Power distribution network control method and system based on graph theory

Abstract

The invention relates to the technical field of graph theory, and discloses a graph theory-based power distribution network control method and system. According to the method, based on current operation data of the power distribution network, an initial power transmission path of a network topology model of the power distribution network is iteratively optimized through a network flow reconstruction algorithm in a graph theory, then the initial power transmission path of the power distribution network and a corresponding initial power distribution scheme are updated based on an optimization result, and the operation state of the power distribution network is controlled. Compared with a traditional power distribution network control method, the method enables the power distribution network to achieve the automatic adjustment of the current power transmission path and the current power distribution scheme based on the change of the environment, so as to maintain the maximization of the resource utilization rate of the power distribution network and the minimization of the transmission loss, improve the stability of the power distribution network in coping with a daily changeable environment, and improve the reliability of the power distribution network. And continuity and stability of power supply of the power distribution network are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of graph theory, and in particular to a distribution network control method and system based on graph theory. Background Art

[0002] With the continuous growth of electricity demand and the expansion of power grid scale, the optimal dispatching and resource allocation of distribution network have become crucial links in the power system. Traditional distribution network usually adopts static optimization strategy, that is, the distribution network is planned and designed according to historical load data and experience. This method can meet the demand of power supply to a certain extent, but its limitations gradually emerge in the face of dynamically changing load conditions. Specifically, it manifests itself in problems such as low resource utilization efficiency, insufficient stability and slow response speed. Traditional distribution network adopts static optimization method, that is, the distribution network is planned and designed according to historical load data and experience. This method relies on fixed load forecast and static resource allocation strategy, which makes it difficult to achieve dynamic dispatching and optimal allocation of resources, resulting in low resource utilization of distribution network and difficulty in coping with peak load changes and emergencies. Due to the lack of dynamic control mechanism, it is difficult for traditional distribution network to adjust power distribution strategy in time when facing the impact of environmental changes, which can easily lead to power supply interruption in local areas and affect the stability of power supply of power grid.

[0003] In the face of the above-mentioned shortcomings and challenges, it is urgent 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 object of the present invention is to provide a distribution network control method and system, which can control the operating state of the distribution network based on changes in current data of the distribution network, so as to improve the resource utilization efficiency, response speed and stability of the distribution network.

[0005] In order to achieve the above object, the present invention provides a distribution network control method based on graph theory, comprising the following steps:

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

[0007] Constructing a network topology model of the power distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0008] Based on the current operation data and the initial load demand, the initial power transmission path is iteratively optimized by a network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan;

[0009] Based on the current power transmission path and the current power distribution plan, the initial power transmission path and the corresponding initial power distribution plan of the power distribution network are updated, thereby achieving control over the operating state of the power distribution network.

[0010] The above method is based on the current operation data of the distribution network, and uses the network flow reconstruction algorithm in graph theory to iteratively optimize the initial power transmission path of the distribution network topology model, and then updates the initial power transmission path of the distribution network and the corresponding initial power distribution plan based on the optimization results to achieve control of the operation status of the distribution network. Compared with the traditional distribution network control method, this method enables the distribution network to automatically adjust the current power transmission path and the current power distribution plan based on environmental changes to maximize the resource utilization of the distribution network and minimize the transmission loss, thereby improving the stability of the distribution network in dealing with daily changing environments and ensuring the continuity and stability of the power supply of the distribution network.

[0011] Among them, 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 a network topology model of the distribution network and a preset network flow reconstruction algorithm. Among them, the network flow reconstruction algorithm includes a shortest path algorithm and a maximum flow algorithm. By analyzing and optimizing the initial power transmission path in the network topology model through the network flow reconstruction algorithm, the optimized current power transmission path and the corresponding current power distribution plan can be obtained, so as to minimize the power transmission loss of the distribution network while meeting the power demand of each node.

[0012] Preferably, the topological model for constructing the distribution network is specifically:

[0013] According to the initial conditions and historical operation data of the distribution network, a directed graph representing the network topology of the distribution network, the initial power transmission path, the initial power distribution plan and the initial load demand are designed, so as to carry out initial power distribution to meet the basic power demand of each node in the distribution network.

