Power distribution network power supply restoration optimization method and system considering micro-grid fusion

Abstract

The invention discloses a power distribution network power supply recovery optimization method and system considering micro-grid fusion. The method comprises the following steps: firstly, establishing a power distribution network topology model based on a graph theory, then respectively constructing a power distribution network power supply recovery path optimization model and a node and line power supply state model considering micro-grid fusion, then constructing a source-grid-load collaborative scheduling optimization model in a power supply recovery process, and finally performing linearization processing and solving on the constructed model. And obtaining a power distribution network power supply recovery optimization result. According to the technical scheme, fusion of the micro-grids is considered in the power supply recovery process of the power distribution network, electric energy complementation among the micro-grids is achieved, the power distribution network can make full use of a local distributed power supply after power supply of a main network is lost, and the recovery process of important loads is effectively accelerated.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular to a method and system for optimizing power supply restoration of a distribution network considering microgrid integration. Background Art

[0002] The frequent occurrence of extreme natural disasters such as typhoons and rainstorms can easily lead to multiple physical failures in the distribution network, which in turn can cause large-scale power outages. In order to cope with the threat of extreme natural disasters to the security of the power grid and ensure the security of energy and electricity, some scholars have proposed the concept of "elastic power grid". Among them, the use of elastic resources such as distributed power sources to build isolated microgrids and restore the power supply of key loads in non-fault power outage areas can effectively reduce the scope of power outages and shorten the power outage time, which is an important means to achieve power supply restoration of elastic distribution networks.

[0003] In order to restore power after a power outage in the distribution network, some scholars have conducted research on power restoration methods that gradually realize the power supply of the power outage grid using distributed power sources. That is, by generating sequential operation instructions for each device, a stable island microgrid is gradually established, thereby realizing power restoration in non-fault power outage areas. However, existing research mainly focuses on the parallel generation of multiple independently operating microgrids, and rarely considers the use of microgrid fusion to improve the rate of power restoration. In fact, there are a large number of distributed new energy sources in the microgrid, which makes it difficult to ensure the balance of supply and demand within the microgrid. Therefore, considering the integration of microgrids in the power restoration process and utilizing the complementary power between microgrids can effectively speed up the recovery process of important loads and thus improve the elasticity of the distribution network. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the above-mentioned prior art, and to provide a distribution network power supply restoration optimization method considering the integration of microgrids, taking into account that making full use of the electric energy complementarity between microgrids can effectively accelerate the recovery process of important loads. First, the restoration path is modeled, and on this basis, the power supply restoration problem considering the integration of microgrids is modeled as a mixed integer nonlinear programming model, and the nonlinear constraints in the model are linearized to facilitate the solution. Compared with the traditional power supply restoration method that does not consider the integration of microgrids, the proposed method can realize the integration of multiple isolated microgrids and improve the problem of low efficiency of traditional power supply restoration methods.

[0005] The technical solution to achieve the purpose of the present invention is: a distribution network power supply restoration optimization method considering microgrid integration, the method comprising the following steps:

[0006] Step 1: Establish a distribution network topology model based on graph theory;

[0007] Step 2: construct a distribution network power supply restoration path optimization model;

[0008] Step 3: construct a node and line power supply status model taking into account microgrid integration;

[0009] Step 4: construct a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0010] Step 5: Linearize and solve the model to obtain the optimized distribution network power restoration plan.

[0011] Furthermore, the distribution network topology model is established based on graph theory in step 1, which specifically includes:

[0012] The distribution network topology is represented by a graph G = (N, L), where N represents the set of nodes in the distribution network that can restore power supply; L represents the set of lines in the distribution network that can restore power supply; the node where the distributed power source with self-starting capability is located is represented by N B indicates that, and

[0013] At the same time, a virtual node f is added to the graph G, and the virtual node f is connected to the nodes where all distributed power sources with self-starting capabilities are located; the distribution network topology after adding the virtual node f can be represented by the graph G'=(N',L').

