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

Abstract

The present application relates to power distribution network planning technical field, disclose a kind of power distribution network thermal stability planning method and device based on node power interval index, for solving the problem that the power distribution network planned in prior art is prone to cause thermal stability due to the uncertainty of distributed photovoltaic spatial distribution. By constructing the graph model of the current power distribution network based on the relevant information of each node;Determine the target node of the power distribution planning, and determine the power supply path from the graph model using the depth-first search algorithm, based on the load and photovoltaic time series characteristics of the area where the target node is located, calculate the node interval index value set of the target node;Based on the node interval index value set, the thermal stability of the power distribution network planning scheme of the target node is evaluated using the preset thermal stability evaluation rule, to obtain the evaluation result;Based on the evaluation result, the node interval index value set is optimized, and the final power distribution network planning scheme of the target node is output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system distribution network planning, in particular to a distribution network thermal stability planning method and device based on node power interval index. BACKGROUND

[0002] Distribution network planning is a technology that coordinates the power supply capacity and carrying capacity of high-voltage distribution networks and medium-voltage distribution networks according to load and distributed power generation demand. In the prior art, high-voltage distribution networks are planned through capacity-load ratio index, and medium-voltage distribution networks are planned based on spatial load prediction results. Although the existing method can achieve a certain degree of coordination, due to the influence of factors such as policy and building conditions, the spatial distribution of distributed photovoltaic power is difficult to determine accurately in the planning stage. On this basis, when the existing distribution network planning method is used for planning, the power grid cannot accurately evaluate the carrying capacity of the distributed power supply, thereby easily causing thermal stability problems. Specifically, the existing planning method cannot accurately quantify the influence of distributed photovoltaic power access on the thermal stability of the distribution network, cannot effectively identify weak link lines in the distribution network, and may result in potential overload risk in the planning scheme. In addition, the traditional method lacks comprehensive consideration of the time sequence characteristics of load and photovoltaic power, and cannot dynamically evaluate the carrying capacity of the power grid in different time periods, which further increases the probability of thermal stability problems in the distribution network. SUMMARY

[0003] The present application provides a distribution network thermal stability planning method and device based on node power interval index, which aims to solve the problem that the spatial distribution of distributed photovoltaic power is uncertain in the prior art, which leads to the planning of the distribution network easily causing thermal stability problems.

[0004] In a first aspect, the present application provides a distribution network thermal stability planning method based on node power interval index, which comprises:

[0005] Collecting a node set in the current distribution network and related information of each node in the node set, and constructing a graph model of the current distribution network based on the related information of each node, wherein the related information includes device parameters of the corresponding node and connection relationship with other nodes;

[0006] Determining a target node for distribution planning, and using a depth-first search algorithm to determine a power supply path from the graph model, and based on the power supply path and the load and photovoltaic time sequence characteristics of the region where the target node is located, calculating a node interval index value set of the target node;

[0007] Based on the node interval index value set, using a preset thermal stability evaluation rule to evaluate the thermal stability of the distribution network planning scheme of the target node, and obtaining an evaluation result;

[0008] optimizing the set of node-to-node index values based on the evaluation result, and outputting a final power distribution network planning scheme of the target node.

[0009] In an optional implementation, the set of nodes in the current power distribution network and the relevant information of each node in the set of nodes are collected, and a graph model of the current power distribution network is constructed based on the relevant information of each node, including:

[0010] According to a predefined power distribution network component node type, all nodes in the current power distribution network that match the power distribution network component node type are identified to obtain a set of nodes, and the power distribution network component node type includes switches, ring network cabinets, power distribution towers, and line endpoints in the power distribution network.

[0011] The devices, device parameters, and connection relationships with other nodes associated with each node in the set of nodes are collected respectively.

[0012] Based on the devices and the connection relationships, a tree structure of nodes-edges 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 to obtain a graph model of the current power distribution network.

[0013] In an optional implementation, the set of node-to-node index values includes a power interval index and a weak link line; the power supply path is determined from the graph model using a depth-first search algorithm, and the set of node-to-node index values of the target node is calculated based on the power supply path and the load and photovoltaic time sequence characteristics of the region where the target node is located, including:

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

[0015] The power interval index and the weak link line of the power supply path are calculated based on the load and photovoltaic time sequence characteristics of the region where the target node is located.

[0016] In an optional implementation, the power supply path of the target node is obtained by tracing forward from the graph model to the power supply starting node of the current power distribution network using a depth-first search algorithm with the target node as the starting point, including:

[0017] The power supply path of the target node that meets the operating state of the current power distribution network is extracted by selecting the upward connection relationship in the target node and traversing all nodes in the graph model in the direction of the power supply starting node with the target node as the starting point.

[0018] In an optional implementation, the calculation of the power interval index and the weak link line of the power supply path based on the load and photovoltaic time sequence characteristics of the area where the target node is located comprises:

[0019] The device parameters of each node in the power supply path are extracted, and the power flow calculation is performed on the power supply path in combination with the load and photovoltaic time sequence characteristics of the area where the target node is located, so as to obtain the power interval index and the weak link line of the target node.

[0020] In an optional implementation, the thermal stability evaluation of the power distribution network planning scheme of the target node based on the node interval index value set and by using a preset thermal stability evaluation rule comprises:

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

[0022] The capacities of the power index interval and the weak link line in the node interval index value set are compared with the evaluation threshold, respectively.

[0023] Based on the comparison result, the potential overload nodes and / or weak link lines in the power supply path are identified, and the identified result is aggregated and analyzed to generate the thermal stability evaluation result of the power distribution network planning scheme of the target node.

[0024] In an optional implementation, the node interval index value set is optimized based on the evaluation result, and the final power distribution network planning scheme of the target node is outputted, comprising:

[0025] The nodes and / or weak link lines that do not meet the thermal stability constraint are identified according to the evaluation result.

