Method and device for determining power supply recovery of fault power distribution network and electronic equipment

Abstract

The invention provides a fault power distribution network power supply recovery determination method and device and electronic equipment, and the method comprises the steps: constructing a fault power distribution network power supply recovery optimization model according to a plurality of first constraint conditions; decomposing the optimization model according to a decomposition algorithm to obtain a fault power distribution network main optimization model and a plurality of normal power distribution network sub optimization models; solving the main optimization model and the plurality of sub optimization models to respectively obtain a first objective function value, a second objective function value and an objective function value, and determining the maximum value of the objective function value of the optimization model according to the sum of the objective function values of the sub optimization models and the first objective function value; determining the minimum value of the objective function value of the optimization model according to the sum of the objective function value of the sub-optimization model and the second objective function value; and comparing the difference between the maximum value and the minimum value with a preset threshold value, and recovering the power supply of the fault power distribution network according to conditions. According to the method, the power supply recovery rate is effectively improved on the premise of controlling the cost.

Description

Technical Field

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

[0002] The distribution network is an important bridge and interactive hub connecting the transmission network and end users, and is an important part of the power system. In recent years, the use of energy mutual assistance between multiple interconnected distribution networks to restore the power supply of faulty distribution networks has become a major direction in the field of distribution network power restoration. However, most of the current research on distribution network power restoration at home and abroad only considers traditional tie switches (TS, Tie Switch) as interconnection switches. However, TS has a single control method, complicated operation steps, and harsh interconnection conditions. In addition, most related studies use a centralized communication architecture for information exchange between distribution networks. For the current distribution network with increasingly complex structure and scale, it is not well qualified for the task of power restoration in fault scenarios. Therefore, it is necessary to adopt a more efficient interconnection mode and communication architecture to improve the efficiency of power restoration of faulty distribution networks.

[0003] In the existing technology, for today's large-scale and complex interconnected distribution networks, most studies only consider TS interconnection with harsh connection conditions or flexible interconnection with high investment costs, and there are few studies on the hybrid coordinated power restoration of the two interconnection methods in practical applications. In addition, the communication architecture of the interconnected distribution network is mostly based on a centralized architecture. It is difficult for existing technologies to improve the power supply restoration rate of the interconnected distribution network while controlling costs, and it faces the problem of dimensionality disaster caused by the large-scale increase in communication volume and computing volume. Summary of the invention

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

[0005] According to one aspect of the present application, a method for determining power supply recovery of a fault distribution network is provided, wherein the distribution network is electrically connected to a tie switch and an intelligent soft switch, and the determination method comprises: a construction step: constructing an optimization model for power supply recovery of the fault 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 transfer power supply of the tie switch and the active power of the transfer power supply of the intelligent soft switch; a first determination step: decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of the fault distribution network and a plurality of sub-optimization models of the normal distribution network; a second determination step: solving the main optimization model and the plurality of sub-optimization models to obtain an objective function value of the sub-optimization model and a first objective function value and a second objective function value of the main optimization model Objective function value, determining the maximum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the first objective function value, the first objective function value and the second objective function value using different parameters for solving; third determination step: determining the minimum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the second objective function value; loop step: when the difference between the maximum value and the minimum value is greater than a preset threshold, re-execute the second determination step and the third determination step at least once; fourth determination 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.

[0006] Optionally, the determination method also includes: establishing 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; performing network reconstruction on the optimization model of the normal distribution network to obtain a network reconstruction model of the normal distribution network; determining the grid topology of the normal distribution network based on the network reconstruction model, and constructing an optimization model for power supply recovery of the faulty distribution network based on the grid topology and multiple first constraints.

[0007] Optionally, solving the main optimization model and the multiple sub-optimization models includes: initializing at least the initial values ​​of the boundary variables of the faulty distribution network; solving the objective function values ​​of the sub-optimization models according to the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network; solving the main optimization model according to the boundary variables of the normal distribution network and the objective function values ​​of the sub-optimization models to obtain the first objective function value; and obtaining the second objective function value at least according to the feasible cut number and feasible cut plane constraints of the faulty distribution network based on all the normal distribution networks.

[0008] Optionally, the determination method also includes: obtaining Lagrange multipliers according to boundary equality constraints of the normal distribution network, the Lagrange multipliers including: active power, reactive power and voltage amplitude square corresponding to the boundary equality constraints; and constructing the feasible cutting plane constraints of the normal distribution network according to the Lagrange multipliers.

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

[0010] Optionally, the constraint conditions of the network reconstruction 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 condition includes at least one of the following: a second linear power flow constraint, an interconnection boundary equality constraint, a node voltage constraint, a photovoltaic capacity constraint, and an energy storage system constraint.

[0012] According to another aspect of the present application, a device for determining power supply recovery of a faulty distribution network is provided, wherein the distribution network is electrically connected to a tie switch and an intelligent soft switch, and comprises: a construction module, configured to execute a construction step: construct an optimization model for power supply recovery of a faulty distribution network according to at least a plurality of first constraints, wherein the parameters of the optimization model at least include the active power of the transfer power supply of the tie switch and the active power of the transfer power supply of the intelligent soft switch; a first determination module, configured to execute a first determination step: decompose the optimization model according to a decomposition algorithm to obtain a main optimization model of a faulty distribution network and a plurality of sub-optimization models of a normal distribution network; and a second determination module, configured to execute a second determination step: solve the main optimization model and the plurality of sub-optimization models to obtain objective function values ​​of the sub-optimization models and first objective function values ​​and second objective function values ​​of the main optimization model. a target function value, determining the 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, wherein the first target function value and the second target function value are solved using different parameters; a third determination module, used to execute a third determination step: determining the minimum 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 second target function value; a loop module, used to re-execute the second determination step and the third determination step at least once when the difference between the maximum value and the minimum value is greater than a preset threshold; a fourth determination module, used to execute a fourth determination step: determining the optimal solution of the optimization model when the difference between the maximum value and the minimum value is less than or equal to a preset threshold, and restoring the power supply of the faulty distribution network according to the optimal solution.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for determining power supply restoration of a faulty distribution network.

