Power distribution network elasticity improvement and first-aid repair personnel scheduling combined optimization scheduling method and system

Abstract

The invention relates to the technical field of energy safety, in particular to a power distribution network elasticity improvement and repair personnel scheduling joint optimization scheduling method and system, and the method comprises the steps: obtaining an active power distribution network topological parameter, a diesel generator and energy storage equipment parameter, and a renewable energy prediction output and load curve and value; constructing a post-disaster recovery model of the active power distribution network by taking elasticity as guidance, and determining an adjustment strategy of distributed power generation resources based on a topology reconstruction and island segmentation strategy method; constructing an optimal scheduling model for coordinating maintenance personnel and network reconstruction by taking elasticity as guidance, and performing relaxation processing by using a second-order cone relaxation method to determine a fault maintenance strategy; the output condition is predicted through active power distribution network topological parameters, schedulable resource parameters and renewable energy sources, and an optimization problem is reconstructed based on an active power distribution network fault scene under a disaster; and solving an optimization problem by taking the minimum fault economic loss as a target to obtain an active power distribution network topological structure and a fault maintenance strategy.

Description

Technical Field

[0001] The present invention relates to the field of energy security technology, and in particular to a method and system for joint optimization and dispatching of distribution network elasticity enhancement and emergency repair personnel. Background Art

[0002] As the global climate continues to change, extreme natural disasters such as typhoons and earthquakes occur frequently, causing large-scale power outages that pose a huge challenge to the safe and stable operation of distribution networks. Improving the resilience of active distribution networks can effectively enhance the distribution network's ability to respond to extreme natural disasters and power grid failures, reduce the scope of power outages and reduce the fault recovery market, and minimize power outage losses.

[0003] After a large-scale fault occurs, network reconstruction can effectively reduce the difficulty of fault repair by adjusting the distribution network structure, reducing the scope of power outage, and reducing load losses, thereby reducing the time and scope of power outages and reducing economic and social losses. During the fault repair process, by collecting real-time data and information from the distribution network, a basis can be provided for network reconstruction, guiding the optimal optimization and dynamic adjustment of the network reconstruction plan. Therefore, network reconstruction and fault repair are interdependent and mutually reinforcing in the fault recovery process. The coordinated optimization of the two can achieve rapid power restoration after a distribution network fault, help effectively improve the elasticity level of the distribution network, and effectively reduce economic losses after a fault.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

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

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for joint optimization and dispatching of distribution network elasticity improvement and emergency repair personnel dispatch, comprising the following steps:

[0007] Obtain active distribution network topology parameters, diesel generator and energy storage equipment parameters, renewable energy forecast output and load curves and values;

[0008] 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 reconstruction and island segmentation strategy method, the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads is determined;

[0009] An optimal scheduling model for coordination between maintenance personnel and network reconstruction is constructed based on elasticity, and the second-order cone relaxation method is used to perform relaxation and determine the fault maintenance strategy.

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

[0011] With the goal of minimizing the economic loss caused by faults, the reconstructed optimization problem is solved to obtain the reconstructed active distribution network topology and fault maintenance strategy.

[0012] Furthermore, the active distribution network post-disaster recovery model includes a flexible power supply 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;

[0013] The flexible power supply model includes a diesel engine power generation model, an energy storage system model and a renewable energy system model.

[0014] Furthermore, the diesel engine power generation model includes:

[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 respectively; N T is the total scheduling time; N DG is the number of diesel generators.

[0017] Furthermore, the energy storage system model includes:

[0018]

[0019] In the formula, Represents the amount of electricity stored in the energy storage device at node i at time t; is the loss coefficient of the energy storage device; as well as are the charging and discharging power and net power of the energy storage device at node i at time t; η c With η D are the charging and discharging efficiency of the energy storage device respectively; and are binary variables representing the charging and discharging status of the energy storage device at node i at time t; Indicates the upper limit of the charging and discharging power of the energy storage device; and They are the upper and lower limits of the amount of electricity that can be stored in the energy storage device; is the capacity of the energy storage device; is the reactive power of the energy storage device at time t; Δt is the dispatching period, which is 1 hour; N ESS is the number of energy storage devices;

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

[0021]

[0022]

