Power distribution network thermal stability planning method and device based on node power interval indexes

Abstract

The invention relates to the technical field of power distribution network planning, discloses a power distribution network thermal stability planning method and device based on node power interval indexes, and is used for solving the problem that a planned power distribution network is easy to cause thermal stability due to uncertain distributed photovoltaic spatial distribution in the prior art. Constructing a graph model of the current power distribution network based on the related information of each node; determining a target node to be subjected to power distribution planning, determining a power supply path from the graph model by using a depth-first search algorithm, and calculating a node interval index value set of the target node based on the power supply path and load and photovoltaic time sequence characteristics of an area where the target node is located; based on the node interval index value set, performing thermal stability evaluation on the power distribution network planning scheme of the target node by using a preset thermal stability evaluation rule to obtain an evaluation result; and optimizing the node interval index value set based on the evaluation result, and outputting a final power distribution network planning scheme of the target node.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network planning technology, specifically a distribution network thermal stability planning method and device based on node power range indices. Background Technology

[0002] Distribution network planning is a technology that coordinates the power supply capacity and carrying capacity of high-voltage and medium-voltage distribution networks based on load and distributed generation demand. In existing technologies, high-voltage distribution networks are planned using capacity-to-load ratio indicators, while medium-voltage distribution networks are planned based on spatial load forecasting results for substation site selection, capacity determination, and network planning. Although existing methods can achieve a certain degree of coordination, the spatial distribution of distributed photovoltaic (PV) power generation is difficult to pinpoint accurately during the planning stage due to policy and building conditions. Therefore, using existing distribution network planning methods cannot accurately assess the grid's capacity to accommodate distributed power sources, easily leading to thermal stability problems. Specifically, existing planning methods struggle to accurately quantify the impact of distributed PV access on the distribution network's thermal stability, cannot effectively identify weak links in the distribution network, and may result in potential overload risks in the planning scheme. Furthermore, traditional methods lack comprehensive consideration of load and PV temporal characteristics, making it difficult to dynamically assess the grid's carrying capacity at different times, further increasing the probability of thermal stability problems in the distribution network. Summary of the Invention

[0003] This invention proposes a distribution network thermal stability planning method and device based on node power range index, aiming to solve the problem that the spatial distribution uncertainty of distributed photovoltaics in the prior art makes the planned distribution network prone to thermal instability.

[0004] In a first aspect, the present invention provides a distribution network thermal stability planning method based on nodal power range indices, the method comprising:

[0005] Collect the set of nodes in the current distribution network and the relevant information of each node in the set of nodes, and construct a graph model of the current distribution network based on the relevant information of each node, wherein the relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes;

[0006] The target node to be planned for power distribution is determined, and the power supply path is determined from the graph model using a depth-first search algorithm. Based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, the set of node interval index values ​​of the target node is calculated.

[0007] Based on the set of index values ​​for the node interval, the thermal stability of the distribution network planning scheme of the target node is evaluated using a preset thermal stability evaluation rule, and the evaluation result is obtained.

[0008] Based on the evaluation results, optimize the set of index values ​​for the node intervals and output the final distribution network planning scheme for the target node.

[0009] In an optional implementation, the step of collecting the set of nodes in the current distribution network and the relevant information of each node in the set, and constructing a graph model of the current distribution network based on the relevant information of each node, includes:

[0010] Based on the predefined distribution network component node types, identify all nodes in the current distribution network that match the distribution network component node types to obtain a node set. The distribution network component node types include switching stations, ring main units, distribution towers, and line endpoints in the distribution network.

[0011] Collect the devices associated with each node in the node set, their device parameters, and their connection relationships with other nodes.

[0012] Based on the devices and the connection relationships, a node-edge tree structure is constructed, and the parameters of the corresponding nodes in the tree structure are generated based on the device parameters of the devices in each node, thus obtaining the graph model of the current power distribution network.

[0013] In an optional implementation, the set of node interval index values ​​includes power interval indexes and weak link lines; the step of using a depth-first search algorithm to determine the power supply path from the graph model, and calculating the set of node interval index values ​​for the target node based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, includes:

[0014] Starting from the target node, the power supply path of the target node is obtained by tracing upwards along the graph model using a depth-first search algorithm to the power supply starting node of the current distribution network.

[0015] Based on the load and photovoltaic timing characteristics of the area where the target node is located, the power range index and weak link lines of the power supply path are calculated.

[0016] In an optional implementation, the step of tracing forward along the graph model from the target node to the current power supply starting node of the distribution network using a depth-first search algorithm to obtain the power supply path of the target node includes:

[0017] Starting from the target node, select the upward connection relationship in the target node, traverse all nodes in the graph model along the direction of the power supply starting node, and extract the power supply path of the target node that satisfies the current distribution network operation status.

[0018] In an optional implementation, calculating the power range index and weak link lines of the power supply path based on the load and photovoltaic timing characteristics of the area where the target node is located includes:

[0019] The equipment parameters of each node in the power supply path are extracted, and combined with the load and photovoltaic timing characteristics of the area where the target node is located, the power flow calculation is performed on the power supply path to obtain the power range index and weak link line of the target node.

[0020] In an optional implementation, the assessment of the thermal stability of the distribution network planning scheme of the target node based on the set of node interval index values ​​and using preset thermal stability assessment rules to obtain the assessment results includes:

[0021] The evaluation threshold of the index is determined according to the preset thermal stability evaluation rules, and the evaluation threshold is the maximum power limit;

[0022] The capacity of the power index range and the weak link line in the set of node interval index values ​​are compared with the evaluation threshold respectively;

[0023] Based on the comparison results, potential overload nodes and / or weak links in the power supply path are identified, and the identification results are aggregated and analyzed to generate the thermal stability assessment results of the distribution network planning scheme for the target node.

[0024] In an optional implementation, optimizing the set of node interval index values ​​based on the evaluation results and outputting the final distribution network planning scheme for the target node includes:

[0025] Based on the evaluation results, identify nodes and / or weak links in the circuit that do not meet the thermal stability constraints;

[0026] Optimize the nodes and / or weak links in the lines, wherein the optimization includes adjusting line parameters, optimizing connection relationships, or reallocating distributed photovoltaic access points;

[0027] The optimization results are updated to the node interval index value set, and the optimized distribution network planning scheme is recalculated until the thermal stability constraint conditions are met.