[0014] According to another aspect of the present 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 are configured to be executed by the one or more processors, and the one or more programs include a determination method for executing any one of the above-described power supply restoration methods for a faulty distribution network.

[0015] Applying the technical solution of the present application, the above-mentioned method for determining power supply restoration of the fault distribution network first constructs an optimization model for power supply restoration of the fault distribution network according to at least multiple first constraints, and the parameters of the optimization model include at least the active power of the interconnecting switch and the active power of the intelligent soft switch; then, according to the decomposition algorithm, the optimization model is decomposed to obtain a main optimization model of the fault distribution network and multiple sub-optimization models of the normal distribution network; then, the main optimization model and the multiple sub-optimization models are solved to obtain the objective function value of the sub-optimization model and the first objective function value and the second objective function value of the main optimization model; according to the objective function value of the sub-optimization model and the sum of the first objective function value, the maximum value of the objective function value of the optimization model is determined, and the parameters used for solving the first objective function value and the second objective function value are different; then, according to the objective function value of the sub-optimization model and the sum of the second objective function value, the minimum value of the objective function value of the optimization model is determined; then, when the difference between the maximum value and the minimum value is greater than the preset threshold, the second determination step and the third determination step are re-executed at least once; finally, when the difference between the maximum value and the minimum value is less than or equal to the preset threshold, the optimal solution of the optimization model is determined, and the power supply of the fault distribution network is restored according to the optimal solution. This method uses a distributed communication architecture to deal with the power supply restoration problem of interconnected distribution networks. By adopting the generalized Benders decomposition (GBD) algorithm, it solves the problem of limited transmission power of distribution network lines and harsh TS interconnection conditions in the existing technology, and the large amount of calculation is prone to dimensional disasters. While protecting the privacy data of different communication subjects, it reduces the communication investment cost and communication burden. In addition, due to the decomposition of the global problem, it also alleviates the calculation pressure to a certain extent and improves the power supply restoration efficiency. At the same time, TS and intelligent soft switches (SOP, Soft Open Point) are used for joint transfer between distribution networks, taking into account the advantages of traditional hard switches and intelligent soft switches, and improving the overall power supply restoration rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for determining power supply restoration of a faulty power distribution network provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic diagram of a flow chart of a method for determining power supply restoration of a faulty distribution network provided according to an embodiment of the present application is shown;

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

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

[0021] Figure 5 A schematic diagram of a flow chart of a GBD distributed algorithm provided according to an embodiment of the present application is shown;

[0022] Figure 6 A schematic diagram of photovoltaic output data of a fault distribution network after convergence of a GBD distributed algorithm is shown according to an embodiment of the present application;

[0023] Figure 7 A schematic diagram showing a second-stage distributed power supply restoration simulation result provided according to an embodiment of the present application is shown;

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

[0025] Fig. 9 A structural block diagram of a device for determining power supply restoration of a faulty power distribution network provided according to an embodiment of the present application is shown.

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

[0027] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

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

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

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

[0035] ZB (Zone Boundary): Zone 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;

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

[0040] As introduced in the background technology, in the prior art, for today's interconnected distribution networks with huge scale and complex structures, there is a lack of hybrid coordination between TS interconnection with harsh connection conditions and flexible interconnection with high investment costs. In addition, the communication architecture of the interconnected distribution network is mostly based on a centralized architecture. The centralized control mode requires the central controller in the distribution network to uniformly collect and calculate the information of the entire network, and then issue operation instructions. It has fast calculation speed and high power restoration efficiency when processing small-scale distribution networks, but the actual distribution network with increasingly large processing scale and increasingly intelligent control methods may have large communication and calculation volume of the central controller, high communication investment cost, and insufficient ability to deal with single-point failures of the communication system. In order to solve the problem of limited transmission power of distribution network lines and harsh TS interconnection conditions in the prior art, and the large amount of calculation is prone to dimensional disasters, the embodiments of the present application provide a method for determining power supply recovery of a faulty distribution network, a device for determining power supply recovery of a faulty distribution network, a computer-readable storage medium, and an electronic device.

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0042] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for determining power supply recovery of a faulty distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0043] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining power supply restoration of a faulty distribution network in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as 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] In this embodiment, a method for determining power supply recovery of a faulty distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0045] Figure 2 1 is a flow chart of a method for determining power supply restoration of a faulty distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0046] Step S201, a construction step: constructing an optimization model for power supply restoration of a faulty distribution network according to at least a plurality of first constraints, wherein the parameters of the optimization model at least include the active power of the power supply transferred by the tie switch and the active power of the power supply transferred by the intelligent soft switch;

[0047] Specifically, according to at least one of the constraints of linear power flow constraint, node voltage constraint, photovoltaic capacity constraint and ESS constraint, a global optimization model of interconnected distribution network power supply restoration based on tie switch and intelligent soft switch transfer is established. Each distribution network is interconnected with the power-lost distribution network through the transfer tie switch or flexible soft switch, and then provides voltage support 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 realize the restoration of power supply of the entire fault distribution network, and fully coordinate the transferred power of TS and SOP, thereby reducing the power restoration cost.

