Radial constraint-based power distribution network post-disaster rapid reconstruction method, system, equipment and medium

Abstract

The invention is suitable for the field of power distribution network post-disaster reconstruction, and discloses a power distribution network post-disaster rapid reconstruction method, system and device based on radial constraint and a medium, and the method comprises the steps: obtaining post-disaster power distribution network data, and constructing a post-disaster reconstruction model; based on the post-disaster reconstruction model, establishing an optimization objective function and various constraint conditions; establishing a mixed integer linear programming model based on the optimization objective function and a plurality of constraint conditions, judging whether the set constraint conditions are met by combining a particle swarm optimization algorithm, and solving an optimization problem of the mixed integer linear programming model to obtain a final decision result; and obtaining a switch operation sequence based on the decision result, calculating a recovery result of each power supply unit, and obtaining a reconstruction decision scheme of the post-disaster power distribution network, thereby improving reconstruction calculation efficiency and engineering applicability, and realizing rapid recovery of the key load and stable operation of the post-disaster power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network post-disaster reconstruction, and in particular to a radial constraint-based power distribution network post-disaster fast reconstruction method, system, device and medium. BACKGROUND

[0002] With the frequent occurrence of extreme natural disasters, the power distribution network is prone to large-area failure and power outage when subjected to external impacts such as typhoons, earthquakes and freezing, which seriously threatens the power supply safety of important users and the stable operation of the social economy. Improving the resilience of the power distribution network, especially the fast recovery of key loads after disasters, has become an important goal of current power system operation and regulation. In this context, post-disaster network reconstruction, as a key link, is widely regarded as an effective technical means to enhance the disaster resistance and recovery capability of the power distribution network. At present, the research on post-disaster reconstruction of the power distribution network is mostly based on the mixed integer programming method, which divides the network into several power supply units through switch operation and provides support by distributed power sources to maximize the recovery of loads. However, this kind of method needs to consider both binary variables and continuous variables when modeling variables, and with the increase in the number of power supply units, the variable scale increases exponentially, leading to a sharp rise in computational complexity, which makes it difficult to meet the demand for fast solution after disasters.

[0003] In order to reduce complexity, existing research has introduced node clustering constraints, connectivity constraints and branch-node relationship constraints to characterize network topology characteristics, and to some extent, has improved the convergence of the model. However, due to the radial structure of the power distribution network, the above constraints often have redundancy, which not only makes the modeling process cumbersome, but also limits the computational efficiency of the optimization solver, making it difficult to achieve fast and effective power recovery in actual post-disaster scenarios. Therefore, while ensuring system resilience, the existing technology still has problems such as excessive modeling scale, insufficient computational efficiency and poor engineering practicability. How to propose a simple and efficient mathematical modeling method that meets the radial structure characteristics of the power distribution network, and quickly realizes network reconstruction and key load recovery after disasters has become a technical problem that needs to be solved in the field. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a radial constraint-based power distribution network post-disaster fast reconstruction method, system, device and medium to solve the problems of increasing number of power supply units, exponential growth of variable scale, sharp rise in computational complexity, while the power distribution network has a radial structure, the constraints are redundant, the modeling process is cumbersome, and the engineering practicability is not high.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The first aspect of the present application provides a radial constraint-based power distribution network post-disaster rapid reconstruction method, comprising:

[0008] Obtaining post-disaster power distribution network data and constructing a post-disaster reconstruction model;

[0009] Based on the post-disaster reconstruction model, an optimization objective function and constraint conditions are established;

[0010] Based on the optimization objective function and constraint conditions, a mixed integer linear programming model is established, and a particle swarm optimization algorithm is combined to obtain a decision result;

[0011] Based on the decision result, a switch operation sequence is obtained, the recovery result of the power supply unit is calculated, the reconstruction decision scheme of the post-disaster power distribution network is obtained, and the stable operation of the post-disaster power grid is realized.

[0012] As a preferred scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction method of the present application, wherein: based on the post-disaster reconstruction model, an optimization objective function and constraint conditions are established, comprising:

[0013] An optimization objective function is established by maximizing the weighted load recovery amount as the target;

[0014] The constraint conditions are established, including operation constraints, connectivity constraints and radial constraints, wherein the operation constraints include: node power balance constraints, branch power flow and voltage relationship constraints, node voltage constraints, distributed power output constraints, power flow constraints, and load recovery constraints.