[0028] Secondly, the present invention provides a distribution network thermal stability planning device based on nodal power range indices, the device comprising:

[0029] The model building module is used to collect the node set in the current distribution network and the relevant information of each node in the node set, and to build a graph model of the current distribution network based on the relevant information of each node, wherein the relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes.

[0030] The index calculation module is used to determine the target node of the power distribution plan, and to use the depth-first search algorithm to determine the power supply path from the graph model. Based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, the module calculates the set of node interval index values ​​of the target node.

[0031] The evaluation module is used to evaluate the thermal stability of the distribution network planning scheme of the target node based on the set of index values ​​of the node interval and using preset thermal stability evaluation rules, and obtain the evaluation result.

[0032] The optimization module is used to optimize the set of index values ​​for the node interval based on the evaluation results, and output the final distribution network planning scheme for the target node.

[0033] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the above-mentioned distribution network thermal stability planning method based on node power range index.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the above-mentioned distribution network thermal stability planning method based on node power range indicators.

[0035] The embodiments of the present invention bring the following beneficial effects:

[0036] This invention provides a method and apparatus for distribution network thermal stability planning based on node power range indices. The method involves collecting a set of nodes in the current distribution network and related information about each node, constructing a graph model of the current distribution network based on this information, including equipment parameters and connections to other nodes. The method then identifies the target node for distribution network planning and uses a depth-first search algorithm to determine the power supply path from the graph model. Based on the power supply path and the load and photovoltaic timing characteristics of the target node's region, it calculates a set of node range index values ​​for the target node. Based on this set of node range index values, it evaluates the thermal stability of the distribution network planning scheme for the target node using preset thermal stability assessment rules, obtaining the evaluation results. Finally, it optimizes the set of node range index values ​​based on the evaluation results and outputs the final distribution network planning scheme for the target node. This method achieves thermal stability planning even when the spatial distribution of distributed photovoltaic power is uncertain, by calculating the set of node range indices for the target node in the distribution network and dynamically optimizing the distribution network planning scheme based on the evaluation results.

[0037] Other features and advantages of the invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 A flowchart of a distribution network thermal stability planning method based on node power range index provided in an embodiment of the present invention;

[0041] Figure 2 This is a flowchart illustrating the set of computation node interval indicators provided in an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the structure of the graphical model provided in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram illustrating the process of searching for a power supply path provided in an embodiment of the present invention;

[0044] Figure 5 A flowchart of power distribution network planning and design provided for embodiments of the present invention;

[0045] Figure 6 A flowchart for assessing the grid carrying capacity of distributed power sources provided in an embodiment of the present invention;

[0046] Figure 7 A schematic diagram of a distribution network thermal stability planning device based on node power range index provided in an embodiment of the present invention;

[0047] Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0050] In related technologies, distribution network planning coordinates the power supply capacity of high-voltage and medium-voltage distribution networks through capacity-to-load ratio indicators and spatial load forecasting. However, the planning of distributed photovoltaic (PV) power generation is affected by factors such as policies and building conditions, making it difficult to accurately determine its spatial distribution during the planning stage. This makes it impossible for existing methods to assess the grid's capacity to accommodate distributed power sources, easily leading to thermal stability problems such as line overload or equipment overheating. For example, when planning to add distributed PV power generation in a certain area, the uncertain location of PV access makes it impossible for traditional methods to dynamically analyze the impact of different access points on the line's carrying capacity, potentially causing local lines to operate under long-term overload.

[0051] To address the aforementioned issues, this invention proposes constructing a power distribution network diagram model to reflect equipment parameters and connection relationships. It also uses a depth-first search algorithm to trace power supply paths, calculates power range indicators based on time-series characteristics, identifies weak links, and finally achieves thermal stability planning through iterative optimization.

[0052] Please see Figure 1 This invention provides a specific implementation of a distribution network thermal stability planning method based on nodal power range indices, which includes the following steps:

[0053] 110. Collect the set of nodes in the current distribution network and the relevant information of each node in the set, and construct a graph model of the current distribution network based on the relevant information of each node. The relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes.

[0054] In this embodiment, each node in the distribution network is a custom node created by the staff based on their experience with distribution networks. This node can actually be understood as a power supply device, tool, or line that can be used to supply power to the target area.

[0055] In practical applications, before collecting data on the nodes and related information of the distribution network, it is necessary to define or configure the constituent nodes of the distribution network. Specifically, the node types of the distribution network are first configured, such as substations, ring main units, and switchgear. In addition to configuring the node types of the distribution network, it is also necessary to define the node index types for each node type, such as maximum power supply capacity, maximum carrying capacity, and weak link lines.

[0056] Then, based on the defined node types, nodes are identified from the current distribution network, that is, power equipment, tools, and lines in the current distribution network that match the definition of each node type, to obtain a node set. Based on each node in the node set, the equipment information of the equipment associated with that node and the connection relationship with other nodes are extracted.

[0057] In practical applications, defining the types of distribution network nodes and node interval indicators includes: specifying the node type as a switchgear, ring main unit, distribution tower, and line endpoint in a medium-voltage distribution network; defining node interval indicators, which include maximum power supply capacity DnCapacity, maximum carrying capacity UpCapacity, and weak link lines DnLimitLine and UpLimitLine, where DnLimitLine is defined as a medium-voltage distribution network line with a limit on maximum power supply capacity DnCapacity, and UpLimitLine is defined as a medium-voltage distribution network line with a limit on maximum carrying capacity UpCapacity.

[0058] It should be noted that this set of nodes can also be understood as the topology nodes corresponding to key equipment such as switching stations and ring main units in the distribution network. Specifically, it can be implemented by identifying predefined equipment types to reflect the physical connection relationship of the distribution network.

[0059] After identifying and extracting all nodes and their corresponding equipment parameters and connections, a corresponding network model is abstracted based on these parameters and connections. This graph model refers to a tree-like structure with nodes as vertices and connections as edges. Specifically, it can be constructed by extracting equipment parameters and topological connections, and is used to carry the electrical parameters and topological information of the distribution network. Specifically, first, nodes such as switching stations and ring main units in the distribution network are identified; then, their equipment parameters and connections are collected; finally, a graph model containing electrical parameters and topological structure is constructed based on the collected equipment parameters and connections.

[0060] 120. Determine the target node for the power distribution plan, and use the depth-first search algorithm to determine the power supply path from the graph model. Based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, calculate the set of node interval index values ​​for the target node.

