Treatment method, device and equipment of power distribution network system, storage medium and product

Abstract

The invention relates to a treatment method, device and equipment of a power distribution network system, a storage medium and a product. The method comprises the following steps: determining N levels of treatment nodes by taking a node with abnormal voltage deviation as an initial treatment node; adjusting the voltage deviation of the initial governance node based on a grid-connected inverter circuit accessed by the initial governance node; if the condition that the voltage deviation of the initial governance node is still larger than the preset voltage deviation exists, aiming at M-level governance nodes in the N-level governance nodes, optimizing and solving the reactive compensation capacity of a grid-connected inverter circuit connected to the M-level governance nodes by adopting a particle swarm algorithm, and adjusting the voltage deviation of the M-level governance nodes according to an optimizing and solving result; and under the condition that the iteration stopping condition is not met, adding 1 to M, and returning to the step of adjusting the voltage deviation of the M-level treatment node for the M-level treatment node in the N-level treatment nodes until the iteration stopping condition is met. The method can improve the power quality treatment effect of the power distribution network.

Description

Technical Field

[0001] The present application relates to the technical field of power quality management of distribution networks, and in particular to a management method, device, equipment, storage medium and product for a distribution network system. Background Art

[0002] As inverter control technology continues to mature, grid-connected inverters also have certain power quality management functions. With the large-scale grid connection of distributed power sources, the use of grid-connected inverters in distribution networks to improve power quality has gradually become a development trend.

[0003] Using the remaining capacity of the inverter to assist the dedicated treatment equipment to treat the harmonic and voltage deviation pollution in the distribution network is of great significance to improving the economy and overall coordination of the power quality treatment of the distribution network and improving the utilization rate of the treatment equipment. However, in the process of power quality treatment of the distribution network, the traditional solution has the problem that each node of the distribution network is easily affected, resulting in poor treatment effect.

[0004] How to solve the problem that each node of the distribution network is easily affected during the power quality management of the distribution network and improve the power quality management effect of the distribution network is still an urgent problem to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a distribution network system management method, device, equipment, storage medium and product that can improve the power quality management effect of the distribution network in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for managing a distribution network system, wherein the distribution network system includes a plurality of nodes, at least one node being connected to a grid-connected inverter circuit; the method includes:

[0007] If a node with abnormal voltage deviation is detected in the distribution network system, the node with abnormal voltage deviation is used as the starting governance node, and the N-level governance nodes of the starting governance node are determined according to the connection relationship between the starting governance node and other nodes in the distribution network system; wherein N is a natural number greater than zero;

[0008] Based on the grid-connected inverter circuit connected to the starting management node, the voltage deviation of the starting management node is adjusted, and the voltage deviation of each management node in the N-level management nodes is obtained after the voltage deviation of the starting management node is adjusted;

[0009] If the voltage deviation of the starting management node is still greater than the preset voltage deviation, the particle swarm algorithm is used to optimize the reactive power compensation capacity of the grid-connected inverter circuit connected to the M-level management node among the N-level management nodes, and the voltage deviation of the M-level management node is adjusted according to the optimization result; the starting value of M is 1 and the ending value is N;

[0010] If the iteration stop condition is not met, M is increased by 1, and the step of adjusting the voltage deviation of the M-level governance node among the N-level governance nodes is returned to the execution step until the iteration stop condition is met;

[0011] Among them, the iteration stop condition includes: the voltage deviation of each management node in the distribution network system is less than or equal to the preset voltage deviation, and / or M reaches the termination value when the voltage deviation of the starting management node is still greater than the preset voltage deviation.

[0012] In one embodiment, the starting governance node is a level 0 governance node, the node with abnormal voltage deviation is taken as the starting governance node, and the N-level governance node of the starting governance node is determined according to the connection relationship between the starting governance node and other nodes in the distribution network system, including:

[0013] The same-feeder line nodes and cross-feeder line nodes directly connected to the a-level governance node are determined as a+1-level governance nodes; where a is a natural number greater than or equal to 0 and less than N-1.

[0014] In one embodiment, m grid-connected inverter circuits are connected to the M-level management node, and the particle swarm algorithm is used to optimize the reactive power of the inverter in the M-level management node, including:

[0015] Randomly generate n particles, and form a particle group based on the n particles; wherein the position of each particle represents the reactive power compensation capacity of the grid-connected inverter circuit, and the search space dimension of each particle is m;

[0016] Under the constraints, the fitness value of each particle for the objective function in each search space dimension is calculated; wherein the objective function is used to minimize the sum of the voltage deviation and reactive power compensation capacity of the M-level governance nodes;

[0017] Based on the fitness value of the particle for the objective function, the individual extreme value and the group extreme value of the particle are updated, and if the end condition is not met, the step of calculating the fitness value of each particle for the objective function in each search space dimension under the constraint condition is returned to the execution step, until the end condition is met, and the final optimal position of the particle and the optimal position of the particle group are output as the optimization solution result;

[0018] The optimal position of the particle represents the optimal reactive power compensation capacity of the grid-connected inverter circuit, and the optimal position of the particle group represents the optimal reactive power compensation capacity of the grid-connected inverter circuit corresponding to the particle group.

[0019] In one embodiment, the method further comprises:

[0020] Acquire a sensitivity factor of each node in the distribution network system, wherein the sensitivity factor is positively correlated with the sensitivity of the grid-connected inverter circuit to which the node is connected to voltage deviation;

[0021] For each node, determine the node importance weight coefficient according to the node sensitivity factor;

[0022] The importance weight coefficient of each node is introduced into the calculation process of the voltage deviation of the M-level governance nodes in the objective function.

[0023] In one of the embodiments, the constraint conditions include power balance flow constraints of the distribution network and capacity upper limit constraints of each grid-connected inverter circuit;

[0024] Among them, the power balance flow constraints of the distribution network include the injected active power of the i-th node and the injected reactive power of the i-th node;

[0025] The injected active power of the i-th node is equal to the injected active power of the i-1-th node minus the active power loss of the line of the i-th node minus the active power of the load of the i-th node;

[0026] The injected reactive power of the ith node is equal to the injected reactive power of the i-1th node minus the reactive power loss of the line of the ith node minus the reactive power of the load of the ith node.

