Power distribution network resilience improvement and joint optimization dispatching method and system of repair personnel scheduling

Abstract

The present application relates to the technical field of energy security, and more particularly to a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system, comprising: obtaining active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output and load curve and numerical value; the post-disaster recovery model of active power distribution network is constructed in the guidance of elasticity, the adjustment strategy of distributed power generation resources is determined based on the topology reconstruction and island segmentation strategy method; the optimization scheduling model of repair personnel and network reconstruction coordination is constructed in the guidance of elasticity, and the second order cone relaxation method is used for relaxation processing to determine the fault repair strategy; through the active power distribution network topology parameters, the adjustable resource parameters and the renewable energy predicted output, the optimization problem is reconstructed based on the active power distribution network fault scene under the disaster; the optimization problem is solved with the minimum fault economic loss as the target, and the active power distribution network topology structure and the fault repair strategy are obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy security, and particularly relates to a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system. BACKGROUND

[0002] With the global climate changing, extreme natural disaster events such as typhoon and earthquake occur frequently, and large-area power outage accidents caused thereby bring great challenges to the safe and stable operation of the power distribution network. Improving the elasticity of the active power distribution network can effectively enhance the response capability of the power distribution network to extreme natural disaster events and power grid faults, reduce the outage range and failure recovery market, and minimize the loss of power outage.

[0003] After a large-scale fault occurs, network reconstruction can effectively reduce the difficulty of fault repair by adjusting the structure of the power distribution network, reducing the outage range, reducing the load loss, and the like, reduce the outage time and influence range, and reduce the economic and social losses. In the process of fault repair, by collecting real-time data and information of the power distribution network, basis can be provided for network reconstruction, and the best optimization and dynamic adjustment of the network reconstruction scheme can be guided. Therefore, network reconstruction and fault repair are interdependent and mutually promoting in the process of fault recovery, and the coordinated optimization of the two can realize the rapid recovery of power supply after the fault of the power distribution network, help to effectively improve the elasticity level of the power distribution network, and effectively reduce the economic loss after the fault.

[0004] The information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present application and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art. SUMMARY

[0005] The present application provides a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system, thereby effectively solving the problems in the background art.

[0006] In order to achieve the above purpose, the technical solution adopted by the present application is as follows: a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method, comprising the following steps:

[0007] Obtaining the topology parameters of the active power distribution network, the parameters of the diesel generator and the energy storage device, the renewable energy predicted output, and the load curve and numerical value;

[0008] Based on the above parameters and numerical values, an active power distribution network post-disaster recovery model is constructed in an elasticity-oriented manner, and based on the topology reconstruction and island segmentation strategy method, the adjustment strategy of the distributed power generation resources when the important load is restored and supplied with energy is determined;

[0009] An optimization scheduling model of maintenance personnel and network reconfiguration coordination is constructed in the elastic direction, and a second-order cone relaxation method is used for relaxation processing to determine the fault maintenance strategy;

[0010] Through active power distribution network topology parameters, dispatchable resource parameters and renewable energy predicted output, the optimization problem of adjustment strategy and fault maintenance strategy containing the distributed power generation resource is reconstructed based on the active power distribution network fault scene under disaster;

[0011] The optimization problem after reconstruction is solved to obtain the topology structure of the reconstructed active power distribution network and the fault maintenance strategy.

[0012] Further, the active power distribution network post-disaster recovery model comprises a flexible power source model, an active power distribution network power flow constraint model, a maintenance personnel allocation and fault maintenance constraint model and a radial active power distribution network operation constraint model;

[0013] The flexible power source model comprises a diesel generator model, an energy storage system model and a renewable energy system model.

[0014] Further, the diesel generator model comprises:

[0015]

[0016] In the formula, and respectively represent the active and reactive power output of the diesel generator at node i at time t; κ i represents the power factor of the diesel generator; is the capacity of the diesel generator at node i; and are the upper and lower limits of the diesel generator power output; N T is the total number of dispatching time points; N DG is the number of diesel generators.