[0061] In this embodiment, the depth-first search algorithm refers to a path search method that traces upwards from the target node to the starting power supply node. Specifically, it can be implemented by recursively traversing the connection relationship of the graph model to determine the power supply path of the target node.

[0062] The set of index values ​​for this node interval refers to dynamic interval data that reflects the power carrying capacity of the power supply path. Specifically, it can be obtained by combining power flow calculation with time-series characteristics and is used to quantitatively assess the power supply capacity limitations at different time periods.

[0063] Specifically, after identifying the target node to be planned, a depth-first search algorithm is used to trace back to the power supply initiation node, selecting power supply paths that meet the current distribution network operation status. Combining the load and photovoltaic time-series curves of the target area, the time is divided into three typical periods: peak, flat, and valley. Power flow calculations are performed on the power supply paths to obtain the maximum power supply capacity and carrying capacity for each period, forming power range indicators. By comparing the indicator values ​​with preset thresholds, overloaded nodes or lines with insufficient capacity in the power supply path are identified and marked as weak links.

[0064] 130. Based on the set of index values ​​of node intervals, the thermal stability of the distribution network planning scheme of the target node is evaluated using the preset thermal stability evaluation rules, and the evaluation results are obtained.

[0065] It should be noted that this thermal stability assessment rule refers to the threshold conditions for judging whether the power index exceeds the limit. Specifically, it can be set through the equipment's rated parameters or historical operating data to identify potential overload risks.

[0066] Understandably, the evaluation threshold or evaluation method for each indicator is first determined based on the preset thermal stability evaluation rules. Each indicator in the node interval indicator set is extracted, and the corresponding evaluation threshold or evaluation method is used to evaluate the indicator. Then, the evaluation results of each indicator are combined to determine the thermal stability of the current distribution network. An evaluation report can even be produced based on the evaluation results.

[0067] 140. Optimize the set of index values ​​for node intervals based on the evaluation results, and output the final distribution network planning scheme for the target node.

[0068] If the assessment results show that the thermal stability does not meet the standard, the line parameters are adjusted or the photovoltaic access point is optimized, the indicators are recalculated until the constraints are met, and the optimized planning scheme is finally output.

[0069] The method provided in this application, by constructing a distribution network diagram model, calculating node interval indicators by combining load and photovoltaic time-series characteristics, and dynamically optimizing the planning scheme based on thermal stability assessment rules, can accurately quantify the impact of distributed photovoltaic access on the thermal stability of the power grid, effectively identify weak links in the lines, and dynamically assess the carrying capacity at different times by combining time-series characteristics. It has the advantages of improving the accuracy of distribution network thermal stability assessment and reducing overload risk.

[0070] In one specific implementation, for step 110, the step of collecting the set of nodes in the current distribution network and the relevant information of each node in the set of nodes, and constructing a graph model of the current distribution network based on the relevant information of each node, includes:

[0071] Based on the predefined distribution network component node types, identify all nodes in the current distribution network that match the distribution network component node types to obtain a node set. The distribution network component node types include switching stations, ring main units, distribution towers, and line endpoints in the distribution network.

[0072] Collect the devices associated with each node in the node set, their device parameters, and their connection relationships with other nodes.

[0073] Based on the devices and the connection relationships, a node-edge tree structure is constructed, and the parameters of the corresponding nodes in the tree structure are generated based on the device parameters of the devices in each node, thus obtaining the graph model of the current power distribution network.

[0074] It should be noted that the node type of the distribution network refers to the node classification corresponding to the key equipment in the distribution network as defined in advance. Specifically, it can be implemented by using standardized equipment classification rules, such as taking switching stations and ring main units as core node types. By matching the types, it can be ensured that the node set covers the key topological elements of the distribution network, thereby providing basic support for subsequent model construction.

[0075] The equipment parameters refer to the operating parameters of the electrical equipment associated with the node. Specifically, they can be obtained using equipment nameplate data or real-time monitoring data, such as line rated current and transformer capacity. Parameter acquisition provides a dynamic data foundation for the graph model.

[0076] The connection relationship refers to the electrical connection topology between nodes, which can be stored using an adjacency matrix or linked list structure. By parsing the physical connection relationship between devices, a tree structure is constructed to accurately reflect the hierarchical power supply path of the distribution network.

[0077] The node-edge tree structure refers to a tree-shaped topology model with nodes as vertices and connections as edges. It can be implemented using graph databases or tree data structures. The tree-like hierarchical relationship expresses the power supply directionality of the distribution network and provides structured support for subsequent path tracing.

[0078] In practical applications, step 110, when constructing the graph model, first selects key equipment nodes in the distribution network based on predefined node types. For example, switching stations and ring main units are used as backbone nodes, while distribution towers and line endpoints are used as branch nodes, forming a complete node set. Then, for each node, the operating parameters of its associated equipment are collected, such as the incoming and outgoing line capacity of the ring main unit and the conductor cross-sectional area of ​​the distribution tower. Simultaneously, the physical connection relationships between nodes are recorded, such as the cable connection path between the ring main unit and the distribution tower. Based on this information, nodes are used as vertices and connection relationships as edges to construct a tree-like topology with a clear power supply direction. Equipment parameters are mapped to the attributes of the corresponding nodes, forming a graph model that includes electrical parameters and topological relationships. This model can dynamically reflect the real-time structure of the distribution network, providing an accurate data foundation for subsequent power supply path analysis and thermal stability assessment.

[0079] By identifying structured node types and collecting parameters, the constructed graph model accurately reflects the actual topology and equipment status of the distribution network, providing reliable data support for subsequent calculations of power range indicators and identification of weak links. For example, when the location of photovoltaic grid connection changes dynamically, the tree-structured graph model can quickly update node parameters and connection relationships, ensuring that the planning scheme always matches the actual operating state of the power grid, thereby effectively avoiding thermal stability risks caused by model deviations.

[0080] Furthermore, the collection of devices associated with each node in the node set, device parameters, and connection relationships with other nodes can be achieved in the following ways:

[0081] Based on each node in the node set, the unique identifiers and parameter information of the distribution transformers, feeders, and switching equipment on each node are collected; wherein the feeders include the power distribution lines between devices in the same node and the connection relationships between different nodes.

[0082] A device attribute table is established based on the unique identifier and the parameter information to obtain the device parameters, wherein the attribute table includes rated voltage, rated capacity and maximum allowable current carrying capacity.

