Method, device and electronic equipment for determining power supply restoration of a faulty distribution network

Abstract

The application provides a method, device and electronic equipment for determining power supply recovery of a fault power distribution network, which comprises the following steps: constructing an optimization model for power supply recovery of the fault power distribution network according to a plurality of first constraint conditions; decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of the fault power distribution network and a plurality of sub optimization models of normal power distribution networks; solving the main optimization model and the plurality of sub optimization models to obtain a first target function value and a second target function value and a target function value, determining a maximum value of the target function value of the optimization model according to the target function value of the sub optimization model and the sum of the first target function value, determining a minimum value of the target function value of the optimization model according to the sum of the target function value of the sub optimization model and the second target function value, and comparing the difference between the maximum value and the minimum value with a preset threshold to determine the power supply recovery of the fault power distribution network. The method effectively improves the power supply recovery rate under the premise of controlling the cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power supply restoration of distribution network, in particular to a method for determining power supply restoration of fault distribution network, a device for determining power supply restoration of fault distribution network, a computer readable storage medium and an electronic device. BACKGROUND

[0002] The distribution network is an important bridge and interaction hub connecting the transmission network and the end user, and is an important part of the power system. In recent years, using the energy mutual aid between multi-interconnected distribution networks to realize the power supply restoration of fault distribution network has become a major direction in the field of power supply restoration of distribution network. However, most of the current domestic and foreign researches on power supply restoration of distribution network only consider traditional tie switch (TS) as the interconnected switch. However, the TS control mode is single, the operation steps are complicated, and the interconnection conditions are harsh. In addition, most of the related researches use centralized communication architecture for information interaction between distribution networks, which cannot well perform the power supply restoration task under the fault scenario for the current complex structure and scale of distribution network. Therefore, a more efficient interconnection mode and communication architecture need to be used to improve the power supply restoration efficiency of fault distribution network.

[0003] In the prior art, for the current large-scale and complex structure of interconnected distribution network, most of the researches only consider the harsh connection condition of TS interconnection or the high investment cost of flexible interconnection, and there are few researches on the mixed and collaborative power restoration of the two interconnection modes in actual application. In addition, the communication architecture of interconnected distribution network is mostly centralized architecture. The prior art cannot improve the power supply restoration rate of interconnected distribution network under the premise of controlling cost, and faces the dimension disaster problem caused by large-scale increase of communication volume and calculation amount. SUMMARY

[0004] The present application provides a method for determining power supply restoration of fault distribution network, a device for determining power supply restoration of fault distribution network, a computer readable storage medium and an electronic device to solve the problem that the transmission power of distribution network line is limited and the TS interconnection condition is harsh in the prior art, and the calculation amount is large and easy to face the dimension disaster problem.

[0005] According to an aspect of the present application, a method for determining power supply recovery of a fault power distribution network is provided. The power distribution network is electrically connected with a tie switch and a smart soft switch. The method comprises: a construction step of constructing an optimization model of power supply recovery of the fault power distribution network according to at least a plurality of first constraint conditions, wherein parameters of the optimization model at least include active power of power transfer of the tie switch and active power of power transfer of the smart soft switch; a first determination step of decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of the fault power distribution network and a plurality of sub-optimization models of normal power distribution networks; a second determination step of solving the main optimization model and the plurality of sub-optimization models to obtain a target function value of the sub-optimization model, a first target function value and a second target function value of the main optimization model, determining a maximum value of a target function value of the optimization model according to a sum of the target function value of the sub-optimization model and the first target function value, and solving the first target function value and the second target function value by using different parameters; a third determination step of determining a minimum value of the target function value of the optimization model according to a sum of the target function value of the sub-optimization model and the second target function value; a loop step of re-executing the second determination step and the third determination step at least once in a case that a difference between the maximum value and the minimum value is greater than a preset threshold; and a fourth determination step of determining an optimal solution of the optimization model in a case that the difference between the maximum value and the minimum value is less than or equal to the preset threshold, and recovering power supply of the fault power distribution network according to the optimal solution.

[0006] Optionally, the method further comprises: establishing an optimization model of the normal power distribution network according to an operation cost of the normal power distribution network and power of power transfer; performing network reconfiguration on the optimization model of the normal power distribution network to obtain a network reconfiguration model of the normal power distribution network; determining a network topology of the normal power distribution network according to the network reconfiguration model, and constructing the optimization model of power supply recovery of the fault power distribution network according to the network topology and the plurality of first constraint conditions.

[0007] Optionally, the solving the main optimization model and the plurality of sub-optimization models comprises: initializing at least an initial value of a boundary variable of the fault power distribution network; solving the target function value of the sub-optimization model according to the initial value of the boundary variable of the fault power distribution network and a boundary equation constraint condition of the normal power distribution network; solving the main optimization model according to the boundary variable of the normal power distribution network and the target function value of the sub-optimization model to obtain the first target function value; and obtaining the second target function value according to at least a feasible cut frequency and a feasible cut plane constraint of the fault power distribution network based on all the normal power distribution networks.

[0008] Optionally, the determination method further includes: obtaining Lagrange multipliers based on the boundary equality constraints of the normal distribution network, wherein the Lagrange multipliers include: active power, reactive power, and the square of voltage magnitude corresponding to the boundary equality constraints; and constructing the feasible cutting plane constraints of the normal distribution network based on the Lagrange multipliers.

[0009] Optionally, the determination method further includes: constructing a relaxation model of the sub-optimization model, solving the relaxation model to obtain Lagrange multipliers, wherein the Lagrange multipliers include: active power, reactive power and voltage amplitude squared corresponding to the boundary equality constraints; and constructing the feasible cutting plane constraints of the normal distribution network based on the Lagrange multipliers.

[0010] Optionally, the constraints of the network reconfiguration model include at least one of the following: a first linear power flow constraint, a node voltage constraint, a photovoltaic capacity constraint, and an energy storage system constraint.

[0011] Optionally, the first constraint includes at least one of the following: a second linear power flow constraint, an interconnection boundary equation constraint, a node voltage constraint, a photovoltaic capacity constraint, and an energy storage system constraint.

