A method for active power distribution network source and load distribution based on node elasticity and inertia distribution

Abstract

The application belongs to the technical field of active power distribution network stable operation control, and discloses a source and load distribution method of an active power distribution network based on node elasticity and inertia distribution, which comprises the following steps: acquiring the topological structure and power quality data of the active power distribution network; extracting the node network topological metric characteristics and node electrical characteristics of the active power distribution network; establishing a node importance evaluation model of the active power distribution network; calculating the inertia and elasticity distribution of the nodes of the active power distribution network, and establishing a source and load distribution order calculation model of the active power distribution network; constructing a source and load distribution model to obtain a source and load distribution matrix considering the overall strength and sparsity of the power grid; and checking the source and load distribution effect, performing power flow calculation on the active power distribution network after the redistribution, and checking whether the node power quality meets the standard requirements. The application analyzes the historical operation data of the active power distribution network, identifies important nodes in the power grid, and proposes a source and load distribution strategy according to the importance identification result and the power grid disturbance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of active power distribution network stable operation control, in particular to a source and load distribution method for active power distribution network based on node elasticity and inertia distribution. BACKGROUND

[0002] With the development of distributed energy technology, active power distribution network has become an indispensable infrastructure for economic development and daily life, and the public shows a high degree of dependence on stable power supply. However, the complexity and variability of the power system make it face various potential risks, especially in active power distribution network, the output fluctuation of distributed energy makes the system operation more unstable. In recent years, with the wide application of renewable energy and distributed generation, the operation mode of power distribution network is more and more complex, and when the network is disturbed, the traditional source and load distribution method has been difficult to meet the needs of modern power system.

[0003] When the active power distribution network is disturbed, unreasonable source and load distribution may lead to system instability and failure. How to achieve efficient and reliable source and load distribution in complex power environment has become one of the key technologies to improve the stability and reliability of power system. The existing technology generally considers only the power value of the node in normal operation, lacks analysis of the overall network, and fails to fully consider the mutual influence and dynamic change between nodes, so as to effectively cope with frequent load changes and sudden operation state. Therefore, how to improve the redistribution model and method and improve the ability of active power distribution network to respond to disturbance is of great significance. SUMMARY

[0004] In view of the above shortcomings of the prior art, the present application provides a source and load distribution method for active power distribution network based on node elasticity and inertia distribution, which aims to analyze the characteristics of the historical operation data of the active power distribution network, identify the important nodes in the power grid, and propose a source and load distribution strategy according to the importance identification result and the power grid disturbance, so as to provide technical support for reducing the load loss of power grid disturbance.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A source and load distribution method for active power distribution network based on node elasticity and inertia distribution, comprising:

[0007] Step 1, obtaining the topology structure and power quality data of the active power distribution network, and processing the original data into standard data format;

[0008] Step 2, facing the complex network structure hole theory, extracting the node network topology measurement characteristics and node electrical characteristics of the active power distribution network, using EFO function to extract the principal component characteristics, and forming the reconstruction characteristic space;

[0009] Step 3, establish an active power distribution network node importance evaluation model based on the improved I-PageRank method, and obtain an important node ranking set;

[0010] Step 4, based on the important node identification result of step 3, calculate the inertia and elasticity distribution of the active power distribution network node, and establish an active power distribution network source-load weight distribution order calculation model to obtain a source-load weight distribution order matrix;

[0011] Step 5, based on the source-load weight distribution order matrix obtained in step 4, a source-load weight distribution model is constructed based on the SSPR method to obtain a source-load weight distribution matrix considering the overall strength and sparsity of the power grid;

[0012] Step 6, perform source-load distribution effect verification, perform power flow calculation on the re-distributed active power distribution network, verify whether the node power quality meets the standard requirements, if yes, issue an instruction; if not, return to step 4 and adjust the to-be-distributed source-load.

[0013] Further, the step 1 comprises:

[0014] The power monitoring system is used to measure the power quality related data of each node of the active power distribution network, and the obtained data is processed by using digital filtering method for data cleaning, missing value processing, noise reduction processing, standardization and normalization processing; at the same time, the connected generators, loads and new energy in the power grid are processed as network nodes, and the processed power grid data includes a label matrix L storing node type information of the active power distribution network, a power grid directed adjacency matrix The matrix A is a power grid directed adjacency matrix, A.arcs[i][j] refers to the element of the i row and j column in the power grid directed adjacency matrix A, the node current matrix I, the frequency matrix f, the voltage matrix V, the node active power matrix P and the reactive power matrix Q.