[0083] For example, the node type of the distribution network is first defined as any location in the medium-voltage distribution network that can be connected to loads or distributed photovoltaics, such as switching stations, ring main units, distribution towers, and line endpoints. The node interval index set is defined as including the maximum power supply capacity DnCapacity, the maximum carrying capacity UpCapacity, and the weak link lines DnLimitLine and UpLimitLine. DnLimitLine is defined as the medium-voltage distribution network line that limits the maximum power supply capacity DnCapacity, and UpLimitLine is defined as the medium-voltage distribution network line that limits the maximum carrying capacity UpCapacity.

[0084] In practical applications, the definition of indicators is specifically achieved by constructing time-series curves using load classification and the new energy resource endowment of the planning area in actual sample calculations, thereby obtaining multiple indicators under time-series conditions for different types of nodes.

[0085] Then, based on the node types defined above, all nodes and equipment parameters under the nodes in the current distribution network are extracted. Based on the extracted data, the distribution network is abstracted into a tree diagram model containing a set of nodes (V) and a set of edges (E), where the set of edges (E) is the distribution lines and the set of nodes (V) contains all the node types defined above.

[0086] Specifically, the power grid is abstracted into a power grid diagram. Figure 2 In this context, V1, V2, etc., are sets of nodes, and L1, L2, etc., are sets of edges. Figure 2 As can be seen, a graph model is essentially a point-edge relationship model; therefore, constructing a power distribution network graph model is as follows: Figure 3 As shown.

[0087] In other words, the construction of the graph model of the distribution network includes: collecting equipment parameters corresponding to predefined node types. The parameters include at least the device ID, upstream power supply connection relationship and power supply line information, and the parameters will serve as the basic data for power supply path search when calculating the node interval index set.

[0088] Building a graph model is essentially about constructing the relationships between nodes and devices. Because the power distribution network has a tree-like structure, it follows a tree-graph structure.

[0089] In one specific implementation, the set of node interval index values ​​includes power interval indexes and weak link lines; in step 120 above, the power supply path is determined from the graph model using a depth-first search algorithm, and based on the power supply path and the load and photovoltaic timing characteristics of the area where the target node is located, the set of node interval index values ​​for the target node is calculated, including:

[0090] Starting from the target node, the power supply path of the target node is obtained by tracing upwards along the graph model using a depth-first search algorithm to the power supply starting node of the current distribution network.

[0091] Based on the load and photovoltaic timing characteristics of the area where the target node is located, the power range index and weak link lines of the power supply path are calculated.

[0092] It should be noted that this power range index refers to the power range that the power supply path can carry in different typical time periods. Specifically, it can be determined by dividing the time periods into peak, flat, and valley periods and calculating the maximum power supply capacity and carrying capacity, which is used to quantitatively evaluate the power supply margin of the distribution network.

[0093] The weak link line refers to the distribution line with the lowest capacity limitation in the power supply path. Specifically, it can be located by identifying the equipment capacity parameters and power flow calculation results in the path, which is used to reveal the key nodes that limit the grid's capacity.

[0094] Specifically, after determining the power supply path to the target node, the time-series curves of load and photovoltaic (PV) power are constructed and divided into typical time periods. Power flow calculation algorithms are then used to perform time-series analysis on the power supply path. For example, during periods of high PV output, the matching relationship between the line capacity and load demand in the path is calculated, thereby determining the upper limit of the power range index. Simultaneously, by comparing the capacity parameters of each line with the power flow calculation results, lines causing power limitations can be identified as weak links. This process enables the planning scheme to dynamically reflect the impact of distributed PV integration on the grid's thermal stability.

[0095] In practical applications, the calculation process for power range indicators and weak link lines in a distribution network specifically involves using a graphical model of the distribution network and applying a depth-first search algorithm to search for the power supply paths of each node. The power supply bottlenecks along these paths represent the node's power supply capacity and carrying capacity constraints, thus yielding the power range indicators. This process includes the following steps:

[0096] First, starting from the target node, select the upward connection relationship in the target node, traverse all nodes in the graph model along the direction of the power supply starting node, and extract the power supply path of the target node that satisfies the current distribution network operation status.

[0097] Specifically, the power supply path is constructed by searching the connectivity relationships between nodes and power sources in the distribution network, thereby calculating the maximum power supply capacity and maximum load capacity of each node. An example is shown below. Figure 4 As shown, V1 is the power supply point. The process of searching for V1 from V7 eventually yields the power supply path: V7-V6-V8-V1.

[0098] Then, the equipment parameters of each node in the power supply path are extracted, and combined with the load and photovoltaic timing characteristics of the area where the target node is located, the power flow calculation is performed on the power supply path to obtain the power range index and weak link line of the target node.

[0099] It should be noted that the equipment parameters refer to the line's resistance, reactance, and capacity limit parameters. Specifically, these can be achieved using the line impedance data recorded by the power distribution automation system or the rated capacity marked on the equipment nameplate, which reflects the line's electrical characteristics and load-bearing capacity.

[0100] The load and photovoltaic time-series characteristics refer to the dynamic changes in electricity demand and photovoltaic power generation in the target area at different points in time. Specifically, they can be achieved by using load curves collected by smart meters and output curves recorded by photovoltaic inverters, which are used to characterize the time-varying characteristics of the power grid operation status.

[0101] Power flow calculation refers to a numerical method for obtaining the voltage and branch power distribution of each node in the power grid by solving the node power balance equation. Specifically, it can be implemented using the Newton-Raphson method or the forward-backward substitution method, and is used to quantitatively analyze the power transmission status of the power supply path.

[0102] Specifically, after obtaining the power supply path, the system first extracts the line impedance parameters and transformer capacity data corresponding to each node on the path from the graph model, and simultaneously obtains the historical load curve and photovoltaic (PV) predicted output curve for the target area. After dividing the time-series data into peak, flat, and valley periods, equivalent grid models for each period are established, and power distribution along the power supply path under different periods is solved through power flow calculations. Based on the calculation results, the maximum allowable transmission power of the lines in each period is determined, and the intersection of the three periods is taken as the power range index. Simultaneously, by comparing the power transmission values ​​of each line with its rated capacity in different periods, lines that consistently approach or exceed the capacity threshold are marked as weak links. For example, during the midday period when PV output is high, if a line is overloaded due to reverse power flow, it is identified as a weak link requiring key upgrades. By integrating time-series characteristics and multi-scenario analysis, the grid's capacity to accommodate distributed power sources can be dynamically assessed, and key lines restricting PV absorption can be accurately identified. This not only provides a more scientific basis for distribution network planning but also significantly reduces the risk of line overload caused by PV fluctuations, ensuring the thermal stability of the grid operation.

