Distributed power distribution network key distribution method based on quantum communication

Abstract

The application discloses a distributed new power distribution network key distribution method based on quantum communication, and comprises the following steps: predicting power flow fluctuation caused by distributed energy output change by establishing a prediction model to obtain power flow distribution data in real time; generating candidate nodes of a key distribution network dynamically based on the obtained power flow distribution data, selecting a high load fluctuation area as a quantum communication distribution center node, and constructing a distributed key distribution network topology; and realizing quantum key distribution link through generating quantum entanglement pairs on the basis of the established network topology. The application can obtain and predict power flow fluctuation caused by distributed energy output change in real time, and then dynamically adjust the key distribution path to cope with the change of power grid operation state.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution networks, in particular to a distributed new power distribution network key distribution method based on quantum communication. BACKGROUND

[0002] In recent years, the domestic distributed energy market has been growing in size, reaching 1077.18 billion yuan in 2023, becoming one of the important markets for global distributed energy investment. As of 2022, the cumulative installed capacity of distributed energy in China was about 25000.49 million kilowatts, showing strong development momentum in the field of distributed energy in China. On March 21, 2024, the State Energy Administration released the "Power Distribution Network Safety Risk Control Key Action Work Plan", which mentioned that the impact of large-scale access of distributed distributed energy sources, energy storage, electric vehicle charging piles and other new grid-connected subjects on the power distribution network needs to be analyzed, and the grid-connected safety management of new grid-connected subjects needs to be investigated and analyzed to ensure the safety of the power grid.

[0003] One of the problems of distributed distributed energy source access to distribution network is to make the power flow distribution of distribution network more complex, for example, Huang Haiquan, Huang Xiaowei, Jiang Wang, etc. New type of distribution network distributed energy storage system scheme and configuration research summary [J]. Southern energy construction, 2024, 11(4) mentioned in the new type of distribution network, the high proportion of distributed photovoltaic access to the new type of distribution network brings heavy burden: (1) the peak period of photovoltaic power generation and the peak period of electricity consumption are inconsistent, and it causes the new type of distribution network to change from unidirectional flow to bidirectional flow, causing uncertain flow and increasing network loss. Chinese patent CN105048451A provides an interval power flow calculation method based on distributed energy generation interval prediction, specifically using the ability of interval two type fuzzy logic to process uncertainty problems to obtain the interval two type fuzzy prediction set of distributed energy generation, wherein the interval two type fuzzy prediction set clearly defines the fluctuation interval range of the generation capacity, which can be the initial iteration interval in the interval power flow calculation, avoiding the problems of non-convergence, too fast convergence and too slow convergence caused by artificially setting the initial interval, thereby improving the accuracy of the calculation. Peng Wei, Zheng Lianqing, Zheng Tianwen. Optimization configuration method of distributed photovoltaic energy storage system [J]. Sichuan electric power technology, 2022, 45(1) uses a double-layer optimization model to reduce the fluctuation caused by the access of distributed photovoltaic to the distribution network as much as possible: the outer optimization target is the access location, power and capacity of the energy storage system; the inner optimization target is the cost of the energy storage system. After the outer layer gives the determined energy storage parameters, the inner layer uses the particle swarm algorithm combined with the MATPOWER power flow calculation tool to optimize the operation strategy of the energy storage system, so that the total cost of the energy storage system is minimized under this configuration; then, the inner layer optimization result is fed back to the outer layer for selection, crossover and mutation operation of genetic algorithm, and the optimal configuration and operation strategy of the energy storage in the feasible region are determined through repeated iteration comparison. At the same time, in 2022, the main line of Jiangbei 10kV Hengshan line in Ningbo, Zhejiang realized quantum encryption intelligent switch full coverage, and in 2023, State Grid Wuhan Power Supply Company realized quantum encryption communication in the distribution automation terminal of the power supply ring network in Wuhan Jinqiao District, so the problem that must be considered now is that quantum key distribution (QKD) relies on stable communication channel to generate and distribute keys, however, the fluctuation of distributed energy output will cause the change of power grid operation state, affecting the generation and distribution of keys. SUMMARY

