Graph theory-based power distribution network control method and system

Abstract

The present invention relates to the technical field of graph theory. Disclosed are a graph theory-based power distribution network control method and system. The method comprises: on the basis of current operating data of a power distribution network, and by means of a network flow reconstruction algorithm in graph theory, performing iterative optimization on an initial power transmission path of a network topology model of the power distribution network; and then, on the basis of an optimization result, updating the initial power transmission path and a corresponding initial power distribution scheme of the power distribution network, thereby achieving control of the operating state of the power distribution network. Compared with traditional power distribution network control methods, the present method enables a power distribution network to achieve automatic adjustment of a current power transmission path and a current power distribution scheme on the basis of environmental changes, thus maximizing the resource utilization rate and minimizing the transmission loss of power distribution networks, improving the stability of power distribution networks in coping with daily changing environments, and ensuring the continuity and stability of power supply of power distribution networks.

Description

A graph theory-based control method and system for power distribution networks Technical Field

[0001] This invention relates to the field of graph theory technology, and in particular to a graph theory-based power distribution network control method and system. Background Technology

[0002] With the continuous growth of electricity demand and the expansion of power grid scale, the optimized scheduling and resource allocation of distribution networks have become crucial links in the power system. Traditional distribution networks typically employ static optimization strategies, that is, planning and designing the distribution network based on historical load data and experience. While this approach can meet electricity supply demands to a certain extent, its limitations become increasingly apparent when facing dynamically changing load conditions. Specifically, this manifests as low resource utilization efficiency, insufficient stability, and slow response speed. The traditional static optimization method, relying on fixed load forecasts and static resource allocation strategies, makes it difficult to achieve dynamic scheduling and optimal resource allocation, resulting in low resource utilization and an inability to cope with peak load changes and sudden events. Lacking dynamic control mechanisms, traditional distribution networks struggle to adjust power allocation strategies in a timely manner when faced with environmental changes, easily leading to power outages in localized areas and affecting the stability of the power grid.

[0003] In response to the aforementioned shortcomings and challenges, there is an urgent need to introduce new optimization methods and control strategies to improve the resource utilization efficiency, response speed, and stability of the distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide a power distribution network control method and system, which can control the operating status of the power distribution network based on changes in current power distribution network data, thereby improving the resource utilization efficiency, response speed and stability of the power distribution network.

[0005] To achieve the above objectives, this invention provides a graph theory-based power distribution network control method, comprising the following steps:

[0006] Obtain the current operating data of the preset distribution network;

[0007] Construct a network topology model of the power distribution network, which includes an initial power transmission path, an initial power allocation scheme, and an initial load demand.

[0008] Based on the current operating data and initial load demand, the initial power transmission path is iteratively optimized using a network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme.

[0009] Based on the current power transmission path and the current power distribution scheme, the initial power transmission path and the corresponding initial power distribution scheme of the distribution network are updated, thereby realizing the control of the operation status of the distribution network.

[0010] The aforementioned method, based on current distribution network operating data, iteratively optimizes the initial power transmission path of the distribution network topology model using a network flow reconstruction algorithm from graph theory. Then, based on the optimization results, it updates the initial power transmission path and corresponding initial power allocation scheme, thereby controlling the operating state of the distribution network. Compared to traditional distribution network control methods, this method enables the distribution network to automatically adjust the current power transmission path and power allocation scheme based on environmental changes, maximizing resource utilization and minimizing transmission losses. This improves the stability of the distribution network in the face of daily changing environments and ensures the continuity and stability of power supply.

[0011] The graph theory described in the above scheme is a mathematical method used to study the properties and applications of various network relationships based on a structure composed of vertices and edges. In this scheme, graph theory is used to construct the network topology model of the distribution network and a preset network flow reconstruction algorithm. The network flow reconstruction algorithm includes shortest path algorithms and maximum flow algorithms. By analyzing and optimizing the initial power transmission paths in the network topology model through the network flow reconstruction algorithm, the optimized current power transmission paths and corresponding current power allocation schemes can be obtained, achieving the goal of minimizing power transmission losses in the distribution network while meeting the power demands of each node.

[0012] Preferably, the topology model for constructing the power distribution network is specifically as follows:

[0013] Based on the initial conditions and historical operating data of the distribution network, a directed graph representing the distribution network topology, initial power transmission paths, initial power allocation schemes, and initial load demands are designed to perform initial power allocation in order to meet the basic power needs of each node in the distribution network.

[0014] Preferably, the network topology model of the distribution network is a directed graph G = (V, E) composed of a set of nodes and a set of edges, used to represent the network topology structure of the distribution network; where V is the set of nodes, including power plants, substations, distribution rooms and load nodes; and E is the set of edges, including the initial power transmission path.

[0015] Furthermore, each edge e∈E has a certain capacity C. e and transmission loss L e Each node can be represented as a power generation node G. v Substation node T v or load node L v The power generation node has a power generation capacity P.g Substation nodes have voltage conversion capabilities, while load nodes have a certain power demand. l .

[0016] The network topology model also includes a load sub-model, which is used to represent the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model.

[0017] The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

[0018] The power flow equations are used to describe the power flow in the distribution network. They typically include capacity constraint equations for power flow, power balance conditions of distribution network nodes, and voltage constraint equations for the distribution network. These equations are constraints that ensure the normal operation of the distribution network and its network topology model.

[0019] Preferably, after the step of controlling the operating status of the distribution network, the method further includes:

[0020] The current power transmission path and current power allocation scheme are fed back to the control center to be stored as historical power transmission paths and historical power allocation schemes. In the next iteration optimization process of the network flow reconstruction algorithm, the historical power transmission path and historical power allocation scheme can also be used as basic data to achieve further optimization of the iterative optimization results.