[0027] In one embodiment, the capacity upper limit constraint of each grid-connected inverter circuit includes:

[0028] In the case where the grid-connected inverter circuit includes an energy storage array, the actual charging reactive power of the energy storage array is within a first charging reactive power range, and the actual discharging reactive power of the energy storage array is within a first discharging reactive power range; wherein the first charging reactive power range is determined based on the charging state value of the energy storage array, the actual charging active power of the energy storage array, the maximum power factor of the energy storage array, and the minimum power factor of the energy storage array; wherein the first discharging reactive power range is determined based on the discharging state value of the energy storage array, the actual discharging active power of the energy storage array, the maximum power factor, and the minimum power factor;

[0029] In the case where the grid-connected inverter circuit includes a photovoltaic array, the reactive power output by any photovoltaic device in the photovoltaic array is within a first reactive power range; wherein the first reactive power range is determined based on the maximum active power that the photovoltaic device can output, the rated capacity of the photovoltaic device, the maximum power factor of the photovoltaic device, and the minimum power factor of the photovoltaic device;

[0030] When the grid-connected inverter circuit includes a charging pile array, the remaining capacity of the inverter in the dispatchable vehicle is equal to the first capacity, and the remaining capacity of the inverter in the non-dispatchable vehicle is equal to the second capacity; wherein the first capacity is determined based on the maximum charging power of the electric vehicle and the maximum value of the inverter capacity in the electric vehicle; wherein the second capacity is determined based on the upper energy boundary, the lower energy boundary and the maximum value of the inverter capacity in the electric vehicle.

[0031] In a second aspect, the present application provides a management device for a distribution network system, wherein the distribution network system includes a plurality of nodes, at least one node is connected to a grid-connected inverter circuit; the device includes:

[0032] The node confirmation module is used to, if a node with abnormal voltage deviation is detected in the distribution network system, take the node with abnormal voltage deviation as the starting governance node, and determine the N-level governance node of the starting governance node according to the connection relationship between the starting governance node and other nodes in the distribution network system; wherein N is a natural number greater than zero;

[0033] A regulating module, used for regulating the voltage deviation of the starting management node based on the grid-connected inverter circuit connected to the starting management node, and obtaining the voltage deviation of each management node in the N-level management nodes after the voltage deviation of the starting management node is regulated;

[0034] The regulating module is further used for, if the voltage deviation of the starting governance node is still greater than the preset voltage deviation, then, for the M-level governance node among the N-level governance nodes, using a particle swarm algorithm to optimize the reactive compensation capacity of the grid-connected inverter circuit connected to the M-level governance node, and adjusting the voltage deviation of the M-level governance node according to the optimization result; wherein the starting value of M is 1 and the ending value is N;

[0035] The adjustment module is further used for, when the iteration stop condition is not met, adding 1 to M, and returning to the step of adjusting the voltage deviation of the M-level governance node among the N-level governance nodes, until the iteration stop condition is met;

[0036] Among them, the iteration stop condition includes: the voltage deviation of each management node in the distribution network system is less than or equal to the preset voltage deviation, and / or M reaches the termination value when the voltage deviation of the starting management node is still greater than the preset voltage deviation.

[0037] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect are implemented.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0039] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0040] The above-mentioned distribution network system governance method, device, equipment, storage medium and product. The radial distribution network system is converted into a system consisting of a starting governance node and N-level governance nodes. The starting governance node is a node with abnormal voltage deviation. In the case of abnormal voltage deviation, the grid-connected inverter circuit connected to the starting governance node and the grid-connected inverter circuit of the N-level governance node connected to the starting governance node are used to provide reactive support to adjust the voltage deviation. In the case that the iteration stop condition is not met, the particle algorithm is used to search for the optimal solution for each level of governance node, and the distribution network is adjusted according to the optimal solution result (optimal reactive compensation capacity) until the iteration stop condition is met. In this way, through hierarchical governance, the effective governance of the faulty node (i.e., the starting governance node) is given priority, and the multifunctional inverter output capacity of multiple resources is reasonably calculated for the heavy load of the faulty node in the distribution network, thereby ensuring that all nodes in the distribution network are not affected by these faults and meeting the requirements of each node for power quality. Compared with traditional solutions, the method provided in this embodiment can solve the problem that each node of the distribution network is easily affected during the power quality management of the distribution network, and improve the power quality management effect of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 An application environment diagram of a method for managing a power distribution network system in an embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a method for managing a power distribution network system in one embodiment;

[0044] Figure 3 is a schematic structural diagram of a grid-connected inverter circuit in one embodiment;

[0045] Figure 4 A partial flow chart of a method for managing a power distribution network system in one embodiment;

[0046] Figure 5 A schematic diagram of the distribution of nodes in a power distribution network system in one embodiment;

[0047] Figure 6 A partial flow chart of a method for managing a power distribution network system in another embodiment;

[0048] Figure 7 A schematic diagram of the distribution of nodes in a power distribution network system in another embodiment;

[0049] Figure 8 It is a schematic diagram of the comparison result of the voltage deviation rate of each node before and after the treatment at a certain moment in an embodiment;

[0050] Fig. 9 It is a structural block diagram of a management device of a power distribution network system in one embodiment;

[0051] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] 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.

[0053] The distribution network system governance method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, a master station of the distribution network system, a voltage deviation detection device, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0054] In an exemplary embodiment, Figure 2 As shown, a method for managing a distribution network system is provided, and the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 202 to 208. Among them:

[0055] Step 202: If a node with abnormal voltage deviation is detected in the distribution network system, the node with abnormal voltage deviation is used as the starting governance node, and the N-level governance node of the starting governance node is determined based on the connection relationship between the starting governance node and other nodes in the distribution network system.

[0056] Wherein, N is a natural number greater than zero.

[0057] The nodes of the distribution network system refer to important points in the distribution network that are connected to power sources, loads, and branches. The distribution network system includes multiple nodes, some of which may be used to access power sources, and some of which may be used to access branches. However, it should be noted that at least one node of the distribution network system provided in this embodiment is connected to a grid-connected inverter circuit.

[0058] Among them, the grid-connected inverter circuit described in this embodiment refers to a multi-functional grid-connected inverter (FGCI for short) control structure circuit, which can also be understood as reactive power management equipment.

[0059] Please refer to Figure 3 The grid-connected inverter circuit mainly includes an energy array (such as photovoltaic, energy storage or charging pile), a boost circuit, and a DC side capacitor C DG , inverter, filter, three-phase voltage (u oa 、u ob 、u oc ). Figure 1 In the middle: L0 is the DC side inductance, which is connected to the DC side capacitor C DG The boost circuit is composed of filter capacitor Cf, damping resistor Rd, filter inductor Lf and equivalent resistor Rf. The boost circuit raises the DC power to a voltage that allows grid-connected inversion. The three-phase inverter converts the DC power into AC power and feeds it into the grid. The LC filter is used to filter out higher harmonics. Through the control method of the above devices, the multifunctional grid-connected inverter, i.e., various resources, can achieve reactive power compensation optimization for the distribution network nodes.

[0060] Among them, the voltage deviation can be understood as the difference between the actual voltage of the node and the nominal voltage of the system. The voltage deviation abnormality can be understood as the voltage deviation exceeding the preset voltage deviation. The reason for the abnormal voltage deviation of the node is generally that the node is connected to a heavy load. For example, a large number of heavy-loaded equipment causes the distribution network to increase sharply, resulting in an increase in voltage loss in the line, which in turn causes the voltage deviation of the distribution network to be abnormal. In this embodiment, the node with abnormal voltage deviation is named the starting governance node. There can be multiple starting governance nodes in the distribution network system, which is not limited in this embodiment. For each starting governance node, the N-level governance node of the starting governance node can be determined.