[0015] As a preferred scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction method of the present application, wherein: the constraint conditions are established, including operation constraints, connectivity constraints and radial constraints, comprising:

[0016] Virtual flow variables are introduced, and connectivity constraints are established by single commodity flow to judge the connectivity of the sub-network;

[0017] Based on the connectivity of the power distribution network, radial constraints are established to judge the radial nature of the network.

[0018] As a preferred scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction method of the present application, wherein: based on the optimization objective function and constraint conditions, a mixed integer linear programming model is established, and a particle swarm optimization algorithm is combined to obtain a decision result, comprising:

[0019] Initializing the switch state sequence and the particle swarm algorithm parameters;

[0020] Based on the initialized particle swarm, it is judged whether the particles satisfy the connectivity constraints, radial constraints and source coverage constraints, if not, the value is assigned to infinity and the next particle is continued to be judged, if satisfied, the corresponding calculation is performed to obtain the decision result.

[0021] As a preferred scheme of the radial constraint-based power distribution network post-disaster fast reconstruction method, the method comprises the following steps:

[0022] If |E + | = 29, the particle satisfies the connectivity, wherein E + is the number of closed branches corresponding to the position vector of the particle;

[0023] If |E k | - |N k | + 1 = 0, the particle satisfies the radial constraint, wherein |N k | is the number of nodes of the kth connected component, and |E k | is the number of branches of the kth connected component;

[0024] If each subarea has a power supply, the source coverage constraint is satisfied.

[0025] As a preferred scheme of the radial constraint-based power distribution network post-disaster fast reconstruction method, the method comprises the following steps:

[0026] The switch combination coded by the particle coordinates is inputted to obtain the operation topology of the power distribution network;

[0027] Based on the operation topology, the mixed integer linear programming MILP model is established in combination with the optimization objective function and the constraint condition;

[0028] The maximum charge recovery amount and the power flow constraint penalty function value are obtained by calculating the optimization problem of the MILP model;

[0029] Based on the maximum charge recovery amount and the power flow constraint penalty function value, the particle fitness is calculated, the particle speed and position are updated, and the decision result is obtained when the particle swarm optimization algorithm converges.

[0030] As a preferred scheme of the radial constraint-based power distribution network post-disaster fast reconstruction method, the method comprises the following steps:

[0031] The switch sequence operation is obtained based on the decision result;

[0032] Based on the switch sequence operation, the power supply range of each distributed power supply and the recovery level of the node charge are obtained as the recovery result of the power supply unit;

[0033] The switch operation sequence and the recovery result of each power supply unit are taken as the reconstruction decision scheme of the post-disaster power distribution network to realize fast recovery of key loads and stable operation of the post-disaster power grid.

[0034] In a second aspect, the present application provides a radial constraint-based post-disaster power distribution network fast reconstruction system, comprising: an acquisition module configured to acquire pipeline parameters and construct a pipeline model;

[0035] An acquisition module is configured to acquire post-disaster power distribution network data and construct a post-disaster reconstruction model.

[0036] A first calculation module is configured to establish an optimization objective function and constraint conditions based on the post-disaster reconstruction model.

[0037] A second calculation module is configured to establish a mixed integer linear programming model based on the optimization objective function and constraint conditions, and obtain a decision result by combining a particle swarm optimization algorithm.

[0038] A decision module is configured to obtain a switch operation sequence based on the decision result, calculate a recovery result of a power supply unit, acquire a reconstruction decision scheme of a post-disaster power distribution network, and realize stable operation of the post-disaster power grid.

[0039] In a third aspect, the present application provides an electronic device, comprising:

[0040] A memory and a processor.

[0041] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which realize the steps of the radial constraint-based post-disaster power distribution network fast reconstruction method.

[0042] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which realize the steps of the radial constraint-based post-disaster power distribution network fast reconstruction method when executed by a processor.

