Optimal planning method for complex electromagnetic ring network partitioning based on Louvain algorithm

Abstract

The present invention discloses a method for optimizing the partitioning of complex electromagnetic ring networks based on the Louvain algorithm. The method comprises simplifying the receiving-end power grid model into a weighted undirected topological graph, partitioning the nodes of the weighted undirected topological graph into multiple communities based on the Louvain algorithm, and assigning each node to the community where its adjacent nodes reside until the modularity of each community reaches its maximum value. When the communities formed after all nodes have been moved and the three-phase short-circuit currents of each partition meet the judgment criteria, a partitioning scheme is output, and all partitioning schemes are evaluated for indicators to select the optimal partitioning scheme. The method provided by the present invention aims to improve the operating efficiency and stability of the power grid while reducing the complexity in power grid planning and operation.

Description

Technical Field

[0001] The present invention relates to the technical field of alternating current power transmission, and in particular to a complex electromagnetic ring network partition optimization planning method based on the Louvain algorithm. Background Art

[0002] With the rapid development of the power system, power grids of different voltage levels are growing simultaneously, forming electromagnetic ring networks (EMNs) spanning multiple voltage levels, such as 1000kV, 500kV, and 220kV. In the early stages of power grid development, these cross-voltage EMNs played a key role in improving transmission capacity and ensuring power supply stability. However, with the gradual improvement of ultra-high voltage AC (UHV) systems and the increase in power flow capacity, these early EMNs have gradually become a potential risk to the safe and stable operation of the power grid. In the event of a fault in the high-voltage grid, power flow could shift to the low-voltage grid, placing enormous pressure on it and potentially triggering a chain reaction, leading to large-scale power outages in localized areas. Therefore, to ensure the stable operation of the power grid, addressing the threat posed by EMNs has become a pressing issue in the power system sector.

[0003] Currently, there are two main methods for zoning electromagnetic ring networks: experience-based zoning and search-based zoning. The experience-based zoning method relies on the expertise and long-term experience of operators, achieving grid zoning by disconnecting specific lines or adjusting the operation of substation buses. However, as grid scale and interconnectivity increase, this method lacks in-depth research into the overall grid structure. The search-based zoning method uses computer-aided analysis to search for specific grid operation issues using the overall topology of the power system. However, existing search-based zoning methods require re-zoning after new projects are commissioned, resulting in insufficient flexibility. Summary of the Invention

[0004] In view of this, the object of the present invention is to provide a complex electromagnetic ring network partition optimization planning method based on the Louvain algorithm, which is characterized by comprising:

[0005] Step 1: Set partition parameters based on the receiving-end grid model and set the short-circuit current interruption value based on the voltage level;

[0006] Step 2: Simplify the receiving-end power grid model into a weighted undirected topology graph, wherein the weighted undirected topology graph uses buses as nodes;

[0007] Step 3: Divide the nodes into multiple communities based on the Louvain algorithm, and calculate the modularity of each community;

[0008] Step 4: Calculate the modularity gain of each community after each node is assigned to the community where its neighboring node is located. If the modularity gain is greater than 0, repeatedly move each node to the community where its neighboring node is located until the modularity of each community reaches a maximum value.

[0009] Step 5: Determine whether the community formed after all nodes have moved satisfies the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, reconstruct the weighted undirected topological graph and repeat step 4.

[0010] Step 6: Determine whether the three-phase short-circuit current of each partition meets the short-circuit current interruption value. If so, output the partitioning scheme; if not, reconstruct the weighted undirected topological graph and repeat step 4;

[0011] Step 7: When all the nodes are divided into the same community, the partitioning scheme is outputted, and an index evaluation is performed on all the partitioning schemes to select the optimal partitioning scheme.

[0012] Preferably, the weighted undirected topological graph has transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and the lines are the weighted values ​​of the edges.