[0014] Furthermore, step 2 of constructing a distribution network power supply restoration path optimization model specifically includes:

[0015] When generating a recovery path, the following constraints must be met:

[0016]

[0017] x ij =1,i∈N'\N,j∈N B

[0018] x ij =0,i≠j,i∈N,j∈N'\N

[0019] x ij +x ji ≤1,i≠j

[0020] x ij ≤H ij ,i≠j

[0021]

[0022]

[0023] Among them, x ij represents the state of the recovery path from node i to node j in the distribution network, x ij =1 means there is a recovery path from node i to node j. Otherwise, xij =0 means there is no recovery path; H ij represents the connectivity matrix of the line ij formed by node i and node j in the distribution network; n represents the number of nodes in the distribution network, N represents the set of nodes in the distribution network that can restore power supply, N' represents the set of nodes in the distribution network that can restore power supply with virtual nodes added, and N B Indicates the node where the distributed power source with self-starting capability is located.

[0024] Furthermore, step 3 of constructing a node and line power supply state model taking into account microgrid integration specifically includes:

[0025] Step 3-1, construct a model for the time when the recovery path arrives at each node:

[0026] t i =1,i∈N'\N

[0027]

[0028]

[0029] Among them, t i represents the time when the recovery path arrives at node i, represents the number of time steps required to restore the path from node i to node j; T max Indicates the duration of power outage in the distribution network; M is a constant; x ij represents the state of the restoration path from node i to node j in the distribution network;

[0030] Step 3-2: Build a node power supply status model based on the time when the recovery path arrives at each node:

[0031]

[0032]

[0033]

[0034] Among them, z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t, z i.t-1 is a 0-1 decision variable, indicating the power supply status of node i at time t-1, x hi represents the state of the restoration path from node h to node i in the distribution network;

[0035] Step 3-3, determine the microgrid boundary nodes based on the restoration path:

[0036]

[0037] Among them, bi is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid; H ij Represents the connectivity matrix of line ij formed by node i and node j in the distribution network;

[0038] Step 3-4, based on the microgrid boundary node, determine the line connecting the two microgrids:

[0039]

[0040] Among them, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids; b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid; B represents the line set;

[0041] Step 3-5, determine the line recovery time:

[0042]

[0043]

[0044] in, is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; z j.t is a 0-1 decision variable, indicating the power supply status of node j at time t; Represents the number of time steps required to implement the synchronous operation of the microgrid.

[0045] Furthermore, step 4 constructs a source-grid-load coordinated dispatch optimization model during power supply restoration, specifically including:

[0046] Step 4-1, taking the maximum weighted load recovery amount within the fault duration as the power supply recovery target, establish the objective function:

[0047]

[0048] in, represents the weight of load l; represents the active power recovery amount of load l at time t; T max represents the duration of power outage in the distribution network, and L represents the total number of loads;

[0049] Step 4-2, construct the power supply status model of distributed power sources, fans and loads:

[0050]

[0051]

[0052]

[0053] in, represents the power supply status of distributed power source g at time t; represents the power supply status of wind turbine w at time t; represents the power supply status of load l at time t; z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t;

[0054] Step 4-3, build a distributed power output model:

[0055]

[0056]

[0057]

[0058] in, They represent the active output of distributed generation g at time t and time t-1 respectively; They represent the reactive power output of distributed generation g at time t and time t-1 respectively; and They represent the lower and upper limits of the active output of the distributed generation g respectively; and They represent the lower and upper limits of reactive power output of distributed generation g respectively; and They represent the allowed downward power and upward power of the distributed generation g in adjacent time steps respectively;

[0059] Step 4-4, build a fan output model:

[0060]

[0061]

[0062] in, represents the active output of wind turbine w at time t; represents the reactive power output of wind turbine w at time t; and They represent the lower and upper limits of the reactive power output of wind turbine w respectively; represents the predicted active power output of wind turbine w at time t;

