Power grid structure form collaborative optimization method and device, terminal equipment and storage medium

Abstract

The invention discloses a power grid structure form collaborative optimization method and device, terminal equipment and a storage medium, and belongs to the technical field of power grid structure optimization, and the method comprises the steps: obtaining the power data of a power grid in a preset time period, and taking the minimum difference between the comprehensive influence of the operation cost and reliability and the flexibility as a target; constructing a grid form collaborative optimization model and corresponding constraint conditions; and finally, under each constraint condition, solving the grid form collaborative optimization model to obtain a switch equipment characteristic parameter, a line electrical cost parameter and a grid topological structure when the difference between the comprehensive influence of the operation cost and the reliability and the flexibility is minimum, and performing grid structure optimization on the power grid. According to the invention, the method can solve a problem that the planning result is unreasonable due to the neglect of the mutual influence between the switch equipment and the network frame form when the network frame topology form of the power grid is planned in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid framework optimization, and particularly relates to a power grid framework form collaborative optimization method and device, a terminal device and a storage medium. BACKGROUND

[0002] The intermediate link of power grid is to transport power from high-voltage transmission network to users, which is an important infrastructure for realizing power conversion and utilization. Scientific distribution system planning is an important part of power grid construction, development and operation. Therefore, for the construction planning of power grid, the planning of the topology form of the framework is very important.

[0003] The existing technical solution mainly plans the topology form of the framework to obtain the optimal topology form, and then determines the type and parameters of the devices such as transformers and transmission lines in the power grid. Although this solution can obtain the optimal framework form scheme, it ignores the mutual influence between the switch devices and the framework form in the power grid. Since the large-capacity switch devices are used to control the on-off of the circuit in the power grid, they play a very important role in ensuring the safe operation of the system. Therefore, the existing technology has the problem that when the topology form of the framework of the power grid is planned, the mutual influence between the switch devices and the framework form is ignored, resulting in unreasonable planning results. SUMMARY

[0004] The present application provides a power grid framework form collaborative optimization method, device, terminal device and storage medium, which can solve the problem that the mutual influence between the switch devices and the framework form is ignored when the topology form of the framework of the power grid is planned in the prior art, resulting in unreasonable planning results.

[0005] An embodiment of the present application provides a power grid framework form collaborative optimization method, comprising:

[0006] Obtaining power data of the power grid in a preset time period; wherein the power data includes switch device operation characteristic parameters, node power failure characteristic parameters, node cost parameters, node power parameters, node voltage parameters and node current parameters;

[0007] According to the power data, a framework form collaborative optimization model and corresponding constraint conditions are constructed with the minimum gap between the comprehensive influence of operation cost and reliability and flexibility as the target; wherein the constraint conditions include electrical constraints, device constraints and spatial constraints;

[0008] Under the above constraints, the grid form collaborative optimization model is solved to obtain the switching device characteristic parameters, line electrical cost parameters, and grid topology structure when the gap between the comprehensive influence of operation cost and reliability and flexibility is the smallest, and the grid is optimized in terms of grid structure according to the switching device characteristic parameters, line electrical cost parameters, and grid topology structure. The switching device characteristic parameters include: device maximum load, device rated capacity, device type construction cost, device type operation and maintenance cost, device type rated current, and device type floor area. The line electrical cost parameters include: line current, line resistance, line operation and maintenance cost, and line construction cost. The grid topology structure includes: node construction variables and line construction variables.

[0009] Further, the construction of the objective function of the grid form collaborative optimization model includes:

[0010] The weight of the power outage time index, the weight of the power outage frequency index, the weight of the power outage influence index, the weight of the topology adjustable index, the weight of the load adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switching device combinations, the total number of switching devices, and the number of ring networks are obtained.

[0011] The reliability evaluation index function is constructed by using the node power outage characteristic parameters, the weight of the power outage time index, the weight of the power outage frequency index, and the weight of the power outage influence index.