[0023] In the formula, and are the sum of active and reactive power outputs of photovoltaic and wind power equipment at time t respectively; 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 power generation, RES represents renewable energy equipment, including PV and WT, PV and WT represent photovoltaic and wind turbine respectively, N PV 、N WT are 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 are the branch sets at time t Active and reactive power of inner branch ij; r ki With x ki are the resistance and reactance of branch ki respectively; I ki,t is the current flowing through branch ki at time t; a(i) represents the set of parent nodes with node i as child node, k is the number of elements in set a(i); b(i) represents the set of child nodes with node i as parent node; U i,t The voltage amplitude of node i at time t; and is the upper and lower limits of the voltage amplitude change at node i; P i,t With Q i,t are respectively the active and reactive power injection size of node i at time t;

[0027]

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

[0029] Furthermore, the maintenance personnel allocation and fault maintenance constraint model includes:

[0030]

[0031] In the formula, Represents the set of fault lines The switch status of branch ij; h is the maximum number of lines that can be repaired at the same time; T f is the fault repair time, F is the set The number of elements in t n , t m They are the time before maintenance and the time of maintenance respectively;

[0032] Each maintenance worker needs to have sufficient equipment capacity to meet the total resource requirements of the line he or she is maintaining:

[0033]

[0034] In the formula, It is a binary variable, and its value is 1. The faulty line ij is repaired by the maintenance personnel τ in the maintenance team Ξ; Indicates maintenance The demand for the mth type of equipment when the failure occurs, M is the number of equipment types; The capacity limit of the mth type of equipment carried by maintenance personnel τ.

[0035] Furthermore, the radial active distribution network operation constraint model includes:

[0036] α ij =μ ij +v ij ;

[0037]

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

[0039] In the formula, α ij is the breaking state of branch ij, μij With v ij are the virtual power flows 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 common nodes; Ω s is the node set of variable islands; Ω i,A represents the branch set of which node i is the parent node; Ω i,c Represents the set of branches whose child nodes are node i.

[0040] Furthermore, the elasticity-oriented optimization scheduling method for coordinating maintenance personnel and network reconstruction uses a second-order cone relaxation method to relax the existing model, including:

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

[0042]

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

[0044] Perform second-order cone relaxation on the power flow constraints of the distribution network:

[0045]

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

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

[0048]

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

[0050]

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

[0052] Furthermore, the objective is to minimize the economic loss caused by failure. Including the cost of cutting off load and the cost of distribution network operation:

[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 operating costs of diesel generator, energy storage equipment, photovoltaic power generation and wind power generation, Ω ADN It is the set of active distribution network nodes.

[0055] The present invention also includes a distribution network elasticity improvement and emergency repair personnel dispatching joint optimization dispatching system, using the above method, including:

[0056] The acquisition unit is used to obtain the topological parameters of the active distribution network, the parameters of the diesel generator and the energy storage equipment, the predicted output of renewable energy, and the load curve and value;

[0057] A modeling unit, used for constructing an active distribution network post-disaster recovery model based on the above parameters and values ​​with elasticity as the orientation, and determining the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads based on topology reconstruction and island segmentation strategy methods;

[0058] The scheduling unit is used to build an optimal scheduling model for coordination between maintenance personnel and network reconstruction based on elasticity, and to determine the fault maintenance strategy by using the second-order cone relaxation method for relaxation.

[0059] A problem reconstruction unit, for reconstructing the optimization problem including the adjustment strategy and the fault repair strategy of the distributed generation resources based on the active distribution network fault scenario under disasters by using the active distribution network topology parameters, the dispatchable resource parameters and the predicted output of renewable energy;

[0060] The solving unit is used to solve the reconstructed optimization problem with the goal of minimizing the economic loss caused by the fault, and obtain the reconstructed active distribution network topology structure and fault maintenance strategy.

[0061] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0062] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0063] The beneficial effects of the present invention are as follows: based on the joint optimization scheduling method for improving the elasticity of distribution networks and scheduling of emergency repair personnel, the present invention first constructs an elasticity-oriented post-disaster recovery model for active distribution networks, and based on the topology reconstruction and island segmentation strategy method, realizes the flexible application of distributed power generation resources, and the energy recovery and supply of important loads, thereby improving the elasticity level of active distribution networks in the face of extreme disasters; then establishes a scheduling method for coordinated elasticity-oriented maintenance personnel and network reconstruction, and uses the second-order cone relaxation method to relax the existing model. Through the active distribution network topology parameters, dispatchable resource parameters and renewable energy predicted output, the optimization problem is reconstructed based on the active distribution network fault scenario under disasters, and the optimization is performed with the goal of minimizing the economic loss of the fault, and finally the reconstructed active distribution network topology structure and fault repair strategy are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 It is the elasticity curve and fault recovery framework diagram based on active distribution network;