[0063] Step 4-5, build load demand model:

[0064]

[0065]

[0066] in, and They represent the lower and upper limits of the active demand of load l respectively; is a power coefficient, which indicates the relationship between the reactive power recovery and active power recovery of load l; represents the reactive power recovery amount of load l at time t;

[0067] Steps 4-6, construct microgrid power flow constraints:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] in, represents the active power flowing through the line (h,i) at time t, represents the active power flowing through line (i, j) at time t; represents the reactive power flowing through the line (h,i) at time t, represents the reactive power flowing through line (i, j) at time t; represents the active output of node i at time t; represents the reactive power output of node i at time t; U i.t represents the square of the voltage amplitude at node i at time t; U h.t represents the square of the voltage amplitude at node h at time t; R hi represents the resistance of line (i, j); X hi represents the reactance of line (i, j); is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; Indicates the minimum active power allowed to flow on line (h,i); Indicates the maximum active power allowed to flow on line (h,i); Indicates the minimum reactive power allowed to flow on line (h,i); Indicates the maximum reactive power allowed to flow on line (h,i); and They represent the minimum and maximum values ​​of the square of the voltage amplitude respectively.

[0076] Furthermore, the model is linearized and solved as described in step 5, specifically including:

[0077] Step 5-1, linearize the microgrid boundary node constraints:

[0078]

[0079] Among them, ε is a positive constant; H ij represents the connectivity matrix of line ij formed by node i and node j in the distribution network; x ij represents the state of the recovery path from node i to node j in the distribution network; M is a constant; b i is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid;

[0080] Step 5-2, linearize the line model connected to the microgrid:

[0081]

[0082] In the formula, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids, b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid;

[0083] Step 5-3, linearize the "min" item in the line restoration time model in step 3-5:

[0084]

[0085] In the formula, π ij A dummy variable represents and The smaller value of ;

[0086] Step 5-4, based on the linearized constraints, solve the models constructed in steps 2, 3 and 4 to obtain the optimal distribution network power supply restoration result considering microgrid integration.

[0087] On the other hand, a distribution network power supply restoration optimization system considering microgrid integration is provided, the system comprising:

[0088] The first module is used to establish a distribution network topology model based on graph theory;

[0089] The second module is used to build a distribution network power supply restoration path optimization model;

[0090] The third module is used to build a node and line power supply status model taking into account microgrid integration;

[0091] The fourth module is used to build a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0092] The fifth module is used to linearize and solve the model to obtain the optimized distribution network power supply restoration plan.

[0093] On the other hand, a computer device is provided, 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 distribution network power supply restoration optimization method considering microgrid integration is implemented.

[0094] On the other hand, a computer storable medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the distribution network power supply restoration optimization method considering microgrid integration is implemented.

[0095] Compared with the prior art, the present invention has the following significant advantages:

[0096] (1) The technical solution of the present invention takes into account the integration of microgrids during the power supply restoration process, realizes the complementarity of electric energy between microgrids during the power supply restoration process, makes greater use of local power generation resources in the distribution network, and accelerates the recovery rate of important loads.

[0097] (2) When considering the integration of microgrids during the power supply restoration process, the present invention first determines the microgrid boundary nodes, and then determines the lines connecting the two microgrids. By optimizing the scheduling, the electric energy is complemented among multiple microgrids through the connecting lines during the power supply restoration process, thereby making greater use of the local power generation resources in the distribution network and accelerating the recovery rate of important loads.

[0098] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 It is a flow chart of the steps of the present invention.

[0100] Figure 2 It is a schematic diagram of an IEEE37 node power distribution system in an embodiment of the present invention.

[0101] Figure 3 The figure is a schematic diagram of a restoration path in a power restoration process in an embodiment of the present invention.

[0102] Figure 4 Schematic diagram of power output during power restoration in an embodiment of the present invention, wherein Figure 4(a) to (c) are respectively the active outputs of two distributed power sources with self-starting capability, the active outputs of two distributed power sources without self-starting capability, and the active outputs of three wind turbines.

