Power distribution network frame planning method, device and equipment with collaborative self-healing control function
By using a distribution network planning method with collaborative self-healing control function, and combining the main network planning model and the secondary network self-healing control model, the configuration of the primary network and secondary system is optimized. This solves the problem that existing technologies cannot take into account both the primary network planning and the secondary system configuration, and realizes a differentiated planning scheme with high power supply reliability and investment optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-27
- Publication Date
- 2026-03-31
Smart Images
Figure CN121769820A_ABST
Abstract
Description
Technical Field
[0001] This application provides embodiments in the field of power distribution network technology, and particularly relates to a power distribution network planning method, apparatus and equipment with collaborative self-healing control function. Background Technology
[0002] Power supply reliability refers to the ability of a power supply system to continuously supply power. It is an important indicator for assessing the power quality of a power supply system and reflects the degree to which the power industry meets the national economy's electricity demand. It has become one of the standards for measuring the level of economic development of a country or region. Power supply reliability is a comprehensive indicator of the construction and operation management level of the distribution network, and it is closely related to factors such as the distribution network structure, equipment level, distribution automation configuration standards, and operation and maintenance management level.
[0003] However, related technologies cannot simultaneously meet the requirements of primary grid planning and secondary system configuration, resulting in the inability to formulate differentiated planning schemes based on the functional positioning and power supply requirements of different areas within a city. Therefore, researching investment cost optimization for distribution network structure and equipment configuration that is suitable for the development of high-reliability urban distribution networks and applicable to various wiring modes is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention
[0004] The embodiments of this application provide a distribution network planning method, device, and equipment with a collaborative self-healing control function, which can coordinate primary network optimization and secondary control system configuration, taking into account technical feasibility, investment economy, and high power supply reliability.
[0005] In a first aspect, embodiments of this application provide a distribution network planning method with collaborative self-healing control function, including: Based on the power distribution network planning data of the target area to be planned, determine the grid connection type, switch station configuration data, and power supply reliability standard data; Based on the switchyard configuration data and grid connection type, the switchyard connection result is determined based on the backbone grid planning model. Based on the wiring results of the switch stations and the distance between the midpoint switch station and the power supply point in the wiring results, the wiring results of the main grid are determined. Based on the wiring results of the main trunk network and the power supply reliability standard data, the configuration of secondary equipment in the secondary trunk network is determined based on the self-healing control model of the secondary trunk network.
[0006] In one alternative implementation, the method for constructing a backbone network planning model includes: Based on the switch station configuration data and the grid connection type, the first decision variable of the backbone grid planning model is determined, and the first decision variable is whether the switch stations are connected. The first objective function and its constraints are generated based on the first decision variable, thus obtaining the backbone network planning model.
[0007] In one optional implementation, the backbone network planning model is obtained by generating a first objective function and constraints on the first objective function based on the first decision variables, including: When the first objective function aims to minimize the total cost of new line construction, the constraints of the first objective function include at least one of the following: The number of switching stations in each switching station line is less than or equal to the first threshold; The capacity of each switching station line is less than or equal to the second threshold; The number of lines at each switching station is unique; The first decision variable is either 0 or 1.
[0008] In one optional embodiment, the network wiring type includes a backbone network wiring type and a secondary backbone network wiring type; the backbone network wiring type includes at least one of the following: double petal wiring, double ring network wiring, and single ring network wiring; the secondary backbone network wiring type includes at least one of the following: double ring network wiring, single ring network wiring, dual access cascade connection line, and dual access wiring; The switch station configuration data includes at least one of the following: the number of switch stations in the target area to be planned, the location of the switch stations, the location of the power supply points, and the number of power supply points; Distribution network planning data includes at least one of the following: economic development data, planning and development positioning data, distribution network infrastructure configuration data, and electricity load forecast data; The backbone network wiring results include the switch station wiring results and the connection relationship between the terminal switch stations and the nearest power supply point; Among them, the endpoint switch station is the switch station located at the endpoint in the switch station wiring result.
[0009] In one optional implementation, based on the backbone network wiring results and power supply reliability standard data, and using the secondary network self-healing control model, the configuration of secondary equipment in the secondary backbone network is determined, including: Based on the wiring results of the main network, the second decision variable of the secondary network self-healing control model is determined. The second decision variable is whether the target switch is equipped with intelligent automation equipment. The target switch includes at least one of the following: switches on the feeder of the main network switch station, switches on the incoming line of the power distribution room, and switches on the outgoing line of the power distribution room. The second objective function is generated based on the second decision variable, and the constraints of the second objective function are used to obtain the self-healing control model of the secondary trunk network.
[0010] In one optional implementation, the secondary trunking network self-healing control model is obtained by generating a second objective function and constraints based on the second decision variable, including: When the second objective function is to minimize the total investment in equipment, the constraints of the second objective function include: the power supply reliability value corresponding to each equipment configuration of the target switch is greater than or equal to the power supply reliability threshold corresponding to the power supply reliability standard data.
[0011] In one optional implementation, the method for determining the power supply reliability value includes: Based on the set of anticipated incidents, the corresponding power outage time is determined according to the equipment configuration of the target switch. Determine the power supply reliability value based on the power outage time; Establish a mapping table between the equipment configuration and power supply reliability values of the target switch; The power supply reliability value corresponding to each device configuration of the target switch is determined based on the mapping table.