[0021] Preferably, the step of obtaining the current operating data of the preset distribution network specifically includes:

[0022] Preset sensors and preset data acquisition devices are deployed to key nodes of the power distribution network to obtain the current operating data of the power distribution network in real time; wherein, the current operating data of the power distribution network includes voltage, current, power and load.

[0023] Data is time-sensitive. If there is a delay in the distribution network operation data, it will affect the timeliness of the iterative optimization results, making the obtained current power transmission path and power distribution scheme not the optimal choice at the current moment. Therefore, the steps mentioned above, such as deploying sensors and data acquisition equipment at key nodes of the distribution network, are crucial for timely and accurate acquisition of distribution network operation data. Timely and accurate distribution network operation data is beneficial for improving the results of iterative optimization of the distribution network mathematical model, increasing the efficiency of distribution network resource utilization, and accelerating the distribution network's response speed to environmental changes.

[0024] Preferably, the above solution further includes the following steps:

[0025] When a power supply failure occurs in the distribution network, the current operating data of the distribution network and the current data of the node set and edge set in the network topology model are obtained to identify the nodes with abnormal or interrupted data, thus enabling the identification of the fault location in the distribution network.

[0026] The aforementioned scheme enables rapid identification of fault locations in the distribution network when encountering sudden events and failures, thereby facilitating rapid fault repair. Furthermore, when a sudden event occurs, the network flow reconstruction algorithm analyzes the distribution network in a timely manner to obtain the optimal current power transmission path and optimal current power allocation scheme for maintaining power supply. This allows for timely adjustments to the power transmission path and power allocation scheme to maintain system power supply capacity. After an event occurs, the scheme can also quickly adjust the current power transmission path and current power allocation scheme to restore normal operation of the distribution network. This enhances the distribution network system's ability to respond quickly to emergencies, ensuring the resilience and stability of power supply in the event of a sudden event. It solves the problem that traditional distribution networks struggle to adjust power allocation strategies in a timely manner when facing sudden events such as natural disasters and equipment failures, which can easily lead to power supply interruptions in some areas.

[0027] Preferably, the steps for controlling the operating status of the distribution network as described in the above scheme specifically include:

[0028] The current operating data and current load demand of the distribution network are processed by a preset state estimation algorithm to obtain the estimated value of the current state of the distribution network.

[0029] Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

[0030] The state estimation algorithm includes the Kalman filter algorithm, and the control law can be designed using linear or nonlinear control methods, such as PID control or adaptive fuzzy control. The operating parameters include voltage regulation, load distribution, and power transmission path.

[0031] The above-described process of controlling the operating state of the distribution network through state estimation algorithms and control laws realizes an adaptive feedback control method. This method can dynamically adjust the operating parameters of the distribution network system by analyzing current operating data to adapt to environmental changes and ensure the stability and optimized performance of the distribution network system. For example, it can promptly increase or decrease power supply when the load increases and promptly decrease power output when the load decreases. This method improves the balance and stability of the distribution network system in the face of environmental changes and effectively avoids local overload of power supply or waste of resources.

[0032] Preferably, the above solution further includes the following steps:

[0033] Obtain historical load demand data from the distribution network;

[0034] Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network;

[0035] Based on the future load demand, new nodes and new power transmission paths in the distribution network are obtained.

[0036] When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model, and some loads are gradually transferred to the newly added nodes, so as to achieve seamless expansion of the network topology model.

[0037] The above scheme realizes a seamless expansion method. Without affecting the existing operation of the distribution network, it achieves seamless expansion of system capacity and function by gradually introducing new nodes and new power transmission paths. This ensures that the expansion process does not affect the operation of the distribution network, enabling the distribution network to maintain stable and efficient operation during the expansion process.

[0038] Demand forecasting for the distribution network is a prerequisite for seamless expansion technology. By analyzing historical operating data, historical load demand, current operating data, and current load demand, future load demand of the distribution network can be predicted, thus determining the necessity of system expansion. Load demand forecasting can be performed using methods such as time series analysis and machine learning.

[0039] Preferably, the design of the new nodes and new power transmission paths in the distribution network should meet the following requirements:

[0040] Capacity requirements: The capacity of the expanded distribution network system should meet the projected future load demand of the distribution network.

[0041] Minimization requirement: The impact on the operation of the distribution network during the expansion process should be minimized.

[0042] Optimization requirements: The newly added nodes and power transmission paths should be able to optimize the power distribution configuration of the distribution network, so as to improve the resource utilization efficiency and system stability of the distribution network.

[0043] Preferably, before introducing new nodes and new power transmission paths, an initial configuration is performed according to the power distribution network expansion scheme. The initial configuration includes the basic settings for the new nodes and new power transmission paths.

[0044] Wherein, the current load of the distribution network meets the preset requirements, which means that when the load of the distribution network system is low, the new node and the new power transmission path are gradually introduced into the distribution network to avoid the impact on the existing operating status; part of the load is gradually transferred to the new node to avoid system instability caused by sudden load changes; preferably, during the introduction process, the new node and the existing node operate in parallel to achieve a smooth transition of the system.

[0045] In the process of gradually introducing new nodes and power transmission paths, the operating parameters of the distribution network system can be adjusted by the above-mentioned adaptive feedback control method, which can realize the control of the operating status during the expansion of the distribution network and thus achieve a smooth transition between the old and new distribution network systems.