[0014] Preferably, the network topology model of the distribution network is specifically a directed graph G=(V, E) consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein V is a node set, including power stations, substations, distribution rooms and load nodes; E is an edge set, including an 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 Pg , the substation node has voltage conversion capability, and the load node has a certain power demand D l .

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

[0018] The power flow equation is used to describe the power flow in the distribution network, and usually includes the capacity constraint equation of the power flow, the power balance condition of the distribution network nodes, and the voltage constraint equation of the distribution network. These equations are the constraint conditions to ensure the normal operation of the distribution network and its network topology model.

[0019] Preferably, after the step of realizing the control of the operating state of the distribution network, the method further includes:

[0020] The current power transmission path and the current power distribution plan are fed back to the control center to be stored as the historical power transmission path and the historical power distribution plan. Before the next iterative optimization process of the network flow reconstruction algorithm, the historical power transmission path and the historical power distribution plan 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 power distribution network specifically includes:

[0022] Deploy preset sensors and preset data acquisition equipment to key nodes of the distribution network to obtain the current operating data of the distribution network in real time; wherein the current operating data of the 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 current power transmission path and the current power distribution plan not the best at the current moment. Therefore, the above-mentioned steps of deploying sensors and data acquisition equipment at key nodes of the distribution network are key steps to obtain distribution network operation data in a timely and accurate manner. Timely and accurate distribution network operation data is conducive to improving the results of iterative optimization of the distribution network mathematical model, improving the efficiency of distribution network resource utilization, and accelerating the response speed of the distribution network to environmental changes.

[0024] Preferably, the above scheme further comprises the following steps:

[0025] When the power supply of the distribution network fails, the current operation data of the distribution network and the current data of the node set and edge set in the network topology model are obtained, so as to obtain the nodes with abnormal or interrupted data and identify the fault location of the distribution network.

[0026] The above scheme enables the distribution network to quickly identify the fault location of the distribution network when encountering emergencies and faults, thereby realizing the rapid repair of the distribution network fault. Furthermore, when the distribution network encounters an emergency, the network flow reconstruction algorithm can be used to timely analyze the distribution network, and the optimal current power transmission path and the optimal current power distribution plan for maintaining power supply can be obtained, so as to timely adjust the power transmission path and the power distribution plan of the distribution network to maintain the power supply capacity of the system; after the event occurs, the above scheme can also quickly adjust the current power transmission path and the current power distribution plan of the distribution network to restore the normal operation of the distribution network, which improves the ability of the distribution network system to respond quickly to emergencies, ensures the resilience and stability of the distribution network to maintain power supply when encountering emergencies, and solves the problem that the traditional distribution network is difficult to adjust the power distribution strategy in time when facing natural disasters, equipment failures and other emergencies, which easily leads to power supply interruptions in local areas.

[0027] Preferably, the steps of realizing control over the operating state of the distribution network in the above scheme specifically include:

[0028] Processing the current operation data and current load demand of the distribution network through a preset state estimation algorithm, thereby obtaining an estimated value of the current state of the distribution network;

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

[0030] Among them, the state estimation algorithm includes a Kalman filter algorithm, and the design of the control law can adopt a linear or nonlinear control method, such as PID control, adaptive fuzzy control; the operating parameters include voltage regulation, load distribution and power transmission path.

[0031] The above-mentioned process of controlling the operating state of the distribution network is realized through the state estimation algorithm and control law, and a method of adaptive feedback control is realized. This method can dynamically adjust the operating parameters of the distribution network system by analyzing the current operating data to adapt to environmental changes and ensure the stability and optimization performance of the distribution network system. For example, when the load increases, the power supply is increased or decreased in time; when the load decreases, the power output is reduced in time. 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 scheme further comprises the following steps:

[0033] Obtain historical operation data and historical load demand of distribution network;

[0034] Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand;

[0035] Based on the future load demand, acquiring new nodes and new power transmission paths in the distribution network;

[0036] When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and part of the load is gradually transferred to the newly added nodes, thereby realizing a seamless expansion of the network topology model.