[0103] Figure 5 Schematic diagram of weighted load recovery amount under different recovery strategies in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0105] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0106] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their 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 at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0107] In one embodiment, in combination Figure 1 , provides a distribution network power supply restoration optimization method considering microgrid integration, the method comprising the following steps:

[0108] Step 1: Establish a distribution network topology model based on graph theory;

[0109] Step 2: construct a distribution network power supply restoration path optimization model;

[0110] Step 3: construct a node and line power supply status model taking into account microgrid integration;

[0111] Step 4: construct a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0112] Step 5: Linearize and solve the model to obtain the optimized distribution network power restoration plan.

[0113] Furthermore, in one embodiment, the step 1 of establishing a distribution network topology model based on graph theory specifically includes:

[0114] The distribution network topology is represented by a graph G = (N, L), where N represents the set of nodes in the distribution network that can restore power supply; L represents the set of lines in the distribution network that can restore power supply; the node where the distributed power source with self-starting capability is located is represented by N B indicates that, and

[0115] At the same time, a virtual node f is added to the graph G, and the virtual node f is connected to the nodes where all distributed power sources with self-starting capabilities are located; the distribution network topology after adding the virtual node f can be represented by the graph G'=(N',L').

[0116] Further, in one embodiment, step 2 of constructing a distribution network power supply restoration path optimization model specifically includes:

[0117] To ensure the radial structure of the microgrid, the following constraints must be met when generating the restoration path:

[0118]

[0119] x ij =1,i∈N'\N,j∈N B

[0120] x ij =0,i≠j,i∈N,j∈N'\N

[0121] x ij +x ji ≤1,i≠j

[0122] x ij ≤H ij ,i≠j

[0123]

[0124]

[0125] Among them, x ij represents the state of the recovery path from node i to node j in the distribution network, x ij =1 means there is a recovery path from node i to node j. Otherwise, x ij =0 means there is no recovery path; H ij represents the connectivity matrix of the line ij formed by node i and node j in the distribution network; n represents the number of nodes in the distribution network, N represents the set of nodes in the distribution network that can restore power supply, N' represents the set of nodes in the distribution network that can restore power supply after adding virtual nodes, and NB Indicates the node where the distributed power source with self-starting capability is located.

[0126] Further, in one embodiment, step 3 of constructing a node and line power supply state model taking into account microgrid integration specifically includes:

[0127] Step 3-1, construct a model for the time when the recovery path arrives at each node:

[0128] t i =1,i∈N'\N

[0129]

[0130]

[0131] Among them, t i represents the time when the recovery path arrives at node i, represents the number of time steps required to restore the path from node i to node j; T max Indicates the duration of power outage in the distribution network; M is a large constant; x ij represents the state of the restoration path from node i to node j in the distribution network;

[0132] Step 3-2: Build a node power supply status model based on the time when the recovery path arrives at each node:

[0133]

[0134]

[0135]

[0136] Among them, z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t, z i.t-1 is a 0-1 decision variable, indicating the power supply status of node i at time t-1, x hi represents the state of the restoration path from node h to node i in the distribution network;

[0137] Step 3-3, determine the microgrid boundary nodes based on the restoration path:

[0138]

[0139] Among them, b i is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid; H ij Represents the connectivity matrix of line ij formed by node i and node j in the distribution network;

[0140] Step 3-4, based on the microgrid boundary node, determine the line connecting the two microgrids:

[0141]

[0142] Among them, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids; b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid; B represents the line set;

[0143] Step 3-5, determine the line recovery time:

[0144]

[0145]

[0146] in, is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; z j.t is a 0-1 decision variable, indicating the power supply status of node j at time t; Represents the number of time steps required to implement the synchronous operation of the microgrid.

