A method, apparatus, electronic device and storage medium for coordinated optimization of main and distribution networks

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for coordinated optimization of a main distribution network. The method includes: acquiring basic data on the power grid's sources, grid, load, and storage; constructing a basic operating model of the main distribution network based on this data; determining the electrical multidimensional characteristic indicators of distributed photovoltaic (PV) units within the main distribution network according to the basic operating model, and dividing each PV unit into at least one distributed PV cluster based on these indicators; determining the fault ride-through severity index of each distributed PV cluster based on main grid fault disturbance conditions, and selecting corresponding coordinated candidate schemes for each PV cluster according to the fault ride-through severity index; constructing a coordinated parameter decision model corresponding to the candidate schemes; and determining the corresponding dispatch control parameters for the main distribution network based on the coordinated parameter decision model. Optimizing grid dispatch by quantifying the fault ride-through severity can improve the reliability of grid dispatch.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to a method, apparatus, electronic device, and storage medium for coordinated optimization of main and distribution networks. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power in distribution networks continues to increase, some areas are exhibiting operational characteristics such as intertwined power sources, bidirectional power flow, and significantly enhanced voltage fluctuations. Distributed PV is connected to the grid via power electronic inverters, and its fault ride-through process is influenced by the depth and duration of voltage sag at the grid connection point, control mode switching, and the allocation of voltage regulation resources. When short-circuit faults, generator shedding, load shedding, or voltage surges occur in the main grid, the response characteristics of different distributed PV clusters vary significantly. Without a unified severity assessment and graded response mechanism, problems such as slow local voltage recovery, reactive power mismatch, and disordered voltage regulation resource allocation can easily arise.

[0003] In existing technologies, one type of method focuses on the transient voltage stability assessment of new energy power systems, which can reflect the stable state of the system from the perspective of voltage drop and control switching, but is usually not effectively combined with the main distribution network coordinated control strategy; another type of method focuses on the division of distributed photovoltaic clusters, distribution network zone voltage regulation or source-grid-load-storage coordinated optimization, which can improve voltage quality under high proportion of photovoltaic access conditions, but lacks quantitative characterization of fault ride-through risk level, and is difficult to implement differentiated topology adjustment and resource coordinated control according to the severity of fault.

[0004] Therefore, there is an urgent need to propose a method that can assess the severity of fault travel, divide distributed photovoltaic clusters, and optimize the main distribution network in a coordinated manner, so that the control actions under different fault disturbance conditions have clear assessment basis and hierarchical response logic, thereby improving the safety, economy and fault recovery capability of the coordinated operation of the main distribution network. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for coordinated optimization of main and distribution networks, in order to solve the problem that the existing fault ride-through state is disconnected from the main and distribution network control process, resulting in the underutilization of the grid regulation characteristics.

[0006] According to one aspect of the present invention, a method for coordinated optimization of a main distribution network is provided, wherein the method includes: Obtain basic data on power generation, grid, load and storage of the main distribution network, and construct a basic operation model of the main distribution network based on the basic data on power generation, grid, load and storage; Based on the basic operation model of the main distribution network, the electrical multidimensional characteristic indicators of the distributed photovoltaic units in the main distribution network are determined, and each of the distributed photovoltaic units is divided into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators. Based on the main grid fault disturbance conditions, the fault ride-through severity index of each of the distributed photovoltaic clusters is determined, and the corresponding collaborative candidate scheme of the distributed photovoltaic cluster is selected according to the fault ride-through severity index. Construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the scheduling control parameters corresponding to the main distribution network based on the collaborative parameter decision model; The main distribution network is optimized collaboratively based on the scheduling and control parameters, and the results of the collaborative optimization of the main distribution network are then subject to rolling correction.

[0007] According to another aspect of the present invention, a main distribution network collaborative optimization device is provided, wherein the device comprises: The model building module is used to acquire basic data of power generation, grid, load and storage of the main distribution network, and to build a basic operation model of the main distribution network based on the basic data of power generation, grid, load and storage. The cluster partitioning module is used to determine the electrical multidimensional characteristic indicators of the distributed photovoltaic units in the main distribution network according to the basic operation model of the main distribution network, and to partition each of the distributed photovoltaic units into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators. The scheme selection module is used to determine the fault ride-through severity index of each of the distributed photovoltaic clusters based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate schemes of the distributed photovoltaic clusters according to the fault ride-through severity index. The optimization execution module is used to construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the scheduling control parameters corresponding to the main distribution network based on the collaborative parameter decision model. The rolling correction module is used to perform collaborative optimization of the main distribution network based on the scheduling control parameters, and to perform rolling correction on the collaborative optimization results of the main distribution network.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the main distribution network collaborative optimization method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the main distribution network coordinated optimization method according to any embodiment of the present invention.

[0010] The technical solution of this invention collects basic data on the power generation, grid, load, and storage of the main distribution network, constructs a basic operation model of the main distribution network corresponding to the basic data, determines the electrical multi-dimensional characteristic indicators of distributed photovoltaic (PV) units within the main distribution network according to the basic operation model, and divides each distributed PV unit into different distributed PV clusters according to the electrical multi-dimensional characteristic indicators. Based on the main grid fault disturbance conditions, it determines the fault ride-through severity index corresponding to each distributed PV cluster, selects collaborative candidate schemes corresponding to the distributed PV clusters based on the fault ride-through severity index, determines the scheduling control parameters corresponding to the main distribution network according to the collaborative parameter decision model of the collaborative candidate schemes, performs collaborative optimization of the main distribution network according to the scheduling control parameters, and performs rolling correction on the collaborative optimization results of the main distribution network. This invention quantifies the fault ride-through severity index of distributed PV clusters corresponding to the main distribution network, guides grid collaborative optimization according to the quantified fault ride-through index, improves the decision reliability of grid control, enhances the stability of the grid after a fault, forms a closed-loop control chain for decision correction, enhances resource allocation efficiency, and improves the speed of grid fault recovery.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a main distribution network collaborative optimization method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of another main distribution network collaborative optimization method provided by Embodiment 2 of the present invention; Figure 3 This is a flowchart of another main distribution network collaborative optimization method provided in Embodiment 3 of the present invention; Figure 4 This is a flowchart of another main distribution network collaborative optimization method provided in Embodiment 4 of the present invention; Figure 5 This is an example diagram of a main distribution network collaborative optimization method provided in Embodiment 5 of the present invention; Figure 6This is an example diagram of a fault passage severity assessment and classification process provided in Embodiment 5 of the present invention; Figure 7 This is a schematic diagram of a main distribution network topology-voltage collaborative optimization framework provided in Embodiment 5 of the present invention; Figure 8 This is a schematic diagram of the structure of a main distribution network collaborative optimization device according to Embodiment Six of the present invention; Figure 9 This is a schematic diagram of the structure of an electronic device that implements the main distribution network collaborative optimization method of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a main distribution network collaborative optimization method according to Embodiment 1 of the present invention. This embodiment is applicable to the scenario of main distribution network topology-voltage collaborative optimization of distributed photovoltaic clusters. This method can be executed by a main distribution network collaborative optimization device, which can be implemented in hardware and / or software, and can be configured in a server or main control unit. Figure 1 As shown, the method includes: Step 110: Obtain the basic data of power generation, grid, load and storage of the main distribution network, and construct the basic operation model of the main distribution network based on the basic data of power generation, grid, load and storage.

[0017] The main distribution network can be a distributed photovoltaic (PV) cluster main distribution network, which can treat the high-voltage main grid, medium- and low-voltage distribution network, and massive distributed PV as a "single network" for coordinated operation, solving the problems of bidirectional power flow, voltage over-limit, absorption, and voltage regulation under high-proportion PV access. The main distribution network can include the main grid, distribution network, PV clusters, etc. According to the hierarchical control architecture, the main distribution network can include the main grid, substations, feeders, and end nodes. The grid source-grid-load-storage basic data can be collected at various points within the main distribution network. The grid source-grid-load-storage basic data can include the load parameters of the main grid lines, substations, feeders, tie switches, and each node, such as network topology data, line parameters, distributed PV access location and capacity data, inverter control type and fault ride-through parameter data, energy storage and reactive power compensation device operation data, etc.

[0018] In this embodiment of the invention, network topology data, line parameters, distributed photovoltaic access location and capacity data, inverter control type and fault ride-through parameter data, and energy storage and reactive power compensation device operation data of the main and distribution networks can be collected as basic data of the power grid source-grid-load-storage system. A basic operation model of the main and distribution networks can be constructed based on the collected basic data of the power grid source-grid-load-storage system. This basic operation model of the main and distribution networks can reflect the topology of the main and distribution networks and the state data of each grid node at different times. For example, the basic operation model of the main and distribution networks can be implemented in the form of topology graph model, time series model, power flow graph model, knowledge graph, etc.

[0019] Step 120: Determine the electrical multidimensional characteristic indicators of distributed photovoltaic units in the main distribution network based on the basic operation model of the main distribution network, and divide each distributed photovoltaic unit into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators.

[0020] Among them, a distributed photovoltaic unit can be the smallest photovoltaic power generation system in the main distribution network with independent grid connection, power regulation, and status monitoring capabilities, while the electrical multidimensional characteristic indicators can include electrical dimension indicators of the distributed photovoltaic unit such as voltage sensitivity, electrical distance, and voltage regulation potential.