[0017] Further, the energy storage system model comprises:

[0018]

[0019] In the formula, represents the stored power of the energy storage device at node i at time t; is the loss coefficient of the energy storage device; and are the charging and discharging power and net power of the energy storage device at node i at time t; η c and η D are the charging and discharging efficiencies of the energy storage device; and Let be binary variables representing the charging and discharging state of the energy storage device at node i at time t; This indicates the upper limit of the charging and discharging power of the energy storage device; and These are the upper and lower limits of the amount of electricity that the energy storage device can store; The capacity of the energy storage device; N represents the reactive power of the energy storage device at time t; Δt represents the dispatch period, taken as 1 hour; N ESS The number of energy storage devices;

[0020] Furthermore, the renewable energy system model includes:

[0021]

[0022]

[0023] In the formula, and These represent the sum of active and reactive power outputs of the photovoltaic and wind power equipment at time t, respectively. Let i be the power factor of renewable energy generation at node i; Let i be the capacity of renewable energy generation at node i; RES represents the maximum power output of renewable energy generation, and RES stands for renewable energy equipment, including PV and WT, where PV and WT represent photovoltaic and wind turbines, respectively. PV N WT These represent the number of photovoltaic and wind turbines, respectively.

[0024] Furthermore, the active distribution network power flow constraint model includes:

[0025]

[0026] In the formula, and The set of branches at time t The active and reactive power of the inner branch ij; r ki With x ki These represent the resistance and reactance of branch ki, respectively; I ki,t Let be the current flowing through branch ki at time t; a(i) represents the set of parent nodes with node i as a child node, and k is the number of elements in set a(i); b(i) represents the set of child nodes with node i as a parent node; U i,t The voltage amplitude at node i at time t; and P represents the upper and lower limits of the voltage amplitude variation at node i; i,t With Q i,t These represent the active and reactive power injection magnitudes at node i at time t;

[0027]

[0028] where λ i is a binary variable indicating whether node i is connected to the load; and are the active and reactive power of node i at time t, respectively.

[0029] Further, the repairman allocation and fault repair constraint model comprises:

[0030]

[0031] where is the branch ij switch state of the fault line set h is the maximum number of lines that can be repaired at the same time in the same time period; T f is the fault repair time, F is the number of elements in the set t n and t m are the time before maintenance and the maintenance time, respectively.

[0032] Each repairman needs to carry sufficient equipment capacity to meet the total resource demand of the lines he repairs:

[0033]

[0034] where is a binary variable, and when the value is 1, it indicates that the fault line ij is repaired by the repairman τ in the repair team Ξ; represents the demand for the mth equipment when repairing the fault, and M is the number of equipment types; is the capacity limit of the mth equipment carried by the repairman τ.

[0035] Further, the radial active power distribution network operation constraint model comprises:

[0036] α ij = μ ij + v ij ;

[0037]

[0038] α ij ∈ {0, 1}, μ ij ∈ {0, 1}, v ij ∈ {0, 1};

[0039] where α ij is the opening state of the branch ij, μij with v ij respectively the virtual power flow direction of branch ij, μ ij = 1 indicates that the actual power flow direction is the same as the default direction, v ij = 1 indicates that the actual power flow direction is opposite to the default direction; Ω n is the set of normal nodes; Ω s is the set of nodes of variable island; Ω i,A denotes the branch set of which node i is the parent node; Ω i,c denotes the branch set of which node i is the child node.

[0040] Further, the optimization scheduling method for coordinating the maintenance personnel and network reconstruction in a flexible manner utilizes a second-order cone relaxation method to relax the existing model, including:

[0041] The active power distribution network flow constraint model is converted into the following form using the Fortuny-Amat conversion method:

[0042]

[0043] In the formula, M is a sufficiently large positive real number; if α ij = 0, then if α ij = 1, it is equivalent to

[0044] The second-order cone relaxation is performed on the power distribution network flow constraint:

[0045]

[0046] In the formula, are the square terms of voltage and current, respectively;

[0047] The relaxation gap of the power distribution network flow is defined as:

[0048]

[0049] The second-order cone relaxation of the diesel generator model and the energy storage model is:

[0050]

[0051] In the formula, is the capacity of the energy storage battery.