[0037] The above scheme implements a method of seamless expansion. Without affecting the existing operating status of the distribution network, it realizes seamless expansion of system capacity and functions by gradually introducing new nodes and new power transmission paths, ensuring that the expansion process does not affect the operating status of the distribution network, so that the distribution network can maintain stable and efficient operation during the expansion process.

[0038] Among them, distribution network demand forecasting is the premise of the sensorless expansion technology. By analyzing historical operation data, historical load demand, current operation data and current load demand, the future load demand of the distribution network can be predicted, and the necessity of system expansion can be determined. Load demand forecasting can be carried out using time series analysis, machine learning and other methods.

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

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

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

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

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

[0044] Among them, the current load of the distribution network meets the preset requirements means that when the load of the distribution network system is low, the new nodes and the new power transmission paths are gradually introduced into the distribution network to avoid affecting the existing operating status; part of the load is gradually transferred to the new nodes to avoid system instability caused by sudden load changes; preferably, during the introduction process, the new nodes run in parallel with the existing nodes to achieve a smooth transition of the system.

[0045] Among them, while gradually introducing new nodes and power transmission paths, the operating parameters of the distribution network system are adjusted through the above-mentioned adaptive feedback control method, which can realize the control of the operating status during the expansion of the distribution network, and then achieve a smooth transition between the old and new distribution network systems.

[0046] Among them, 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, and then the current power transmission path of the distribution network and the corresponding current power distribution plan are updated to achieve the optimal operation of the expanded system under new load conditions.

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

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

[0049] A model building module, used to build a network topology model of the distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0050] an iterative optimization module, wherein the module is preset with a network flow reconstruction algorithm in graph theory, and is used to iteratively optimize the initial power transmission path based on the current operation data and the initial load demand through the network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan;

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

[0052] Preferably, the data acquisition module is preset with a data acquisition system, which is used to acquire the current operation data of the distribution network in real time by deploying preset sensors and preset data acquisition equipment to each key node of the distribution network; wherein the current operation data of the 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 consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein the node set includes a power station, a substation, a distribution room and a load node, and the edge set includes an initial power transmission path;

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

[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 the power supply of the distribution network fails, so as to obtain nodes with abnormal data or data interruption, and realize 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 is preset with a state estimation algorithm for implementing the following steps:

[0058] Processing the current operation data and current load demand of the distribution network by the state estimation algorithm, thereby obtaining a current state estimation value of the distribution network;

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

[0060] Preferably, the system further comprises a non-sensing expansion module, and the non-sensing expansion module is used to perform the following steps:

[0061] Obtain historical operation data and historical load demand of distribution network;

[0062] Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand;

[0063] Based on the future load demand, acquiring new nodes and new power transmission paths in the distribution network;

[0064] When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and part of the load is gradually transferred to the newly added nodes, thereby realizing a seamless expansion of the network topology model.

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

[0066] Firstly, the network flow reconstruction algorithm in graph theory is used to optimize the initial power transmission path and initial power allocation scheme of the distribution network to ensure the continuity and stability of power supply when the distribution network copes with environmental changes.

[0067] Secondly, by quickly identifying the fault location of the distribution network, the distribution network fault can be quickly repaired. At the same time, the distribution network can be analyzed in time through the network flow reconstruction algorithm to obtain the optimal current power transmission path and the optimal current power distribution plan to maintain power supply, thereby improving the resilience and stability of the distribution network in maintaining power supply when encountering emergencies.

[0068] Thirdly, the process of controlling the operating state of the distribution network is realized through the state estimation algorithm and control law, realizing 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 the future load demand of the distribution network and expanding the distribution network accordingly, by gradually updating the newly added nodes and the newly added power transmission paths to the network topology model, the operating parameters of the distribution network are adjusted and the operating state of the distribution network is controlled, so as to realize the seamless expansion of the network topology model, ensure that the expansion process does not affect the operating state of the distribution network, and ensure the stability of the distribution network during the expansion process. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0071] Figure 1 This is a flow chart of a distribution network control method based on graph theory according to an embodiment of the present invention.

[0072] Figure 2 It is a flow chart of a distribution network control method based on graph theory preferred in an embodiment of the present invention.

[0073] Figure 3 It is a flow chart of a seamless expansion method according to an embodiment of the present invention.