[0043] Compared with the prior art, the present application has the beneficial effects that: the present application greatly reduces the number of binary variables and continuous variables in the model and simplifies the overall modeling structure by introducing radial constraints instead of traditional node clustering, connectivity and branch-node and other redundant constraints; the radial constraint relationship of "branch number = node number - distributed power supply number" is converted into the topological characteristics of the distribution network, and combined with the particle swarm optimization algorithm for fast solution, which can significantly shorten the calculation time of post-disaster reconstruction; since the required number of new variables is only linearly related to the number of branches, the model size will not expand exponentially with the increase of the number of microgrids, so it can also maintain good solving performance in large-scale distribution network post-disaster scenarios, and has strong engineering application value; while ensuring the operation constraints such as voltage, power flow and power output, the radial structure of the power supply unit can be quickly formed to realize the efficient recovery of key loads and improve the overall resilience and power supply reliability of the post-disaster power system. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 The overall flowchart of the radial constraint-based distribution network post-disaster rapid reconstruction method of an embodiment of the present application.

[0046] Figure 2 The network reconstruction diagram of the radial constraint-based distribution network post-disaster rapid reconstruction method of an embodiment of the present application.

[0047] Figure 3 The voltage diagram of each node of the radial constraint-based distribution network post-disaster rapid reconstruction method of an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0049] Embodiment 1, refer to Figure 1 An embodiment of the present application provides a radial constraint-based distribution network post-disaster rapid reconstruction method, which comprises:

[0050] S100: Obtain post-disaster power distribution network data and construct a post-disaster reconstruction model;

[0051] S200: Based on the post-disaster reconstruction model, establish an optimization objective function and constraint conditions;

[0052] S300: Based on the optimization objective function and constraint conditions, establish a mixed integer linear programming model, and obtain a decision result by combining a particle swarm optimization algorithm;

[0053] S400: Based on the decision result, obtain a switch operation sequence, calculate the recovery result of the power supply unit, obtain a reconstruction decision scheme of the post-disaster power distribution network, and realize stable operation of the post-disaster power grid.

[0054] It should be noted that the existing method usually relies on node clustering, connectivity and branch-node constraints to ensure that the network topology is reasonable, but these constraint forms are complex and the variable scale is large. When the number of distributed power sources and the number of divided units increases, the binary variables and continuous variables of the model increase by several times, resulting in a significant increase in the computational burden, which seriously restricts the efficiency and practicality of post-disaster rapid recovery.

[0055] Therefore, in view of the above-mentioned problems of post-disaster power distribution network reconstruction, through the steps of S100-S400, the connectivity and radiality requirements of the power distribution network are converted into simple mathematical constraint forms, the topology characteristics are realized by the single commodity flow method and the relationship of "branch number = node number - distributed power source number", and the node clustering and branch-node constraints in the existing method are avoided. On the basis of the mixed integer linear programming modeling, the particle swarm optimization algorithm is introduced for fast solution, which can significantly shorten the calculation time of post-disaster reconstruction while ensuring the voltage, power flow and power output constraint conditions, and improve the solution efficiency and engineering applicability of large-scale power distribution networks.

[0056] Embodiment 2, refer to Figure 1 For an embodiment of the present application, based on the above embodiment, a post-disaster rapid reconstruction method for power distribution network based on radial constraints is provided.

[0057] In the present application, the steps S100-S200 of obtaining post-disaster power distribution network data and constructing a post-disaster reconstruction model include:

[0058] The basic data of the post-disaster power distribution network is read, including node load and its importance coefficient, distributed power output prediction, fault branch state and initial switch sequence, etc., to provide input for subsequent optimization modeling.

[0059] In an optional embodiment, the post-disaster power distribution network data obtained in step S100 can also be collected by a drone carrying sensors, and the optional sensors include infrared cameras, laser radars, ultraviolet imagers and sniffers, to realize fast and large-scale data collection.

[0060] In another alternative embodiment, the post-disaster power distribution network data obtained in step S100 can also be obtained through inverter operation data of grid-connected distributed power sources, such as output voltage, frequency, protection action information, and the like, which are important island operation state data sources and can reflect the live state and power quality of the line section in which the distributed power sources are connected.