[0052] Further, the target function is set to minimize the economic loss caused by the fault: including the cost of cutting off the load and the operation cost of the power distribution network:

[0053]

[0054] In the formula, θ i is the importance coefficient of the load at node i; C DG , C ESS , C PV , C WT are the operation costs of diesel generators, energy storage devices, photovoltaic power generation, and wind power generation, respectively, and Ω ADN is the set of nodes of the active power distribution network.

[0055] The application also includes a power distribution network resilience improvement and repair personnel scheduling joint optimization scheduling system using the method described above, comprising:

[0056] A collection unit is configured to obtain active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output, and load curve and numerical values.

[0057] A modeling unit is configured to construct an active power distribution network post-disaster recovery model based on the above parameters and values, and to determine the adjustment strategy of the distributed power generation resources when important loads are restored and supplied with energy based on a topology reconfiguration and island segmentation strategy method.

[0058] A scheduling unit is configured to construct an optimization scheduling model for repair personnel and network reconfiguration coordination based on resilience, and to determine a fault repair strategy by using a second-order cone relaxation method for relaxation processing.

[0059] A problem reconfiguration unit is configured to reconfigure the optimization problem of the adjustment strategy of the distributed power generation resources and the fault repair strategy based on the active power distribution network fault scenario under disasters based on active power distribution network topology parameters, adjustable resource parameters, and renewable energy predicted output.

[0060] A solving unit is configured to solve the reconfigured optimization problem with the goal of minimizing economic loss, to obtain the reconfigured active power distribution network topology structure and the fault repair strategy.

[0061] The application also includes a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0062] The application also includes a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method described above.

[0063] The beneficial effects of this invention are as follows: Based on the joint optimization scheduling method of distribution network resilience enhancement and emergency repair personnel scheduling, this invention first constructs a resilience-oriented active distribution network post-disaster recovery model. Based on topology reconstruction and islanding strategies, it realizes the flexible application of distributed generation resources, restores and supplies energy to important loads, and improves the resilience of the active distribution network in the face of extreme disasters. Then, it establishes a scheduling method that coordinates maintenance personnel and network reconstruction based on resilience, and uses the second-order cone relaxation method to relax the existing model. Through the active distribution network topology parameters, schedulable resource parameters, and renewable energy predicted output, the optimization problem is reconstructed based on the active distribution network fault scenario under disaster, with the goal of minimizing the economic loss of the fault. Finally, the reconstructed active distribution network topology and fault maintenance strategy are obtained. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of the method of the present invention;

[0066] Figure 2 This is a diagram illustrating the elasticity curves and fault recovery framework for active distribution networks.

[0067] Figure 3 A graph showing the voltage management and resilience of active power distribution networks;

[0068] Figure 4 This is a schematic diagram of the system structure of the present invention;

[0069] Figure 5 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0070] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0071] like Figure 1 As shown: A method for joint optimization scheduling of distribution network resilience enhancement and emergency repair personnel dispatch, comprising the following steps:

[0072] Obtain active power distribution network topology parameters, diesel generator and energy storage device parameters, and renewable energy predicted output and load curves and values;

[0073] Based on the above parameters and values, an active distribution network post-disaster recovery model is constructed with elasticity as the guide. Based on the topology reconfiguration and islanding strategy, the adjustment strategy of distributed generation resources is determined when energy restoration and supply are carried out for important loads.

[0074] An optimized scheduling model for coordinating maintenance personnel and network reconfiguration is constructed with flexibility as the guiding principle, and a second-order cone relaxation method is used for relaxation processing to determine the fault maintenance strategy.

[0075] By using the active distribution network topology parameters, dispatchable resource parameters, and predicted renewable energy output, the optimization problem of adjustment strategies and fault repair strategies involving the distributed generation resources is reconstructed based on the active distribution network fault scenarios under disasters.

[0076] With the goal of minimizing economic losses from faults, the optimization problem after reconstruction is solved to obtain the reconstructed active distribution network topology and fault maintenance strategy.