[0021] In this embodiment of the invention, the electrical multidimensional characteristic indicators of each distributed photovoltaic unit in the main distribution network can be statistically analyzed according to the basic operation model of the main distribution network. Based on the multidimensional characteristic indicators of each distributed photovoltaic unit, each distributed photovoltaic unit can be divided into one or more distributed photovoltaic clusters. For example, the multidimensional characteristic indicators can be classified according to specific indicator values, and the distributed photovoltaic units corresponding to each category of multidimensional characteristic indicators can be respectively regarded as a distributed photovoltaic cluster. Alternatively, the multidimensional characteristic indicators of each distributed photovoltaic unit can be clustered, and the distributed photovoltaic units corresponding to each cluster in the clustering results can be respectively regarded as a distributed photovoltaic cluster.

[0022] Step 130: Determine the fault traversal severity index of each distributed photovoltaic cluster based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate scheme for the distributed photovoltaic cluster according to the fault traversal severity index.

[0023] Among them, the main grid fault disturbance conditions can be the causes of deviations in system voltage, frequency, power flow, phase angle, etc. from the normal steady-state operating point caused by the main grid. The main grid fault disturbance conditions can include faults, equipment switching, power supply or load changes, etc. The fault ride-through severity index can be a quantitative indicator that measures the ability of the power grid to operate continuously without disconnecting from the grid as required and to provide dynamic support. The fault ride-through severity index can indicate the threat level of system equipment safety, grid connection stability and grid cascading disconnection risk. The fault ride-through severity index can be jointly determined by information such as the voltage deviation degree of the main distribution network, fault type, fault duration, fault electrical distance, etc.

[0024] In this embodiment of the invention, a fault ride-through severity index for distributed photovoltaic (PV) clusters can be generated according to the main grid fault disturbance conditions. For example, the fault disturbance boundary can be determined according to the main grid fault disturbance conditions. Based on the fault disturbance boundary and the basic operation model of the main distribution network, simulation or calculation can be performed to determine the voltage, frequency, fault timing, etc., of each distributed PV cluster. Quantitative values ​​generated based on information such as voltage, frequency, and fault timing can be used as the fault ride-through severity index for the distributed PV cluster. For each distributed PV cluster, a corresponding collaborative candidate scheme can be selected according to the obtained fault ride-through severity index. This collaborative candidate scheme can be a control strategy scheme that optimizes the distributed PV cluster. The collaborative candidate scheme can include different optimization operations, such as switching operations, transformer tap adjustment, capacitor bank switching, and distribution network reconfiguration. It is understood that collaborative candidate schemes can correspond to different fault ride-through severity levels, and a corresponding collaborative candidate scheme can be matched for the distributed PV cluster according to the fault ride-through severity index.

[0025] Step 140: Construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the dispatch control parameters corresponding to the main distribution network based on the collaborative parameter decision model.

[0026] Among them, the collaborative parameter decision model can be a model used to determine the scheduling control parameters. The collaborative parameter decision model can be composed of the objective function of the scheduling control parameters and the constraints. The collaborative parameter decision model can correspond to the collaborative candidate scheme.

[0027] In this embodiment of the invention, for each distributed photovoltaic cluster, a corresponding objective function of the collaborative parameter decision model is constructed and corresponding constraints are determined. The objective function and constraints can be used as the collaborative parameter decision model, and the objective function within the collaborative parameter decision model can be solved according to the constraints, thereby obtaining the scheduling control parameters for optimizing the main distribution network.

[0028] Step 150: Perform coordinated optimization of the main distribution network based on the scheduling control parameters, and perform rolling correction on the coordinated optimization results of the main distribution network.

[0029] Specifically, the main distribution network can be regulated according to the scheduling control parameters of each distributed photovoltaic cluster, thereby improving the operating status of the distributed photovoltaic cluster. After regulating the main distribution network according to the scheduling control parameters, the collaborative optimization results of the main distribution network can be collected. The scheduling control parameters for the next cycle can be determined according to the collaborative optimization results. If the scheduling control parameters for the next cycle do not meet the conditions for rolling correction, the main distribution network can be re-optimized according to steps 110-150. If the scheduling control parameters for the next cycle meet the conditions for rolling correction, the main distribution network can be optimized according to the scheduling control parameters generated by the collaborative optimization results, so as to reduce the waiting delay for determining the scheduling control parameters and improve the optimization efficiency of the main distribution network.

[0030] This invention, in its embodiments, collects basic data on the power grid's sources, grid, load, and storage, and constructs a basic operating model of the main distribution network corresponding to this data. Based on this model, it determines the multi-dimensional electrical characteristics of distributed photovoltaic (PV) units within the main distribution network and divides each PV unit into different distributed PV clusters. Based on main grid fault disturbance conditions, it determines the fault ride-through severity index corresponding to each distributed PV cluster. The fault ride-through severity index selects collaborative candidate schemes for the distributed PV clusters. Following the collaborative parameter decision model of the collaborative candidate schemes, it determines the scheduling and control parameters corresponding to the main distribution network. The main distribution network is then collaboratively optimized according to these parameters, and the results of this optimization are continuously corrected. This invention quantifies the fault ride-through severity index of distributed PV clusters corresponding to the main distribution network. Using this quantified index to guide grid collaborative optimization improves the reliability of grid control decisions, enhances grid stability after a fault, forms a closed-loop control chain for decision correction, improves resource allocation efficiency, and increases grid fault recovery speed.

[0031] Example 2 Figure 2 This is a flowchart of another main distribution network collaborative optimization method provided by Embodiment 2 of the present invention. This embodiment of the present invention describes the construction process of the basic operation model of the main distribution network. See [link to documentation]. Figure 2 The method provided in this embodiment of the invention specifically includes the following steps: Step 210: Collect network topology data, line parameters, distributed photovoltaic access locations, capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network as basic data for power grid source-grid-load-storage.

[0032] In this embodiment of the invention, network topology data, line parameters, distributed photovoltaic (PV) grid connection locations, capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data can be collected from the main distribution network as basic data for the power grid's power generation, grid-load, and energy storage systems. Specifically, network topology data can be basic data describing the physical connections between grid nodes, branches, and equipment within the main distribution network. This data may include node information, branch connections, switchgear status, and network partitioning. Line parameters can be parameters representing the electrical and physical characteristics of transmission lines, cables, overhead conductors, etc., within the main distribution network. These parameters may include line length, conductor type, conductor diameter, electrical steady-state parameters, and limit parameters. Distributed PV grid connection locations can indicate the grid connection locations of distributed PV nodes within the main distribution network. These locations may include the PV grid connection point number, the connected power supply level, and geographical location. Capacity data can be the rated installed capacity of a single distributed PV unit, and may include the maximum active power output upper limit or minimum output lower limit, and the rated power range. Inverter control type can reflect the operation control mode or adjustment strategy of grid-connected inverters within the main distribution network. Inverter control type can include active power control mode, reactive power control mode, grid-connected mode, etc. Fault ride-through parameters can be the action threshold, response characteristics, and constraint parameters for maintaining grid connection during voltage dips, short circuits, and disturbance faults in the main distribution network. Fault ride-through parameters can include fault voltage threshold, fault duration tolerance time, and voltage recovery rate during the fault recovery phase. Energy storage information can be relevant information about grid-connected energy storage units within the main distribution network. Energy storage information can include energy storage unit number, rated capacity, voltage level, type, and state of charge, etc. Reactive power compensation device operation data can be the real-time operating status data of reactive power compensation devices within the main distribution network. Reactive power compensation devices can include parallel capacitors, reactors, SVG, SVC, static var generators, etc. Reactive power compensation device operation data can include basic device information, real-time operating data, and control and protection parameters, etc.

[0033] Step 220: Determine the topological connection relationship between each grid device in the main distribution network based on network topology data, line parameters and distributed photovoltaic access location. Construct the nodes and node connection relationship of the basic operation model of the main distribution network according to the topological connection relationship of each grid device. The grid devices include the main grid equivalent boundary node, substation, distribution network common connection node, feeder node and terminal node.

[0034] In this embodiment of the invention, the power grid equipment can be various devices within the main distribution network. These devices may include main grid equivalent boundary nodes, substations, distribution network common connection nodes, feeder nodes, and terminal nodes. Main grid equivalent boundary nodes can be equivalent power sources and impedances on the boundary of the main power grid, thereby reducing the simulation and computational scale. Distribution network common connection nodes can be the electrical demarcation points of various distribution networks.

[0035] Specifically, the topological connection relationships of various electrical devices within the main distribution network can be determined by using network topology data, line parameters, and the location of distributed photovoltaic access. Each electrical device can be used as a node in the basic operation model of the main distribution network, and the topological connection relationships of the electrical devices can be set as the node connection relationships corresponding to the nodes. The topology structure of the basic operation model of the main distribution network can be constructed through the nodes and node connection relationships. It can be understood that the basic operation model of the main distribution network can use the main network equivalent boundary nodes as the upper-level interface and the distribution network common connection points and feeder nodes as the downlink control objects to construct a multi-layer model. Furthermore, the basic operation model of the main distribution network can be described in the form of distribution network branch power flow.

[0036] Step 230: Map the capacity data, inverter control type, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network to the corresponding nodes of the main distribution network basic operation model according to the corresponding grid equipment.

[0037] In this embodiment of the invention, the collected capacity data, inverter control type, fault ride-through parameters, energy storage information, and reactive power compensation device operation data can be mapped to the corresponding nodes of the main distribution network basic operation model according to their respective grid equipment, thereby serving as the association information of the corresponding nodes in the main distribution network basic operation model.

