Power distribution network black start partitioning method and device based on multiple types of distributed power sources

Abstract

This invention relates to a black-start partitioning method for distribution networks based on multiple types of distributed power sources. The method includes the following steps: S1. Obtaining grid information, constructing an undirected graph representing the grid, determining the number of partitions based on the black-start power sources, and dividing the nodes in the undirected graph into a preliminary partitioning node set and a node set to be partitioned. If the node set to be partitioned is empty, the preliminary partitioning node set is used as the partitioning result, and S3 is executed; otherwise, S2 is executed. S2. Taking the minimization of the recovery time difference and total recovery cost of each subsystem as the optimization objective, the power source nodes in the node set to be partitioned are used as control variables to construct a partitioning optimization model, and further partitioning optimization is performed to obtain the partitioning result. S3. Correcting the partitioning result according to the black-start radius to obtain the final partition. Compared with the prior art, this invention has advantages such as comprehensive optimization objectives, reasonable model construction, scientific partitioning method, efficient system recovery strategy, and improved grid resilience and security.

Description

Technical Field

[0001] This invention relates to the field of black start technology for distribution networks, and in particular to a black start zoning method and apparatus for distribution networks based on multiple types of distributed power sources. Background Technology

[0002] When a transmission network failure leads to a large-scale power outage, distributed resources within the distribution network can play two roles in the power restoration process. First, when a power system failure causes partial or complete load loss, independently operating distributed resources can act as startup power sources for the distribution network, enabling sequential restoration of functionality from subsystems to the entire system, achieving a "black start" and self-repair. Second, when critical power sources in the main grid cannot start automatically due to insufficient startup power or lack black start capability, self-starting distributed resources such as energy storage and micro gas turbines in the surrounding distribution network can coordinate and aggregate wind and solar power sources through inverters, providing flexible power support for the startup of large power plant units at critical grid nodes, thereby accelerating the overall system recovery process.

[0003] When a distribution network undergoes a black start, it is first necessary to determine the division of the distribution network into zones, and then restore the power supply of the distribution network according to the order of the zones. Existing black start methods for distribution networks, such as CN115102220A, disclose a load self-recovery optimization method suitable for black start of distribution networks. The method discloses that the objective of the optimization of each node in the system is to minimize the recovery time difference between each zone of the distribution network and the overall system recovery cost. A genetic algorithm is used to randomly generate chromosomes to represent the zone to which each node belongs, ensuring that each zone contains at least one black start power source.

[0004] Existing technologies primarily focus on minimizing the recovery time difference between different zones in the distribution network and the overall system recovery cost. They employ genetic algorithms to randomly generate chromosomes representing the zones to which each node belongs, ensuring that each zone contains at least one black-start power source. However, the problem remains that some zones may not be able to support the startup of critical nodes in the main network. Summary of the Invention

[0005] This invention provides a black-start partitioning method and apparatus for distribution networks based on multiple types of distributed power sources. It not only considers recovery time difference and overall system recovery cost, but also comprehensively considers the black-start capability of power sources and the minimization of the recovery path weights of distributed resource nodes to be started, achieving multi-objective optimization. Simultaneously, the preliminary partitioning results are corrected based on the black-start radius to ensure that each partition can support the startup of critical nodes in the main network.

[0006] The objective of this invention can be achieved through the following technical solutions: A black-start partitioning method for distribution networks based on multiple types of distributed power sources, the method comprising the following steps: S1. Obtain power grid information, construct an undirected graph representing the power grid, determine the number of partitions based on the black-start power source, use the shortest path algorithm to perform preliminary partitioning of the undirected graph, divide the nodes in the undirected graph into the preliminary partition node set and the node set to be partitioned. If the node set to be partitioned is empty, the preliminary partition node set is used as the partitioning result, and S3 is executed; otherwise, S2 is executed. S2. Taking the minimum recovery time difference and total recovery cost of each subsystem as the optimization objective, the power nodes in the set of nodes to be partitioned are used as control variables to construct a partitioning optimization model, and further partitioning optimization is carried out to obtain the partitioning result. S3. Based on the black boot radius correction partition results, the final partition is obtained.

[0007] Furthermore, the undirected graph of the power grid includes black-start power nodes, heterogeneous distributed power nodes, and load nodes.

[0008] Furthermore, the shortest path algorithm is used to perform preliminary partitioning of the undirected graph. The specific steps for dividing the nodes in the undirected graph into the preliminary partitioning node set and the unpartitioned node set are as follows: The reactance value X between nodes and the line operating time T are selected as the line weights. The node where the black-start power source is located is taken as the root node. Dijkstra's algorithm is used to calculate the shortest electrical distance from all distributed resource nodes to the black-start power source node. Q ij , Q ij This represents the shortest electrical distance from the i-th distributed resource node to the j-th black-start power node, where the distributed resource node is a non-black-start power node; Iterate through all distributed resource nodes. For the i-th distributed resource node, if its two smallest shortest electrical distances are... Q ij1 and Q ij2 The absolute value of the difference between them is greater than the initial partition limit, where Q ij1 ≤ Q ij2 , Q ij1 and Q ij Let represent the shortest electrical distance from the i-th distributed resource node to the j1-th and j2-th black-start power nodes. Then, the i-th distributed resource node will be assigned to partition j1, which belongs to the j1-th black-start power node. Conversely, the i-th distributed resource node is assigned to the set of nodes to be partitioned.

[0009] Furthermore, the shortest electrical distance is the sum of the line weights of all edges between the distributed resource node and the black-start power node, where the line weights include the reactance value X and the line operating time T.

[0010] Furthermore, the function for optimizing the objective is: in, Ψ D Indicates the total number of partitions. T d Representation Subsystem d The recovery time cost, with a one-to-one correspondence between subsystems and partitions, This represents the average recovery time cost of each subsystem. , g 1,d and g 2,d Representing subsystems d The number of charging reactive power and switching operations in the recovery path.

[0011] Furthermore, the constraints of the partitioned optimization model include black-start power constraints, node branch partitioning constraints, and connectivity constraints.