[0061] Among them, there is a direct connection or indirect connection relationship between multiple nodes in the distribution network, and the connection relationship between the starting governance node and other nodes in the distribution network system includes at least a direct connection relationship, and may also include an indirect connection relationship. Direct connection can be understood as a connection relationship between two nodes that is not established through other nodes. For example, when a connection relationship is directly established between node one and node two, node one and node two are directly connected. Indirect connection can be understood as a connection relationship between two nodes through another node. For example, when a connection relationship is established between node one and node two through node three, node one and node two are indirectly connected. According to the direct connection relationship and indirect connection of the starting governance node, the N-level governance node of the starting governance node can be determined. For example, the node directly connected to the starting governance node is determined as a level 1 governance node, and the node indirectly connected to the starting governance node is determined as a level 2 governance node.

[0062] Step 204, based on the grid-connected inverter circuit connected to the starting management node, adjust the voltage deviation of the starting management node, and obtain the voltage deviation of each management node in the N-level management nodes after adjusting the voltage deviation of the starting management node.

[0063] In the hierarchical management mode of grid-connected inverter circuits in the distribution network system, the inverter in the grid-connected inverter circuit connected to the starting management node is first considered to be managed to adjust the voltage deviation of the starting management node. By controlling the devices in the grid-connected inverter circuit, the voltage deviation of the node can be adjusted to achieve reactive power compensation optimization of the node.

[0064] In one embodiment, while or after adjusting the voltage deviation of the starting management node based on the grid-connected inverter circuit connected to the starting management node, it can be considered to use multiple inverters on the N-level management nodes (such as the first-level management node and the second-level management node) of the starting management node for coordinated adjustment to adjust the voltage deviation of the starting management node more quickly. In other words, once the starting management node appears, the voltage deviation of the starting management node is adjusted according to the grid-connected inverter circuit connected to the starting management node and the grid-connected inverter connected to the N-level management node.

[0065] In one embodiment, the voltage deviation of the starting governance node is first adjusted based on the grid-connected inverter circuit connected to the starting governance node, and then the voltage deviation of each governance node in the N-level governance nodes is obtained after the voltage deviation of the starting governance node is adjusted. According to the voltage deviation of each governance node in the N-level governance nodes, it is determined whether to use multiple inverters on the N-level governance nodes (e.g., the first-level governance node and the second-level governance node) for coordinated adjustment. In this way, when the voltage deviation of the starting governance node has been adjusted based on the grid-connected inverter circuit connected to the starting governance node, the N-level governance nodes can no longer be used to coordinately adjust the voltage deviation of the starting governance node, saving resources.

[0066] Step 206, if the voltage deviation of the starting management node is still greater than the preset voltage deviation, then for the M-level management nodes among the N-level management nodes, a particle swarm algorithm is used to optimize the reactive compensation capacity of the grid-connected inverter circuit connected to the M-level management node, and the voltage deviation of the M-level management node is adjusted according to the optimization result.

[0067] The starting value of M is 1 and the ending value is N.

[0068] When the voltage deviation of the initial management node is still greater than the preset voltage deviation, the voltage deviation of the nodes in the distribution network system is readjusted based on the 1st to Nth level management nodes among the Nth level management nodes.

[0069] In the particle swarm algorithm, each potential solution to the optimization problem is a bird in the search space, called a particle. All particles have a fitness value determined by the function being optimized, and each particle also has a speed that determines the direction and distance they fly. The particles follow the current optimal particle to search in the solution space and find the optimal solution through iteration. In each iteration, the particle updates itself by tracking two extreme values: the first extreme value is the optimal solution found by the particle itself, called the individual extreme value (pBest); the other extreme value is the optimal solution currently found by the entire population, called the global extreme value (gBest).

[0070] The particle swarm algorithm is used to optimize the reactive compensation capacity of the grid-connected inverter circuit connected to the M-level governance node. The objective function used to calculate the fitness value in the particle swarm algorithm can be used to minimize the voltage deviation of the M-level governance node. The position of the particle in the particle swarm algorithm can represent the reactive compensation capacity of the grid-connected inverter circuit, and the reactive compensation capacity can also be understood as the remaining capacity of the inverter. When there are m grid-connected inverter circuits connected to the M-level governance node, the search space dimension of each particle is m.

[0071] Among them, the optimization solution result can be understood as the optimal reactive power compensation capacity of the M-level governance node. The voltage deviation of the M-level governance node is adjusted according to the optimal reactive power compensation capacity of the M-level governance node, that is, by controlling the devices in the grid-connected inverter circuit connected to the M-level governance node, the inverter residual capacity in the grid-connected inverter circuit connected to the M-level governance node reaches the optimal reactive power compensation capacity. It should be noted that the M-level governance node is connected to at least one grid-connected inverter circuit.

[0072] Step 208, if the iteration stop condition is not met, M is increased by 1, and the execution returns to the step of adjusting the voltage deviation of the M-level governance nodes among the N-level governance nodes until the iteration stop condition is met.

[0073] Among them, the iteration stop condition includes: the voltage deviation of each governance node in the distribution network system is less than or equal to the preset voltage deviation, or, when the voltage deviation of the starting governance node is still greater than the preset voltage deviation, M reaches the termination value. In other words, the iteration stop condition can be that the voltage deviation of each governance node in the distribution network system is less than or equal to the preset voltage deviation. The iteration stop condition can also be that when the voltage deviation of the starting governance node is still greater than the preset voltage deviation, M reaches the termination value (the termination value is N).

[0074] If the iteration stop condition is not met, M is increased by 1, and the step of adjusting the voltage deviation of the M-level governance node in the N-level governance node in step 206 is returned. The termination value of M is N. After M is increased by 1 to form a new M, the termination value of the new M is also N. Assume that there are 2 levels of governance nodes, the starting value of M is 1, and the termination value is 2. First, adjust the voltage deviation based on the 1-level governance node. If the iteration stop condition is not met, adjust the voltage deviation based on the 2-level governance node until the iteration stop condition is met. As described above, the iteration stop condition can be that the voltage deviation of each governance node in the distribution network system is less than or equal to the preset voltage deviation, or it can be that the voltage deviation of the starting governance node is still greater than the preset voltage deviation. M reaches the termination value refers to M reaching the termination value in step 206.