[0013] Preferably, simplifying the receiving-end power grid model into the weighted undirected topology graph includes:

[0014] Step 201: Equating the external power source to a series circuit model formed by an infinite power source and an internal impedance, and calculating the per-unit reactance value of the internal impedance;

[0015] Step 202: Calculate the per-unit short-circuit reactance of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation according to the actual situation of the receiving-end power grid;

[0016] Step 203: Calculate the per-unit reactance of the line between the 500 kV substation and the 220 kV substation according to the actual situation of the receiving-end power grid;

[0017] Step 204: Simplify the receiving-end power grid model into the weighted undirected topology graph based on the per-unit reactance value, the per-unit short-circuit reactance value, and the per-unit line reactance value.

[0018] Preferably, dividing the nodes into a plurality of communities based on the Louvain algorithm, and calculating the modularity of each community includes:

[0019] Determine whether the node is partitioned and running. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community. If not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community.

[0020] The expression of the modularity Q is as follows:

[0021]

[0022] Where A ij is the edge weight between node i and node j; k i and k j are the degrees of the node i and the node j, that is, the sum of the weights of the edges owned by the node i and the node j; c i and c j is the community ID to which the node i and the node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , the node i and the node j belong to the same community, and the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all the edges in the weighted undirected topological graph.

[0023] Preferably, the module gain ΔQ is expressed as:

[0024]

[0025] Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0026] Preferably, during the node movement process, the connection relationship and electrical parameters between the nodes, and the overall improvement degree of the modularity are used as reference factors.

[0027] Preferably, reconstructing the weighted undirected topological graph includes:

[0028] The community in the weighted undirected topological graph generated in step 2 is used as a new node, the connections between all the nodes in the community in step 2 and the nodes in other communities are used as new edges, and the connection strength between the communities in the weighted undirected topological graph generated in step 2 is used as the weight of the new edge.

[0029] Preferably, all the partitioning schemes are evaluated for indicators, and the optimal partitioning scheme is selected, including:

[0030] Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators for partition scheme i;

[0031] Step 702: Calculate the power grid evaluation vector X of the partition scheme i i Model|X i |;

[0032] Step 703: Alignment i | Sort and select the partitioning scheme with the largest modulus value as the optimal partitioning scheme.

[0033] Preferably, the power grid evaluation indicators include: modularity index Q, fault line overload index R1, load shedding index R2, short-circuit current index I S , Power balance margin index P mar .

[0034] The beneficial effects of the present invention are as follows:

[0035] This paper provides an electromagnetic ring network zoning optimization planning method based on the Louvain algorithm, aiming to improve the operational efficiency and stability of power grids while reducing the complexity of grid planning and operation. Compared to traditional search-based partitioning methods, this method can optimize an initial grid partitioning scheme with complete node splitting. It can also optimize a partitioning scheme based on an existing, operational partitioning scheme.

[0036] The method provided by the present invention is particularly suitable for the discovery problem of large-scale networks. The algorithm converges very quickly and can achieve community division in large-scale networks in a relatively short period of time. At the same time, this method provides hierarchical partitioning results and can discover hierarchical community structures at different granularity levels, providing more solutions for the optimization of complex electromagnetic ring networks.

[0037] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A schematic diagram of a flow chart of a method for optimizing electromagnetic ring network partitioning planning based on the Louvain algorithm provided in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a single-station power supply model for an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention;

[0042] Figure 3 Schematic diagram of a two-station hand-in-hand power supply model for an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention;

[0043] Figure 4 Schematic diagram of a three-station chain partition power supply model for an electromagnetic ring network partition optimization planning method based on the Louvain algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] To facilitate understanding of this embodiment, Figure 1 The electromagnetic ring network partition optimization planning method based on the Louvain algorithm disclosed in the embodiment of the present invention is introduced in detail.

[0046] Example 1:

[0047] Step 1: Set partition parameters based on the receiving-end grid model and set the short-circuit current tripping value based on the voltage level.

[0048] Among them, the receiving-end power grid model is a typical 1000kV, 500kV, and 220kV receiving-end power grid model, including: a single-station power supply model, a two-station hand-in-hand power supply model, and a three-station chain partition power supply model.