[0103] In practical applications, determining the power supply path to the target node is actually preparation for subsequent distribution network capacity assessment. That is, the distribution network's capacity is limited by the minimum available capacity (i.e., the power supply bottleneck) along the power supply path from the target node to the power source. Therefore, the calculation of the node interval index value set for the target node is specifically as follows:

[0104] In a typical radial distribution network, there is only one power supply path between any load node and the main power source. (A ring network also finds an effective power supply path based on the current operating status.)

[0105] Traverse all equipment (including lines and transformers) along the power supply path, calculate the remaining capacity of each link, and extract the minimum value, which is the maximum connectable capacity (carrying capacity) of the target node.

[0106] After determining the load-bearing capacity, power flow calculations can be performed based on path impedance parameters to verify whether the connection will cause heavy overload, etc.

[0107] In one specific implementation, the step of combining the load and photovoltaic timing characteristics of the area where the target node is located to perform power flow calculation on the power supply path, and obtaining the power range index and weak link line of the target node, includes:

[0108] Based on the new energy resource endowment and load classification characteristics of the region where the target node is located, a time-series curve of load and photovoltaic power is constructed.

[0109] The time-series curve is divided into three typical periods: peak, flat, and valley. The power flow calculation algorithm is used to perform time-series power flow calculation on the power supply path to obtain the maximum power supply capacity and maximum carrying capacity of the target node under each typical period.

[0110] Based on the maximum power supply capacity and maximum carrying capacity of the target node under each typical time period, the power range index of the target node is determined, and the weak link line in the power supply path that causes power limitation is identified.

[0111] It should be noted that the new energy resource endowment and load classification characteristics refer to the distribution characteristics of photovoltaic power generation resources and the differences in electricity load types within the target area. Specifically, this can be achieved by cluster analysis using historical power generation data and electricity consumption behavior data, which is used to reflect the dynamic matching relationship between photovoltaic output and load demand in different areas.

[0112] This time-series curve refers to the continuous variation curve of load demand and photovoltaic power generation generated according to the time series. Specifically, it can be generated by the moving average method or the probability density estimation method, and is used to quantify the time-series fluctuation characteristics of photovoltaic output and load demand.

[0113] Among them, the three typical periods of peak, flat and valley refer to the high, medium and low electricity consumption periods divided according to the characteristics of the load curve. Specifically, the K-means clustering algorithm or the ternary method can be used to divide the periods, which is used to establish the power grid carrying capacity assessment benchmark under different operating scenarios.

[0114] This time-series power flow calculation refers to the steady-state analysis of a power system that takes into account the dynamic changes in photovoltaic output and load. Specifically, it can be implemented using the Newton-Raphson method or the forward-backward substitution method to calculate the line power distribution and node voltage state at different time periods.

[0115] The power flow calculation algorithm consists of the following three parts:

[0116] Calculate the voltage distribution at each node along the path;

[0117] The power flow distribution is determined based on the node voltage and line impedance;

[0118] The power supply limit and load-bearing limit of a node are determined by the extreme values ​​of the power flow distribution.

[0119] Specifically, firstly, based on the target area's photovoltaic (PV) installed capacity, solar irradiance data, and historical data on industrial, commercial, and residential loads, load and PV output curves with typical daily characteristics are generated. For example, for concentrated industrial and commercial areas, the PV output curve can be corrected by incorporating the installation tilt angle and shading rate of rooftop PV systems, and the load curve can distinguish the time-of-day distribution characteristics of production equipment and office power consumption. Next, the 24-hour operating cycle is divided into three periods—peak, flat, and valley—using a ternary method. The duration of each period can be dynamically adjusted according to the fluctuation characteristics of the actual load curve. During power flow calculation, a power grid operation model is established for each typical period. By iteratively solving the node power balance equations, the maximum transmission power of the lines and equipment operating parameters are obtained. For example, during peak PV output periods, the impact of reverse power flow on line current carrying capacity needs to be calculated, and information on line segments that cause equipment overload is recorded. Finally, by comparing the calculation results of the three periods, the power fluctuation range of the target nodes is determined, and lines that exhibit capacity bottlenecks in multiple periods are identified as weak links.

[0120] Furthermore, the step of determining the power range index of the target node based on the maximum power supply capacity and maximum carrying capacity of the target node under each typical time period, and identifying the weak link line in the power supply path that causes power limitation, includes:

[0121] Based on the maximum power supply capacity and maximum carrying capacity of the target node under each typical time period, determine the upper limit of power supply and the upper limit of carrying capacity of the target node;

[0122] Based on the power supply limit and the load limit, the power range index of the target node in each typical time period is constructed;

[0123] Identify the power distribution lines in the power supply path that have minimum capacity limitations, and designate these power distribution lines as the weak links of the target node.

[0124] The power supply limit refers to the maximum power supply that the target node can provide within a specific time period. It can be determined by combining power flow calculation algorithms with equipment capacity constraint parameters, and is used to characterize the limit value of the node's power supply capacity.

[0125] The upper limit of the load capacity refers to the maximum power load that the target node can withstand within a specific time period. It can be calculated by analyzing the line impedance characteristics and equipment operating parameters, and is used to characterize the boundary conditions of the node's load capacity.

[0126] Specifically, the maximum power supply capacity and maximum load capacity of the target node are calculated under three typical periods: peak, flat, and valley. For example, during the flat and valley periods when photovoltaic output is high, the power supply capacity may exceed the load demand, while during the peak load period, the power supply capacity may be insufficient. By dynamically matching the upper limits of power supply capacity and load capacity, power range indicators for each period are formed. For example, the upper limit of power supply is set to 90% of the transformer capacity, and the upper limit of load capacity is set to 85% of the line current carrying capacity. At the same time, the capacity parameters of all distribution lines in the power supply path are traversed, and the line with the smallest capacity is selected as the weak link. For example, if the rated current of a certain cable is only 200A, while other lines all reach 500A or more, then the cable is marked as the weak link.

[0127] In practical applications, based on the device parameters corresponding to the node types defined in the graph model, and combined with the new energy resource endowment and load classification characteristics of the planning area, a time-series curve of load and photovoltaic is constructed to obtain the uplink distributed photovoltaic access and downlink load power supply capacity space of the device at different time periods.