[0061] In the embodiments of the present application, the optimization objective function and the constraint condition are established based on the post-disaster reconstruction model in step S200, including the following steps A1-A8:

[0062] A1: An optimization objective function is established by maximizing the weighted load recovery amount as the target;

[0063] Specifically, in the post-disaster scenario, in order to restore power supply to important users as much as possible, the weighted load recovery amount is maximized as the optimization objective function:

[0064]

[0065] wherein, α j is the load weight coefficient of node j, indicating the importance of the load of the node; P L,j is the actual recovered active load of node j after the disaster; and N is the node set of the power distribution network.

[0066] A2: A node power balance constraint is established;

[0067] Specifically, in order to ensure the balance between power supply and demand, the active and reactive power balance relationship needs to be met at each node:

[0068]

[0069] wherein, P jm and Q jm are the active and reactive power flow of branch (j, m); and are the parent node set and child node set of node j; P kj and P kj are the active and reactive power from node k to j; P DG,j and Q DG,j are the active and reactive output of the distributed power source at node j; P L,j and Q L,j are the recovered active and reactive load of node j.

[0070] A3: A branch power flow and voltage relationship constraint is established;

[0071] Specifically, in order to depict the relationship between branch power flow and node voltage, a linearized DistFlow approximation model is adopted, which converts the coupling relationship between voltage and power flow into an approximate linear constraint, so as to ensure that the power flow distribution of each sub-network after network reconstruction meets the physical law, and the overall optimization problem can be efficiently solved in the mixed integer framework. The formula of the linearized DistFlow approximation model is as follows:

[0072] -M(1-ξ ij )+(r ij P ij +x ij Q ij ) / V0≤V j -V i ≤M(1-ξ ij )+(r ij P ij +x ij Q ij )

[0073] Where V i and V j are the node voltage amplitudes; ξ ij is the switch state variable of branch (i, j), which takes 1 to represent the closed branch and 0 to represent the disconnected branch; P ij and Q ij are the active and reactive power transmitted on branch (i, j) respectively; r ij and x ij are the resistance and reactance of branch (i, j) respectively; M is a large enough penalty coefficient.

[0074] A4: Establish node voltage constraints;

[0075] Specifically, in order to ensure that each node voltage is within the allowable range, the following constraints need to be met:

[0076] V min ≤V j ≤V max

[0077] Where V j is the voltage amplitude of node j; V min and V max are the lower and upper limits of voltage amplitude respectively.

[0078] A5: Establish distributed power output constraints;

[0079] Specifically, in order to ensure that the distributed power is running within the rated output range, the following constraints need to be set:

[0080]

[0081] where, and are the active and reactive power output upper limits of the distributed generator at node j, respectively; and are the active and reactive power output lower limits of the distributed generator at node j, respectively.

[0082] A5: Establish the power flow constraint;

[0083] Specifically, the power flow constraint expression is:

[0084]

[0085] where, P ij and Q ij are the active and reactive power of branch ij, respectively; and are the active and reactive transmission capacity upper limits of branch ij, respectively.

[0086] A6: Establish the load restoration constraint.

[0087] Specifically, to ensure that the load restoration amount does not exceed the pre-disaster rated demand, the following needs to be met:

[0088]

[0089] where, is the rated active load of node j under normal operating conditions.

[0090] A7: Introduce virtual flow variables to establish connectivity constraints through single commodity flow to determine the connectivity of the sub-network;

[0091] Specifically, in the reconstruction process of the post-disaster distribution network, it is difficult to ensure that the formed sub-network remains connected in topology only by relying on the power flow equation constraint. Therefore, virtual flow variables are introduced to describe the reachability between nodes through single commodity flow. Specifically, it is assumed that each node containing a distributed generator is a source point and other nodes are sink points, the source point outputs virtual flow, and the sink point consumes a unit of flow. When the virtual flow can meet the energy balance in the network, it indicates that the sub-network is connected. The mathematical form is as follows:

[0092]

[0093] In the formula, F kj is the virtual flow variable from node k to node j, F jm is the virtual flow variable from node j to node m, N DG is used to describe the transmission capacity of branch (i, j) in connectivity determination; |n j | is the number of nodes of the subgraph containing the distributed generator; NDG a set of nodes where the distributed power sources are located.