[0046] After the introduction of new nodes and new power transmission paths is completed, the network flow reconstruction algorithm is used to iteratively optimize the current power transmission path, thereby updating the current power transmission path and the corresponding current power allocation scheme of the distribution network, so as to achieve the optimal operation of the expanded system under new load conditions.

[0047] The present invention also provides a graph theory-based power distribution network control system, comprising:

[0048] The data acquisition module is used to acquire the current operating data of the preset power distribution network;

[0049] The model building module is used to build a network topology model of the power distribution network, which includes the initial power transmission path, the initial power allocation scheme, and the initial load demand.

[0050] The iterative optimization module is pre-loaded with a network flow reconstruction algorithm in graph theory. Based on the current operating data and initial load demand, the module iteratively optimizes the initial power transmission path using the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme.

[0051] The distribution network control module is used to update the initial power transmission path and the corresponding initial power distribution scheme of the distribution network based on the current power transmission path and the current power distribution scheme, thereby realizing the control of the operation status of the distribution network.

[0052] Preferably, the data acquisition module includes a pre-set data acquisition system for acquiring real-time operating data of the power distribution network by deploying pre-set sensors and pre-set data acquisition devices to key nodes of the power distribution network; wherein the current operating data of the power distribution network includes voltage, current, power, and load;

[0053] Preferably, in the model building module, the network topology model of the distribution network is a directed graph composed of a set of nodes and a set of edges, which is used to represent the network topology structure of the distribution network; wherein, the set of nodes includes power plants, substations, distribution rooms and load nodes, and the set of edges includes the initial power transmission path;

[0054] The network topology model also includes a load sub-model, which is used to represent the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model.

[0055] The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

[0056] Preferably, the system also includes a fault node identification module, which is used to obtain the current operating data of the distribution network and the current data of the node set and edge set in the network topology model when a power supply failure occurs in the distribution network, thereby obtaining the nodes with abnormal data or interrupted data, and realizing the identification of the fault location of the distribution network.

[0057] Preferably, the distribution network control module further includes an adaptive feedback control submodule, which has a preset state estimation algorithm for implementing the following steps:

[0058] The current operating data and current load demand of the distribution network are processed by the state estimation algorithm to obtain the current state estimate of the distribution network.

[0059] Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

[0060] Preferably, the system further includes a seamless expansion module, which is used to perform the following steps:

[0061] Obtain historical operating data and historical load demand of the power distribution network;

[0062] Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network;

[0063] Based on the future load demand, new nodes and new power transmission paths in the distribution network are obtained.

[0064] When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model, and some loads are gradually transferred to the newly added nodes, so as to achieve seamless expansion of the network topology model.

[0065] The graph theory-based power distribution network control method and system provided by the present invention have at least the following advantages compared with the prior art:

[0066] First, by utilizing network flow reconstruction algorithms in graph theory to optimize the initial power transmission path and initial power allocation scheme of the distribution network, the continuity and stability of power supply in response to environmental changes in the distribution network are ensured.

[0067] Secondly, by quickly identifying the location of distribution network faults, rapid repair of distribution network faults can be achieved. At the same time, by analyzing the distribution network in a timely manner through network flow reconstruction algorithms, the optimal current power transmission path and the optimal current power distribution scheme for maintaining power supply can be obtained, thereby improving the resilience and stability of the distribution network in maintaining power supply when encountering emergencies.

[0068] Furthermore, the process of controlling the operating state of the distribution network through state estimation algorithms and control laws realizes an adaptive feedback control method, which significantly improves the resource utilization efficiency, stability and rapid response capability of the distribution network.

[0069] Finally, when predicting future load demand of the distribution network and expanding the distribution network accordingly, the operating parameters of the distribution network are adjusted and the operating status of the distribution network is controlled simultaneously by gradually updating the newly added nodes and new power transmission paths into the network topology model. This achieves seamless expansion of the network topology model, ensuring that the expansion process does not affect the operating status of the distribution network and guaranteeing the stability of the distribution network during the expansion process. Attached Figure Description

[0070] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0071] Figure 1 is a flowchart of a graph theory-based power distribution network control method according to an embodiment of the present invention.

[0072] Figure 2 is a flowchart of a preferred embodiment of the power distribution network control method based on graph theory according to the present invention.

[0073] Figure 3 is a flowchart of a non-intrusive expansion method according to an embodiment of the present invention.

[0074] Figure 4 is a schematic diagram of a power distribution network control system based on graph theory according to an embodiment of the present invention.

[0075] Figure 5 is a comparison of the resource utilization rate changes of the traditional static optimization method provided in this application and a graph theory-based distribution network control method according to an embodiment of the present invention over 24 hours.

[0076] Figure 6a is a comparison of voltage deviations between the traditional PID control method provided in this application and a graph theory-based power distribution network control method according to an embodiment of the present invention over 24 hours.

[0077] Figure 6b is a comparison of the frequency deviation between the traditional PID control method provided in this application and a graph theory-based power distribution network control method according to an embodiment of the present invention over 24 hours.

[0078] Figure 7 is a comparison of the power supply recovery rate after a simulated natural disaster between the traditional manual recovery method provided in this application and a graph theory-based distribution network control method according to an embodiment of the present invention.

[0079] Figure 8a is a comparison of transmission loss over 24 hours between the traditional static optimization method provided in this application and a graph theory-based distribution network control method according to an embodiment of the present invention.

[0080] Figure 8b is a comparison of the load fluctuation adaptability of the traditional static load allocation method provided in this application and a graph theory-based distribution network control method according to an embodiment of the present invention within 24 hours.