[0038] Step 240: Set branch power flow constraints and node voltage constraints for the basic operation model of the main distribution network according to the power flow exchange relationship, voltage-power correspondence relationship and voltage upper and lower limit relationship of the main distribution network.

[0039] In this embodiment of the invention, the power flow exchange relationship can be a mathematical and physical constraint relationship between adjacent nodes in the main distribution network and the bidirectional transmission and dynamic balance of active / reactive power formed by transmission lines. The power flow exchange relationship can be expressed as: ,in, and Branch roads Resistance and reactance, and Time periods branch road Active power and reactive power, and These are the squared voltage variables at the beginning and end nodes of the branch, respectively. The squared variable is the branch current.

[0040] Specifically, the voltage-power correspondence can indicate the relationship between branch power and node voltage within the main distribution network. This relationship can be specifically a second-order cone relationship, that is, the voltage-power correspondence can be expressed as: ,in, This is a second-order cone relationship term between branch power and node voltage; For the active power of the branch circuit, This refers to the reactive power of the branch circuit. The voltage upper and lower limits relationship can be expressed as the voltage upper and lower limits that each grid device within the main distribution network needs to meet. ,in, and They are nodes The lower and upper voltage limits, For time period node The voltage amplitude.

[0041] In this embodiment of the invention, the resistance, reactance, voltage, active power or reactive power of each grid device in the main distribution network can be obtained to construct the power flow exchange relationship, the power of the branch formed by each grid device and the node voltage can be used to construct the voltage-power correspondence relationship, and the upper and lower voltage limits of each grid device can be used to construct the voltage upper and lower limit relationship. The obtained power flow exchange relationship, voltage-power correspondence relationship and voltage upper and lower limit relationship can be used as the branch power flow constraint and node voltage constraint of the main distribution network basic operation model, so as to use the main distribution network basic operation model as the basis for subsequent simulation or calculation.

[0042] Step 250: Determine the electrical multidimensional characteristic indicators of distributed photovoltaic units in the main distribution network based on the basic operation model of the main distribution network, and divide each distributed photovoltaic unit into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators.

[0043] Step 260: Determine the fault traversal severity index of each distributed photovoltaic cluster based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate scheme for the distributed photovoltaic cluster according to the fault traversal severity index.

[0044] Step 270: Construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the dispatch control parameters corresponding to the main distribution network based on the collaborative parameter decision model.

[0045] Step 280: Perform coordinated optimization of the main distribution network based on the scheduling control parameters, and perform rolling correction on the coordinated optimization results of the main distribution network.

[0046] In this embodiment of the invention, network topology data, line parameters, distributed photovoltaic (PV) access locations, capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network are collected as basic data for the power grid's source-grid-load-storage system. The topology connections are determined according to the network topology data, line parameters, and distributed PV access locations. Based on these topology connections, the nodes and node connections of the main distribution network's basic operation model are constructed. The capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network are mapped to the corresponding nodes of the main distribution network's basic operation model according to the corresponding grid equipment. This determines the power flow exchange relationship, voltage-power correspondence, and voltage upper and lower limit relationship of the main distribution network. The basic operation model of the main distribution network is established using the voltage-power correspondence and voltage upper and lower limit relationships as branch power flow constraints and node voltage constraints. Based on this model, the electrical multidimensional characteristic indicators of distributed photovoltaic (PV) units within the main distribution network are determined, and each PV unit is divided into different distributed PV clusters according to these indicators. Based on the main grid fault disturbance conditions, the fault ride-through severity index corresponding to each PV cluster is determined. The fault ride-through severity index is used to select collaborative candidate schemes for the PV clusters. The collaborative parameter decision model of the collaborative candidate schemes is used to determine the corresponding dispatch control parameters for the main distribution network. The main distribution network is then collaboratively optimized according to these parameters, and the results of the collaborative optimization are continuously corrected. This embodiment of the invention constructs a basic operation model of the main distribution network based on grid source-grid-load-storage data at different times within the main distribution network. This provides a unified computing platform for subsequent main distribution network control optimization, improves the reliability of data simulation or calculation, enhances the stability of system optimization, and allows for rapid identification of affected regional nodes, key interconnection channels, and available voltage regulation resources after a fault occurs. This provides a unified data foundation for subsequent fault ride-through severity assessment and main distribution network topology-voltage collaborative optimization.

[0047] Example 3 Figure 3 This is a flowchart of another main distribution network collaborative optimization method provided by Embodiment 3 of the present invention. This embodiment of the present invention describes the process of dividing distributed photovoltaic clusters within the main distribution network. See [link to documentation]. Figure 3 The method provided in this embodiment of the invention specifically includes the following steps: Step 310: Obtain the basic data of power generation, grid, load and storage of the main distribution network, and construct the basic operation model of the main distribution network based on the basic data of power generation, grid, load and storage.

[0048] Step 320: Determine the influence relationship between the injected power change and the node voltage of each node according to the basic operation model of the main distribution network, and construct the power flow Jacobian matrix according to the influence relationship.

[0049] Among them, the change in injected power can be the change of injected active power over time. The injected active power can be the net active power injected into the power network after subtracting the active power consumed by all loads of a node from the active power generated by all power sources in the basic operation model of the main distribution network.

[0050] In this embodiment of the invention, the injected power change and node voltage of each node in the basic operation model of the main distribution network at different times can be statistically analyzed. The influence relationship between each node and the node voltage of other nodes according to its corresponding injected power change can be represented by the power flow Jacobian matrix. ,in, and These are the changes in node phase angle and voltage, respectively. and These represent the changes in active power injection and reactive power injection at the nodes, respectively. The trend Jacobian matrix can be used as a trend Jacobian matrix. H represents the sensitivity of active power injection to phase angle, N represents the sensitivity of active power injection to voltage, M represents the sensitivity of reactive power injection to phase angle, and L represents the sensitivity of reactive power injection to voltage.

[0051] Step 330: Construct the node voltage sensitivity matrix based on the inverse mapping of the power flow Jacobian matrix.

[0052] In this embodiment of the invention, the voltage sensitivity matrix can be determined based on the obtained power flow Jacobian matrix, and the inverse mapping of the power flow Jacobian matrix can be obtained: This allows us to extract the sub-blocks H, N, M, and L within the power flow Jacobian matrix, and solve for the inverse mapping of the power flow Jacobian matrix according to the sub-blocks H, N, M, and L. , , and The voltage sensitivity matrix is ​​constructed by extracting sub-blocks C and D from the inverse mapping of the power flow Jacobian matrix. It can be understood that sub-block C can be the sensitivity of active power injection to voltage amplitude, and sub-block D can be the sensitivity of reactive power injection to voltage amplitude. The sensitivities can be arranged according to their corresponding nodes to form the voltage sensitivity matrix.

[0053] Step 340: Determine the electrical distance between different nodes based on the node voltage sensitivity matrix.

[0054] In this embodiment of the invention, the electrical distance between different nodes can be determined according to the constructed node voltage sensitivity matrix. The electrical distance can be defined as the difference in the sensitivity of voltage amplitude between different nodes. The difference in voltage amplitude sensitivity can include the sensitivity of active power injection to voltage amplitude and / or the sensitivity of reactive power injection to voltage amplitude. It is understood that the above-mentioned difference in voltage amplitude sensitivity can be the difference or ratio of the sensitivity of active power injection to voltage amplitude between two nodes.

[0055] For example, electrical distance can be defined as: ,in, Represents a node i Active power injection to nodes i Sensitivity of voltage amplitude Represents a node i Active power injection to nodes j Sensitivity to voltage amplitude.

[0056] Step 350: Divide each node in the basic operation model of the main distribution network into at least one electrical zone according to each electrical distance.

[0057] In this embodiment of the invention, nodes in the basic operation model of the main distribution network can be classified according to the electrical distance between different nodes. The set of nodes corresponding to each classification result can be regarded as an electrical region. For example, K-means clustering can be performed according to the electrical distance between different nodes to obtain each electrical distance.

[0058] Step 360: For each electrical area, determine the clustering characteristics of distributed photovoltaic units within the electrical area. The clustering characteristics include at least one of the following: voltage regulation potential, clustering identification information, electrical distance characteristics, grid-connected voltage dynamic response characteristics, and low voltage ride-through parameter characteristics.

[0059] In this embodiment of the invention, for each electrical region, the voltage regulation potential, cluster identification information, electrical distance characteristics, grid-connected voltage dynamic response characteristics, low voltage ride-through parameter characteristics, etc. of each distributed photovoltaic unit in the electrical region can be statistically analyzed as clustering features.

[0060] The clustering identification information can be generated for all distributed photovoltaic units within each electrical area, and this clustering identification information can be the average electrical distance of each distributed photovoltaic unit within the electrical area. The voltage regulation potential can be quantitative information based on the regulation capabilities of reactive power compensation devices and energy storage resources within the electrical area. The voltage regulation potential can be: In the formula, For the voltage regulation potential of electrical region c, The voltage regulation capability of reactive power compensation resources within electrical zone c. Voltage regulation capability for energy storage or active power regulation resources within electrical zone c. and Let be the weight coefficient, and satisfy... .

[0061] The electrical distance characteristic can be the electrical distance of a distributed photovoltaic unit, while the grid-connected voltage dynamic response characteristic can include the voltage rise or fall time, voltage change rate, overshoot, adjustment time, and maximum dynamic voltage deviation of the distributed photovoltaic unit. The low voltage ride-through parameter characteristic can include the inverter type, current duration during faults, voltage recovery rate, reactive current support, and active current limit.