[0026] The nodes and / or weak link lines are optimized, wherein the optimization comprises adjusting the line parameters, optimizing the connection relationship, or reallocating the distributed photovoltaic access point.

[0027] The optimized result is updated to the node interval index value set, and the optimized power distribution network planning scheme is recalculated until the thermal stability constraint condition is met.

[0028] In a second aspect, the present application provides a power distribution network thermal stability planning device based on node power interval index, comprising:

[0029] A model construction module is configured to collect a node set in a current power distribution network and related information of each node in the node set, and construct a graph model of the current power distribution network based on the related information of each node, wherein the related information comprises device parameters of the corresponding node and connection relationship with other nodes.

[0030] An index calculation module is configured to determine a target node of a power distribution planning, and determine a power supply path from the graph model by using a depth-first search algorithm, and calculate a node interval index value set of the target node based on the power supply path and load and photovoltaic time sequence characteristics of a region where the target node is located.

[0031] An evaluation module is configured to evaluate thermal stability of a power distribution network planning scheme of the target node based on the node interval index value set by using a preset thermal stability evaluation rule, and obtain an evaluation result.

[0032] An optimization module is configured to optimize the node interval index value set based on the evaluation result, and output a final power distribution network planning scheme of the target node.

[0033] In a third aspect, the present application provides an electronic device, which comprises a processor and a memory, the memory storing machine executable instructions capable of being executed by the processor, and the processor executes the machine executable instructions to implement the power distribution network thermal stability planning method based on node power interval indexes provided above.

[0034] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions, when invoked and executed by a processor, cause the processor to implement the power distribution network thermal stability planning method based on node power interval indexes provided above.

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

[0036] The power distribution network thermal stability planning method and device based on node power interval indexes provided by the present application collect a node set in a current power distribution network and related information of each node in the node set, and construct a graph model of the current power distribution network based on the related information of each node, wherein the related information comprises device parameters of the corresponding node and a connection relationship with other nodes; determine a target node of a power distribution planning, and determine a power supply path from the graph model by using a depth-first search algorithm, and calculate a node interval index value set of the target node based on the power supply path and load and photovoltaic time sequence characteristics of a region where the target node is located; evaluate thermal stability of a power distribution network planning scheme of the target node based on the node interval index value set by using a preset thermal stability evaluation rule, and obtain an evaluation result; optimize the node interval index value set based on the evaluation result, and output a final power distribution network planning scheme of the target node. In the method, thermal stability is evaluated by calculating a node interval index set of a target node in a power distribution network, and a power distribution network planning scheme is dynamically optimized based on an evaluation result, so that thermal stability of the power distribution network planning can be ensured even in the case of uncertain spatial distribution of distributed photovoltaics.

[0037] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application as hereinafter described, or can be learned by practice of the application.

[0038] In order to make the above objectives, features and advantages of the present application more apparent, the following will specifically describe a preferred embodiment in combination with the accompanying drawings, and the detailed description is as follows. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0040] Figure 1 A flow chart of the power distribution network thermal stability planning method based on the node power interval index provided by the embodiment of the present application;

[0041] Figure 2 A flow chart of the node interval index set provided by the embodiment of the present application;

[0042] Figure 3 A structural schematic diagram of the graph model provided by the embodiment of the present application;

[0043] Figure 4 A process schematic diagram of searching for a power supply path provided by the embodiment of the present application;

[0044] Figure 5 A power distribution network planning and design flow chart provided by the embodiment of the present application;

[0045] Figure 6 A distributed power supply access to power grid carrying capacity evaluation flow chart provided by the embodiment of the present application;

[0046] Figure 7 A structural schematic diagram of the power distribution network thermal stability planning device based on the node power interval index provided by the embodiment of the present application;

[0047] Figure 8 A structural schematic diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the drawings in the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0050] In the related art, power distribution network planning coordinates the power supply capacity of high-voltage and medium-voltage power distribution networks through the capacity-load ratio index and spatial load forecasting. However, the planning of distributed photovoltaics is affected by factors such as policy and building conditions, and its spatial distribution is difficult to accurately determine at the planning stage, which leads to the fact that existing methods cannot evaluate the power grid's ability to accommodate distributed power, and easily causes thermal stability problems such as line overload or equipment overheating. For example, when a certain area plans to add distributed photovoltaics, due to the uncertainty of the photovoltaic access location, the traditional method cannot dynamically analyze the impact of different access points on the line carrying capacity, which may cause local line long-term overload operation.

[0051] In order to solve the above problems, the present application proposes to construct a power distribution network graph model to reflect device parameters and connection relationships, and to trace back the power supply path based on a depth-first search algorithm, calculate power interval indicators combined with time sequence characteristics, identify weak links, and finally realize thermal stability planning through iterative optimization.

[0052] Referring to Figure 1 The present application provides a specific embodiment of a power distribution network thermal stability planning method based on node power interval indicators, which specifically includes the following steps:

[0053] 110, collect a node set in the current power distribution network and related information of each node in the node set, and construct a graph model of the current power distribution network based on the related information of each node, the related information including device parameters of the corresponding node and connection relationships with other nodes.

[0054] In this embodiment, each node in the power distribution network is a node defined by the staff according to the experience of the power distribution network. The node can be understood as a power supply device, tool, line and the like that can be used to realize power supply to a target area.

[0055] In practical applications, before collecting the relevant information of nodes and nodes of the power distribution network, the constituent nodes of the power distribution network need to be defined or configured. Specifically, first, the node types of the power distribution network are configured, for example, the node types can be switch stations, ring network cabinets, switch devices, etc. In addition to configuring the node types of the power distribution network, the node definition index types for each node type are also included, for example, the index types include the maximum power supply capacity, the maximum carrying capacity, and the weak link line.

[0056] Then, based on the defined node types, the nodes in the current power distribution network are identified, that is, the power supply devices, tools, and lines in the current power distribution network that match the definitions of various node types are identified, a node set is obtained, and based on each node in the node set, the device information of the devices associated with the node and the connection relationship with other nodes are extracted.