[0012] The flexibility evaluation index function is constructed by using the switching device operation characteristic parameters, the weight of the topology adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switching device combinations, the total number of switching devices, the number of ring networks, and the weight of the load adjustable index.

[0013] The operation cost function is constructed by using the node cost parameters, the grid topology structure, the line electrical cost parameters, and the switching device characteristic parameters.

[0014] The objective function is constructed according to the reliability evaluation index function, the flexibility evaluation index function, and the operation cost function.

[0015] Further, the electrical constraint is:

[0016]

[0017] U min ≤U j ≤U max

[0018] I i ≤I max

[0019] In the formula, P jactive power of node j, U j voltage of node j, U k voltage of node k, G jk real part in admittance matrix constructed by node j and node k, θ jk voltage phase angle difference between node j and node k, B jk imaginary part in node admittance matrix constructed by node j and node l, Q j reactive power of node j, U min lower limit of node voltage, U max upper limit of node voltage, I max maximum allowed current of line, N represents a node set.

[0020] Further, the above device constraints are:

[0021]

[0022] wherein, y j whether the switch device is constructed at node j, device breaking capacity of node j, breaking capacity of device type d, short-circuit current of node j, device rated current of node j, D represents a device type set.

[0023] Further, the above space constraints are specifically: for each node, the sum of the floor area of the device type selected by the node and the remaining floor area of the node except the floor area, is not less than the maximum floor area allowed by the node.

[0024] On the basis of the above method embodiment, the application correspondingly provides a device embodiment;

[0025] The application provides a power grid framework mode cooperative optimization device, which comprises:

[0026] a power data acquisition module, an optimization model construction module and an optimization model solving module.

[0027] The power data acquisition module is used for acquiring power data of the power grid in a preset time period; wherein the power data comprises switch device operation characteristic parameters, node power-off characteristic parameters, node cost parameters, node power parameters, node voltage parameters and node current parameters.

[0028] The optimization model construction module is configured to construct a grid form collaborative optimization model and corresponding constraint conditions according to the power data, with the minimum gap between the comprehensive influence of operation cost and reliability and flexibility as the target; wherein the constraint conditions include electrical constraints, equipment constraints, and spatial constraints.

[0029] The optimization model solving module is configured to solve the grid form collaborative optimization model under each constraint condition, to obtain the switch device characteristic parameters, line electrical cost parameters, and grid topology structure when the gap between the comprehensive influence of operation cost and reliability and flexibility is the minimum, and to optimize the grid structure according to the switch device characteristic parameters, line electrical cost parameters, and grid topology structure; wherein the switch device characteristic parameters include device maximum load, device rated capacity, device type construction cost, device type operation and maintenance cost, device type rated current, and device type floor area; the line electrical cost parameters include line current, line resistance, line operation and maintenance cost, and line construction cost; and the grid topology structure includes node construction variables and line construction variables.

[0030] Further, the construction of the objective function of the grid form collaborative optimization model includes:

[0031] obtaining the weight of the power outage time index, the weight of the power outage frequency index, the weight of the power outage influence index, the weight of the topology adjustable index, the weight of the load adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switch device combinations, the total number of switch devices, and the number of ring networks;

[0032] constructing a reliability evaluation index function based on the node power outage characteristic parameters, the weight of the power outage time index, the weight of the power outage frequency index, and the weight of the power outage influence index;

[0033] constructing a flexibility evaluation index function based on the switch device operation characteristic parameters, the weight of the topology adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switch device combinations, the total number of switch devices, the number of ring networks, and the weight of the load adjustable index;

[0034] constructing an operation cost function based on the node cost parameters, the grid topology structure, the line electrical cost parameters, and the switch device characteristic parameters;

[0035] constructing the objective function based on the reliability evaluation index function, the flexibility evaluation index function, and the operation cost function.