[0062] Step 370: Cluster each distributed photovoltaic unit in the electrical area based on the clustering characteristics to obtain at least one distributed photovoltaic cluster.

[0063] Specifically, for each electrical area, all distributed photovoltaic units within the electrical area can be clustered again according to their corresponding clustering characteristics, and the clustering results can be used as a distributed photovoltaic cluster.

[0064] Step 380: Determine the fault traversal severity index of each distributed photovoltaic cluster based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate scheme for the distributed photovoltaic cluster according to the fault traversal severity index.

[0065] Step 390: Construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the dispatch control parameters corresponding to the main distribution network based on the collaborative parameter decision model.

[0066] Step 3100: Perform coordinated optimization of the main distribution network based on the scheduling control parameters, and perform rolling correction on the coordinated optimization results of the main distribution network.

[0067] In this embodiment of the invention, basic data of the power grid source-grid-load-storage system of the main distribution network are acquired. Based on this data, a basic operation model of the main distribution network is constructed. The influence relationship between the injected power change and node voltage of each node in the basic operation model is determined. A power flow Jacobian matrix is ​​generated according to the influence relationship. A node voltage sensitivity matrix is ​​constructed based on the inverse mapping of the power flow Jacobian matrix. The electrical distance between nodes is generated according to the node voltage sensitivity matrix. Each node in the basic operation model of the main distribution network is divided into at least one electrical region according to the electrical distance. Clustering features are obtained for each distributed photovoltaic unit in the electrical region. The distributed photovoltaic units are clustered again in the electrical region according to the clustering features to obtain distributed photovoltaic clusters. The fault ride-through severity index corresponding to each distributed photovoltaic cluster is determined based on the main grid fault disturbance conditions. The fault ride-through severity index selects the corresponding collaborative candidate scheme for the distributed photovoltaic cluster. The collaborative parameter decision model of the collaborative candidate scheme is used to determine the dispatch control parameters corresponding to the main distribution network. The main distribution network is collaboratively optimized according to the dispatch control parameters, and the collaborative optimization results of the main distribution network are rolled over. This invention determines the electrical distances between nodes in the basic operation model of the main distribution network based on the inverse mapping of the power flow Jacobian matrix. This reduces the difficulty of solving for electrical distances and improves the computational efficiency of the model. By using electrical distances, distributed photovoltaic (PV) units within the main distribution network are divided into different distributed PV clusters. Units with similar electrical coupling characteristics, voltage dynamic response, and low-voltage ride-through behavior can be grouped together. This enables local coordinated voltage regulation, accurately suppressing voltage fluctuations and transient overvoltages, and improving the voltage control effect of the distribution network. It also ensures that PV units within the same group maintain synchronized operation during grid faults, enhancing low-voltage ride-through capability and system transient stability, and suppressing the large-scale spread of disturbances and oscillations. By dividing the PV units into distributed clusters, equivalent modeling of PV units is achieved, effectively reducing the simulation computation scale, improving computational accuracy, simplifying communication architecture and operation and maintenance management, reducing cross-regional data interaction pressure, and comprehensively optimizing the operation, control, simulation, and maintenance level of distributed PV clusters.

[0068] Furthermore, based on the above embodiments of the invention, determining the electrical distance between different nodes based on the node voltage sensitivity matrix includes: The electrical influence degree between different nodes is determined according to the node voltage sensitivity matrix; within each electrical influence degree, the target electrical influence degree of any two nodes in the main distribution network basic operation model corresponding to the third node is determined; for each third node, the difference between the target electrical influence degrees of the two nodes is determined; the average value of the difference corresponding to each third node is taken as the electrical distance between the two nodes.

[0069] In this embodiment of the invention, the electrical influence degree between different nodes can be determined according to the node voltage sensitivity matrix, and this electrical influence degree can be defined as: ,in, Represents the node under operating condition π i Active power injection to nodes i Sensitivity of voltage amplitude Represents the node under operating condition π i Active power injection to nodes j The sensitivity of voltage amplitude. Based on the determined electrical influence between different nodes, within the obtained electrical influence, the electrical influence of any two nodes on the same third node can be recorded as the target electrical influence. Any two nodes currently determining the electrical distance can be recorded as node A and node B. The third node can be any other node in the main distribution network basic operation model other than the initial nodes A and B. For each third node, the difference between the target electrical influence of node A and node B can be obtained. The average of the differences in the target electrical influence of each third node can be used as the electrical distance between node A and node B. Based on the above process, the electrical distance between all nodes in the main distribution network basic operation model can be determined.

[0070] In other embodiments of the invention, the fault ride-through severity index of each distributed photovoltaic cluster is determined based on the main grid fault disturbance conditions, and the corresponding collaborative candidate schemes for the distributed photovoltaic clusters are selected according to the fault ride-through severity index, including: Obtain pre-configured main grid fault disturbance conditions, which include at least one of the following: short-circuit fault conditions, node tripping fault conditions, power disturbance conditions, voltage regulation equipment disturbance conditions, system oscillation disturbance conditions, and extreme operating condition disturbance conditions. Distributed photovoltaic clusters are disturbed according to the main grid fault disturbance conditions, and the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and key node influence coefficient of the distributed photovoltaic clusters are collected. The normalized weighted sum of the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and key node influence coefficient of each distributed photovoltaic cluster is used as the fault ride-through severity index. For each distributed photovoltaic cluster, a collaborative candidate scheme matching the fault ride-through severity index is selected, where the collaborative candidate scheme includes at least one of the following: main grid interconnection channel switching strategy and distribution network interconnection switch reconfiguration strategy.

[0071] In this embodiment of the invention, at least one of the pre-configured short-circuit fault conditions, node tripping fault conditions, power disturbance conditions, voltage regulation equipment disturbance conditions, system oscillation disturbance conditions, and extreme operating condition disturbance conditions can be obtained as the main grid fault disturbance condition. Examples of the above main grid fault disturbance conditions are shown in the table below: In this embodiment of the invention, distributed photovoltaic (PV) clusters can be simulated according to pre-configured main grid fault conditions to determine their operating status information under the corresponding main grid fault conditions. This operating status information may include minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and key node influence coefficient. The collected operating status information, including minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and key node influence coefficient, can be normalized and weighted to serve as a fault ride-through severity indicator. Each collaborative candidate scheme can be configured with its own corresponding fault ride-through severity indicator range or fault ride-through severity indicator level. The corresponding collaborative candidate scheme can be matched according to the determined fault ride-through severity indicator of the distributed PV cluster. This allows for the switching of the main grid connection channel and / or the reconfiguration of the distribution network connection switch within the collaborative candidate scheme, thereby adjusting the topology of the main grid and / or distribution network of the distributed PV cluster.

[0072] Example 4 Figure 4 This is a flowchart of another main and distribution network collaborative optimization method provided in Embodiment 4 of the present invention. The embodiments of the present invention illustrate the closed-loop control process of scheduling. See [link to documentation]. Figure 4 The method provided in this embodiment of the invention specifically includes the following steps: Step 410: Obtain the basic data of power generation, grid, load and storage of the main distribution network, and construct the basic operation model of the main distribution network based on the basic data of power generation, grid, load and storage.

[0073] Step 420: Determine the electrical multidimensional characteristic indicators of distributed photovoltaic units in the main distribution network based on the basic operation model of the main distribution network, and divide each distributed photovoltaic unit into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators.

[0074] Step 430: Determine the fault traversal severity index of each distributed photovoltaic cluster based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate scheme for the distributed photovoltaic cluster according to the fault traversal severity index.

[0075] Step 440: For distributed photovoltaic clusters, generate the node voltage deviation item, network loss item, switching action cost item, and active power limitation item corresponding to the distributed photovoltaic cluster according to the collaborative candidate scheme.

[0076] In this embodiment of the invention, a corresponding collaborative candidate scheme can be obtained for each distributed photovoltaic cluster. The distributed photovoltaic cluster is simulated according to the collaborative candidate scheme. During the simulation, the impact of the distributed photovoltaic cluster on node voltage deviation, network loss, switching action cost, and active power limitation when executing the collaborative candidate scheme is determined. Based on the above impact on node voltage deviation, network loss, switching action cost, and active power limitation, a node voltage deviation item, a network loss item, a switching action cost item, and an active power limitation item are constructed.

[0077] Step 450: Use the weighted sum of the node voltage deviation term, network loss term, switching action cost term, and active power limitation term as the objective function of the collaborative parameter decision model, and construct the constraints of the collaborative parameter decision model according to the energy storage information of the distributed photovoltaic cluster, the inverter control type, and the reactive power compensation device operation data.

[0078] In this embodiment of the invention, the weighted sum of the node voltage deviation term, network loss term, switching action cost term, and active power limitation term can be used as the objective function of the collaborative parameter decision model. Constraints for the collaborative parameter decision model are constructed based on the energy storage information, inverter control type, and reactive power compensation device operation data of the distributed photovoltaic cluster. These constraints can include distributed photovoltaic inverter constraints, energy storage constraints, on-load tap-regulating inverter constraints, and reactive power compensation device constraints. The distributed photovoltaic inverter constraint can be expressed as: , For time period node Distributed photovoltaic power generation has active power output; For time period node The maximum available active power output of distributed photovoltaic power; For time period node Distributed photovoltaic reactive power output; For nodes Apparent power capacity of distributed photovoltaic inverters.