[0094] It should be noted that the connectivity constraint can ensure that each node in the power supply range of the distributed power source is connected therewith, thereby avoiding the occurrence of isolated nodes.

[0095] A8: Based on the power distribution network connectivity, a radial constraint is established to determine the radiality of the network.

[0096] Specifically, under the premise of ensuring connectivity, it is also necessary to avoid forming a loop, thereby ensuring that the post-disaster power supply unit maintains the typical radial structure of the power distribution network. If N represents the total number of nodes in the network, N DG represents the total number of distributed power source nodes, then when the network is divided into N DG sub-networks, if the topology is a radial structure, then it must satisfy "branch number = node number - sub-network number". Accordingly, the following constraint can be obtained:

[0097]

[0098] It should be noted that the inherent radiality of the power distribution network is the core modeling idea, which directly uses the radial constraint to replace the traditional redundant topology constraint, thereby fundamentally reducing the number of variables and the complexity of the constraint, and improving the simplicity and calculation efficiency of the model. The radial constraint forcibly ensures that the multiple sub-networks formed after the disconnection of part of the fault branch are tree structures, thereby avoiding the existence of loops.

[0099] In the embodiments of the present application, based on the optimization objective function and the constraint condition, a mixed integer linear programming model is established in step S300, and a decision result is obtained by combining a particle swarm optimization algorithm, including the following steps B1-B2:

[0100] B1: Initialize the switch state sequence and the particle swarm optimization algorithm parameters;

[0101] Specifically, the switch state variable ξ ij in the power distribution network is taken as the decision variable of the particle, and the position vector x p of the particle is constructed, wherein each component corresponds to the switch state of a branch. m particles are randomly generated as the initial population, and a random initial speed v p is assigned thereto.

[0102] B2: Based on the initialized particle swarm, it is determined whether the particles satisfy the connectivity constraint, the radial constraint and the source coverage constraint, including:

[0103] If |E + | = 29, the particle satisfies the connectivity, wherein E + is the number of closed branches corresponding to the position vector of the solution particle;

[0104] If |E k |-|N k |+1=0, the particle satisfies the radial constraint, where |N k | is the number of nodes of the kth connected component, |E k | is the number of branches of the kth connected component.

[0105] By intuitive judgment, if each partition has a power supply, the source coverage constraint is satisfied.

[0106] If the particle does not satisfy any constraint, the value is assigned to be infinite and the next particle is entered for continuous judgment. If all constraints are satisfied, corresponding calculation is continued.

[0107] In the embodiment of the present application, after the B2 step is completed in the step S300, the following steps B3-B6 are further included.

[0108] Specifically, if the particle satisfies the connectivity constraint, the radial constraint and the source coverage constraint, corresponding calculation is continued.

[0109] B3: inputting the switch combination coded by the particle coordinates to obtain the operation topology of the power distribution network;

[0110] B4: based on the operation topology, combining the optimization objective function and the constraint condition, a mixed integer linear programming MILP model is established;

[0111] B5: by calculating the MILP model optimization problem, the maximum charge recovery amount and the power flow constraint penalty function value are obtained;

[0112] Specifically, the MILP optimization problem based on the DistFlow model is solved based on the current operation topology structure, and the maximum load recovery amount and the power flow constraint penalty function value are obtained.

[0113] Further, under the operation topology, the loads of the nodes that can be restored to power supply are counted, and the maximum charge recovery amount expression is calculated as:

[0114]

[0115] Further, through the MILP optimization problem based on the DistFlow model, the satisfaction of the power flow constraint is obtained, a penalty function of constraint violation is established, and the penalty function is calculated according to the power flow constraint violation amount:

[0116]

[0117] where, Φ(x p ) is the penalty function of the solution x p , Φ1 is the voltage out-of-limit penalty, |N| is the total number of nodes. Φ2 is the power flow out-of-limit penalty.

[0118] B6: Calculate the particle fitness based on the maximum charge recovery amount and the flow constraint penalty function value, update the particle velocity and position, and obtain the decision result when the particle swarm optimization algorithm converges.