[0081] Figure 8c is a comparison of the expansion effects of the traditional power distribution network expansion method provided in this application and the non-inductive power distribution network expansion method of this invention within 24 hours.

[0082] Figure 8d is a comparison of the operating costs of the traditional static optimization method provided in this application and a graph theory-based distribution network control method in this embodiment of the invention within 24 hours. Detailed Implementation

[0083] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0084] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0085] As shown in Figure 1, this embodiment of the invention provides a graph theory-based power distribution network control method, including the following steps:

[0086] S101. Obtain the current operating data of the preset distribution network;

[0087] Construct a network topology model of the power distribution network, which includes an initial power transmission path, an initial power allocation scheme, and an initial load demand.

[0088] S102. Based on the current operating data and initial load demand, the initial power transmission path is iteratively optimized using the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme.

[0089] S103. Based on the current power transmission path and the current power distribution scheme, update the initial power transmission path and the corresponding initial power distribution scheme of the distribution network, thereby realizing the control of the operation status of the distribution network.

[0090] In step S101, the method for obtaining the current operating data of the preset distribution network includes:

[0091] By deploying preset sensors and preset data acquisition devices to key nodes of the power distribution network, the current voltage, current, power and load of the power distribution network can be obtained in real time.

[0092] In step S101, the network topology model of the distribution network is specifically a directed graph G = (V, E) composed of a set of nodes and a set of edges, used to represent the network topology of the distribution network; where V is the set of nodes, including power plants, substations, distribution rooms, and load nodes; and E is the set of edges, including the initial power transmission paths. Each edge e ∈ E has a certain capacity C. e and transmission loss L e Each node v∈V can be represented as a generator node G. v Substation node T v or load node L v The power generation node has a power generation capacity P. g Substation nodes have voltage conversion capabilities, while load nodes have a certain power demand. l .

[0093] The network topology model also includes a load sub-model, which represents the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model. Assume that at time t, load node L... v The electricity demand is D v If (t), then the load demand of the entire distribution network can be expressed as Equation 1, as shown below:

[0094] Where D(t) is the load demand of the entire distribution network, v is the node, and V L It is the set of all load nodes, D v (t) is the load node L v The electricity demand.

[0095] The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

[0096] Specifically, the constraints on the attributes of each node and edge in the network topology model are used to establish the constraints on the power flow equations of the target distribution network. These constraints include:

[0097] For each edge e∈E in the network topology model, the power flow F e The capacity constraint is satisfied, as shown in Equation 2 below:

[0098] Among them, F e Indicates the flow of electricity, C e Represents power capacity, and e∈E represents an edge in the network topology model;

[0099] For each node v∈V in the network topology model, the power flow F e The power balance equation is satisfied, as shown in Equation 3 below:

[0100] Among them, E out (v) represents the set of edges originating from node v, E in (v) represents the set of edges leading to node v; P v D is the power generation of node v. v F is the power demand of node v. e It refers to the electrical flow at the nodes;

[0101] For each substation node T v Its voltage V v The voltage constraint should be satisfied, as shown in Equation 4 below:

[0102] Among them, V T It is the set of all substation nodes; V min V is the minimum permissible value of the voltage. max This is the maximum permissible voltage value;

[0103] For the transmission loss L in the power transmission process e It can be represented by the following formula 5: L e =k e ·F e 2 (Equation 5)

[0104] Where, k e It is the loss factor, which reflects the physical characteristics of the transmission line, F.e It represents the flow of electricity.

[0105] From equations 1 to 5 in the above power flow sub-model, the objective function for optimizing the network topology model can be derived, as shown in equation 6 below:

[0106] Where e∈E represents an edge in the network topology model, v∈V represents a node in the network topology model, and k e It is the loss coefficient, F e P represents the flow of electricity. v D represents the power generation of node v. v Represents the power demand of node v; α and β are weighting coefficients used to balance transmission loss, resource balance, and system stability; Var(F v Var(F) represents the variance of the electrical flow at node v. Specifically, Var(F) v It can reflect the stability of the system.

[0107] The optimization objective function shown in Equation 6 can minimize the power transmission loss of the distribution network, achieve a balanced allocation of power resources in the distribution network, and improve the stability of the distribution network system.

[0108] In step S102, the network flow reconstruction algorithm iteratively optimizes the initial power transmission path using the objective function shown in Equation 7 below:

[0109] Where e∈E represents an edge in the network topology model, L e k represents the transmission loss of each edge e. e It is the loss coefficient, F e It represents the flow of electricity.

[0110] To ensure that the current power transmission path and corresponding power allocation scheme obtained through iterative optimization can meet the normal operation of the distribution network, the process of iteratively optimizing the initial power transmission path using the network flow reconstruction algorithm in graph theory needs to satisfy the capacity constraint shown in Equation 8, the power balance equation shown in Equation 9, and the voltage constraint shown in Equation 10.

[0111] Among them, F e Indicates the flow of electricity, C e Represents power capacity, and e∈E represents an edge in the network topology model;

[0112] For each node v∈V in the network topology model, its power balance equation is shown in Equation 9 below:

[0113] Among them, E out(v) represents the set of edges originating from node v, E in (v) represents the set of edges leading to node v; P v D is the power generation of node v. v F is the power demand of node v. e It refers to the electrical flow at the nodes;

[0114] For each substation node T v Its voltage V v The voltage constraint should be satisfied, as shown in Equation 10 below:

[0115] Among them, V T It is the set of all substation nodes; V min V is the minimum permissible value of the voltage. max This represents the maximum permissible voltage value.