[0012] Secondly, embodiments of this application provide a power grid planning device with collaborative self-healing control function, comprising: The data determination module determines the grid connection type, switch station configuration data, and power supply reliability standard data based on the distribution network planning data of the target area to be planned. The wiring determination module determines the wiring results of the switch station based on the configuration data of the switch station and the wiring type of the grid structure, and on the backbone grid planning model. The power supply determination module determines the backbone network wiring results based on the switch station wiring results and the distance between the midpoint switch station and the power supply point in the switch station wiring results. The configuration determination module determines the configuration of secondary equipment in the secondary trunk network based on the wiring results of the main trunk network and power supply reliability standard data, and on the self-healing control model of the secondary trunk network.
[0013] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method provided in embodiments of this application.
[0014] Fourthly, embodiments of this application provide a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed in a computer, causes the computer to execute the method provided in embodiments of this application.
[0015] The technical solution provided in this application determines the grid connection type, switch station configuration data, and power supply reliability standard data based on the distribution network planning data of the target area to be planned; based on the switch station configuration data and grid connection type, the switch station connection result is determined based on the backbone grid planning model; based on the switch station connection result and the distance between the endpoint switch station and the power supply point in the switch station connection result, the backbone grid connection result is determined; based on the backbone grid connection result and power supply reliability standard data, the secondary equipment configuration in the secondary backbone grid is determined based on the secondary backbone grid self-healing control model, coordinating the primary grid optimization and secondary control system configuration to achieve the goal of balancing technical feasibility, investment economy, and high power supply reliability. Attached Figure Description
[0016] Figure 1 This is a flowchart of a power distribution network planning method with collaborative self-healing control function provided in an embodiment of this application.
[0017] Figure 2A This is a schematic diagram of a double-petal wiring mode for a power distribution network backbone provided in an embodiment of this application.
[0018] Figure 2B This is a schematic diagram of a dual-ring network mode of a distribution network backbone provided in an embodiment of this application.
[0019] Figure 2C This is a schematic diagram of a single-ring network connection mode of a power distribution network backbone provided in an embodiment of this application.
[0020] Figure 3 This is a schematic diagram of a typical wiring mode of a secondary trunk network of a distribution network provided in an embodiment of this application.
[0021] Figure 4 This is a schematic diagram of a power distribution network planning scenario provided in an embodiment of this application.
[0022] Figure 5 This is a schematic diagram of the wiring result of a backbone network provided in an embodiment of this application.
[0023] Figure 6 This is a schematic diagram illustrating the relationship between power supply reliability calculation results and equipment configuration costs, provided in an embodiment of this application.
[0024] Figure 7 This is a schematic diagram of a fault outage time determination provided in an embodiment of this application.
[0025] Figure 8 This is a schematic diagram of a fault scenario provided in an embodiment of this application.
[0026] Figure 9 This is a structural block diagram of a power distribution network planning device with collaborative self-healing control function provided in an embodiment of this application.
[0027] Figure 10 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation
[0028] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0029] Figure 1 This is a flowchart of a distribution network planning method with collaborative self-healing control function provided in an embodiment of this application. The method can be executed by a distribution network planning device with collaborative self-healing control function. The device can be implemented by software and / or hardware and can be configured in electronic devices such as computers.
[0030] like Figure 1 As shown, the technical solution provided in this application includes the following steps: S110: Based on the distribution network planning data of the target area to be planned, determine the grid connection type, switch station configuration data, and power supply reliability standard data.
[0031] In this embodiment, the distribution network planning data includes, but is not limited to, at least one of the following: economic development data, planning and development positioning data, distribution network infrastructure configuration data, and electricity load forecasting data. Economic development data includes population, GDP, and power supply area; planning and development positioning data includes power supply area planning positioning and land use planning positioning. Distribution network infrastructure configuration data includes the main wiring types of the existing network structure, the configuration of secondary equipment such as the coverage level of self-healing control or distribution automation equipment, and the current level of power supply reliability development.
[0032] In one embodiment, the basic configuration data of the power distribution network can be determined based on economic development data and planning development positioning data.
[0033] For example, for core areas such as central urban areas or national strategic carrying areas clearly defined in the city's development positioning, it is necessary to set high power supply reliability planning targets and correspondingly select high power supply reliability network connection types and equipment configuration standards. Taking cable networks as an example, the distribution network structure includes levels such as the main network and secondary networks, where the main network connection type includes, for example, […]. Figure 2A The "double petal connection" shown is as follows: Figure 2B The "double ring network connection" shown is as follows: Figure 2C The above-mentioned distribution network planning wiring diagrams, such as the "single-ring network connection," demonstrate a decreasing theoretical power supply reliability level from high to low. Secondary trunk network connection types include, for example... Figure 3 The theoretical power supply reliability levels of the above-mentioned distribution network planning wiring, such as "double ring network wiring", "single ring network wiring", "double access level connection line", and "double access wiring", decrease from high to low.