[0057] In practical applications, for defining the node types and node interval indexes of the power distribution network, the node types are explicitly defined as switch stations, ring network cabinets, distribution towers, and line endpoints in the medium-voltage distribution network; the node interval indexes are defined, including the maximum power supply capacity DnCapacity, the maximum carrying capacity UpCapacity, and the weak link line DnLimitLine and UpLimitLine, where 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.

[0058] It should be noted that the node set can also be understood as the topological nodes corresponding to key devices such as switch stations and ring network cabinets in the power distribution network, which can be implemented by identifying pre-defined device types, and is used to reflect the physical connection relationship of the power distribution network.

[0059] After identifying and extracting all nodes and corresponding device parameters and connection relationships, a corresponding graph model is abstracted based on the device parameters and connection relationships, where the graph model refers to a tree structure with nodes as vertices and connection relationships as edges, which can be constructed by extracting device parameters and topological connections, and is used to carry electrical parameters and topological information of the power distribution network. Specifically, first, switch stations, ring network cabinets, and other nodes in the power distribution network are identified, then their device parameters and connection relationships are collected, and finally a graph model containing electrical parameters and topological structure is constructed based on the collected device parameters and connection relationships.

[0060] 120, determine the target node of the power distribution planning, and use the depth-first search algorithm to determine the power supply path from the graph model, and based on the power supply path and the load and photovoltaic time sequence characteristics of the region where the target node is located, calculate the node interval index value set of the target node.

[0061] In this embodiment, the depth-first search algorithm refers to a path search method that traces back to the power supply starting node from the target node as the starting point, which can be implemented by recursively traversing the connection relationship of the graph model, and is used to determine the power supply path of the target node.

[0062] The node interval index value set refers to dynamic interval data reflecting the power supply path power carrying capacity, which can be obtained by power flow calculation combined with time sequence characteristics, and is used to quantitatively evaluate the power supply capacity limit in different time periods.

[0063] Specifically, after determining the target node to be planned, the depth-first search algorithm is used to trace back to the power supply starting node from the target node as the starting point, and the power supply path that meets the current power distribution network operating state is selected. According to the time sequence curves of the target area load and photovoltaic, the time is divided into three typical periods of peak, flat and valley, and the power flow calculation is performed on the power supply path to obtain the maximum power supply capacity and carrying capacity in each period, forming the power interval index. By comparing the index value with the preset threshold value, the overload nodes or lines with insufficient capacity in the power supply path are identified and marked as weak links.

[0064] 130, based on the node interval index value set, the power distribution network planning scheme of the target node is evaluated for thermal stability using a preset thermal stability evaluation rule, and an evaluation result is obtained.

[0065] It should be noted that the thermal stability evaluation rule refers to a threshold condition for judging whether the power index is out of limit, which can be set by device rated parameters or historical operation data, and is used to identify potential overload risks.

[0066] It can be understood that the evaluation threshold or evaluation method of each index is determined based on the preset thermal stability evaluation rule, each index in the node interval index set is extracted, and the corresponding evaluation threshold or evaluation method is used to evaluate the index. Then, the thermal stability of the current power distribution network is determined by comprehensively evaluating the evaluation results of each index, and even an evaluation report can be given based on the evaluation results.

[0067] 140, based on the evaluation result, the node interval index value set is optimized, and the final power distribution network planning scheme of the target node is output.

[0068] If the evaluation result shows that the thermal stability does not meet the standard, the line parameters are adjusted or the photovoltaic access point is optimized, the index is recalculated until the constraint condition is met, and finally the optimized planning scheme is output.

[0069] The method provided by the application can accurately quantify the influence of distributed photovoltaic access on the thermal stability of the power grid, effectively identify weak link lines, dynamically evaluate the carrying capacity of different time periods in combination with the time sequence characteristics, and has the advantages of improving the evaluation accuracy of the thermal stability of the distribution network and reducing the overload risk.

[0070] In a specific implementation, for step 110, the node set in the current distribution network and the related information of each node in the node set are collected, and a graph model of the current distribution network is constructed based on the related information of each node, including:

[0071] According to the pre-defined distribution network component node type, all nodes in the current distribution network that match the distribution network component node type are identified to obtain a node set, and the distribution network component node type includes open and closed switches, ring network cabinets, distribution towers and line endpoints in the distribution network;

[0072] The devices associated with each node in the node set, the device parameters and the connection relationship with other nodes are collected respectively;

[0073] Based on the devices and the connection relationship, a tree structure of nodes and edges 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 to obtain a graph model of the current distribution network.

[0074] It should be noted that the distribution network component node type refers to the pre-defined node classification corresponding to the key devices in the distribution network, and can be implemented by using standardized device classification rules, for example, open and closed switches, ring network cabinets and the like are taken as core node types, and the node set is ensured to cover the key topological elements of the distribution network through type matching, thereby providing a basic support for subsequent model construction.

[0075] The device parameters refer to the operating parameters of the electrical devices associated with the nodes, and can be obtained by using device nameplate data or real-time monitoring data, for example, line rated current, transformer capacity and the like, and dynamic data basis is provided for the graph model through parameter collection.

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

[0077] The node-edge tree structure refers to a tree topology model with nodes as vertices and connection relationships as edges, and can be implemented by using a graph database or a tree data structure, and the power supply directionality of the distribution network is expressed through the tree hierarchical relationship, thereby providing structured support for subsequent path tracing.