[0036] Further, the electrical constraints are as follows:

[0037]

[0038] Umin ≤U j ≤U max

[0039] I i ≤I max

[0040] In the formula, P j represents the active power of node j, U j represents the voltage of node j, U k represents the voltage of node k, G jk represents the real part in the admittance matrix constructed by node j and node k, θ jk represents the voltage phase angle difference between node j and node k, B jk represents the imaginary part in the node admittance matrix constructed by node j and node l, Q j represents the reactive power of node j, U min represents the lower limit of node voltage, U max represents the upper limit of node voltage, I max represents the maximum allowable current of the line, and N represents a node set.

[0041] On the basis of the above-mentioned method embodiment, the application correspondingly provides a terminal device embodiment;

[0042] The application provides a terminal device, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor executes the computer program to realize the power grid framework coordination optimization method in any one of the embodiments of the application.

[0043] On the basis of the above-mentioned method embodiment, the application correspondingly provides a storage medium embodiment;

[0044] The application provides a storage medium, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor executes the computer program to realize the power grid framework coordination optimization method in any one of the embodiments of the application.

[0045] The embodiments of the application have the following beneficial effects:

[0046] This invention provides a method, apparatus, terminal equipment, and storage medium for collaborative optimization of power grid structure. The method includes: acquiring power data of the power grid within a preset time period; wherein the power data includes: operating characteristic parameters of switching equipment, node outage characteristic parameters, node cost parameters, node power parameters, node voltage parameters, and node current parameters; subsequently, based on the power data, constructing a collaborative optimization model of the power grid structure and corresponding constraints with the objective of minimizing the gap between the combined impact of operating cost and reliability and flexibility; wherein the constraints include: electrical constraints, equipment constraints, and spatial constraints; and then, under each of the above constraints, optimizing the collaborative optimization model of the power grid structure. The solution process yields the optimal switching equipment characteristic parameters, line electrical cost parameters, and grid topology when the gap between the combined impact of operating costs and reliability and flexibility is minimized. Based on these parameters, the power grid structure is optimized. The switching equipment characteristic parameters include: maximum load, rated capacity, construction cost, maintenance cost, rated current, and floor space. The line electrical cost parameters include: line current, line resistance, maintenance cost, and construction cost. The grid topology includes node construction variables and line construction variables. Therefore, this invention constructs a collaborative optimization model for grid morphology based on the switching equipment operating characteristic parameters and corresponding constraints. Consequently, when optimizing the power grid structure based on the solved switching equipment characteristic parameters, line electrical cost parameters, and grid topology, the influence between switching equipment and grid morphology is considered, resulting in a more reasonable planning outcome. Attached Figure Description

[0047] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for collaborative optimization of power grid structure according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of a power grid structure collaborative optimization device provided in an embodiment of the present invention. Detailed Implementation

[0050] In order to make the objects, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the scope of the present application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the terms "comprising," "comprises" and "including" and "has" and any variations thereof used herein are intended to cover a non-exclusive inclusion.

[0052] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0053] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, or necessarily alternatives to other embodiments. It will be explicitly and implicitly appreciated by a person of ordinary skill in the art that the embodiments described herein can be combined with other embodiments.

[0054] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0055] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0056] In the description of the embodiments of the present application, unless explicitly defined and limited otherwise, the technical terms "mounting", "connection", "linking", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through an intermediate medium, can be internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0057] Referring to Figure 1 To solve the problem that the mutual influence between the switch device and the network topology form is ignored when the network topology form is planned, resulting in unreasonable planning results. An embodiment of the present application provides a power grid network form collaborative optimization method, comprising:

[0058] Step S101: Obtain power data of the power grid in a preset time period; wherein the power data includes: switch device operating characteristic parameters, node power failure characteristic parameters, node cost parameters, node power parameters, node voltage parameters and node current parameters;

[0059] Specifically, the switch device operating characteristic parameters include: the number of effective switch device combinations, the total number of switch devices, the maximum adjustment capacity of the device, the device availability probability, the device sustainable adjustment time and the breaking capacity of the device type.