[0012] According to another aspect of this application, a device for determining power restoration in a faulty distribution network is provided, wherein the distribution network is electrically connected to a tie switch and a smart soft switch, comprising: a construction module for performing a construction step: constructing an optimization model for power restoration in the faulty distribution network based on at least a plurality of first constraints, wherein the parameters of the optimization model include at least the active power of the power supplied by the tie switch and the active power of the power supplied by the smart soft switch; a first determination module for performing a first determination step: decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of the faulty distribution network and a plurality of sub-optimization models of the normal distribution network; and a second determination module for performing a second determination step: solving the main optimization model and the plurality of sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model. The optimization model is divided into four modules: a target function value module and a third determination module. The first module determines the maximum value of the objective function value based on the sum of the sub-optimization model's objective function value and the second objective function value, where the parameters used to solve the first and second objective function values ​​are different. The second module performs a third determination step: determining the minimum value of the objective function value based on the sum of the sub-optimization model's objective function value and the second objective function value. A loop module is used to re-execute the second and third determination steps at least once if the difference between the maximum and minimum values ​​is greater than a preset threshold. The third module performs a fourth determination step: determining the optimal solution of the optimization model if the difference between the maximum and minimum values ​​is less than or equal to a preset threshold, and restoring power supply to the faulty distribution network based on the optimal solution.

[0013] 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 execute any of the aforementioned methods for determining power restoration of a faulted power distribution network.

[0014] 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 determination method for performing any of the aforementioned fault distribution network power restoration methods.

[0015] Applying the technical solution of this application, the above-mentioned method for determining power supply restoration of a faulty distribution network firstly constructs an optimization model for power supply restoration of the faulty distribution network based on at least several first constraints. The parameters of the optimization model include at least the active power of the power supplied by the tie switch and the active power of the power supplied by the smart soft switch. Then, the optimization model is decomposed according to a decomposition algorithm to obtain a main optimization model of the faulty distribution network and multiple sub-optimization models of the normal distribution network. Next, the main optimization model and multiple sub-optimization models are solved to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model. The maximum value of the objective function value of the optimization model is determined based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value. The parameters used to solve the first and second objective function values ​​are different. Then, the minimum value of the objective function value of the optimization model is determined based on the sum of the objective function values ​​of the sub-optimization models and the second objective function value. Then, if the difference between the maximum and minimum values ​​is greater than a preset threshold, the second and third determination steps are repeated at least once. Finally, if the difference between the maximum and minimum values ​​is less than or equal to the preset threshold, the optimal solution of the optimization model is determined, and the power supply of the faulty distribution network is restored based on the optimal solution. This method employs a distributed communication architecture to address the power restoration problem in interconnected distribution networks. By utilizing the Generalized Benders Decomposition (GBD) distributed algorithm, it solves the problems of limited transmission power of distribution network lines, stringent TS interconnection conditions, large computational load, and susceptibility to dimensionality curse in existing technologies. While protecting the privacy of different communication entities, it reduces communication investment costs and burden. Furthermore, by decomposing the global problem, it alleviates computational pressure to some extent and improves power restoration efficiency. Simultaneously, it uses TS and smart soft switches (SOPs) for joint power transfer between distribution networks, combining the advantages of traditional hard switches and smart soft switches, thus improving the overall power restoration rate. Attached Figure Description

[0016] 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:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining power restoration of a faulty distribution network, provided in an embodiment of this application, is shown.

[0018] Figure 2 A flowchart illustrating a method for determining power restoration in a faulted distribution network according to an embodiment of this application is shown.

[0019] Figure 3A schematic diagram of a multi-interconnection distribution network topology provided according to an embodiment of this application is shown;

[0020] Figure 4 A schematic diagram of the structure of each optimal topology of a normal distribution network obtained in the first stage according to an embodiment of this application is shown;

[0021] Figure 5 A flowchart illustrating a GBD distributed algorithm according to an embodiment of this application is shown.

[0022] Figure 6 A schematic diagram is shown of photovoltaic output data of a faulted distribution network after convergence using the GBD distributed algorithm, according to an embodiment of this application.

[0023] Figure 7 A schematic diagram of a simulation result of a second-stage distributed power supply recovery according to an embodiment of this application is shown;

[0024] Figure 8 A schematic diagram of a second-stage distributed iterative convergence process of the global objective function value according to an embodiment of this application is shown.

[0025] Figure 9 A structural block diagram of a device for determining power supply restoration in a faulted distribution network according to an embodiment of this application is shown.

[0026] The above figures include the following reference numerals:

[0027] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] 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.

[0031] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0032] GBD (Generalized Benders Decomposition): A generalized Benders algorithm;

[0033] TS (Tie Switch): Handling switch;

[0034] SOP (Soft Open Point): Intelligent soft switch;

[0035] ZB (Zone Boundary): The region boundary of the network reconstruction optimization model;

[0036] ADN (Active Distribution Network): Active distribution network;

[0037] ESS (Energy Storage System): Energy storage system;

[0038] PV (Photovoltaic): Photovoltaic power generation;

[0039] SOC (State of Charge): State of charge.

[0040] As described in the background section, existing technologies for today's large-scale and complex interconnected distribution networks lack hybrid coordination between demanding TS interconnections and costly flexible interconnections. Furthermore, the communication architecture of interconnected distribution networks is mostly centralized. Centralized control requires a central controller within the distribution network to collect and process network-wide information before issuing operational commands. While this approach is fast and efficient for small-scale distribution networks, it faces challenges in handling increasingly large-scale and intelligent distribution networks, including high communication and computational loads for the central controller, high communication investment costs, and insufficient capacity to handle single-point failures in the communication system. To address the limitations of existing distribution network lines in terms of transmission power, demanding TS interconnection conditions, and the risk of dimensionality curse due to large computational loads, embodiments of this application provide a method for determining power restoration in a faulty distribution network, a device for determining power restoration in a faulty distribution network, a computer-readable storage medium, and an electronic device.

[0041] 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.

[0042] 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 method of determining power restoration in a faulty distribution network 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.

[0043] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining power restoration of a faulty distribution network 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 may also include 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.

[0044] This embodiment provides a method for determining the restoration of power supply to a faulty power distribution network 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. Furthermore, 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.

[0045] Figure 2 This is a flowchart illustrating a method for determining power restoration in a faulty distribution network according to an embodiment of this application. For example... Figure 2 As shown, the method includes the following steps:

[0046] Step S201, Construction Step: Based on at least several first constraints, construct an optimization model for power supply restoration of the faulted distribution network. The parameters of the optimization model shall include at least the active power of the power supply transferred by the tie switch and the active power of the power supply transferred by the smart soft switch.

[0047] Specifically, a global optimization model for power restoration of interconnected distribution networks based on tie switches and smart soft switches is established according to at least one of the following constraints: linear power flow constraint, node voltage constraint, photovoltaic capacity constraint, and ESS constraint. Each distribution network is interconnected with the out-of-power distribution network through tie switches or flexible soft switches, thereby providing voltage and power support to the faulty distribution network. The implementation process of the proposed distributed power restoration method is as follows: Figure 3 As shown, the above method can restore power to the entire faulty distribution network and fully coordinate the transfer power of TS and SOP, thereby reducing the cost of power restoration.

