A cloud-edge collaborative optimization method and device for fault recovery of a flexible interconnected power distribution network

Abstract

The application discloses a cloud-edge collaborative optimization method and equipment for fault recovery of a flexible interconnected power distribution network, and belongs to the field of fault recovery of the flexible interconnected power distribution network. The method comprises a pre-computation step performed by each edge network deployed in an edge layer, and a fault recovery collaborative optimization step based on the pre-computation. In the application, each power distribution network operation strategy and strategy execution originally centrally calculated are decentralized to the network edge, and the optimization model dimension and scale are limited within a single power distribution network, so that the problems of slow response speed of the power distribution network fault recovery optimization problem, difficulty in integrating resources between the power distribution networks, and inability to fully utilize the mutual aid capacity between the multiple networks adopting the flexible interconnected equipment caused by the centralized calculation of the traditional power distribution network fault recovery are overcome.

Description

Technical Field

[0001] This invention relates to the field of fault recovery in flexible interconnected distribution networks, and in particular to a cloud-edge collaborative optimization method and device for fault recovery in flexible interconnected distribution networks. Background Technology

[0002] Distribution network fault recovery is a nonlinear, multi-objective combinatorial optimization problem. The dimensionality and scale of the fault recovery model increase with the scale of flexible interconnection in the distribution network. Solving this problem involves a large amount of equipment operating information, and the information domain and scope of the fault recovery optimization strategy extend to multiple feeder networks. These factors all increase the computational and execution difficulty of the fault recovery optimization strategy. Flexible interconnection devices enable power exchange between multiple networks during emergency faults in the distribution network. However, traditional centralized algorithms cannot meet the response speed requirements for fault recovery between multiple flexible interconnected networks. This makes it difficult to fully utilize the flexibility and controllability of the active distribution network and the multi-network power exchange capabilities of flexible interconnected devices (FIDs) to ensure continuous and reliable power supply in emergency situations. Summary of the Invention

[0003] In order to at least partially solve one of the technical problems existing in the prior art, the purpose of this invention is to provide a cloud-edge collaborative optimization method and device for fault recovery of flexible interconnected distribution networks.

[0004] The technical solution adopted in this invention is:

[0005] A cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network includes a pre-computation step performed by various edge networks deployed at the edge layer, and a fault recovery collaborative optimization step based on the pre-computation.

[0006] The pre-calculation step includes:

[0007] A1. When the triggering condition is detected, pre-computation is started to collect the operation information of the edge network;

[0008] A2. Based on the operational information, set parameters for the edge network pre-computation model, inject power into the flexible interconnect devices and perform point-by-point scanning, optimize and solve the pre-computation model to obtain the results for each scanning point. Corresponding optimization target value

[0009] A3. Calculate the different scan points The target value for optimizing the edge network ADN-x Perform piecewise linear fitting, upload the obtained piecewise linear correlation function to the cloud; and upload each scan point... The corresponding optimization target values ​​form the optimal operating instruction set and are saved to the edge network for the edge gateway to call when receiving FID power interaction instructions from the cloud;

[0010] The fault recovery collaborative optimization step based on pre-calculation includes:

[0011] B1. Upon receiving the operation information and fault segment location information uploaded by the fault edge network ADN-y, start the fault recovery optimization calculation, retrieve the pre-calculated piecewise linear correlation function of the adjacent non-faulty ADN, instantiate the parameters for fault recovery collaborative optimization, and perform optimization solution.

[0012] B2. Send the optimal operating command values ​​of each device in the fault edge network ADN-y obtained by solving, as well as the active power command values ​​of each port of the flexible interconnection device, to the corresponding edge smart gateway.

[0013] B3. The faulty edge network ADN-y forwards the recovery and control instructions issued by the cloud to each intelligent monitoring and control terminal; the non-faulty ADN edge intelligent gateway searches and interpolates the optimal operating instructions of each device from the optimal control instruction set pre-calculated by each edge network according to the power instructions of the flexible interconnection device port, and sends them to each intelligent monitoring and control terminal.