[0049] Specifically, the partition parameters are set based on the receiving-end power grid model, and the short-circuit current interruption value is set based on the voltage level, including: based on the receiving-end power grid model, setting the upper limit and lower limit of the number of transformers in the partition and the upper limit of the capacity of the units in the partition according to the historical data and / or actual operation of the power grid; setting the short-circuit current interruption value based on the voltage level;

[0050] Among them, according to the historical maximum load value and the historical minimum load value, the upper limit and lower limit of the number of transformers in the zone are set based on the receiving end power grid model; the upper limit of the unit capacity in the zone is set according to the maximum power generation capacity of the unit; and the short-circuit current tripping value is set based on the voltage level.

[0051] The structural diagrams of the single-station power supply model, the two-station hand-in-hand power supply model, and the three-station chain partition power supply model are shown in the following diagrams: Figure 2 、 3 ,4, combined Figure 3 、 Figure 4 In the two-station hand-in-hand power supply model and the three-station chain partition power supply model, the upper limit of the number of 500kV transformers is 8, and the lower limit is 3; the short-circuit current interruption value of the 1000kV power grid is 63kA, the short-circuit current interruption value of the 500kV power grid is 63kA, and the short-circuit current interruption value of the 220kV power grid is 50kA.

[0052] In step 1, the safe and stable operation of the power grid is ensured by setting the partition parameters; by setting the short-circuit current interruption value, it is ensured that the fault current can be cut off in time when a short-circuit fault occurs, thereby protecting the power grid equipment.

[0053] Step 2: Simplify the receiving-end power grid model into a weighted undirected topology graph with buses as nodes.

[0054] Preferably, the weighted undirected topological graph uses transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and lines are the weight values ​​of the edges.

[0055] The busbars include 1000kV, 500kV, and 220kV buses. In this embodiment, busbar nodes represent the voltage level and location of the receiving grid; transformer and line edges represent the connection relationship and electrical parameters of the receiving grid; and the inverse of the reactance of the transformer and line serves as the edge weight, reflecting the transmission loss and stability of the receiving grid.

[0056] Preferably, simplifying the receiving-end power grid model into a weighted undirected topology graph includes:

[0057] Step 201: Equating the external power source to a series circuit model formed by an infinite power source and an internal impedance, and calculating the per-unit reactance value of the internal impedance.

[0058] Reactance per unit value Xs ' is expressed as follows:

[0059]

[0060] Where, E s ' is the per-unit value of the wireless high-power power supply voltage; I k Injects current into the external system after a 1000kV busbar three-phase short circuit; I B is the system reference current; S B is the system base capacity, which is 100MVA; U B It is the system reference voltage, which is 1050kV.

[0061] The reason for equating the out-of-region power source to a series circuit model formed by an infinite power source and an internal impedance is that, in order to simplify the analysis, the receiving-end power grid model is regarded as the internal system and the out-of-region power source as the external system. Based on the Thevenin theorem, the situation where the out-of-region power source injects a short-circuit current into the receiving-end power grid is simulated.

[0062] Step 202: Calculate the per-unit short-circuit reactance of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation according to the actual situation of the receiving-end power grid.

[0063] Short-circuit reactance per unit value X' T The expression is as follows:

[0064]

[0065] Where U k % is the percentage of transformer short-circuit voltage; S T is the rated capacity of the transformer; S B is the rated capacity of the system, which is 100MVA.

[0066] Step 203: Calculate the per-unit value of the line reactance between the 500 kV substation and the 220 kV substation based on the actual situation of the receiving-end power grid.

[0067] Line reactance per unit value X' L The expression is as follows:

[0068]

[0069] Where x L is the line reactance per unit length; l is the line length; Z B is the system reference impedance, S B is the system base capacity, which is 100MVA, U B It is the system reference voltage, and its value is 525kV or 230kV according to the actual voltage level of the line.

[0070] Step 204: Simplify the receiving-end power grid model into a weighted undirected topology graph based on the per-unit reactance value, the per-unit short-circuit reactance value, and the per-unit line reactance value.

[0071] In step 2, by simplifying the receiving-end power grid model, the key characteristics of the actual receiving-end power grid can be retained, which facilitates subsequent algorithm processing.

[0072] Step 3: Divide the nodes into multiple communities based on the Louvain algorithm and calculate the modularity of each community.