[0147] Further, in one of the embodiments, the step 4 of constructing a source-grid-load coordinated dispatch optimization model during power supply restoration specifically includes:

[0148] Step 4-1, taking the maximum weighted load recovery amount within the fault duration as the power supply recovery target, establish the objective function:

[0149]

[0150] in, represents the weight of load l; represents the active power recovery amount of load l at time t; T max represents the duration of power outage in the distribution network, and L represents the total number of loads;

[0151] Step 4-2, construct the power supply status model of distributed power sources, fans and loads:

[0152]

[0153]

[0154]

[0155] in, represents the power supply status of distributed power source g at the tth moment; represents the power supply status of wind turbine w at time t; represents the power supply status of load l at time t; z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t;

[0156] Step 4-3, build a distributed power output model:

[0157]

[0158]

[0159]

[0160] in, They represent the active output of distributed generation g at time t and time t-1 respectively; They represent the reactive power output of distributed generation g at time t and time t-1 respectively; and They represent the lower and upper limits of the active output of the distributed generation g respectively; and They represent the lower and upper limits of reactive power output of distributed generation g respectively; and They represent the allowed downward power and upward power of the distributed generation g in adjacent time steps respectively;

[0161] Step 4-4, build a fan output model:

[0162]

[0163]

[0164] in, represents the active output of wind turbine w at time t; represents the reactive power output of wind turbine w at time t; and They represent the lower and upper limits of the reactive power output of wind turbine w respectively; represents the predicted active power output of wind turbine w at time t;

[0165] Step 4-5, build load demand model:

[0166]

[0167]

[0168] in, and They represent the lower and upper limits of the active demand of load l respectively; is a power coefficient, which indicates the relationship between the reactive power recovery and active power recovery of load l; represents the reactive power recovery amount of load l at time t;

[0169] Steps 4-6, construct microgrid power flow constraints:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] in, represents the active power flowing through the line (h,i) at time t, represents the active power flowing through line (i, j) at time t; represents the reactive power flowing through the line (h,i) at time t, represents the reactive power flowing through line (i, j) at time t; represents the active output of node i at time t; represents the reactive power output of node i at time t; U i.t represents the square of the voltage amplitude at node i at time t; U h.t represents the square of the voltage amplitude at node h at time t; R hi represents the resistance of line (i, j); X hi represents the reactance of line (i, j); is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; Indicates the minimum active power allowed to flow on line (h,i); Indicates the maximum active power allowed to flow on line (h,i); Indicates the minimum reactive power allowed to flow on line (h,i); Indicates the maximum reactive power allowed to flow on line (h,i); and They represent the minimum and maximum values ​​of the square of the voltage amplitude respectively.

[0178] Further, in one embodiment, the linearization and solving of the model in step 5 specifically includes:

[0179] Step 5-1, linearize the microgrid boundary node constraints:

[0180]

[0181] Among them, ε is a small positive constant; H ij represents the connectivity matrix of line ij formed by node i and node j in the distribution network; x ij represents the state of the recovery path from node i to node j in the distribution network; M is a large constant; b i is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid;

[0182] Step 5-2, linearize the line model connected to the microgrid:

[0183]

[0184] In the formula, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids, b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid;

[0185] Step 5-3, linearize the "min" item in the line restoration time model in step 3-5:

[0186]

[0187] In the formula, π ij is a dummy variable, indicating and The smaller value of ;

[0188] Step 5-4, based on the linearized constraints, solve the models constructed in steps 2, 3 and 4 to obtain the optimal distribution network power supply restoration result considering microgrid integration.

[0189] In one embodiment, a distribution network power supply restoration optimization system considering microgrid integration is provided, the system comprising:

[0190] The first module is used to establish a distribution network topology model based on graph theory;

[0191] The second module is used to build a distribution network power supply restoration path optimization model;

[0192] The third module is used to build a node and line power supply status model taking into account microgrid integration;

[0193] The fourth module is used to build a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0194] The fifth module is used to linearize and solve the model to obtain the optimized distribution network power supply restoration plan.