[0048] In the first stage, after reconstructing the parallel networks of each normal distribution network into an optimal transfer network topology, each distribution network is interconnected with the power-outage distribution network through transfer tie lines or flexible soft switches, thereby providing voltage and power support to the faulty distribution network. The optimization model for power restoration of the faulty distribution network is as follows: In the formula, λ loss For network loss costs; σ line For the set of lines; R ij P represents the resistance of line ij in a normal distribution network. ij and Q ij These represent the active power and reactive power flowing through line ij in a normal distribution network, respectively; U base λ is the square of the voltage at the saturation point. PV Cost of photovoltaic power generation; σ PV P is the set of photovoltaic access nodes; dec,j σ represents the reduction in photovoltaic power at photovoltaic access node j; unode k represents the set of nodes in a faulty distribution network. j P represents the load weight level at node j during load picking. re,j This represents the load pickup amount at photovoltaic access node j.

[0049] The determination method also includes the following steps:

[0050] Step S301: Establish an optimization model for the normal distribution network based on the operating cost of the normal distribution network and the power of the transferred power.

[0051] Step S302: Perform network reconfiguration on the optimization model of the normal distribution network to obtain the network reconfiguration model of the normal distribution network;

[0052] Step S303: Based on the network reconfiguration model, determine the network topology of the normal distribution network, and construct an optimization model for power supply restoration of the faulty distribution network based on the network topology and multiple first constraints.

[0053] Specifically, each normal distribution network aims to reduce its own operating costs and increase the transfer power. An optimization model for each normal distribution network is constructed, and the optimal network topology is obtained and fixed through network reconstruction. This provides a basis for subsequent power restoration operations, thereby improving power restoration efficiency while saving operating costs.

[0054] In one alternative approach, the topology of the multi-interconnected distribution network is as follows: Figure 3 As shown, the topology in the scheme consists of two IEEE-33 node distribution systems and two IEEE-69 node distribution systems interconnected via TS or SOP. ADN-1 is the power outage distribution network area due to a main transformer fault, while the rest are normally operating distribution network areas. The three normal distribution networks, ADN-2, ADN-3, and ADN-4, each establish their own distribution network optimization models with the optimization objectives of operating costs and power transfer revenue within their respective networks. ADN-1 and ADN-4 each include ESS1~ESS3 (energy storage system nodes) and PV1~PV5 (photovoltaic system nodes); ADN-2 and ADN-3 each include ESS1~ESS2 and PV1~PV5. The optimal network topology for each normal distribution network is obtained and fixed through network reconfiguration, ready for the second phase of power transfer restoration operations.

[0055] The network reconfiguration optimization model for the first stage of the normal distribution network is as follows: In the formula σ line For the set of lines in a normal distribution network; σ PV For the set of photovoltaic access nodes; σ ZB λ is the set of boundary interaction nodes. loss For network loss cost; λ PV Cost of photovoltaic power generation; R ij P represents the resistance of line ij in a normal distribution network. ij and Q ij These represent the active power and reactive power flowing through line ij in a normal distribution network, respectively; U base P represents the squared value of the voltage at the saturation point. dec,j P represents the reduction in photovoltaic (PV) capacity at PV access node j; ZB,x The active power output to ZB from the xth normal distribution network includes the power supplied via TS. And flexible transfer using SOP These represent the active power input from photovoltaic access node j to the region boundary.

[0056] The constraints of the network reconfiguration model include at least one of the following: first linear power flow constraint, node voltage constraint, photovoltaic capacity constraint, and energy storage system constraint.

[0057] Specifically, the first linear power flow constraint is:

[0058] In the formula P ij and Q ij These represent the active power and reactive power flowing through line ij in a normal distribution network, respectively; P j and Q j P represents the active and reactive power of the load at load node j in the distribution network, respectively. jk and Q jk These represent the active power and reactive power flowing through line jk in a normal distribution network, respectively. These are the active and reactive power of the photovoltaic device input to the load node j of the distribution network, respectively. These are the ESS discharge power and charging power of the energy storage device input to the load node j of the distribution network, respectively. These represent the net active power and net reactive power of the load at load node j in the distribution network, respectively; P dec,j σ represents the reduction in photovoltaic power at load node j in the distribution network; node This is the set of nodes in a normal power grid.

[0059] Since the state of line switches changes during optimal network reconfiguration, the power flow model needs to consider the opening and closing of lines. Therefore, the Big M method is introduced to establish the relationship between line power flow and line switch state variables, resulting in the following constraint equations for line power flow and node voltage applicable to network reconfiguration. These constraint equations can be expressed by equations (1), (2), and (3):

[0060]

[0061] In the formula, M is a large positive number; σ line The set of lines in a normal distribution network; R ij P represents the resistance of line ij in a normal distribution network. ij and Q ij These represent the active power and reactive power flowing through line ij in a normal distribution network, respectively; U j and U i α represents the squared voltage values ​​of load nodes j and i in the distribution network, respectively; ij Let be the switch state variable on line ij.

[0062] Additionally, the node voltage constraint is expressed as In the formula, V j,min and V j,max These are the minimum and maximum values ​​of the voltage at photovoltaic access node j, respectively; α ij Let be the switch state variables on line ij; the photovoltaic capacity constraint is expressed as... In the formula, θ = cos -1 PF minPower factor limitation for photovoltaic power generation output power, PF min This is the minimum power factor. These represent the active and reactive power of the photovoltaic system at photovoltaic access node j, respectively. dec,j Let be the reduction in photovoltaic power at photovoltaic access node j; in the optional scheme of this application, the power factor cosθ = 0.9 is taken; the energy storage system constraints are expressed by equations (4) and (5):

[0063]

[0064] In the formula, It is a Boolean variable. These are the ESS discharge power and charging power of distribution network node j, respectively; Δt represents the maximum power limit for charging and discharging of the ESS at distribution network node j; Δt is the time during which this ADN needs to provide power support to the adjacent ADN. The initial SOC stored in the ESS for distribution network node j; η is the maximum amount of electricity that the ESS at distribution network node j can store; η is the charging and discharging efficiency of the ESS, which is taken as 0.9 in the optional scheme of this application.

[0065] Based on the above model, the three normally operating distribution networks initiate network reconfiguration optimization calculations based on optimal power transfer to determine and fix their respective optimal network topologies. This allows for power support based on the optimal network topology during the second phase of power restoration of the faulty distribution network, thereby improving power restoration efficiency while saving operating costs. The optimal network topologies of the three normally operating distribution networks obtained in the first phase are as follows: Figure 4 As shown, the introduction of each part is as follows: Figure 3 Consistent.