[0014] Furthermore, the operational information includes switch operating status, load power demand, DG output, remaining energy storage capacity, and short-term forecasts of load power demand and DG power generation.

[0015] Furthermore, the pre-calculation model in step A2 is the optimal power flow model;

[0016] Step A2 includes:

[0017] Establish constraints, including: Distflow power flow constraints, network node voltage and line current constraints, DG capacity constraints, grouped capacitor (CB) constraints, energy storage (ESS) constraints, and FID constraints.

[0018] The optimization objective of the pre-computation model is defined as follows: For normal network operation, considering network line loss, FID power transmission loss, and the minimum number of CB operations, the pre-computation optimization objective for the edge network ADN-x is expressed as:

[0019]

[0020] Among them, Ω FID Ω CB These represent the sets of nodes connected to FID and CB in ADN-x, respectively. λ represents the number of CBs deployed and the initial number of CBs deployed at node j, respectively; loss , λ CB The weight coefficients for network loss and the number of CB switching operations are respectively; the objective function... The value obtained after each round of optimization is pre-calculated, which is the sum of ADN-x and the value obtained in that round of calculation. The optimal operating parameter corresponding to the value.

[0021] Furthermore, the expression for the Distflow power flow constraint is:

[0022]

[0023]

[0024]

[0025] Among them, P ij and Q ij P represents the active power and reactive power flowing through line ij, respectively; j and Q j These represent the active power and reactive power injected into node j, respectively. and Let D represent the active power and reactive power injected into node j by device D, respectively, where D∈{DG,ESS,FID,load}. and These represent the charging and discharging power of the energy storage ESS (Energy Storage System). This represents the squared value of the voltage at node j; R represents the square of the current in line ij; ij and X ij Ω represents the resistance and reactance of line ij, respectively. node Ω line Represents the set of nodes and paths in a network;

[0026] The expressions for the node voltage and line current constraints of the network are as follows:

[0027]

[0028] Among them, U j,min U represents the minimum voltage limit at node j. j,max I represents the maximum voltage limit at node j. ij,max This indicates the maximum limit of the current in line ij;

[0029] The expression for the capacity constraint of the DG is:

[0030]

[0031] in, This represents the reactive power generated by the DG. This indicates the active power generated by the DG. This represents the maximum output power tracking value of the DG at node j. Indicates the power factor angle limit of the DG;

[0032] The expression for the CB constraint of the grouped capacitor is:

[0033]

[0034] in, This represents the compensated reactive power of each CB group; This indicates the number of CB operations performed on node j; This represents the maximum number of times a node j can perform a CB operation; This represents the reactive power of the CB injected into node j;

[0035] The expression for the energy storage ESS constraint is:

[0036]

[0037]

[0038] in, ΔT represents the charging and discharging time of the ESS, which are 0-1 variables respectively. These represent the charging and discharging power of the ESS at node j, respectively.

[0039] The expression for the FID constraint is:

[0040]

[0041]

[0042] in This represents the active power loss of the FID at node j; This represents the FID capacity of node j.

[0043] Further, step A3 includes:

[0044] For different scan points The target value for optimizing the edge network ADN-x Perform piecewise linear fitting, assuming that each scan in the edge network ADN-x selects... They are respectively corresponding They are respectively Connecting each point sequentially yields a piecewise linear correlation function.

[0045]

[0046]

[0047] in, It is a set of continuous auxiliary variables; A set of 0-1 auxiliary variables used to limit The value of , thus and The segmented interval can be accessed via Sure.

[0048] Further, step B1 includes:

[0049] Establish Distflow power flow constraints for the fault edge network ADN-y;

[0050] Establish radial and connectivity topological constraints for the faulty network ADN-y;

[0051] Establish load constraints for the fault edge network ADN-y;

[0052] Establish operational constraints for each device in the fault edge network ADN-y;

[0053] Determine the objective function for fault recovery and solve the fault recovery model.