[0075] In the above-mentioned distribution network system governance method, the radial distribution network system is converted into a system consisting of a starting governance node and N-level governance nodes. The starting governance node is a node with abnormal voltage deviation. In the case of abnormal voltage deviation, the grid-connected inverter circuit connected to the starting governance node and the grid-connected inverter circuit of the N-level governance node connected to the starting governance node are used to provide reactive support to adjust the voltage deviation. In the case that the iteration stop condition is not reached, the particle algorithm is used to search for the optimal solution for each level of governance node, and the distribution network is adjusted according to the optimal solution result (optimal reactive compensation capacity) until the iteration stop condition is reached. In this way, through hierarchical governance, the effective governance of the faulty node (i.e., the starting governance node) is given priority, and the multifunctional inverter output capacity of multiple resources is reasonably calculated for the heavy load of the faulty node in the distribution network, so as to ensure that all nodes in the distribution network are not affected by these faults, and the requirements of each node for power quality are met. Compared with the traditional solution, the method provided in this embodiment can solve the problem that each node of the distribution network is easily affected in the process of power quality governance of the distribution network, and improve the power quality governance effect of the distribution network.

[0076] For some examples, see Figure 4, the starting governance node is a level 0 governance node, step 202 takes the node with abnormal voltage deviation as the starting governance node, and determines the N-level governance node of the starting governance node according to the connection relationship between the starting governance node and other nodes in the distribution network system, including:

[0077] Step 402: determine the same-feeder line nodes and cross-feeder line nodes directly connected to the a-level governance node as a+1-level governance nodes.

[0078] Where a is a natural number greater than or equal to 0 and less than N-1. The starting value of a is 0 and the ending value is N-1.

[0079] The same-feeder line nodes and cross-feeder line nodes directly connected to the level 0 governance node (i.e., the starting governance node) are determined as level 1 governance nodes. The same-feeder line nodes and cross-feeder line nodes directly connected to the level 1 governance node are determined as level 2 governance nodes. The same-feeder line nodes and cross-feeder line nodes directly connected to the level 2 governance node are determined as level 3 governance nodes.

[0080] See also Figure 5 , assuming that node 5 is the starting governance node, the same-feeder line nodes directly connected to node 5 include node 2, node 6, node 7 and node 8, and the cross-feeder line nodes directly connected to node 5 include node 9. Then nodes 2, node 6, node 7, node 8 and node 9 are level 1 governance nodes. The same-feeder line nodes directly connected to node 9 in the level 1 governance node include node 10 and node 11, and nodes 10 and node 11 are level 2 governance nodes. The cross-feeder line nodes directly connected to node 2 in the level 1 governance node are node 3 and node 4. Then the level 2 governance nodes include node 3, node 4, node 10 and node 11. The same-feeder line nodes directly connected to node 3 in the level 2 governance node include node 12, node 13 and node 14, and the cross-feeder line nodes directly connected to node 3 include node 15, node 16 and node 17. The same-feeder line nodes directly connected to node 4 in the level 2 governance node include node 18, node 19 and node 20. The level 3 governance nodes include node 15, node 16, node 17, node 18, node 19 and node 20.

[0081] The method provided in this embodiment innovatively considers the structural characteristics of the radial distribution network and divides the nodes in the distribution network system into different management levels to support different levels of management nodes to provide coordinated reactive power compensation and improve the voltage deviation regulation effect.

[0082] In some embodiments, the M-level management node is connected to m grid-connected inverter circuits, see Figure 6 Step 206 uses a particle swarm algorithm to optimize the reactive power of the inverter in the M-level management node, including:

[0083] Step 602, randomly generate n particles, and form a particle group based on the n particles; wherein the position of each particle represents the reactive power compensation capacity of the grid-connected inverter circuit, and the search space dimension of each particle is m.

[0084] First, n particles are randomly generated, and their initial positions in the population are initialized to complete the particle swarm initialization. The position of each particle represents the reactive power compensation capacity of the grid-connected inverter circuit.

[0085] The dimension of each particle is the same as the dimension of the search space. When there are m grid-connected inverter circuits connected to the M-level governance node, the dimension of the search space of each particle is m.

[0086] Step 604: Under the constraint conditions, calculate the fitness value of each particle for the objective function in each search space dimension.

[0087] The objective function is used to minimize the sum of the voltage deviation and reactive power compensation capacity of the M-level management nodes.

[0088] Among them, the objective function can be expressed as min f M =f M1 +f M2 , where f M is the objective function, f M1 is the total node voltage deviation function, f M2 It is the inverter reactive power optimization function.

[0089] in, Where ξ is the set of nodes in the distribution network whose voltage deviation exceeds the preset voltage deviation (such as the national standard limit), U i is the voltage amplitude of node i, and U0 is the reference voltage amplitude.

[0090] Among them, f M2 =∑ i∈ψ S i , where ψ is the set of nodes connected to the inverter in the distribution network, S i The capacity used by each inverter to manage voltage deviation.

[0091] The objective function is to minimize the power quality index corresponding to the node voltage deviation rate and reactive power compensation amount, and use the remaining capacity of the inverter to continue voltage deviation and reactive power optimization to optimize the power quality of the node.

[0092] Under the constraints, the reactive power compensation capacity (also known as the remaining capacity of the inverter) of the grid-connected inverter circuit connected to each node and the voltage deviation (also known as the voltage offset rate) of each node are calculated. Then, based on the objective function, the fitness value of each particle for the objective function in each search space dimension is determined.

[0093] Step 606, based on the fitness value of the particle for the objective function, update the individual extreme value and the group extreme value of the particle, and if the end condition is not met, return to the execution step of calculating the fitness value of each particle for the objective function in each search space dimension under the constraint condition, until the end condition is met, and output the final optimal position of the particle and the optimal position of the particle group as the optimization solution result.

[0094] The optimal position of the particle represents the optimal reactive power compensation capacity of the grid-connected inverter circuit, and the optimal position of the particle group represents the optimal reactive power compensation capacity of the grid-connected inverter circuit corresponding to the particle group.

[0095] If the termination condition is not met, the particle position and particle velocity are updated, and the fitness value of each particle to the objective function is calculated again until the termination condition is met, and the optimal solution result is output. The termination condition can be that the particle swarm algorithm reaches the maximum number of iterations or meets other termination requirements.

[0096] In some embodiments, the method for managing the power distribution network system further includes:

[0097] Step 1: Obtain the sensitivity factor of each node in the distribution network system. The sensitivity factor is positively correlated with the sensitivity of the grid-connected inverter circuit to which the node is connected to voltage deviation.

[0098] In order to optimize the voltage distortion and voltage offset of the distribution network, it is necessary to determine the importance weight coefficient of each node according to the different tolerance of grid-connected load equipment at different nodes to voltage offset, and use the importance weight coefficient to characterize the node's requirement for voltage offset.

[0099] The node sensitivity factor α is introduced, and its value reflects the sensitivity of the load equipment connected to the node to voltage deviation. The larger the α value, the higher the voltage deviation index requirement of the node.

[0100] Step 2: For each node, determine the importance weight coefficient of the node according to the sensitivity factor of the node.

[0101] The node importance weight coefficient is defined by using the node sensitivity factor ratio method.