[0067] Figure 3 It is a graph of voltage management and resilience level of active distribution network;

[0068] Figure 4 It is a structural schematic diagram of the system of the present invention;

[0069] Figure 5 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0071] like Figure 1 As shown: A method for joint optimization and dispatching of distribution network elasticity and emergency repair personnel dispatching includes the following steps:

[0072] Obtain active distribution network topology parameters, diesel generator and energy storage equipment parameters, renewable energy forecast 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 reconstruction and island segmentation strategy method, the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads is determined;

[0074] An optimal scheduling model for coordination between maintenance personnel and network reconstruction is constructed based on elasticity, and the second-order cone relaxation method is used to perform relaxation and determine the fault maintenance strategy.

[0075] Through the active distribution network topology parameters, dispatchable resource parameters and renewable energy predicted output, the optimization problem of the adjustment strategy and fault repair strategy including the distributed generation resources is reconstructed based on the active distribution network fault scenario under disaster;

[0076] With the goal of minimizing the economic loss caused by faults, the reconstructed optimization problem 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] The flexible power source model includes diesel engine power generation model, energy storage system model and renewable energy system model.

[0079] The diesel generator model includes:

[0080]

[0081] 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 respectively; N T is the total scheduling time; N DG is the number of diesel generators.

[0082] The energy storage system model includes:

[0083]

[0084] In the formula, Represents the amount of electricity stored in the energy storage device at node i at time t; is the loss coefficient of the energy storage device; as well as are the charging and discharging power and net power of the energy storage device at node i at time t; η C With η D are the charging and discharging efficiency of the energy storage device respectively; and are binary variables representing the charging and discharging status of the energy storage device at node i at time t; Indicates the upper limit of the charging and discharging power of the energy storage device; and They are the upper and lower limits of the amount of electricity that can be stored in the energy storage device; is the capacity of the energy storage device; is the reactive power of the energy storage device at time t; Δt is the dispatching period, which is 1 hour; N ESS is the number of energy storage devices;

[0085] The renewable energy system model includes:

[0086]

[0087] In the formula, and are the sum of active and reactive power outputs of photovoltaic and wind power equipment at time t respectively; 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 power generation, RES represents renewable energy equipment, including PV and WT, PV and WT represent photovoltaic and wind turbine respectively, N PV 、N WT are the number of photovoltaic and wind turbines, respectively.

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

[0089]

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

[0091]

[0092] In the formula, the binary variable λ i Indicates whether node i is connected to the load; and are the active and reactive power of the load of node i at time t respectively.

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

[0094]

[0095] In the formula, Represents the set of fault lines The switch status of branch ij; h is the maximum number of lines that can be repaired at the same time; T f is the fault repair time, F is the set The number of elements in t n , t m They are the time before maintenance and the time of maintenance respectively;

[0096] Each maintenance worker needs to have sufficient equipment capacity to meet the total resource requirements of the line he or she is maintaining:

[0097]

[0098] In the formula, It is a binary variable, and its value is 1. The faulty line ij is repaired by the maintenance personnel τ in the maintenance team Ξ; Indicates maintenance The demand for the mth type of equipment when the failure occurs, M is the number of equipment types; The capacity limit of the mth type of equipment carried by maintenance personnel τ.

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

[0100] α ij =μ ij +v ij ;

[0101]

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

[0103] In the formula, α ij is the breaking state of branch ij, μ ij With ν ij are the virtual power flows of branch ij, μ ij =1 indicates that the actual power flow direction is the same as the default direction, ν ij =1 indicates that the actual power flow direction is opposite to the default direction; Ω n is the set of common nodes; Ω s is the node set of variable islands; Ω i,A represents the branch set of which node i is the parent node; Ω i,C Represents the set of branches whose child nodes are node i.

[0104] In this embodiment, an optimization scheduling method for coordination between maintenance personnel and network reconstruction is constructed with flexibility as the guide, and the existing model is relaxed using a second-order cone relaxation method, including:

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

[0106]

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

[0108] Perform second-order cone relaxation on the power flow constraints of the distribution network:

[0109]

[0110]

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

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

[0113]

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

[0115]

[0116] In the formula, is the capacity of the energy storage battery.

[0117] The goal is to minimize the economic loss of failure, and the objective function Including the cost of cutting off load and the cost of distribution network operation:

[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 operating costs of diesel generator, energy storage equipment, photovoltaic power generation and wind power generation, Ω ADN It is the set of active distribution network nodes.