[0078] In practical applications, this step 110 first filters out key equipment nodes in the power distribution network according to predefined node types when building a graph model, such as open-close stations and ring network cabinets as backbone nodes, and distribution towers and line endpoints as branch nodes, to form a complete node set. Subsequently, for each node, the operating parameters of its associated equipment are collected, such as the incoming and outgoing line capacities of a ring network cabinet and the conductor cross-sectional area of a distribution tower, and the physical connection relationships between nodes are recorded, such as the cable connection path between a ring network cabinet and a distribution tower. Based on this information, the nodes are taken as vertices and the connection relationships are taken as edges to build a tree-like topology structure with a clear power supply direction, and the device parameters are mapped to the attributes of the corresponding nodes to form a graph model containing electrical parameters and topological relationships. This model can dynamically reflect the real-time structure of the power distribution network and provide accurate data basis for subsequent power supply path analysis and thermal stability evaluation.

[0079] Here, through structured node type identification and parameter collection, the graph model built can accurately reflect the actual topology and equipment state of the power distribution network, providing reliable data support for subsequent calculation of power interval indicators and identification of weak links. For example, when the location of photovoltaic access changes dynamically, the graph model based on tree-like structure 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 deviation.

[0080] Further, for collecting the equipment associated with each node in the node set, the device parameters, and the connection relationships with other nodes, the following methods can be used:

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

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

[0083] Exemplarily, first, the node types of the power distribution network are defined as any accessible load or distributed photovoltaic position in the open-close switch, ring network cabinet, power distribution tower and line end point in the medium-voltage distribution network; the node interval index set is defined to include the maximum power supply capacity DnCapacity, the maximum carrying capacity UpCapacity, and the weak link line DnLimitLine and UpLimitLine, wherein the DnLimitLine is defined as the medium-voltage distribution network line limiting the maximum power supply capacity DnCapacity, and the UpLimitLine is defined as the medium-voltage distribution network line limiting the maximum carrying capacity UpCapacity.

[0084] In actual application, the definition of the index is specifically obtained by applying the load classification and the new energy resource endowment of the planning area to construct the time sequence curve in actual sample calculation, so as to obtain multiple indexes of different types of nodes in the time sequence.

[0085] Then, based on the above-defined node types, all nodes and device parameters under the nodes of the current power distribution network are extracted, and the power distribution network is abstracted into a tree graph model containing a node set (V) and an edge set (E), wherein the edge set (E) is a power distribution line, and the node set (V) contains all the above-defined node types;

[0086] Specifically, the power grid is abstracted into a power grid graph, Figure 2 wherein V1, V2, etc. are node sets, and L1, L2, etc. are edge sets. From the above, Figure 2 It can be seen that the graph model is essentially a point-edge relationship model, so the power distribution network graph model is constructed as shown in Figure 3 .

[0087] That is, the construction of the graph model of the power distribution network includes: collecting device parameters corresponding to the pre-defined node types, the parameters at least including device ID, superior power connection relationship and power supply line information, and the parameters will be used as basic data for power supply path search when calculating the node interval index set.

[0088] The construction of the graph model is essentially the construction of the relationship between the nodes and the devices. Because the power distribution network is a tree structure, the tree graph structure is used.

[0089] In a specific implementation, the node interval index value set includes the power interval index and the weak link line; in the step 120, the power supply path is determined from the graph model by using the depth-first search algorithm, and the node interval index value set of the target node is calculated based on the power supply path and the load and photovoltaic time sequence characteristics of the region where the target node is located, including:

[0090] tracing, by a depth-first search algorithm, from the target node as a starting point, to a power supply starting node of a current power distribution network along the graph model to obtain a power supply path of the target node;

[0091] Based on the load and photovoltaic time sequence characteristics of the area where the target node is located, the power interval index and weak link line of the power supply path are calculated.

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

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

[0094] Specifically, after determining the power supply path of the target node, the time sequence curve of the load and photovoltaic is constructed and divided into typical periods, and the power supply path is analyzed in time sequence by combining the power flow calculation algorithm. For example, in the period when the photovoltaic output is high, the matching relationship between the carrying capacity of the line in the path and the load demand is calculated, so as to determine the upper limit value of the power interval index. At the same time, by comparing the capacity parameters and power flow calculation results of each line, the line causing power limitation can be identified as the weak link. This process enables the planning scheme to dynamically reflect the influence of distributed photovoltaic access on the thermal stability of the power grid.

[0095] In actual application, the calculation process of the power interval index and the weak link line of the power distribution network is as follows: the graph model of the power distribution network is used, and a depth-first search algorithm is applied to search the power supply path of each node, and the power supply bottleneck on the power supply path, i.e. the power supply capacity and carrying capacity constraints of the node, to obtain the power interval index. The specific steps include:

[0096] First, the target node is taken as a starting point, the upward connection relationship in the target node is selected, all nodes in the graph model are traversed in the direction of the power supply starting node, and the power supply path of the target node meeting the current power distribution network operation state is extracted.

[0097] Specifically, the connection relationship between the nodes and the power supply points in the power distribution network is searched to construct the power supply path, so as to calculate the maximum power supply capacity and the maximum carrying capacity of each node. For example, Figure 4 As shown in the figure, V1 is a power supply point, and the process of searching V1 from V7 finally obtains the power supply path V7-V6-V8-V1.

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

[0099] It should be noted that the device parameters refer to the resistance, reactance, and capacity limit parameters of the line, which can be realized by using the line impedance data recorded by the power distribution automation system or the rated capacity marked on the device nameplate, and are used to reflect the electrical characteristics and carrying capacity of the line.

[0100] The load and photovoltaic time sequence characteristics refer to the dynamic change law of the electricity demand and photovoltaic power generation of the target area at different time points, which can be realized by using the load curve collected by the smart meter and the output curve recorded by the photovoltaic inverter, and are used to represent the time-varying characteristics of the power grid operation state.

[0101] The 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, which can be realized by using the Newton-Raphson method or the forward-backward substitution method, and is used for quantitative analysis of the power transmission state of the power supply path.