[0015] Further, the step 2 comprises:

[0016] Step 2.1, extract the node network topology structure hole feature set F of the active power distribution network topo , which includes the following features: active power distribution network node degree centrality matrix K, node betweenness centrality matrix C RB , node closeness centrality matrix C AC , and the expressions are as follows:

[0017]

[0018]

[0019]

[0020] d(h,j,t)=log(α h,j (t),αj,h (t))

[0021]

[0022] where, is the out-degree of active distribution network node h at time t, is the in-degree of node h at time t, n is the number of active distribution network nodes, hj (t) is the value of arcs[h][j] of the power grid directed adjacency matrix A at time t, jh (t) is the value of arcs[j][h] of the power grid directed adjacency matrix A at time t, sw (h,t) is the number of s→w shortest paths passing through node h at time t, sw (t) is the number of s→w shortest paths at time t, d(h,j,t) is the electrical distance from node h to node j at time t, h,j (t) is the voltage sensitivity of node h to node j at time t, j,h (t) is the voltage sensitivity of node j to node h at time t, h (t) is the voltage of node h at time t, j (t) is the voltage of node j at time t, j (t) is the active power of node j at time t, j (t) is the reactive power of node j at time t.

[0023] Step 2.2, extract the electrical feature set F of active distribution network nodes ele , which includes the following features: consider the real-time power flow tracking matrix P of distributed power sources GL , network efficiency matrix E, and node constraint coefficient matrix Co, expressed as follows:

[0024]

[0025]

[0026]

[0027] Co(h,t)=∑ k∈Γ(h) (p hk (t)+∑ j∈(Γ(h)IΓ(k)) (p hj (t)p jk (t))) 2

[0028]

[0029] where, P G(h, t) is the power injected by the generator of the power supply node h at time t, P(h, t) is the injected power of node h at time t, [A d ] ij is the downstream distribution value from node i to node j, P L (k, t) is the power of the load node k at time t, N G is the set of power supply nodes, N L is the set of load nodes, β i is the set of all nodes connected to node i and drawing power from node i, d(h, i, t) is the electrical distance between node h and node i at time t, P ij is the active power transmitted by node i to node j, P j is the total injected active power of node j, N is the set of nodes of the active distribution network, Γ(h) is the neighbor node of node h, Γ(k) is the neighbor node of node k, p hk (t) is the ratio of the power injected by node h to node k to the total outgoing power of node h, p hj (t) is the ratio of the power injected by node h to node j to the total outgoing power of node h, p jk (t) is the ratio of the power injected by node j to node k to the total outgoing power of node j, p ij (t) is the ratio of the power injected by node i to node j to the total outgoing power of node i, P ij (t) is the power injected by node i to node j at time t, P ig (t) is the power injected by node i to node g at time t.

[0030] Step 2.3, using empirical orthogonal function EOF to reconstruct the node network topology metric features and node electrical features with high correlation coefficient respectively to form feature space: normalizing the active distribution network node features and constructing matrix X m×n , solving to get the principal components of the node feature vectors, where is the transpose of the feature vector matrix; accumulating the variance contribution rates of the first n feature vectors, when the accumulated value exceeds the preset threshold γ, the process ends, and the features participating in the accumulation are extracted to reconstruct the feature matrix R o×n , where o is the number of reconstructed features.

[0031] Further, the step 3 includes:

[0032] Step 3.1, using the improved k-shell method to decompose the active distribution network, iteratively deleting nodes according to the in-degree from small to large to get the k-shell number K S (h, t), and record the iteration number n(h, t) when the node is deleted; combining the topological connection, calculate the core number KCore (h,t), which is expressed as follows:

[0033] K Core (h,t) = K S (h,t) x K(h,t) + n(h,t) + μ i D(h,t)

[0034] where K(h,t) is the degree of node h at time t, D(h,t) is the number of second-neighbor nodes of node h, and μ i is the influence coefficient;

[0035] Step 3.2, the weight w o×n between nodes is calculated according to the reconstructed feature space R ij , and the active power distribution network weight matrix W is obtained, which is expressed as follows:

[0036] w ij = (ω1*r 1,i + ω2*r 2,i + L + ω o *r o,i )*(ω1*r 1,j + ω2*r 2,j + L + ω o *r o,j )

[0037] where ω1, ω2, L, and ω o are benign adjustment factors corresponding to the features of the nodes, r 1,i , r 2,i , L, and r o,i are the eigenvalues of the corresponding node i in the reconstruction matrix, and r 1,j , r 2,j , L, and r o,j are the eigenvalues of the corresponding node j in the reconstruction matrix;