[0102] The importance weight coefficient of the node is expressed as c i Specifically, α irepresents the sensitivity factor of node i, and N represents the total number of N nodes in the distribution network system.

[0103] Step three, introducing the importance weight coefficient of each node into the calculation process of the voltage deviation of the M-level governance nodes in the objective function.

[0104] f M1 is the total node voltage deviation function. Specifically, Where ξ is the set of nodes in the distribution network whose voltage deviation exceeds the preset voltage deviation (such as the national standard limit), U i is the voltage amplitude of node i, U0 is the reference voltage amplitude, c i Represents the importance weight coefficient of the i-node.

[0105] The method provided in this embodiment introduces the importance weight coefficient of each node to match the tolerance of grid-connected load devices at different nodes to voltage deviation, so as to optimize the optimization effect of voltage distortion and voltage deviation of the distribution network.

[0106] In some embodiments, the constraint conditions include power balance flow constraints of the distribution network and capacity upper limit constraints of each grid-connected inverter circuit.

[0107] The power balance flow constraints of the distribution network include the injected active power of the i-node and the injected reactive power of the i-node.

[0108] The injected active power of the i-th node is equal to the injected active power of the i-th node minus the active power loss of the line of the i-th node, minus the active power of the i-th node load. The injected reactive power of the i-th node is equal to the injected reactive power of the i-th node minus the reactive power loss of the line of the i-th node, minus the reactive power of the i-th node load.

[0109] The power balance flow constraint of the distribution network can be expressed as

[0110] Where: P i is the injected active power of the node, Q i is the injected reactive power of the node. ΔP loss,i is the active power loss of the line at node i, ΔQ loss,i is the reactive power loss of the line at node i. L,i is the active power of the load at node i, Q L,i is the reactive power of the load at node i. D,i It injects reactive power into the grid-connected inverter circuit (i.e. reactive power management equipment).

[0111] After adding reactive power control equipment, the voltage of nodes in the distribution network system cannot exceed the national standard. The constraint formula is V i,min ≤Vi ≤V i,max Where V i is the voltage of node i, V i,min is the minimum voltage allowed for node i, V i,max is the maximum voltage allowed for node i.

[0112] The capacity upper limit constraint of the grid-connected inverter circuit is associated with the type of energy array contained in the grid-connected inverter. The following is a detailed description of the case where the energy array is an energy storage array, a photovoltaic array, and a charging pile array.

[0113] In the first case, when the grid-connected inverter circuit includes an energy storage array, the actual charging reactive power of the energy storage array is within a first charging reactive power range, and the actual discharging reactive power of the energy storage array is within a first discharging reactive power range; wherein the first charging reactive power range is determined based on the charging state value of the energy storage array, the actual charging active power of the energy storage array, the maximum power factor of the energy storage array, and the minimum power factor of the energy storage array; wherein the first discharging reactive power range is determined based on the discharge state value of the energy storage array, the actual discharging active power of the energy storage array, the maximum power factor, and the minimum power factor.

[0114] During the day, when the photovoltaic power generation is large and cannot be fully absorbed by the distribution network, the excess electricity can be absorbed by the energy storage array (i.e., the energy storage system). In the evening, when the photovoltaic power generation is insufficient and the power demand is high, the energy storage array releases active power to make up for the insufficient power of the distribution network. The state of charge (SOC) of the energy storage array is an important indicator to measure the charging and discharging capacity of the energy storage array. On the basis of meeting the upper and lower limit constraints of its capacity, the energy storage array must also meet the energy balance constraint during the process of energy storage charging and discharging. In addition, in order to ensure the sustainability of energy storage use, the charge level at the initial time of each day must be consistent to take into account that the charging and discharging processes cannot be carried out at the same time.

[0115] The energy storage array is a typical multi-time period coupling device and needs to satisfy the following constraints:

[0116]

[0117] Where T is the number of time nodes that need to be optimized. Represents the charging state of the energy storage array, Represents the discharge state of the energy storage array, which is a 0-1 variable. Represents the actual charging active power of the energy storage array, Represents the actual discharge active power of the energy storage array. Represents the actual charging reactive power of the energy storage array, Represents the actual discharge reactive power of the energy storage array. Represents the maximum value of the power factor, min represents the minimum value of the power factor. Represents the maximum discharge power of the energy storage array, Represents the minimum discharge power of the energy storage array. Represents the maximum charging power of the energy storage array, Represents the minimum charging power of the energy storage array. Represents the charging efficiency of the energy storage array, Represents the discharge efficiency of the energy storage array. In order to extend the service life of the energy storage array and reduce investment costs, full charging and full discharging of the energy storage array are not allowed. is the minimum state of charge of the energy storage array, is the maximum state of charge of the energy storage array. is the rated capacity of the energy storage array. The energy storage array must meet SOC constraints, maximum charge and discharge power constraints, maximum and minimum state of charge constraints, and power factor constraints.

[0118] In the second case, when the grid-connected inverter circuit includes a photovoltaic array, the reactive power output by any photovoltaic device in the photovoltaic array is within a first reactive power range; wherein the first reactive power range is determined based on the maximum active power that the photovoltaic device can output, the rated capacity of the photovoltaic device, the maximum power factor of the photovoltaic device, and the minimum power factor of the photovoltaic device.

[0119] The photovoltaic array can be understood as a distributed photovoltaic array.

[0120] The photovoltaic array meets the following constraints:

[0121]

[0122] In the formula, Represents the active power output by the jth photovoltaic device at time t. Represents the reactive power output by the jth photovoltaic device at time t, that is, the reactive power output by any photovoltaic device. It represents the maximum active power that the j-th photovoltaic can output at time t, and is generally taken as the predicted maximum photovoltaic output value. Represents the rated capacity of the jth PV. Represents the maximum power factor of the photovoltaic device, Represents the minimum power factor of the photovoltaic device.

[0123] In the third case, when the grid-connected inverter circuit includes a charging pile array, the remaining capacity of the inverter in the dispatchable vehicle is equal to the first capacity, and the remaining capacity of the inverter in the non-dispatchable vehicle is equal to the second capacity; wherein the first capacity is determined based on the maximum charging power of the electric vehicle and the maximum value of the inverter capacity in the electric vehicle; wherein the second capacity is determined based on the upper energy boundary, the lower energy boundary and the maximum value of the inverter capacity in the electric vehicle.

[0124] Before dispatching electric vehicles, it is necessary to establish a dispatchable capacity assessment model for electric vehicles, that is, to quantify the capacity of electric vehicles that can participate in grid dispatch in each dispatch period while meeting user needs.