[0102] Specifically, after obtaining the power supply path, first, the line impedance parameters and transformer capacity data corresponding to each node on the path are extracted from the graph model, and the historical load curve and photovoltaic predicted output curve of the target area are obtained. After dividing the time sequence data into peak, flat, and valley periods, the equivalent model of the power grid in each period is established, and the power distribution of the power supply path in different periods is solved by power flow calculation. According to the calculation results, the maximum allowable transmission power of each line in each period is determined, and the intersection of the three periods is taken as the power interval index. At the same time, the power transmission value of each line in different periods is compared with its rated capacity, and the line that is close to or exceeds the capacity threshold for a long time is marked as a weak link. For example, during the noon period when the photovoltaic output is high, if a certain line is overloaded due to reverse power flow, it is identified as a weak link that needs to be transformed. By fusing the time sequence characteristics and multi-scenario analysis, the power grid's ability to accommodate distributed power can be dynamically evaluated, and the key line that restricts photovoltaic consumption can be accurately identified. This not only provides a more scientific basis for power distribution network planning, but also significantly reduces the risk of line overload caused by photovoltaic fluctuations, ensuring the thermal stability of power grid operation.

[0103] In practical applications, determining the power supply path of the target node is actually to prepare for subsequent power distribution network carrying capacity evaluation, that is, the carrying capacity of the power distribution network is limited by the minimum available capacity (i.e., the power supply bottleneck) on the power supply path from the target node to the power source. Therefore, the calculation of the node interval index value set of the target node is as follows:

[0104] In a typical radial distribution network, there is only one power supply path between any load node and the main power supply point. (The ring network is also combined with the current operating state to find an effective power supply path.)

[0105] Traverse all devices (including lines and transformers) on the power supply path, calculate the remaining capacity of each link, and extract the minimum value as the maximum accessible capacity (carrying capacity) of the target node.

[0106] After determining the carrying capacity, further power flow calculation can be performed based on the path impedance parameters to check whether the access causes overload, etc.

[0107] In a specific implementation, the power flow calculation of the power supply path based on the load and photovoltaic time series characteristics of the area where the target node is located is performed to obtain the power interval index and weak link line of the target node, including:

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

[0109] The time series curve is divided into three typical time periods, peak, flat, and valley, and a time series 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 in each typical time period.

[0110] According to the maximum power supply capacity and maximum carrying capacity of the target node in each typical time period, the power interval 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 photovoltaic power generation resource distribution characteristics and the difference in electric load types in the target area, which can be realized by clustering analysis of historical power generation data and power consumption behavior data, and is used to reflect the dynamic matching relationship between photovoltaic output and load demand in different areas.

[0112] The time series curve refers to a continuous change curve of load demand and photovoltaic power generation power generated according to time sequence, which can be generated by using a moving average method or a 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 time periods of peak, flat, and valley refer to the high, medium, and low power consumption time periods divided according to the load curve characteristics, which can be divided into time periods by using a K-means clustering algorithm or a three-part method, and is used to establish a power grid carrying capacity evaluation benchmark under different operating scenarios.

[0114] The time sequence power flow calculation refers to the power system steady-state analysis considering the dynamic changes of photovoltaic output and load, and can be realized by using Newton-Raphson method or forward-backward substitution method, and is used for calculating the line power distribution and node voltage state in different time periods.

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

[0116] Calculate the voltage distribution of each node on the path;

[0117] Obtain the power flow distribution according to the node voltage and line impedance;

[0118] Determine the power supply limit and carrying limit of the node by the extreme value of the power flow distribution.

[0119] Specifically, first, based on the photovoltaic installed capacity of the target area, the sunshine intensity data and the historical data of industrial and commercial, residential load, the load and photovoltaic output curve with typical daily characteristics is generated. For example, for the industrial and commercial concentrated area, the photovoltaic output curve can be corrected in combination with the installation inclination and shading rate of roof photovoltaic, and the load curve can distinguish the time period distribution characteristics of production equipment and office electricity. Then, the 24-hour operation period is divided into peak, flat and valley three periods by using the three quantile method, and the duration of each period can be dynamically adjusted according to the fluctuation characteristics of the actual load curve. In the process of power flow calculation, the power grid operation model is established for each typical period, and the maximum transmission power and equipment operation parameters are obtained by iteratively solving the node power balance equation. For example, in the peak period of photovoltaic output, the influence of reverse power flow on line carrying capacity needs to be calculated, and the line segment information causing equipment overload is recorded. Finally, by comparing the calculation results of the three periods, the power fluctuation range of the target node is determined, and the line which appears capacity bottleneck in multiple periods is selected as the weak link.

[0120] Further, the maximum power supply capacity and the maximum carrying capacity of the target node in each typical period are determined, and the weak link line causing power limitation in the power supply path is identified, including:

[0121] According to the maximum power supply capacity and the maximum carrying capacity of the target node in each typical period, the upper limit of power supply and the upper limit of carrying of the target node are determined;

[0122] Based on the upper limit of power supply and the upper limit of carrying, the power interval index of the target node in each typical period is constructed;

[0123] Identify the distribution line with the minimum capacity limitation in the power supply path, and take the distribution line as the weak link line of the target node.

[0124] The power supply upper limit refers to the maximum power supply amount that the target node can provide within a certain period of time, which can be determined by a power flow calculation algorithm combined with device capacity constraint parameters, and is used to represent the limit value of the node power supply capability.

[0125] The load upper limit refers to the maximum power load that the target node can withstand within a certain period of time, which can be calculated by analyzing the line impedance characteristics and device operating parameters, and is used to represent the boundary condition of the node load capacity.

[0126] Specifically, the maximum power supply capability and the maximum load capacity of the target node are calculated in the peak, flat, and valley three typical periods, for example, in the flat valley period with high photovoltaic output, the power supply capability may be higher than the load demand, while in the load peak period, the power supply capability may be insufficient. By dynamically matching the upper limit values of the power supply capability and the load capacity, the power interval index of each period is formed, for example, the power supply upper limit is set to 90% of the transformer capacity, and the load upper limit is set to 85% of the line 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, the rated current of a certain cable is only 200A, while other lines are above 500A, then the cable is marked as a weak link.