[0012] Furthermore, the connectivity constraint is: in, F lk For subsystem k Central route l The virtual traffic on the line is defined as flowing from nodes with smaller node numbers to nodes with larger node numbers in the positive direction. l ( m , i ) indicates the destination is i The side road, l ( i , n ), indicating the starting point is i The branch, M, is a sufficiently large positive number. Indicates whether node i belongs to the region. k 0-1 variables, b i Represents a node i Is it a black-start power node? This indicates the number of nodes in the system.

[0013] Furthermore, based on the black boot radius correction partitioning results, the specific steps to obtain the final partition are as follows: Taking the black-start power node of each partition in the partitioning results as the starting node, the available output of the black-start power node at the time of power outage is defined as the initial black-start radius, which is used as the current black-start radius. A breadth-first search is used to prioritize the search for heterogeneous distributed power nodes within the partition with smaller path weights to the black-start power node. The searched heterogeneous distributed power nodes are aggregated, and the current black-start radius and partitioning results are updated. The search stops when the sum of the current black-start radii of all partitions can support the startup of the main network critical nodes, thus forming the final partition.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Obtain power grid information and construct an undirected graph: Obtain the topology and parameter information of the power grid, including nodes (black-start power generation nodes, heterogeneous distributed power generation nodes, load nodes) and lines (reactance values, line operating times, etc.). Based on this information, construct an undirected graph representing the power grid, providing a foundation for subsequent partitioning and optimization.

[0015] 2. Preliminary Partitioning: Based on the location of the black-start power source, a shortest path algorithm (such as Dijkstra's algorithm) is used to perform preliminary partitioning of the undirected graph. Reactance and line operating time are selected as line weights, and the shortest electrical distance from each distributed resource node to the black-start power source node is calculated. According to the preliminary partitioning limits, nodes are assigned to the preliminary partition node set and the unpartitioned node set.

[0016] 3. Further Partition Optimization: A partition optimization model is constructed with the goal of minimizing the recovery time difference and total recovery cost of each subsystem. Power nodes in the set of nodes to be partitioned are used as control variables, and a genetic algorithm is employed for further partition optimization.

[0017] 4. Final Partition Correction: The preliminary partitioning results are corrected based on the black start radius to ensure that each partition can support the startup of critical nodes in the mainnet. A breadth-first search is used to aggregate heterogeneous distributed power nodes with smaller path weights within each partition, and the current black start radius and partitioning results are updated.

[0018] 5. Device Implementation: A black-start partitioning device for a distribution network based on multiple types of distributed power sources is designed, including a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements the aforementioned black-start partitioning method. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention; Figure 2 This is a preliminary partitioning example diagram of the present invention; Figure 3 Result diagram for Scenario 1, Solution 1; Figure 4 The total active and reactive power output of distributed energy in scenario one, solution one, 24 hours; Figure 5 For Scenario 1, Solution 1, Black Start Solution for Power Outage at 10:00 AM; Figure 6 For Scenario 1, Solution 1, Black Start Solution for Power Outage at 19:00; Figure 7 Configure the result image for Scenario 2, Option 1; Figure 8 For the 24-hour active and reactive power output of distributed energy in Scheme 1 of Scenario 2; Figure 9 For Scenario 2, Option 1, the black start option is implemented at 10:00 AM during a power outage. Figure 10 For Scenario 2, Option 1, the black start solution is implemented at 19:00 during the power outage. Figure 11 Configure the result diagram for Scenario 3, Solution 1; Figure 12 For Scheme 1 of Scenario 3, the active and reactive power output of distributed energy is calculated for 24 hours. Figure 13 The scenario is a black start solution for scenario three, scheme one, where the power outage occurs at 10:00. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0021] Example 1: When a transmission network failure leads to a large-scale power outage, distributed resources within the distribution network can play two roles in the power restoration process. First, when a power system failure causes partial or complete load loss, independently operating distributed resources can act as startup power sources for the distribution network, enabling sequential restoration of functionality from subsystems to the entire system, achieving a "black start" and self-repair. Second, when critical power sources in the main grid cannot start automatically due to insufficient startup power or lack black start capability, self-starting distributed resources such as energy storage and micro gas turbines in the surrounding distribution network can coordinate and aggregate wind and solar power sources through inverters, providing flexible power support for the startup of large power plant units at critical grid nodes, thereby accelerating the overall system recovery process.

[0022] Considering the characteristics of various types of "source-load-storage" distributed resources, such as gas turbines, distributed wind / solar power, and electric vehicle charging stations, and taking into account the comprehensive characteristics of distribution network source-load-storage resources and the requirements of zoned black start technology, distributed resources are classified and aggregated to improve their utilization efficiency. Based on this aggregation, and aiming to fully utilize the power supply capacity of distributed resources and minimize the recovery time of power plant auxiliary equipment, the distribution network surrounding key nodes of the main grid is zoned, providing a theoretical basis for the next step of preliminary black start within each zone. Based on the characteristics of various distributed resources, they are divided into three categories: (1) Based on whether they have black-start capability, they are divided into: black-start DER (BDER) and non-black-start DER (NBDER). BDERs are mostly power sources with energy storage devices and units that can generate electricity independently. They can act as "black-start" power sources or operate in islanded mode after an unexpected failure in the network. NBDERs do not have the ability to supply electricity independently, but some can act as backup power sources. BDERs mainly include combined generator sets, passive inverters and separately excited generator sets, wind power and solar power with energy storage devices, electric vehicle charging stations, etc.; NBDERs mainly include self-excited generator sets and wind power and solar power without energy storage devices, etc.

[0023] (2) Based on whether they can maintain connection with the distribution network and continue operating after a fault occurs, they can be divided into SDER (Survived DER) and NSDER (Non-Survived DER). SDER means that after a fault occurs, it can still maintain connection with the distribution network and can act as a backup power source for the main grid to alleviate the power supply pressure on the main grid. NSDER means that after a fault occurs, it cannot participate in the power supply of the main grid and needs to exit the grid-connected mode. SDER mainly includes: micro gas turbines, fuel cells, electric vehicle charging stations, wind power generation with energy storage devices, and solar power generation, etc.; NSDER mainly includes resources such as solar and wind power that are greatly affected by weather and are not equipped with energy storage devices.