[0195] For the specific limitations of the distribution network power supply restoration optimization system considering microgrid integration, please refer to the limitations of the distribution network power supply restoration optimization method considering microgrid integration mentioned above, which will not be repeated here. Each module in the above-mentioned distribution network power supply restoration optimization system considering microgrid integration can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0196] In one embodiment, a computer device is provided, 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 following is achieved:

[0197] Step 1: Establish a distribution network topology model based on graph theory;

[0198] Step 2: construct a distribution network power supply restoration path optimization model;

[0199] Step 3: construct a node and line power supply status model taking into account microgrid integration;

[0200] Step 4: construct a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0201] Step 5: Linearize and solve the model to obtain the optimized distribution network power restoration plan.

[0202] For the specific limitations of each step, please refer to the above limitations on the distribution network power supply restoration optimization method considering microgrid integration, which will not be repeated here.

[0203] In one embodiment, a computer storable medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements:

[0204] Step 1: Establish a distribution network topology model based on graph theory;

[0205] Step 2: construct a distribution network power supply restoration path optimization model;

[0206] Step 3: construct a node and line power supply status model taking into account microgrid integration;

[0207] Step 4: construct a source-grid-load coordinated dispatch optimization model during power supply restoration;

[0208] Step 5: Linearize and solve the model to obtain the optimized distribution network power restoration plan.

[0209] For the specific limitations of each step, please refer to the above limitations on the distribution network power supply restoration optimization method considering microgrid integration, which will not be repeated here.

[0210] As a specific example, the present invention is further described in one of the embodiments.

[0211] Taking the improved IEEE37 node distribution system as an example, the effectiveness of the distribution network power supply restoration method proposed in this invention is verified. The power grid topology is as follows: Figure 2 shown.

[0212] Assume that the main grid has a power outage due to an extreme natural disaster, and the distribution network is completely power-off. The system is equipped with 4 distributed power sources, of which G1 and G2 have self-starting capabilities, while G3 and G4 do not have self-starting capabilities; the relevant parameters of the distributed power sources are shown in Table 1. In addition, the system is also equipped with 3 wind turbines to assist the distribution network in power supply recovery.

[0213] Table 1 Distributed power supply parameters

[0214]

[0215] In the power restoration plan decision-making process, the variables and constraints of each time step need to be defined. Therefore, the selection of the number of time steps will affect the efficiency of the power restoration decision. When the number of time steps is too small, it will lead to the generation of a local optimal solution; when the time step length is too large, the calculation speed will slow down. In this example, it is assumed that the interval between two consecutive time steps in the sequential restoration process is set to 10 minutes, and the total restoration time is 12 time steps.

[0216] By optimizing the decision, the two distributed power sources with self-starting capability gradually expand their power supply range, generate two independently operated microgrids in the first three time steps, and merge the two microgrids into a larger microgrid in the fourth time step. Figure 3 It can be seen that during the power supply restoration process, the microgrid always maintains radial structure operation.

[0217] The power output during power restoration is as follows: Figure 4 shown.

[0218] Node 3 where distributed power source G3 is located resumes power supply at the 3rd time step, so G3 starts to participate in power supply restoration from the 3rd time step and gradually increases power output. Node 35 where distributed power source G4 is located resumes power supply at the 4th time step, so G4 starts to participate in power supply restoration from the 5th time step and gradually increases power output. As important loads gradually resume, the power output in the microgrid gradually increases to meet the power supply demand of the loads during the power supply restoration process.

[0219] The power supply restoration method proposed in the present invention takes into account the integration of microgrids. In order to compare and illustrate the advantages of the proposed method, this example compares the power supply restoration strategy that takes into account the integration of microgrids with the traditional power supply restoration strategy that does not take into account the integration of microgrids.