[0048] Among them, in the first stage, after the reconstruction of the distributed parallel networks of each normal distribution network is determined to be the optimal transfer grid topology, each distribution network is interconnected with the power-off distribution network through the transfer tie line or flexible soft switch, and then provides voltage support and power support to the faulty distribution network. The optimization model for power supply recovery of the faulty distribution network is: In the formula, λ loss is the network loss cost; line is the line set; R ij is the resistance of line ij in the normal distribution network; P ij and Q ij are respectively the active power and reactive power flowing through line ij in the normal distribution network; U base is the square value of the voltage at the equilibrium node; PV is the photovoltaic power generation cost; PV is the set of photovoltaic access nodes; P dec,j is the reduction of photovoltaic power at photovoltaic access node j; σ unode is the node set of the fault distribution network; k j represents the load weight level at node j when the load is picked up, P re,j is the load pickup amount at the photovoltaic access node j.

[0049] The determination method further comprises the following steps:

[0050] Step S301, establishing an optimization model of a normal distribution network according to the operating cost of the normal distribution network and the power of the transferred power supply;

[0051] Step S302, performing network reconstruction on the optimization model of the normal distribution network to obtain a network reconstruction model of the normal distribution network;

[0052] Step S303: determine the grid topology of the normal distribution network according to the network reconstruction model, and construct an optimization model for power supply restoration of the faulty distribution network according to the grid topology and a plurality of first constraints.

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

[0054] In one alternative, the topology of the multi-interconnected distribution network is as follows: Figure 3 As shown in the figure, the topology in the scheme is that two IEEE-33 node distribution systems and two IEEE-69 node distribution systems are interconnected through TS or SOP, among which ADN-1 is the power-off distribution network area due to the failure of the main transformer, and the rest are the normal operating distribution network areas. The three normal distribution networks ADN-2, ADN-3, and ADN-4 all take the operation cost and power transfer income within the network as the optimization objectives, and establish their own distribution network optimization models. ADN-1 and ADN-4 both include ESS1~ESS3 (energy storage system nodes) and PV1~PV5 (photovoltaic system nodes); ADN-2 and ADN-3 both include ESS1~ESS2 and PV1~PV5. The optimal grid topology of each normal distribution network is obtained and fixed through network reconstruction, in preparation for the second stage of power transfer and restoration operations.

[0055] Among them, the network reconstruction optimization model of the normal distribution network in the first stage is: Where σ line is the line set in the normal distribution network; σ PV is the set of photovoltaic access nodes; σ ZB is the set of boundary interaction nodes; loss is the network loss cost; PV is the photovoltaic power generation cost; R ij is the resistance of line ij in the normal distribution network; P ij and Q ij are respectively the active power and reactive power flowing through line ij in the normal distribution network; U base is the square value of the voltage at the equilibrium node; P dec,j is the reduction of photovoltaic power at photovoltaic access node j; P ZB,x The active power output from the xth normal distribution network to ZB, including the active power transferred by TS And the use of SOP flexible transfer They represent the active power input from PV access node j to the regional boundary.

[0056] Among them, the constraints of the network reconstruction 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.

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

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

[0059] Since the state of the line switches will change when the optimal grid is reconfigured, the power flow model needs to consider the disconnection of the line. For this purpose, the Big M method is introduced to establish the relationship between the line power flow and the line switch state variables, and the following constraint equations for the line power flow and node voltage applicable to network reconstruction are obtained. The constraint equations can be expressed by equations (1), (2), and (3):

[0060]

[0061] In the formula, M is a large positive number; σ line is the line set in the normal distribution network; R ij is the resistance of line ij in the normal distribution network; P ij and Q ij are respectively the active power and reactive power flowing through line ij in the normal distribution network; U j and U i are the square values ​​of the voltages at the load nodes j and i in the distribution network respectively; α ij is the switch state variable on line ij.

[0062] In addition, the node voltage constraint is expressed as Where V j,min and V j,max are the minimum and maximum voltages of photovoltaic access node j respectively; α ij is the switch state variable on line ij; the photovoltaic capacity constraint is expressed as Where θ = cos -1 PF minThe power factor limit of the photovoltaic generator set output power, PF min is the minimum power factor, are the PV active power and reactive power of PV access node j, P dec,j is the reduction of photovoltaic power at photovoltaic access node j; in the optional scheme of this application, the power factor cosθ=0.9; the energy storage system constraints are expressed by equations (4) and (5):

[0063]

[0064] In the formula, is a Boolean variable, are the ESS discharge power and charging power of distribution network node j respectively; are the maximum power limits of ESS charging and discharging at distribution network node j, respectively; Δt is the time that the ADN needs to provide power support to the adjacent ADN; is the initial SOC stored in the ESS of distribution network node j; is the maximum amount of electricity that can be stored in the ESS of the distribution network node j; η is the charging and discharging efficiency of the ESS, and in the optional solution of this application, η=0.9.

[0065] According to the above model, the three normally operating distribution networks start the network reconstruction optimization calculation based on the optimal power transfer, determine the optimal grid topology of each and fix it, so that the fault distribution network can start power restoration based on the optimal grid topology for power support in the second stage, thereby improving the power restoration efficiency while saving operating costs. The optimal grid topology of the three normal distribution networks obtained in the first stage is as follows: Figure 4 As shown, the introduction of each part is as follows Figure 3 Consistent.

[0066] The first constraint condition 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.