[0024] (3) Based on whether they have communication capabilities and control protocols with the distribution network dispatch center, they are divided into: Controllable DER (CDER) and Non-Controllable DER (NCDER), mainly classified according to the specific connection of the DER. CDER is mostly connected to high-voltage distribution networks, while NCDER is mostly connected to 0.38kV voltage level distribution networks.

[0025] In distribution networks, the operating states of DERs (Power Deployers) can be broadly categorized into grid-connected and islanded operation. Grid-connected operation involves the DER acting as a backup power source, working in conjunction with the main power source to supply power to the loads. In this mode, the DER can alleviate pressure during peak electricity demand, contributing to network stability and reliability. In the event of a fault, the system can implement a planned islanding scheme for the BDER (Base Deployers), disconnecting it from the grid to operate with loads within its capacity. This islanded operation mode ensures uninterrupted operation of critical loads within its range, which is crucial for distribution network fault recovery.

[0026] This invention proposes a black-start partitioning method for distribution networks based on multiple types of distributed power sources. The flowchart of the method is as follows: Figure 1 As shown, the method includes the following steps: S1. Obtain power grid information, construct an undirected graph representing the power grid, determine the number of partitions based on the black-start power source, use the shortest path algorithm to perform preliminary partitioning of the undirected graph, divide the nodes in the undirected graph into the preliminary partition node set and the node set to be partitioned. If the node set to be partitioned is empty, the preliminary partition node set is used as the partitioning result, and S3 is executed; otherwise, S2 is executed. S2. Taking the minimum recovery time difference and total recovery cost of each subsystem as the optimization objective, the power nodes in the set of nodes to be partitioned are used as control variables to construct a partitioning optimization model, and further partitioning optimization is carried out to obtain the partitioning result. S3. Based on the black boot radius correction partition results, the final partition is obtained.

[0027] First, without altering the power grid's topology, and considering its actual conditions, the power grid can be abstracted as an undirected graph G={V, E}, where V is the vertex set containing all nodes, and E is the edge set containing all lines and transformer branches. Based on this network topology, the black-start partitioning problem is simplified to a node partitioning problem within the topology graph. Given several black-start power sources, the goal is to partition the system's other power plants, important substations, and important load nodes into groups represented by the black-start power sources. The partitioning results in several subsystems originating from the black-start power sources, each containing some nodes and lines from the system. Once the partitioning of nodes and important load nodes is determined, the regional partitioning and recovery of other load nodes are further considered. Using the search for optimal recovery paths, the recovery sequence and recovery paths of generating units are determined while the target nodes are partitioned into different partitions, ultimately determining the network structure and recovery scheme for each partition.

[0028] This paper analyzes the grid zoning problem for black start, taking into account the principles of subsystem partitioning during black start and the actual conditions of the power grid, such as the fact that the power grid is mostly a large sending-end and large receiving-end grid, and that the feasible control of the power grid is based on the provincial power grid. The subsystem partitioning principles adopted are as follows: (1) Black start capability. Each subsystem should include a self-starting unit, such as energy storage or a micro gas turbine, so that the system structure is similar to that during normal operation after recovery. If the black start power supply is not reasonably distributed for zone recovery, measures should be taken to allow the black start power supply closest to the subsystem without a start power supply to supply power to that subsystem.

[0029] (2) Compact internal network. Each small system should form a compact network in terms of topology to prevent the recovery process from being terminated due to faults or accidents in some lines or nodes under abnormal conditions. This facilitates path transfer when equipment in the network fails to be put into operation, thereby improving the reliability of system recovery.

[0030] (3) Insufficient electrical connections between systems. Too many electrical connections will increase the complexity of operation when the system is connected to the grid and delay the recovery process.

[0031] (4) The partition sizes should be roughly the same. Roughly the same partition size is beneficial to improving the speed of parallel recovery of multiple subsystems. Large differences in size between subsystems will cause asynchronous recovery times between subsystems, prolonging the recovery time of the entire system.

[0032] Distribution network zoning is the foundation for parallel system recovery. The parallel distribution network recovery scheme refers to dividing the distribution network into multiple sub-regions after a large-scale power outage, simultaneously activating available power sources within each sub-region, restoring power lines, shortening the overall system recovery time, and achieving the startup of auxiliary units at key nodes of the main grid through synchronous interconnection of subsystems. Since the black-start grid reconfiguration process of the distribution network is relatively short, while the startup of power plant units after grid reconfiguration is relatively long, the time-varying characteristics of wind and solar power output have a negligible dynamic impact on the black-start grid reconfiguration scheme. However, when formulating the preliminary zoning scheme, the impact of the time-varying output of wind and solar power units on the subsequent black-start process should be considered, and a certain reserve capacity should be reserved for the already started power sources. Based on the predicted output values ​​of typical wind and solar power days at the time of the power outage as known data, the distribution network is initially divided into zones, fully considering the impact of the time-varying output of wind and solar power units on the safe recovery of auxiliary units, and determining the preliminary zoning results at different outage times, laying the foundation for the implementation of the next step of the black-start scheme. Therefore, the specific steps of this invention are as follows: (1) Determine the number of zones based on black-start power sources. Based on the time-varying characteristics of the "source load", determine the available output of distributed resources in the distribution network at the time of the fault; select energy storage or micro gas turbines with self-starting capability and good voltage and frequency regulation capability as black-start power sources, and determine the number of distribution network zones based on the location of the black-start power sources.