[0004] In order to solve the stability problem of quantum key distribution of distributed energy distribution network, the present application provides a distributed new type of distribution network key distribution method based on quantum communication, which can obtain and predict the power flow fluctuation caused by the change of distributed energy output in real time, and then dynamically adjust the key distribution path to cope with the change of power grid operation state.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] The distributed new power distribution network key distribution method based on quantum communication comprises:

[0007] By establishing a prediction model, the power flow fluctuation caused by the change of distributed energy output is predicted to obtain the power flow distribution data in real time; based on the obtained power flow distribution data, the candidate nodes of the key distribution network are dynamically generated, and the high load fluctuation area is selected as the quantum communication distribution center node, so as to construct the distributed key distribution network topology; on the basis of the established network topology, the quantum key distribution link is realized by generating quantum entanglement pairs.

[0008] Further, the power flow fluctuation caused by the change of distributed energy output is predicted by establishing a prediction model to obtain the power flow distribution data in real time, comprising:

[0009] The historical power flow data, real-time distributed energy output data, load data, node voltage and weather condition information of each node in the integrated power grid are integrated.

[0010] The spatiotemporal sequence data is converted into a quantum feature vector, and the quantum feature vector is convolved by a quantum convolution layer to realize the extraction and enhancement of local features.

[0011] The quantum feature enhanced data is input into the spatiotemporal convolution neural network.

[0012] The spatiotemporal convolution neural network comprises a power grid spatiotemporal graph structure and a spatiotemporal convolution layer.

[0013] The power grid spatiotemporal graph structure comprises regarding each node in the power grid as a node in the graph structure, regarding the power grid connection line as an edge in the graph, thereby constructing the spatial graph structure of the power grid, and the data in the time dimension as the time sequence input of the graph.

[0014] The spatiotemporal convolution layer comprises mapping the quantum feature vector after the convolution operation to the spatiotemporal relationship of each node by spatiotemporal convolution operation, capturing the electrical coupling relationship between different nodes, and gradually obtaining the comprehensive influence of the adjacent node on the power flow by inputting the quantum feature vector as the data of each node in the spatiotemporal convolution process.

[0015] The graph embedding of each node is generated, and the graph embedding of each node represents the power flow feature distribution of the node at the current time and future time, and reflects the influence of the adjacent nodes, thereby obtaining the global distribution of the power flow of the power grid.

[0016] The node embedding of the spatiotemporal convolution layer is input into the dynamic prediction layer, and the recurrent neural network is used for power flow prediction.

[0017] Further, the candidate nodes of the key distribution network are dynamically generated based on the obtained power flow distribution data, and the high-load fluctuation area is selected as the quantum communication distribution center node, so as to construct the distributed key distribution network topology, comprising:

[0018] Obtaining power flow prediction results and calibrating high-load fluctuation nodes;

[0019] Generating a set of key distribution candidate nodes;

[0020] Selecting a center node;

[0021] Constructing a distributed key distribution network topology.

[0022] Further, the obtaining of the power flow prediction results and the calibrating of the high-load fluctuation nodes comprise:

[0023] Based on the power flow prediction data, the nodes with large load fluctuation, i.e. the high-load fluctuation nodes, are calibrated by using the node load variance and the power change rate.

[0024] Further, the generating of the set of key distribution candidate nodes comprises:

[0025] The high-load fluctuation nodes and their adjacent nodes form a set of candidate nodes, in which the connectivity of the set is verified by using a network connectivity verification algorithm to ensure that all candidate nodes form a distribution network topology graph with multi-path redundancy.