[0067] Specifically, since the fault distribution network is prioritized according to the importance of the load, the logic of picking up important loads first must be met during the power supply restoration process to ensure the normal operation of important loads. The second linear power flow constraint applicable to the fault distribution network is improved to In the formula, Z j is a Boolean variable representing load picking. When its value is 0, it means that the corresponding load is picked up, and when its value is 1, it means that the corresponding load is not picked up; σ unode represents the node set of the faulty distribution network; σ uline represents the line set of the fault distribution network; P j and Q j are the active power and reactive power of faulty distribution network node j respectively; are the ESS discharge power and charging power of all nodes j in the faulty distribution network, respectively; are the net active power and net reactive power of the load of the faulty distribution network node j input by the energy storage device respectively; are the PV active power and reactive power of the PV device input to the fault distribution network node j; P jk and Q jk are the active power and reactive power flowing through line jk respectively. The corresponding voltage constraint is improved to and Among them, X ij is the reactance of line ij, V j,min and V j,max are the minimum and maximum voltages of fault distribution network node j, σ line is the line set in the normal distribution network, R ij is the resistance of line ij in the normal distribution network, P ij and Q ij are respectively the active power and reactive power flowing through line ij in the normal distribution network, U j and U i are the square values ​​of the voltages at faulty power supply and distribution network nodes j and i respectively.

[0068] The constraints related to the flexible interconnection between faulty and normal distribution networks using SOP are given by equations (6) and (7):

[0069]

[0070] In the formula, is the capacity input to SOP by distribution network node j; is the active power input to SOP from distribution network node j; is the reactive power input to SOP from the distribution network node; is the port loss corresponding to the converter port at the photovoltaic access node j, η SOP is the loss coefficient of the SOP port; is the active power input to SOP from distribution network x.

[0071] The interconnect boundary equality constraint is expressed as Among them, P ZB,x is the active power output from the xth normal distribution network to ZB, U ZB,x is the square value of the voltage at the xth normal distribution network access node j, Q ZB,x is the reactive power output from the xth normal distribution network to ZB, σ unode A collection of nodes representing the faulty distribution network.

[0072] The node voltage constraints of the faulty distribution network, the net load constraints of all distribution networks, and the PV and ESS constraints are consistent with the constraints of the above network reconstruction model.

[0073] Step S202, a first determination step: decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of a fault distribution network and a plurality of sub-optimization models of normal distribution networks;

[0074] Specifically, according to the GBD decomposition technology and the actual operation control mode of the distribution network, and under the premise of protecting the privacy data of each distribution network, the global optimization problem is decomposed into the main optimization model of power supply restoration of the faulty distribution network and the sub-optimization models of voltage support and power transfer of each normal distribution network. Among them, the main optimization model of power supply restoration of the faulty distribution network and the sub-optimization models of each normal distribution network are respectively

[0075] Among them, σ uline represents the set of faulty distribution network lines, σ unode represents the node set of the faulty distribution network, σ PV is the set of photovoltaic access nodes, λ loss is the network loss cost, λ PV is the photovoltaic power generation cost, U base is the square value of the voltage at the equilibrium node, P dec,j is the reduction of photovoltaic power at distribution network node j, R ij is the resistance of line ij in the normal distribution network, P ij and Q ij are respectively the active power and reactive power flowing through line ij in the normal distribution network, k j represents the load weight level at node j when the load is picked up, P re,j is the load pickup at the distribution network node j, P dec,j is the reduction of photovoltaic power at the node j of the distribution network. This step decomposes the global optimization problem, realizes global optimization scheduling through the interaction of boundary information, reduces the communication burden, and at the same time, has low computing power requirements for a single controller, avoiding the dimensionality disaster.

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

[0077] Step S203, a second determination 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 objective function value and the second objective function value of the main optimization model, and determining the maximum objective function value of the optimization model according to the sum of the objective function value of the sub-optimization model and the first objective function value, wherein the first objective function value and the second objective function value are solved using different parameters;

[0078] Specifically, the specific process of using the GBD distributed algorithm to solve the main optimization model and multiple sub-optimization models is as follows: Figure 5 As shown, the maximum and minimum values ​​of the global optimization objective function value are initialized, the fault distribution network solves the main optimization model based on the cutting plane constraint and updates the minimum value of the global optimization objective function value, the fault distribution network transmits the boundary decision variables to the normal distribution network sub-optimization model, the normal distribution network brings the boundary decision variables into the sub-optimization model, and determines whether the normal distribution network sub-optimization model has a feasible solution. When the first judgment indication structure is no, the normal distribution network solves the relaxed model sub-optimization model, constructs and sends the feasible cutting plane constraint to the fault distribution network, and re-executes the fault distribution network to solve the main optimization model based on the cutting plane constraint and update the minimum value of the global optimization objective function value. The fault distribution network transmits the boundary decision variables to the normal distribution network sub-optimization model. The normal distribution network brings the boundary decision variables into the sub-optimization model until the first judgment result indicates yes, so that the normal distribution network transmits the obtained objective function value and the constructed optimal cutting plane constraint to the faulty distribution network, and judges whether the maximum value-minimum value is less than the preset value a. When the second judgment result indicates no, the faulty distribution network updates the maximum value of the global optimization objective function, re-executes the main optimization model based on the cutting plane constraint of the faulty distribution network and updates the minimum value of the global optimization objective function value. The faulty distribution network transmits the boundary decision variables to the normal distribution network sub-optimization model, and the normal distribution network brings the 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] The main optimization model and multiple sub-optimization models are solved, including the following steps:

[0080] Step S401, initializing at least the initial value of the boundary variable of the faulty distribution network;