[0060] Specifically, the node power failure characteristic parameters include: node fault power failure probability, node affected user number and node power failure duration.

[0061] Specifically, the node cost parameters include: node construction cost and node operation and maintenance cost.

[0062] Specifically, the node power parameters include: node active power and node reactive power.

[0063] Specifically, the node voltage parameters include: node voltage, voltage phase angle difference between nodes, node voltage upper limit and node voltage lower limit.

[0064] Specifically, the node current parameters include: device rated current of the node and short-circuit current of the node.

[0065] Step S102: According to the power data, the difference between the comprehensive influence of operation cost and reliability and flexibility is minimized as the target, a network form collaborative optimization model and corresponding constraint conditions are constructed; wherein the constraint conditions include: electrical constraint, device constraint and space constraint;

[0066] In a preferred embodiment, the construction of the objective function of the grid form optimization model comprises:

[0067] obtaining the weight of the power outage time index, the weight of the power outage frequency index, the weight of the power outage influence index, the weight of the topology adjustable index, the weight of the load adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switch device combinations, the total number of switch devices, and the number of ring networks;

[0068] constructing a reliability evaluation index function based on the node power outage characteristic parameters, the weight of the power outage time index, the weight of the power outage frequency index, and the weight of the power outage influence index;

[0069] constructing a flexibility evaluation index function based on the switch device operation characteristic parameters, the weight of the topology adjustable index, the weight corresponding to the adjustment capacity index, the number of effective switch device combinations, the total number of switch devices, the number of ring networks, and the weight of the load adjustable index;

[0070] constructing an operation cost function based on the node cost parameters, the grid topology structure, the line electrical cost parameters, and the switch device characteristic parameters;

[0071] constructing the objective function based on the reliability evaluation index function, the flexibility evaluation index function, and the operation cost function.

[0072] Specifically, the objective function is:

[0073] min f = w1C + w2S - w3F

[0074]

[0075]

[0076] In the formula, f represents the value of the objective function, w1 represents the weight corresponding to the operation cost, C represents the function value corresponding to the operation cost function, w2 represents the weight corresponding to the reliability evaluation index, S represents the function value corresponding to the reliability evaluation index function, w3 represents the weight corresponding to the flexibility evaluation index, F represents the function value corresponding to the flexibility evaluation index function, and w 21 represents the weight of the power outage time index, N represents the node set, λ j represents the failure outage probability of node j, H j represents the number of users affected by node j, w 22 represents the weight of the power outage frequency index, T j represents the power outage duration of node j, w 23 represents the weight of the power outage influence index, w 31Weight representing topology adjustable index, m1 represents number of effective switch device combination of power grid, m2 represents total number of switch device of power grid, m3 represents number of ring network, w 32 Weight representing load adjustable index, E represents set of controllable device of power grid, ΔP e,max Maximum adjustable capacity of device e, η e Available probability of device e, T e Sustainable adjustable time of device e, T ref Reference time, w 33 Weight representing adjustable capacity index, B represents set of all devices of power grid, c b Rated capacity of device b, l b,max Maximum load of device b in preset period, w 34 Weight corresponding to improvement adaptation index, N A Total number of improvement projects, A represents set of projects that can be improved in future, P a Occurrence probability of improvement project a, C a Performance index of power grid after improvement project a, L represents set of lines, x i Whether line i is constructed, C i Construction cost of line i, C iw Operation and maintenance cost of line i, x j Whether node j is constructed, C jw Operation and maintenance cost of node j, D represents set of device types, x jd Whether device type d is selected for node j, C d Construction cost of device type d, C dw Operation and maintenance cost of device type d, c r Electricity price, I i Current of line i, r i Resistance of line i, T represents running time.