[0038] Step 3.3, the PR value of the node is calculated, and the calculation formula is as follows:

[0039]

[0040] where i is the iteration number for PR value calculation, d is the damping factor, PR(h) i is the PR value of node h in the i-th iteration, the decay coefficient is the core number K Core (j) of node j, p jh (t) is the ratio of the power injected by node j into node h to the total power flowing out of node j, and w jh is the weight between node j and node h;

[0041] Step 3.4, according to the label matrix L of the node type information, the active power distribution network node set is divided into a power supply node set, a load node set, and an intermediate node set, and the active power distribution network power supply node set, load node set, and intermediate node set are sorted according to the PR value of the node to determine the key nodes in the power grid.

[0042] Further, the step 4 comprises:

[0043] Step 4.1, first, the inertia and elasticity values of the nodes in the power grid are calculated, and the expression is as follows:

[0044]

[0045] wherein Hs(h) is the inertia value of the active power distribution network node, used to represent the inertia of the node; f is the frequency corresponding to the node, the unit is Hz; ΔP h is the increment of the unbalanced active power of the node h caused by the disturbance; Re(h) is the elasticity value of the node, R o (h, t) is the electrical function retention rate of the node h, specifically the load retention rate and the source retention rate of the node, t e is the disturbance occurrence time, t pr is the node elasticity calculation cutoff time, R true (h, t) is the actual value of the electrical function, R nom (h, t) is the electrical function value under normal operation;

[0046] Step 4.2, identify the fault nodes of the active power distribution network after the disturbance and the nodes whose source and load exceed their maximum capacity, to obtain the node set N assign to be allocated; determine the source and load redistribution amount according to the inertia and elasticity of the nodes in the power grid, to obtain the target adjacency matrix Λ after redistribution.

[0047] Further, the specific steps of the step 4.2 are as follows:

[0048] Step 4.2.1, traverse the node set N assign to be allocated, and for the node q that needs to be redistributed, traverse its neighbor nodes g, calculate the inter-node redistribution source and load amount ΔF g , and the expression is as follows:

[0049]

[0050] wherein F p is the source and load amount of the node to be allocated under normal operation, Γ p is the neighbor node of the node p, and α1, α2 are the elasticity and inertia weight adjustment factors, d pgLet Hs(g) be the electrical distance between node p and node g, Hs(g) and Hs(k) be the inertia values ​​of nodes g and k respectively, and Re(g) and Re(k) be the elasticity values ​​of nodes g and k respectively.

[0051] Step 4.2.2: After calculating the redistribution amount for all nodes, determine whether any node has triggered a fault condition, i.e., whether the node's source load exceeds its maximum capacity. If it does, add the node to set N. assign In the middle, redistribution is performed again; otherwise, the redistribution calculation ends, and the target adjacency matrix Λ after redistribution of the active distribution network is obtained.

[0052] Further, step 5 includes:

[0053] Step 5.1: Determine the allocatable node matrix Z based on the active distribution network topology;

[0054] Step 5.2: Calculate the initial source-load transfer matrix Ψ based on the original active power matrix P of the active distribution network and the target adjacency matrix Λ after redistribution. initial ;

[0055] Step 5.3, regarding the transition amount Ψ between node i and node j i,j The source load allocation optimization amount Δω is calculated iteratively. i,j,k,l And update the transfer amount Ψ between the corresponding nodes in sequence. i,j ,Ψ k,l ,Ψ i,l ,Ψ k,j Ψ i，j Ψ represents the transition amount between node i and node j. k，l Ψ represents the transition amount between node k and node l. i，l Ψ represents the transfer amount between node i and node l. k，j Let represent the transfer amount between node k and node j. The transfer amounts of all nodes are calculated iteratively to obtain the final source-load transfer matrix Ψ, expressed as follows:

[0056] Ψ initial =P-Λ

[0057]

[0058]

[0059] Among them, z k,l ,z i,l ,z k,j For each element in the distributable node matrix Z, we have: whether nodes k and l are distributable, whether nodes i and l are distributable, and whether nodes k and j are distributable.

[0060] Compared with the prior art, the application has the beneficial effects that:

[0061] 1) The application establishes a node characteristic calculation formula of the active power distribution network considering the complex network structure hole, comprehensively integrates the topological structure characteristics and electrical characteristics of the node, solves the problem that the traditional method ignores the complex relationship between nodes when analyzing the importance of the power grid node, and thus provides more accurate and comprehensive node importance evaluation characteristic indexes.