[0220] Figure 5 The weighted load restoration amounts under the two power restoration ideas are compared. Figure 5 It can be seen that considering the integration of microgrids in the power supply restoration process can speed up the recovery of important loads and thus improve the power supply restoration capability of the system. This is because the complementary power between microgrids can effectively improve the utilization efficiency of distributed power sources, realize the full utilization of limited power generation resources, and thus ensure the continuous and reliable power supply of important loads.

[0221] The above embodiments show and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A distribution network power supply restoration optimization method considering microgrid integration, characterized in that: The method comprises the following steps: Step 1: Establish a distribution network topology model based on graph theory; Step 2: construct a distribution network power supply restoration path optimization model; Step 3: construct a node and line power supply status model taking into account microgrid integration; Step 4: construct a source-grid-load coordinated dispatch optimization model during power supply restoration; Step 5: Linearize and solve the model to obtain the optimized distribution network power restoration plan.

2. The method for optimizing power supply restoration of a distribution network considering microgrid integration according to claim 1, characterized in that: Step 1 describes establishing a distribution network topology model based on graph theory, which specifically includes: The distribution network topology is represented by a graph G = (N, L), where N represents the set of nodes in the distribution network that can restore power supply; L represents the set of lines in the distribution network that can restore power supply; the node where the distributed power source with self-starting capability is located is represented by N B indicates that, and At the same time, a virtual node f is added to the graph G, and the virtual node f is connected to the nodes where all distributed power sources with self-starting capabilities are located; the distribution network topology after adding the virtual node f can be represented by the graph G'=(N',L').

3. The method for optimizing power supply restoration of a distribution network considering microgrid integration according to claim 1, characterized in that: Step 2 constructs a distribution network power supply restoration path optimization model, specifically including: When generating a recovery path, the following constraints must be met: x ij =1,i∈N'\N,j∈N B x ij =0,i≠j,i∈N,j∈N'\N x ij +x ji ≤1,i≠j x ij ≤H ij ,i≠j Among them, x ij represents the state of the recovery path from node i to node j in the distribution network, x ij =1 means there is a recovery path from node i to node j. Otherwise, x ij =0 means there is no recovery path; H ij represents the connectivity matrix of the line ij formed by node i and node j in the distribution network; n represents the number of nodes in the distribution network, N represents the set of nodes in the distribution network that can restore power supply, N' represents the set of nodes in the distribution network that can restore power supply after adding virtual nodes, and N B Indicates the node where the distributed power source with self-starting capability is located.

4. The method for optimizing power supply restoration of a distribution network considering microgrid integration according to claim 1, characterized in that: Step 3 constructs a node and line power supply state model taking into account microgrid integration, specifically including: Step 3-1, construct a model for the time when the recovery path arrives at each node: t i =1,i∈N'\N Among them, t i represents the time when the recovery path arrives at node i, represents the number of time steps required to restore the path from node i to node j; T max Indicates the duration of power outage in the distribution network; M is a constant; x ij represents the state of the restoration path from node i to node j in the distribution network; Step 3-2: Build a node power supply status model based on the time when the recovery path arrives at each node: Among them, z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t, z i.t-1 is a 0-1 decision variable, indicating the power supply status of node i at time t-1, x hi represents the state of the restoration path from node h to node i in the distribution network; Step 3-3, determine the microgrid boundary nodes based on the restoration path: Among them, b i is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid; H ij Represents the connectivity matrix of line ij formed by node i and node j in the distribution network; Step 3-4, based on the microgrid boundary node, determine the line connecting the two microgrids: Among them, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids; b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid; B represents the line set; Step 3-5, determine the line recovery time: in, is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; z j.t is a 0-1 decision variable, indicating the power supply status of node j at time t; Represents the number of time steps required to implement the synchronous operation of the microgrid.