[0054] Furthermore, the optimization objective is to minimize the amount of power loss load, the number of switching operations, line losses, FID active power losses, and the number of CB switching operations.

[0055] The objective function for fault recovery is expressed as follows:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] Among them, f Load f Switch f CB f Loss These represent the objective functions for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively; Ω ADN This represents the set of ADNs that are flexibly interconnected with ADN-y; Indicates the amount of power outage load; ω j α represents the importance weight of the load at node j; ij, Indicates the initial switching state of line ij; β j,0 λ represents the initial state of the uncontrollable load at node j; Load , λ Switch , λ CB , λ Loss These are the weighting coefficients of the objective function for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively.

[0062] Furthermore, the expression for the Distflow power flow constraint is:

[0063]

[0064]

[0065] Where M represents a positive number; α ij It is a 0-1 variable representing the state of the circuit switch; P ij and q ij These represent the active power and reactive power flowing through line ij, respectively. R represents the square of the current in line ij; ij and X ij These represent the resistance and reactance of line ij, respectively; This represents the square of the voltage at node i; This represents the square of the voltage at node j.

[0066] Furthermore, the load constraints for establishing the fault edge network ADN-y include:

[0067] ADN internal loads are divided into controllable loads and uncontrollable loads:

[0068]

[0069]

[0070] Among them, Ω cnode Ω unode These represent the sets of nodes representing controllable and uncontrollable loads, respectively. These represent the maximum active and reactive power demand of node j, respectively; β j It is a 0-1 variable, representing the state of the uncontrollable load of node j.

[0071] Another technical solution adopted in this invention is:

[0072] An electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network as described above.

[0073] The beneficial effects of this invention are: This invention decentralizes the operation strategies and execution of each distribution network, which were originally centrally calculated, to the network edge, and restricts the dimension and scale of the optimization model to a single distribution network. This overcomes the problems of slow response speed, difficulty in integrating resources between distribution networks, and inability to fully utilize the mutual support capabilities between multiple networks using flexible interconnection equipment caused by the centralized calculation of traditional distribution network fault recovery. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart illustrating the pre-calculation step in an embodiment of the present invention;

[0076] Figure 2 This is a flowchart illustrating the collaborative optimization steps for fault recovery based on pre-computation of the edge network in an embodiment of the present invention.

[0077] Figure 3 This is a schematic diagram of network fault recovery results and active power interaction between networks in an embodiment of the present invention. Detailed Implementation

[0078] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0079] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0080] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0081] Furthermore, in the description of this invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0082] In the description of this invention, unless otherwise explicitly defined, terms such as "setting," "installing," and "connecting" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0083] Terminology Explanation:

[0084] ADN: Active Distribution Network.

[0085] DG: Distributed Generation.

[0086] CB: Capacitors banks, grouped capacitors.

[0087] Based on existing technical problems, this invention provides a cloud-edge collaborative optimization method for fault recovery in flexible interconnected distribution networks. By dividing the network edge and fault recovery module, the operation strategies and execution of each distribution network, which were originally centrally calculated, are decentralized to the network edge. The optimization model dimension and scale are limited to a single distribution network, overcoming the problems of slow response speed, difficulty in integrating resources between distribution networks, and inability to fully utilize the mutual support capabilities between multiple networks using flexible interconnection equipment caused by the centralized calculation of traditional distribution network fault recovery.

[0088] In addition, the present invention follows a three-layer network architecture of cloud-pipe-edge. The implementation of the algorithm requires the joint implementation of the pre-computation module deployed on each edge network in the edge layer and the fault recovery collaborative optimization module deployed in the cloud. The fault recovery collaborative optimization module in the cloud performs collaborative optimization calculation on the pre-computation results of each edge network.

[0089] like Figure 1 and Figure 2 As shown, this embodiment provides a cloud-edge collaborative optimization method for fault recovery in flexible interconnected distribution networks, including the following steps:

[0090] S1, Pre-computation deployed in various edge networks.