[0077] Specifically, the set of all devices B of the power grid includes controllable devices and uncontrollable devices of the power grid, and the controllable devices include switch devices and non-switch devices.

[0078] Specifically, the power outage time index, the power outage frequency index, the power outage influence index, the topology adjustable index, the load adjustable index, the adjustable capacity index and the improvement adaptation index are calculated by the following formula:

[0079]

[0080] In the formula, S1 represents a power outage time index, S2 represents a power outage frequency index, S3 represents a power outage influence index, F1 represents a topology adjustable index, F2 represents a load adjustable index, F3 represents an adjustable capacity index, and F4 represents an improvement adaptation index.

[0081] In this preferred embodiment, a target function of a network frame coordination optimization model is obtained by designing a minimum value of a difference between a sum of a running cost and a reliability evaluation index and a flexibility evaluation index.

[0082] In another preferred embodiment, the electrical constraint is:

[0083]

[0084] U min ≤U j ≤U max

[0085] I i ≤I max

[0086] In the formula, P j represents active power of node j, U j represents voltage of node j, U k represents voltage of node k, G jk represents a real part in an admittance matrix constructed by node j and node k, θ jk represents a voltage phase angle difference between node j and node k, B jk represents an imaginary part in a node admittance matrix constructed by node j and node l, Q j represents reactive power of node j, U min represents a lower limit of node voltage, U max represents an upper limit of node voltage, I max represents a maximum allowable current of a line, and N represents a node set.

[0087] Specifically, the electrical constraint is specifically a power flow constraint, a node voltage constraint, and a line constraint. The power flow constraint is:

[0088]

[0089] The node voltage constraint is:

[0090] U min ≤U j ≤U max

[0091] The line current constraint is:

[0092] I i ≤I max

[0093] In this preferred embodiment, the electrical constraints are constructed according to the power data and include the power flow constraints, the node voltage constraints and the line current constraints.

[0094] In another preferred embodiment, the device constraints are:

[0095]

[0096] where y j denotes whether the switch device is built at node j, denotes the device breaking capacity at node j, denotes the breaking capacity of device type d, denotes the short-circuit current at node j, denotes the device rated current at node j, and D denotes the set of device types.

[0097] Specifically, the device constraints are specifically device coupling constraints and device capacity constraints. The device coupling constraints are:

[0098]

[0099] The device capacity constraints are:

[0100]

[0101] In this preferred embodiment, the device constraints are constructed according to the power data and include the device coupling constraints and the device capacity constraints.

[0102] In another preferred embodiment, the space constraints are specifically that, for each node, the sum of the floor area of the device type selected by the node and the remaining floor area of the node other than the floor area is not less than the maximum floor area allowed by the node.

[0103] Specifically, the space constraints are represented by the following formula:

[0104]

[0105] where A d denotes the floor area of device type d, A j denotes the remaining floor area of node j, denotes the maximum floor area allowed by node j.

[0106] In this preferred embodiment, the space constraints of the grid form collaborative optimization model are constructed by the power data.

[0107] Step S103: Under the constraints described above, solve the collaborative optimization model of the power grid structure to obtain the characteristic parameters of the switching equipment, the electrical cost parameters of the lines, and the topology of the power grid when the gap between the combined impact of operating cost and reliability and flexibility is minimized. Based on the characteristic parameters of the switching equipment, the electrical cost parameters of the lines, and the topology of the power grid, optimize the power grid structure. The characteristic parameters of the switching equipment include: maximum load of the equipment, rated capacity of the equipment, construction cost of the equipment type, operation and maintenance cost of the equipment type, rated current of the equipment type, and floor area of ​​the equipment type. The electrical cost parameters of the lines include: line current, line resistance, line operation and maintenance cost, and line construction cost. The topology of the power grid includes: node construction variables and line construction variables.