[0128] The construction of the time series curve also includes: dividing the load characteristics and photovoltaic output characteristics of typical days, months or years into at least 3 time periods (peak, flat and valley), and using the capacity space of each time period as the dynamic boundary condition for power flow calculation to adapt to the dynamic power demand of the defined node type.

[0129] In one specific implementation, for step 130, the thermal stability of the distribution network planning scheme of the target node is evaluated based on the set of node interval index values ​​using preset thermal stability evaluation rules, and the evaluation results are obtained, including:

[0130] The evaluation threshold of the index is determined according to the preset thermal stability evaluation rules, and the evaluation threshold is the maximum power limit;

[0131] The capacity of the power index range and the weak link line in the set of node interval index values ​​are compared with the evaluation threshold respectively;

[0132] Based on the comparison results, potential overload nodes and / or weak links in the power supply path are identified, and the identification results are aggregated and analyzed to generate the thermal stability assessment results of the distribution network planning scheme for the target node.

[0133] The thermal stability assessment rule refers to a set of standards used to determine whether a power distribution network planning scheme meets the thermal stability requirements. Specifically, it can be implemented using the maximum power limit as the core parameter. By setting threshold constraints under different scenarios, it ensures that the equipment operates within a safe range.

[0134] The assessment threshold refers to the maximum allowable power limit, which can be calculated by comprehensively considering factors such as the rated capacity of the equipment, the current carrying capacity of the line, and the ambient temperature. It is used to determine whether the power index exceeds the safe range.

[0135] Specifically, the assessment process begins by setting a maximum power limit as the assessment threshold based on equipment parameters and environmental conditions. For example, 80% of the transformer capacity or cable current carrying capacity is set as the safety upper limit. Then, the power index range of the target node and the capacity of the weak link line are compared with the corresponding thresholds. If the upper limit of the power index range exceeds the threshold or the capacity of the weak link line is lower than the threshold, it is marked as a potential overload node or weak link. Further, by aggregating and analyzing all marked points, their distribution density and impact range are statistically analyzed. For example, consecutive overload nodes on the same power supply path are identified as high-risk areas. Finally, a thermal stability assessment result containing risk level and location is generated.

[0136] In some specific implementations, the evaluation threshold can be dynamically adjusted based on historical operating data. For example, the threshold can be appropriately lowered during high-temperature periods in summer to address the decrease in heat dissipation efficiency. Furthermore, the aggregation analysis can employ a weighted allocation method to calculate a comprehensive risk value based on factors such as overload duration and power deviation, thereby prioritizing the handling of high-risk areas.

[0137] By using dynamic thresholds and aggregation analysis, overloaded nodes and weak lines can be accurately located, providing a clear direction for subsequent optimization. This enhances the distribution network's capacity to support high proportions of distributed power sources and improves its operational safety. At the same time, it can effectively identify potential thermal stability issues caused by distributed photovoltaic access in distribution network planning.

[0138] Furthermore, for step 140, optimizing the set of node interval index values ​​based on the evaluation results and outputting the final distribution network planning scheme for the target node includes:

[0139] Based on the evaluation results, identify nodes and / or weak links in the circuit that do not meet the thermal stability constraints;

[0140] Optimize the nodes and / or weak links in the lines, wherein the optimization includes adjusting line parameters, optimizing connection relationships, or reallocating distributed photovoltaic access points;

[0141] The optimization results are updated to the node interval index value set, and the optimized distribution network planning scheme is recalculated until the thermal stability constraint conditions are met.

[0142] Thermal stability constraints refer to the limitations imposed on power distribution equipment during operation when the temperature rise exceeds allowable values ​​due to current overload. This can be achieved by setting heat resistance thresholds for equipment materials to prevent insulation melting or overheating failures. Weak link lines refer to the section of the power supply path with the smallest capacity or highest impedance. This can be identified by comparing the difference between the line's current carrying capacity and the load demand, thus pinpointing bottlenecks that limit power supply capacity. Distributed photovoltaic (PV) grid connection points refer to the grid connection locations of PV power generation equipment. Site selection can be optimized by analyzing building parameters such as roof area and sunlight conditions to balance power generation and consumption demands in a local area.

[0143] Specifically, when the assessment results indicate an overload risk, the specific lines or nodes causing insufficient thermal stability are first identified. For example, for lines with insufficient capacity, line parameters can be adjusted by increasing the conductor cross-sectional area or replacing the transformer with a higher capacity one; for nodes with unreasonable connections, the network topology can be restructured to shorten the power supply distance; for areas with overly concentrated photovoltaic access, some of the power generation units can be relocated to load center areas. The optimized parameters are synchronously updated to the indicator set, and the thermal stability is verified through iterative calculations. If constraint violations still exist, the optimization process is restarted until all nodes and lines meet the operating temperature limits.

[0144] Furthermore, updating the optimization results to the node interval index value set and recalculating the optimized distribution network planning scheme until the thermal stability constraint condition is met includes:

[0145] Monitor the operating status and equipment parameters of each device at each node in the current power distribution network model;

[0146] When a change in network structure or device parameters is detected, the node interval index value of the node is automatically recalculated.

[0147] The thermal stability of the distribution network was reassessed based on the updated metrics.

[0148] In this embodiment, by applying the calculated set of node interval indicators to the post-planning evaluation of the distribution network, the capacity of distributed power generation access is verified and weak links are optimized, thus forming a planning closed loop.

[0149] Specifically, firstly, according to the "Technical Guidelines for Distribution Network Planning and Design," the input quantities, output quantities, and planning basis involved in each planning stage of the distribution network are as follows: Figure 5 As shown.

[0150] Then, the distribution network planning post-evaluation process is carried out. The distribution network planning process is the process of planning high and medium distribution networks based on the load and grid connection requirements of distributed photovoltaics. The distributed power grid access carrying capacity assessment is a post-verification process. In the distribution network, this process depends on information such as grid structure, parameters, and operation mode. According to the distribution network planning process, it can be included in the "planning post-evaluation" stage to verify whether the planned grid meets the requirements for safety and reliability.

[0151] According to the "Guidelines for Assessment of the Bearing Capacity of Distributed Power Generation Access to the Power Grid," due to uncertainties in load levels and external power grids during the distribution network planning stage, thermal stability is used as a constraint condition for bearing capacity. The bearing capacity assessment process is as follows: Figure 6 As shown.