[0034] Understandable. Figure 2A The A, B, C, and D switch stations, A and B substations shown in 2B and 2C are for illustrative purposes only. Figure 3 The switch station A, switch station B, power distribution room A, power distribution room B, power distribution room C, and power distribution room D shown are for illustrative purposes only.
[0035] For example, the core urban area has the highest load importance and volume, and therefore needs to be planned and constructed according to the highest power supply reliability development target standards; general towns have the next highest load importance and volume, and can be planned and constructed according to the second highest power supply reliability development target standards; suburban areas have the lowest load importance and volume, and can be planned and constructed according to the low power supply reliability development target standards to improve economic efficiency. Based on the differentiated development positioning of the three types of areas, the grid planning route is determined to be a double-petal connection for the main grid in the core urban area, a double-ring network connection for general towns, and a single-ring network connection for the suburban areas.
[0036] The characteristics of each major wiring mode are compared in the table below.
[0037] Table 1. Comparison of Main Characteristics of Distribution Network Backbone Network Planning Structure Connection Types
[0038] Table 2 Comparison of Main Characteristics of Secondary Trunk Network Planning Network Wiring Types
[0039] In one embodiment, the power supply facility configuration standards and requirements are determined based on the distribution network planning data of the target area to be planned, and the power supply indicators of distribution network power facilities such as a single switch station in the target area are determined, thereby determining the switch station configuration data in the target area. The switch station configuration data includes, but is not limited to, at least one of the following: the number of switch stations in the target area to be planned, the location of the switch stations, the location of power supply points, and the number of power supply points. For example, the land use planning of the target area is determined, the land use attributes of different areas within the area are clarified (such as commercial, industrial, residential, etc.), and the typical electricity load density indicators (electricity load demand per unit area) of each type of land use are combined, multiplied and accumulated, and a certain electricity simultaneity rate (which can be between 0.8 and 1) is considered to determine the electricity load; then, according to the power supply facility configuration standards of the area, i.e., the electricity load that a single switch station can meet, which is usually between 4 and 6 MW; finally, the number of switch stations required in the area is obtained by dividing the electricity load by the electricity load that a single switch station can meet. For the known number of switch stations, their layout and construction scheme within the area is determined, adhering to the principles of uniform distribution, proximity to roads, and consideration of land use attributes. This step can also be done by directly inputting known boundaries, or by determining them manually. For example... Figure 4The switch station configuration data shown includes 2 power supply points (Power Supply Point 1, Power Supply Point 2) and 32 switch stations (K1-K32).
[0040] It should be noted that the location of the switch station should be as close as possible to areas with high load density, and should be arranged in accordance with the actual construction conditions.
[0041] S120: Based on the switch station configuration data and grid connection type, determine the switch station connection result based on the backbone grid planning model.
[0042] In this embodiment, based on the switch station configuration data and the grid connection type, whether multiple switch stations are connected is used as the first decision variable and input into the grid planning model to determine the switch station connection result.
[0043] In this embodiment, the first decision variable The connection matrix formed by the first decision variable is The calculation is as follows:
[0044] Among them, the first decision variable ( ) reflects the The switch station and the first The connection relationship of the switch stations. A value of 1 indicates a connection, and vice versa.
[0045] The matrix of connections consists of all the first decision variables.
[0046] In this embodiment, a first objective function and constraints of the first objective function are generated based on the first decision variable to obtain the backbone network planning model.
[0047] In this embodiment, the backbone network planning model aims to optimize the cost of new line construction, that is, the first objective function is to minimize the total cost of new line construction. The formula for calculating the first objective function is as follows:
[0048] in, For switch station , The cost of constructing new lines per unit length between them; For switch station , The length of the line between them; This refers to the number of circuits on the inter-station path between connected switching stations.
[0049] It should be noted that: for single-ring network connection type, take 1; for double-ring network and double-petal connection type, take 2.
[0050] It should be noted that the cost of the newly built line is calculated at 500,000 yuan per kilometer.
[0051] In this embodiment, based on determining the configuration data (quantity, location) of the switching stations, the planning scenario is recorded. The set consisting of the upstream power supply points of each switching station is , The set of switch stations is For ease of description, the wiring configuration includes power supply points on both sides and several switch stations. ( , ) is a series of wires, denoted as The set formed by the series wires is .
[0052] In one embodiment, the backbone network planning model must satisfy the constraint on the number of switchyard connections. The number of switchyards in each switchyard line is less than or equal to a first threshold. Taking the number of switchyards in each switchyard line being equal to the first threshold as an example, the calculation formula is as follows:
[0053] For the first Group( The number of switch stations included in the wiring; The first threshold is the maximum threshold value of the switching stations included in each series connection, for example, a value of 4.
[0054] by Taking a value of 4 as an example, The computational logic is to execute the following process: for any ,make ;like ,make ;like and , Increment the value by 1, and set... , ,Will The value is reset to zero, and the process is repeated. If it does not exist, the process ends, and the result is obtained. The calculation results are as follows. Through the above logic, it can be determined whether the wiring meets the constraint of 4, that is, to ensure that every 4 switch stations form 1 wiring series.