[0026] Further, the selecting of the center node comprises:

[0027] The nodes with high load fluctuation area and high connection degree with other candidate nodes are preferentially selected as the quantum key distribution center node by using the load change rate of the candidate nodes and the power flow amount of the node neighborhood, and combining the graph theory algorithm.

[0028] Further, the constructing of the distributed key distribution network topology comprises:

[0029] Based on the candidate nodes and the center node, the topology structure of the distributed key distribution network is constructed by using a graph structure generation algorithm.

[0030] Further, the quantum key distribution link is realized by generating quantum entangled pairs on the basis of the established network topology, comprising:

[0031] Quantum entangled pairs are generated between the selected distribution center node and its adjacent candidate nodes;

[0032] Based on the quantum key distribution protocol shared by the entangled pairs, the key link is constructed between the distribution center node and the adjacent nodes.

[0033] Compared with the prior art, the technical progress achieved by the present application is that:

[0034] The present application can identify potential high load fluctuation areas after completing the power flow prediction, and by analyzing the characteristics of these areas, it can determine the nodes suitable for quantum key distribution center. Using these designated nodes, the present application can build a preliminary candidate node set, and on this basis, it can confirm the connectivity of the candidate nodes through graph theory algorithm, and finally form a distribution network topology with multiple path redundancy. Importantly, this topology is not static, but dynamically adjusted with real-time power flow data.

[0035] In the present application, the characteristics of quantum entanglement are applied to adjust the key distribution path through the optimized network topology after monitoring the changes in new energy output and its impact on the power grid load. This flexibility and adaptability ensures that even in the case of new energy output fluctuations, quantum key distribution can still maintain high efficiency and stability. Therefore, through the present application, the key distribution network can respond to load fluctuations in real time in a constantly changing power environment, ensuring the continuity and security of key generation and distribution.

[0036] In the present application, the characteristics of quantum entanglement are applied to adjust the key distribution path through the optimized network topology after monitoring the changes in new energy output and its impact on the power grid load. This flexibility and adaptability ensures that even in the case of new energy output fluctuations, quantum key distribution can still maintain high efficiency and stability. Therefore, through the present application, the key distribution network can respond to load fluctuations in real time in a constantly changing power environment, ensuring the continuity and security of key generation and distribution. DETAILED DESCRIPTION

[0037] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below.

[0038] The present application discloses a distributed new power distribution network key distribution method based on quantum communication, comprising the following steps:

[0039] Step 1: Establish a prediction model to predict the power flow fluctuations caused by changes in distributed energy output, to obtain real-time power flow distribution data.

[0040] Step 2: Based on the obtained power flow distribution data, dynamically generate candidate nodes for the key distribution network, and select high load fluctuation areas as quantum communication distribution center nodes, thereby constructing a distributed key distribution network topology.

[0041] Step 3: Based on the established network topology, quantum key distribution link is realized by generating quantum entanglement pairs.

[0042] Specifically, in step 1, in order to establish a prediction model, the embodiment realizes accurate prediction of power flow distribution under distributed energy output change by combining quantum feature enhancement, spatio-temporal convolution layer and dynamic prediction layer.

[0043] The model is constructed based on quantum feature enhancement technology, which is used for high-dimensional feature extraction of distributed energy output real-time data, and uses spatio-temporal convolution network for spatio-temporal dynamic modeling of power grid power flow to generate short-term prediction of power flow distribution. The final goal of the model is to generate power flow distribution prediction data at future time, which is convenient for subsequent optimization and adjustment of quantum key distribution path. The specific implementation steps are as follows:

[0044] 1. The historical power flow data of each node in the integrated power grid, real-time distributed energy output data (such as wind power and photovoltaic output), load data, node voltage and other information are integrated and processed as spatio-temporal sequence data, and then the original data is converted into quantum feature vector by using quantum state mapping method, so as to more effectively capture complex nonlinear relationship. Then, quantum convolution layer is used to perform convolution operation on quantum features to extract and enhance local features, especially to extract key features in the node area with frequent distributed energy fluctuations, so as to better adapt to the real-time volatility of distributed energy output. Finally, the quantum feature enhanced data is input into the next spatio-temporal convolution neural network as the basic feature of power flow distribution dynamic modeling.