[0062] 2) The application establishes an active power distribution network node importance evaluation model based on an improved I-PageRank method, the model can deeply analyze the relative importance of each node in the power grid, fully considers the mutual influence between nodes and the network structure characteristics, and introduces the node core number of the K-shell algorithm as a decay coefficient in the traditional PageRank algorithm, and further improves the performance of identifying nodes.

[0063] 3) The application establishes a source-load heavy distribution magnitude calculation model of the active power distribution network, combines the node importance identification result of the active power distribution network, considers the elasticity and inertia distribution of the network, calculates the source-load transfer probability between nodes, determines the source-load heavy distribution magnitude, and can improve the stability and reliability of the system while retaining as many sources and loads as possible.

[0064] 4) A source-load heavy distribution model based on the SSPR method is established, and when multiple node source-load heavy distribution, single node multiple heavy distribution, multiple node multiple heavy distribution and other complex scenarios occur in the active power distribution network, the model can calculate the optimal scheme of network strength and sparsity while achieving distribution. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The flowchart of the application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0067] Please refer to Figure 1 , a source and load heavy distribution method of an active power distribution network based on node elasticity and inertia distribution, comprising:

[0068] Step 1, active power distribution network topology based on IEEE118 nodes, including 8 photovoltaic nodes, 5 wind turbine nodes, 3 synchronous generator nodes and 18 load nodes. Obtain the active power distribution network topology and power quality data, and process the original data into standard data format, including:

[0069] The power quality related data of each node in the active power distribution network is measured by the power monitoring system. The obtained data is processed by digital filtering method for data cleaning, missing value processing, noise reduction processing, standardization and normalization processing. At the same time, the generators, loads and new energy connected in the power grid are processed as network nodes. The processed power grid data includes label matrix L storing active power distribution network node type information, power grid directed adjacency matrix Matrix A is the power grid directed adjacency matrix, A.arcs[i][j] refers to the element of i row and j column in the power grid directed adjacency matrix A. Node current matrix I, frequency matrix f, voltage matrix V, node active power matrix P and reactive power matrix Q.

[0070] Step 2, based on the complex network structure hole theory, extract the node network topology measurement characteristics and node electrical characteristics of the active power distribution network, use EFO function to extract principal component characteristics, form a reconstructed feature space, including:

[0071] Step 2.1, extract node network topology structure hole feature set F topo of the active power distribution network, which includes the following characteristics: active power distribution network node degree centrality matrix K, node betweenness centrality matrix C RB , node closeness centrality matrix C AC , the specific calculation is as follows:

[0072]

[0073]

[0074]

[0075] d(h,j,t)=log(α h,j (t),α j,h (t))

[0076]

[0077] wherein, is the out-degree of active power distribution network node h at time t, is the in-degree of node h at time t, n is the number of active power distribution network nodes, a hj (t) is the value of power grid directed adjacency matrix A.arcs[h][j] at time t, a jh(t) is the value of the grid directed adjacency matrix A.arcs[j][h] at time t, σ sw (h, t) is the number of s→w shortest paths passing through node h at time t, σ sw (t) is the number of s→w shortest paths at time t, d(h, j, t) is the electrical distance from node h to node j at time t, α h,j (t) is the voltage sensitivity of node h to node j, α j,h (t) is the voltage sensitivity of node j to node h, U h (t) is the voltage of node h at time t, U j (t) is the voltage of node j at time t, P j (t) is the active power of node j at time t, Q j (t) is the reactive power of node j at time t;

[0078] Step 2.2, extract the node electrical feature set F for the active power distribution network ele , which contains the following features: consider the distributed power real-time power flow tracking matrix P GL , network efficiency matrix E, node constraint coefficient matrix Co, the specific calculation is as follows:

[0079]

[0080]

[0081]

[0082] Co(h, t) = ∑ k∈Γ(h) (p hk (t) + ∑ j∈(Γ(h)IΓ(k)) (p hj (t) p jk (t)) 2

[0083]