[0055] In one embodiment, the backbone network planning model must meet line capacity constraints, ensuring that the capacity of each switch station line is less than or equal to a second threshold, and guaranteeing that the sum of the planned power supply loads of all switch stations in the same series does not exceed the maximum allowable transmission capacity of the line. The calculation formula is as follows:
[0056] in, The second threshold is the maximum transmission capacity allowed to pass through each segment of a certain string connection.
[0057] This represents the maximum allowable transport capacity of the line.
[0058] It should be noted that, according to the aforementioned high-reliability planning requirements, the load must meet the transfer conditions after a fault occurs. The line load capacity and the predicted load of the switching station have been pre-verified. Therefore... The calculation can be simplified by using 50% of the sum of the loads of all stations on the line.
[0059] In one embodiment, the backbone network planning model must satisfy the requirement that the number of lines in each switch station is unique, i.e., the uniqueness constraint of switch stations in series, ensuring that each switch station, after optimization, will only belong to one series of connections, and there are no switch stations connected to two or more series of connections at the same time. The calculation formula is as follows:
[0060] In one embodiment, the backbone network planning model needs to satisfy the 0-1 variable constraint, that is, the first decision variable is 0 or 1, and the calculation formula is as follows:
[0061] Through the above embodiments, the connection relationship between switch stations under different wiring types (switch station wiring results) is calculated.
[0062] S130: Determine the main grid wiring result based on the switch station wiring result and the distance between the terminal switch station and the power supply point in the switch station wiring result.
[0063] In one embodiment, the main difference between the grid types lies in the power supply point and the switching station at the beginning of each string of lines. , The differences lie in the connection methods. Double-ring and single-ring networks have the same wiring path, differing only in the number of circuit loops. Taking a network structure including double-petal, double-ring, and single-ring networks, with each series connection containing 4 switch stations as an example: A double-petal network is characterized by starting from one power source point, connecting 4 switch stations in series, and returning to that power source point. Double-ring and single-ring networks are characterized by starting from one power source point, connecting 4 switch stations in series, and returning to another power source point. Given the starting switch station for each connection group, find the nearest power source point. For a double-petal network, find the closest one to the starting switch station. For double-ring and single-ring networks, find the power source point closest to the starting switch station on one side and the power source point closest to the starting switch station on the other side (i.e., the endpoint switch station), respectively.
[0064] In one embodiment, for double-ring and single-ring network connection types, each switching station is grouped according to its distance from each power source point. The grouping method is as follows: for the first... Switch station ( ), and assign it to the nearest power source. ( In the corresponding group, the distance is recorded as , The set of distances to all switching stations forms the classification group. , ,…, The aforementioned model calculates the switching station connection relationships in each series circuit. Then, take the terminal switch station of each series connection. Corresponding to its group according to distance { , ,…, Determine the power supply point connected to it.
[0065] In one embodiment, the terminal switch stations of each string are calculated. The two nearest power sources. (Note) , The distance from the station to each power source is , , , The power supply points on both sides of each series connection are calculated by solving the following optimization model:
[0066]
[0067]
[0068] In the above embodiments, each switch station is grouped according to its distance from each power source. After optimizing the wiring scheme of each series of switch stations, their connection relationship with the upstream substation is determined. The optimization objective is still based on the shortest distance to the power source. Thus, the connection relationship between the downstream switch stations and the nearest power source for different wiring types, and the connection relationship between switch stations (switch station wiring results), are calculated, i.e., the backbone network wiring results, such as... Figure 5 As shown in the diagram. The endpoint switch station is the switch station located at the endpoint in the switch station wiring results.
[0069] In one embodiment, the secondary backbone network can be customized with its own wiring configuration, such as considering a double-ring network access method. In another embodiment, a secondary backbone network planning model can be established to obtain the network wiring configuration. The specific configuration can be referred to the main backbone network planning model, and the comparison will not be elaborated further.
[0070] S140: Based on the main backbone network wiring results and power supply reliability standard data, determine the configuration of secondary equipment in the secondary backbone network based on the secondary backbone network self-healing control model.
[0071] In this embodiment, based on the wiring results of the main trunk network, a second decision variable for the secondary trunk network self-healing control model is determined. The second decision variable is whether the target switch is equipped with intelligent automation equipment. The target switch includes at least one of the following: a switch on the feeder of the main trunk network switch station, a switch on the incoming line of the power distribution room, and a switch on the outgoing line of the power distribution room. The second decision variable is input into the secondary trunk network self-healing control model to obtain the configuration of secondary equipment in the secondary trunk network.
[0072] It should be noted that, based on the above-obtained backbone network wiring results, the switch combinations at both ends of the line are also determined. For example, the switches on the feeder of the backbone network switch station, the switches on the incoming line of the distribution room, and the switches on the outgoing line of the distribution room need to be determined by whether the above switches are equipped with intelligent automation equipment as the second decision variable. Based on the secondary backbone network self-healing control model, the configuration of secondary equipment in the secondary backbone network, i.e. whether the switches are equipped with intelligent automation equipment, is obtained.
[0073] In one embodiment, each switch needs to decide whether to configure intelligent automation equipment; the configuration variable is set to 1 otherwise, and 0 otherwise. In another embodiment, a certain type of switch is uniformly configured or not configured, for example, all switches on the main grid feeder are configured or not configured; all switches on the distribution room's incoming and outgoing lines are configured or not configured.