[0033] (2) Preliminary partitioning is performed using the shortest path algorithm. The reactance value X and the line operation time T are selected as the line weights, and the node where the black-start power source is located is taken as the root node. Dijkstra's algorithm is used to calculate the shortest electrical distance Q from all distributed resource nodes to the black-start power source node. By setting a preliminary partitioning limit ALim, the shortest electrical distance from each node to different black-start power source nodes is compared to perform preliminary partitioning of distributed resource nodes at the spatial level. For power source nodes that are less than the preliminary partitioning limit and cannot be selected to belong to a partition, they are temporarily assigned to the node set Ψnet to be partitioned and await further partitioning optimization.

[0034] by Figure 2 Taking nodes 2, 3, and 4 as black-start power sources and nodes 1, 5, 6, 7, 8, 9, and 10 as non-black-start power sources, subsystems are divided using these three black-start power source nodes as root nodes. An initial partitioning limit of Alim = 0.5 is set. The shortest electrical distance Qij from other non-black-start power source nodes i to black-start power source nodes 2, 3, and 4 is calculated. The absolute value of the difference between the two smallest Q values ​​of a node is determined by whether it is greater than Alim. If it is, the node is assigned to the subsystem of the black-start power source with the smallest Q value; otherwise, it cannot be partitioned. After calculation, nodes 5 and 6 belong to partition one, nodes 7 and 8 belong to partition two, nodes 9 and 10 belong to partition three, and node 1 is temporarily assigned to the unpartitioned node set for further optimization. The analysis focuses on nodes 1 and 5.

[0035] For node 1: (31) Clearly, node 1 does not meet the initial partitioning requirements and requires further partitioning optimization.

[0036] For node 5: (3-2) (3-3) Clearly, node 5 meets the initial partitioning requirements and does not require further partitioning optimization. 52 If the value is the smallest, it will be assigned to partition one of the black startup power supply nodes 2.

[0037] (3) If all partition nodes meet the preliminary partitioning requirements in (2), that is, the node set Ψnet to be partitioned does not contain any nodes, then (4) is executed directly. Otherwise, the partitioning is further optimized with the goal of minimizing the recovery time difference and total recovery cost of each subsystem. According to the partitioning scheme, based on the black start scheme of the distribution network partition around the key nodes of the main network, considering that the parallel recovery of the distribution network partition should meet the principle of matching the subsystem scale and recovery time, the recovery time cost setting should take into account the minimum recovery time difference of each subsystem. The total recovery cost includes the time cost of the entire system recovery process, the line operation cost, and the line charging capacitor cost. The power nodes in the node set Ψnet to be partitioned are used as control variables, and the genetic algorithm is used to solve the problem. The objective function is: (3-4) In the formula: Ψ D Indicates the total number of partitions. T d Representation Subsystem d The recovery time cost, This represents the average recovery time cost of each subsystem. g1,d and g2,d represent the charging reactive power and the number of switching operations in the recovery path of subsystem d, respectively. g 1,d and g 2,d , T d The recovery cost of the jointly composed subsystem d The constraints of the partitioning optimization model are: 1) Black start power supply constraints (3-5) in, b i Indicates whether node i is a black-start power node. If node i is a black-start power node, then node i is a black-start power node. i If it is a black start power supply, then b i =1, otherwise b i =0; Equation (3-5) indicates that each region must have a black start power node.

[0038] 2) Node branch partitioning constraints (3-6) (3-7) Equation (3-6) indicates that each node can only belong to one partition. Equation (3-7) indicates that each branch can belong to at most one partition. When a branch does not belong to any partition, it is considered a connecting branch.

[0039] 3) Connectivity constraints To ensure connectivity within each sub-region while maintaining isolation between them, subsystem connectivity constraints are constructed based on network flow theory. The basic principle of network flow theory is: flow is injected into the system from the source node, and the flow reaches every node through lines. All nodes consume unit flow; when every node receives flow, network connectivity is ensured. Therefore, the black-start unit node can be defined as the system source node, and the units to be started and load nodes can be defined as sink nodes. When the network satisfies the network flow constraints, the connectivity of the resulting sub-regions can be guaranteed. Integer variables are defined. F lk Let be the virtual flow on line l in subsystem k, and define the direction of the virtual flow as flowing from the node with the smaller node number to the node with the larger node number.

[0040] (3-8) (3-9) (3-10) (3-11) (3-12) Where M is a sufficiently large positive number. l ( m , i ), indicating the endpoint is i The side road, l (i,n) represents the starting point. i The branch. Equation (3-8) indicates that virtual traffic only flows on lines within the subsystem, and there is no traffic on the inter-section connecting lines. y lk For 0-1 variables, y lk =1 means F lk =1, meaning there is data flow on the line. y lk =0 means F lk =0, meaning there is no flow on the line. Equation (3-9) indicates that the flow consumed by nodes other than the black-start power node is greater than or equal to 1 unit. Equation (3-10) indicates that the flow flowing into nodes other than the black-start power node in the subsystem is greater than 1. Equation (3-11) indicates that the flow flowing out of the black-start power node is equal to the total number of nodes in the area minus 1. Equation (3-12) indicates that the flow flowing into the black-start power node is 0.

[0041] (4) Correct the initial partition target based on the black start radius. Based on the initial partition in the previous step, take the black start power source in the partition as the starting node, and define the available output of the black start power source at the time of power outage as the black start radius. Considering the influence of factors such as intermittent resource output fluctuations, the black start radius needs to reserve a certain margin. Use breadth-first traversal to prioritize the search for DERs with smaller path weights to the black start power source in the partition, aggregate the searched DERs, and update the black start radius at the same time. Stop the search when the sum of the black start radii of all partitions can support the startup of the main network critical nodes, and form the final partition. The size of each partition should not differ much.

[0042] The following is a practical case analysis: Scenario 1: The feasibility range for a black start is 100%. In this scenario, the power distribution network meets the black start requirements within 24 hours.

[0043] (1) Scenario 1 Configuration Scheme 1: The total capacity of the pre-installed power supply is 7900kW. The pre-installed power supply of Scenario 1 Configuration Scheme 1 is shown in Table 1.