[0084] Where P G (h, t) is the generator injection power of power source node h at time t, P(h, t) is the injection power of node h at time t, [A d ] ij is the downstream distribution value from node i to node j, P L (k, t) is the power of load node k at time t, N G is the power source node set, N L is the load node set, P ij is the active power transmitted from node i to node j, P jPj(t) is the total active power injected into node j, N is the set of active distribution network nodes, Γ(h) is the neighbor nodes of node h, Γ(k) is the neighbor nodes of node k, phk(t) is the ratio of the power injected into node k by node h to the total power out of node h, p hj (t) is the ratio of the power injected into node j by node h to the total power out of node h, p jk (t) is the ratio of the power injected into node k by node j to the total power out of node j, p ij (t) is the ratio of the power injected into node j by node i to the total power out of node i, P ij (t) is the power injected into node j by node i at time t, P ig (t) is the power injected into node g by node i at time t;

[0085] Step 2.3, using EOF to reconstruct the node network topology metric features and node electrical features with high correlation coefficients into feature spaces respectively: normalize the features of the active distribution network nodes, and construct matrices X m×n , solve to obtain the principal components of the node feature vectors, where is the transpose of the feature vector matrix; accumulate the variance contribution rates of the first n feature vectors, and end when the accumulated value exceeds the preset threshold γ, while extracting the features participating in the accumulation to reconstruct the feature matrix R o×n , where o is the number of reconstructed features.

[0086] Step 3, for the active distribution network, establish a node importance evaluation model based on the improved I-PageRank method to obtain the importance node ranking set, including:

[0087] Step 3.1, use the improved k-shell method to decompose the active distribution network, iteratively delete nodes according to the in-degree from small to large, and obtain the k-shell number K S (h, t) of the nodes, and record the iteration number n(h, t) when the node is deleted; combined with the topology connection, calculate the core number K Core (h, t) of the active distribution network nodes, the expression is as follows:

[0088] K Core (h, t) = K S (h, t) × K(h, t) + n(h, t) + μ i D(h, t)

[0089] Where K(h, t) is the degree of node h at time t, D(h, t) is the number of second neighbors of the node, μ i is the influence coefficient;

[0090] Step 3.2, according to the reconstruction feature space R o×n Calculate the weight w between nodes ij , get the weight matrix W of the active power distribution network, the specific calculation is as follows:

[0091] w ij =(ω1*r 1,i +ω2*r 2,i +L+ω o *r o,i )*(ω1*r 1,j +ω2*r 2,j +L+ω o *r o,j )

[0092] Where ω1,ω2L,ω o are the benign adjustment factors of the node corresponding features, r 1,i ,r 2,i ,L,r o,i are the eigenvalues of the corresponding node i in the reconstruction matrix, r 1,j ,r 2,j ,L,r o,j are the eigenvalues of the corresponding node j in the reconstruction matrix.

[0093] Step 3.3, calculate the PR value of the node, the calculation formula is as follows:

[0094]

[0095] Where i is the iteration number of PR value calculation, d is the damping factor, PR(h) i is the PR value of node h in the i th iteration, and the decay coefficient is the core number K Core (j) of node j.

[0096] Step 3.4, according to the label matrix L of node type information, the node set of active power distribution network is divided into power supply node set, load node set and intermediate node set, according to the PR value of the node, the importance of the power supply node set, load node set and intermediate node set of active power distribution network is sorted, and the key node in the power grid is determined.

[0097] Step 4, assuming that the output of photovoltaic node fluctuates sharply due to the mutation of irradiance during operation, which leads to a large drop in the voltage of load node 3. In view of this source side disturbance, based on the important node identification result of step 3, the inertia and elasticity distribution of the nodes in the active power distribution network are calculated, an active power distribution network source load distribution order calculation model is established, and the source load distribution order matrix is obtained. The specific steps are as follows:

[0098] Step 4.1, first calculate the inertia and elasticity value of the grid node, as follows:

[0099]

[0100] where Hs(h) is the inertia value of the active distribution network node, representing the inertia of the node; f is the frequency corresponding to the node, dimensionless; ΔP h is the increment of the unbalanced active power of node h caused by the disturbance; Re(h) is the resilience value of the node, R o (h, t) is the electrical function retention rate of node h, specifically the load retention rate and the source retention rate of the node, t e is the disturbance occurrence time, t pr is the node resilience calculation cutoff time, R true (h, t) is the actual value of the electrical function, R nom (h, t) is the electrical function value under normal operation;

[0101] Step 4.2, identify the fault nodes of the active distribution network after the disturbance and the nodes whose source load exceeds their maximum capacity, to obtain the set of nodes to be allocated N assign ; determine the source load redistribution amount according to the inertia and resilience of the grid nodes, and obtain the target adjacency matrix Λ after redistribution, the specific steps are as follows:

[0102] Step 4.2.1, traverse the set of nodes to be allocated N assign , for the node q that needs to be redistributed, traverse its neighbor nodes g, calculate the inter-node redistribution source load ΔF g , the expression is as follows:

[0103]

[0104] where F p is the source load of the node to be allocated under normal operation, Γ p is the neighbor node of node p, α1, α2 are the resilience and inertia weight adjustment factors, d pg is the electrical distance between node p and node g, Hs(g), Hs(k) are the inertia values of nodes g and k, respectively, Re(g), Re(k) are the resilience values of nodes g and k, respectively;

[0105] Step 4.2.2, after calculating the allocation amount of all nodes to be allocated, judge whether all nodes trigger the fault condition, i.e., whether the source load of the node exceeds its maximum capacity, if yes, put the node into the set N assign , and redistribute again, otherwise end the redistribution amount calculation, and obtain the target adjacency matrix Λ of the active distribution network after redistribution.

[0106] Step 5, based on the source load redistribution amount level matrix obtained in step 4, construct a source load redistribution model based on the SSPR method, and obtain a source load redistribution matrix considering the overall strength and sparsity of the grid, including:

[0107] Step 5.1, determine the allocable node matrix Z based on the active power distribution network network topology;

[0108] Step 5.2, calculate the initial source load transfer matrix Ψ according to the active power matrix P of the active power distribution network and the target adjacency matrix Λ after redistribution initial ;

[0109] Step 5.3, for the transfer amount Ψ i,j between node i and node j, iteratively calculate the source load redistribution optimization amount Δω i,j,k,l , and update the transfer amount Ψ i,j between the corresponding nodes in turn, Ψ k,l , Ψ i,l , Ψ k,j , Ψ i，j represents the transfer amount between node i and node j, Ψ k，l represents the transfer amount between node k and node l, Ψ i，l represents the transfer amount between node i and node l, and Ψ k，j represents the transfer amount between node k and node j, iteratively calculate the transfer amount of all nodes to obtain the final source load transfer matrix Ψ, the expression is as follows:

[0110] Ψ initial = P-Λ

[0111]

[0112]

[0113] wherein z k,l , z i,l , z k,j are the corresponding elements of the allocable node matrix Z, respectively representing whether the nodes k, l are allocable, whether the nodes i, l are allocable, and whether the nodes k, j are allocable. It is indicated that the constraint condition is that only the allocable node set can participate in the optimization redistribution process.

[0114] Step 6, perform source load distribution effect verification, perform power flow calculation on the active power distribution network after redistribution, verify whether the node power quality meets the standard requirement, if yes, issue an instruction; if not, return to step 4 and adjust the to-be-distributed source load amount.

[0115] Although the embodiments of the present application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for power and load distribution in an active power distribution network based on nodal elasticity and inertia distribution, characterized in that, include: Step 1: Obtain the active distribution network topology and power quality data, and process the raw data into a standard data format; Step 2: Based on the hole theory of complex network structures, extract the network topology metric features and node electrical features of active distribution network nodes, and use the EFO function to extract principal component features to form a reconstructed feature space; Step 3: Establish an active distribution network node importance assessment model based on the improved I-PageRank method to obtain a ranking set of important nodes. Step 3 includes: Step 3.1: Decompose the active distribution network using the improved k-shell method, iteratively deleting nodes according to their in-degree from smallest to largest, to obtain the k-shell level K of each node. S (h,t), and record the iteration number n(h,t) when a node is deleted; combine the topology connection situation to calculate the number of active distribution network node cores K. Core (h,t), the expression is as follows: K Core (h,t)=K S (h,t)×K(h,t)+n(h,t)+μ i D(h,t) Where K(h,t) is the degree of node h at time t, D(h,t) is the number of second-order neighbor nodes of node h, and μ i This is the influence coefficient; Step 3.2, based on the reconstructed feature space R o×n Calculate the weights w between nodes ij The weight matrix W of the active distribution network is obtained, and its expression is as follows: w ij =(ω1*r 1,i +ω2*r 2,i +L+ω o *r o,i )*(ω1*r 1,j +ω2*r 2,j +L+ω o *r o,j ) Where ω1, ω2L, ω o r is a benign moderating factor for the features corresponding to the nodes. 1,i ,r 2,i ,L,r o,i To reconstruct the eigenvalues ​​of the corresponding node i in the matrix, r 1,j ,r 2,j ,L,r o,j To reconstruct the eigenvalues ​​of the corresponding node j in the matrix; Step 3.3, calculate the PR value of the node, using the following formula: In the formula, i is the number of iterations for calculating the PR value, d is the damping factor, and PR(h) i Let PR be the PR value of node h in the i-th iteration, and let K be the decay coefficient of the number of cores of node j. Core (j), p jh (t) represents the ratio of the power supplied by node j to node h to the total power outflow from node j. jh Let be the weight between node j and node h; Step 3.4: Based on the label matrix L of node type information, the active distribution network node set is divided into power supply node set, load node set, and intermediate node set. Based on the PR value of the nodes, the power supply node set, load node set, and intermediate node set of the active distribution network are sorted by importance to determine the key nodes in the power grid. Step 4: Based on the important node identification results in Step 3, calculate the node inertia and elasticity distribution of the active distribution network, establish a calculation model for the source load distribution magnitude of the active distribution network, and obtain the source load distribution magnitude matrix. Step 5: Based on the source load distribution magnitude matrix obtained in Step 4, construct the source load distribution model based on the SSPR method to obtain the source load distribution matrix that considers the overall strength and sparsity of the power grid. Step 6: Verify the source load allocation effect. Perform power flow calculation on the redistributed active distribution network to verify whether the node power quality meets the standard requirements. If it does, issue an instruction; if it does not, return to step 4 and adjust the amount of source load to be allocated.