[0116] In step S103, the step of controlling the operating status of the distribution network specifically includes:

[0117] The current operating data and current load demand of the distribution network are processed by a preset state estimation algorithm to obtain the estimated value of the current state of the distribution network.

[0118] Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

[0119] Specifically, the current operating data and current load demand of the distribution network can be represented as the state vector of the distribution network system at time t. The equation representing the state vector is shown in Equation 11: x(t)=[P(t),V(t),I(t),D(t)] (Equation 11)

[0120] Where x(t) is the state vector of the distribution network system at time t, P(t) is the power of the distribution network, V(t) is the voltage of the distribution network, I(t) is the current of the distribution network, and D(t) is the load demand of the distribution network.

[0121] Specifically, the preset state estimation algorithm includes the Kalman filter algorithm; the process of processing the current operating data and current load demand of the distribution network through the state estimation algorithm to obtain the estimated value of the current state of the distribution network is shown in Equation 12:

[0122] in, Z is the state estimate at time t, K(t) is the Kalman gain, z(t) is the measured value, and H is the measurement matrix.

[0123] Specifically, the design of the control law includes linear control methods and nonlinear control methods, such as PID control and adaptive fuzzy control.

[0124] This embodiment preferably uses a control law design method, as shown in Equation 13:

[0125] Where u(t) is the control input and K is the control gain matrix.

[0126] Specifically, the adjustment of the distribution network operating parameters based on the control law includes voltage regulation, load allocation, and power transmission path updating.

[0127] As shown in Figure 2, a preferred embodiment of the present invention provides a distribution network control method based on graph theory, comprising the following steps:

[0128] S201. Construct a network topology model of the power distribution network, the network topology model including initial power transmission paths, initial power allocation schemes and initial load demands;

[0129] Based on the network topology model and historical data of the distribution network, the initial network flow allocation of the distribution network is carried out to meet the basic power demand of each node of the distribution network.

[0130] S202. Obtain the current operating data of the preset distribution network;

[0131] S203. Based on the current operating data and initial load demand, the initial power transmission path is iteratively optimized using the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme.

[0132] S204. Based on the current power transmission path and the current power distribution scheme, update the initial power transmission path and the corresponding initial power distribution scheme of the distribution network, thereby realizing the control of the operating status of the distribution network.

[0133] The current power transmission path and the current power allocation scheme are fed back to the control center to be stored as historical power transmission paths and historical power allocation schemes. In this way, before the next iterative optimization process of the network flow reconstruction algorithm, the historical power transmission path and historical power allocation scheme can also be used as basic data to achieve further optimization of the iterative optimization results.

[0134] S205. When a power supply failure occurs in the distribution network, the current operating data of the distribution network and the current data of the node set and edge set in the network topology model are obtained to identify the nodes with abnormal or interrupted data, thereby realizing the identification of the fault location in the distribution network.

[0135] The above embodiments enable rapid identification of fault locations in the distribution network when encountering sudden events and failures, thereby facilitating rapid repair of distribution network faults. Furthermore, when the distribution network encounters a sudden event, timely analysis of the distribution network through network flow reconstruction algorithms can yield the optimal current power transmission path and optimal current power allocation scheme for maintaining power supply. This allows for timely adjustment of the power transmission path and power allocation scheme to maintain system power supply capacity. After the event occurs, the above scheme can also quickly adjust the current power transmission path and current power allocation scheme to restore normal operation of the distribution network, ensuring the resilience and stability of the power supply in the event of a sudden event. This solves the problem that traditional distribution networks struggle to adjust power allocation strategies in a timely manner when facing sudden events such as natural disasters and equipment failures, which can easily lead to power supply interruptions in some areas.

[0136] Preferably, as shown in FIG3, any of the above embodiments further includes the following steps:

[0137] Demand forecasting: Obtain historical load demand data from the distribution network.

[0138] Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network;

[0139] Extended solution design: Based on the aforementioned future load demand, obtain new nodes and new power transmission paths for the distribution network;

[0140] Initialization: Perform initial basic settings for newly added nodes and new power transmission paths in the power distribution network;

[0141] Gradual introduction: When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model. By dynamically adjusting the operating parameters of the existing system, a smooth transition between the old and new systems is achieved.

[0142] Seamless switching: While gradually updating the network topology model with new nodes and new power transmission paths, some loads are gradually transferred to the new nodes to avoid system instability caused by sudden load changes and achieve seamless expansion of the network topology model;

[0143] Preferably, during the update process, the new node and the existing node run in parallel to ensure a smooth system transition;

[0144] Specifically, the four steps mentioned above—initialization, gradual introduction, seamless switching, and system optimization—complete the dynamic switching process.

[0145] System optimization: After the new nodes and new power transmission paths are gradually updated into the network topology model, the network flow reconstruction algorithm is used to iteratively optimize the current power transmission paths, thereby updating the current power transmission paths and corresponding current power allocation schemes of the distribution network, so as to achieve the optimal operation of the expanded system under new load conditions;

[0146] Specifically, the demand forecasting step includes time series analysis and machine learning for predicting the future load of the distribution network. This embodiment preferably provides a method for forecasting the future load demand of the distribution network as shown in Equation 14:

[0147] in, Let f be the future load demand of the distribution network, D(t) be the load demand of the distribution network at time t, and f be the future load demand prediction function.

[0148] Preferably, the steps in designing the extended scheme should meet the following requirements:

[0149] Capacity requirements: The capacity of the expanded distribution network system should meet the projected future load demand of the distribution network.

[0150] Minimization requirement: The impact on the operation of the distribution network during the expansion process should be minimized.