[0119] Specifically, the particle fitness is calculated, and the fitness function expression is:

[0120] f(x p )=P rec (x p )-λ·Φ(x p )

[0121] Wherein, f(x p ) is the fitness of particle x p ; λ is the penalty factor.

[0122] In each iteration, the position and velocity of the particle are corrected according to the standard update formula of the particle swarm algorithm:

[0123]

[0124] Wherein, And The velocity and position of the i-th particle at iteration t are respectively; ω is the inertia weight; c1 and c2 are the individual learning factor and group learning factor respectively; r1 and r2 are random numbers between 0 and 1; pbest i The historical optimal position of particle i; gbest is the global historical optimal position.

[0125] When the maximum number of iterations is reached or the optimal fitness does not improve significantly for consecutive iterations, the algorithm is considered to have converged. At this time, the switch state scheme corresponding to the global optimal position gbest of the population is taken as the final decision result of the post-disaster power distribution network reconstruction.

[0126] In the embodiments of the present application, the switch operation sequence is obtained based on the decision result in step S400, the recovery result of the power supply unit is calculated, and the reconstruction decision scheme of the post-disaster power distribution network is obtained, realizing the stable operation of the post-disaster power grid, including the following steps C1-C3:

[0127] C1: Obtain the switch sequence operation based on the decision result;

[0128] Specifically, when the particle swarm algorithm converges, the vector corresponding to the global optimal solution gbest is the switch state variable ξ ij of the branch, wherein ξ ij =1 represents the closed branch, and ξ ij =0 represents the disconnected branch. Compare this set of ξ ij with the initial state of the switch before the disaster, and extract the branches whose state changes, that is, obtain the switch operation sequence Ξ that needs to be implemented.

[0129] C2: based on the switching sequence operation, obtaining the power supply range of each distributed power supply and the recovery level of the node charge as the recovery result of the power supply unit;

[0130] Specifically, under a given switching operation, the power supply range of each distributed power supply is determined according to the branch power flow and voltage relationship constraint:

[0131] S k ={j∈N|P L,j >0,j powered by the kth DG}

[0132] Wherein, S k represents the node set powered by the kth distributed power supply.

[0133] According to the branch power flow and voltage relationship constraint, the voltage level and load recovery of each node can also be determined. If the node voltage satisfies the constraint, the node load is considered to be recovered; if the constraint is not satisfied, it is determined to be unrecoverable or partially recovered.

[0134] C3: output the switching operation set Ξ and the recovery result S k of each power supply unit together as the reconstruction decision scheme of the post-disaster distribution network. The scheme can be directly used to guide the switching operation of the dispatch personnel, so as to realize the rapid recovery of the key load and the stable operation of the post-disaster power grid.

[0135] In summary, the connectivity and radiality requirements of the distribution network are converted into simple mathematical constraint forms, the topological characteristics are realized by the single commodity flow method and the relationship of "branch number = node number - distributed power supply number", and a large number of redundant node clusters and branch-node constraints in the existing method are avoided, thereby effectively reducing the model complexity and variable scale; on the basis of mixed integer linear programming modeling, the particle swarm optimization algorithm is introduced for rapid solution, which can significantly shorten the calculation time of post-disaster reconstruction while ensuring the voltage, power flow and power supply output constraint conditions, and improve the solution efficiency and engineering applicability of large-scale distribution network. Through the combination of modeling method and algorithm framework, the power supply unit meeting the radial structure can be quickly formed in the post-disaster scenario, the efficient recovery of the key load is realized, the resilience and power supply reliability of the distribution network are significantly enhanced, and the method has outstanding innovation and promotion value.