[0045] 2. The spatial graph structure of the power grid is constructed, and each node in the power grid is regarded as a node in the graph structure, and the connection line of the power grid is represented as an edge in the graph, so as to construct the spatial graph structure of the power grid. The data in the time dimension is input as the time sequence of the graph. Then, the spatio-temporal convolution layer is performed, and the quantum feature data is mapped to the spatio-temporal relationship of each node through spatio-temporal convolution operation to capture the electrical coupling relationship between different nodes. In the process of spatio-temporal convolution, the quantum feature is input as the data of each node, and the comprehensive influence of its neighborhood nodes on power flow is gradually obtained. Quantum parallel computing is introduced in the spatio-temporal convolution layer to enhance the speed and computing efficiency of data processing, and the graph embedding of each node is generated. Each node embedding represents the power flow feature distribution of the node at the current time and future time, and reflects the influence of its neighborhood nodes, so as to obtain the global distribution of power flow of the power grid. The output of the spatio-temporal convolution layer is the power flow feature embedding of each node, which provides feature data with spatio-temporal relationship for the next step of dynamic power flow prediction.

[0046] 3. The nodes of the spatio-temporal convolution layer are embedded into the input dynamic prediction layer, which uses a recurrent neural network (such as GRU) for short-term power flow prediction. This layer predicts the power flow distribution changes at future time points based on the current power flow characteristics. To improve the real-time accuracy of the prediction, the quantum attention mechanism gives higher weights to the nodes that have a greater impact on the prediction process, to adapt to the local fluctuations of the power flow in real time. The attention mechanism adjusts the weights through quantum computing, enhancing the focus on important nodes and thus improving the prediction accuracy of the model when the distributed energy output fluctuates dramatically. The results of the dynamic prediction layer combined with the quantum attention mechanism are the power flow predictions for future time points, which can reflect the real-time power flow distribution under the fluctuation of distributed energy output.

[0047] 4. To ensure the accuracy and training efficiency of the model under high-dimensional data, quantum gradient descent can be used to optimize the model parameters, improving the convergence speed and prediction performance of the model under dynamic data. The optimized model is applied to power flow distribution prediction, and the power flow distribution at future time points is output in chart form, indicating the predicted power of each node, node voltage, power flow direction and size, etc. These prediction results are used for dynamic adjustment of the key distribution path.

[0048] Specifically, in step 2, the goal is to dynamically generate candidate nodes for the key distribution network based on the power flow prediction results from step 1, and to set high-load fluctuation areas as the main quantum communication distribution center nodes through graph theory algorithms, to construct a distributed key distribution network topology. The specific implementation steps are as follows:

[0049] 1. Obtain the power flow prediction results and identify high-load fluctuation nodes

[0050] Based on the power flow prediction data, the node load fluctuation analysis method is used to identify nodes with high load fluctuations. These nodes may experience high load changes due to fluctuations in distributed energy output and need to be prioritized as candidates for quantum key distribution center nodes. To identify and label high-load fluctuation nodes in the quantum key distribution network, so as to select them as potential quantum key distribution center node candidates, the following steps are included:

[0051] From step 1, obtain the power flow prediction data for the future time period, which includes the load, power flow, node voltage, and historical data of each node. Clean the collected prediction data by removing outliers and noise, and use the load fluctuation analysis method to define the load fluctuation index of each node, including load variance and power change rate, with the following calculation formulas:

[0052] Load variance calculation formula:

[0053]

[0054] where, L i,t denotes the load value of node i at time t, μ i is the average load of node i, N is the total number of time points.

[0055] Power change rate calculation formula:

[0056]

[0057] where R i denotes the power change rate of node i at time t.