[0151] Optimization requirements: The newly added nodes and power transmission paths should be able to optimize the power distribution configuration of the distribution network, so as to improve the resource utilization efficiency and system stability of the distribution network.

[0152] Specifically, the objective function of the extended scheme can be expressed as Equation 15, which is shown below:

[0153] Among them, E new It is a newly added set of transmission lines, C e It is the cost of the transmission line, V new It is the set of newly added nodes, P v γ is the configuration cost of the node, and γ is the weighting coefficient.

[0154] As shown in Figure 4, this embodiment of the invention also provides a graph theory-based power distribution network control system, including:

[0155] The data acquisition module is used to acquire the current operating data of the preset power distribution network;

[0156] Preferably, the data acquisition module includes a pre-set data acquisition system for acquiring real-time operating data of the power distribution network by deploying pre-set sensors and pre-set data acquisition devices to key nodes of the power distribution network; wherein the current operating data of the power distribution network includes voltage, current, power, and load;

[0157] The model building module is used to build a network topology model of the power distribution network, which includes the initial power transmission path, the initial power allocation scheme, and the initial load demand.

[0158] Specifically, in the model construction module, the network topology model of the distribution network is a directed graph composed of a set of nodes and a set of edges, which is used to represent the network topology structure of the distribution network; wherein, the set of nodes includes power plants, substations, distribution rooms and load nodes, and the set of edges includes the initial power transmission path;

[0159] The network topology model also includes a load sub-model, which is used to represent the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model.

[0160] The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

[0161] The iterative optimization module is pre-loaded with a network flow reconstruction algorithm in graph theory. Based on the current operating data and initial load demand, the module iteratively optimizes the initial power transmission path using the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme.

[0162] The distribution network control module is used to update the initial power transmission path and the corresponding initial power distribution scheme of the distribution network based on the current power transmission path and the current power distribution scheme, thereby realizing the control of the operating status of the distribution network;

[0163] Specifically, the power distribution network control module also includes an adaptive feedback control submodule, which has a preset state estimation algorithm for implementing the following steps:

[0164] The current operating data and current load demand of the distribution network are processed by the state estimation algorithm to obtain the current state estimate of the distribution network.

[0165] Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

[0166] The fault node identification module is used to obtain the current operating data of the distribution network and the current data of the node set and edge set in the network topology model when a power supply failure occurs in the distribution network, thereby identifying the nodes with abnormal data or interrupted data, and realizing the identification of the fault location in the distribution network.

[0167] The seamless expansion module is used to perform the following steps:

[0168] Obtain historical operating data and historical load demand of the power distribution network;

[0169] Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network;

[0170] Based on the future load demand, new nodes and new power transmission paths in the distribution network are obtained.

[0171] When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model, and some loads are gradually transferred to the newly added nodes, so as to achieve seamless expansion of the network topology model.

[0172] Preferably, embodiments of the present invention also provide a graph theory-based distribution network control system for optimizing provincial disaster recovery distribution network resources, including:

[0173] The data acquisition module is used to acquire and store the current operating data of the preset distribution network;

[0174] Preferably, the data acquisition module includes a pre-set data acquisition system for acquiring real-time operating data of the power distribution network by deploying pre-set sensors and pre-set data acquisition devices to key nodes of the power distribution network; wherein the current operating data of the power distribution network includes voltage, current, power, and load;

[0175] The data analysis module is used to process the current operating data and construct a network topology model of the distribution network based on the current operating data. The network topology model includes the initial power transmission path, the initial power allocation scheme, and the initial load demand.

[0176] The network flow reconstruction algorithm module is pre-set with a network flow reconstruction algorithm in graph theory. Based on the current operating data and initial load demand, the module uses the network flow reconstruction algorithm in graph theory to iteratively optimize the initial power transmission path to obtain the current power transmission path and the corresponding current power allocation scheme.

[0177] The adaptive feedback control module is used to implement the following steps:

[0178] The current operating data and current load demand of the distribution network are processed by the state estimation algorithm to obtain the current state estimate of the distribution network.

[0179] Based on the state estimate, a control law is designed, and the operating parameters of the distribution network are adjusted based on the control law. At the same time, based on the current power transmission path and the current power distribution scheme, the initial power transmission path and the corresponding initial power distribution scheme of the distribution network are updated, thereby realizing the dynamic adjustment of the distribution network control strategy.

[0180] To more intuitively demonstrate the application effects and technical achievements of this invention in power distribution networks, embodiments of this invention also provide a comparison of the effects of several traditional static optimization methods for power distribution networks and the method of this invention when applied to power distribution network systems, specifically including the following:

[0181] This embodiment uses the technical specifications and operational data of the actual distribution network to establish a simulation model of the actual distribution network and configure an appropriate hardware environment using MATLAB and Simulink platforms. By simulating different load conditions and sudden events on the simulation model, the actual effect of the graph theory-based distribution network control method and other traditional distribution network optimization methods of this invention on the distribution network is tested.

[0182] The power distribution network simulation model includes power distribution network nodes, power transmission paths, and control systems to reflect the actual operating characteristics and control behavior of the power distribution network. The simulation model is used to simulate various operating states, including normal operation, sudden faults, and load fluctuations, to test the system's adaptability and robustness.

[0183] The specific parameter settings for the simulation model are shown in Table 1 below:

[0184] Table 1 Simulation Model Parameter Settings

[0185] The parameter settings for the network flow reconstruction algorithm, adaptive feedback control algorithm, dynamic balancing strategy, and seamless scaling technique are shown in Table 2 below:

[0186] Table 2 Algorithm Settings

[0187] The adaptive feedback control algorithm includes a state estimation algorithm and a control law design.