[0127] In actual application, based on the device parameters corresponding to the node types defined in the graph model, combined with the new energy resource endowment and load classification characteristics of the planning area, the time sequence curve of load and photovoltaic is constructed, and the device uplink distributed photovoltaic access and downlink load power supply capacity space in different periods are obtained;

[0128] Among them, the construction of the time sequence curve further includes: based on the load characteristics and photovoltaic output characteristics of a typical day, month or year, at least three periods (peak, flat, valley) are divided, and the capacity space of each period is taken as the dynamic boundary condition of power flow calculation to adapt to the dynamic power demand of the defined node type.

[0129] In a specific implementation, for step 130, the power supply network planning scheme of the target node is evaluated for thermal stability based on the set of node interval index values using a preset thermal stability evaluation rule, and an evaluation result is obtained, including:

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

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

[0132] identify potential overload nodes and / or weak link lines in the power supply path based on the comparison results, and perform aggregated analysis on the identified results to generate a thermal stability evaluation result of the power distribution network planning scheme of the target node.

[0133] The thermal stability evaluation rule refers to a set of standards for judging whether the power distribution network planning scheme meets the thermal stability, and specifically can be implemented by using the maximum power limit as the core parameter. By setting threshold constraints in different scenarios, it is ensured that the equipment operates within a safe range.

[0134] The evaluation threshold refers to the maximum power limit value allowed, which can be calculated by comprehensively considering the rated capacity of the equipment, the line carrying capacity, and the environmental temperature factor, and is used to determine whether the power index exceeds the safe range.

[0135] Specifically, in the evaluation process, first, the maximum power limit is set as the evaluation threshold according to the equipment parameters and environmental conditions, for example, 80% of the transformer capacity or cable carrying capacity is set as the upper limit of safety. Then, the power index interval of the target node and the capacity of the weak link line are compared with the corresponding threshold. If the upper limit of the power index interval 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 a weak link. Further, by aggregating all marked points, the distribution density and influence range are calculated, for example, consecutive overload nodes on the same power supply path are determined as a high-risk area, and finally the thermal stability evaluation result including the risk level and location is generated.

[0136] In some embodiments, the evaluation threshold can be dynamically adjusted in combination with historical operation data, for example, appropriately reducing the threshold during the summer high temperature period to cope with the decrease in heat dissipation efficiency. In addition, the aggregated analysis can use a weight allocation method to calculate a comprehensive risk value according to factors such as overload duration and power deviation degree, so as to prioritize high-risk areas.

[0137] By dynamically adjusting the threshold and aggregating the analysis, the overload nodes and weak lines are accurately located, providing a clear direction for subsequent optimization, thereby improving the carrying capacity and operation safety of the power distribution network for a high proportion of distributed power sources, and effectively identifying potential thermal stability problems caused by the access of distributed photovoltaic power in the power distribution network planning.

[0138] Further, for step 140, the node interval index value set is optimized based on the evaluation result, and the final power distribution network planning scheme of the target node is output, including:

[0139] According to the evaluation result, identify the nodes and / or weak link lines that do not meet the thermal stability constraints;

[0140] optimizing the node and / or weak link line, wherein the optimization comprises adjusting line parameters, optimizing connection relationships, or redistributing distributed photovoltaic access points;

[0141] updating the results of the optimization to the node interval index value set, and recalculating the optimized power distribution network planning scheme until the thermal stability constraint condition is met.

[0142] The thermal stability constraint refers to the limitation condition that the temperature rise of the power distribution equipment exceeds the allowable value due to current overload during operation. It can be realized by setting the heat resistance threshold of the equipment material to prevent line insulation layer melting or equipment overheating failure. The weak link line refers to the line section with the smallest capacity or the largest impedance in the power supply path. It can be identified by comparing the difference between the line carrying capacity and the load demand, and its function is to locate the bottleneck position that limits the power supply capacity. The distributed photovoltaic access point refers to the grid-connected position of the photovoltaic power generation equipment in the power distribution network. It can be optimized by analyzing building parameters such as roof area and lighting conditions to balance the local power generation and power demand.

[0143] Specifically, when the evaluation result indicates that there is an overload risk, first locate the specific line or node that causes insufficient thermal stability. For example, for lines with insufficient capacity, the line parameters can be adjusted by increasing the conductor cross-sectional area or replacing high-capacity transformers; for nodes with unreasonable connection relationships, the network topology can be reconstructed to shorten the power supply distance; for areas with too concentrated photovoltaic access, part of the power generation units can be migrated to the load center area. The optimized parameters are updated to the index set synchronously, and the thermal stability is verified by iterative calculation. If there is still a constraint violation, the optimization process is started again until all nodes and lines meet the operating temperature limit.

[0144] Further, the updating of the optimization results to the node interval index value set and the recalculation of the optimized power distribution network planning scheme until the thermal stability constraint condition is met comprises:

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

[0146] When changes in network structure or equipment parameters are detected, the recalculation of the node interval index value of the node is automatically triggered;

[0147] re-evaluating the thermal stability of the power distribution network according to the updated index.

[0148] In this embodiment, the calculated node interval index set is applied to the post-evaluation of the power distribution network planning to verify the distributed power supply access bearing capacity and optimize the weak links, 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. In the calculation of node interval index, the power supply path is traced back by depth-first search, and the weak line limiting capacity is directly located by combining device parameters (such as line carrying capacity, transformer capacity), so as to avoid the resource waste caused by "empirical expansion" in traditional planning, and improve the identification efficiency of weak links.