[0079] Energy storage constraints can be expressed as: In the formula For time period node Energy storage charging power; For nodes Maximum charging power of energy storage; It is a binary variable representing the energy storage charging state. For time period node Energy storage discharge power; For nodes Maximum discharge power of energy storage; It is a binary variable representing the energy storage discharge state. This is a charging state variable; The state variable is the discharge state variable; this formula indicates that energy storage cannot be charged and discharged simultaneously in the same period. For time period node Energy storage state of charge; and These are the energy storage charging efficiency and the discharging efficiency, respectively. To control the cycle. and These are the lower and upper limits of the energy storage state of charge, respectively.

[0080] The constraints of an on-load tap-changing transformer can be expressed as follows: In the formula, For time period No. The turns ratio of an on-load tap-changing transformer; This is the initial ratio; For gear position variables; This refers to the single-gear shift ratio step size. The first The minimum and maximum tap positions of the on-load tap-changing transformer.

[0081] The constraints of reactive power compensation equipment can be expressed as follows: In the formula, For time period node Total output of the capacitor bank; The number of throw groups; This refers to the capacitance of a single capacitor bank. For nodes Maximum number of capacitor banks that can be switched on / off. (Period) node Output of the static var compensator; and These are its lower and upper limits of output, respectively.

[0082] Step 460: Solve the objective function according to the constraints to obtain the scheduling and control parameters of the corresponding distributed photovoltaic cluster.

[0083] In this embodiment of the invention, the objective function can be solved according to the constraints determined above. This solution process can be implemented through nonlinear programming, genetic algorithms, deep reinforcement learning models, etc., and the objective function can be expressed as: in, To comprehensively optimize the objectives; , , , These are the weights of the node voltage deviation term, network loss term, switching action cost term, and active power limitation cost term, respectively. For time period node Voltage; Reference voltage; branch road The resistance; The square of the branch current is a variable; Cost of switching action; For time period node The photovoltaic power generation is limited.

[0084] Specifically, the scheduling and control parameters of the corresponding distributed photovoltaic cluster can be obtained through the above objective function. These scheduling and control parameters may include information such as inverter reactive power output, active power generation limit, tap position of on-load tap changer, on-container switching status, sectionalizing switch operation scheme, and node voltage amplitude.

[0085] Step 470: Extract the scheduling and control parameters of the distributed photovoltaic clusters in the corresponding main distribution network, and schedule the distributed photovoltaic clusters according to the scheduling and control parameters.

[0086] In this embodiment of the invention, the distributed photovoltaic cluster can be adjusted according to the determined scheduling control parameters. For example, the reactive power output of the inverter, the active power generation limit, the tap position of the on-load tap changer, the switching status of the container, the operation scheme of the sectionalizing switch, and the node voltage amplitude can be adjusted to achieve optimized control of the distributed photovoltaic cluster. It is understood that each distributed photovoltaic cluster in the main distribution network can have its own corresponding scheduling control parameters, and each distributed photovoltaic cluster can be controlled according to the determined scheduling control parameters.

[0087] Step 480: Collect the changes in edge node voltage, tie-line power, and severity index of each distributed photovoltaic cluster after scheduling.

[0088] Specifically, the changes in edge node voltage, tie-line power, and severity index of the distributed cluster after scheduling can be collected. These changes can include the changes of each distributed photovoltaic cluster before and after optimization according to the scheduling control parameters, or the changes over a time interval after optimization by the scheduling control parameters.

[0089] Step 490: If the changes in edge node voltage, tie-line power, and severity index are less than the preset threshold, then generate the scheduling control parameters for the next cycle according to the current scheduling control parameters.

[0090] In this embodiment of the invention, the changes in edge node voltage, tie-line power, and severity index can be compared with corresponding preset thresholds. If the changes in edge node voltage, tie-line power, and severity index all meet the preset thresholds, the scheduling control parameters for the next cycle can be adjusted according to the current scheduling control parameters of the distributed photovoltaic cluster. This generation process can be implemented in the following way: For the first The optimization variable vector for each control cycle; For the first The optimization variable vector for each control cycle; The edge node voltage deviation vector; This is the tie-line power deviation vector; This represents the change in severity index; , , This refers to the feedback gain matrix or feedback gain coefficient.

[0091] In this embodiment of the invention, basic data on the power grid, source, grid, load, and storage of the main distribution network are acquired. A basic operation model of the main distribution network is constructed based on this data. Electrical multi-dimensional characteristic indicators of distributed photovoltaic (PV) units within the main distribution network are generated according to the basic operation model. Each distributed PV unit is divided into at least one distributed PV cluster according to these electrical multi-dimensional characteristic indicators. The fault ride-through severity index of each distributed PV cluster is determined based on the main grid fault disturbance conditions. Collaborative candidate schemes corresponding to the distributed PV clusters are selected according to the fault ride-through severity index. Node voltage deviation, network loss, switching action cost, and active power limitation are determined for each distributed PV cluster according to the collaborative candidate schemes. The weighted sum of these factors is used as the collaborative parameter decision model. The objective function is defined, along with constraints for constructing a collaborative parameter decision model based on the energy storage information, inverter control type, and reactive power compensation device operation data of the distributed photovoltaic (PV) cluster. The objective function is solved based on these constraints to obtain the corresponding scheduling control parameters for the distributed PV cluster. The distributed PV cluster is then scheduled according to these parameters. Changes in edge node voltage, tie-line power, and severity indicators for each distributed PV cluster are collected after scheduling. When the changes meet a preset threshold, scheduling control parameters for the next cycle are generated based on the current scheduling control parameters, thus achieving rolling correction of the distributed PV cluster. The optimization range of the distributed PV cluster is limited by a preset threshold, and matching scheduling control parameters are generated based on this optimization range. This reduces the computational burden of determining scheduling control parameters, thereby improving the efficiency of optimized scheduling of the distributed PV cluster.

[0092] Example 5 Figure 5 This is an example diagram of a main distribution network collaborative optimization method provided in Embodiment 5 of the present invention. This embodiment of the invention can realize main distribution network topology-voltage collaborative optimization based on fault ride-through severity assessment. See also... Figure 5 The method provided in this embodiment of the invention specifically includes the following steps: Step S1: Establish the basic operation model of the main and distribution networks. Collect parameters of the main grid lines, substations, feeders, tie switches, and loads at each node. Collect data on distributed photovoltaic access nodes, installed capacity, inverter type, low-voltage ride-through parameters, energy storage capacity, and reactive power compensation device configuration, and form a unified data interface. The basic operation model of the main and distribution networks uses the main grid equivalent boundary nodes as the upper-level interface and the distribution network common connection points and feeder nodes as the lower-level control objects.

[0093] The basic operation model of the main distribution network adopts the power flow form of the distribution network branches and introduces the following variables: in, For time period node The squared voltage variable, For time period node The voltage amplitude. For time period branch road The squared variable of the current; For time period branch road The current amplitude.

[0094] The basic operation model of the main distribution network can adopt the branch power flow form, and the branch power flow constraints are as follows: in, and Branch roads Resistance and reactance; and Time periods branch road Active power and reactive power; and These are the squared voltage variables at the beginning and end nodes of the branch, respectively; Let be the square variable of the branch current. The above formula must satisfy: , This is a second-order cone relationship term between branch power and node voltage; The active power of the branch circuit; This represents the reactive power of the branch circuit.

[0095] In this embodiment of the invention, the node voltages in the main distribution network basic operation model also satisfy upper and lower voltage limit constraints: ,in, and They are nodes The lower and upper voltage limits; For time period node The voltage amplitude.

[0096] The aforementioned basic operation model of the main distribution network can serve as a unified computing platform for subsequent cluster partitioning, severity assessment, and collaborative optimization.

[0097] In some embodiments of the invention, the basic operation model of the main and distribution networks uses the equivalent boundary of the main network as the upper-level interface and the common connection point and feeder node of the distribution network as the lower-level control objects to uniformly describe the electrical connection relationship and operating status of the main network interconnection channel, distribution feeder, distributed photovoltaic cluster, energy storage system, on-load tap-changing transformer, static var compensator and capacitor bank.

[0098] The aforementioned equivalent boundary nodes of the main grid are used to characterize the voltage support capability, short-circuit capacity level, and tie-line power flow exchange characteristics of the study area, and serve as information exchange interfaces for topology adjustment on the main grid side and coordinated control on the distribution network side. Distribution network common connection points and feeder nodes are used to characterize the access location, power flow distribution, and voltage variation of distributed photovoltaic clusters and various voltage regulation resources, and serve as the direct targets for distribution network-side topology reconfiguration, voltage regulation, and rolling correction.

[0099] Based on the above-described embodiments of the invention, the construction process of the main distribution network basic operation model includes: First, establishing a hierarchical network structure of main network—substation—feeder—end node according to the main network wiring method, interconnection channel capacity, substation hierarchical relationship, and distribution network feeder topology; Second, completing the parameter mapping of source, network, load, storage, and compensation resources according to the load power, distributed photovoltaic installed capacity, inverter operation mode, energy storage state of charge, and reactive power compensation equipment output limit of each node; Then, forming an integrated network model that can be used for fault assessment and optimization control based on the power flow exchange relationship between the main and distribution networks and node voltage constraints.

[0100] The basic operation model of the main distribution network not only describes the static topology connections but also synchronously records the operating status information of each device within the current control cycle. This operating status information includes at least: main grid tie line power flow, transformer tap position, feeder switch status, active / reactive power output of distributed photovoltaic inverters, energy storage charging and discharging power, static var compensator output, and capacitor bank switching status. Through this operating status information, affected regional nodes, critical tie channels, and available voltage regulation resources can be quickly identified after a fault occurs, providing a unified data foundation for subsequent fault ride-through severity assessment and main distribution network topology-voltage coordinated optimization.