[0188] Figure 5 shows a test result from this embodiment. Figure 5 illustrates the resource utilization changes of the distribution network system over 24 hours when the traditional static optimization method and the graph theory-based distribution network control method of this embodiment are applied to the distribution network system, respectively. As shown in Figure 5, the resource utilization of the distribution network using the traditional static optimization method fluctuates within 24 hours, reaching higher values ​​during peak electricity consumption periods, including morning and evening, but lower during off-peak periods at night and midday. The distribution network system using the method of this invention exhibits higher resource utilization across all time periods compared to the traditional static optimization method. Notably, during peak load periods, the resource utilization improvement effect of the method of this invention compared to the traditional static optimization method is more significant.

[0189] Specifically, when using the traditional static optimization method, the resource utilization rate is at most 85% during peak periods, including 8 to 10 a.m. and 6 to 8 p.m., while when using the method of this invention, the resource utilization rate can reach about 93% during the same period, which is about 8% higher than the traditional static optimization method.

[0190] In summary, compared to traditional static optimization methods for distribution networks, the graph theory-based distribution network control method of this invention has significant advantages in resource optimization and can effectively improve the resource utilization rate of the distribution network; in particular, its effect is especially prominent during peak electricity load periods. Therefore, the graph theory-based distribution network control method of this invention has outstanding potential value and broad application prospects in practical application scenarios.

[0191] The test results of this embodiment are shown in Figures 6a and 6b, illustrating a comparison of the voltage and frequency deviations of a distribution network system over 24 hours when a traditional PID control method and a graph theory-based distribution network control method of this embodiment are applied to the distribution network system. Figure 6a shows the voltage deviation comparison of the distribution network system over 24 hours, and Figure 6b shows the frequency deviation comparison of the distribution network system over 24 hours.

[0192] As shown in Figure 6a, the test results reveal that when using the traditional PID control method, the voltage deviation of the distribution network system fluctuates significantly throughout the day. Specifically, during peak hours in the morning and evening, the voltage deviation reaches 6.5V to 7.5V. This indicates that the traditional PID control method is ineffective in regulating voltage during peak load periods in the distribution network, leading to large voltage deviations. However, when using the method of this invention, the voltage deviation of the distribution network system is significantly smaller than that obtained using the traditional PID control method, with a maximum value not exceeding 4.7V. In particular, during peak load periods in the distribution network, the method of this invention can significantly reduce voltage deviation, improving the system's voltage regulation capability and stability.

[0193] As shown in Figure 6b, the frequency deviation of the distribution network system fluctuates significantly when using the traditional PID control method. Specifically, during periods of drastic load changes, including morning and evening peak hours, the frequency deviation can reach 0.07 Hz. This indicates that the traditional PID control method is insufficient in regulating the frequency of the distribution network, resulting in poor frequency stability. However, when using the method of this invention, the frequency deviation of the distribution network system is significantly smaller than that obtained by the traditional PID control algorithm, with a maximum value not exceeding 0.047 Hz. This demonstrates that when using the method of this invention, the distribution network system can maintain high frequency stability even under significant load variations.

[0194] In summary, the distribution network using the method of this invention exhibits significantly better voltage and frequency stability than that using traditional PID control methods. Specifically, when using the method of this invention, the voltage and frequency deviations of the system are lower than those of the traditional method, and the curve fluctuation amplitude is smaller, especially during peak load periods in the distribution network. By comparing the application effect of the method of this invention with that of traditional PID control methods, the technical effect of the method of this invention in improving the voltage and frequency stability of the distribution network system is clearly demonstrated, illustrating that the graph theory-based distribution network control method of this invention has outstanding potential value and broad application prospects in practical application scenarios.

[0195] Figure 7 shows a test result of this embodiment. Figure 7 illustrates the performance of the traditional power distribution network post-disaster recovery method and the graph theory-based power distribution network control method of this embodiment in the power distribution network system in terms of simulating the power supply capacity of the power distribution network after a disaster.

[0196] As shown in Figure 7, the traditional method for post-disaster power distribution network recovery exhibits a slow recovery rate, indicating low efficiency. In contrast, the method of this invention achieves a significantly faster recovery rate than traditional methods, restoring most of the power supply to the distribution network within a short time. This result demonstrates that the graph-based distribution network control method of this invention has a significant effect on post-disaster power distribution network recovery.

[0197] The test results of this embodiment are shown in Figures 8a, 8b, 8c and 8d, which demonstrate the comparison of different optimization methods in four aspects: transmission loss, load fluctuation adaptability, seamless expansion effect and operating cost.

[0198] As can be seen from the test results in Figure 8a, the efficiency of reducing power transmission losses in the distribution network by applying the traditional distribution network optimization method is significantly lower than that of reducing power transmission losses in the distribution network by applying the method of the present invention.

[0199] As shown in the test results of Figure 8b, when the traditional method is used to control the distribution network, the load fluctuation of the distribution network is relatively large; especially during the peak load period, the load fluctuation is obvious, indicating that the traditional control method has poor system adaptability. However, when the distribution network is controlled by the graph theory-based distribution network control method of this invention, the load change of the distribution network is relatively smooth, indicating that the method of this invention can better adapt to the load fluctuation of the distribution network compared with the traditional method, thus improving the stability and response speed of the distribution network system.