[0152] Among them, the power range index of distribution network nodes is applied to the planning process. Figure 5 In the distribution network planning process, the power range indicators of distribution network nodes do not affect the "medium-voltage distribution network planning" process. This process, based on planning guidelines and according to power supply reliability and capacity requirements, selects a "10kV grid structure" to establish a medium-voltage distribution network supplying power to spatial loads on a plot-by-plot basis. During the selection of the "10kV grid structure," there is no spatial distribution information for distributed photovoltaic (PV) power; therefore, distributed PV integration need not be considered throughout the entire process.

[0153] The power index of distribution network nodes is applied to and influences the "post-grid planning evaluation" process. This process assesses the carrying capacity of distributed generation after it is connected to the grid. Addressing the uncertainty of the spatial distribution of distributed photovoltaic (PV) power in distribution network planning, this invention proposes establishing node power interval indices. Given a clear distribution network plan, this provides the available distributed PV capacity for each location within the distribution network, performs stability calculations, and optimizes weak links in the grid. This constitutes a closed-loop distributed PV planning process.

[0154] The node interval index is a dynamically updated index: when the distribution network topology, equipment parameters or operating mode change, steps 110-120 are re-executed to calculate new index values ​​for the defined node type using the updated graph model and parameters.

[0155] This invention significantly improves the scientific nature, dynamic adaptability, and thermal stability assurance capabilities of distribution network planning through three major innovations: node interval index quantification, time-series dynamic evaluation, and closed-loop iterative optimization. Specific effects are as follows:

[0156] 1. When calculating node interval indicators, the power supply path is traced through depth-first search, and weak lines with limited capacity are directly located by combining equipment parameters (such as line current carrying capacity and transformer capacity). This avoids the waste of resources caused by "experience-based expansion" in traditional planning and improves the efficiency of weak link identification.

[0157] 2. When calculating node interval indicators, the time-series characteristics of load and photovoltaic (peak / flat / valley time period division) are also integrated to construct a dynamic power flow calculation model, which upgrades the static capacity assessment to a dynamic analysis with "spatiotemporal dual dimensions", accurately calculates the capacity space at different time periods, thereby improving the grid planning for distributed photovoltaic access capacity and avoiding curtailment.

[0158] 3. The solution provided in this application realizes closed-loop control of "planning-evaluation-optimization-re-evaluation" and dynamic updating of indicators, realizes the planning and adaptation of the entire life cycle of distribution network control, ensures the long-term effectiveness of the planning scheme, and reduces the secondary transformation costs caused by system changes.

[0159] The above describes the distribution network thermal stability planning method based on node power range indices in the embodiments of the present invention. The following describes the distribution network thermal stability planning device based on node power range indices in the embodiments of the present invention; please refer to... Figure 7 One embodiment of the distribution network thermal stability planning device based on node power range index in this invention includes:

[0160] The model building module 710 is used to collect the node set in the current distribution network and the relevant information of each node in the node set, and to build a graph model of the current distribution network based on the relevant information of each node, wherein the relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes.

[0161] The index calculation module 720 is used to determine the target node of the power distribution planning, and to use the depth-first search algorithm to determine the power supply path from the graph model, and to calculate the node interval index value set of the target node based on the power supply path and the load and photovoltaic time sequence characteristics of the area where the target node is located.

[0162] The evaluation module 730 is used to evaluate the thermal stability of the distribution network planning scheme of the target node based on the set of index values ​​of the node interval and using preset thermal stability evaluation rules, and obtain the evaluation result.

[0163] The optimization module 740 is used to optimize the set of index values ​​for the node interval based on the evaluation results, and output the final distribution network planning scheme for the target node.

[0164] Optionally, the model building module 710 is specifically used for:

[0165] Based on the predefined distribution network component node types, identify all nodes in the current distribution network that match the distribution network component node types to obtain a node set. The distribution network component node types include switching stations, ring main units, distribution towers, and line endpoints in the distribution network.

[0166] Collect the devices associated with each node in the node set, their device parameters, and their connection relationships with other nodes.

[0167] Based on the devices and the connection relationships, a node-edge tree structure is constructed, and the parameters of the corresponding nodes in the tree structure are generated based on the device parameters of the devices in each node, thus obtaining the graph model of the current power distribution network.

[0168] Optionally, the set of node interval index values ​​includes power interval indexes and weak link lines; the index construction module 720 is specifically used for:

[0169] Starting from the target node, the power supply path of the target node is obtained by tracing upwards along the graph model using a depth-first search algorithm to the power supply starting node of the current distribution network.

[0170] Based on the load and photovoltaic timing characteristics of the area where the target node is located, the power range index and weak link lines of the power supply path are calculated.

[0171] Optionally, the indicator construction module 720 is specifically used for:

[0172] Starting from the target node, select the upward connection relationship in the target node, traverse all nodes in the graph model along the direction of the power supply starting node, and extract the power supply path of the target node that satisfies the current distribution network operation status.

[0173] Optionally, the indicator construction module 720 is specifically used for:

[0174] The equipment parameters of each node in the power supply path are extracted, and combined with the load and photovoltaic timing characteristics of the area where the target node is located, the power flow calculation is performed on the power supply path to obtain the power range index and weak link line of the target node.

[0175] Optionally, the evaluation module 730 is specifically used for:

[0176] The evaluation threshold of the index is determined according to the preset thermal stability evaluation rules, and the evaluation threshold is the maximum power limit;

[0177] The capacity of the power index range and the weak link line in the set of node interval index values ​​are compared with the evaluation threshold respectively;

[0178] Based on the comparison results, potential overload nodes and / or weak links in the power supply path are identified, and the identification results are aggregated and analyzed to generate the thermal stability assessment results of the distribution network planning scheme for the target node.

[0179] Optionally, the optimization module 740 is specifically used for:

[0180] Based on the evaluation results, identify nodes and / or weak links in the circuit that do not meet the thermal stability constraints;

[0181] Optimize the nodes and / or weak links in the lines, wherein the optimization includes adjusting line parameters, optimizing connection relationships, or reallocating distributed photovoltaic access points;

[0182] The optimization results are updated to the node interval index value set, and the optimized distribution network planning scheme is recalculated until the thermal stability constraint conditions are met.