[0066] The first constraint includes at least one of the following: second linear power flow constraint, interconnection boundary equation constraint, node voltage constraint, photovoltaic capacity constraint, and energy storage system constraint.

[0067] Specifically, since the faulty distribution network prioritizes loads based on their importance, the power restoration process must prioritize picking up critical loads to ensure their normal operation. Therefore, the second linear power flow constraint applicable to the faulty distribution network is improved as follows: In its formula Z j This is a Boolean variable representing load picking; a value of 0 indicates that the corresponding load is picked, and a value of 1 indicates that the corresponding load is not picked. unode σ represents the set of nodes in a faulty distribution network; uline P represents the set of lines in a faulty distribution network. j and Q j These are the active power and reactive power of the faulty distribution network node j, respectively. These are the ESS discharge power and charging power of all nodes j in the faulty distribution network, respectively. These are the net active power and net reactive power of the load at the faulty distribution network node j, respectively, input to the energy storage device. These represent the active and reactive power of the photovoltaic system at the faulty distribution network node j, respectively; P jk and Q jk These represent the active power and reactive power flowing through line jk, respectively. The corresponding voltage constraint is then improved as follows: and Among them, X ij V is the reactance of line ij. j,min and V j,max Let σ be the minimum and maximum values ​​of the voltage at node j in the faulty distribution network, respectively. line R is the set of lines in a normal distribution network. ij P represents the resistance of line ij in a normal distribution network. ij and Q ij These represent the active and reactive power flowing through line ij in a normal distribution network, respectively. j and U i These are the squared voltage values ​​of nodes j and i in the faulty power supply and distribution network, respectively.

[0068] The relevant constraints for flexible interconnection between fault and normal distribution networks using SOP are given by equations (6) and (7):

[0069]

[0070] In the formula, The capacity input from distribution network node j to SOP; The active power input from distribution network node j to SOP; The reactive power input from the distribution network node to the SOP; η represents the port loss corresponding to the converter port at photovoltaic access node j. SOP The loss factor for the SOP port; The active power input to SOP in the distribution network x.

[0071] Interconnection boundary equality constraints are expressed as Among them, P ZB,x U is the active power output to ZB by the xth normal distribution network. ZB,x Let Q be the squared voltage value of the x-th normal distribution network access node j. ZB,x Let σ be the reactive power output to ZB by the xth normal distribution network. unode This represents the set of nodes in a faulty distribution network.

[0072] The node voltage constraints, net load constraints, PV and ESS constraints of the faulted distribution network are consistent with the constraints of the above network reconfiguration model.

[0073] Step S202, First determination step: Decompose the optimization model according to the decomposition algorithm to obtain the main optimization model of the faulty distribution network and multiple sub-optimization models of the normal distribution network;

[0074] Specifically, based on GBD decomposition technology and the actual operation and control mode of the distribution network, and considering the protection of privacy data of each distribution network, the global optimization problem is decomposed into a main optimization model for power supply restoration of faulty distribution networks and sub-optimization models for voltage support and power transfer of each normal distribution network. The main optimization model for power supply restoration of faulty distribution networks and the sub-optimization models for each normal distribution network are as follows:

[0075] Where, σ uline σ represents the set of lines in a faulty distribution network. unode σ represents the set of nodes in a faulty distribution network. PV Let λ be the set of photovoltaic access nodes. loss For network loss costs, λ PV For the cost of photovoltaic power generation, U base P is the squared value of the voltage at the slack node. dec,j R represents the reduction in photovoltaic power at node j of the distribution network. ij P represents the resistance of line ij in a normal distribution network. ij and Q ij These represent the active and reactive power flowing through line ij in a normal distribution network, respectively, and k j P represents the load weight level at node j during load picking. re,j Let P be the load pickup at node j of the distribution network. dec,j Let represent the reduction in photovoltaic power at node j in the distribution network. This step decomposes the global optimization problem and achieves global optimization scheduling through the interaction of boundary information, reducing the communication burden. At the same time, it places lower demands on the computing power of a single controller, thus avoiding the curse of dimensionality.

[0076] After decomposing the global optimization model into a main optimization model for a faulty distribution network and multiple sub-optimization models for normal distribution networks, the distributed iterative calculation process of GBD is executed. The GBD algorithm has good convergence performance, and its converged results are consistent with those of the traditional centralized algorithm. However, this algorithm has a smaller communication burden, lower communication investment costs, and lower computing power requirements for the controller, which can better improve the overall power supply recovery rate.

[0077] Step S203, Second Determination Step: Solve the main optimization model and multiple sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model. Based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value, determine the maximum value of the objective function value of the optimization model. The parameters used to solve the first and second objective function values ​​are different.

[0078] Specifically, the detailed process of solving the main optimization model and multiple sub-optimization models using the GBD distributed algorithm is as follows: Figure 5 As shown, the global optimization objective function is initialized to its maximum and minimum values. The faulty distribution network solves the master optimization model based on the cutting plane constraint and updates the minimum value of the global optimization objective function. The faulty distribution network transmits boundary decision variables to the normal distribution network sub-optimization model. The normal distribution network substitutes the boundary decision variables into the sub-optimization model and determines whether the normal distribution network sub-optimization model has a feasible solution. If the first determination indicates no, the normal distribution network solves the relaxation model sub-optimization model, constructs and sends feasible cutting plane constraints to the faulty distribution network, and re-executes the process of the faulty distribution network solving the master optimization model based on the cutting plane constraint and updating the minimum value of the global optimization objective function. The faulty distribution network transmits boundary decision variables to the normal distribution network sub-optimization model. The normal distribution network inputs boundary decision variables into the sub-optimization model until the first judgment result indicates yes. Then, the normal distribution network transmits the obtained objective function value and the constructed optimal cutting plane constraint to the fault distribution network. It judges whether the maximum value minus the minimum value is less than the preset value 'a'. If the second judgment result indicates no, the fault distribution network updates the maximum value of the global optimization objective function, re-executes the fault distribution network to solve the main optimization model based on the cutting plane constraint and updates the minimum value of the global optimization objective function. The fault distribution network transmits boundary decision variables to the normal distribution network sub-optimization model. The normal distribution network inputs boundary decision variables into the sub-optimization model until the second judgment result indicates yes, and outputs the maximum or minimum value of the optimized structure.

[0079] Solving the main optimization model and multiple sub-optimization models involves the following steps:

[0080] Step S401: Initialize the initial values ​​of the boundary variables of the faulty distribution network at least once.