[0074] In this embodiment, the secondary backbone network involved... The set of switches (including switches on feeders of main grid substations and switches on incoming and outgoing lines of distribution rooms, excluding busbar sectionalizing switches within substations) is denoted as […]. , record Base switch ( The open / closed state of ) is The initial value is 1 when the switch is closed and 0 when the switch is open. Its initial value describes the switch's open / closed state under normal operating conditions. Second decision variable. Description of the Whether an intelligent automation device is configured at the switch, i.e., the status of the intelligent automation device, is indicated by a value of 1 indicating configuration and 0 indicating non-configuration. The set of these configurations is denoted as […]. The model optimization output is a 0-1 combination of switch configurations for intelligent automation equipment.
[0075] In this embodiment, the second objective function is to minimize the total investment in equipment, and the calculation formula is as follows:
[0076] in, For switch The cost of configuring intelligent automation devices in the area; The cost of configuring conventional protection devices.
[0077] In this embodiment, the constraints of the second objective function include: the power supply reliability value corresponding to each device configuration of the target switch is greater than or equal to the power supply reliability threshold corresponding to the power supply reliability standard data, and the calculation formula is as follows:
[0078] in, Refers to the anticipated accident collection and smart device configuration solutions The calculated reliability index level is the power supply reliability value.
[0079] This refers to the preset reliability index thresholds according to planning requirements, i.e., the power supply reliability thresholds corresponding to the standard data for power supply reliability. Taking the central town as an example in this case, the power supply reliability thresholds are... Take 0.99999.
[0080] In this embodiment, based on the set of anticipated accidents, the corresponding power outage time is determined according to the equipment configuration of the target switch, and the power supply reliability value is determined according to the power outage time; a mapping table between the equipment configuration of the target switch and the power supply reliability value is established; and the power supply reliability value corresponding to each equipment configuration of the target switch is determined according to the mapping table.
[0081] It's understandable that intelligent automation equipment costs more than conventional protection equipment, but it offers shorter power outage times and higher power supply reliability. For example, the configuration cost of conventional protection equipment... The cost is 120,000 yuan per set for the configuration of intelligent automation equipment. The price is 150,000 yuan per set.
[0082] like Figure 6 As shown, when the number of intelligent automation devices is small, there is no improvement in the overall reliability index. When intelligent automation devices cover all incoming and outgoing line equipment in all distribution rooms of the secondary trunk network, the overall reliability is significantly improved and meets the threshold constraint. When the automation devices are further extended to the outgoing line side of the switch station, the reliability improvement obtained only comes from the time reduction caused by the faster action of intelligent devices compared to conventional protection after a fault occurs.
[0083] Specifically, a set of anticipated faults is set, and the corresponding power outage time for different anticipated fault sets is determined. The operating time of the conventional protection is denoted as... The action time (from the occurrence of a fault to the completion of location and isolation) of intelligent distributed feeder automation is: The self-healing process takes time. The time required for the secondary trunk power distribution room to activate the low-voltage backup power supply is The power outage repair time is For example, the action time of conventional protection. Take 1 second as the action time of intelligent automated equipment. Take 0.1s as the time from self-healing to power restoration. Take 15 seconds for the low-voltage backup power supply to be activated in the power distribution room. Take 6 seconds for power outage repair due to cable and busbar faults. Take 6h and 8h respectively. The cable failure rate is taken as 0.01 times / km·year, and the bus failure rate is taken as 0.001 times / section·year.
[0084] It should be noted that the contingency set can be selected by referring to the analysis of historical power outage events in the distribution network of the region, focusing on scenarios that are concentrated or necessary to consider. For a specific fault scenario in the contingency set, the theoretical power outage event following its occurrence is analyzed.
[0085] Analytical methods such as Figure 7 As shown: First, determine whether intelligent automation equipment is installed at the location of the fault. Record the circuit breaker numbers of the fault and the line adjacent to the fault point. ,like The fault point can be quickly located and addressed using automated equipment (by pressing...). Otherwise, the standard protective action (according to) (Calculation). After the switch on the adjacent line at the fault location trips, it is necessary to change the state of the tripping switch to make the tripping switch... status Next, assess the power loss status of each busbar section. Based on the network topology and the status of the line switches after the fault, determine the connectivity between each busbar section in the distribution room and the busbar at the upstream switchyard. If a switch is open on the line from the switchyard to the distribution room busbar, the busbar is de-energized. At this time, the self-healing control activates (according to...). (Calculation), and at the same time, reassess the busbar power loss situation, i.e., whether a self-healing reconfiguration device is configured. The switches on the standby lines under normal operating conditions... Close, make Under this switch opening / closing combination, the connectivity between each bus section and the upstream switchyard bus is then determined, i.e., whether there is a power outage load. If there is still a power outage load after the self-healing control action, it is further determined whether the low-voltage side backup power supply can be activated. If the backup power supply is activated, the power outage duration is as follows: If there is no backup power supply, the power outage time will be calculated as follows: It is understandable that the self-healing and reconfiguration devices of conventional protection equipment have lower self-healing capabilities than those of intelligent automation equipment.