[0058] Based on the above defined method, the load variance and power change rate of all nodes are calculated in turn to evaluate the load fluctuation degree of the nodes in the predicted time period, a threshold of load fluctuation is set according to historical data analysis or industry standard, so as to distinguish high load fluctuation nodes and normal nodes, according to the calculated load fluctuation index, the set threshold is compared, the nodes whose load fluctuation exceeds the set threshold are marked, and a group of high load fluctuation nodes is obtained, these nodes may have larger load changes due to distributed energy output fluctuation, and need to be preferentially selected as the distribution center node candidate of quantum key.

[0059] 2. Generating a set of key distribution candidate nodes

[0060] The high load fluctuation nodes and their adjacent nodes form a preliminary candidate node set to improve the coverage of the fluctuation area and ensure that the key distribution network can more efficiently adapt to future possible load changes, in the candidate node set, the connectivity verification algorithm is used to confirm the connectivity of the set to ensure that all candidate nodes form a distribution network topology graph with multi-path redundancy, specifically:

[0061] First, set a neighborhood radius R, which is used to define the range of adjacent nodes of each high load fluctuation node, for each marked high load fluctuation node i, according to the set neighborhood radius R, the adjacent node set N i is identified by the following formula:

[0062] N i = {j | d(i, j) ≤ R}

[0063] where d(i, j) represents the distance between node i and node j, if the distance is less than or equal to the neighborhood radius R, then node j is considered to be the adjacent node of node i.

[0064] Finally, the adjacent node set N i corresponding to each high load fluctuation node is obtained.

[0065] All high load fluctuation nodes and their corresponding adjacent nodes are merged to form a preliminary candidate node set C, and the preliminary candidate node set C is regarded as a graph model, wherein a node represents a candidate node, and an edge represents a connection relationship between nodes. The graph is represented by an adjacency matrix A, wherein A[i][j] = 1 represents that there is a connection between node i and node j, and vice versa.

[0066] The connectivity of the candidate node set is verified using a breadth-first search (BFS) algorithm starting from any candidate node:

[0067] From any node s in the set, mark it as visited and add it to the queue.

[0068] Iterate through each node in the queue, check all adjacent nodes and add unvisited adjacent nodes to the queue, and mark them as visited.

[0069] When the queue is empty, if the number of visited nodes is equal to the size of the candidate node set C, the set is connected, otherwise it is not connected.

[0070] If the connectivity verification is passed, the preliminary candidate node set C can be confirmed as the final candidate node set C f , which is the basis for subsequent key distribution network topology construction, that is, a key distribution candidate node set with connectivity and coverage is generated.

[0071] 3. Center node selection based on graph theory

[0072] Using the load fluctuation intensity indicators in the candidate node set, including load change rate, node neighborhood power flow flow amount, and combining graph theory algorithm, the nodes with high connection degree in the load fluctuation area are preferentially selected as quantum key distribution center nodes, specifically:

[0073] From the candidate node set, extract the load fluctuation intensity indicators, including the load change rate R i of the node and the power flow flow amount F i of the node neighborhood, wherein the power flow flow amount F i of the node neighborhood is represented as:

[0074]

[0075] wherein P i,j represents the power flow from node i to adjacent node j, and N i is the set of adjacent nodes of node i.

[0076] For each node i in the candidate node set, calculate the load fluctuation intensity indicator H i , which can be represented by the following formula:

[0077] H i = αR i + βF i

[0078] where α and β are weight coefficients, representing the importance of load change rate and flow amount.

[0079] The candidate node set is regarded as a graph, with nodes representing candidate nodes and edges representing the connection relationship between nodes. The graph is described by an adjacency matrix A, where A[i][j] = 1 indicates that there is a connection between node i and node j.