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

[0075] Figure 5It is a comparison chart of resource utilization changes within 24 hours between the traditional static optimization method provided in the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0076] Figure 6a It is a comparison chart of voltage deviation within 24 hours between the traditional PID control method provided in the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0077] Figure 6b It is a comparison chart of frequency deviation within 24 hours between the traditional PID control method provided by the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0078] Figure 7 It is a comparison chart of power supply restoration rates after simulating natural disasters between a traditional manual restoration method provided in the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0079] Figure 8a It is a comparison chart of transmission losses within 24 hours between the traditional static optimization method provided in the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0080] Figure 8b It is a comparison chart of load fluctuation adaptability within 24 hours between the traditional static load distribution method provided in the present application and a distribution network control method based on graph theory in an embodiment of the present invention.

[0081] Figure 8c It is a comparison chart of the expansion effects within 24 hours between the traditional distribution network expansion method provided in the present application and a distribution network non-sensing expansion method according to an embodiment of the present invention.

[0082] Figure 8d This is a comparison chart of the operating costs within 24 hours between a traditional static optimization method provided in this application according to an embodiment of the present invention and a distribution network control method based on graph theory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0084] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0085] like Figure 1As shown, an embodiment of the present invention provides a distribution network control method based on graph theory, comprising the following steps:

[0086] S101, obtaining current operation data of a preset distribution network;

[0087] Constructing a network topology model of the power distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0088] S102, based on the current operation data and the initial load demand, the initial power transmission path is iteratively optimized by a network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan;

[0089] S103: Based on the current power transmission path and the current power allocation plan, update the initial power transmission path and the corresponding initial power allocation plan of the distribution network, thereby achieving control over the operating state of the distribution network.

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

[0091] By deploying preset sensors and preset data acquisition equipment to each key node of the distribution network, the current voltage, current, power and load of the 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) consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein V is a node set, including power stations, substations, distribution rooms and load nodes; E is an edge set, including the initial power transmission path. Each edge e∈E has a certain capacity C e and transmission loss L e , each node v∈V 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 , the substation node has voltage conversion capability, and the load node has a certain power demand D l .

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

[0094]

[0095] Where D(t) is the load demand of the entire distribution network, v is the node, V L is the set of all load nodes, D v (t) is the load node L v power demand.

[0096] 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 distribution plan.

[0097] Specifically, constraining the attributes of each node and edge in the network topology model, that is, establishing the constraint conditions of the power flow equation of the target distribution network, includes:

[0098] For each edge e∈E in the network topology model, the power flow F e Satisfy the capacity constraint, which is shown in Formula 2 below:

[0099]

[0100] Among them, F e represents the flow of electricity, C e represents the power capacity, e∈E represents the edge in the network topology model;

[0101] For each node v∈V in the network topology model, the power flow F e The power balance equation is satisfied, and the power balance equation is shown in the following equation 3:

[0102]

[0103] Among them, E out (v) represents the set of edges starting from node v, E in (v) represents the set of edges that reach node v; P v is the power generated by node v, D v is the power demand of node v, F e is the power flow of the node;

[0104] For each substation T v , its voltage V v The voltage constraint should be satisfied, and the voltage constraint is shown in equation 4 below:

[0105]

[0106] Among them, V Tis the set of all substation nodes; V min is the minimum allowable voltage, V max is the maximum allowable value of voltage;

[0107] For the transmission loss L during power transmission e , which can be expressed by the following formula 5:

[0108]

[0109] Among them, k e is the loss factor, which reflects the physical characteristics of the transmission line, F e Represents the flow of electricity.

[0110] From equations 1 to 5 in the above power flow sub-model, an objective function for optimizing the network topology model can be derived, and the objective function is shown in equation 6 below:

[0111]

[0112] Among them, e∈E represents the edge of the network topology model, v∈V represents the node of the network topology model, and k e is the loss factor, F e represents the power flow, P v represents the power generation of node v, D v represents the power demand of node v; α and β are weight coefficients used to balance transmission loss, resource balance and system stability; Var(F v ) represents the variance of the power flow at node v. In particular, Var(F v ) can reflect the stability of the system.