[0081] In one optional implementation, the faulty distribution network ADN-1 and the normal distribution network ADN-4 are flexibly interconnected using a Standard Operating Procedure (SOP). Since the SOP has excellent characteristics in controlling the converter output voltage, only active and reactive power are exchanged between the two interconnected distribution networks. The SOP switches the control mode on the fault side to Vf mode and the control mode on the normal side to Vdc-Q mode. The fault-side output voltage is treated as an independent optimization variable. Therefore, the feasible initial value of the fault-side boundary variable for the flexible interconnection is... These represent the active power and reactive power at node j of the converter in the faulty distribution network, respectively.

[0082] Step S402: Solve the objective function value of the sub-optimization model based on the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network.

[0083] Specifically, if the sub-optimization model of the normal distribution network ADN-x has a feasible solution, the optimal cut number is increased by 1, and the Lagrange multipliers corresponding to the boundary equality constraints are solved. The maximum value of the objective function of the model is updated using the obtained sub-optimization objective function value of ADN-x, and the optimal cut plane constraint-complementary master optimization is constructed. If the sub-optimization model of the normal distribution network does not have a feasible solution, the feasible cut number is increased by 1, and slack variables are introduced. Relax the sub-optimization model of the distribution network.

[0084] Step S403: Solve the main optimization model based on the boundary variables of the normal distribution network and the objective function value of the sub-optimization model to obtain the first objective function value;

[0085] Specifically, the faulty distribution network is based on the boundary variable y transmitted by each normal distribution network. j The objective function values ​​of each normal distribution network sub-optimization model are used to solve the power supply restoration model of this network. The maximum value of the objective function value of the global optimization model is updated by the sum of the objective function values ​​of all normal distribution network sub-optimization models and the objective function value of the faulty distribution network main optimization model, which is the upper bound UB.

[0086] Step S404: Obtain the second objective function value based at least on the number of feasible cuts and the feasible cut plane constraints of the faulty distribution network based on all normal distribution networks.

[0087] Specifically, the faulty distribution network solves the main optimization model of the faulty distribution network based on the optimal number of cuts, optimal cut plane constraints or feasible number of cuts, feasible cut plane constraints, and all variables y in the main optimization model of the faulty distribution network, and obtains the second objective function value of the main optimization model of the faulty distribution network after cut set processing.

[0088] The above-mentioned fault distribution network main optimization model is expressed in a compact form. In the formula, f fault (y) represents the objective function value of the main optimization of the fault distribution network; Y includes s, t, and y represents the set of all variables in the main optimization model of the fault distribution network; Z fault (y) represents all constraints in the fault distribution network master optimization model; LBD x This represents the optimal value obtained under the optimal cutting plane constraint returned by the x-th normal distribution network. Indicates the optimal cutting plane constraint; This indicates a feasible cutting plane constraint.

[0089] The maximum and minimum values ​​of the objective function of the global optimization model for power supply restoration, the number of iterations, the number of optimal cutting planes for the normal distribution network, the number of feasible cutting planes, and the feasible initial values ​​of the boundary variables for the faulty distribution network are determined. These are feasible initial values ​​for the active power, reactive power, and squared voltage amplitude at node j of the converter in the faulty distribution network. The values ​​of active power, reactive power, and voltage, which are pre-set by the program to ensure the feasibility of the first iteration, are understood as feasible variable values, not optimal variable values.

[0090] The determination method also includes the following steps:

[0091] Step S501: Based on the boundary equality constraints of the normal distribution network, obtain the Lagrange multipliers. The Lagrange multipliers include: active power, reactive power, and voltage amplitude squared corresponding to the boundary equality constraints.

[0092] Step S502: Construct feasible cutting plane constraints for the normal distribution network based on the Lagrange multipliers.

[0093] Specifically, if the sub-optimization model of the normal distribution network ADN-x has a feasible solution, the optimal cut number increases by 1, and the Lagrange multipliers corresponding to the boundary equality constraints are calculated as follows: in, These are the Lagrange multipliers for active power, reactive power, and the square of the voltage magnitude corresponding to the boundary equality constraints of ADN-x. Similarly, the Lagrange multipliers for the boundary equality constraints of ADN-4 do not include the voltage term. The obtained ADN-x sub-optimizes the objective function value f. norm,x Update the upper bound UB of the objective function value of the model. x And construct the optimal cutting plane constraint to complete the principal optimization, the optimal cutting plane constraint satisfies In the formula, P represents the optimal cutting plane constraint. ZB,x U is the active power output to ZB by the xth normal distribution network. ZB,x Let Q be the squared voltage value of the x-th normal distribution network access node j. ZB,x Let P be the reactive power output to ZB of the xth normal distribution network. j and Q j These represent the active power and reactive power of photovoltaic access node j, respectively. j Let be the square of the voltage at a normal photovoltaic access node j. These are the Lagrange multipliers for active power, reactive power, and the square of voltage magnitude, respectively, corresponding to the boundary equality constraints of ADN-x.

[0094] The determination method also includes the following steps:

[0095] Step S601: Construct a relaxation model of the sub-optimization model, solve the relaxation model to obtain the Lagrange multipliers, which include: active power, reactive power and voltage amplitude squared corresponding to the boundary equality constraints;

[0096] Step S602: Construct feasible cutting plane constraints for the normal distribution network based on the Lagrange multipliers.

[0097] Specifically, if the normal distribution network sub-optimization model has no feasible solution, the feasible cut number is increased by 1, and slack variables are introduced. The distribution network sub-optimization model is relaxed, and the objective function of the sub-optimization model is transformed into Equation (8). The sub-optimization model is then solved. Based on the original distribution network sub-optimization model, the relaxation methods of the distribution network sub-optimization are shown in Equations (8), (9) and (10):

[0098]

[0099] In the formula, the objective function of the distribution network sub-optimization model is changed to Equation (8), which means minimizing the sum of relaxed variables; the boundary equality constraint is changed to Equation (10), which means relaxing the boundary equality constraint. P is a slack variable. ZB,x U is the active power output to ZB by the xth normal distribution network. ZB,x Let Q be the squared voltage value of the x-th normal distribution network access node j. ZB,x This represents the reactive power output to ZB from the xth normal distribution network.

[0100] The optimal boundary variable values ​​are obtained by optimizing the above-mentioned distribution network relaxation model. And the Lagrange multipliers λ1~λ6 corresponding to the above three relaxation equality constraints, let in, To optimize the solution The optimal variable value, Let j be the optimal value of active power transmitted at the boundary of distribution network node j. and These represent the optimal values ​​for reactive power and voltage, respectively. Since the output voltage of the distribution network interconnected via SOPs can be autonomously controlled, the ADN-4 relaxation model through flexible interconnection does not include voltage relaxation equality constraints, and similarly, does not generate corresponding Lagrange multipliers. The upper bound UB of the objective function of the distribution network sub-optimization model is... xKeeping the constraints unchanged, construct a feasible cutting plane constraint-based master optimization. The feasible cutting plane constraint satisfies: In the formula, This represents a feasible cut plane constraint. The feasible cut constraint constructed by ADN-4 does not include the voltage term of the cut plane constraint polynomial mentioned above; P ZB,x U is the active power output to ZB by the xth normal distribution network. ZB,x Let Q be the squared voltage value of the x-th normal distribution network access node j. ZB,x Let P be the reactive power output to ZB of the xth normal distribution network. j and Q j These represent the active and reactive power of node j in the distribution network, U. j Let be the squared voltage value of load node j in the distribution network.