[0081] In an optional implementation scheme, the fault distribution network ADN-1 and the normal distribution network ADN-4 are connected by SOP. Since SOP has the excellent characteristic of controlling the converter outlet voltage, only active power and reactive power are exchanged between the two distribution networks connected by SOP. The control mode of SOP switching fault side is Vf mode, and the control mode of normal side is Vdc-Q mode. The outlet voltage of the fault side is used as an independent optimization variable. Then the feasible initial value of the boundary variable on the fault side of the flexible interconnection is are the active power and reactive power at the fault distribution network converter node j respectively;

[0082] Step S402, solving the objective function value of the sub-optimization model according to 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 number of optimal cuts increases by 1, and the Lagrange multipliers corresponding to the boundary equality constraints are solved at the same time. The obtained ADN-x sub-optimization objective function value is used to update the maximum value of the objective function value of the model, and the optimal cutting plane constraint is constructed to fill the main optimization. If there is no feasible solution to the sub-optimization model of the normal distribution network, the number of feasible cuts increases by 1, and the slack variable is introduced Relax the distribution network sub-optimization model.

[0084] Step S403, solving the main optimization model according to the boundary variables of the normal distribution network and the objective function value of the sub-optimization model to obtain a first objective function value;

[0085] Specifically, the fault distribution network is based on the boundary variables 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 the network, and the sum of the objective function values ​​of all normal distribution network sub-optimization models and the objective function values ​​of the main optimization model of the faulty distribution network is used to update the maximum value of the objective function value of the global optimization model, that is, the upper bound UB.

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

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

[0088] The above fault distribution network main optimization model is expressed as 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 of the main optimization model of the fault distribution network; Z fault (y) represents all constraints in the main optimization model of the fault distribution network; LBD x represents the optimal value obtained under the optimal cutting plane constraint returned by the x-th normal distribution network; represents the optimal cutting plane constraint; Represents a feasible cutting plane constraint.

[0089] Initialize the maximum and minimum values ​​of the objective function value of the global optimization model for power 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 of the fault distribution network are the feasible initial values ​​of active power, reactive power and square of voltage amplitude at converter node j of the faulty distribution network. The values ​​of active power, reactive power and voltage at the boundaries that are artificially set in advance to ensure the feasibility of the first iteration of the iterative program are understood to be only feasible variable values, not optimal variable values.

[0090] The determination method further comprises the following steps:

[0091] Step S501, according to the boundary equation constraint conditions of the normal distribution network, obtain the Lagrange multiplier, the Lagrange multiplier includes: the active power, reactive power and voltage amplitude square corresponding to the boundary equation constraint;

[0092] Step S502: construct feasible cutting plane constraints of the normal distribution network according to Lagrange multipliers.

[0093] Specifically, if the sub-optimization model of the normal distribution network ADN-x has a feasible solution, the number of optimal cuts increases by 1, and the Lagrange multiplier corresponding to the boundary equality constraint is solved as follows: in, are the Lagrange multipliers of active power, reactive power, and voltage amplitude squared corresponding to the boundary equality constraints of ADN-x. Similarly, the Lagrange multipliers corresponding to the boundary equality constraints of ADN-4 do not contain voltage terms. The obtained ADN-x sub-optimization 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 fill the main optimization, the optimal cutting plane constraint satisfies In the formula, represents the optimal cutting plane constraint, P ZB,x is the active power output from the xth normal distribution network to ZB, U ZB,x is the square value of the voltage at the xth normal distribution network access node j, Q ZB,x is the reactive power output from the xth normal distribution network to ZB, P j and Q j are the active power and reactive power of PV access node j, U j is the square value of the voltage at the normal photovoltaic access node j, They are the Lagrange multipliers of active power, reactive power and voltage amplitude square corresponding to the ADN-x boundary equality constraints.

[0094] The determination method further comprises the following steps:

[0095] Step S601, constructing a relaxation model of the sub-optimization model, solving the relaxation model, and obtaining Lagrange multipliers, wherein the Lagrange multipliers include: active power, reactive power, and voltage amplitude square corresponding to boundary equality constraints;

[0096] Step S602: construct feasible cutting plane constraints of the normal distribution network according to Lagrange multipliers.

[0097] Specifically, if there is no feasible solution to the normal distribution network sub-optimization model, the number of feasible cuts is increased by 1, and the slack variable is introduced The distribution network sub-optimization model is relaxed, and the objective function of the sub-optimization model is transformed into formula (8) to further solve the sub-optimization model. On the basis of retaining the original distribution network sub-optimization model, the relaxation method of the distribution network sub-optimization is shown in formulas (8), (9) and (10):

[0098]

[0099] In the formula, the objective function of the distribution network sub-optimization model is changed to formula (8), which means that the sum of the slack variables is minimized; the boundary equality constraint is changed to formula (10), which means the relaxed boundary equality constraint. is the slack variable, P ZB,x is the active power output from the xth normal distribution network to ZB, U ZB,x is the square value of the voltage at the xth normal distribution network access node j, Q ZB,x It is the reactive power outputted to ZB by the xth normal distribution network.

[0100] The above distribution network relaxation model is optimized and solved to obtain the optimal boundary variable value And the Lagrange multipliers λ1~λ6 corresponding to the above three relaxed equality constraints, let in, After optimizing the solution The optimal variable value of is the optimal value of active power transmitted at the boundary of distribution network node j, and are the optimal values ​​of reactive power and voltage, respectively. Since the outlet voltage of the distribution network interconnected by SOP can be controlled autonomously, the ADN-4 relaxation model through flexible interconnection does not contain the voltage relaxation equation constraint, and similarly does not generate the corresponding Lagrange multiplier. The upper bound of the objective function of the distribution network optimization model is UB xKeep unchanged, construct a feasible cutting plane constraint to fill the main optimization, and the feasible cutting plane constraint satisfies: In the formula, represents the feasible cutting plane constraint. The feasible cutting plane constraint constructed by ADN-4 does not contain the voltage term of the above cutting plane constraint polynomial; P ZB,x is the active power output from the xth normal distribution network to ZB, U ZB,x is the square value of the voltage at the xth normal distribution network access node j, Q ZB,x is the reactive power output from the xth normal distribution network to ZB, P j and Q j are the active power and reactive power of distribution network node j, U j is the square value of the voltage at the load node j in the distribution network.