[0113] The optimization objective function shown in Equation 6 can minimize the power transmission loss of the distribution network, achieve balanced allocation of power resources in the distribution network and improve the stability of the distribution network system.

[0114] In step S102, the optimization objective function of the network flow reconstruction algorithm for iteratively optimizing the initial power transmission path is as shown in Formula 7 below:

[0115]

[0116] Among them, e∈E represents the edge of the network topology model, L e represents the transmission loss of each edge e, k e is the loss factor, F e Represents the flow of electricity.

[0117] In order to ensure that the current power transmission path obtained by iterative optimization and the corresponding current power distribution plan can meet the normal operation of the distribution network, the process of iteratively optimizing the initial power transmission path by the network flow reconstruction algorithm in graph theory needs to meet the capacity constraint condition shown in the following formula 8, the power balance equation shown in the following formula 9 and the voltage constraint condition shown in the following formula 10:

[0118]

[0119] Among them, F e represents the flow of electricity, C e represents the power capacity, e∈E represents the edge in the network topology model;

[0120] For each node v∈V in the network topology model, the power balance equation is as follows:

[0121] As shown in formula 9:

[0122]

[0123] Among them, E out (v) represents the set of edges starting from node v, E in (v) represents the set of edges that reach node v; P v is the power generated by node v, D v is the power demand of node v, F e is the power flow of the node;

[0124] For each substation T v , its voltage V v The voltage constraint should be satisfied, and the voltage constraint is shown in the following equation 10:

[0125]

[0126] Among them, V T is the set of all substation nodes; V min is the minimum allowable voltage, V max is the maximum allowable voltage value.

[0127] In step S103, the step of controlling the operating state of the distribution network specifically includes:

[0128] Processing the current operation data and current load demand of the distribution network through a preset state estimation algorithm, thereby obtaining an estimated value of the current state of the distribution network;

[0129] Based on the state estimation value, a control law is designed, and then based on the control law, the operating parameters of the distribution network are adjusted to achieve control of the operating state of the distribution network.

[0130] Specifically, the current operation data and current load demand of the distribution network can be expressed as a state vector of the distribution network system at time t, and the expression equation of the state vector is shown in Formula 11:

[0131] x(t)=[P(t),V(t),I(t),D(t)] (Formula 11)

[0132] Among them, x(t) is the state vector of the distribution network system at time t, P(t) is the distribution network power, V(t) is the distribution network voltage, I(t) is the distribution network current, and D(t) is the distribution network load demand.

[0133] Specifically, the preset state estimation algorithm includes a Kalman filter algorithm; the process of processing the current operation data and the current load demand of the distribution network by the state estimation algorithm to obtain the current state estimation value of the distribution network is shown in Formula 12:

[0134]

[0135] in, is the state estimate at time t, K(t) is the Kalman gain, z(t) is the measurement value, and H is the measurement matrix.

[0136] Specifically, the design of the control law includes linear control methods and nonlinear control methods, such as PID control, adaptive fuzzy control, etc.

[0137] This embodiment preferably uses a control law design method, as shown in Formula 13:

[0138]

[0139] Where u(t) is the control input and K is the control gain matrix.

[0140] Specifically, the control law-based adjustment of the operating parameters of the distribution network includes voltage regulation, load distribution and power transmission path update.

[0141] like Figure 2 As shown, the embodiment of the present invention preferably adopts a distribution network control method based on graph theory, comprising the following steps:

[0142] S201, constructing a network topology model of the distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0143] Performing initial network flow allocation for the distribution network according to the network topology model and the historical data of the distribution network to meet the basic power demand of each node in the distribution network;

[0144] S202, obtaining current operation data of a preset power distribution network;

[0145] S203, based on the current operation data and the initial load demand, the initial power transmission path is iteratively optimized by a network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan;

[0146] S204: Based on the current power transmission path and the current power allocation plan, update the initial power transmission path and the corresponding initial power allocation plan of the distribution network, thereby realizing control over the operating state of the distribution network;

[0147] Feedback the current power transmission path and the current power distribution plan to the control center to store them as the historical power transmission path and the historical power distribution plan, and then before the next iterative optimization process of the network flow reconstruction algorithm, the historical power transmission path and the historical power distribution plan can also be used as basic data to further optimize the iterative optimization results;

[0148] S205. When a power supply failure occurs in the distribution network, the current operation data of the distribution network and the current data of the node set and the edge set in the network topology model are obtained, so as to obtain nodes with abnormal or interrupted data and identify the fault location of the distribution network.