[0125] The dispatchable capacity of a single electric vehicle is modeled using the energy and power boundary method, that is, each electric vehicle has an upper energy boundary, a lower energy boundary, an upper power boundary, and a lower power boundary. The energy boundary and the power boundary describe the set of all possible charging trajectories of a single electric vehicle after it is connected to a charging pile. The upper energy boundary and the lower energy boundary represent the fastest and slowest charging energy trajectories, respectively, and the upper power boundary and the lower power boundary represent the maximum charging power and the maximum discharging power, respectively.

[0126] For non-dispatchable cars, this type of electric vehicle is not connected to the charging pile for long enough and must be charged at maximum power throughout the parking process. The energy boundary and power boundary of the electric vehicle are not flexible. For this type of electric vehicle, its power boundary is simply calculated as follows:

[0127]

[0128] In the formula, is the charging power at time t. The moment to connect your electric car to a charging station. The time when the electric vehicle is expected to leave the charging station. is the maximum charging power.

[0129] At this time, the maximum capacity of electric vehicles that can be used for power quality management (that is, the remaining capacity of the inverter in the dispatchable vehicle) is as follows:

[0130]

[0131] In the formula, It is the remaining capacity of the inverter at time t, representing the remaining capacity of the inverter in the non-dispatchable car.

[0132] is the maximum inverter capacity at time t.

[0133] For dispatchable electric vehicles, the expected parking time of such electric vehicles is longer than the charging time, and they have flexible charging and discharging margins, and can charge and discharge to the grid under the control of the aggregator. Dispatched electric vehicles can charge and discharge to the grid during the entire parking period. Similarly, their power boundary and energy boundary can be calculated as follows:

[0134]

[0135] In the formula, is the upper energy boundary of the electric vehicle at time t, is the lower bound of the energy of the electric vehicle at time t. is the upper power limit of the electric vehicle at time t, is the lower bound of the power of the electric vehicle at time t. is the rated charging power of the electric vehicle, is the rated discharge power of the electric vehicle. Δt is the scheduling period, which can be 15 minutes in this embodiment.

[0136] At each moment, the energy boundaries and power boundaries of all electric vehicles connected to the charging pile are superimposed to obtain the aggregated energy boundaries and power boundaries. The remaining capacity of the inverter in the non-dispatchable car is as follows:

[0137]

[0138] In the formula, is the remaining capacity of the inverter at time t, which represents the remaining capacity of the inverter in the dispatchable vehicle.

[0139] is the maximum inverter capacity at time t.

[0140] The above is the capacity upper limit constraint of the grid-connected inverter circuit under the different types of energy arrays contained in the grid-connected inverter.

[0141] The method provided in this embodiment, when the grid-connected inverter circuit is composed of a photovoltaic array, an energy storage array or a charging pile array, manages the faulty nodes through the mutual coordination of the management devices of the local and multi-level management nodes with preset priorities, takes into account the structural characteristics of the complex distribution network, and is suitable for complex new distribution networks with strong randomness and dispersion.

[0142] In some embodiments, a radial distribution network structure with 20 nodes is used as a reference to illustrate the effect of the distribution network system governance method provided by the embodiment of the present application. Figure 7 , Figure 7 The numbers from 1 to 20 represent the node numbers.

[0143] The first step is to set the node parameters. For specific line parameters, see Table 1.

[0144] Table 1:

[0145] Starting Node Termination Node Line resistance value Line reactance value 1 2 0.448 0.744 1 3 0.56 0.93 1 4 0.42 0.6975 2 5 0.5264 0.8742 5 6 0.4872 0.8091 6 7 0.5264 0.8742 7 8 0.128 0.144 2 9 0.48 0.54 9 10 0.664 0.747 10 11 0.152 0.171 3 12 0.144 0.162 12 13 0.64 0.72 13 14 0.616 0.693 3 15 0.552 0.621 15 16 0.664 0.747 15 17 0.736 0.828 4 18 0.9624 0.9702 4 19 0.9612 0.96135 4 20 0.96128 0.96144

[0146] The reactive power of some nodes is adjusted to represent the access of heavy loads, and the selected heavy load access nodes are nodes 3, 6, 9, 13, 16 and 20. The particle swarm algorithm sets the initial number of particles to 200 and the maximum number of iterations to 400.

[0147] In the second step, it is assumed that there are two charging stations distributed at nodes 7 and 14 for the distribution network management, with rated capacities of 140KW and 300KW respectively. Five photovoltaic arrays are distributed at nodes 8, 9, 12, 17, and 19, with capacities of 100KW, 30KW, 220KW, and 75KW respectively. Among them, nodes 7, 8, 9, 12, 17, and 19 are all equipped with 100KW energy storage arrays. The capacity of their distributed energy inverters is shown in Table 2. For voltage-sensitive loads, the sensitivity factors of the nodes are set to 10 and 5 respectively, and the other nodes are all 1.

[0148] Table 2:

[0149] Resource Type Distributed Nodes Inverter capacity / KW Photovoltaic 8,9,12,17,19 100,30,220,75 Charging Station 7,14 140,300 Energy Storage 7,8,9,12,17,19 100

[0150] In the third step, the inverter remaining capacity of photovoltaic, energy storage and charging piles is used as the optimization variable to compensate the reactive power value. According to the established global optimization model, the optimal compensation reactive value of the grid-connected inverter circuit is solved by power flow calculation combined with particle swarm algorithm. The global optimization results of the inverter equipment of each control node are shown in Table 3.

[0151] Table 3:

[0152]

[0153] The sum of the voltage deviation rates of all nodes in the network is used as the power quality comparison index. The comparison results of the voltage deviation rates of each node before and after the treatment at a certain moment (the voltage deviation rate can be understood as voltage deviation) are as follows: Figure 8 As shown, Figure 8 Before optimization refers to the period before the distribution network system governance method provided by this embodiment is adopted, and after optimization refers to the period after the distribution network system governance method provided by this embodiment is adopted. Figure 8It can be seen that the voltage offset level of each node before treatment is at a very high level, with the highest offset rate reaching about 20%. After using the corresponding location resource inverter to manage the power, the voltage offset of each node has decreased compared with before treatment, and the overall rate remains at about 5% and 2%. The node voltage offset rate after connecting to the voltage management equipment meets the national standard limit of 10%, reaching the qualified level of power quality.

[0154] For nodes 3 and 12 connected to sensitive loads, the voltage distortion of these sensitive nodes has been effectively reduced after the treatment compared to before the treatment. Considering that some nodes are more sensitive to voltage offset and have higher requirements for voltage offset, these nodes are given larger weights in the global optimization objective. Therefore, nodes with larger weights will have lower voltage offset after optimization.

[0155] At the same time, the method provided in the present application can achieve targeted management of corresponding nodes by setting corresponding importance weights for nodes within the compensation capacity according to the actual power quality requirements of users, which verifies the rationality of the method provided in the present application.