[0044] Table 1 Scenario 1 Configuration Scheme 1 Pre-installed Power Supply The configuration result diagram of Scenario 1, Solution 1 is as follows: Figure 3 As shown. Based on the configuration results, the active and reactive power output curves of the distributed energy source within 24 hours are calculated and plotted as follows. Figure 4 As shown. By Figure 4 It can be seen that the output of distributed energy in the distribution network can meet the black start requirements of key nodes in the main network within 24 hours.

[0045] (2) Scenario 1 Configuration Scheme 1 Black Boot Scheme: Power outage occurred at 10:00.

[0046] Based on the distributed resource optimization allocation scheme of the distribution network and the power output status at 10:00 AM when the power outage occurs, the distribution network partitioning results and black start scheme are obtained as follows: Figure 5 As shown. Figure 5 In the diagram, a solid red node indicates that the power supply to that node has been activated, and a solid red line with an arrow indicates a line that has been charged.

[0047] Black Start Scheme Analysis: The power outage occurred at 10:00 AM. For photovoltaic (PV) units, sunlight was relatively abundant at this time, and the available output of PV units in the distribution network system was approximately 68%. However, the PV unit capacity in the system configuration scheme was relatively small, with only 300kW of PV installed at node 17. For wind turbine units, the configuration scheme included a large number of wind turbine units, with a total configured capacity of 2100kW. However, the wind speed in the surrounding environment was low at this time, resulting in an available output of approximately 17% for wind turbine units in the distribution network system. Considering that the energy storage and gas turbine resources in the configuration scheme were sufficient to support the smooth recovery of auxiliary equipment at node 1, in order to reduce the impact of increased recovery time, increased reactive power from path charging, and increased number of switching operations caused by starting PV and wind turbine units with low available output, it was not necessary to start all PV and wind turbine units in the system.

[0048] When performing a black start operation, firstly, based on the black start capability of the power sources in the distribution network system, gas turbines and energy storage at nodes 21 and 33 are selected as black start power sources. Each black start power source is used as a root node, and the line electrical parameters and line operating time are used as line weights. The weights of all lines in the path constitute the path weight. Dijkstra's algorithm is then used to divide the distribution network into two partitions: Partition 1 = {Node 3, Node 10, Node 14, Node 21, Node 23}, and Partition 2 = {Node 17, Node 30, Node 33}.

[0049] Based on the initial partitioning in the previous step, the black start power supplies at nodes 21 and 33 within the partitions are taken as the starting nodes. The available output of the black start power supply at the time of power outage is defined as the black start radius. A breadth-first search is used to aggregate the power supplies with low path weights within the partitions until the available output of the started power supplies in both partitions meets the requirements for auxiliary machine startup. The partitions are then updated, and the unstarted power supplies are removed from the partitions. The final partitioning results are: Partition 1 = {node 21, node 23}, Partition 2 = {node 30, node 33}.

[0050] Power outage occurred at 19:00.

[0051] Based on the distributed resource optimization allocation scheme of the distribution network and the power output status at the time of the power outage (19:00), the distribution network zoning results and black start scheme are obtained as follows: Figure 6 As shown. Figure 6 In the diagram, a solid red node indicates that the power supply to that node has been activated, and a solid red line with an arrow indicates a line that has been charged.

[0052] Black Start Scheme Analysis: The power outage occurred at 19:00. For photovoltaic (PV) units, sunlight was extremely scarce at this time, and the available output of PV units in the distribution network system was approximately 0%. Therefore, the PV units configured at node 17 could not be utilized. For wind turbine units, the configuration scheme included a large number of wind turbine units with a total configured capacity of 2100kW. At this time, the wind speed in the surrounding environment was relatively high, and the available output of wind turbine units in the distribution network system was approximately 60%. Wind power resources were relatively abundant. In order to make full use of renewable resources in the system and reduce the use of energy storage and gas turbines to improve economic efficiency, the wind turbine units were prioritized for startup. By regulating the energy storage, gas turbines, and wind power resources in the system, the auxiliary equipment at node 1 was smoothly restored.

[0053] During black start, based on the black start capability of the power sources in the distribution network system, gas turbines at nodes 21 and 23 are selected as black start power sources for partition 1 and partition 2, respectively. Each partition is rooted at a black start power source, and line electrical parameters and line operating time are used as line weights. The total line weights in the path constitute the path weights. Dijkstra's algorithm is used to divide the distribution network into two partitions: Partition 1 = {Node 10, Node 14, Node 17, Node 21}, and Partition 2 = {Node 3, Node 23, Node 30, Node 33}.

[0054] Based on the initial partitioning in the previous step, the black start power supplies at nodes 21 and 23 in the partitions are taken as the starting nodes respectively. The available output of the black start power supply at the time of power outage is defined as the black start radius. A breadth-first search is used to aggregate the power supplies with low path weights in the partitions until the available output of the started power supplies in the two partitions meets the requirements for auxiliary machine startup. The partitions are then updated again, and the unstarted power supplies are removed from the partitions. The final partitioning results are: Partition 1 = {Node 14, Node 21}, Partition 2 = {Node 3, Node 23, Node 30}.

[0055] Scenario 2: The feasibility range for a black start is 75%. In this scenario, the distributed power output of the distribution network can meet the black start requirements of key nodes in the main network within 24 hours if it reaches 75% or more of its capacity.

[0056] (1) Scenario 2 Configuration Scheme 1: The total capacity of the pre-installed power supply is 7200kW. The pre-installed power supply of Scenario 2 Configuration Scheme 1 is shown in Table 2.

[0057] Table 2 Scenario 2 Configuration Scheme 1 Pre-installed Power Supply The configuration result of Scenario 2, Option 1 is shown in the figure below. Figure 7 As shown. Scenario 2, Scheme 1: Distributed energy's active and reactive power output over 24 hours is as follows. Figure 8 As shown. From Figure 8It can be seen that during the approximately 5h44min period of low power demand, the output of distributed power sources cannot meet the black start requirements, while the reactive power output meets the black start requirements for 24 hours.