[0108] Specifically, a genetic algorithm is used to solve the network structure morphology collaborative optimization model: First, an initial combination of decision variables (i.e., switchgear characteristic parameters, line electrical cost parameters, and network topology) is generated as the initial population of the genetic algorithm. Then, the objective function is used as the fitness function of the genetic algorithm, and the fitness of each individual is calculated iteratively. When the results converge, the optimal combination of decision variables is output. This solution process is existing technology and will not be elaborated further here.

[0109] Preferably, after obtaining an optimal combination of decision variables, the feasibility of the project can be verified first. If the verification is successful, the power grid structure can be optimized according to this optimal combination of decision variables. If the verification fails, the above constraints and objective function can be adjusted and the solution can be performed again.

[0110] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0111] like Figure 2 As shown, one embodiment of the present invention provides a power grid structure collaborative optimization device, comprising:

[0112] The module includes a power data acquisition module, an optimization model construction module, and an optimization model solving module.

[0113] The aforementioned power data acquisition module is used to acquire power data of the power grid within a preset time period; wherein, the aforementioned power data includes: operating characteristic parameters of switching equipment, power outage characteristic parameters of nodes, cost parameters of nodes, power parameters of nodes, voltage parameters of nodes, and current parameters of nodes;

[0114] The aforementioned optimization model construction module is used to construct a grid configuration collaborative optimization model and corresponding constraints based on the aforementioned power data, with the objective of minimizing the gap between the combined impact of operating costs and reliability and flexibility; wherein the aforementioned constraints include: electrical constraints, equipment constraints and spatial constraints;

[0115] The optimization model solving module is configured to solve the grid structure collaborative optimization model under each constraint condition to obtain the switch device characteristic parameter, the line electrical cost parameter, and the grid topology structure when the gap between the comprehensive influence of the operation cost and the reliability and the flexibility is the smallest, and to optimize the grid structure of the power grid according to the switch device characteristic parameter, the line electrical cost parameter, and the grid topology structure. The switch device characteristic parameter includes a device maximum load, a device rated capacity, a device type construction cost, a device type operation and maintenance cost, a device type rated current, and a device type floor area. The line electrical cost parameter includes a line current, a line resistance, a line operation and maintenance cost, and a line construction cost. The grid topology structure includes a node construction variable and a line construction variable.

[0116] In a preferred embodiment, the construction of the objective function of the grid structure collaborative optimization model includes:

[0117] obtaining a weight of a power outage time index, a weight of a power outage frequency index, a weight of a power outage influence index, a weight of a topology adjustable index, a weight of a load adjustable index, a weight corresponding to an adjustment capacity index, an effective switch device combination number, a total number of switch devices, and a number of ring networks;

[0118] constructing a reliability evaluation index function based on the node power outage characteristic parameter, the weight of the power outage time index, the weight of the power outage frequency index, and the weight of the power outage influence index;

[0119] constructing a flexibility evaluation index function based on the switch device operation characteristic parameter, the weight of the topology adjustable index, the weight corresponding to the adjustment capacity index, the effective switch device combination number, the total number of switch devices, the number of ring networks, and the weight of the load adjustable index;

[0120] constructing an operation cost function based on the node cost parameter, the grid topology structure, the line electrical cost parameter, and the switch device characteristic parameter;

[0121] constructing the objective function based on the reliability evaluation index function, the flexibility evaluation index function, and the operation cost function.

[0122] In another preferred embodiment, the electrical constraint is as follows:

[0123]

[0124] U min ≤U j ≤U max

[0125] I i ≤Imax

[0126] In the formula, P j U represents the active power of node j. j U represents the voltage at node j. k G represents the voltage at node k. jk θ represents the real part of the admittance matrix constructed from nodes j and k. jk B represents the voltage phase angle difference between node j and node k. jk Q represents the imaginary part of the nodal admittance matrix constructed from nodes j and l. j U represents the reactive power of node j. min U represents the lower limit of the node voltage. max I represents the upper limit of the node voltage. max This represents the maximum allowable current of the line, and N represents the set of nodes.