[0073] Preferably, nodes are divided into multiple communities based on the Louvain algorithm, and the modularity of each community is calculated including:

[0074] Determine whether the node is partitioned. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community. If not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community.

[0075] The expression of modularity Q is as follows:

[0076]

[0077] Where A ij is the edge weight between node i and node j; k i and k j are the degrees of node i and node j, that is, the sum of the weights of the edges owned by node i and node j; c i and c j is the community ID to which node i and node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , node i and node j belong to the same community, the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0078] Among them, when all nodes in the weighted undirected topological graph are divided into communities, the modularity is the difference between the sum of the weights of the edges within each community and the sum of the weights of all edges connected to the community nodes.

[0079] Step 4: Calculate the modularity gain of each community after each node is assigned to the community where its neighboring nodes are located. If the modularity gain is greater than 0, repeatedly move each node to the community where its neighboring nodes are located until the modularity of each community reaches the maximum value.

[0080] Preferably, the module gain ΔQ is expressed as:

[0081]

[0082] Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

[0083] Preferably, during the node movement process, the connection relationship and electrical parameters between nodes, as well as the overall improvement degree of modularity are used as reference factors.

[0084] The significance of this step is that after the node is moved, the connection relationship and electrical parameters between the nodes are considered to ensure that the community structure in the receiving network is reasonable; the overall improvement in modularity is considered to ensure that the community structure can improve the modularity of the receiving network. For example, if the connection relationship between two nodes is strong, or the electrical parameters are similar, this indicates that the functions or properties of the two nodes in the network are relatively close. Merging the two nodes into the same community can enhance the connection density within the community, and the community structure after the move is reasonable. This step is one of the core goals of the community detection algorithm, which can identify community structures with tight internal connections and loose external connections.

[0085] Step 5: Determine whether the community formed after all nodes move meets the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, rebuild the weighted undirected topology graph and repeat step 4.

[0086] The principle of calculating three-phase short-circuit current using the equivalent voltage source method is as follows:

[0087] Step 501: When a short-circuit fault point exists in the power system, a virtual voltage source is introduced into the short-circuit fault point, and the virtual voltage source is used as the only voltage source of the network.

[0088] Step 502: The actual electromotive force of other power sources in the power system is regarded as 0, and the electromotive force of other power sources is replaced by the self-reactance;

[0089] Step 503: Calculate the system equivalent reactance of the short-circuit fault point based on the Thevenin equivalent algorithm;

[0090] Step 504: Calculate the three-phase short-circuit current I at the short-circuit fault point based on the system equivalent reactance and the virtual voltage source. k .

[0091] Three-phase short-circuit current I k The expression is:

[0092]

[0093] Where c is the voltage coefficient; U0 is the nominal system voltage at the short-circuit fault point, i.e. the rated line voltage, in kV; Z k is the system positive sequence equivalent reactance at the short-circuit fault point, in Ω.

[0094] Step 6: Determine whether the three-phase short-circuit current of each partition meets the short-circuit current interruption value. If so, output the partitioning scheme; if not, reconstruct the weighted undirected topology graph and repeat step 4.

[0095] In steps 5 and 6, preferably, reconstructing the weighted undirected topological graph includes: using the communities in the weighted undirected topological graph generated in step 2 as new nodes, using the connections between all nodes in the community in step 2 and nodes in other communities as new edges, and using the connection strengths between communities in the weighted undirected topological graph generated in step 2 as weights of the new edges.

[0096] Among them, the new node is the reconstructed weighted undirected topological graph

[0097] Step 7: When all nodes are divided into the same community, the partitioning scheme is output, and the indicators of all partitioning schemes are evaluated to select the optimal partitioning scheme.

[0098] Preferably, all partitioning schemes are evaluated and the optimal partitioning scheme is selected, including:

[0099] Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators for partition scheme i.

[0100] Step 702: Calculate the grid evaluation vector X for partition scheme i i Model|X i |.

[0101] Step 703: Alignment i |Sort and select the partitioning scheme with the largest modulus value as the optimal partitioning scheme.