[0156] In summary, the method provided in the embodiment of the present application, the grid-connected inverter circuit is composed of a distributed photovoltaic array, an energy storage array and a charging pile array, and the faulty nodes are governed by the management equipment of the local and multi-level management nodes in a coordinated manner with preset priorities, taking into account the structural characteristics of complex distribution networks, and being suitable for complex new distribution networks with strong randomness and dispersion, so that the method has a better effect on the power quality pollution control of new distribution networks than the existing technology, while reducing equipment costs and improving equipment utilization. In addition, by selecting distributed energy for governance in a hierarchical manner, giving priority to the effective governance of faulty nodes, and for the heavy load of faulty nodes in the distribution network, the multifunctional inverter output capacity of multiple resources is reasonably calculated, thereby ensuring that all nodes in the distribution network are not affected by these faults, and meeting the power quality requirements of each node.

[0157] It should be understood that, although the steps in the flowcharts of the embodiments described above are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0158] Based on the same inventive concept, the embodiment of the present application also provides a distribution network system governance device for implementing the distribution network system governance method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more distribution network system governance devices provided below can refer to the limitations of the distribution network system governance method above, and will not be repeated here.

[0159] In an exemplary embodiment, the power distribution network system includes a plurality of nodes, at least one of which is connected to a grid-connected inverter circuit. Fig. 9 As shown, a distribution network system management device 90 is provided, including: a node confirmation module 901 and a regulation module 902, wherein:

[0160] The node confirmation module 901 is used to, if a node with abnormal voltage deviation is detected in the distribution network system, take the node with abnormal voltage deviation as the starting governance node, and determine the N-level governance node of the starting governance node according to the connection relationship between the starting governance node and other nodes in the distribution network system; wherein N is a natural number greater than zero;

[0161] The adjustment module 902 is used to adjust the voltage deviation of the starting management node based on the grid-connected inverter circuit connected to the starting management node, and obtain the voltage deviation of each management node in the N-level management nodes after adjusting the voltage deviation of the starting management node;

[0162] The regulating module 902 is also used for, if the voltage deviation of the starting governance node is still greater than the preset voltage deviation, using a particle swarm algorithm to optimize the reactive compensation capacity of the grid-connected inverter circuit connected to the M-level governance node among the N-level governance nodes, and adjusting the voltage deviation of the M-level governance node according to the optimization result; wherein the starting value of M is 1 and the ending value is N;

[0163] The adjustment module 902 is further configured to, if the iteration stop condition is not met, add 1 to M and return to the step of adjusting the voltage deviation of the M-level governance node among the N-level governance nodes until the iteration stop condition is met;

[0164] Among them, the iteration stop condition includes: the voltage deviation of each management node in the distribution network system is less than or equal to the preset voltage deviation, and / or M reaches the termination value when the voltage deviation of the starting management node is still greater than the preset voltage deviation.

[0165] In one embodiment, the starting governance node is a level 0 governance node, and the node confirmation module 901 is used to determine the same-feeder line nodes and cross-feeder line nodes directly connected to the a-level governance node as a+1-level governance nodes; wherein a is a natural number greater than or equal to 0 and less than N-1.

[0166] In one embodiment, m grid-connected inverter circuits are connected to the M-level governance node, and the adjustment module 902 is used to randomly generate n particles and form a particle group based on the n particles; wherein the position of each particle represents the reactive compensation capacity of the grid-connected inverter circuit, and the search space dimension of each particle is m; under the constraint condition, the fitness value of each particle in each search space dimension for the objective function is calculated; wherein the objective function is used to minimize the sum of the voltage deviation and reactive compensation capacity of the M-level governance node; based on the fitness value of the particle for the objective function, the individual extreme value and the group extreme value of the particle are updated, and if the end condition is not met, the step of calculating the fitness value of each particle in each search space dimension for the objective function under the constraint condition is returned to the step of executing until the end condition is met, and the final optimal position of the particle and the optimal position of the particle group are output as the optimization solution result; wherein the optimal position of the particle represents the optimal reactive compensation capacity of the grid-connected inverter circuit, and the optimal position of the particle group represents the optimal reactive compensation capacity of the grid-connected inverter circuit corresponding to the particle group.

[0167] In one embodiment, the regulation module 902 is also used to obtain the sensitivity factor of each node in the distribution network system, which is positively correlated with the sensitivity of the grid-connected inverter circuit to which the node is connected to voltage deviation; for each node, the importance weight coefficient of the node is determined according to the sensitivity factor of the node; and the importance weight coefficient of each node is introduced into the calculation process of the voltage deviation of the M-level governance node in the objective function.

[0168] In one embodiment, the constraint conditions include power balance flow constraints of the distribution network and capacity upper limit constraints of each grid-connected inverter circuit; wherein, the power balance flow constraints of the distribution network include the injected active power of the i-th node and the injected reactive power of the i-th node; the injected active power of the i-th node is equal to the injected active power of the i-1th node minus the line active loss of the i-th node, and then minus the active power of the i-th node load; the injected reactive power of the i-th node is equal to the injected reactive power of the i-1th node minus the line reactive loss of the i-th node, and then minus the reactive power of the i-th node load.

[0169] In one embodiment, the capacity upper limit constraint of each grid-connected inverter circuit includes: when the grid-connected inverter circuit includes an energy storage array, the actual charging reactive power of the energy storage array is within a first charging reactive power range, and the actual discharging reactive power of the energy storage array is within a first discharging reactive power range; wherein the first charging reactive power range is determined based on the charging state value of the energy storage array, the actual charging active power of the energy storage array, the maximum power factor of the energy storage array, and the minimum power factor of the energy storage array; wherein the first discharging reactive power range is determined based on the discharge state value of the energy storage array, the actual discharging active power of the energy storage array, the maximum power factor, and the minimum power factor; when the grid-connected inverter circuit includes a photovoltaic array In the case of a column, the reactive power output by any photovoltaic device in the photovoltaic array is within a first reactive power range; wherein the first reactive power range is determined based on the maximum active power that the photovoltaic device can output, the rated capacity of the photovoltaic device, the maximum power factor of the photovoltaic device, and the minimum power factor of the photovoltaic device; in the case where the grid-connected inverter circuit includes an array of charging piles, the remaining capacity of the inverter in the dispatchable vehicle is equal to the first capacity, and the remaining capacity of the inverter in the non-dispatched vehicle is equal to the second capacity; wherein the first capacity is determined based on the maximum charging power of the electric vehicle and the maximum value of the inverter capacity in the electric vehicle; wherein the second capacity is determined based on the upper energy boundary, the lower energy boundary, and the maximum value of the inverter capacity in the electric vehicle.

[0170] Each module in the above-mentioned distribution network system management device can be implemented in whole or in part by software, hardware and a combination thereof. Each of 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 each of the above modules.

[0171] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as voltage deviation and reactive power compensation capacity. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for managing a distribution network system is implemented.