[0086] In one embodiment, the connectivity determination method can be the conventional minimum path method. The minimum path method is an improvement on the fault consequence search method based on Failure Mode and Effects Analysis (FMEA). For a single load point, facilities can be divided into two categories: facilities on the minimum path and facilities on the non-minimum path. Facilities on the path from a load point to the power source in the opposite direction of the power flow are considered minimum path facilities, while facilities not on this path are considered non-minimum path facilities. The minimum path method searches for the minimum path for each load point, transfers the impact of failures of facilities on the non-minimum path to the nodes of the corresponding minimum path, and then calculates the reliability index of a single load point by analyzing the facilities and nodes on the minimum path. The reliability index of the system is obtained by combining the reliability indices of all load points. The specific calculation steps are as follows: a) Determine the minimum and non-minimum road facilities for a single load point; b) Transfer the impact of non-minimum road facility failures at the load point to the corresponding minimum road nodes; c) Enumerate the minimum road facility failures at the load point to form a list of failure outage rates and annual failure outage times for that load point, thereby obtaining the reliability index of that load point; d) Calculate the reliability index of each load point in sequence, and calculate the system reliability index based on these.
[0087] In this embodiment, under different intelligent automation equipment configuration scenarios, the power loss load and power outage duration, i.e., the power outage time, can be calculated according to the above control logic under different sets of anticipated accidents and fault occurrence location boundaries. Let the set of accident consequences (including combinations of power loss load and power outage duration) corresponding to different configuration schemes be denoted as... .
[0088] like Figure 8 As shown, the set of anticipated accidents in reliability analysis include Three different fault points, which can be determined according to Figure 7 The illustrated embodiments analyze the power outage time after a fault occurs at different locations.
[0089] The fault point is Furthermore, the switches upstream and downstream of the line are only equipped with conventional protection devices. If there is no self-healing reconfiguration device, it is necessary to wait for the protection to operate after a fault occurs. Furthermore, the only recourse is to activate the low-voltage side backup power supply or restore power after the fault is repaired.
[0090] The fault point is At this location, intelligent automated equipment is installed upstream and downstream of the line, and the operation time is [not specified]. After the switch activates and the self-healing device operates, power is restored to the distribution room, but the II section busbar in the distribution room remains de-energized. In this situation, power loss can be prevented by closing the busbar sectionalizing switch or activating the low-voltage side backup power supply. Based on this, the fault outage time under different secondary system configuration standards can be obtained.
[0091] In this embodiment, the calculation formula for the fault outage time obtained under different combinations of secondary system configuration standards and ranges of the secondary trunk network is as follows:
[0092] This indicates the set of intelligent automation configurations. Each configuration scheme in the system.
[0093] Anticipated accident collection Each combination of failure scenarios in the set of accident consequences There are corresponding combinations of load loss and power outage duration in each of them.
[0094] This application includes a primary grid wiring scheme and a secondary power distribution automation configuration scheme (responding to the current requirements of primary and secondary coordinated planning). On the primary side, it's clear that higher redundancy and better reliability in the adopted wiring pattern lead to higher investment and lower economic efficiency. Therefore, the wiring pattern is first determined based on regional location, and then path optimization is carried out according to this wiring pattern. This involves optimizing the wiring after knowing the power supply points and switch station layout, minimizing the overall wiring path and investment. After determining the primary wiring scheme, the secondary side also optimizes the secondary system configuration scheme with reliability as a constraint. This model considers two types of secondary equipment configurations: conventional secondary equipment configurations and intelligent secondary equipment configurations with self-healing capabilities (the impact time of power outages differs, with the latter being shorter). The variable in the research problem refers to whether intelligent secondary equipment should be configured at the switches at both ends of each line in the primary grid wiring; the variable is 1 if configured at a switch and 0 otherwise. Clearly, more intelligent secondary equipment improves reliability but increases investment. Therefore, it is necessary to study the minimum range of intelligent secondary equipment configurations that meets the target reliability constraint. To this end, it is necessary to first establish a specific quantitative relationship between secondary equipment configuration and theoretical reliability through analysis using a set of anticipated fault events. For a known primary wiring scheme, first assume a combination of secondary equipment configurations and analyze the theoretical reliability of that combination; repeat the above operation until the correlation between reliability and the secondary equipment configuration combination is obtained.