[0101] Step S204, Third Determination Step: Determine the minimum value of the objective function of the optimization model based on the sum of the objective function values ​​of the sub-optimization model and the second objective function value;

[0102] Specifically, the minimum objective function value of the global optimization model, i.e., the lower bound LB, is updated using the sum of the first and second objective function values ​​of the main optimization of the faulty distribution network and the sub-optimization objective function values ​​of all normal distribution networks. This is achieved by using the boundary variable y. j The optimal value is The value is assigned for use in the next round of iteration.

[0103] Step S205, Looping Step: If the difference between the maximum and minimum values ​​is greater than a preset threshold, re-execute the second determination step and the third determination step at least once;

[0104] Specifically, the first, second, and third determination steps are repeated until the upper and lower bound deviations of the objective function value of the global optimization model are less than a preset value 'a'. Then, the solution result of the global optimization model for fault recovery in this application converges, yielding the globally optimal solution for distributed power supply recovery in a multi-interconnected distribution network. In one specific embodiment of this application, the preset value 'a' is 10. -6 The iterative convergence process of the global objective function value is as follows: Figure 6 As shown. After multiple iterations, at iteration number 4, the global optimization objective function value UB (or LB) obtained after algorithm convergence is 106.2997 yuan. The simulation results of distributed power supply restoration of interconnected distribution networks based on the GBD algorithm are as follows. Figure 7 As shown, the introduction of each part is as follows: Figure 3 Consistent.

[0105] Step S206, Fourth Determination Step: When the difference between the maximum and minimum values ​​is less than or equal to a preset threshold, determine the optimal solution of the optimization model, and restore the power supply to the faulty distribution network according to the optimal solution.

[0106] Specifically, after the GBD algorithm converges in the second stage, the simulation results of distributed power supply recovery obtained show the situation of active photovoltaic power output-photovoltaic system nodes (PV1~PV5) and reactive photovoltaic power output-photovoltaic system nodes in the faulted distribution network as follows: Figure 8 As shown. Comparing the distributed simulation results obtained in the second stage with the centralized simulation results, the boundary transmission power, the output voltage optimized by SOP, and the grid reconfiguration results of the faulty distribution network obtained by the two methods are consistent. Among them, the global optimization objective function value under the centralized architecture is 106.3002 yuan, which is basically consistent with the result obtained by the strategy in this application, and the power supply recovery rate is 100% in both cases.

[0107] After the power is restored through information exchange and coordination between the distribution networks, the power supplied by the SOP with a larger transmission capacity is about 4 to 5 times that supplied by the TS. It plays an important role in the power restoration process. While giving full play to the flexible power flow control capability of the SOP, it also alleviates the power supply pressure of the TS to a certain extent and avoids the problem of limited line capacity when the TS is transmitting.

[0108] 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, and 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.

[0109] This application also provides a device for determining power restoration in a faulty distribution network. It should be noted that this device can be used to execute the method for determining power restoration in a faulty distribution network 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.

[0110] The following describes the device for determining power restoration of a faulty distribution network provided in the embodiments of this application.

[0111] Figure 9 This is a schematic diagram of a device for determining the power supply restoration of a faulty distribution network according to an embodiment of this application. Figure 9As shown, the device includes: a construction module 10, a first determination module 20, a second determination module 30, a third determination module 40, a loop module 50, and a fourth determination module 60. The construction module 10 is used to perform the construction step, that is, to construct an optimization model for power supply restoration of the faulted distribution network based on at least a number of first constraints. The parameters of the optimization model include at least the active power of the power transferred by the tie switch and the active power of the power transferred by the smart soft switch. The first determination module 20 is used to perform the first determination step, that is, to decompose the optimization model according to the decomposition algorithm to obtain the main optimization model of the faulted distribution network and a number of sub-optimization models of the normal distribution network. The second determination module 30 is used to perform the second determination step, that is, to solve the main optimization model and the number of sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first objective function of the main optimization model. The first objective function value is determined by summing the objective function value of the sub-optimization model and the first objective function value. The parameters used to solve for the first and second objective function values ​​are different. The third determination module 40 is used to execute the third determination step, that is, to determine the minimum objective function value of the optimization model based on the sum of the objective function value of the sub-optimization model and the second objective function value. The loop module 50 is used to execute the loop step, that is, if the difference between the maximum and minimum values ​​is greater than a preset threshold, the second and third determination steps are re-executed at least once. The fourth determination module 60 is used to execute the fourth determination step, that is, if the difference between the maximum and minimum values ​​is less than or equal to a preset threshold, the optimal solution of the optimization model is determined, and the power supply to the faulty distribution network is restored based on the optimal solution.

[0112] The aforementioned device for determining power restoration of a faulty distribution network according to this application includes: a construction module, a first determination module, a second determination module, a third determination module, a loop module, and a fourth determination module. The construction module is used to construct an optimization model for power restoration of the faulty distribution network based on at least a plurality of first constraints. The parameters of the optimization model include at least the active power of the power supplied by the tie switch and the active power of the power supplied by the smart soft switch. The first determination module is used to decompose the optimization model according to a decomposition algorithm to obtain a main optimization model of the faulty distribution network and a plurality of sub-optimization models of the normal distribution network. The second determination module is used to solve the main optimization model and the plurality of sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first constraint values ​​of the main optimization model. The objective function value and the second objective function value are summed based on the objective function value of the sub-optimization model and the first objective function value to determine the maximum value of the objective function value of the optimization model. The parameters used to solve the first and second objective function values ​​are different. The third determination module determines the minimum value of the objective function value of the optimization model based on the sum of the objective function values ​​of the sub-optimization model and the second objective function value. The loop module re-executes the second and third determination steps at least once if the difference between the maximum and minimum values ​​is greater than a preset threshold. The fourth determination module determines the optimal solution of the optimization model if the difference between the maximum and minimum values ​​is less than or equal to a preset threshold, and restores power supply to the faulty distribution network based on the optimal solution. This device solves the problems of limited transmission power of distribution network lines, stringent TS interconnection conditions, large computational load, and susceptibility to dimensionality curse in existing technologies. It reduces communication investment costs and communication burden while protecting the privacy data of different communication entities. Furthermore, by decomposing the global problem, it alleviates computational pressure to some extent and improves power restoration efficiency. Simultaneously, the use of TS and SOP for joint power transfer between distribution networks combines the advantages of traditional hard switches and intelligent soft switches, improving the overall power restoration rate.