[0157] 2. In the calculation of node interval index, the time sequence characteristics (peak / flat / valley period division) of load and photovoltaic are also integrated, a dynamic power flow calculation model is constructed, the static capacity evaluation is upgraded to "time-space two-dimensional" dynamic analysis, the capacity space in different time periods is accurately calculated, so as to improve the access capacity of distributed photovoltaic in distribution network planning, and avoid the phenomenon of light abandonment.

[0158] 3. The scheme provided in the application realizes the closed-loop control of "planning-evaluation-optimization-re-evaluation" and the dynamic update of the index, realizes the planning adaptation of the whole life cycle of the distribution network control, ensures that the planning scheme is long-term effective, and reduces the secondary modification cost caused by system changes.

[0159] The above describes the distribution network thermal stability planning method based on the node power interval index in the embodiment of the application. The distribution network thermal stability planning device based on the node power interval index in the embodiment of the application is described below. Please refer to Figure 7 One embodiment of the distribution network thermal stability planning device based on the node power interval index in the embodiment of the application includes:

[0160] The model construction module 710 is configured to collect a node set in a current distribution network and related information of each node in the node set, and construct a graph model of the current distribution network based on the related information of each node, wherein the related information includes device parameters of the corresponding node and a connection relationship with other nodes.

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

[0162] The evaluation module 730 is configured to evaluate the distribution network planning scheme of the target node based on the node interval index value set and using a preset thermal stability evaluation rule, and obtain an evaluation result.

[0163] The optimization module 740 is configured to optimize the node interval index value set based on the evaluation result, and output a final distribution network planning scheme of the target node.

[0164] Optionally, the model construction module 710 is specifically configured to:

[0165] According to a predefined power distribution network component node type, all nodes in the current power distribution network that match the power distribution network component node type are identified to obtain a node set, the power distribution network component node type including switches, ring network cabinets, distribution towers and line endpoints in the power distribution network;

[0166] The devices, device parameters and connection relationships with other nodes associated with each node in the node set are collected respectively;

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

[0168] Optionally, the node interval indicator value set includes a power interval indicator and a weak link line; and the indicator construction module 720 is specifically configured to:

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

[0170] Based on the load and photovoltaic time sequence characteristics of the region where the target node is located, the power interval indicator and the weak link line of the power supply path are calculated.

[0171] Optionally, the indicator construction module 720 is specifically configured to:

[0172] Starting from the target node, the connection relationship upwards in the target node is selected, all nodes in the graph model are traversed in the direction of the power supply starting node, and the power supply path of the target node that meets the operating state of the current power distribution network is extracted.

[0173] Optionally, the indicator construction module 720 is specifically configured to:

[0174] The device parameters of each node in the power supply path are extracted, and the power flow calculation of the power supply path is performed in combination with the load and photovoltaic time sequence characteristics of the region where the target node is located to obtain the power interval indicator and the weak link line of the target node.

[0175] Optionally, the evaluation module 730 is specifically configured to:

[0176] According to a preset thermal stability evaluation rule, an evaluation threshold of the indicator is determined, the evaluation threshold being a maximum power limit;

[0177] The capacities of the power indicator interval and the weak link line in the node interval indicator value set are compared with the evaluation threshold respectively;

[0178] Based on the comparison result, a potential overload node and / or weak link line in the power supply path are identified, and the identified result is aggregated and analyzed to generate a thermal stability evaluation result of the power distribution network planning scheme of the target node.

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

[0180] Based on the evaluation result, a node and / or weak link line that does not meet the thermal stability constraint are identified;

[0181] The node and / or weak link line are optimized, wherein the optimization includes adjusting a line parameter, optimizing a connection relationship, or reallocating a distributed photovoltaic access point;

[0182] The optimized result is updated to the node interval index value set, and the optimized power distribution network planning scheme is recalculated until the thermal stability constraint condition is met.

[0183] In the embodiment, by constructing a power distribution network graph model, calculating a node interval index in combination with load and photovoltaic time sequence characteristics, and dynamically optimizing a planning scheme based on a thermal stability evaluation rule, the influence of distributed photovoltaic access on the thermal stability of the power grid can be accurately quantified, a weak link line can be effectively identified, and the carrying capacity in different time periods can be dynamically evaluated in combination with time sequence characteristics, thereby having the advantages of improving the evaluation precision of the thermal stability of the power distribution network and reducing the overload risk.

[0184] The above Figure 7 The node power interval index-based power distribution network thermal stability planning device in the embodiment of the application is described in detail from the perspective of a modular functional entity, and the electronic device in the embodiment of the application is described in detail from the perspective of hardware processing.

[0185] Figure 8 is a structural schematic diagram of an electronic device provided by the embodiment of the application. The electronic device 800 can have great differences due to different configurations or performances, and can include one or more processors (central processing units, CPUs) 810 (for example, one or more processors) and a memory 820, and one or more storage media 830 (for example, one or more mass storage devices) storing an application program 833 or data 832. The memory 820 and the storage media 830 can be temporary storage or persistent storage. The program stored in the storage media 830 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the electronic device 800. Further, the processor 810 can be configured to communicate with the storage media 830, execute a series of instruction operations in the storage media 830 on the electronic device 800, to implement the node power interval index-based power distribution network thermal stability planning method provided by the above-described embodiment.

[0186] The electronic device 800 can 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 appreciate that the electronic device structure shown is not intended to limit the electronic device provided by the present application, and can include more or fewer components than shown, or combine some components, or arrange the components differently. Figure 8 The electronic device structure shown is not intended to limit the electronic device provided by the present application, and can include more or fewer components than shown, or combine some components, or arrange the components differently.

[0187] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the power interval index based power distribution network thermal stability planning method provided by the above-described embodiments.