[0058] (2) Scenario 2 Configuration Scheme 1 Black Boot Scheme Scenario 2, Solution 1: Power outage at 10:00 AM, black start solution as follows: Figure 9 As shown.

[0059] Black start scheme analysis: The power outage occurred at 10:00. For photovoltaic (PV) units, the sunlight was relatively abundant at this time, and the available output of PV units in the distribution network system was about 68%. Moreover, the system configuration scheme had a large capacity of PV units, with 500kW PV units configured at nodes 23 and 33 respectively. For wind turbine units, the wind speed in the surrounding environment was low at this time, resulting in the available output of wind turbine units in the distribution network system being about 17%. However, the total capacity of energy storage and gas turbines in the distribution network system was 4100kW. Utilizing only energy storage, gas turbines, and PV resources was insufficient to support the smooth restoration of the auxiliary equipment at node 1, and the wind turbine units in the system needed to be started.

[0060] During black start operation, based on the black start capability of the power sources in the distribution network system, gas turbines and energy storage at nodes 21 and 30 are selected as black start power sources. Each black start power source is used as the root node, and the line electrical parameters and line operating time are used as line weights. The weights of all lines in the path constitute the path weight. Dijkstra's algorithm is used to divide the distribution network into two partitions: Partition 1 = {Node 2, Node 14, Node 17, Node 21, Node 22}, and Partition 2 = {Node 23, Node 25, Node 30, Node 33}.

[0061] Based on the initial partitioning in the previous step, the black-start power supplies at nodes 21 and 30 within the partitions are taken as the starting nodes. The available output of the black-start power supply at the time of power outage is defined as the black-start radius. A breadth-first search is used to aggregate power supplies with low path weights within the partitions until the available output of the powered supplies in both partitions meets the requirements for auxiliary machine startup. The partitions are then updated, and the unstarted power supplies are removed from the partitions. The final partitioning results are: Partition 1 = {Node 2, Node 14, Node 17, Node 21, Node 22}, and Partition 2 = {Node 23, Node 25, Node 30, Node 33}.

[0062] Scenario 2, Solution 1: Power outage at 19:00, black start solution as follows: Figure 10 As shown.

[0063] Black Start Scheme Analysis: The power outage occurred at 19:00. For photovoltaic (PV) units, sunlight was extremely scarce at this time, and the available output of PV units in the distribution network system was approximately 0%. Therefore, the PV units configured at nodes 23 and 33 could not be utilized, leaving only energy storage, gas turbines, and wind power resources. For wind turbine units, the configuration scheme included a large number of wind turbine units with a total configured capacity of 2100kW. At this time, the wind speed in the surrounding environment was relatively high, and the available output of wind turbine units in the distribution network system was approximately 60%, indicating relatively abundant wind power resources. To support the smooth restoration of the auxiliary equipment at node 1, it is necessary to fully utilize the energy storage, gas turbines, and wind power resources in the system.

[0064] During black start operation, based on the black start capability of the power sources in the distribution network system, gas turbines at nodes 21 and 25 are selected as black start power sources. Each black start power source is used as the root node, and the line electrical parameters and line operating time are used as line weights. The weights of all lines in the path constitute the path weight. Dijkstra's algorithm is used to divide the distribution network into two partitions: Partition 1 = {Node 14, Node 17, Node 21, Node 22}, and Partition 2 = {Node 2, Node 23, Node 25, Node 30, Node 33}.

[0065] Based on the initial partitioning in the previous step, the black-start power supplies at nodes 21 and 25 within the partitions are taken as the starting nodes. The available output of the black-start power supply at the time of power outage is defined as the black-start radius. A breadth-first search is used to aggregate the power supplies with low path weights within the partitions until the available output of the powered supplies in both partitions meets the requirements for auxiliary machine startup. The partitions are then updated, and the unstarted power supplies are removed from the partitions. The final partitioning results are: Partition 1 = {Node 14, Node 17, Node 21, Node 22}, and Partition 2 = {Node 2, Node 25, Node 30}.

[0066] Scenario 3: The feasibility range for a black start is 50%. (1) Scheme 1 for Scenario 3: The total capacity of the pre-installed power supply is 7000kW. The pre-installed power supply for Scheme 1 for Scenario 3 is shown in Table 3.

[0067] Table 3. Configuration Scheme 1 for Scenario 3: Pre-installed Power Supply The configuration result of Scenario 3, Option 1 is shown in the figure below. Figure 11 As shown, the active and reactive power output of distributed energy in Scenario 3, Scheme 1, is as follows: Figure 12 As shown.

[0068] from Figure 12 It can be seen that within a period of approximately 11 hours and 10 minutes, the output of the distributed power source cannot meet the black start requirement, while the reactive power output meets the black start requirement for 24 hours.

[0069] (2) Scenario 3 Configuration Scheme 1 Black Boot Scheme: Power outage occurred at 10:00.

[0070] Scenario 3, Solution 1: Power outage at 10:00 AM - Black Start Solution (as follows) Figure 13 As shown.

[0071] Black Start Scheme Analysis: The power outage occurred at 10:00 AM. For photovoltaic (PV) units, sunlight was abundant at this time, with approximately 68% of their output available in the distribution network. The system configuration also included a significant number of PV units, with 900kW and 400kW PV units deployed at nodes 23 and 33, respectively. For wind turbines, wind speeds were low, resulting in approximately 17% of their output available in the distribution network. A 1700kW wind turbine was deployed at node 17. However, the total capacity of energy storage and gas turbines in the distribution network was 4000kW. To support the smooth recovery of auxiliary equipment at node 1, all energy storage, gas turbine, PV, and wind power resources in the system needed to be fully utilized.

[0072] During black start operation, based on the black start capability of the power sources in the distribution network system, gas turbines and energy storage at nodes 21 and 30 are selected as black start power sources. Each black start power source is used as the root node, and the line electrical parameters and line operating time are used as line weights. The weights of all lines in the path constitute the path weight. Dijkstra's algorithm is used to divide the distribution network into two partitions: Partition 1 = {Node 17, Node 21}, and Partition 2 = {Node 23, Node 25, Node 30, Node 33}.