[0102] Furthermore, the grid evaluation indicators include: modularity index Q, line overload index R1 under fault conditions, load shedding index R2, short-circuit current index I S , Power balance margin index P mar .

[0103] Specifically, the line overload index R1 is a quantitative indicator of the operational risk caused by line overload due to the occurrence of N-1 faults. The line overload index is established by comprehensively considering the possibility and severity of the accident.

[0104] The expression of line overload index R1 under fault is:

[0105]

[0106] Where n F is the total number of failures; n l is the total number of failures; h i is the ratio of annual fault duration to annual utilization hours, i.e., the probability of fault i occurring; I ij is the current value of line j when fault i occurs, I ij,lim is the thermal stability limit of line j; when fault i occurs, if there is a line overload, γ ij =1, otherwise γ ij =0.

[0107] The load shedding index, R2, is a quantitative indicator of the load shedding required to prevent severe overloads in a zone. The reason for reducing the equipment load factor is to measure the potential inability of the power supply to meet the zone's load requirements due to equipment thermal stability constraints when a critical line in the zone fails or fails during maintenance. This reduces the load factor to prevent severe overloads.

[0108] The expression of load shedding index R2 is:

[0109]

[0110] Where n F is the total number of failures; h i is the probability of fault i occurring; P int,i is the load shedding amount when fault i occurs, P load For partition load.

[0111] Short-circuit current index I S In order to achieve this, the short-circuit current of the receiving power grid is divided into several different levels from high to low according to the actual situation, and different correction coefficients are set for different levels. S It can reflect different short-circuit current levels under different partitioning schemes.

[0112] Short-circuit current index I S The expression is:

[0113]

[0114] Where N is the number of short-circuit current intervals; δ i is the correction coefficient for the short-circuit current in the ith interval, and the correction coefficients are 0.8, 0.6, 0.4, and 0.2 respectively; g i is the number of buses with short-circuit current in the ith interval; I j , I NThey are busbar short-circuit current and rated breaking current respectively.

[0115] In this embodiment, N is set to 4, and the short-circuit current intervals are (48,50], (45,48], (40,45], and (0,40] respectively.

[0116] Power balance margin index P mar The expression is:

[0117]

[0118] Where, P iMAX P is the power limit of the partition, GMAX is the installed capacity of power plants in the area; P load is the partition load; η is the network loss rate.

[0119] Due to the community modularity Q, power balance margin P mar The larger the index value is, the better the partitioning scheme is. The smaller the index value of the line overload index R1 and the load shedding index R2 is, the better the partitioning scheme is. Therefore, the above evaluation indicators are processed in the same direction to obtain the evaluation vector X of the i-th partitioning scheme. i , the expression of the partition scheme evaluation vector is as follows:

[0120] X i =[Q i ,1-R 1,i ,1-R 2,i ,P mar,i ] T

[0121] Where Q i is the modularity index of partition scheme i; R 1,i is the line overload index under fault in partition scheme i; R 2,i is the load shedding index of partition scheme i; P mar,i is the power balance margin index of partition scheme i.

[0122] In combination with this embodiment, the advantage of this application is that the partitioning method provided by this application can take any partitioning state as the initial state, and can optimize the existing partitioning after the new project is put into production to form a new partitioning scheme; the partitioning method provided by this application can take the complete split state as the initial state to construct an ideal power grid partitioning scheme.