[0113] In some examples, the construction module includes a first construction submodule, a second construction submodule, and a third construction submodule. The first construction submodule is used to establish an optimization model of the normal distribution network based on the operating cost of the normal distribution network and the power of the transferred power. The second construction submodule is used to perform network reconstruction on the optimization model of the normal distribution network to obtain a network reconstruction model of the normal distribution network. The third construction submodule is used to determine the network topology of the normal distribution network based on the network reconstruction model, and to construct an optimization model for power restoration of the faulty distribution network based on the network topology and multiple first constraints. By constructing optimization models for each normal distribution network and obtaining and fixing the optimal network topology of each normal distribution network through network reconstruction, a foundation is provided for subsequent power transfer and restoration operations, thereby improving power restoration efficiency while saving operating costs.

[0114] In some instances, the constraints of the second construction submodule include at least one of the following: a first linear power flow constraint, a node voltage constraint, a photovoltaic capacity constraint, and an energy storage system constraint. These constraints of the second construction submodule are used to construct an accurate distribution network optimization model, providing a foundation for restoring power to the entire faulted distribution network and reducing restoration costs.

[0115] In some instances, the first constraint in the building module includes at least one of the following: second linear power flow constraint, interconnection boundary equality constraint, node voltage constraint, photovoltaic capacity constraint, and energy storage system constraint. The first constraint in the building module is used to construct an accurate global optimization model for power restoration of the interconnected distribution network, providing a foundation for achieving full power restoration of the entire faulted distribution network and reducing restoration costs.

[0116] In some instances, the second determining module includes a first determining submodule, a second determining submodule, a third determining submodule, and a fourth determining submodule. The first determining submodule is used to initialize the initial values ​​of the boundary variables of the faulty distribution network at least once. The second determining submodule is used to solve for the objective function value of the sub-optimization model based on the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network. The third determining submodule is used to solve for the main optimization model based on the boundary variables of the normal distribution network and the objective function value of the sub-optimization model to obtain the first objective function value. The fourth determining submodule is used to obtain the second objective function value based at least on the feasible cut number and feasible cut plane constraints of the faulty distribution network based on all normal distribution networks. The GBD algorithm has good convergence performance, and its converged results are consistent with those of the traditional centralized algorithm. However, this algorithm has a smaller communication burden, lower communication investment cost, and lower computing power requirements for the controller, and can better improve the overall power restoration rate.

[0117] In some instances, the second determining module further includes a fifth determining submodule and a sixth determining submodule. The fifth determining submodule is used to obtain the Lagrange multipliers based on the boundary equality constraints of the normal distribution network. The Lagrange multipliers include: active power, reactive power, and the square of voltage magnitude corresponding to the boundary equality constraints. The sixth determining submodule is used to construct feasible cutting plane constraints for the normal distribution network based on the Lagrange multipliers. This is applicable when the sub-optimization model of the normal distribution network ADN-x has feasible solutions.

[0118] In some instances, the second determining module further includes a seventh determining submodule and an eighth determining submodule. The seventh determining submodule is used to construct a relaxed model of the sub-optimization model, solve the relaxed model to obtain Lagrange multipliers, and the Lagrange multipliers include: active power, reactive power, and the square of voltage magnitude corresponding to the boundary equality constraints. The eighth determining submodule is used to construct feasible cutting plane constraints for the normal distribution network based on the Lagrange multipliers. This is applicable when the sub-optimization model of the normal distribution network ADN-x does not have a feasible solution.

[0119] 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.

[0120] 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.

[0121] 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 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0129] 1) The method for determining power supply restoration of the faulty distribution network described in this application firstly constructs an optimization model for power supply restoration of the faulty distribution network based on at least several first constraints. The parameters of the optimization model include at least the active power of the power supplied by the tie switch and the active power of the power supplied by the smart soft switch. Then, the optimization model is decomposed according to a decomposition algorithm to obtain a main optimization model of the faulty distribution network and multiple sub-optimization models of the normal distribution network. Then, the main optimization model and multiple sub-optimization models are solved to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model. The maximum value of the objective function value of the optimization model is determined based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value. The parameters used to solve the first and second objective function values ​​are different. Then, the minimum value of the objective function value of the optimization model is determined based on the sum of the objective function values ​​of the sub-optimization models and the second objective function value. Then, if the difference between the maximum and minimum values ​​is greater than a preset threshold, the second and third determination steps are repeated at least once. Finally, if the difference between the maximum and minimum values ​​is less than or equal to the preset threshold, the optimal solution of the optimization model is determined, and the power supply of the faulty distribution network is restored based on the optimal solution. This method employs a distributed communication architecture to address the power restoration problem in interconnected distribution networks. By using the GBD distributed algorithm, it solves the problems of limited transmission power of distribution network lines, stringent TS interconnection conditions, large computational load, and susceptibility to dimensionality curse in existing technologies. While protecting the privacy of different communication entities, it reduces communication investment costs and burden. Furthermore, by decomposing the global problem, it alleviates computational pressure to some extent and improves power restoration efficiency. Simultaneously, it uses TS and SOP for joint power transfer between distribution networks, combining the advantages of traditional hard switches and intelligent soft switches, thus improving the overall power restoration rate.

[0130] 2) The above-mentioned device for determining power restoration of a faulty distribution network according to this application includes: a construction module, a first determination module, a second determination module, a third determination module, a loop module, and a fourth determination module. The construction module is used to construct an optimization model for power restoration of the faulty distribution network based on at least a plurality of first constraints. The parameters of the optimization model include at least the active power of the power supplied by the tie switch and the active power of the power supplied by the smart soft switch. The first determination module is used to decompose the optimization model according to a decomposition algorithm to obtain a main optimization model of the faulty distribution network and a plurality of sub-optimization models of the normal distribution network. The second determination module is used to solve the main optimization model and the plurality of sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the fourth determination model of the main optimization model. The device employs a first objective function value and a second objective function value. Based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value, the maximum value of the objective function value of the optimization model is determined. Different parameters are used to solve for the first and second objective function values. A third determination module determines the minimum value of the objective function value of the optimization model based on the sum of the objective function values ​​of the sub-optimization models and the second objective function value. A loop module re-executes the second and third determination steps at least once if the difference between the maximum and minimum values ​​exceeds a preset threshold. A fourth determination module determines the optimal solution of the optimization model if the difference between the maximum and minimum values ​​is less than or equal to a preset threshold, and restores power supply to the faulty distribution network based on the optimal solution. This device solves the problems of limited transmission power in distribution network lines, stringent TS interconnection conditions, large computational load, and susceptibility to dimensionality curse in existing technologies. It reduces communication investment costs and burdens while protecting the privacy data of different communication entities. Furthermore, by decomposing the global problem, it alleviates computational pressure to some extent and improves power restoration efficiency. Simultaneously, the use of TS and SOP for joint power transfer between distribution networks combines the advantages of traditional hard switches and intelligent soft switches, improving the overall power restoration rate.