[0120] like Figure 2 and 3 As shown, in this embodiment, based on the joint optimization scheduling method of distribution network elasticity improvement and emergency repair personnel scheduling, firstly, a elasticity-oriented active distribution network post-disaster recovery model is constructed, and based on the topology reconstruction and island segmentation strategy method, the flexible application of distributed power generation resources is realized, the energy recovery and supply of important loads are realized, and the elasticity level of the active distribution network in the face of extreme disasters is improved; then, a scheduling method for elasticity-oriented maintenance personnel and network reconstruction coordination is established, and the second-order cone relaxation method is used to relax the existing model. Through the active distribution network topology parameters, dispatchable resource parameters and renewable energy predicted output, the optimization problem is reconstructed based on the active distribution network fault scenario under the disaster, and the optimization is performed with the goal of minimizing the economic loss of the fault, and finally the reconstructed active distribution network topology structure and fault repair strategy are obtained, which have good application effects.

[0121] like Figure 4 As shown, this embodiment also includes a distribution network elasticity improvement and emergency repair personnel dispatching joint optimization dispatching system, using the above method, including:

[0122] The acquisition unit is used to obtain the topological parameters of the active distribution network, the parameters of the diesel generator and the energy storage equipment, the predicted output of renewable energy, and the load curve and value;

[0123] A modeling unit, used for constructing an active distribution network post-disaster recovery model based on the above parameters and values ​​with elasticity as the orientation, and determining the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads based on topology reconstruction and island segmentation strategy methods;

[0124] The scheduling unit is used to build an optimal scheduling model for coordination between maintenance personnel and network reconstruction based on elasticity, and to determine the fault maintenance strategy by using the second-order cone relaxation method for relaxation.

[0125] A problem reconstruction unit, for reconstructing the optimization problem including the adjustment strategy and the fault repair strategy of the distributed generation resources based on the active distribution network fault scenario under disasters by using the active distribution network topology parameters, the dispatchable resource parameters and the predicted output of renewable energy;

[0126] The solving unit is used to solve the reconstructed optimization problem with the goal of minimizing the economic loss caused by the fault, and obtain the reconstructed active distribution network topology and fault maintenance strategy.

[0127] See also Figure 5 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0128] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

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

[0130] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0131] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0132] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0133] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0137] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for joint optimization of distribution network flexibility improvement and repair personnel dispatching, It is characterized in that The steps include: Obtain active distribution network topology parameters, diesel generator and energy storage equipment parameters, renewable energy forecast output and load curves and values; 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 reconstruction and island segmentation strategy method, the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads is determined; An optimal scheduling model for coordination between maintenance personnel and network reconstruction is constructed based on elasticity, and the second-order cone relaxation method is used to perform relaxation and determine the fault maintenance strategy. Through the active distribution network topology parameters, dispatchable resource parameters and renewable energy predicted output, the optimization problem of the adjustment strategy and fault repair strategy including the distributed generation resources is reconstructed based on the active distribution network fault scenario under disaster; With the goal of minimizing the economic loss caused by faults, the reconstructed optimization problem is solved to obtain the reconstructed active distribution network topology and fault maintenance strategy.

2. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 1, It is characterized in that The active distribution network post-disaster recovery model includes a flexible power supply model, an active distribution network power flow constraint model, a maintenance personnel allocation and fault maintenance constraint model, and a radial active distribution network operation constraint model; The flexible power supply model includes a diesel engine power generation model, an energy storage system model and a renewable energy system model.

3. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 2, It is characterized in that The diesel engine power generation model includes: 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 diesel generator power output respectively; N T is the total scheduling time; N DG is the number of diesel generators.

4. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 3, It is characterized in that The energy storage system model includes: In the formula, Represents the amount of electricity stored in the energy storage device at node i at time t; is the loss coefficient of the energy storage device; as well as are the charging and discharging power and net power of the energy storage device at node i at time t; η C With η D are the charging and discharging efficiency of the energy storage device respectively; and are binary variables representing the charging and discharging states of the energy storage device at node i at time t; Indicates the upper limit of the charging and discharging power of the energy storage device; and They are the upper and lower limits of the amount of electricity that can be stored in the energy storage device; is the capacity of the energy storage device; is the reactive power of the energy storage device at time t; Δt is the dispatching period, which is 1 hour; N ESS is the number of energy storage devices.

5. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 4, It is characterized in that The renewable energy system model includes: In the formula, RES stands for renewable energy equipment, including PV and WT. PV and WT stand for photovoltaic and wind turbine respectively. and are the sum of active and reactive power outputs of photovoltaic and wind power equipment at time t respectively; 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, N PV 、N WT are the number of photovoltaic and wind turbines, respectively.

6. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 3, It is characterized in that The active distribution network power flow constraint model includes: In the formula, and are the branch sets at time t Active and reactive power of inner branch ij; r ki With x ki are the resistance and reactance of branch ki respectively; I ki,t is the current flowing through branch ki at time t; a(i) represents the set of parent nodes with node i as child node, k is the number of elements in set a(i); b(i) represents the set of child nodes with node i as parent node; U i,t The voltage amplitude of node i at time t; and is the upper and lower limits of the voltage amplitude change at node i; P i,t With Q i,t are the active and reactive power injection sizes of node i at time t respectively; In the formula, the binary variable λ i Indicates whether node i is connected to the load; and are the active and reactive power of the load of node i at time t respectively.

7. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 2, It is characterized in that The maintenance personnel allocation and fault maintenance constraint model includes: In the formula, Represents the set of fault lines The switch state of branch ij, F is the set The number of elements in; h is the maximum number of lines that can be repaired at the same time; T f is the fault repair time, t n , t m They are the time before maintenance and the time of maintenance respectively; Each maintenance worker needs to have sufficient equipment capacity to meet the total resource requirements of the line he or she is maintaining: In the formula, It is a binary variable, and its value is 1. The faulty line ij is repaired by the maintenance personnel τ in the maintenance team Ξ; Indicates maintenance The demand for the mth type of equipment when the failure occurs, M is the number of equipment types; The capacity limit of the mth type of equipment carried by maintenance personnel τ.

8. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 2, It is characterized in that The radial active distribution network operation constraint model includes: a ij =μ ij +v ij ; a ij ∈{0,1}, μ ij ∈{0,1},v ij ∈{0,1}; In the formula, α ij is the breaking state of branch ij, μ ij With v ij are the virtual power flows 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 common nodes; Ω s is the node set of variable islands; Ω i,A represents the branch set of which node i is the parent node; Ω i,C Represents the branch set of which node i is a child node.

9. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 6, It is characterized in that The elasticity-oriented optimization scheduling method for coordinating maintenance personnel and network reconstruction uses a second-order cone relaxation method to relax the existing model, including: The Fortuny-Amat conversion method is used to transform the active distribution network power flow constraint model into the following form: Where M is a sufficiently large positive real number; if α ij =0, then If α ij =1, it is equivalent to Perform second-order cone relaxation on the power flow constraints of the distribution network: In the formula, are the square terms of voltage and current respectively; The slack gap of the 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, is the capacity of the energy storage battery.

10. The method for optimizing the dispatching of distribution network elasticity and emergency repair personnel according to claim 9, It is characterized in that The objective is to minimize the economic loss caused by the failure. The objective function O includes the cost of cutting off the load and the cost of operating the distribution network: 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 operating costs of diesel generator, energy storage equipment, photovoltaic power generation and wind power generation, Ω ADN It is the set of active distribution network nodes.

11. A joint optimization dispatching system for improving the elasticity of distribution networks and dispatching emergency repair personnel, It is characterized in that Use of the method according to any one of claims 1 to 10, comprising: The acquisition unit is used to obtain the topological parameters of the active distribution network, the parameters of the diesel generator and energy storage equipment, the predicted output of renewable energy, and the load curve and value; A modeling unit, used for constructing an active distribution network post-disaster recovery model based on the above parameters and values ​​with elasticity as the orientation, and determining the adjustment strategy of distributed generation resources when restoring and supplying energy to important loads based on topology reconstruction and island segmentation strategy methods; The scheduling unit is used to build an optimal scheduling model for coordination between maintenance personnel and network reconstruction based on elasticity, and to use the second-order cone relaxation method to perform relaxation and determine the fault maintenance strategy; A problem reconstruction unit, for reconstructing the optimization problem including the adjustment strategy and the fault repair strategy of the distributed generation resources based on the active distribution network fault scenario under disasters by using the active distribution network topology parameters, the dispatchable resource parameters and the predicted output of renewable energy; The solving unit is used to solve the reconstructed optimization problem with the goal of minimizing the economic loss caused by the fault, and obtain the reconstructed active distribution network topology and fault maintenance strategy.

12. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

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

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