[0172] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0173] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0174] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0175] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for managing a distribution network system, characterized in that: The distribution network system includes a plurality of nodes, at least one of which is connected to a grid-connected inverter circuit; the method includes: If a node with abnormal voltage deviation is detected in the distribution network system, the node with abnormal voltage deviation is used as the starting governance node, and the N-level governance nodes of the starting governance node are determined according to the connection relationship between the starting governance node and other nodes in the distribution network system; wherein N is a natural number greater than zero; Based on the grid-connected inverter circuit connected to the starting management node, the voltage deviation of the starting management node is adjusted, and the voltage deviation of each management node in the N-level management nodes is obtained after the voltage deviation of the starting management node is adjusted; If the voltage deviation of the starting management node is still greater than the preset voltage deviation, the particle swarm algorithm is used to optimize the reactive power compensation capacity of the grid-connected inverter circuit connected to the M-level management node among the N-level management nodes, and the voltage deviation of the M-level management node is adjusted according to the optimization result; the starting value of M is 1 and the ending value is N; If the iteration stop condition is not met, M is increased by 1, and the step of adjusting the voltage deviation of the M-level governance node among the N-level governance nodes is returned to the execution step until the iteration stop condition is met; Among them, the iteration stop condition includes: the voltage deviation of each management node in the distribution network system is less than or equal to the preset voltage deviation, and / or M reaches the termination value when the voltage deviation of the starting management node is still greater than the preset voltage deviation.

2. The method according to claim 1, characterized in that The starting governance node is a level 0 governance node, the node with abnormal voltage deviation is taken as the starting governance node, and the N-level governance node of the starting governance node is determined according to the connection relationship between the starting governance node and other nodes in the distribution network system, including: The same-feeder line nodes and cross-feeder line nodes directly connected to the a-level governance node are determined as a+1-level governance nodes; where a is a natural number greater than or equal to 0 and less than N-1.

3. The method according to claim 1, characterized in that There are m grid-connected inverter circuits connected to the M-level management node. The particle swarm algorithm is used to optimize the reactive power of the inverter in the M-level management node, including: Randomly generate n particles, and form a particle group based on the n particles; wherein the position of each particle represents the reactive power compensation capacity of the grid-connected inverter circuit, and the search space dimension of each particle is m; Under the constraints, the fitness value of each particle for the objective function in each search space dimension is calculated; wherein the objective function is used to minimize the sum of the voltage deviation and reactive power compensation capacity of the M-level governance nodes; Based on the fitness value of the particle for the objective function, the individual extreme value and the group extreme value of the particle are updated, and if the end condition is not met, the step of calculating the fitness value of each particle for the objective function in each search space dimension under the constraint condition is returned to the execution step, until the end condition is met, and the final optimal position of the particle and the optimal position of the particle group are output as the optimization solution result; The optimal position of the particle represents the optimal reactive power compensation capacity of the grid-connected inverter circuit, and the optimal position of the particle group represents the optimal reactive power compensation capacity of the grid-connected inverter circuit corresponding to the particle group.

4. The method according to claim 3, characterized in that The method further comprises: Acquire a sensitivity factor of each node in the distribution network system, wherein the sensitivity factor is positively correlated with the sensitivity of the grid-connected inverter circuit to which the node is connected to voltage deviation; For each node, determine the node importance weight coefficient according to the node sensitivity factor; The importance weight coefficient of each node is introduced into the calculation process of the voltage deviation of the M-level governance nodes in the objective function.

5. The method according to claim 3, characterized in that: The constraints include power balance flow constraints of the distribution network and capacity upper limit constraints of each grid-connected inverter circuit; Among them, the power balance flow constraints of the distribution network include the injected active power of the i-th node and the injected reactive power of the i-th node; The injected active power of the i-th node is equal to the injected active power of the i-1-th node minus the active power loss of the line of the i-th node minus the active power of the load of the i-th node; The injected reactive power of the ith node is equal to the injected reactive power of the i-1th node minus the reactive power loss of the line of the ith node minus the reactive power of the load of the ith node.

6. The method according to claim 5, characterized in that The capacity upper limit constraints of each grid-connected inverter circuit include: In the case where the grid-connected inverter circuit includes an energy storage array, the actual charging reactive power of the energy storage array is within a first charging reactive power range, and the actual discharging reactive power of the energy storage array is within a first discharging reactive power range; wherein the first charging reactive power range is determined based on the charging state value of the energy storage array, the actual charging active power of the energy storage array, the maximum power factor of the energy storage array, and the minimum power factor of the energy storage array; wherein the first discharging reactive power range is determined based on the discharging state value of the energy storage array, the actual discharging active power of the energy storage array, the maximum power factor, and the minimum power factor; In the case where the grid-connected inverter circuit includes a photovoltaic array, the reactive power output by any photovoltaic device in the photovoltaic array is within a first reactive power range; wherein the first reactive power range is determined based on the maximum active power that the photovoltaic device can output, the rated capacity of the photovoltaic device, the maximum power factor of the photovoltaic device, and the minimum power factor of the photovoltaic device; When the grid-connected inverter circuit includes a charging pile array, the remaining capacity of the inverter in the dispatchable vehicle is equal to the first capacity, and the remaining capacity of the inverter in the non-dispatchable vehicle is equal to the second capacity; wherein the first capacity is determined based on the maximum charging power of the electric vehicle and the maximum value of the inverter capacity in the electric vehicle; wherein the second capacity is determined based on the upper energy boundary, the lower energy boundary and the maximum value of the inverter capacity in the electric vehicle.

7. A management device for a distribution network system, characterized in that: The distribution network system includes a plurality of nodes, at least one of which is connected to a grid-connected inverter circuit; the device includes: The node confirmation module is used to, if a node with abnormal voltage deviation is detected in the distribution network system, take the node with abnormal voltage deviation as the starting governance node, and determine the N-level governance node of the starting governance node according to the connection relationship between the starting governance node and other nodes in the distribution network system; wherein N is a natural number greater than zero; A regulating module, used for regulating the voltage deviation of the starting management node based on the grid-connected inverter circuit connected to the starting management node, and obtaining the voltage deviation of each management node in the N-level management nodes after the voltage deviation of the starting management node is regulated; The regulating module is further used for, if the voltage deviation of the starting governance node is still greater than the preset voltage deviation, then, for the M-level governance node among the N-level governance nodes, using a particle swarm algorithm to optimize the reactive compensation capacity of the grid-connected inverter circuit connected to the M-level governance node, and adjusting the voltage deviation of the M-level governance node according to the optimization result; wherein the starting value of M is 1 and the ending value is N; The adjustment module is further used for, when the iteration stop condition is not met, adding 1 to M, and returning to the step of adjusting the voltage deviation of the M-level governance node among the N-level governance nodes, until the iteration stop condition is met; Among them, the iteration stop condition includes: the voltage deviation of each management node in the distribution network system is less than or equal to the preset voltage deviation, and / or M reaches the termination value when the voltage deviation of the starting management node is still greater than the preset voltage deviation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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