[0095] For example, simulation studies were conducted on the reliability levels of 11 typical network architectures, combining backbone networks with double-petal, double-ring, and single-ring network configurations, and secondary backbone networks with dual-access, dual-access cascade, single-ring, and double-ring network configurations. When the secondary backbone networks used the same configuration, the reliability levels of the backbone networks decreased in the order of double-petal, double-ring, and single-ring network configurations. Similarly, when the backbone networks used the same configuration, the reliability levels of the secondary backbone networks decreased in the order of double-ring, dual-access, dual-access cascade, and single-ring network configurations. The combination of a double-petal backbone network and a double-ring secondary backbone network resulted in the highest power supply reliability, while the combination of a single-ring backbone network and a single-ring secondary backbone network resulted in the lowest power supply reliability. The backbone network wiring results and the secondary equipment configuration schemes and results in the secondary backbone networks are shown in the following table: Table 3 Recommended Scheme for High-Reliability Smart Distribution Network Cascade Planning
[0096] In the above embodiments, differentiated power supply reliability development goals and distribution network construction standards are set based on the actual situation and development positioning of the planning area. Following the principle of tiered planning, this invention first establishes a backbone network planning model. Combining typical network connection types, it optimizes line investment costs under specific connection modes to meet the planning needs of different regions and connection modes. Secondly, a secondary backbone network self-healing control model is established. Based on the determined distribution network self-healing control strategy, a correspondence between the investment scale of intelligent automation equipment and distribution network reliability is established. This is used to construct a mathematical model to optimize equipment coverage and provide recommended tiered planning schemes. Solving the above model with a solver yields a distribution network tiered planning scheme with coordinated self-healing control function under the guidance of distribution network power supply reliability goals that balances economy and technology. This provides a reference for formulating distribution network planning and development schemes under high power supply reliability development goals, optimizing the configuration of primary and secondary networks in terms of both technology and economy, and guiding the upgrading and transformation of the existing power grid and the planning and layout of the future network.
[0097] Figure 9 This is a structural block diagram of a power distribution network planning device with collaborative self-healing control function provided in an embodiment of this application, such as... Figure 9 As shown, the device includes: The data determination module 910 determines the grid connection type, switch station configuration data, and power supply reliability standard data based on the distribution network planning data of the target area to be planned. The wiring determination module 920 determines the wiring results of the switch station based on the configuration data of the switch station and the wiring type of the grid structure, and on the backbone grid planning model. The power supply determination module 930 determines the backbone network wiring result based on the switch station wiring result and the distance between the midpoint switch station and the power supply point in the switch station wiring result. The configuration determination module 940 determines the configuration of secondary equipment in the secondary trunk network based on the wiring results of the main trunk network and the power supply reliability standard data, and on the self-healing control model of the secondary trunk network.
[0098] In one alternative implementation, the method for constructing a backbone network planning model includes: Based on the switch station configuration data and the grid connection type, the first decision variable of the backbone grid planning model is determined, and the first decision variable is whether the switch stations are connected. The first objective function and its constraints are generated based on the first decision variable, thus obtaining the backbone network planning model.
[0099] In one optional implementation, the backbone network planning model is obtained by generating a first objective function and constraints on the first objective function based on the first decision variables, including: When the first objective function aims to minimize the total cost of new line construction, the constraints of the first objective function include at least one of the following: The number of switching stations in each switching station line is less than or equal to the first threshold; The capacity of each switching station line is less than or equal to the second threshold; The number of lines at each switching station is unique; The first decision variable is either 0 or 1.
[0100] In one optional embodiment, the network wiring type includes a backbone network wiring type and a secondary backbone network wiring type; the backbone network wiring type includes at least one of the following: double petal wiring, double ring network wiring, and single ring network wiring; the secondary backbone network wiring type includes at least one of the following: double ring network wiring, single ring network wiring, dual access cascade connection line, and dual access wiring. The switch station configuration data includes at least one of the following: the number of switch stations in the target area to be planned, the location of the switch stations, the location of the power supply points, and the number of power supply points; Distribution network planning data includes at least one of the following: economic development data, planning and development positioning data, distribution network infrastructure configuration data, and electricity load forecast data; The backbone network wiring results include the switch station wiring results and the connection relationship between the terminal switch stations and the nearest power supply point; Among them, the endpoint switch station is the switch station located at the endpoint in the switch station wiring result.
[0101] In one optional implementation, the method for constructing a self-healing control model for a secondary trunk network includes: Based on the wiring results of the main network, the second decision variable of the secondary network self-healing control model is determined. The second decision variable is whether the target switch is equipped with intelligent automation equipment. The target switch includes at least one of the following: switches on the feeder of the main network switch station, switches on the incoming line of the power distribution room, and switches on the outgoing line of the power distribution room. The second objective function is generated based on the second decision variable, and the constraints of the second objective function are used to obtain the self-healing control model of the secondary trunk network.
[0102] In one optional implementation, the secondary trunking network self-healing control model is obtained by generating a second objective function and constraints based on the second decision variable, including: When the second objective function is to minimize the total investment in equipment, the constraints of the second objective function include: the power supply reliability value corresponding to each equipment configuration of the target switch is greater than or equal to the power supply reliability threshold corresponding to the power supply reliability standard data.
[0103] In one optional implementation, the method for determining the power supply reliability value includes: Based on the set of anticipated incidents, the corresponding power outage time is determined according to the equipment configuration of the target switch. Determine the power supply reliability value based on the power outage time; Establish a mapping table between the equipment configuration and power supply reliability values of the target switch; The power supply reliability value corresponding to each device configuration of the target switch is determined based on the mapping table.