[0183] In this embodiment, by constructing a distribution network diagram model, calculating node interval indicators by combining load and photovoltaic time-series characteristics, and dynamically optimizing the planning scheme based on thermal stability assessment rules, the impact of distributed photovoltaic access on grid thermal stability can be accurately quantified, weak link lines can be effectively identified, and the carrying capacity of different time periods can be dynamically assessed by combining time-series characteristics. This has the advantages of improving the accuracy of distribution network thermal stability assessment and reducing overload risk.

[0184] above Figure 7 The distribution network thermal stability planning device based on node power range index in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The electronic equipment in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0185] Figure 8 This is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of the present invention. The electronic device 800 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 833 or data 832. The memory 820 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 800. Furthermore, the processor 810 may be configured to communicate with the storage media 830 and execute a series of instruction operations on the storage media 830 on the electronic device 800 to implement the distribution network thermal stability planning method based on node power range indicators provided in the above embodiment.

[0186] Electronic device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input / output interfaces 860, and / or one or more operating systems 831, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 8 The illustrated electronic device structure does not constitute a limitation on the electronic device provided by the present invention, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0187] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the distribution network thermal stability planning method based on node power range indicators provided in the above embodiments.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0189] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0190] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network thermal stability planning method based on nodal power range indices, characterized in that, The method includes: Collect the set of nodes in the current distribution network and the relevant information of each node in the set of nodes, and construct a graph model of the current distribution network based on the relevant information of each node, wherein the relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes; The target node to be planned for power distribution is determined, and the power supply path is determined from the graph model using a depth-first search algorithm. Based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, the set of node interval index values ​​of the target node is calculated. Based on the set of index values ​​for the node interval, the thermal stability of the distribution network planning scheme of the target node is evaluated using a preset thermal stability evaluation rule, and the evaluation result is obtained. Based on the evaluation results, optimize the set of index values ​​for the node intervals and output the final distribution network planning scheme for the target node.

2. The distribution network thermal stability planning method based on nodal power range indices according to claim 1, characterized in that, The process of collecting the set of nodes in the current distribution network and the relevant information of each node in the set, and constructing a graph model of the current distribution network based on the relevant information of each node, includes: Based on the predefined distribution network component node types, identify all nodes in the current distribution network that match the distribution network component node types to obtain a node set. The distribution network component node types include switching stations, ring main units, distribution towers, and line endpoints in the distribution network. Collect the devices associated with each node in the node set, their device parameters, and their connection relationships with other nodes. Based on the devices and the connection relationships, a node-edge tree structure is constructed, and the parameters of the corresponding nodes in the tree structure are generated based on the device parameters of the devices in each node, thus obtaining the graph model of the current power distribution network.

3. The distribution network thermal stability planning method based on nodal power range indices according to claim 1, characterized in that, The set of node interval index values ​​includes power interval indexes and weak link lines; the method utilizes a depth-first search algorithm to determine the power supply path based on the graph model, and calculates the set of node interval index values ​​for the target node based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, including: Starting from the target node, the power supply path of the target node is obtained by tracing upwards along the graph model using a depth-first search algorithm to the power supply starting node of the current distribution network. Based on the load and photovoltaic timing characteristics of the area where the target node is located, the power range index and weak link lines of the power supply path are calculated.

4. The distribution network thermal stability planning method based on nodal power range index as described in claim 3, characterized in that, The process of obtaining the power supply path to the target node by tracing forward along the graph model to the current power supply starting node of the distribution network, starting from the target node, using a depth-first search algorithm, includes: Starting from the target node, select the upward connection relationship in the target node, traverse all nodes in the graph model along the direction of the power supply starting node, and extract the power supply path of the target node that satisfies the current distribution network operation status.

5. The distribution network thermal stability planning method based on nodal power range index according to claim 3, characterized in that, The calculation of the power range index and weak link lines of the power supply path based on the load and photovoltaic timing characteristics of the area where the target node is located includes: The equipment parameters of each node in the power supply path are extracted, and combined with the load and photovoltaic timing characteristics of the area where the target node is located, the power flow calculation is performed on the power supply path to obtain the power range index and weak link line of the target node.

6. The distribution network thermal stability planning method based on nodal power range index according to claim 1, characterized in that, The method involves evaluating the thermal stability of the distribution network planning scheme for the target node based on the set of node interval index values ​​and using preset thermal stability evaluation rules, yielding evaluation results including: The evaluation threshold of the index is determined according to the preset thermal stability evaluation rules, and the evaluation threshold is the maximum power limit; The capacity of the power index range and the weak link line in the set of node interval index values ​​are compared with the evaluation threshold respectively; Based on the comparison results, potential overload nodes and / or weak links in the power supply path are identified, and the identification results are aggregated and analyzed to generate the thermal stability assessment results of the distribution network planning scheme for the target node.

7. The distribution network thermal stability planning method based on nodal power range index according to claim 1, characterized in that, The process of optimizing the set of index values ​​for the node interval based on the evaluation results and outputting the final distribution network planning scheme for the target node includes: Based on the evaluation results, identify nodes and / or weak links in the circuit that do not meet the thermal stability constraints; Optimize the nodes and / or weak links in the lines, wherein the optimization includes adjusting line parameters, optimizing connection relationships, or reallocating distributed photovoltaic access points; The optimization results are updated to the node interval index value set, and the optimized distribution network planning scheme is recalculated until the thermal stability constraint conditions are met.

8. A distribution network thermal stability planning device based on nodal power range indices, characterized in that, The device includes: The model building module is used to collect the node set in the current distribution network and the relevant information of each node in the node set, and to build a graph model of the current distribution network based on the relevant information of each node, wherein the relevant information includes the equipment parameters of the corresponding node and the connection relationship with other nodes. The index calculation module is used to determine the target node of the power distribution plan, and to use the depth-first search algorithm to determine the power supply path from the graph model. Based on the power supply path and the load and photovoltaic time-series characteristics of the area where the target node is located, the module calculates the set of node interval index values ​​of the target node. The evaluation module is used to evaluate the thermal stability of the distribution network planning scheme of the target node based on the set of index values ​​of the node interval and using preset thermal stability evaluation rules, and obtain the evaluation result. The optimization module is used to optimize the set of index values ​​for the node interval based on the evaluation results, and output the final distribution network planning scheme for the target node.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the distribution network thermal stability planning method based on node power range index as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the distribution network thermal stability planning method based on node power range indices as described in any one of claims 1 to 7.

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