[0101] For the main grid portion, equivalent boundary node and tie-line parameters are used to describe the support and constraint effects of the main grid on the distribution network, thereby reducing the overall model size. For the distribution network portion, detailed electrical relationships of points of common coupling, feeder nodes, and distributed photovoltaic cluster access points are retained to ensure the accuracy of characterization of local voltage changes, power flow transfer, and fault propagation processes. This modeling approach can reflect the propagation characteristics of main grid fault disturbances to the distribution network while balancing model solution efficiency and engineering feasibility.

[0102] Step S2: Forming a distributed photovoltaic (PV) cluster. A two-layer cluster partitioning method is used to establish a voltage sensitivity matrix based on the impact of node injected power changes on node voltage, and the electrical distance between nodes is calculated. Then, a comprehensive index is constructed based on the grid connection point voltage response, low-voltage ride-through control parameters, inverter control methods, and the adjustment margin of energy storage and reactive power compensation resources within the cluster. A clustering algorithm is then used to form several PV clusters. Units with tight electrical coupling and similar fault ride-through dynamic characteristics are preferentially grouped into the same cluster.

[0103] In this embodiment of the invention, the definitions of each symbol are uniformly explained: the node set is denoted as The set of branches is denoted as The time period set is denoted as Node numbering adopts Cluster numbering uses Time period numbering uses The active and reactive power outputs of distributed photovoltaic systems are respectively denoted as... , Energy storage charging power and discharging power are respectively denoted as... , The output of the static var compensator is denoted as... The output of the capacitor bank is denoted as... The tap position of an on-load tap-changing transformer is denoted as... The fault ride severity index is uniformly denoted as: .

[0104] 1) The node sensitivity matrix can be determined in the following way: Based on the relationship between changes in node injected power and node voltage, an inverse mapping of the power flow Jacobian matrix is ​​established: In the formula, and These are the node phase angle and voltage change, respectively. and These represent the changes in active and reactive power injection at the nodes, respectively. The current Jacobian matrix is ​​used. Based on this, operating conditions can be constructed. The node voltage sensitivity matrix below: in, For working conditions The node voltage sensitivity matrix below; For matrix elements; The total number of nodes. for: Specifically, Represents a node Active power change for nodes The degree of influence of voltage amplitude; For nodes The voltage amplitude; For nodes Injecting active power; This refers to the operating conditions.

[0105] 2) The electrical distance between nodes can be determined in the following way: Nodes can be further defined based on voltage sensitivity. i For nodes j Electrical impact: , For working conditions Next node For nodes The degree of electrical impact; For nodes The coefficient of influence on its own voltage; For nodes For nodes Voltage influence coefficient. The electrical distance between node i and node j is determined based on the electrical influence degree as follows: in, For nodes With nodes Electrical distance; The total number of nodes; and They are nodes ,node For nodes The degree of electrical impact.

[0106] 3) Region division and region clustering identification number According to the electrical distance matrix The K-means algorithm is used to initially divide the power distribution area. For the first... Sub-regions Its squared error function can be defined as: in, The sum of squared errors for region partitioning; Number of regions; For the first One region; For the first One sample point; For the first Cluster centers for each region.

[0107] After the distributed cluster is partitioned into regions, the average electrical distance between nodes within each region is defined as the region cluster identifier. in, For the first The region cluster identification number for each area; For the region Number of nodes contained; For regional representative nodes and nodes within the region The electrical distance. In this embodiment of the invention, after the region is divided, the average electrical distance of nodes within the region is defined as the region cluster identification number, which serves as one of the features for subsequent joint clustering of distributed photovoltaic units.

[0108] 4) Potential for cluster voltage regulation To comprehensively consider the regulation capabilities of reactive power compensation devices and energy storage resources, voltage regulation potential can be defined for each cluster. For example, the voltage regulation potential of cluster c is: Specifically, For clusters Voltage regulation potential; Voltage regulation capability for reactive power compensation resources within the cluster; Voltage regulation capability for energy storage or active power regulation resources within the cluster; and Let be the weight coefficient, and satisfy... .

[0109] 5) Joint clustering By integrating regional cluster identifiers, electrical distances, grid connection point voltage dynamic response, inverter low-voltage ride-through parameters, and cluster voltage regulation potential, a joint characteristic unit for photovoltaic units is constructed: In the formula, For the first The joint feature vector of the distributed photovoltaic units; This is the cluster identifier for the region where the unit is located; Electrical distance characteristics; The dynamic response characteristics of the grid connection point voltage; Inverter type and low voltage ride-through parameter characteristics; This represents the potential characteristics for cluster voltage regulation.

[0110] Based on the aforementioned joint feature vector, the K-means clustering algorithm is used to jointly cluster the distributed photovoltaic units within the region. The clustering error function can be: in, The sum of squared errors in the joint clustering of distributed photovoltaic systems; Number of clusters; For the first A distributed photovoltaic cluster; For the first Cluster centers of each cluster.

[0111] Step S3, assess the severity of fault traversal, such as Figure 6 As shown, when disturbances such as three-phase short circuits, single-phase grounding, voltage drops, or generator tripping occur in the main grid, the minimum voltage at the grid connection point of each photovoltaic cluster, voltage recovery time, active power output loss of faulty devices, reactive power support deviation, and control switching fluctuation information are collected. After normalization processing, the severity index of each cluster is calculated. I sev And based on the threshold η 1 and η 2 Complete the classification of mild, moderate and severe.

[0112] In this embodiment of the invention, under the condition of main grid fault disturbance, the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and key node influence coefficient of each photovoltaic cluster grid-connected store are extracted, and a fault ride-through severity index is constructed.

[0113] The severity index for fault ride-through is: in, This is an indicator of fault passthrough severity. This is the normalized value of the minimum voltage amplitude deviation at the grid connection point; This is the normalized value for voltage recovery time; This is the normalized value of the active power loss rate; This is the normalized value of the reactive power support deviation; To control the normalized value of switching volatility; This represents the normalized value of the impact coefficient of key nodes; , , , , , These are the corresponding weights, and they satisfy... In the formula, , , , , , These are the weighting coefficients for each severity sub-indicator.

[0114] The minimum voltage amplitude deviation is: ,in, This is the normalized value of the minimum voltage amplitude deviation; This refers to the steady-state grid connection point voltage before the fault. This is the lowest voltage at the grid connection point during the fault period; This is the minimum permissible voltage threshold.

[0115] The normalized value for voltage recovery time is: ,in, This is the actual voltage recovery time. The reference time can be the upper limit of voltage recovery, the electromechanical time constant, or the characteristic time reference of fault disturbance, etc.

[0116] The active power loss rate is: ,in, This is the normalized value of the active power loss rate; This represents the steady-state active power output before the fault. This represents the average active power output during the fault period or within a short time window after the fault is cleared.

[0117] The reactive power support deviation is: , This is the normalized value of the reactive power support deviation; This is the reference reactive power support required by the fault ride-through strategy; This represents the actual reactive power support.

[0118] Considering the repeated switching of inverter low-voltage ride-through control, traditional maximum Lyapunov exponent-type indices tend to oscillate around zero, making it difficult to accurately characterize the non-smooth dynamic characteristics caused by control switching. Therefore, the switching compensated Lyapunov exponent (SCMLE) is used to describe the control switching volatility. SCMLE corrects the calculation error at the control switching moment through the switching compensation matrix, making it more suitable for reflecting the transient voltage trajectory changes in LVRT switching scenarios.

[0119] Control switching volatility is: , To control the normalized value of switching volatility; To switch to compensate for the Lyapunov index; and These are the minimum and maximum values ​​in the selected sample set, respectively.

[0120] The impact coefficient of the key node is: In the formula, This represents the normalized value of the impact coefficient of key nodes; A set of key nodes; As a key node The weights; As a key node Voltage deviation; As a key node The reference voltage.

[0121] In this embodiment of the invention, the severity classification of the fault ride-through severity index can be as follows: ,in, This is an indicator of fault passthrough severity. Thresholds were set for mild and moderate faults; Thresholds were set for moderate and severe faults.

[0122] Step S4: Real-time main and distribution network topology-voltage collaborative optimization, such as... Figure 7 As shown, firstly, candidate schemes for switching main grid interconnection channels and reconfiguring distribution network interconnection switches are screened based on severity levels. Then, targeting node voltage deviation, network losses, switching operation costs, and active power curtailment, the active and reactive power outputs of distributed photovoltaic inverters, the charging and discharging power of energy storage systems, the tap positions of on-load tap changers, and the output of reactive power compensation equipment are jointly optimized. This step is performed in the order of main grid topology, distribution network topology, and cluster voltage collaborative optimization.

[0123] In this embodiment of the invention, the objective function for main and distribution network topology-voltage collaborative optimization is defined as: in, To comprehensively optimize the objectives; , , , These are the weights of the node voltage deviation term, network loss term, switching action cost term, and active power limitation cost term, respectively. For time period node Voltage; Reference voltage; branch road The resistance; The square of the branch current is a variable; Cost of switching action; For time period node The photovoltaic power generation is limited.

[0124] The cost of switching actions can be: in, , , These are the unit cost coefficients for switching operations, transformer tap adjustment, and capacitor bank switching, respectively. This refers to the change in the state of the circuit breaker. This refers to the change in tap position of an on-load tap-changing transformer. This represents the change in the number of capacitor banks switched on and off.