[0149] The above-mentioned embodiments enable the distribution network to quickly identify the fault location of the distribution network when encountering emergencies and faults, thereby realizing rapid repair of the distribution network fault. Furthermore, when the distribution network encounters an emergency, the network flow reconstruction algorithm can be used to timely analyze the distribution network, and the optimal current power transmission path and the optimal current power distribution plan for maintaining power supply can be obtained, so as to timely adjust the power transmission path and the power distribution plan of the distribution network to maintain the power supply capacity of the system; after the event occurs, the above-mentioned scheme can also quickly adjust the current power transmission path and the current power distribution plan of the distribution network to restore the normal operation of the distribution network, ensuring the resilience and stability of the power supply of the distribution network when encountering emergencies, and solving the problem that the traditional distribution network is difficult to adjust the power distribution strategy in time when facing emergencies such as natural disasters and equipment failures, which easily leads to power supply interruptions in local areas.

[0150] Preferably, Figure 3 As shown, any of the above embodiments further includes the following steps:

[0151] Demand forecasting: obtaining historical operation data and historical load demand of the distribution network;

[0152] Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand;

[0153] Expansion plan design: based on the future load demand, obtain new nodes and new power transmission paths in the distribution network;

[0154] Initialization: performing initial basic settings for the newly added nodes and newly added power transmission paths of the distribution network;

[0155] Gradual introduction: When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and the smooth transition of the old and new systems is achieved by dynamically adjusting the operating parameters of the existing system;

[0156] Senseless switching: while gradually updating the newly added nodes and the newly added power transmission paths to the network topology model, part of the load is gradually transferred to the newly added nodes to avoid system instability caused by sudden load changes and realize senseless expansion of the network topology model;

[0157] Preferably, during the update process, the newly added nodes run in parallel with the existing nodes to ensure a smooth transition of the system;

[0158] Specifically, the above four steps of initialization, gradual introduction, senseless switching and system optimization complete the dynamic switching process;

[0159] System optimization: After gradually updating the newly added nodes and the newly added power transmission paths to the network topology model, the network flow reconstruction algorithm is used to iteratively optimize the current power transmission path, thereby updating the current power transmission path of the distribution network and the corresponding current power distribution plan, so as to achieve the optimal operation of the expanded system under the new load conditions;

[0160] Specifically, in the step of demand forecasting, the forecasting of future load of the distribution network includes time series analysis and machine learning. In this embodiment, a method for forecasting future load demand of the distribution network is preferably shown in Formula 14:

[0161]

[0162] in, is the future load demand of the distribution network, D(t) is the load demand of the distribution network at time t, and f is the future load demand prediction function.

[0163] Preferably, the steps of designing the expansion scheme should meet the following requirements:

[0164] Capacity requirement: The capacity of the expanded distribution network system should meet the predicted future load demand of the distribution network.

[0165] Minimization requirement: The impact on the operating status of the distribution network during the expansion process should be minimized.

[0166] Optimization requirements: The newly added nodes and newly added power transmission paths should be able to achieve the effect of optimizing the power distribution configuration of the distribution network to improve the resource utilization efficiency and system stability of the distribution network.

[0167] Specifically, the objective function of the extended solution can be expressed as Formula 15, which is as follows:

[0168]

[0169] Among them, E new is the set of newly added transmission lines, C e is the cost of the transmission line, V new is the set of newly added nodes, P v is the configuration cost of the node, and γ is the weight coefficient.