[0101] Step S204, a third determination step: determining the minimum value of the objective function value of the optimization model according to the sum of the objective function value of the sub-optimization model and the second objective function value;

[0102] Specifically, the minimum value of the objective function value of the global optimization model, i.e., the lower bound LB, is updated using the sum of the first objective function value and the second objective function value of the main optimization of the fault distribution network and the objective function values ​​of all normal distribution network sub-optimizations, and the boundary variable y is used to calculate the minimum value of the objective function value of the global optimization model. j The optimal value is Assignment for the next round of iterative calculation.

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

[0104] Specifically, the first determination step, the second determination step and the third determination step are repeated until the upper and lower bound deviations of the objective function value of the global optimization model are less than the preset value a, then the solution result of the global optimization model of fault recovery of the present application converges, and the global optimal solution of the distributed power supply restoration of the multi-interconnected distribution network is obtained. In a specific embodiment of the present application, the preset value a is 10 -6 , the iterative convergence process of the global objective function value is as follows Figure 6 After multiple iterations, when the number of iterations is 4, the global optimization objective function value UB (or LB) obtained after the algorithm converges is 106.2997 yuan, and the simulation results of distributed power supply restoration of interconnected distribution networks based on the GBD algorithm are shown in 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 value and the minimum value is less than or equal to the 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.

[0106] Specifically, after the second-stage GBD algorithm converges, the simulation results of distributed power supply restoration are obtained, including the active photovoltaic output force-photovoltaic system nodes (PV1~PV5) and reactive photovoltaic output force-photovoltaic system nodes in the fault distribution network. Figure 8 As shown. Comparing the distributed simulation results obtained in the second phase with the centralized simulation results, the boundary transmission power, the SOP-optimized export voltage, and the grid reconstruction results of the fault 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 results obtained by the strategy of this application, and the power supply recovery rate is 100%.

[0107] After information interaction and coordination between distribution networks, the power transferred by SOP with larger transmission capacity was about 4 to 5 times that of TS, which played an important role in the power supply restoration process. While exerting the flexible power flow control capability of SOP, it also alleviated the power transfer pressure of TS to a certain extent, avoiding the problem of limited line capacity during TS transmission.

[0108] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0109] The embodiment of the present application also provides a device for determining the restoration of power supply of a faulty distribution network. It should be noted that the device for determining the restoration of power supply of a faulty distribution network in the embodiment of the present application can be used to execute the method for determining the restoration of power supply of a faulty distribution network provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0110] The following is an introduction to the device for determining power supply recovery of a faulty distribution network provided in an embodiment of the present application.

[0111] Fig. 9 Schematic diagram of a device for determining power supply restoration of a faulty distribution network according to an embodiment of the present application. Fig. 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 circulation module 50 and a fourth determination module 60, the construction module 10 is used to execute the construction step, that is, to construct an optimization model for power supply recovery of a fault distribution network according to at least a plurality of first constraints, and the parameters of the optimization model at least include the active power of the transfer power supply of the tie switch and the active power of the transfer power supply of the intelligent soft switch; the first determination module 20 is used to execute the first determination step, that is, to decompose the optimization model according to the decomposition algorithm to obtain a main optimization model of the fault distribution network and a plurality of normal distribution network sub-optimization models; the second determination module 30 is used to execute the second determination step, that is, 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 objective function values ​​of the main optimization model The function value and the second objective function value of the optimization model are determined according to the objective function value of the sub-optimization model and the sum of the first objective function value, and the parameters used for solving the first objective function value and the second objective function value are different; the third determination module 40 is used to execute the third determination step, that is, according to the objective function value of the sub-optimization model and the sum of the second objective function value, determine the minimum value of the objective function value of the optimization model; the loop module 50 is used to execute the loop step, that is, when the difference between the maximum value and the minimum value is greater than the preset threshold, re-execute the second determination step and the third determination step at least once; the fourth determination module 60 is used to execute the fourth determination step, that is, when the difference between the maximum value and the minimum value is less than or equal to the 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.

[0112] The above-mentioned determination device for power supply recovery of the fault distribution network of the present application includes: a construction module, a first determination module, a second determination module, a third determination module, a circulation module and a fourth determination module, wherein the construction module is used to construct an optimization model for power supply recovery of the fault distribution network according to at least a plurality of first constraints, and the parameters of the optimization model include at least the active power of the transfer power supply of the tie switch and the active power of the transfer power supply of the intelligent soft switch; the first determination module is used to decompose the optimization model according to the decomposition algorithm to obtain a main optimization model of the fault 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 value of the sub-optimization model and the first constraint value of the main optimization model The objective function value and the second objective function value are used to determine the maximum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the first objective function value. The first objective function value and the second objective function value are solved using different parameters; the third determination module is used to determine the minimum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the second objective function value; the loop module is used to re-execute the second determination step and the third determination step at least once when the difference between the maximum value and the minimum value is greater than the preset threshold; the fourth determination module is used to determine the optimal solution of the optimization model when the difference between the maximum value and the minimum value is less than or equal to the preset threshold, and restore the power supply of the faulty distribution network according to the optimal solution. The device solves the problem of limited transmission power of the distribution network line and harsh TS interconnection conditions in the prior art, and the large amount of calculation is prone to dimensional disasters. While protecting the privacy data of different communication subjects, it reduces the communication investment cost and communication burden, and because the global problem is decomposed, it also alleviates the calculation pressure to a certain extent and improves the power supply recovery efficiency. At the same time, the use of TS and SOP for joint transfer between distribution networks takes into account the advantages of traditional hard switches and intelligent soft switches, and improves the overall power supply recovery rate.