[0127] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort. The above schematic diagrams are merely examples of a power grid structure collaborative optimization device and do not constitute a limitation on a power grid structure collaborative optimization device. It may include more or fewer components than illustrated, or combine certain components, or use different components.

[0128] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0129] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power grid structure collaborative optimization method described in any embodiment of the present invention.

[0130] For example, in this embodiment, the computer program described above can be divided into one or more modules, the one or more modules are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the device;

[0131] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The device can include, but is not limited to, a processor, a memory, and the like.

[0132] The processor can be a central processing module (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or any conventional processor, and the like. The processor is the control center of the device, and is connected to various parts of the device through various interfaces and lines.

[0133] The memory can be used to store the computer program and / or the module, and the processor realizes various functions of the device by running or executing the computer program and / or the module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like; in addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0134] On the basis of the method embodiment, the present application correspondingly provides a storage medium embodiment.

[0135] Another embodiment of the present application provides a storage medium including a stored computer program, wherein the computer program, when executed, controls a device in which the storage medium is located to perform the power grid framework coordination optimization method of any one of the embodiments of the present application.

[0136] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and the like.

[0137] The above is the preferred embodiment of the present application, and it should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A method for collaborative optimization of power grid structure, characterized in that, include: Acquire power data of the power grid within a preset time period; wherein, the power data includes: operating characteristic parameters of switching equipment, power outage characteristic parameters of nodes, cost parameters of nodes, power parameters of nodes, voltage parameters of nodes, and current parameters of nodes; Based on the power data, with the goal of minimizing the gap between the combined impact of operating costs and reliability and flexibility, a collaborative optimization model of grid structure and corresponding constraints are constructed; wherein, the constraints include: electrical constraints, equipment constraints and spatial constraints; Under the aforementioned constraints, the collaborative optimization model for the power grid morphology is solved to obtain the characteristic parameters of switching equipment, electrical cost parameters of lines, and the power grid topology when the gap between the combined impact of operating cost and reliability and flexibility is minimized. Based on the characteristic parameters of switching equipment, electrical cost parameters of lines, and the power grid topology, the power grid structure is optimized. The characteristic parameters of switching equipment include: maximum load of equipment, rated capacity of equipment, construction cost of equipment type, operation and maintenance cost of equipment type, rated current of equipment type, and floor area of ​​equipment type. The electrical cost parameters of lines include: line current, line resistance, line operation and maintenance cost, and line construction cost. The power grid topology includes: node construction variables and line construction variables.

2. The method for collaborative optimization of power grid structure according to claim 1, characterized in that, The construction of the objective function of the collaborative optimization model for grid morphology includes: Obtain the weights of the power outage time index, power outage frequency index, power outage impact index, topology adjustability index, load adjustability index, adjustment capability index, effective switchgear combination, total number of switchgear, and number of ring networks. A reliability assessment index function is constructed using the node outage characteristic parameters, the weights of the outage time index, the outage frequency index, and the outage impact index. A flexibility evaluation index function is constructed using the operating characteristic parameters of the switching equipment, the weight of the topology adjustability index, the weight of the adjustment capability index, the number of effective switching equipment combinations, the total number of switching equipment, the number of ring networks, and the weight of the load adjustability index. An operating cost function is constructed using the node cost parameters, network topology, line electrical cost parameters, and switchgear characteristic parameters. The objective function is constructed based on the reliability assessment index function, the flexibility assessment index function, and the operating cost function.

3. The method for collaborative optimization of power grid structure according to claim 2, characterized in that, The electrical constraints are: IN min ≤U j ≤U max I i ≤I max In the formula, P j U represents the active power of node j. j U represents the voltage at node j. k G represents the voltage at node k. jk θ represents the real part of the admittance matrix constructed from nodes j and k. jk B represents the voltage phase angle difference between node j and node k. jk Q represents the imaginary part of the nodal admittance matrix constructed from nodes j and l. j U represents the reactive power of node j. min U represents the lower limit of the node voltage. max I represents the upper limit of the node voltage. max This represents the maximum allowable current of the line, and N represents the set of nodes.