[0170] like Figure 4 As shown, an embodiment of the present invention further provides a distribution network control system based on graph theory, comprising:

[0171] A data acquisition module is used to obtain the current operating data of the preset power distribution network;

[0172] Preferably, the data acquisition module is preset with a data acquisition system, which is used to acquire the current operation data of the distribution network in real time by deploying preset sensors and preset data acquisition equipment to each key node of the distribution network; wherein the current operation data of the distribution network includes voltage, current, power and load;

[0173] A model building module, used to build a network topology model of the distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0174] Specifically, in the model building module, the network topology model of the distribution network is a directed graph consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein the node set includes a power station, a substation, a distribution room and a load node, and the edge set includes an initial power transmission path;

[0175] The network topology model further includes a load sub-model, which is used to represent the power demand of each load node in the network topology model and the overall load demand, thereby obtaining the initial load demand of the network topology model;

[0176] 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 distribution plan;

[0177] an iterative optimization module, wherein the module is preset with a network flow reconstruction algorithm in graph theory, and is used to iteratively optimize the initial power transmission path based on the current operation data and the initial load demand through the network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan;

[0178] A distribution network control module, configured to update an initial power transmission path and a corresponding initial power distribution plan of the distribution network based on the current power transmission path and the current power distribution plan, thereby realizing control over the operation state of the distribution network;

[0179] Specifically, the distribution network control module also includes an adaptive feedback control submodule, which is preset with a state estimation algorithm for implementing the following steps:

[0180] Processing the current operation data and current load demand of the distribution network by the state estimation algorithm, thereby obtaining a current state estimation value of the distribution network;

[0181] Based on the state estimation value, a control law is designed, and then based on the control law, an operating parameter of the distribution network is adjusted to achieve control of the operating state of the distribution network;

[0182] The fault node identification module is used to obtain the current operation data of the distribution network and the current data of the node set and edge set in the network topology model when the power supply of the distribution network fails, so as to obtain the nodes with abnormal data or data interruption, and realize the identification of the fault location of the distribution network;

[0183] The sensorless expansion module is used to perform the following steps:

[0184] Obtain historical operation data and historical load demand of distribution network;

[0185] Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand;

[0186] Based on the future load demand, acquiring new nodes and new power transmission paths in the distribution network;

[0187] When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and part of the load is gradually transferred to the newly added nodes, thereby realizing a seamless expansion of the network topology model.

[0188] Preferably, an embodiment of the present invention further provides a distribution network control system based on graph theory, which is used for optimizing provincial disaster recovery distribution network resources, including:

[0189] A data acquisition module, used to obtain and store the current operating data of the preset power distribution network;

[0190] Preferably, the data acquisition module is preset with a data acquisition system, which is used to acquire the current operation data of the distribution network in real time by deploying preset sensors and preset data acquisition equipment to each key node of the distribution network; wherein the current operation data of the distribution network includes voltage, current, power and load;

[0191] A data analysis module, configured to process the current operation data and construct a network topology model of the power distribution network based on the current operation data, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand;

[0192] A network flow reconstruction algorithm module, which is preset with a network flow reconstruction algorithm in graph theory, and is used to iteratively optimize the initial power transmission path based on the current operating data and the initial load demand through the network flow reconstruction algorithm in graph theory to obtain the current power transmission path and the corresponding current power distribution plan;

[0193] The adaptive feedback control module is used to implement the following steps:

[0194] Processing the current operation data and current load demand of the distribution network by the state estimation algorithm, thereby obtaining a current state estimation value of the distribution network;

[0195] Based on the state estimation value, 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 plan, the initial power transmission path and the corresponding initial power distribution plan of the distribution network are updated, thereby realizing dynamic adjustment of the distribution network control strategy.

[0196] In order to more intuitively demonstrate the application effect and technical effect of the present invention in the distribution network, the embodiment of the present invention also provides several traditional static optimization methods of the distribution network and the effect comparison of the method of the present invention when applied to the distribution network system, which specifically includes the following contents:

[0197] This embodiment is based on the technical specifications and operating data of the actual distribution network. The simulation model of the actual distribution network is established using the MATLAB and Simulink platforms and an adaptive hardware environment is configured. By simulating different load conditions and emergencies on the simulation model, the actual effects of a distribution network control method based on graph theory and other traditional distribution network optimization methods when applied to the distribution network are tested.

[0198] Among them, the distribution network simulation model includes distribution network nodes, distribution network power transmission paths and control systems, which are used to reflect the operating characteristics and control behaviors of the actual distribution network; the simulation model is simulated in various operating states including normal operating state, sudden failures and load fluctuations to test the adaptability and robustness of the system.