[0200] As shown in the test results of Figure 8c, when adding nodes and power transmission paths to the distribution network system using the traditional expansion method, the distribution network system is significantly affected by the expansion process, resulting in a substantial decrease in resource utilization. However, when applying the non-intrusive expansion method of this invention to add nodes and power transmission paths to the distribution network system, the resource utilization of the distribution network decreases less compared to the traditional expansion method. This indicates that the method of this invention can effectively reduce disturbances to the existing system and maintain system stability during the expansion process.

[0201] As shown in Figure 8d, the test results indicate that when using traditional methods to control the distribution network, the operating cost increases significantly over time, indicating that the system's operating cost is high. However, when using the graph theory-based distribution network control method of this invention, the increase in operating cost over time is smaller compared to traditional control methods, demonstrating that the method of this invention can significantly reduce the operating cost of the distribution network system compared to traditional methods.

[0202] In summary, the graph theory-based distribution network control method of this invention demonstrates significant progress and technical effectiveness compared to traditional distribution network control and optimization methods in four aspects: reducing power transmission losses, mitigating load fluctuations, maintaining stability during distribution network expansion, and reducing operating costs. This indicates that the graph theory-based distribution network control method and system of this invention have outstanding potential value and broad application prospects in practical application scenarios.

[0203] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A graph theory-based distribution network control method, characterized in that, Includes the following steps: Obtain the current operating data of the preset distribution network; Construct a network topology model of the power distribution network, which includes an initial power transmission path, an initial power allocation scheme, and an initial load demand. Based on the current operating data and initial load demand, the initial power transmission path is iteratively optimized using a network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme. Based on the current power transmission path and the current power distribution scheme, the initial power transmission path and the corresponding initial power distribution scheme of the distribution network are updated, thereby realizing the control of the operation status of the distribution network.

2. The distribution network control method based on graph theory according to claim 1, characterized in that, The network topology model of the distribution network is a directed graph composed of a set of nodes and a set of edges, which is used to represent the network topology structure of the distribution network; wherein, the set of nodes includes power plants, substations, distribution rooms and load nodes, and the set of edges includes the initial power transmission path; The network topology model also includes a load sub-model, which is used to represent the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model. The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

3. The distribution network control method based on graph theory according to claim 2, characterized in that, It also includes the following steps: When a power supply failure occurs in the distribution network, the current operating data of the distribution network and the current data of the node set and edge set in the network topology model are obtained to identify the nodes with abnormal or interrupted data, thus enabling the identification of the fault location in the distribution network.

4. The distribution network control method based on graph theory according to claim 2, characterized in that, The steps for controlling the operating status of the distribution network specifically include: The current operating data and current load demand of the distribution network are processed by a preset state estimation algorithm to obtain the estimated value of the current state of the distribution network. Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

5. The distribution network control method based on graph theory according to claim 4, characterized in that, It also includes the following steps: Obtain historical operating data and historical load demand of the power distribution network; Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network; Based on the future load demand, new nodes and new power transmission paths in the distribution network are obtained. When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model, and some loads are gradually transferred to the newly added nodes, so as to achieve seamless expansion of the network topology model.

6. A graph theory-based power distribution network control system, characterized in that, include: The data acquisition module is used to acquire the current operating data of the preset power distribution network; The model building module is used to build a network topology model of the power distribution network, which includes the initial power transmission path, the initial power allocation scheme, and the initial load demand. The iterative optimization module is pre-loaded with a network flow reconstruction algorithm in graph theory. Based on the current operating data and initial load demand, the module iteratively optimizes the initial power transmission path using the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power allocation scheme. The distribution network control module is used to update the initial power transmission path and the corresponding initial power distribution scheme of the distribution network based on the current power transmission path and the current power distribution scheme, thereby realizing the control of the operation status of the distribution network.

7. A graph-based power distribution network control system according to claim 6, characterized in that, In the model construction module, the network topology model of the distribution network is a directed graph composed of a set of nodes and a set of edges, which is used to represent the network topology structure of the distribution network; wherein, the set of nodes includes power plants, substations, distribution rooms and load nodes, and the set of edges includes the initial power transmission path; The network topology model also includes a load sub-model, which is used to represent the power demand of each load node and the overall load demand in the network topology model, thereby obtaining the initial load demand of the network topology model. The network topology model also includes a power flow sub-model, which is used to constrain the attributes of each node and edge in the network topology model, thereby obtaining an initial power allocation scheme.

8. A graph-based power distribution network control system according to claim 6, characterized in that, Also includes: The fault node identification module is used to obtain the current operating data of the distribution network and the current data of the node set and edge set in the network topology model when a power supply failure occurs in the distribution network, thereby identifying the nodes with abnormal data or interrupted data, and realizing the identification of the fault location in the distribution network.

9. A graph-based power distribution network control system according to claim 6, characterized in that, The power distribution network control module also includes an adaptive feedback control submodule, which has a preset state estimation algorithm for implementing the following steps: The current operating data and current load demand of the distribution network are processed by the state estimation algorithm to obtain the current state estimate of the distribution network. Based on the state estimate, a control law is designed, and then the operating parameters of the distribution network are adjusted based on the control law to achieve control over the operating state of the distribution network.

10. A graph-based power distribution network control system according to claim 6, characterized in that, It also includes a seamless expansion module, which is used to perform the following steps: Obtain historical operating data and historical load demand of the power distribution network; Based on historical operating data, historical load demand, current operating data, and current load demand, predict the future load demand of the distribution network; Based on the future load demand, new nodes and new power transmission paths in the distribution network are obtained. When the current load of the distribution network meets the preset requirements, the newly added nodes and newly added power transmission paths are gradually updated into the network topology model, and some loads are gradually transferred to the newly added nodes, so as to achieve seamless expansion of the network topology model.

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