2. The method for power and load allocation in an active power distribution network based on nodal elasticity and inertia distribution according to claim 1, characterized in that, Step 1 includes: Power quality data at various nodes of an active distribution network are measured using a power monitoring system. The obtained data is then cleaned, missing value removed, noise reduced, and standardized and normalized using digital filtering methods. Simultaneously, generators, loads, and renewable energy sources connected to the grid are treated as network nodes. The processed grid data includes a label matrix L storing active distribution network node type information and a directed adjacency matrix. Matrix A is the directed adjacency matrix of the power grid. A.arcs[i][j] refers to the element in row i and column j of the directed adjacency matrix A of the power grid, which includes the node current matrix I, frequency matrix f, voltage matrix V, node active power matrix P, and reactive power matrix Q.

3. The method for power and load distribution in an active power distribution network based on nodal elasticity and inertia distribution according to claim 1, characterized in that, Step 2 includes: Step 2.1: Extract the hole feature set F of the active distribution network node network topology. topo It includes the following characteristics: the degree centrality matrix K of the active distribution network nodes, and the betweenness centrality matrix C of the nodes. RB The nodes are close to the centrality matrix C. AC The expression is as follows: d(h,j,t)=log(α h,j (t),α j,h (t)) in, It is the out-degree of node h in the active distribution network at time t. Let h be the in-degree of node h at time t, n be the number of nodes in the active distribution network, and a be the in-degree of node h at time t. hj (t) represents the value of the directed adjacency matrix A.arcs[h][j] of the power grid at time t, a jh (t) represents the value of the directed adjacency matrix A.arcs[j][h] of the power grid at time t, σ sw (h,t) represents the number of shortest paths from node h to w at time t, and σ sw (t) represents the number of shortest paths from s to w at time t, d(h,j,t) represents the electrical distance from node h to node j at time t, and α h,j (t) represents the voltage sensitivity from node h to node j, α j,h (t) represents the voltage sensitivity from node j to node h, U h (t) represents the voltage at node h at time t, U j (t) represents the voltage at node j at time t, P j Q(t) represents the active power of node j at time t. j (t) represents the reactive power of node j at time t; Step 2.2, extract the electrical feature set F of active distribution network nodes. ele It includes the following features: considering the real-time power flow tracking matrix P of distributed sources. GL The network efficiency matrix E and the node constraint coefficient matrix Co are expressed as follows: Co(h,t)=∑ k∈Γ(h) (p hk (t)+∑ j∈(Γ(h)IΓ(k)) (p hj (t) p jk (t))) 2 Among them, P G (h,t) represents the generator-injected power at power node h at time t, and P(h,t) represents the injected power at node h at time t. [A d ] ij It is the downstream allocation value from node i to node j, P L (k,t) represents the power of load node k at time t, N G Let N be the set of power nodes. L For the set of load nodes, β i Let d(h,i,t) be the set of all nodes connected to node i and drawing power from node i, and let d(h,i,t) be the electrical distance between node h and node i at time t. ij P represents the active power transmitted from node i to node j. j Let N be the set of active power injected into node j, N be the set of active distribution network nodes, Γ(h) be the neighboring nodes of node h, Γ(k) be the neighboring nodes of node k, and p be the total active power injected into node j. hk (t) represents the ratio of the power supplied from node h to node k to the total power flowing out of node h, p hj (t) represents the ratio of the power supplied from node h to node j to the total power outflow from node h, p jk (t) represents the ratio of the power supplied from node j to node k to the total power flowing out of node j, p ij (t) represents the ratio of the power supplied from node i to node j to the total power flowing out of node i. ij (t) represents the power injected from node i to node j at time t, P ig (t) represents the power injected by node i into node g at time t; Step 2.3: Using the empirical orthogonal function EOF, the feature space is reconstructed from the network topology metric features and node electrical features with high correlation coefficients. The node features of the active distribution network are normalized and constructed into matrices X. m×n Solve The principal components of the node feature vectors are obtained, where It is the transpose of the eigenvector matrix; the variance contribution rates of the first n eigenvectors are accumulated, and the process ends when the accumulated value exceeds a preset threshold γ. At the same time, the features involved in the accumulation are extracted and reconstructed into the feature matrix R. o×n , where o is the number of reconstructed features.