[0080] The centrality C of the candidate node is calculated using the PageRank algorithm i , which is represented by the following formula:

[0081]

[0082] where d is the damping factor, N(i) is the set of adjacent nodes of node i, and |N(j)| is the number of connections of adjacent node j.

[0083] The degree D of the candidate node is calculated i :

[0084] D i = |N(i)|

[0085] Nodes with higher connectivity are selected as potential central nodes.

[0086] Combining the load fluctuation intensity indicator H i and the centrality indicator C i or D i , a threshold is set, and the node with the highest comprehensive score is selected as the central node N c for quantum key distribution.

[0087] 4. Building a distributed key distribution network topology

[0088] Based on the candidate nodes and central nodes, the initial topology structure of the distributed key distribution network is constructed using a graph structure generation algorithm, ensuring high connectivity and redundancy between nodes in the network.

[0089] First, the goal of building a distributed key distribution network needs to be clearly defined, including high connectivity, redundancy, and fault tolerance, so that the network can maintain its functions in the case of node failure or load change.

[0090] Then set the required topology parameters, such as the maximum connection distance between nodes, connection weight threshold (set according to node load and connection stability), and the required number of redundant paths, and finally determine the network requirements and topology parameters, which lay the foundation for subsequent topology generation.

[0091] Based on the candidate node set C f and the selected center node N c , construct an adjacency matrix A, where A[i][j] = 1 indicates that there is an effective connection between node i and node j, and the effectiveness of the connection can be judged according to the node load and connection weight. If the connection weight W ij is higher than the set threshold W threshold , the connection is effective.

[0092] Define the connection weight W ij between nodes, which can be evaluated based on load fluctuation intensity and geographic distance factors:

[0093]

[0094] Where d(i,j) is the physical distance between nodes i and j, H i and H j are the load fluctuation intensity indicators of nodes i and j, respectively.

[0095] Finally, the constructed adjacency matrix A is obtained, and the result generated by the minimum spanning tree algorithm forms the initial topology G init , G init There is at least one path between all nodes to ensure network connectivity.

[0096] In order to optimize the topology to enhance the redundancy, on the basis of the initial topology, for each pair of nodes (i,j), find additional paths to enhance the network redundancy, and ensure that in the event of a connection failure, communication can still be carried out through other paths. In this embodiment, the path finding algorithm in graph theory is used (find multiple paths between nodes and select according to path weight, preferentially select paths with lower weight as redundant paths, add redundant paths to the network topology, and form the optimized topology G opt , i.e. the distributed key distribution network topology.

[0097] Specifically, in step 2, the final optimized topology G opt has been generated, which contains high load fluctuation nodes, adjacent candidate nodes and multi-path redundancy, and is the basis for quantum key distribution network topology. Therefore, in step 3, this embodiment utilizes the quantum entanglement characteristics and G optThe provided multi-path redundancy establishes a key distribution link with high security and anti-interference capability for a distributed key distribution network.

[0098] First, quantum entanglement links are selected from G opt In G opt , according to the connection strength and load fluctuation strength of each node, node pairs suitable for quantum key distribution paths are selected to ensure that these links can cover high load fluctuation areas and their adjacent nodes to improve the robustness of the key distribution network. Based on the weight information (i.e. load fluctuation strength, transmission distance) contained in the adjacency matrix in G opt , the transmission reliability R ij of the quantum entangled pairs on each link is evaluated, and the formula is:

[0099]

[0100] Where d(i,j) is the distance between node i and node j, H i and H j are load fluctuation strength indicators, and alpha and beta are adjustment parameters.

[0101] Then, quantum entanglement-based key distribution is constructed on the selected links. In G opt , entangled state pairs are generated in sequence on the selected links, and the entangled state pairs are distributed to the communication nodes through these links. To ensure the stability and anti-interference of key distribution, the generation and distribution of quantum bits are completed using the QKD protocol.

[0102] Finally, it should be noted that the above description is only for the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the scope of protection of the claims of the present application.