[0077] In this embodiment, the active distribution network post-disaster recovery model includes a flexible power source model, an active distribution network power flow constraint model, a maintenance personnel allocation and fault repair constraint model, and a radial active distribution network operation constraint model.

[0078] Flexible power source models include diesel generator models, energy storage system models, and renewable energy system models.

[0079] Diesel engine power generation models include:

[0080]

[0081] In the formula, and κ represents the active and reactive power outputs of the diesel generator at node i at time t, respectively; i The power factor of a diesel generator; Let be the capacity of the diesel generator at node i; and These represent the upper and lower limits of the diesel generator's power output; N T N represents the total number of scheduling time slots. DG This refers to the number of diesel generators.

[0082] Energy storage system models include:

[0083]

[0084] In the formula, This represents the amount of electricity stored by the energy storage device at node i at time t; The loss coefficient of the energy storage device; as well as Pi,net(t) and Pi,dis(t) are the net power and discharging power of the energy storage device at node i at time t, respectively; η C and η D are the charging and discharging efficiency of the energy storage device, respectively; and are binary variables representing the charging and discharging state of the energy storage device at node i at time t; is the upper limit of the power of the energy storage device charging and discharging power; and are the upper and lower limits of the energy storage device's storable power; is the capacity of the energy storage device; is the reactive power of the energy storage device at time t; Δt is the scheduling period, taking 1 hour; N ESS is the number of energy storage devices;

[0085] The renewable energy system model includes:

[0086]

[0087] wherein, and are the total active and reactive power output of the photovoltaic and wind power devices at time t; is the power factor of renewable energy generation at node i; is the capacity of renewable energy generation at node i; is the maximum power output of renewable energy generation, RES represents renewable energy devices, including PV, WT, PV and WT represent photovoltaic and wind turbine, respectively, N PV and N WT are the number of photovoltaic and wind turbine, respectively.

[0088] wherein, the active power distribution network flow constraint model includes:

[0089]

[0090] wherein, and are the active and reactive power of branch ij in branch set at time t; r ki and x ki are the resistance and reactance of branch ki; I ki,t is the current flowing through branch ki at time t; a(i) represents the parent node set with node i as a child node, and k is the number of elements in set a(i); b(i) represents the child node set with node i as a parent node; U i,t is the voltage amplitude of node i at time t; and are the upper and lower limits of the voltage amplitude change of node i; Pi,t with Q i,t are the active and reactive power injection of node i at time t, respectively;

[0091]

[0092] where λ i is a binary variable, and are the active and reactive power of node i at time t, respectively.

[0093] As a preferred embodiment of the above, the maintenance personnel allocation and fault repair constraint model comprises:

[0094]

[0095] where λ is a binary variable, and is the branch ij switch state of the fault line set f h is the maximum number of lines that can be repaired at the same time in the same time period, T n is the fault repair time, F is the number of elements in the set t m are the time before maintenance and the maintenance time, respectively.

[0096] Each maintenance personnel needs to carry sufficient equipment capacity to meet the total resource demand of the lines he maintains:

[0097]

[0098] where λ is a binary variable, and is a binary variable, and is the demand for the mth equipment when repairing the fault of M is the number of equipment types; is the capacity limit of the mth equipment carried by the maintenance personnel τ.

[0099] The radial active power distribution network operation constraint model comprises:

[0100] α ij = μ ij + v ij ;

[0101]

[0102] α ij ∈ {0, 1}, ν ij ∈ {0, 1}, v ij ∈ {0, 1}; ​

[0103] where α ij is the open state of branch ij, μ ij and v ij are the virtual power flow direction of branch ij, μ ij = 1 indicates that the actual power flow direction is the same as the default direction, v ij = 1 indicates that the actual power flow direction is opposite to the default direction; Ω n is the set of normal nodes; Ω s is the set of variable islands; Ω i,A represents the branch set of node i as the parent node; Ω i,C represents the branch set of node i as the child node.