[0113] In some instances, 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 a normal distribution network according to 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 grid topology of the normal distribution network according to the network reconstruction model, and to construct an optimization model for power supply restoration of the faulty distribution network according to the grid topology and multiple first constraints. The optimization model of each normal distribution network is constructed, and the optimal grid topology of each normal distribution network is obtained and fixed through network reconstruction, which provides a basis for the subsequent execution of the transfer and restoration operation, thereby improving the efficiency of power supply restoration while saving operating costs.

[0114] In some examples, the constraint conditions 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. The constraint conditions of the second construction submodule are used to construct an accurate distribution network optimization model, which provides a basis for restoring power to the entire fault distribution network and reducing the cost of power restoration.

[0115] In some examples, the first constraint in the building module includes at least one of the following: a second linear power flow constraint, an interconnection boundary equality constraint, a node voltage constraint, a photovoltaic capacity constraint, and an 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 basis for restoring power to the entire fault distribution network and reducing the cost of restoration.

[0116] In some instances, the second determination module includes a first determination submodule, a second determination submodule, a third determination submodule and a fourth determination submodule, wherein the first determination submodule is used to initialize at least the initial value of the boundary variable of the fault distribution network; the second determination submodule is used to solve the objective function value of the sub-optimization model according to the initial value of the boundary variable of the fault distribution network and the boundary equation constraint of the normal distribution network; the third determination submodule is used to solve the main optimization model according to 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 determination submodule is used to obtain the second objective function value according to at least the feasible cut times and feasible cut plane constraints of the fault distribution network based on all normal distribution networks. The GBD algorithm has good convergence performance, and its converged results are consistent with the traditional centralized results, but the algorithm has a smaller communication burden, lower communication investment cost and lower computing power requirements for the controller, which can better improve the overall power supply recovery rate.

[0117] In some examples, the second determination module further includes a fifth determination submodule and a sixth determination submodule, wherein the fifth determination submodule is used to obtain Lagrange multipliers according to the boundary equation constraints of the normal distribution network, and the Lagrange multipliers include: active power, reactive power and voltage amplitude square corresponding to the boundary equation constraints; the sixth determination submodule is used to construct feasible cutting plane constraints of the normal distribution network according to the Lagrange multipliers. It is applicable to the case where the sub-optimization model of the normal distribution network ADN-x has a feasible solution.

[0118] In some examples, the second determination module also includes a seventh determination submodule and an eighth determination submodule, the seventh determination submodule is used to construct a relaxation model of the sub-optimization model, solve the relaxation model, and obtain Lagrange multipliers, the Lagrange multipliers include: active power, reactive power and voltage amplitude square corresponding to the boundary equality constraint; the eighth determination submodule is used to construct a feasible cutting plane constraint of the normal distribution network according to the Lagrange multiplier. It is applicable to the case where there is no feasible solution to the sub-optimization model of the normal distribution network ADN-x.

[0119] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions 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] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0126] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0128] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0129] 1) The method for determining power supply restoration of the above-mentioned fault distribution network of the present application first constructs an optimization model for power supply restoration of the fault distribution network according to at least multiple first constraints, and the parameters of the optimization model include at least the active power of the interconnection switch and the active power of the intelligent soft switch; then, the optimization model is decomposed according to the decomposition algorithm to obtain a main optimization model of the fault distribution network and multiple sub-optimization models of the normal distribution network; then, the main optimization model and the multiple sub-optimization models are solved to obtain the objective function value of the sub-optimization model and the first objective function value and the second objective function value of the main optimization model; according to the objective function value of the sub-optimization model and the sum of the first objective function value, the maximum value of the objective function value of the optimization model is determined, and the parameters used for solving the first objective function value and the second objective function value are different; then, according to the objective function value of the sub-optimization model and the sum of the second objective function value, the minimum value of the objective function value of the optimization model is determined; then, when the difference between the maximum value and the minimum value is greater than the preset threshold, the second determination step and the third determination step are re-executed at least once; finally, when the difference between the maximum value and the minimum value is less than or equal to the preset threshold, the optimal solution of the optimization model is determined, and the power supply of the fault distribution network is restored according to the optimal solution. This method uses a distributed communication architecture to deal with the power supply restoration problem of interconnected distribution networks. By adopting the GBD distributed algorithm, it solves the problem of limited transmission power of distribution network lines and harsh TS interconnection conditions in the existing technology, and the problem of large computational complexity and dimensionality hazards. While protecting the privacy data of different communication subjects, it reduces the communication investment cost and communication burden. In addition, due to the decomposition of the global problem, it also alleviates the computational pressure to a certain extent and improves the power supply restoration efficiency. At the same time, the use of TS and SOP for joint transfer between distribution networks takes into account the advantages of traditional hard switches and intelligent soft switches, and improves the overall power supply restoration rate.