[0136] Embodiment 3, refer to Figures 2-3 , an embodiment of the present application, based on the above-mentioned embodiment, provides a simulation experiment of a distribution network post-disaster rapid reconstruction method based on radial constraint, to verify its implementability and effect;

[0137] To verify the effectiveness and engineering application value of the method, the IEEE 33-node distribution network is selected as an example system for testing. In the experiment, it is assumed that part of the branches of the system fail, causing the original distribution network to be unable to maintain a normal radial power supply structure. In this embodiment, distributed power sources are configured in the distribution network, among which photovoltaic power sources are connected to nodes 7, 13 and 27, and the capacities are 500 kW, 300 kW and 400 kW respectively; wind turbine generators are connected to nodes 10, 16, 17, 30 and 33, and the capacities are 500 kW, 300 kW, 200 kW, 200 kW and 300 kW respectively. The above-mentioned distributed power sources serve as independent power sources of each sub-network and provide a basic condition for forming a radial power supply unit after a disaster. The method maximizes the recovery of key loads, constructs operation constraints and radial constraints, and solves them by using a particle swarm optimization algorithm combined with a mixed integer programming model.

[0138] The simulation results show that the model obtains 71 constraints and 301 variables after preprocessing, among which only 37 binary variables are included, which is significantly lower than the variable scale of the traditional modeling method, and the total running time is about 3.9 seconds, which can quickly obtain a feasible solution in the post-disaster scenario, verifying the advantages of the method in calculation efficiency. Compared with existing methods, the method has obvious advantages in modeling simplicity, calculation efficiency and engineering applicability, and provides an effective solution for improving the post-disaster resilience of distribution networks.

[0139] As shown in Figure 2 , the network reconfiguration result after fault handling is given. It can be seen that the optimization result divides the original network into several power supply sub-networks, each of which is supported by a distributed power source and meets the radial requirement of "branch number = node number - distributed power source number". Each sub-network formed is connected and does not have a loop, proving the effectiveness of the radial constraint in ensuring the rationality of the topology structure.

[0140] As shown in Figure 3 , the voltage distribution of each node is shown. The results show that the voltage of all nodes is within the range of 0.97-1.05 p.u., meeting the voltage constraint requirement. Although the voltage of some nodes fluctuates near the lower limit, the overall operation is stable, and there is no voltage out-of-limit or power flow divergence. This shows that the method can not only achieve rapid reconstruction after a disaster, but also ensure the safety and reliability of system operation.

[0141] Embodiment 4, the above is a schematic scheme of a radial constraint-based power distribution network post-disaster rapid reconstruction method. It should be noted that the technical scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction system is the same as the technical scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction method described above. The technical details of the radial constraint-based power distribution network post-disaster rapid reconstruction system in this embodiment are not described in detail, and can be referred to the description of the technical scheme of the radial constraint-based power distribution network post-disaster rapid reconstruction method described above.

[0142] The embodiment also provides a radial constraint-based power distribution network post-disaster rapid reconstruction system, comprising:

[0143] An acquisition module is configured to acquire post-disaster power distribution network data and construct a post-disaster reconstruction model.

[0144] A first calculation module is configured to establish an optimization objective function and constraint conditions based on the post-disaster reconstruction model.

[0145] A second calculation module is configured to establish a mixed integer linear programming model based on the optimization objective function and constraint conditions, and obtain a decision result by combining a particle swarm optimization algorithm.

[0146] A decision module is configured to obtain a switch operation sequence based on the decision result, calculate a recovery result of a power supply unit, acquire a reconstruction decision scheme of the post-disaster power distribution network, and realize stable operation of the post-disaster power grid.

[0147] The embodiment also provides an electronic device suitable for the radial constraint-based power distribution network post-disaster rapid reconstruction, comprising a memory and a processor. The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the radial constraint-based power distribution network post-disaster rapid reconstruction method proposed in the above embodiment.

[0148] The embodiment also provides a storage medium having a computer program stored thereon. The program is executed by a processor to implement the radial constraint-based power distribution network post-disaster rapid reconstruction method proposed in the above embodiment.

[0149] The storage medium proposed in the embodiment and the radial constraint-based power distribution network post-disaster rapid reconstruction method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0150] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for rapid reconfiguration of a distribution network after a disaster based on radial constraints, characterized in that, include: Acquire post-disaster power distribution network data and construct a post-disaster reconstruction model; Based on the post-disaster reconstruction model, an optimization objective function and constraints are established. Based on the aforementioned objective function and constraints, a mixed-integer linear programming model is established, and the decision result is obtained by combining the particle swarm optimization algorithm. Based on the decision results, the switching operation sequence is obtained, the recovery result of the power supply unit is calculated, the reconstruction decision scheme of the post-disaster distribution network is obtained, and the stable operation of the post-disaster power grid is achieved.

2. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 1, characterized in that, Based on the post-disaster reconstruction model, an optimization objective function and constraints are established, including: An optimization objective function is established by maximizing the weighted load recovery amount. Establish constraints, including operational constraints, connectivity constraints, and radial constraints. The operational constraints include: node power balance constraints, branch power flow and voltage relationship constraints, node voltage constraints, distributed generation output constraints, power flow constraints, and load recovery constraints.

3. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 2, characterized in that, Establish constraints, including operational constraints, connectivity constraints, and radial constraints, including: Virtual flow variables are introduced, and connectivity constraints are established through single-product flow to determine the connectivity of sub-networks; Based on the connectivity of the distribution network, radial constraints are established to determine the radiality of the network.

4. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 3, characterized in that, Based on the aforementioned objective function and constraints, a mixed-integer linear programming model is established, and the decision results are obtained by combining the particle swarm optimization algorithm, including: Initialize the switch state sequence and particle swarm algorithm parameters; Based on the initial particle swarm, it is determined whether the particles satisfy connectivity constraints, radial constraints, and source coverage constraints. If they do not satisfy these constraints, they are assigned an infinity value and the process moves to the next particle to continue the determination. If they satisfy these constraints, the corresponding calculations are performed to obtain the decision result.

5. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 4, characterized in that, Determine whether a particle satisfies connectivity constraints, radial constraints, and source coverage constraints, including: If |E + If |=29, then the particles satisfy connectivity, where E + The number of closed branches corresponding to the position vector of the solution particle; If |E k |-|N k If |+1=0, the particle satisfies the radial constraint, where |N k | represents the number of nodes in the k-th connected component, |E k | represents the number of branches in the k-th connected component; If each partition has a power source, then the source coverage constraint is satisfied.

6. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 5, characterized in that, If the particles satisfy connectivity constraints, radial constraints, and source coverage constraints, then corresponding calculations are performed, including: By inputting switch combinations encoded by particle coordinates, the operating topology of the distribution network is obtained; Based on the operational topology, and combined with the optimization objective function and constraints, a mixed integer linear programming (MILP) model is established. By calculating the optimization problem of the MILP model, the maximum charge recovery and the power flow constraint penalty function values ​​are obtained; Based on the maximum charge recovery and the power flow constraint penalty function value, the particle fitness is calculated, the particle velocity and position are updated, and the decision result is obtained when the particle swarm optimization algorithm converges.

7. The method for rapid reconfiguration of a distribution network based on radial constraints as described in claim 6, characterized in that, Based on the decision results, a switching operation sequence is obtained, the recovery result of the power supply unit is calculated, and a reconfiguration decision scheme for the post-disaster distribution network is acquired to achieve stable operation of the post-disaster power grid, including: The switching sequence operation is obtained based on the decision result; Based on the switching sequence operation, the power supply range and node charge recovery level of each distributed power source are obtained as the recovery result of the power supply unit. The switching operation sequence and the recovery results of each power supply unit are used together as the decision-making scheme for the reconstruction of the distribution network after the disaster, so as to achieve rapid recovery of critical loads and stable operation of the power grid after the disaster.

8. A rapid reconfiguration system for a distribution network after a disaster based on radial constraints, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire post-disaster power distribution network data and build a post-disaster reconstruction model. The first calculation module is used to establish the optimization objective function and constraints based on the post-disaster reconstruction model; The second calculation module is used to establish a mixed integer linear programming model based on the optimization objective function and constraints, and to obtain the decision result by combining the particle swarm optimization algorithm. The decision-making module is used to obtain the switching operation sequence based on the decision results, calculate the recovery results of the power supply unit, obtain the reconstruction decision scheme of the post-disaster distribution network, and realize the stable operation of the post-disaster power grid.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the radial constraint-based rapid reconfiguration method for distribution networks after disasters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes storing computer-executable instructions that, when executed by a processor, implement the steps of the radial constraint-based rapid reconfiguration method for distribution networks after disasters as described in any one of claims 1 to 7.

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