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

[0189] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that makes a contribution to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0190] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for thermal stability planning of a power distribution network based on node power interval index, characterized in that, The method comprises: collecting a node set in a current power distribution network and related information of each node in the node set, and constructing a graph model of the current power distribution network based on the related information of each node, wherein the related information comprises device parameters of the corresponding node and connection relationships with other nodes; determining a target node of a power distribution planning, and determining a power supply path from the graph model 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 a region where the target node is located; based on the node interval index value set, using a preset thermal stability evaluation rule to evaluate the power distribution network planning scheme of the target node for thermal stability, to obtain an evaluation result; based on the evaluation result, optimizing the node interval index value set, and outputting a final power distribution network planning scheme of the target node; based on the node interval index value set, using a preset thermal stability evaluation rule to evaluate the power distribution network planning scheme of the target node for thermal stability, to obtain an evaluation result, comprising: determining an evaluation threshold of the index according to the preset thermal stability evaluation rule, the evaluation threshold being a maximum power limit; comparing the capacity of the power index interval and the weak link line in the node interval index value set with the evaluation threshold respectively; based on the comparison result, identifying potential overload nodes and / or weak link lines in the power supply path, and performing aggregated analysis on the identified result to generate a thermal stability evaluation result of the power distribution network planning scheme of the target node; based on the evaluation result, optimizing the node interval index value set, and outputting a final power distribution network planning scheme of the target node, comprising: identifying nodes and / or weak link lines that do not meet the thermal stability constraint according to the evaluation result; optimizing the nodes and / or weak link lines, wherein the optimization comprises adjusting line parameters, optimizing connection relationships or redistributing distributed photovoltaic access points; updating the optimized result to the node interval index value set, and recalculating the optimized power distribution network planning scheme until the thermal stability constraint condition is met.

2. The method for thermal stability planning of power distribution network based on node power interval indicator according to claim 1, characterized in that, the collection of a node set in a current power distribution network and related information of each node in the node set, and the construction of a graph model of the current power distribution network based on the related information of each node, comprising: according to a predefined power distribution network component node type, identifying all nodes in the current power distribution network that match the power distribution network component node type, to obtain a node set, the power distribution network component node type comprising open and closed switches, ring network cabinets, power distribution towers and line endpoints in the power distribution network; collecting devices, device parameters and connection relationships with other nodes associated with each node in the node set respectively; based on the devices and the connection relationships, constructing a tree structure of nodes-edges, and generating parameters of the corresponding nodes in the tree structure based on the device parameters of the devices in each node, to obtain a graph model of the current power distribution network.

3. The method for thermal stability planning of power distribution network based on node power interval indicator as claimed in claim 1, wherein, The node interval index value set includes a power interval index and a weak link line; the power supply path is determined based on the graph model by using a depth-first search algorithm, and the node interval index value set of the target node is calculated based on the power supply path and load and photovoltaic time sequence characteristics of the region where the target node is located, including: The power supply path of the target node is obtained by tracing upwards along the graph model to the power supply starting node of the current power distribution network by using a depth-first search algorithm with the target node as the starting point; The power interval index and the weak link line of the power supply path are calculated based on the load and photovoltaic time sequence characteristics of the region where the target node is located.

4. The method for thermal stability planning of power distribution network based on node power interval indicator according to claim 3, characterized in that, The power supply path of the target node is obtained by tracing forward along the graph model to the power supply starting node of the current power distribution network by using a depth-first search algorithm with the target node as the starting point, including: The power supply path of the target node is obtained by tracing forward along the graph model to the power supply starting node of the current power distribution network by using a depth-first search algorithm with the target node as the starting point, including:

5. The method for thermal stability planning of power distribution network based on node power interval indicator according to claim 3, characterized in that, The power interval index and the weak link line of the power supply path are calculated based on the load and photovoltaic time sequence characteristics of the region where the target node is located, including: The power interval index and the weak link line of the power supply path are calculated based on the load and photovoltaic time sequence characteristics of the region where the target node is located, including:

6. A power distribution network thermal stability planning device based on node power interval indicator, characterized by, The device includes: A model construction module is configured to collect a node set in a current power distribution network and related information of each node in the node set, and construct a graph model of the current power distribution network based on the related information of each node, wherein the related information includes device parameters of the corresponding node and connection relationships with other nodes; An index calculation module is configured to determine a target node for power distribution planning, and determine a power supply path from the graph model by using a depth-first search algorithm, and calculate a node interval index value set of the target node based on the power supply path and load and photovoltaic time sequence characteristics of the region where the target node is located; An evaluation module is configured to evaluate the power distribution network planning scheme of the target node based on the node interval index value set by using a preset thermal stability evaluation rule to obtain an evaluation result; An optimization module is configured to optimize the node interval index value set based on the evaluation result, and output a final power distribution network planning scheme of the target node; The evaluation module evaluates the power distribution network planning scheme of the target node based on the node interval index value set by using a preset thermal stability evaluation rule to obtain an evaluation result, and specifically includes: An evaluation threshold of the index is determined according to a preset thermal stability evaluation rule, and the evaluation threshold is a maximum power limit; The capacities of the power index interval and the weak link line in the node interval index value set are compared with the evaluation threshold, respectively; Based on the comparison result, a potential overload node and / or weak link line in the power supply path are identified, and the identified result is aggregated and analyzed to generate a thermal stability evaluation result of the power distribution network planning scheme of the target node; The optimization module optimizes the set of node interval index values based on the evaluation result, and outputs a final power distribution network planning scheme of the target node, specifically including: Based on the evaluation result, a node and / or weak link line that does not meet the thermal stability constraint are identified; The node and / or weak link line are optimized, wherein the optimization includes adjusting line parameters, optimizing connection relationships, or redistributing distributed photovoltaic access points; The optimized result is updated to the set of node interval index values, and the optimized power distribution network planning scheme is recalculated until the thermal stability constraint condition is met.

7. An electronic device, comprising: The processor executes the machine executable instructions to implement the power distribution network thermal stability planning method based on the node power interval index according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by the processor, the computer executable instructions cause the processor to implement the power distribution network thermal stability planning method based on the node power interval index according to any one of claims 1 to 5.

Images