Claims

1. A distributed new power distribution network key distribution method based on quantum communication, characterized in that, The application relates to a power grid dynamic key distribution network topology construction method based on quantum communication. The power flow fluctuation caused by the distributed energy output change is predicted by establishing a prediction model to obtain real-time power flow distribution data, including: The historical power flow data of each node in the integrated power grid, real-time distributed energy output data, load data, node voltage, and meteorological condition information are processed into time-space sequence data; The time-space sequence data is converted into a quantum feature vector, and a quantum convolution layer is used to perform convolution operation on the quantum feature vector to realize local feature extraction and enhancement; The quantum feature enhanced data is input into a time-space convolution neural network; The time-space convolution neural network comprises a power grid time-space graph structure and a time-space convolution layer; The power grid time-space graph structure comprises regarding each node in the power grid as a node in the graph structure, regarding the power grid connection line as an edge in the graph, thereby constructing the spatial graph structure of the power grid, and regarding the data in the time dimension as the time sequence input of the graph; The time-space convolution layer comprises mapping the quantum feature vector after the convolution operation into the time-space relationship of each node through time-space convolution operation, capturing the electrical coupling relationship between different nodes, and gradually obtaining the comprehensive influence of the neighborhood nodes on the power flow in the time-space convolution process by taking the quantum feature vector as the data input of each node. The graph embedding of each node is generated, the graph embedding of each node represents the power flow feature distribution of the node at the current time and the future time, and reflects the influence of the neighborhood nodes, so that the global distribution of the power flow of the power grid is obtained; The node embedding of the time-space convolution layer is input into a dynamic prediction layer, and a recurrent neural network is used for power flow prediction. Based on the obtained power flow distribution data, candidate nodes of the key distribution network are dynamically generated, a high load fluctuation area is selected as a quantum communication distribution center node, and a distributed key distribution network topology is constructed; and a quantum entangled pair is generated based on the established network topology to realize quantum key distribution link. 2.The quantum communication based distributed novel power distribution network key distribution method according to claim 1, wherein, The method comprises the following steps: obtaining power flow prediction results and marking high load fluctuation nodes; generating a key distribution candidate node set; selecting a center node; constructing a distributed key distribution network topology. 3.The quantum communication based distributed novel power distribution network key distribution method of claim 2, wherein, The method comprises the following steps: based on the power flow prediction data, using the node load variance and the power change rate to mark the nodes with large load fluctuation, i.e. high load fluctuation nodes.

4. The distributed novel power distribution network key distribution method based on quantum communication according to claim 2, characterized in that, The method comprises the following steps: the high load fluctuation nodes and their adjacent nodes are combined to form a candidate node set, the connectivity of the set is verified by using a network connectivity verification algorithm in the candidate node set, and it is ensured that all the candidate nodes form a distribution network topology graph with multiple path redundancies.

5. The distributed novel power distribution network key distribution method based on quantum communication according to claim 4, characterized in that, The method comprises the following steps: using the load change rate of the candidate nodes and the power flow amount of the node neighborhood, and combining a graph theory algorithm, a node with high connection degree in the load fluctuation area and connected with other candidate nodes is preferentially selected as the quantum key distribution center node.

6. The distributed novel power distribution network key distribution method based on quantum communication according to claim 5, characterized in that, The method comprises the following steps: Based on the candidate nodes and the center nodes, a graph structure generation algorithm is used to construct a topology structure of the distributed key distribution network.

7. The distributed novel power distribution network key distribution method based on quantum communication according to claim 6, characterized in that, The quantum key distribution link is implemented by generating quantum entanglement pairs based on the established network topology, and the quantum key distribution link comprises: Quantum entanglement pairs are generated between the selected distribution center nodes and their adjacent candidate nodes; Based on the quantum key distribution protocol shared by the entanglement pairs, a key link is constructed between the distribution center nodes and the adjacent nodes.

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