[0091] Step S1 specifically includes steps S1.1-S1.3:

[0092] S1.1: When the timed trigger or event trigger conditions are met, pre-calculation is initiated to collect real-time operational information of the edge network, including switch operating status, load power demand, DG output, remaining energy storage capacity, and other real-time operational information, as well as short-term forecasts of load power demand and DG main force.

[0093] S1.2: Based on the collected real-time operating information and short-term prediction data of the edge network, set the parameters for the pre-computation model of the edge network, and inject power into the FID according to the principle of equal intervals. Perform point-by-point scanning, optimize and solve the pre-calculated model, and obtain the results for each scan point. Corresponding optimization target value

[0094] S1.3: The calculated differences The target value for ADN-x optimization is as follows Perform piecewise linear fitting, and upload the resulting piecewise linear correlation function to the cloud for storage in the cloud data center, so that it can be called upon by the cloud when starting optimization calculations; and then perform piecewise linear fitting on each sample. The corresponding optimization target values ​​form the optimal operating instruction set and are saved to the edge network for the edge gateway to call when receiving FID power interaction instructions from the cloud.

[0095] S2. Collaborative optimization of fault recovery based on pre-computation of edge network.

[0096] Step S2 specifically includes steps S2.1-S2.3:

[0097] S2.1: The cloud computing center receives the operational data and fault segment location information from the faulty network ADN-y, initiates fault recovery optimization calculations, and retrieves the pre-calculated piecewise linear correlation function of the adjacent non-faulty ADNs in the cloud computing center. The parameters for fault recovery collaborative optimization are instantiated and configured, and then optimization solutions are obtained.

[0098] S2.2: Send the optimal operating command values ​​of each device of the faulty ADN-y obtained by solving, as well as the active power command values ​​of each port of FID, to the corresponding edge smart gateway.

[0099] S2.3: The faulty edge network ADN-y forwards the recovery control instructions issued by the cloud to each intelligent monitoring and control terminal; the non-faulty ADN edge intelligent gateway searches and interpolates the optimal operating instructions of each device from the optimal control instruction set pre-calculated by each edge network according to the power instructions of the FID port, and sends them to each intelligent monitoring and control terminal.

[0100] In some embodiments, step S1.1 specifically includes:

[0101] S1.1.1: Obtain the current operating parameters of each device within the network. These device operating parameters include the maximum output power tracking value of the DG within the next prediction period. and load demand power factor Maximum and minimum voltage values ​​U at node j j,max U j,min The maximum line current I of line ij ij, The maximum number of operable switching operations for the capacitor banks (CB) of node j. The maximum charge and discharge power of the energy storage ESS at node j are respectively The charge / discharge efficiency is η, and the maximum energy that the ESS can store is The power loss rate of node j at FID is η FID Load demand forecast and

[0102] S1.1.2: Obtain the current operating status of each device within the network. The current operating status of the device includes: on / off state α ij Actual output of DG ESS current battery level and initial power

[0103] In some embodiments, the pre-calculation model in step S1.2 is an optimal power flow model; preferably, it can be a mixed integer second-order cone programming (MISOCP) model.

[0104] Step S1.2 specifically includes:

[0105] S1.2.1: Establish Distflow power flow constraints:

[0106]

[0107]

[0108]

[0109] Where P ij and Q ij P represents the active power and reactive power flowing through line ij, respectively; j and Q j These represent the active power and reactive power injected into node j, respectively. and Let D represent the active power and reactive power injected into node j by device D, respectively, where D∈{DG,ESS,FID,load}. and These represent the charging and discharging power of the energy storage ESS (Energy Storage System). This represents the squared value of the voltage at node j; R represents the square of the current in line ij; ij and X ij Ω represents the resistance and reactance of line ij, respectively. nide Ω line This represents the set of nodes and lines in a network.