[0104] In this embodiment, an optimization scheduling method for coordinating maintenance personnel and network reconstruction is constructed in a flexible manner. A second-order cone relaxation method is used to relax the existing model, including:

[0105] The active power distribution network flow constraint model is converted into the following form using the Fortuny-Amat conversion method:

[0106]

[0107] where M is a sufficiently large positive real number; if α ij = 0, then if α ij = 1, then it is equivalent to

[0108] The second-order cone relaxation is performed on the power distribution network flow constraint:

[0109]

[0110]

[0111] where are the square terms of voltage and current, respectively;

[0112] The relaxation gap of the power distribution network flow is defined as:

[0113]

[0114] The second-order cone relaxation of the diesel generator model and the energy storage model is:

[0115]

[0116] where is the capacity of the energy storage battery.

[0117] The minimum economic loss of fault is taken as the target, and the objective function is The cost of cutting off the load payment and the operation cost of the power distribution network:

[0118]

[0119] In the formula, θ i is the importance coefficient of the load at node i; C DG , C ESS , C PV , C WT are the operation costs of the diesel generator, the energy storage device, the photovoltaic power generation, and the wind power generation, respectively, and Ω ADN is the set of nodes of the active power distribution network.

[0120] As shown in Figure 2 and 3 , in the embodiment, the active power distribution network disaster recovery model is first constructed based on the elasticity-oriented joint optimization scheduling method of the power distribution network elasticity improvement and the repair personnel scheduling. Based on the topology reconstruction and island segmentation strategy method, the flexible application of the distributed power generation resource is realized, the energy recovery and supply of the important load are realized, and the elasticity level of the active power distribution network under extreme disasters is improved. Then, the dispatching method of the repair personnel and the network reconstruction coordination is established based on the elasticity orientation, and the existing model is relaxed by using the second-order cone relaxation method. Through the active power distribution network topology parameters, the adjustable resource parameters, and the renewable energy predicted output, the optimization problem is reconstructed based on the fault scene of the active power distribution network under disasters, and the minimum economic loss of the fault is taken as the optimization target. Finally, the reconstructed active power distribution network topology structure and the fault repair strategy are obtained, and good application effect is achieved.

[0121] As shown in Figure 4 , the embodiment also includes a power distribution network elasticity improvement and repair personnel scheduling joint optimization scheduling system, which uses the method as described above, including:

[0122] The acquisition unit is used to acquire the active power distribution network topology parameters, the diesel generator and energy storage device parameters, the renewable energy predicted output, and the load curve and numerical value.

[0123] The modeling unit is used to construct the active power distribution network disaster recovery model based on the elasticity orientation based on the above parameters and values, to determine the adjustment strategy of the distributed power generation resource when the energy recovery and supply of the important load are realized based on the topology reconstruction and island segmentation strategy method.

[0124] The scheduling unit is used to construct the optimization scheduling model of the repair personnel and the network reconstruction coordination based on the elasticity orientation, and to determine the fault repair strategy by using the second-order cone relaxation method for relaxation.

[0125] A problem reconfiguration unit is configured to reconfigure, based on a fault scenario of the active power distribution network under the disaster, an optimization problem of an adjustment strategy and a fault repair strategy of the distributed power generation resource according to the active power distribution network topology parameter, the dispatchable resource parameter and the renewable energy predicted output.

[0126] A solution unit is configured to solve the reconfigured optimization problem to obtain the reconfigured active power distribution network topology and the fault repair strategy with the minimum fault economic loss as the target.

[0127] Please refer to Figure 5 The computer device provided by the embodiment of the application is shown in a structural schematic diagram. The computer device 400 provided by the embodiment of the application comprises a processor 410 and a memory 420, the memory 420 stores a computer program executable by the processor 410, and the computer program is executed by the processor 410 to perform the method as above.

[0128] The embodiment of the application further provides a storage medium 430, and the storage medium 430 stores a computer program, and the computer program is executed by the processor 410 to perform the method as above.

[0129] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0130] In the description of the application, the terms "first", "second" are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. The meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0131] In the present application, unless specifically defined otherwise and limited in the specification, the terms "mount", "connect", "connection", "contact", and the like are to be construed in their broadest possible sense, such as to include fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections, or connections made by means of intermediate medium; direct connections, or indirect connections via intermediate medium; internal connections between elements, or interaction between elements. The specific meaning of the above terms in the present application can be understood by those skilled in the art according to the specific circumstances.