[0130] 2) The above-mentioned determination device for power supply recovery of the fault distribution network of the present application includes: a construction module, a first determination module, a second determination module, a third determination module, a circulation module and a fourth determination module, wherein the construction module is used to construct an optimization model for power supply recovery of the fault distribution network according to at least a plurality of first constraints, and the parameters of the optimization model include at least the active power of the transfer power supply of the tie switch and the active power of the transfer power supply of the intelligent soft switch; the first determination module is used to decompose the optimization model according to the decomposition algorithm to obtain a main optimization model of the fault 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 value of the sub-optimization model and the first constraint value of the main optimization model The first objective function value and the second objective function value are used to determine the maximum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the first objective function value. The first objective function value and the second objective function value are solved using different parameters; the third determination module is used to determine the minimum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the second objective function value; the loop module is used to re-execute the second determination step and the third determination step at least once when the difference between the maximum value and the minimum value is greater than the preset threshold; the fourth determination module is used to determine the optimal solution of the optimization model when the difference between the maximum value and the minimum value is less than or equal to the preset threshold, and restore the power supply of the faulty distribution network according to the optimal solution. The device solves the problem of limited transmission power of the distribution network line and harsh TS interconnection conditions in the prior art, and the large amount of calculation is prone to dimensional disasters. While protecting the privacy data of different communication subjects, it reduces the communication investment cost and communication burden, and because the global problem is decomposed, it also alleviates the calculation pressure to a certain extent and improves the power supply recovery efficiency. At the same time, the use of TS and SOP for joint transfer between distribution networks takes into account the advantages of traditional hard switches and intelligent soft switches, and improves the overall power supply recovery rate.

[0131] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining power supply restoration of a faulty distribution network, characterized in that: The distribution network is electrically connected to the tie switch and the intelligent soft switch, and the determination method includes: Construction step: constructing an optimization model for power supply restoration of a faulty distribution network at least according to a plurality of first constraints, wherein the parameters of the optimization model at least include the active power of the power supply transferred by the tie switch and the active power of the power supply transferred by the intelligent soft switch; The first determination step: decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of a fault distribution network and a plurality of sub-optimization models of normal distribution networks; The 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 objective function value and the second objective function value of the main optimization model, and determining the maximum objective function value of the optimization model according to the sum of the objective function values ​​of the sub-optimization models and the first objective function value, wherein the first objective function value and the second objective function value are solved using different parameters; The third determination step: determining the minimum value of the objective function value of the optimization model according to the sum of the objective function value of the sub-optimization model and the second objective function value; Looping step: when the difference between the maximum value and the minimum value is greater than a preset threshold, re-performing the second determining step and the third determining step at least once; Fourth determination 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.

2. The determination method according to claim 1, characterized in that: The determination method further comprises: Establishing an optimization model of the normal distribution network according to the operating cost of the normal distribution network and the power of the transferred power supply; Performing network reconstruction on the optimization model of the normal distribution network to obtain a network reconstruction model of the normal distribution network; According to the network reconstruction model, the grid topology of the normal distribution network is determined, and according to the grid topology and a plurality of the first constraints, an optimization model for power supply restoration of the faulty distribution network is constructed.

3. The determination method according to claim 1, characterized in that: The solving of the main optimization model and the plurality of sub-optimization models comprises: Initializing at least the initial values ​​of the boundary variables of the faulty distribution network; Solving the objective function value of the sub-optimization model according to the initial values ​​of the boundary variables of the faulty distribution network and the boundary equality constraints of the normal distribution network; Solving the main optimization model according to 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 second objective function value is obtained at least according to the number of feasible cuts and feasible cutting plane constraints of the faulty power distribution network based on all the normal power distribution networks.

4. The determination method according to claim 3, characterized in that: The determination method further comprises: According to the boundary equation constraint condition of the normal power distribution network, a Lagrange multiplier is obtained, wherein the Lagrange multiplier includes: active power, reactive power and voltage amplitude square corresponding to the boundary equation constraint; The feasible cutting plane constraints of the normal power distribution network are constructed according to the Lagrange multipliers.

5. The determination method according to claim 3, characterized in that: The determination method further comprises: 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 square corresponding to the boundary equality constraint; The feasible cutting plane constraints of the normal power distribution network are constructed according to the Lagrange multipliers.

6. The determination method according to claim 2, characterized in that: The constraint conditions of the network reconstruction 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.

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

8. A device for determining power supply restoration of a faulty distribution network, characterized in that: The distribution network is electrically connected to the tie switch and the intelligent soft switch, and includes: A construction module, used to execute the construction step: construct an optimization model for power supply restoration of a faulty distribution network according to at least a plurality of first constraints, wherein the parameters of the optimization model at least include the active power of the power supply transferred by the tie switch and the active power of the power supply transferred by the intelligent soft switch; A first determination module is used to perform a first determination step: decomposing the optimization model according to a decomposition algorithm to obtain a main optimization model of a faulty distribution network and a plurality of sub-optimization models of normal distribution networks; A second determination module is used to perform a second determination step: solving the main optimization model and the plurality of sub-optimization models to obtain objective function values ​​of the sub-optimization models and a first objective function value and a second objective function value of the main optimization model, and determining a maximum objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the first objective function value, wherein the first objective function value and the second objective function value are solved using different parameters; A third determination module is used to perform a third determination step: determine the minimum value of the objective function value of the optimization model according to the objective function value of the sub-optimization model and the sum of the second objective function value; a loop module, configured to execute the loop step, and re-execute the second determining step and the third determining step at least once when the difference between the maximum value and the minimum value is greater than a preset threshold; The fourth determination module is used to execute the fourth determination 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.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for determining power supply restoration of a faulty distribution network according to any one of claims 1 to 7.

10. 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 are configured to be executed by the one or more processors, and the one or more programs include a determination method for executing power supply restoration of a fault distribution network as described in any one of claims 1 to 7.

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