5. The method for optimizing power supply restoration of a distribution network considering microgrid integration according to claim 1, characterized in that: Step 4 constructs a source-grid-load coordinated dispatch optimization model during power supply restoration, specifically including: Step 4-1, taking the maximum weighted load recovery amount within the fault duration as the power supply recovery target, establish the objective function: in, represents the weight of load l; represents the active power recovery amount of load l at time t; T max represents the duration of power outage in the distribution network, and L represents the total number of loads; Step 4-2, construct the power supply status model of distributed power sources, fans and loads: in, represents the power supply status of distributed power source g at the tth moment; represents the power supply status of wind turbine w at time t; represents the power supply status of load l at time t; z i.t is a 0-1 decision variable, indicating the power supply status of node i at time t; Step 4-3, build a distributed power output model: in, They represent the active output of distributed generation g at time t and time t-1 respectively; They represent the reactive power output of distributed generation g at time t and time t-1 respectively; and They represent the lower and upper limits of the active output of the distributed generation g respectively; and They represent the lower and upper limits of reactive power output of distributed generation g respectively; and They represent the allowed downward power and upward power of the distributed generation g in adjacent time steps respectively; Step 4-4, build a fan output model: in, represents the active output of wind turbine w at time t; represents the reactive power output of wind turbine w at time t; and They represent the lower and upper limits of reactive power output of wind turbine w respectively; represents the predicted active power output of wind turbine w at time t; Step 4-5, build load demand model: in, and They represent the lower and upper limits of the active demand of load l respectively; is a power coefficient, which indicates the relationship between the reactive power recovery and active power recovery of load l; represents the reactive power recovery amount of load l at time t; Steps 4-6, construct microgrid power flow constraints: in, represents the active power flowing through the line (h,i) at time t, represents the active power flowing through line (i, j) at time t; represents the reactive power flowing through the line (h,i) at time t, represents the reactive power flowing through line (i, j) at time t; represents the active output of node i at time t; represents the reactive power output of node i at time t; U i.t represents the square of the voltage amplitude at node i at time t; U h.t represents the square of the voltage amplitude at node h at time t; R hi represents the resistance of line (i, j); X hi represents the reactance of line (i, j); is a 0-1 decision variable, indicating the power supply status of line (i, j) at time t; Indicates the minimum active power allowed to flow on line (h,i); Indicates the maximum active power allowed to flow on line (h,i); Indicates the minimum reactive power allowed to flow on line (h,i); Indicates the maximum reactive power allowed to flow on line (h,i); and They represent the minimum and maximum values ​​of the square of the voltage amplitude respectively.

6. The method for optimizing power supply restoration of a distribution network considering microgrid integration according to claim 4, characterized in that: The model is linearized and solved as described in step 5, specifically including: Step 5-1, linearize the microgrid boundary node constraints: Among them, ε is a positive constant; H ij represents the connectivity matrix of line ij formed by node i and node j in the distribution network; x ij represents the state of the recovery path from node i to node j in the distribution network; M is a constant; b i is a 0-1 decision variable, indicating whether node i is a boundary node of the microgrid; Step 5-2, linearize the line model connected to the microgrid: In the formula, c ij is a 0-1 decision variable, indicating whether line (i, j) is a line connecting two microgrids, b j is a 0-1 decision variable, indicating whether node j is a boundary node of the microgrid; Step 5-3, linearize the "min" item in the line restoration time model in step 3-5: In the formula, π ij is a dummy variable, indicating and The smaller value of ; Step 5-4, based on the linearized constraints, solve the models constructed in steps 2, 3 and 4 to obtain the optimal distribution network power supply restoration result considering microgrid integration.

7. A distribution network power supply restoration optimization system considering microgrid integration based on the method described in any one of claims 1 to 6, characterized in that: The system comprises: The first module is used to establish a distribution network topology model based on graph theory; The second module is used to build a distribution network power supply restoration path optimization model; The third module is used to build a node and line power supply status model taking into account microgrid integration; The fourth module is used to build a source-grid-load coordinated dispatch optimization model during power supply restoration; The fifth module is used to linearize and solve the model to obtain the optimized distribution network power supply restoration plan.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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