4. The method for collaborative optimization of power grid structure according to claim 3, characterized in that, The device constraints are as follows: In the formula, y j Indicates whether node j has a switchgear installed. This represents the device disconnection capacity of node j. This indicates the breaking capacity of equipment type d. This represents the short-circuit current at node j. Let represent the rated current of the device at node j, and D represent the set of device types.

5. The method for collaborative optimization of power grid structure according to claim 3, characterized in that, The spatial constraint is specifically defined as follows: for each node, the sum of the floor area of ​​the device type selected by the node and the remaining floor area of ​​the node excluding the selected floor area is not less than the maximum allowed floor area of ​​the node.

6. A power grid structure collaborative optimization device, characterized in that, include: The module includes a power data acquisition module, an optimization model construction module, and an optimization model solving module. The power data acquisition module is used to acquire power data of the power grid within a preset time period; wherein, the power data includes: operating characteristic parameters of switching equipment, power outage characteristic parameters of nodes, cost parameters of nodes, power parameters of nodes, voltage parameters of nodes, and current parameters of nodes. The optimization model construction module is used to construct a grid configuration collaborative optimization model and corresponding constraints based on the power data, with the goal of minimizing the gap between the combined impact of operating costs and reliability and flexibility; wherein the constraints include: electrical constraints, equipment constraints and spatial constraints; The optimization model solving module is used to solve the network structure collaborative optimization model under various constraints to obtain the switchgear characteristic parameters, line electrical cost parameters, and network topology when the gap between the combined impact of operating cost and reliability and flexibility is minimized. Based on these parameters, the module optimizes the power grid structure. The switchgear characteristic parameters include: maximum load, rated capacity, construction cost, maintenance cost, rated current, and floor space. The line electrical cost parameters include: line current, line resistance, maintenance cost, and construction cost. The network topology includes: node construction variables and line construction variables.

7. The power grid structure collaborative optimization device according to claim 6, characterized in that, The construction of the objective function of the collaborative optimization model for grid morphology includes: Obtain the weights of the power outage time index, power outage frequency index, power outage impact index, topology adjustability index, load adjustability index, adjustment capability index, effective switchgear combination, total number of switchgear, and number of ring networks. A reliability assessment index function is constructed using the node outage characteristic parameters, the weights of the outage time index, the outage frequency index, and the outage impact index. A flexibility evaluation index function is constructed using the operating characteristic parameters of the switching equipment, the weight of the topology adjustability index, the weight of the adjustment capability index, the number of effective switching equipment combinations, the total number of switching equipment, the number of ring networks, and the weight of the load adjustability index. An operating cost function is constructed using the node cost parameters, network topology, line electrical cost parameters, and switchgear characteristic parameters. The objective function is constructed based on the reliability assessment index function, the flexibility assessment index function, and the operating cost function.

8. The power grid structure collaborative optimization device according to claim 7, characterized in that, The electrical constraints are as follows: IN min ≤U j ≤U max I i ≤I max In the formula, P j U represents the active power of node j. j U represents the voltage at node j. k G represents the voltage at node k. jk θ represents the real part of the admittance matrix constructed from nodes j and k. jk B represents the voltage phase angle difference between node j and node k. jk Q represents the imaginary part of the nodal admittance matrix constructed from nodes j and l. j U represents the reactive power of node j. min U represents the lower limit of the node voltage. max I represents the upper limit of the node voltage. max This represents the maximum allowable current of the line, and N represents the set of nodes.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a power grid morphology collaborative optimization method as described in any one of claims 1 to 5.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute a power grid structure collaborative optimization method as described in any one of claims 1 to 5.

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