[0104] The above embodiments take into account both the requirements of primary grid planning and secondary system configuration, and are applicable to the development of high-reliability urban distribution networks, as well as investment cost optimization scenarios for distribution network optimization and equipment configuration under various wiring modes. Differentiated planning schemes are formulated according to the functional positioning and power supply requirements of different areas within the city, improving the targeting of planning for different areas while optimizing investment. like Figure 10 As shown in the figure, this application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114. Memory 113 is used to store computer programs; In one embodiment of this application, when the processor 111 executes a program stored in the memory 113, it implements the method provided in any of the foregoing method embodiments, including: Based on the distribution network planning data of the target area to be planned, determine the grid connection type, switch station configuration data, and power supply reliability standard data; based on the switch station configuration data and the grid connection type, determine the switch station connection results based on the backbone grid planning model; based on the switch station connection results and the distance between the endpoint switch stations and the power supply points in the switch station connection results, determine the backbone grid connection results; based on the backbone grid connection results and the power supply reliability standard data, determine the secondary equipment configuration in the secondary backbone grid based on the secondary backbone grid self-healing control model.
[0105] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.
[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0108] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.
Claims
1. A power distribution network framework planning method of a synergistic self-healing control function, characterized in that, The method comprises the following steps: determining a network architecture connection type, a switch station configuration data, and a power supply reliability standard data according to power distribution network planning data of a target region to be planned; determining a switch station connection result based on a main network architecture planning model according to the switch station configuration data and the network architecture connection type; determining a main network architecture connection result according to the switch station connection result and distances between endpoint switch stations and power supply points in the switch station connection result; determining a secondary device configuration in a secondary network based on a secondary network self-healing control model according to the main network architecture connection result and the power supply reliability standard data.
2. The method of claim 1, wherein, The main network architecture planning model construction method comprises the following steps: determining a first decision variable of the main network architecture planning model according to the switch station configuration data and the network architecture connection type, wherein the first decision variable is whether the switch stations are connected; generating a first objective function and constraint conditions of the first objective function according to the first decision variable to obtain the main network architecture planning model.
3. The method of claim 2, wherein, The method of generating a first objective function and constraint conditions of the first objective function according to the first decision variable to obtain the main network architecture planning model comprises the following steps: when the first objective function is to minimize the total new line cost, the constraint conditions of the first objective function comprise at least one of the following: the number of switch stations in each switch station line is less than or equal to a first threshold value; the capacity of each switch station line is less than or equal to a second threshold value; the number of each switch station line is unique; the first decision variable is 0 or 1.
4. The method of claim 1, wherein, The network architecture connection type comprises a main network connection type and a secondary network connection type; the main network connection type comprises at least one of the following: a double-petal connection, a double-loop network connection, and a single-loop network connection; the secondary network connection type comprises at least one of the following: a double-loop network connection, a single-loop network connection, a double-access cascade connection, and a double-access connection; The switch station configuration data comprises at least one of the following: the number of switch stations in the target region to be planned, the positions of the switch stations, the positions of the power supply points, and the number of the power supply points; The power distribution network planning data comprises at least one of the following: economic development data, planning development positioning data, power distribution network basic configuration data, and electricity load prediction data; The main network architecture connection result comprises the switch station connection result and a connection relationship between an endpoint switch station and a nearest power supply point. The endpoint switch station is a switch station at an endpoint in the switch station connection result.
5. The method of claim 1, wherein, The secondary network self-healing control model construction method comprises the following steps: determining a second decision variable of the secondary network self-healing control model according to the main network architecture connection result, wherein the second decision variable is whether a target switch is configured with intelligent automatic equipment; the target switch comprises at least one of the following: a switch on a main network switch station feeder line, a switch on a distribution room incoming line, and a switch on a distribution room outgoing line; generating a second objective function and constraint conditions of the second objective function according to the second decision variable to obtain the secondary network self-healing control model.
6. The method of claim 5, wherein, The method of generating a second objective function and constraint conditions of the second objective function according to the second decision variable to obtain the secondary network self-healing control model comprises the following steps: When the second objective function is the minimum total investment of equipment, the constraint condition of the second objective function comprises: the power supply reliability value corresponding to each equipment configuration of the target switch is greater than or equal to the power supply reliability threshold value corresponding to the power supply reliability standard data.
7. The method of claim 6, wherein, The method for determining the power supply reliability value comprises: determining the corresponding fault outage time according to the equipment configuration of the target switch based on the set of expected accidents; determining the power supply reliability value according to the fault outage time; establishing a mapping table of the equipment configuration of the target switch and the power supply reliability value; determining the power supply reliability value corresponding to each equipment configuration of the target switch according to the mapping table.
8. A power distribution network grid planning device with a synergistic self-healing control function, characterized by, The method comprises: a data determination module configured to determine the network connection type, the switch station configuration data, and the power supply reliability standard data according to the power distribution network planning data of the target region to be planned; a connection determination module configured to determine the switch station connection result based on the main network planning model according to the switch station configuration data and the network connection type; a power supply determination module configured to determine the main network connection result according to the switch station connection result and the distance between the endpoint switch station in the switch station connection result and the power supply point; a configuration determination module configured to determine the secondary equipment configuration in the secondary network based on the secondary network self-healing control model according to the main network connection result and the power supply reliability standard data.
9. An electronic device, comprising: The computer program product comprises a memory and a processor, wherein the memory stores the computer program, and the processor executes the computer program to implement the method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer program product comprises a memory and a processor, wherein the memory stores the computer program, and the processor executes the computer program to implement the method according to any one of claims 1-7.