[0073] Based on the initial partitioning in the previous step, the black start power supplies at nodes 21 and 30 within the partitions are taken as the starting nodes. The available output of the black start power supply at the time of power outage is defined as the black start radius. A breadth-first search is used to aggregate power supplies with low path weights within the partitions until the available output of the started power supplies in both partitions meets the requirements for auxiliary machine startup. The partitions are then updated again, and the unstarted power supplies are removed from the partitions. The final partitioning results are: Partition 1 = {Node 17, Node 21}, Partition 2 = {Node 23, Node 25, Node 30, Node 33}.

[0074] Power outage time: 19:00 Black start scheme analysis: The power outage occurred at 19:00. For photovoltaic units, sunlight was very scarce at this time, and the available output of photovoltaic units in the distribution network system was about 0%. Therefore, the photovoltaic units configured at nodes 23 and 33 could not be utilized, leaving only energy storage, gas turbines, and wind power resources. For wind turbine units, the configuration scheme only configured a 1700kW wind turbine unit at node 17. Although the wind speed in the surrounding environment was relatively high at this time, and the available output of wind turbine units in the distribution network system was about 60%, the wind power resources were relatively abundant. However, relying on all the energy storage, gas turbines, and wind power resources in the system, it was not enough to support the smooth recovery of the auxiliary equipment at node 1. The power outage time did not meet the black start conditions.

[0075] This invention proposes an optimized strategy for black-starting large-scale generating units in the main grid, utilizing distributed resources in the distribution network to assist the main grid's large-scale generating units. This strategy uses self-starting distributed resources such as energy storage and micro gas turbines within the distribution network surrounding key nodes in the main grid as black-start power sources. Inverters coordinate and control the startup of non-black-start power sources such as wind power and photovoltaic power, collectively providing sufficient power support for the subsequent restoration of large-scale thermal power units in the main grid. This invention deeply analyzes the black-starting zoning principles of the distribution network and the characteristics of various DER types, and formulates a black-starting zoning scheme for the distribution network based on the support of multiple types of distributed power sources.

[0076] Beneficial effects compared to existing technologies: 1. Comprehensiveness of Optimization Objectives: This invention not only considers the recovery time difference and the overall system recovery cost, but also comprehensively considers the black-start capability of the power source and the weight minimization of the recovery path of the distributed resource nodes to be started, thus achieving multi-objective optimization. This comprehensiveness can more accurately reflect the actual operating needs and constraints of the distribution network, improving the efficiency and reliability of system recovery.

[0077] 2. Rationality of Model Construction: The optimization model based on the undirected graph of the local network can accurately reflect the topology and node relationships of the distribution network. When constructing the power source start-up sequence optimization model and the network reconfiguration recovery path optimization model, various factors (such as power source black start capability, recovery path weights, node voltages, line thermal stability limits, power balance, etc.) were fully considered, making the optimization results more consistent with actual operational requirements.

[0078] 3. Scientific nature of the zoning method: Through three steps—preliminary zoning, further optimization, and final correction—a scientific zoning of the distribution network is achieved. The zoning results not only meet the optimization objectives but also satisfy actual operational needs, ensuring that each zone has self-recovery capabilities and improving the resilience and disaster resistance of the distribution network.

[0079] 4. Efficiency of the system recovery strategy: Combining the optimal startup sequence and optimal recovery path, a system recovery strategy for the grid reconfiguration phase was obtained. This strategy can guide the distribution network to quickly and effectively restore power supply under fault or abnormal conditions, reducing power outage time and losses.

[0080] 5. Improving the utilization efficiency of distributed resources: This approach explicitly considers the characteristics of various types of distributed power sources and categorizes and differentiates them. By rationally regulating and optimizing the configuration of different types of distributed power sources, the advantages of various distributed resources can be fully utilized, improving their utilization efficiency during the black start process of the distribution network.

[0081] In summary, this invention significantly improves the recovery efficiency and reliability of the distribution network during black start through a scientific and reasonable zoning strategy and optimization method, providing a strong guarantee for the safe and stable operation of the power grid.

[0082] Example 2: This invention also provides a schematic structural diagram of an apparatus corresponding to the black-start zoning method for distribution networks based on multiple types of distributed power sources in Embodiment 1. At the hardware level, this black-start zoning apparatus for distribution networks based on multiple types of distributed power sources includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for other operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the above-mentioned... Figure 1 The data acquisition method described above. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0083] Improvements in a technology can be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology can now be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement in methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0084] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0085] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0086] For ease of description, the above apparatus is described in terms of its functions, divided into various units. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware components.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0093] Example 3: This invention also proposes a computer-readable storage medium on which a program is stored, which, when executed, implements the method described in Embodiment 1. Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0097] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0098] The advantages of this invention are: 1. Optimize the comprehensiveness and innovativeness of the objectives. Multi-objective optimization: This invention not only considers the black-start capability of the power source, but also minimizes the weight of the recovery path for the distributed resource nodes to be started. This multi-objective optimization method can more comprehensively reflect the actual operating needs and constraints of the distribution network, improving the efficiency and reliability of system recovery.

[0099] Innovation: Unlike existing technologies that often focus on only a single objective (such as optimizing the power-on sequence or recovery path), this invention achieves a more comprehensive optimization effect by comprehensively considering multiple objectives, which is a major innovation of traditional black-start optimization methods.

[0100] 2. The rationality and accuracy of model construction Based on local network undirected graphs: This invention constructs an optimization model based on local network undirected graphs, which can accurately reflect the topology and node relationships of the distribution network, providing a solid foundation for optimization solutions.

[0101] Refined modeling: When constructing the power supply startup sequence optimization model and the grid reconfiguration recovery path optimization model, various factors such as the black start capability of the power supply, the weight of the recovery path, the node voltage, the thermal stability limit of the line, the power balance, and the hot start time of the thermal power unit are fully considered, so that the optimization results are more in line with the actual operation requirements.