[0110] S1.2.2: Establish node voltage and line current constraints for the network:

[0111]

[0112] S1.2.3: Establish capacity constraints for DG:

[0113]

[0114] in This indicates the reactive power generated by the DG.

[0115] S1.2.4: Grouped capacitor CB constraint:

[0116]

[0117] in This represents the compensated reactive power of each CB group; This represents the number of CB operations performed on node j.

[0118] S1.2.5: Energy Storage ESS Constraints:

[0119]

[0120]

[0121] in ΔT represents the charging and discharging time of the ESS, which are 0-1 variables respectively. These represent the charging and discharging power of the ESS at node j.

[0122] S1.2.6: FID constraint:

[0123]

[0124]

[0125] in This represents the active power loss of the FID at node j; This represents the FID capacity of node j.

[0126] S1.2.7: Pre-calculated optimization objective.

[0127] Considering the normal operation of the network, minimizing network line loss, FID power transmission loss, and the number of CB operations, the pre-calculation optimization objective of ADN-x can be expressed as:

[0128]

[0129] Where Ω FID Ω CB These represent the sets of nodes connected to FID and CB in ADN-x, respectively. λ represents the number of CBs deployed and the initial number of CBs deployed at node j, respectively; loss , λ CB These represent the weight coefficients for network loss and the number of CB switching operations, respectively. The objective function... The value obtained after each round of optimization is pre-calculated, which is the sum of ADN-x and the value obtained in that round of calculation. The optimal operating parameter corresponding to the value.

[0130] In some embodiments, step S1.3 specifically includes:

[0131] For different The target value for ADN-x optimization is as follows Perform piecewise linear fitting, assuming that in ADN-x each scan selects... They are respectively corresponding They are respectively Connecting each point sequentially yields a piecewise linear correlation function.

[0132]

[0133]

[0134] in It is a set of continuous auxiliary variables; A set of 0-1 auxiliary variables used to limit The value of , thus and The segmented interval can be accessed via Sure.

[0135] In some embodiments, step S2.1 specifically includes:

[0136] S2.1.1: Establishing improved Distflow power flow constraints for faulty network ADN-y:

[0137]

[0138]

[0139] Where M represents a very large positive number; α ij It is a 0-1 variable that represents the state of the circuit switch.

[0140] S2.1.2: Establish radial and connectivity topological constraints for the faulty network ADN-y:

[0141] After network reconstruction and islanding, the ADN needs to satisfy radial and connectivity constraints, and each island must contain a DG or FID with black-start capability as the island's balancing power source. When reconstructing the radial and connectivity constraints, a new "virtual node 0" is added. When the network forms an island, this node will connect to the balancing node through a "virtual branch" to ensure network connectivity. Discrete "virtual power" is introduced, assumed to be emitted by the "virtual node," ensuring that the virtual power injected into a energized node is one unit more than the virtual power flowing out of the node, and that the number of energized nodes in the ADN is one less than the number of closed circuits. The branch state of a faulty branch must be open. Therefore, the radial and connectivity constraints of the ADN can be expressed as:

[0142]

[0143]

[0144]

[0145] α ij -1≤x i -x j ≤1-α ij (19)

[0146] α ij=0 (ij∈Ω) fault (20)

[0147] Where α 0j Indicates the state of a "virtual branch"; Ω slack This represents the set of nodes that can serve as balancing nodes; Indicates "virtual power"; x j Indicates the charged state of a node; Ω fault This represents the set of network fault branches.

[0148] S2.1.3: Establish load constraints for the faulty network ADN-y:

[0149] The internal load of an ADN can be divided into controllable load and uncontrollable load, and its constraints can be expressed as:

[0150]

[0151]

[0152] Where Ω cnode Ω unode These represent the sets of nodes representing controllable and uncontrollable loads, respectively. These represent the maximum active and reactive power demand of node j, respectively; β j It is a 0-1 variable, representing the state of the uncontrollable load of node j.