[0132] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the skilled person can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0133] Any process or method descriptions or descriptions of the flow diagrams in the present application can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for performing specific logic functions or steps in the process, and the preferred embodiments of the present application include additional implementations in which the order of steps can be changed, including use of the same step more than once, use of the same step in different orders, use of the same step in different ways, use of different steps in the same order, use of different steps in different orders, use of different steps in different ways, and so on, as will be appreciated by those skilled in the art.

[0134] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be realized in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include an electronic connection (an electronic device), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium can even be paper or other suitable medium upon which the program is printed, as the program can be electronically captured, for example, by optically scanning the paper or other suitable medium, then electronically converted into a form that is suitable for use by the instruction execution system, apparatus, or device, and then stored in computer memory.

[0135] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0136] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium, and when executed, include one or a combination of steps of the method embodiments.

[0137] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A power distribution network resilience improvement and repair crew scheduling joint optimization scheduling method, characterized in that, The method comprises the following steps: obtaining active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output, and load curve and numerical value; based on the above parameters and values, an active power distribution network post-disaster recovery model is constructed in an elasticity-oriented manner, and based on a topology reconstruction and island segmentation strategy method, an adjustment strategy of distributed power generation resources when important loads are supplied with energy is determined; an optimization scheduling model of maintenance personnel and network reconstruction coordination is constructed in an elasticity-oriented manner, and a second-order cone relaxation method is used for relaxation processing to determine a fault maintenance strategy; based on the active power distribution network fault scenario under the disaster, the optimization problem of the adjustment strategy of the distributed power generation resources and the fault maintenance strategy is reconstructed through the active power distribution network topology parameters, the adjustable resource parameters and the renewable energy predicted output; the optimization problem after reconstruction is solved with the minimum fault economic loss as the target to obtain the reconstructed active power distribution network topology structure and the fault maintenance strategy.

2. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 1, wherein, The active power distribution network post-disaster recovery model comprises a flexible power source model, an active power distribution network power flow constraint model, a maintenance personnel allocation and fault maintenance constraint model, and a radial active power distribution network operation constraint model; The flexible power source model comprises a diesel engine power generation model, an energy storage system model, and a renewable energy system model.

3. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 2, wherein, The diesel engine power generation model comprises: In the formula, and κ represents the active and reactive power outputs of the diesel generator at node i at time t, respectively; i The power factor of a diesel generator; Let be the capacity of the diesel generator at node i; and These represent the upper and lower limits of the diesel generator's power output, respectively; N T N represents the total number of scheduling time slots. DG This refers to the number of diesel generators.

4. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 3, wherein, The energy storage system model comprises: wherein, represents the amount of electricity stored by the energy storage device at node i at time t; is the loss coefficient of the energy storage device; and are the charging and discharging power and the net power of the energy storage device at node i at time t, respectively; η C and η D are the charging and discharging efficiency of the energy storage device, respectively; and are binary variables representing the charging and discharging state of the energy storage device at node i at time t, respectively; represents the upper limit of the power of the charging and discharging power of the energy storage device; and are the upper and lower limits of the amount of electricity that can be stored by the energy storage device, respectively; is the capacity of the energy storage device; is the reactive power of the energy storage device at time t; Δt is the scheduling period, which is 1 hour; N ESS is the number of energy storage devices.

5. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 4, wherein, The renewable energy system model comprises: where RES represents the renewable energy resources, including PV, WT, PV, WT represent photovoltaic and wind turbine, respectively, With PQ, Q are the total active and reactive power output of PV and WT at time t, respectively; cos(φi) is the power factor of renewable energy generation at node i; Ci is the capacity of renewable energy generation at node i; Pmax is the maximum power output of renewable energy generation, N PV , N WT are the number of PV and WT, respectively.

6. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 3, wherein, The active power distribution network power flow constraint model comprises: wherein, and are the active and reactive power of the branch set at time t are the active and reactive power of the inner branch ij; r ki and x ki are the resistance and reactance of the branch ki; I ki,t is the current flowing through the branch ki at time t; a(i) represents the parent node set with node i as a child node, and k is the number of elements in the set a(i); b(i) represents the child node set with node i as a parent node; U i,t is the voltage amplitude of node i at time t and are the upper and lower limits of the voltage amplitude of node i; P i,t and Q i,t are the active and reactive power injection sizes of node i at time t where the binary variable λ i represents whether node i is connected to the load; and are the active and reactive power of the load at node i at time t, respectively.

7. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 2, wherein, The maintenance personnel allocation and fault maintenance constraint model comprises: wherein represents the branch ij switch state of the fault line set F is the number of elements in the set h is the maximum number of lines that can be repaired at the same time within the same time period; T f is the fault repair time, t n , t m are the time before maintenance and the time of maintenance, respectively Each maintenance personnel needs to have sufficient equipment capacity to meet the total resource demand of the line being maintained: wherein, is a binary variable, and takes the value 1 if the faulty line ij is repaired by the repair crew τ in the repair team Ξ; represents the repair of the mth equipment when a fault occurs, and M is the number of equipment types; is the capacity limit of the mth equipment carried by the repair crew τ.

8. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 2, wherein, The radial active power distribution network operation constraint model comprises: a ij = μ ij + v ij ; α ij ∈{0,1}, μ ij ∈{0,1}, v ij ∈{0,1} ; wherein α ij is the open state of branch ij, μ ij and v ij are the virtual power flow directions of branch ij, μ ij = 1 indicates that the actual power flow direction is the same as the default direction, v ij = 1 indicates that the actual power flow direction is opposite to the default direction; Ω n is the set of normal nodes; Ω s is the set of nodes of variable islands; Ω i,A denotes the branch set of which node i is the parent node; Ω i,C denotes the branch set of which node i is the child node.

9. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 6, wherein, The optimization scheduling method of maintenance personnel and network reconstruction coordination in an elasticity-oriented manner uses a second-order cone relaxation method to relax the existing model, comprising: The active power distribution network power flow constraint model is converted into the following form using the Fortuny-Amat conversion method: where M is a sufficiently large positive real number; if a ij = 0, then if a ij = 1, then is equivalent to The second-order cone relaxation of the power distribution network power flow constraint is: wherein are the square terms of voltage and current, respectively; The relaxation gap of the power distribution network power flow is defined as: The second-order cone relaxation of the diesel engine power generation model and the energy storage model is: In the formula, C is the capacity of the energy storage battery.

10. The power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling method of claim 9, wherein, The target function O with the minimum fault economic loss as the target comprises the cost of cutting off the load and the operation cost of the power distribution network: where θ i is the importance coefficient of the load at node i; C DG , C ESS , C PV , C WT are the operating costs of diesel generators, energy storage devices, photovoltaic power generation, and wind power generation, respectively, and Ω ADN is the set of active distribution network nodes.

11. A power distribution network resilience enhancement and repair crew scheduling joint optimization scheduling system, characterized in that, The method comprises the following steps: an acquisition unit is configured to obtain active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output, and load curve and numerical value; a modeling unit is configured to construct an active power distribution network post-disaster recovery model in an elasticity-oriented manner based on the above parameters and values, and determine an adjustment strategy of distributed power generation resources when important loads are supplied with energy based on a topology reconstruction and island segmentation strategy method; a scheduling unit is configured to construct an optimization scheduling model of maintenance personnel and network reconstruction coordination in an elasticity-oriented manner, and use a second-order cone relaxation method for relaxation processing to determine a fault maintenance strategy. A problem reconstruction unit is configured to reconstruct, based on a fault scenario of the active power distribution network under the disaster, an optimization problem of an adjustment strategy and a fault repair strategy of the distributed power generation resource, by using active power distribution network topology parameters, dispatchable resource parameters, and renewable energy predicted output. A solution unit is configured to solve the reconstructed optimization problem to obtain a reconstructed active power distribution network topology and a fault repair strategy, with the objective of minimizing fault economic loss.

12. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-10.

13. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method of any one of claims 1-10. The computer program is executed by the processor to implement the method of any one of claims 1-10.

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