[0125] For distribution network reconfiguration, the topological feasible region constraint uses a virtual flow approach to ensure radial operation: in, It is a sufficiently large positive number; For nodes Whether it is a binary variable used as the root node; For time period branch road Virtual stream; For nodes A set of connected branches. For time period branch road The switch state is a binary variable; when the branch is closed... When disconnected . This represents the total number of nodes in the network. For branch set; A set of nodes; This is a variable used to identify the root node.

[0126] The constraints of distributed photovoltaic inverters can be expressed as: ,in, For time period node Distributed photovoltaic power generation has active power output; For time period node The maximum available active power output of distributed photovoltaic power; For time period node Distributed photovoltaic reactive power output; For nodes Apparent power capacity of distributed photovoltaic inverters.

[0127] Energy storage constraints can be expressed as follows: In the formula, For time period node Energy storage charging power; For nodes Maximum charging power of energy storage; It is a binary variable representing the energy storage charging state. For time period node Energy storage discharge power; For nodes Maximum discharge power of energy storage; It is a binary variable representing the energy storage discharge state. This is a charging state variable; The state variable is the discharge state variable; this formula indicates that energy storage cannot be charged and discharged simultaneously in the same period. For time period node Energy storage state of charge; and These are the energy storage charging efficiency and the discharging efficiency, respectively. To control the cycle. and These are the lower and upper limits of the energy storage state of charge, respectively.

[0128] The constraints of an on-load tap-changing transformer can be expressed as follows: For time period No. The turns ratio of an on-load tap-changing transformer; This is the initial ratio; For gear position variables; This refers to the single-gear shift ratio step size. The first The minimum and maximum tap positions of the on-load tap-changing transformer.

[0129] The constraints on reactive power compensation equipment are as follows: in, For time period node Total output of the capacitor bank; The number of throw groups; This refers to the capacitance of a single capacitor bank. For nodes Maximum number of capacitor banks that can be switched on / off. (Period) node Output of the static var compensator; and These are its lower and upper limits of output, respectively.

[0130] Step S5: Perform rolling correction and output control results. After each control cycle, collect changes in edge node voltage, tie-line power, and severity indicators to reassess the system status and update optimization variables. When the critical node voltage deviation, tie-line power deviation, and severity change are all less than preset thresholds in two adjacent control cycles, rolling correction is considered to have converged, and the final main distribution network reconfiguration scheme and voltage regulation resource control commands are output.

[0131] To improve the timing adaptability of the main and distribution network coordinated optimization, a rolling correction mechanism based on changes in edge node voltage, tie-line power, and severity is introduced. Its update rule is defined as follows: in, For the first The optimization variable vector for each control cycle; For the first The optimization variable vector for each control cycle; The edge node voltage deviation vector; This is the tie-line power deviation vector; This represents the change in fault passthrough severity. , , This refers to the feedback gain matrix or feedback gain coefficient.

[0132] The convergence criterion for rolling correction is defined as follows: in, For the first Voltage deviation at key nodes in the next control cycle; For the first Next control cycle tie-line power deviation; and These are the fault ride-through severity indices for two adjacent control cycles; , , These are the convergence thresholds for voltage deviation, tie-line power deviation, and severity variation, respectively.

[0133] In this embodiment of the invention, when the convergence criteria of rolling correction are met simultaneously, the rolling correction process is determined to have converged, and the main distribution network topology reconstruction scheme, inverter active / reactive power control command, energy storage charging and discharging command, on-load tap-changing transformer tap adjustment command, and reactive power compensation equipment switching command are output.

[0134] Based on the above embodiments of the invention, if a distribution area experiences a deep voltage drop and slow recovery at multiple photovoltaic cluster grid connection points after a main grid fault, it is determined to be a severe fault in step S3. Subsequently, in step S4, the main grid interconnection channel and the power supply path of the downstream substation are first adjusted, then the distribution network feeder is transferred and the interconnection switch is reconfigured, and finally the reactive power support of the inverter, the charging and discharging of energy storage and the tap position of the on-load tap changer are coordinated to achieve rapid voltage recovery and suppression of the fault's impact range.

[0135] Example 6 Figure 8 This is a schematic diagram of the structure of a main distribution network collaborative optimization device according to Embodiment Six of the present invention, as shown below. Figure 8As shown, the device includes: The model building module 510 is used to acquire the basic data of the power grid source, grid, load and storage of the main distribution network, and to build a basic operation model of the main distribution network based on the basic data of the power grid source, grid, load and storage.

[0136] The cluster division module 520 is used to determine the electrical multidimensional characteristic indicators of the distributed photovoltaic units in the main distribution network according to the basic operation model of the main distribution network, and to divide each of the distributed photovoltaic units into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators.

[0137] The scheme selection module 530 is used to determine the fault ride-through severity index of each of the distributed photovoltaic clusters based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate scheme of the distributed photovoltaic cluster according to the fault ride-through severity index.

[0138] The optimization execution module 540 is used to construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the scheduling control parameters corresponding to the main distribution network based on the collaborative parameter decision model.

[0139] The rolling correction module 550 is used to perform collaborative optimization of the main distribution network based on the scheduling control parameters, and to perform rolling correction on the collaborative optimization results of the main distribution network.

[0140] In this embodiment of the invention, a model building module collects basic data on the power grid sources, grid, load, and storage of the main distribution network and constructs a basic operating model of the main distribution network corresponding to this data. A cluster division module determines the electrical multidimensional characteristic indicators of distributed photovoltaic (PV) units within the main distribution network according to the basic operating model and divides each PV unit into different distributed PV clusters based on these indicators. A scheme selection module determines the fault ride-through severity index corresponding to each distributed PV cluster based on the main grid fault disturbance conditions. The fault ride-through severity index selects collaborative candidate schemes for the distributed PV clusters. An optimization execution module determines the scheduling control parameters corresponding to the main distribution network according to the collaborative parameter decision model of the collaborative candidate schemes. A rolling correction module performs collaborative optimization of the main distribution network according to the scheduling control parameters and performs rolling correction on the collaborative optimization results. This embodiment of the invention quantifies the fault ride-through severity index of distributed PV clusters corresponding to the main distribution network. Using this quantified fault ride-through index to guide grid collaborative optimization can improve the reliability of grid control decisions, enhance the stability of the grid after a fault, form a closed-loop control chain for decision correction, improve resource allocation efficiency, and increase the speed of grid fault recovery.

[0141] Based on the above embodiments of the invention, the model building module 510 includes: The data acquisition unit is used to collect network topology data, line parameters, distributed photovoltaic access locations, capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network as basic data of the power grid source-grid-load-storage system.

[0142] The topology architecture unit is used to determine the topological connection relationship between each grid device in the main distribution network based on the network topology data, the line parameters and the location of the distributed photovoltaic access, and to construct the nodes and node connection relationship of the basic operation model of the main distribution network according to the topological connection relationship of each grid device. The grid devices include main grid equivalent boundary nodes, substations, distribution network common connection nodes, feeder nodes and terminal nodes.

[0143] The data association unit is used to map the capacity data, inverter control type, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network to the corresponding nodes of the main distribution network basic operation model according to the corresponding grid equipment.

[0144] The constraint configuration unit is used to set branch power flow constraints and node voltage constraints for the basic operation model of the main distribution network according to the power flow exchange relationship, voltage-power correspondence relationship and voltage upper and lower limit relationship of the main distribution network.

[0145] Based on the above embodiments of the invention, the cluster partitioning module 520 includes: The influence relationship unit is used to determine the influence relationship between the injected power change and the node voltage of each node according to the basic operation model of the main distribution network, and to construct the power flow Jacobian matrix according to the influence relationship.

[0146] Sensitivity unit, used to construct node voltage sensitivity matrix based on the inverse mapping of the power flow Jacobian matrix.

[0147] An electrical distance unit is used to determine the electrical distance between different nodes based on the node voltage sensitivity matrix.

[0148] A region division unit is used to divide each node in the basic operation model of the main distribution network into at least one electrical region according to the electrical distances described.

[0149] A clustering feature unit is used to determine the clustering features of distributed photovoltaic units within each electrical region, wherein the clustering features include at least one of voltage regulation potential, clustering identification information, electrical distance features, grid-connected voltage dynamic response features, and low voltage ride-through parameter features.

[0150] The cluster grouping unit is used to cluster each distributed photovoltaic unit in the electrical area based on the clustering features to obtain at least one distributed photovoltaic cluster.

[0151] Based on the above embodiments of the invention, the electrical distance unit is specifically used for: determining the electrical influence degree between different nodes according to the node voltage sensitivity matrix; determining the target electrical influence degree of any two nodes in the main distribution network basic operation model corresponding to a third node within each of the electrical influence degrees; determining the difference between the target electrical influence degrees of the two nodes for each of the third nodes; and taking the average value of the difference corresponding to each of the third nodes as the electrical distance between the two nodes.

[0152] Based on the above embodiments of the invention, the scheme selection module 530 is specifically used to: obtain pre-configured main network fault disturbance conditions, wherein the main network fault disturbance conditions include at least one of short-circuit fault conditions, node tripping fault conditions, power disturbance conditions, voltage regulating equipment disturbance conditions, system oscillation disturbance conditions, and extreme operating condition disturbance conditions; The distributed photovoltaic cluster is disturbed according to the main grid fault disturbance conditions, and the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation and key node influence coefficient of the distributed photovoltaic cluster are collected. The normalized weighted sum of the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and critical node influence coefficient of each of the distributed photovoltaic clusters is used as an indicator of the severity of fault ride-through. For each of the distributed photovoltaic clusters, a collaborative candidate scheme matching the fault ride-through severity index is selected, wherein the collaborative candidate scheme includes at least one of the main grid interconnection channel switching strategy and the distribution network interconnection switch reconfiguration strategy.