[0153] S2.1.4: Establish operational constraints for each device in the faulty network ADN-y:

[0154] Voltage and current constraints are the same as in S2.2; DG, CB, ESS and FID are the same as in S2.3, S2.4, S2.5 and S2.6 respectively.

[0155] S2.1.5: Objective function for fault recovery

[0156] With the optimization objectives of minimizing the power outage load, number of switching operations, line loss, FID active power loss, and CB switching frequency, the optimization objective of the fault network can be expressed as:

[0157]

[0158]

[0159]

[0160]

[0161]

[0162] Where f Loadf Switch f CB f Loss These represent the objective functions for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively; Ω ADN This represents the set of ADNs that are flexibly interconnected with ADN-y; Indicates the amount of power outage load; ω j α represents the importance weight of the load at node j; ij,0 Indicates the initial switching state of line ij; β j,0 λ represents the initial state of the uncontrollable load at node j; Load , λ Switch , λ CB , λ Loss These are the weighting coefficients of the objective function for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively.

[0163] S2.1.6: Solving the Fault Recovery Model

[0164] The above method will be explained in detail below with reference to the accompanying drawings and specific embodiments.

[0165] See Figure 3 When the ADN-3 distribution master station loses power, the root node is unable to provide voltage and power support for the ADN-3 load. Including the DG and energy storage in the network, ADN-3 experienced a severe power deficit of 1302.1kW. Relying solely on the power supply and energy storage in the network cannot meet the power supply needs of all load nodes. It is necessary to seek power support from the adjacent network through FID. The ADN-3 edge device sends fault information and fault recovery requests to the cloud.

[0166] When the cloud receives fault information and a fault recovery request from the ADN-3 edge, it retrieves the pre-calculated piecewise linear correlation function data from the adjacent ADN edges and then initiates the solution of the fault recovery collaborative optimization model. This calculates the optimal operating instruction set for the faulty ADN-3 and the active power instructions for each FID port. The former is sent to the edge of the faulty ADN-3 for execution, while the latter is sent to the edges of other non-faulty ADNs. The non-faulty ADN edges, based on the active power instructions for the FID ports issued by the cloud, search and interpolate from the optimal operating instruction set obtained from the pre-calculated results to determine the optimal operating instructions for each device, thus obtaining the operating instruction adjustment scheme for all devices in the fault recovery of the flexible interconnected distribution network.

[0167] In the results of the fault recovery optimization solution, the power operation command adjustment schemes of controllable devices in each ADN are listed in Table 1, including the active power injected into FID, the reactive power injected out of FID port, the reactive power injected out of DG, the active power injected out of ESS, the reactive power injected out of SVC, and the number of CB groups. The switching control scheme of ADN-3, the active power interaction between each network, and the corresponding optimal power flow objective function value of the ADN are as follows: Figure 3 As shown.

[0168] Table 1 Optimization results of optimal power operation commands for each ADN fault recovery device

[0169]

[0170] Depend on Figure 3 As can be seen, the line switches between root node 1 and node 2 of ADN-3, which are affected by the accident, are disconnected. Based on the optimization results, the proposed fault recovery scheme is as follows: Figure 3 In the middle red branch, 27-65 changed from out of operation to operational status, and 54-55 changed from operational status to out of operation. Then, the operating commands of each ADN controllable device were adjusted according to the optimization results in Table 1. ADN-3 obtained 1740kW of active power support from FID and switched from DG of node 6 to V / f control mode to act as the islanding balance node, thus switching to islanding operation.

[0171] After the solution was implemented, the power outage of the faulty ADN-3 load was 196kW, the network loss was 79.89kW, the number of CB actions was 15, the number of switch actions was 2, and 94.84% of the load was restored to power. Among them, Class I, Class II, and ordinary loads all achieved a 100% power restoration rate, and 46.15% of the controllable loads were restored to power. It is evident that the method proposed in this application restores power to as many loads as possible. If the inter-network power mutual assistance provided by FID is not considered, and the fault recovery optimization solution is only performed based on the power supply and regulation equipment within the ADN-3 network itself, the load outage power is 1682.4kW, and only 55.7% of the load is restored to power. It is clear that configuring FID can effectively expand the restoration area of ​​fault recovery and reduce the power outage range of the fault.