4. The method for power and load distribution in an active power distribution network based on nodal elasticity and inertia distribution according to claim 1, characterized in that, Step 4 includes: Step 4.1: First, calculate the inertia and elasticity values ​​of the power grid nodes, as shown in the following expressions: Where Hs(h) is the inertia value of an active distribution network node, used to characterize the node's inertia; f is the corresponding node frequency, per unit value; ΔP h It is the increment of unbalanced active power at node h caused by the disturbance; Re(h) is the resilience value of the node, R o (h,t) represents the electrical function retention rate of node h, specifically the load retention rate and source retention rate of the node, and t e Let t be the time when the disturbance occurs. pr For node elasticity calculation of deadline, R true (h,t) represents the actual values ​​of the electrical function, R nom (h,t) represents the electrical function values ​​under normal operating conditions; Step 4.2: Identify the faulty nodes in the active distribution network after disturbance and the nodes whose source load exceeds their maximum capacity, to obtain the set N of nodes to be assigned. assign The source load allocation is determined based on the inertia and elasticity of the power grid nodes, and the target adjacency matrix Λ after redistribution is obtained.

5. The method for power and load distribution in an active power distribution network based on nodal elasticity and inertia distribution according to claim 4, characterized in that, The specific steps of step 4.2 are as follows: Step 4.2.1, traverse the set of nodes to be assigned N assign For the node q that needs to be redistributed, traverse its neighboring nodes g and calculate the redistribution source load ΔF between the nodes. g The expression is as follows: Among them, F p Γ represents the source load of the node to be allocated under normal operating conditions. p Let be the neighboring nodes of node p, α1 and α2 be the elasticity and inertia weight adjustment factors, and d pg Let Hs(g) be the electrical distance between node p and node g, Hs(g) and Hs(k) be the inertia values ​​of nodes g and k respectively, and Re(g) and Re(k) be the elasticity values ​​of nodes g and k respectively. Step 4.2.2: After calculating the redistribution amount for all nodes, determine whether any node has triggered a fault condition, i.e., whether the node's source load exceeds its maximum capacity. If it does, add the node to set N. assign In the middle, redistribution is performed again; otherwise, the redistribution calculation ends, and the target adjacency matrix Λ after redistribution of the active distribution network is obtained.

6. The method for power and load distribution in an active power distribution network based on nodal elasticity and inertia distribution according to claim 1, characterized in that, Step 5 includes: Step 5.1: Determine the allocatable node matrix Z based on the active distribution network topology; Step 5.2: Calculate the initial source-load transfer matrix Ψ based on the original active power matrix P of the active distribution network and the target adjacency matrix Λ after redistribution. initial ; Step 5.3, regarding the transition amount Ψ between node i and node j i,j The source load allocation optimization amount Δω is calculated iteratively. i,j,k,l And update the transfer amount Ψ between the corresponding nodes in sequence. i,j ,Ψ k,l ,Ψ i,l ,Ψ k,j Ψ i，j Ψ represents the transition amount between node i and node j. k，l Ψ represents the transition amount between node k and node l. i，l Ψ represents the transfer amount between node i and node l. k，j Let represent the transfer amount between node k and node j. The transfer amounts of all nodes are calculated iteratively to obtain the final source-load transfer matrix Ψ, expressed as follows: P initial =P-Λ Among them, z k,l ,z i,l ,z k,j For each element in the distributable node matrix Z, we have: whether nodes k and l are distributable, whether nodes i and l are distributable, and whether nodes k and j are distributable.

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