[0123] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A complex electromagnetic ring network partition optimization planning method based on Louvain algorithm, characterized by: include: Step 1: Set partition parameters based on the receiving-end grid model and set the short-circuit current interruption value based on the voltage level; Step 2: Simplify the receiving-end power grid model into a weighted undirected topology graph, wherein the weighted undirected topology graph uses buses as nodes; Step 3: Divide the nodes into multiple communities based on the Louvain algorithm, and calculate the modularity of each community; Step 4: Calculate the modularity gain of each community after each node is assigned to the community where its neighboring node is located. If the modularity gain is greater than 0, repeatedly move each node to the community where its neighboring node is located until the modularity of each community reaches a maximum value. Step 5: Determine whether the community formed after all nodes have moved satisfies the partition scale. If so, calculate the three-phase short-circuit current of each node according to the equivalent voltage source method; if not, reconstruct the weighted undirected topological graph and repeat step 4. Step 6: Determine whether the three-phase short-circuit current of each partition meets the short-circuit current interruption value, and if so, output the partitioning scheme; If not, reconstruct the weighted undirected topological graph and repeat step 4; Step 7: When all the nodes are divided into the same community, the partitioning scheme is outputted, and all the partitioning schemes are evaluated for indicators to select the optimal partitioning scheme; All the partitioning schemes are evaluated and the optimal partitioning scheme is selected, including: Step 701: Obtain receiving-end power grid data and calculate power grid evaluation indicators for partition scheme i; The grid evaluation indicators include: modularity index Q, line overload index R1 under fault conditions, load shedding index R2, power balance margin index P mar ; Step 702: Calculate the power grid evaluation vector X of the partition scheme i i Model|X i |; The expression of the partition scheme evaluation vector is as follows: X i =[Q i ,1-R 1,i ,1-R 2,i ,P mar,i ] T Where Q i is the modularity index of partition scheme i; R 1,i is the line overload index under fault conditions of partition scheme i; R 2,i is the load shedding index of partition scheme i; P mar,i is the power balance margin index of partition scheme i; Step 703: Alignment i | Sort and select the partitioning scheme with the largest modulus value as the optimal partitioning scheme.

2. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: The weighted undirected topological graph has transformers and lines as edges, and the reciprocals of the reactance values ​​of the transformers and the lines are weighted values ​​of the edges.

3. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Simplifying the receiving-end power grid model into the weighted undirected topology graph includes: Step 201: Equating the external power source to a series circuit model formed by an infinite power source and an internal impedance, and calculating the per-unit reactance value of the internal impedance; Step 202: Calculate the per-unit short-circuit reactance of the main transformer of the 1000 kV hub substation and the main transformer of the 500 kV hub substation according to the actual situation of the receiving-end power grid; Step 203: Calculate the per-unit reactance of the line between the 500 kV substation and the 220 kV substation according to the actual situation of the receiving-end power grid; Step 204: Simplify the receiving-end power grid model into the weighted undirected topology graph based on the per-unit reactance value, the per-unit short-circuit reactance value, and the per-unit line reactance value.

4. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Dividing the nodes into a plurality of communities based on the Louvain algorithm, and calculating the modularity of each community includes: Determine whether the node is partitioned and running. If so, assign the nodes in the same partition to the same community based on the Louvain algorithm, and calculate the modularity of each community. If not, treat each node as a community based on the Louvain algorithm, and calculate the modularity of each community. The expression of the modularity Q is as follows: Where A ij is the edge weight between node i and node j; k i and k j are the degrees of the node i and the node j, that is, the sum of the weights of the edges owned by the node i and the node j; c i and c j is the community ID to which the node i and the node j belong; δ(c i ,c j ) is the indicator function, if c i =c j When , the node i and the node j belong to the same community, and the indicator function takes the value of 1, otherwise it is 0; m is the sum of the weights of all the edges in the weighted undirected topological graph.

5. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: The expression of the module gain ΔQ is: Among them, k i,in is the sum of the weights of the edges from node i to the nodes in community B where its neighboring nodes are located; k i is the sum of the weights of the edges of node i; ∑ tot is the sum of the weights of the edges of all nodes in community B; m is the sum of the weights of all edges in the weighted undirected topological graph.

6. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: During the node movement process, the connection relationship and electrical parameters between the nodes, and the overall improvement degree of the modularity are used as reference factors.

7. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: Reconstructing the weighted undirected topological graph includes: The community in the weighted undirected topological graph generated in step 2 is used as a new node, the connections between all the nodes in the community in step 2 and the nodes in other communities are used as new edges, and the connection strength between the communities in the weighted undirected topological graph generated in step 2 is used as the weight of the new edge.

8. The complex electromagnetic ring network partition optimization planning method based on Louvain algorithm according to claim 1 is characterized in that: The power grid evaluation indicators include: short-circuit current index I S .

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