[0172] This embodiment also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement... Figure 1 and Figure 2 The method shown.

[0173] An electronic device in this embodiment can execute a cloud-edge collaborative optimization method for fault recovery of a flexible interconnected distribution network provided in the method embodiment of the present invention. It can execute any combination of implementation steps of the method embodiment and has the corresponding functions and beneficial effects of the method.

[0174] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the electronic device to perform... Figure 1-2 The method shown.

[0175] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0176] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0177] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0179] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0180] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0181] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0182] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0183] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A cloud-edge collaborative optimization method for fault recovery in flexible interconnected distribution networks, characterized in that, This includes a pre-computation step performed by each edge network deployed at the edge layer, and a fault recovery collaborative optimization step based on the pre-computation. The pre-calculation step includes: A1. When the triggering condition is detected, pre-computation is started to collect the operation information of the edge network; A2. Based on the operational information, set parameters for the edge network pre-computation model, inject power into the flexible interconnect devices and perform point-by-point scanning, optimize and solve the pre-computation model to obtain the results for each scanning point. Corresponding optimization target value A3. Calculate the different scan points The target value for optimizing the edge network ADN-x Perform piecewise linear fitting, upload the obtained piecewise linear correlation function to the cloud; and upload each scan point... The corresponding optimization target values ​​form the optimal operating instruction set and are saved to the edge network for the edge gateway to call when receiving FID power interaction instructions from the cloud; The fault recovery collaborative optimization step based on pre-calculation includes: B1. Upon receiving the operation information and fault segment location information uploaded by the fault edge network ADN-y, start the fault recovery optimization calculation, retrieve the pre-calculated piecewise linear correlation function of the adjacent non-faulty ADN, instantiate the parameters for fault recovery collaborative optimization, and perform optimization solution. B2. Send the optimal operating command values ​​of each device in the fault edge network ADN-y obtained by solving, as well as the active power command values ​​of each port of the flexible interconnection device, to the corresponding edge smart gateway. B3. The faulty edge network ADN-y forwards the recovery and control instructions issued by the cloud to each intelligent monitoring and control terminal; the non-faulty ADN edge intelligent gateway searches and interpolates the optimal operating instructions of each device from the optimal control instruction set pre-calculated by each edge network according to the power instructions of the flexible interconnection device port, and sends them to each intelligent monitoring and control terminal.

2. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 1, characterized in that, The operational information includes switch operating status, load power demand, DG output, remaining energy storage capacity, and short-term forecasts of load power demand and DG power generation.

3. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 1, characterized in that, The pre-calculation model in step A2 is the optimal power flow model; Step A2 includes: Establish constraints, including: Distflow power flow constraints, network node voltage and line current constraints, DG capacity constraints, grouped capacitor (CB) constraints, energy storage (ESS) constraints, and FID constraints. The optimization objective of the pre-computation model is defined as follows: For normal network operation, considering network line loss, FID power transmission loss, and the minimum number of CB operations, the pre-computation optimization objective for the edge network ADN-x is expressed as: Among them, Ω FID Ω CB These represent the sets of nodes connected to FID and CB in ADN-x, respectively. λ represents the number of CBs deployed and the initial number of CBs deployed at node j, respectively; loss , λ CB The weight coefficients for network loss and the number of CB switching operations are respectively; the objective function... The value obtained after each round of optimization is pre-calculated, which is the sum of ADN-x and the value obtained in that round of calculation. The optimal operating parameter corresponding to the value.

4. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 3, characterized in that, The expression for the Distflow power flow constraint is: Among them, P ij and Q ij P represents the active power and reactive power flowing through line ij, respectively; j and Q j These represent the active power and reactive power injected into node j, respectively. and Let D represent the active power and reactive power injected into node j by device D, respectively, where D∈{DG,ESS,FID,load}. and These represent the charging and discharging power of the energy storage ESS (Energy Storage System). This represents the squared value of the voltage at node j; R represents the square of the current in line ij; ij and X ij Ω represents the resistance and reactance of line ij, respectively. node Ω line Represents the set of nodes and paths in a network; The expressions for the node voltage and line current constraints of the network are as follows: Among them, U j,min U represents the minimum voltage limit at node j. j,max I represents the maximum voltage limit at node j. ij,max This indicates the maximum limit of the current in line ij; The expression for the capacity constraint of the DG is: in, This represents the reactive power generated by the DG. This indicates the active power generated by the DG. This represents the maximum output power tracking value of the DG at node j. Indicates the power factor angle limit of the DG; The expression for the CB constraint of the grouped capacitor is: in, This represents the compensated reactive power of each CB group; This indicates the number of CB operations performed on node j; This represents the maximum number of times a node j can perform a CB operation; This represents the reactive power of the CB injected into node j; The expression for the energy storage ESS constraint is: in, ΔT represents the charging and discharging time of the ESS, which are 0-1 variables respectively. These represent the charging and discharging power of the ESS at node j, respectively. The expression for the FID constraint is: Where P j FID,L This represents the active power loss of the FID at node j; This represents the FID capacity of node j.

5. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 1, characterized in that, Step A3 includes: For different scan points The target value for optimizing the edge network ADN-x Perform piecewise linear fitting, assuming that each scan in the edge network ADN-x selects... They are respectively corresponding They are respectively Connecting each point sequentially yields a piecewise linear correlation function. in, It is a set of continuous auxiliary variables; A set of 0-1 auxiliary variables used to limit The value of , thus and The segmented interval can be accessed via Sure.

6. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 1, characterized in that, Step B1 includes: Establish Distflow power flow constraints for the fault edge network ADN-y; Establish radial and connectivity topological constraints for the faulty network ADN-y; Establish load constraints for the fault edge network ADN-y; Establish operational constraints for each device in the fault edge network ADN-y; Determine the objective function for fault recovery and solve the fault recovery model.

7. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 6, characterized in that, The optimization objective is to minimize the amount of power outage load, the number of switching operations, line losses, FID active power losses, and the number of CB switching operations. The objective function for fault recovery is expressed as follows: Among them, f Load f Switch f CB f Loss These represent the objective functions for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively; Ω ADN This represents the set of ADNs that are flexibly interconnected with ADN-y; Indicates the amount of power outage load; ω j α represents the importance weight of the load at node j; ij,0 Indicates the initial switching state of line ij; β j,0 λ represents the initial state of the uncontrollable load at node j; Load , λ Switch , λ CB , λ Loss These are the weighting coefficients of the objective function for load restoration, number of switching operations, number of CB switching operations, and network loss, respectively.

8. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 6, characterized in that, The expression for the Distflow power flow constraint is: Where M represents a positive number; α ij It is a 0-1 variable representing the state of the circuit switch; P ij and Q ij These represent the active power and reactive power flowing through line ij, respectively. R represents the square of the current in line ij; ij and X ij These represent the resistance and reactance of line ij, respectively; This represents the square of the voltage at node i; This represents the square of the voltage at node j.

9. The cloud-edge collaborative optimization method for fault recovery in a flexible interconnected distribution network according to claim 6, characterized in that, The load constraints for establishing the fault edge network ADN-y include: ADN internal loads are divided into controllable loads and uncontrollable loads: Among them, Ω cnode Ω unode These represent the sets of nodes representing controllable and uncontrollable loads, respectively. These represent the maximum active and reactive power demand of node j, respectively; β j It is a 0-1 variable, representing the state of the uncontrollable load of node j.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the cloud-edge collaborative optimization method for fault recovery of a flexible interconnected distribution network as described in any one of claims 1-9.

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