[0131] The above are merely embodiments of this application and are not intended to limit the scope of 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 scope of the claims of this application.

Claims

1. A method for determining power restoration in a faulted distribution network, characterized in that, The power distribution network is electrically connected to tie switches and smart soft switches, and the determination method includes: Construction steps: Based on at least several first constraints, construct an optimization model for power supply restoration of the faulted distribution network. The parameters of the optimization model include at least the active power of the power transferred by the tie switch and the active power of the power transferred by the smart soft switch. First determination step: Decompose the optimization model according to the decomposition algorithm to obtain the main optimization model of the faulty distribution network and multiple sub-optimization models of the normal distribution network; The second determination step is to solve the main optimization model and the multiple sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model. Based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value, the maximum value of the objective function value of the optimization model is determined. The first objective function value and the second objective function value are solved using different parameters. The third determination step: Based on the sum of the objective function values ​​of the sub-optimization model and the second objective function value, determine the minimum value of the objective function of the optimization model; Looping step: If the difference between the maximum value and the minimum value is greater than a preset threshold, the second determination step and the third determination step are re-executed at least once; Fourth determination step: If the difference between the maximum value and the minimum value is less than or equal to a preset threshold, determine the optimal solution of the optimization model, and restore the power supply to the faulty distribution network according to the optimal solution; Based on the operating cost of the normal distribution network and the power of the transferred power supply, an optimization model of the normal distribution network is established; the optimization model of the normal distribution network is reconstructed to obtain a network reconstruction model of the normal distribution network; based on the network reconstruction model, the network topology of the normal distribution network is determined; based on the network topology and multiple first constraints, an optimization model for power supply restoration of the faulty distribution network is constructed. Solving the main optimization model and the multiple sub-optimization models includes: The initial values ​​of the boundary variables of the faulty distribution network are initialized at least once; the objective function value of the sub-optimization model is solved based on the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network; the main optimization model is solved based on the boundary variables of the normal distribution network and the objective function value of the sub-optimization model to obtain the first objective function value; and the second objective function value is obtained based at least on the feasible cut number and feasible cut plane constraints of the faulty distribution network based on all the normal distribution networks.

2. The determination method according to claim 1, characterized in that, The determination method further includes: Based on the boundary equality constraints of the normal distribution network, the Lagrange multipliers are obtained, which include: the active power, reactive power, and voltage amplitude squared corresponding to the boundary equality constraints. Based on the Lagrange multipliers, construct the feasible cutting plane constraints for the normal distribution network.

3. The determination method according to claim 1, characterized in that, The determination method further includes: Construct a relaxation model of the sub-optimization model, solve the relaxation model to obtain the Lagrange multipliers, the Lagrange multipliers include: the active power, reactive power and voltage amplitude squared corresponding to the boundary equality constraints; Based on the Lagrange multipliers, construct the feasible cutting plane constraints for the normal distribution network.

4. The determination method according to claim 1, characterized in that, The constraints of the network reconfiguration model include at least one of the following: first linear power flow constraint, node voltage constraint, photovoltaic capacity constraint, and energy storage system constraint.

5. The determination method according to claim 1, characterized in that, The first constraint includes at least one of the following: second linear power flow constraint, interconnection boundary equation constraint, node voltage constraint, photovoltaic capacity constraint, and energy storage system constraint.

6. A device for determining power restoration in a faulted distribution network, characterized in that, The distribution network is electrically connected to tie switches and intelligent soft switches, including: A construction module is used to perform the construction steps: constructing an optimization model for power supply restoration of the faulted distribution network based on at least a number of first constraints, wherein the parameters of the optimization model include at least the active power of the power supply transferred by the tie switch and the active power of the power supply transferred by the smart soft switch; The first determining module is used to perform the first determining step: decompose the optimization model according to the decomposition algorithm to obtain the main optimization model of the faulty distribution network and multiple sub-optimization models of the normal distribution network; The second determining module is used to perform the second determining step: solving the main optimization model and the multiple sub-optimization models to obtain the objective function values ​​of the sub-optimization models and the first and second objective function values ​​of the main optimization model; determining the maximum value of the objective function value of the optimization model based on the sum of the objective function values ​​of the sub-optimization models and the first objective function value; the parameters used to solve the first objective function value and the second objective function value are different. The third determining module is used to perform the third determining step: determining the minimum value of the objective function value of the optimization model based on the sum of the objective function value of the sub-optimization model and the second objective function value; A loop module is used to execute loop steps, and if the difference between the maximum value and the minimum value is greater than a preset threshold, to re-execute the second determination step and the third determination step at least once; The fourth determining module is used to perform the fourth determining step: when the difference between the maximum value and the minimum value is less than or equal to a preset threshold, determine the optimal solution of the optimization model, and restore the power supply of the faulty distribution network according to the optimal solution; The construction module includes a first construction submodule, a second construction submodule, and a third construction submodule. The first construction submodule is used to establish an optimization model of the normal distribution network based on the operating cost of the normal distribution network and the power of the transferred power supply. The second construction submodule is used to perform network reconstruction on the optimization model of the normal distribution network to obtain a network reconstruction model of the normal distribution network. The third construction submodule is used to determine the network topology of the normal distribution network based on the network reconstruction model, and construct an optimization model for power supply restoration of the faulty distribution network based on the network topology and multiple first constraints. The second determining module includes a first determining submodule, a second determining submodule, a third determining submodule, and a fourth determining submodule. The first determining submodule is used to initialize the initial values ​​of the boundary variables of the faulty distribution network at least once. The second determining submodule is used to solve the objective function value of the sub-optimization model based on the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network. The third determining submodule is used to solve the main optimization model based on the boundary variables of the normal distribution network and the objective function value of the sub-optimization model to obtain the first objective function value. The fourth determining submodule is used to obtain the second objective function value based at least on the feasible cut number and feasible cut plane constraints of the faulty distribution network based on all the normal distribution networks.

7. 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 method for determining the restoration of power supply to a faulty distribution network as described in any one of claims 1 to 5.

8. 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 performing a determination method for restoring power supply to a faulted distribution network as described in any one of claims 1 to 5.

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