[0102] Improved accuracy: Through refined modeling, this invention can more accurately describe the recovery process of the power distribution network, thereby improving the accuracy and reliability of the optimization results.

[0103] 3. The scientific nature and flexibility of the zoning method Scientific Zoning: This invention proposes a black-start zoning method for distribution networks based on multiple types of distributed power sources. By comprehensively considering factors such as black-start capability, internal network compactness, electrical connections between systems, and zoning scale, it achieves scientific zoning of the distribution network.

[0104] Flexibility: This method is applicable to different types of distributed power sources (such as wind power, photovoltaics, and energy storage), exhibiting strong adaptability and flexibility. By rationally regulating and optimizing the configuration of different types of distributed power sources, flexible operation and efficient recovery of the distribution network can be achieved.

[0105] 4. The efficiency and practicality of system recovery strategies Combining optimal startup sequence and recovery path: This invention combines optimal startup sequence and optimal recovery path to obtain a system recovery strategy for the grid reconfiguration phase. This strategy can guide the distribution network to quickly and effectively restore power supply under fault or abnormal conditions, reducing power outage time and losses.

[0106] High-efficiency recovery: By optimizing the startup sequence and recovery path of distributed power sources, this invention can significantly improve the recovery efficiency of the distribution network, shorten the power outage time, and reduce the impact on society and the economy.

[0107] Highly practical: The system recovery strategy of this invention fully considers the actual operating requirements and constraints of the power distribution network, and has high practicality and operability.

[0108] 5. Improve the resilience and security of the power grid. Enhanced resilience: By optimizing the recovery process of the distribution network, this invention helps to improve the resilience of the power grid, enabling the grid to restore power supply more quickly when it suffers a fault or attack.

[0109] Enhanced safety: During the restoration process, this invention fully considers safety factors such as the thermal stability limit of the line and node voltage to ensure the safety and reliability of the restoration process and avoid secondary accidents.

[0110] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A black-start partitioning method for distribution networks based on multiple types of distributed power sources, characterized in that, The method includes the following steps: S1. Obtain power grid information, construct an undirected graph representing the power grid, determine the number of partitions based on the black-start power source, use the shortest path algorithm to perform preliminary partitioning of the undirected graph, divide the nodes in the undirected graph into the preliminary partition node set and the node set to be partitioned. If the node set to be partitioned is empty, the preliminary partition node set is used as the partitioning result, and S3 is executed; otherwise, S2 is executed. S2. Taking the minimum recovery time difference and total recovery cost of each subsystem as the optimization objective, the power nodes in the set of nodes to be partitioned are used as control variables to construct a partitioning optimization model, and further partitioning optimization is carried out to obtain the partitioning result. S3. Correct the partitioning results based on the black boot radius to obtain the final partition.

2. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 1, characterized in that, The undirected graph of the power grid includes black-start power nodes, heterogeneous distributed power nodes, and load nodes.

3. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 2, characterized in that, The specific steps for performing preliminary partitioning of an undirected graph using the shortest path algorithm, dividing the nodes in the undirected graph into the preliminary partition set and the unpartitioned set, are as follows: The reactance value X between nodes and the line operating time T are selected as the line weights. The node where the black-start power source is located is taken as the root node. Dijkstra's algorithm is used to calculate the shortest electrical distance from all distributed resource nodes to the black-start power source node. Q ij , Q ij This represents the shortest electrical distance from the i-th distributed resource node to the j-th black-start power node, where the distributed resource node is a non-black-start power node; Iterate through all distributed resource nodes. For the i-th distributed resource node, if its two smallest shortest electrical distances are... Q ij1 and Q ij2 The absolute value of the difference between them is greater than the initial partition limit, where Q ij1 ≤ Q ij2 , Q ij1 and Q ij Let represent the shortest electrical distance from the i-th distributed resource node to the j1-th and j2-th black-start power nodes. Then, the i-th distributed resource node will be assigned to partition j1, which belongs to the j1-th black-start power node. Conversely, the i-th distributed resource node is assigned to the set of nodes to be partitioned.

4. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 3, characterized in that, The shortest electrical distance is the sum of the line weights of all edges between the distributed resource node and the black-start power node, where the line weights include the reactance value X and the line operation time T.

5. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 1, characterized in that, The function for optimizing the objective is: in, Ψ D Indicates the total number of partitions. T d Representation Subsystem d The recovery time cost, with a one-to-one correspondence between subsystems and partitions, This represents the average recovery time cost of each subsystem. , g 1,d and g 2,d Representing subsystems d The number of charging reactive power and switching operations in the recovery path.

6. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 5, characterized in that, The constraints of the partitioned optimization model include black-start power constraints, node branch partitioning constraints, and connectivity constraints.

7. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 6, characterized in that, The connectivity constraint is: in, F lk For subsystem k Central route l The virtual traffic on the line is defined as flowing from nodes with smaller node numbers to nodes with larger node numbers in the positive direction. l ( m , i ) indicates the destination is i The side road, l ( i , n ), indicating the starting point is i The branch, M, is a sufficiently large positive number. Indicates whether node i belongs to the region. k 0-1 variables, b i Represents a node i Is it a black-start power node? This indicates the number of nodes in the system.

8. The black-start partitioning method for distribution networks based on multiple types of distributed power sources according to claim 1, characterized in that, The specific steps to obtain the final partition based on the black boot radius correction partition results are as follows: Taking the black-start power node of each partition in the partitioning results as the starting node, the available output of the black-start power node at the time of power outage is defined as the initial black-start radius, which is used as the current black-start radius. A breadth-first search is used to prioritize the search for heterogeneous distributed power nodes within the partition with smaller path weights to the black-start power node. The searched heterogeneous distributed power nodes are aggregated, and the current black-start radius and partitioning results are updated. The search stops when the sum of the current black-start radii of all partitions can support the startup of the main network critical nodes, thus forming the final partition.

9. A black-start zoning device for a distribution network based on multiple types of distributed power sources, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-8.

10. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-8.

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