[0153] Based on the above embodiments of the invention, the optimization execution module 540 is specifically used to: generate, according to the cooperative candidate scheme, a node voltage deviation item, a network loss item, a switching action cost item, and an active power limitation item for the distributed photovoltaic cluster; The weighted sum of the node voltage deviation term, the network loss term, the switching action cost term, and the active power limitation term is used as the objective function of the collaborative parameter decision model. The constraints of the collaborative parameter decision model are constructed according to the energy storage information of the distributed photovoltaic cluster, the inverter control type, and the reactive power compensation device operation data. The objective function is solved according to the constraints to obtain the scheduling and control parameters corresponding to the distributed photovoltaic cluster.

[0154] Based on the above embodiments of the invention, the rolling correction module 550 is specifically used for: extracting the scheduling control parameters of the distributed photovoltaic clusters corresponding to the main distribution network, scheduling the distributed photovoltaic clusters according to the scheduling control parameters; collecting the changes in edge node voltage, tie line power, and severity index of each distributed photovoltaic cluster after scheduling; if the changes in edge node voltage, tie line power, and severity index are less than a preset threshold, generating the scheduling control parameters for the next cycle according to the current scheduling control parameters.

[0155] The main distribution network collaborative optimization device provided in the embodiments of the present invention can execute the main distribution network collaborative optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0156] Example 7 Figure 9 This is a schematic diagram of the structure of an electronic device implementing the main distribution network coordinated optimization method of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0157] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0158] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0159] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the main distribution network collaborative optimization method.

[0160] In some embodiments, the main distribution network coordinated optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the main distribution network coordinated optimization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the main distribution network coordinated optimization method by any other suitable means (e.g., by means of firmware).

[0161] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0162] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0163] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0165] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0166] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0167] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0168] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for coordinated optimization of main and distribution networks, characterized in that, The method includes: Obtain basic data on power generation, grid, load and storage of the main distribution network, and construct a basic operation model of the main distribution network based on the basic data on power generation, grid, load and storage; Based on the basic operation model of the main distribution network, the electrical multidimensional characteristic indicators of the distributed photovoltaic units in the main distribution network are determined, and each of the distributed photovoltaic units is divided into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators. Based on the main grid fault disturbance conditions, the fault ride-through severity index of each of the distributed photovoltaic clusters is determined, and the corresponding collaborative candidate scheme of the distributed photovoltaic cluster is selected according to the fault ride-through severity index. Construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the scheduling control parameters corresponding to the main distribution network based on the collaborative parameter decision model; The main distribution network is optimized collaboratively based on the scheduling and control parameters, and the results of the collaborative optimization of the main distribution network are then subject to rolling correction.

2. The method according to claim 1, characterized in that, The process of acquiring basic data on the power grid sources, grid, load, and storage of the main distribution network, and constructing a basic operation model of the main distribution network based on this data, includes: The network topology data, line parameters, distributed photovoltaic access locations, capacity data, inverter control types, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network are collected as the basic data of the power grid source-grid-load-storage system. Based on the network topology data, the line parameters, and the location of distributed photovoltaic access, the topological connection relationship between each grid device in the main distribution network is determined. The nodes and node connection relationships of the basic operation model of the main distribution network are constructed according to the topological connection relationship of each grid device. The grid devices include main grid equivalent boundary nodes, substations, distribution network common connection nodes, feeder nodes, and terminal nodes. The capacity data, inverter control type, fault ride-through parameters, energy storage information, and reactive power compensation device operation data of the main distribution network are mapped to the corresponding nodes of the main distribution network basic operation model according to the corresponding grid equipment. Based on the power flow exchange relationship, voltage-power correspondence, and voltage upper and lower limit relationship of the main distribution network, branch power flow constraints and node voltage constraints are set for the basic operation model of the main distribution network.

3. The method according to claim 1, characterized in that, The step of determining the electrical multidimensional characteristic indicators of distributed photovoltaic units within the main distribution network based on the main distribution network basic operation model, and dividing each distributed photovoltaic unit into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators, includes: The influence relationship between the injected power change and the node voltage of each node is determined according to the basic operation model of the main distribution network, and the power flow Jacobian matrix is ​​constructed according to the influence relationship. Construct the node voltage sensitivity matrix based on the inverse mapping of the power flow Jacobian matrix; The electrical distance between different nodes is determined based on the node voltage sensitivity matrix. Each node in the basic operation model of the main distribution network is divided into at least one electrical region according to the aforementioned electrical distance; For each of the aforementioned electrical regions, the clustering characteristics of the distributed photovoltaic units within the electrical region are determined, wherein the clustering characteristics include at least one of voltage regulation potential, clustering identification information, electrical distance characteristics, grid-connected voltage dynamic response characteristics, and low voltage ride-through parameter characteristics; Based on the clustering features, each distributed photovoltaic unit in the electrical area is clustered to obtain at least one distributed photovoltaic cluster.

4. The method according to claim 3, characterized in that, Determining the electrical distance between different nodes based on the node voltage sensitivity matrix includes: The electrical influence between different nodes is determined according to the node voltage sensitivity matrix. Within each of the aforementioned electrical impact degrees, determine the target electrical impact degree of any two nodes in the basic operation model of the main distribution network corresponding to the third node; For each of the third nodes, determine the difference between the target electrical impact of the two nodes; The average of the differences corresponding to each of the third nodes is taken as the electrical distance between the two nodes.

5. The method according to claim 1, characterized in that, The process of determining the fault ride-through severity index of each distributed photovoltaic cluster based on the main grid fault disturbance condition, and selecting the corresponding collaborative candidate scheme for the distributed photovoltaic cluster according to the fault ride-through severity index, includes: Obtain pre-configured main network fault disturbance conditions, wherein the main network fault disturbance conditions include at least one of the following: short circuit fault conditions, node tripping fault conditions, power disturbance conditions, voltage regulating equipment disturbance conditions, system oscillation disturbance conditions, and extreme operating condition disturbance conditions; The distributed photovoltaic cluster is disturbed according to the main grid fault disturbance conditions, and the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation and key node influence coefficient of the distributed photovoltaic cluster are collected. The normalized weighted sum of the minimum voltage amplitude deviation, voltage recovery time, active power loss rate, reactive power support deviation, control switching fluctuation, and critical node influence coefficient of each of the distributed photovoltaic clusters is used as the fault ride-through severity index. For each of the distributed photovoltaic clusters, a collaborative candidate scheme matching the fault ride-through severity index is selected, wherein the collaborative candidate scheme includes at least one of the main grid interconnection channel switching strategy and the distribution network interconnection switch reconfiguration strategy.

6. The method according to claim 1, characterized in that, The step of constructing a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determining the dispatch control parameters corresponding to the main distribution network based on the collaborative parameter decision model, includes: For the distributed photovoltaic cluster, the node voltage deviation item, network loss item, switching action cost item, and active power limitation item corresponding to the distributed photovoltaic cluster are generated according to the collaborative candidate scheme. The weighted sum of the node voltage deviation term, the network loss term, the switching action cost term, and the active power limitation term is used as the objective function of the collaborative parameter decision model. The constraints of the collaborative parameter decision model are constructed according to the energy storage information, inverter control type, and reactive power compensation device operation data of the distributed photovoltaic cluster. The objective function is solved according to the constraints to obtain the scheduling and control parameters corresponding to the distributed photovoltaic cluster.

7. The method according to claim 1, characterized in that, The step of performing coordinated optimization of the main distribution network based on the scheduling control parameters, and performing rolling correction on the coordinated optimization results of the main distribution network, includes: Extract the scheduling control parameters corresponding to the distributed photovoltaic clusters within the main distribution network, and schedule the distributed photovoltaic clusters according to the scheduling control parameters; The changes in edge node voltage, tie-line power, and severity index of each distributed photovoltaic cluster after the data collection and scheduling were recorded. If the changes in the edge node voltage, the tie-line power, and the severity index are less than a preset threshold, then the scheduling control parameters for the next cycle are generated according to the current scheduling control parameters.

8. A main distribution network collaborative optimization device, characterized in that, The device includes: The model building module is used to acquire basic data of power generation, grid, load and storage of the main distribution network, and to build a basic operation model of the main distribution network based on the basic data of power generation, grid, load and storage. The cluster partitioning module is used to determine the electrical multidimensional characteristic indicators of the distributed photovoltaic units in the main distribution network according to the basic operation model of the main distribution network, and to partition each of the distributed photovoltaic units into at least one distributed photovoltaic cluster based on the electrical multidimensional characteristic indicators. The scheme selection module is used to determine the fault ride-through severity index of each of the distributed photovoltaic clusters based on the main grid fault disturbance conditions, and select the corresponding collaborative candidate schemes of the distributed photovoltaic clusters according to the fault ride-through severity index. The optimization execution module is used to construct a collaborative parameter decision model corresponding to the collaborative candidate scheme, and determine the scheduling control parameters corresponding to the main distribution network based on the collaborative parameter decision model. The rolling correction module is used to perform collaborative optimization of the main distribution network based on the scheduling control parameters, and to perform rolling correction on the collaborative optimization results of the main distribution network.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the main distribution network collaborative optimization method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the main distribution network collaborative optimization method according to any one of claims 1-7.

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