[0199] The specific parameter settings of the simulation model are shown in Table 1 below:

[0200] Table 1 Simulation model parameter settings

[0201]

[0202] The parameter settings of the network flow reconstruction algorithm, adaptive feedback control algorithm, dynamic balancing strategy and senseless expansion technology are shown in Table 2 below:

[0203] Table 2 Algorithm settings

[0204]

[0205] Wherein, the adaptive feedback control algorithm includes a state estimation algorithm and a control law design.

[0206] The test results of this embodiment are as follows Figure 5 As shown, Figure 5 The paper shows the resource utilization change of the distribution network system within 24 hours when the traditional static optimization method of the distribution network and the distribution network control method based on graph theory in the embodiment of the present invention are applied to the distribution network system respectively. Figure 5 The test results show that the resource utilization of the distribution network using the traditional static optimization method shows certain fluctuations within 24 hours, especially reaching higher values ​​during peak power consumption periods including morning and evening, but the resource utilization is lower during the power consumption valley periods at night and noon. The resource utilization of the distribution network system using the method of the present invention is higher than that of the traditional static optimization method in each time period. It is worth noting that during the peak load period, the resource utilization improvement effect of the method of the present invention is more significant than that of the traditional static optimization method.

[0207] Specifically, when the traditional static optimization method is applied, the resource utilization rate in the peak hours including 8 to 10 in the morning and 18 to 20 in the evening is as high as 85%, while when the method of the present invention is applied, the resource utilization rate in the same period can reach about 93%, which is about 8% higher than the traditional static optimization method.

[0208] In summary, compared with the traditional static optimization method of the distribution network, the distribution network control method based on graph theory in the embodiment of the present invention has significant advantages in resource optimization of the distribution network, and can effectively improve the resource utilization rate of the distribution network; in particular, its effect is particularly prominent during the peak period of power load. It can be seen that the distribution network control method based on graph theory in the embodiment of the present invention has outstanding potential value and broad application prospects in practical application scenarios.

[0209] The test results of this embodiment are as follows Figure 6a and Figure 6b As shown, a comparison of the voltage and frequency deviation of the distribution network system within 24 hours is shown when a traditional PID control method and a distribution network control method based on graph theory according to an embodiment of the present invention are respectively applied to the distribution network system. Figure 6a is the voltage deviation comparison of the distribution network system within 24 hours, Figure 6b It is a comparison of frequency deviation of the distribution network system within 24 hours.

[0210] Depend on Figure 6a The obtained test results show that when the traditional PID control method is applied, the voltage deviation of the distribution network system fluctuates significantly within a day. In particular, during the peak hours in the morning and evening, the voltage deviation value reaches 6.5V to 7.5V. This result shows that the traditional PID control method has a poor voltage regulation effect when facing the peak load of the distribution network, resulting in a large voltage deviation. When the method of the present invention is applied, the voltage deviation value of the distribution network system is significantly smaller than the result of applying the traditional PID control method, and its maximum value does not exceed 4.7V. In particular, during the peak load period of the distribution network, the application of the method of the present invention can significantly reduce the voltage deviation and improve the voltage regulation ability and stability of the system.

[0211] Depend on Figure 6b The obtained test results show that when the traditional PID control method is applied, the frequency deviation of the distribution network system fluctuates greatly. In particular, during the period when the distribution network load changes dramatically, including the morning and evening peaks, the frequency deviation can reach 0.07Hz. This result shows that the traditional PID control method is insufficient in regulating the frequency of the distribution network system, resulting in poor frequency stability of the distribution network. When the method of the present invention is applied, the frequency deviation value of the distribution network system is significantly smaller than the result obtained by the traditional PID control algorithm, and its maximum value does not exceed 0.047Hz, indicating that when the method of the present invention is applied, the distribution network system can maintain a high frequency stability even when the load changes greatly.

[0212] In summary, the voltage and frequency stability of the distribution network when the method of the present invention is applied are significantly better than when the traditional PID control method is applied. Specifically, when the method of the present invention is applied, the voltage and frequency deviation values ​​of the system are lower than those of the traditional method, and the curve fluctuation amplitude is smaller, especially during the peak load period of the distribution network. By comparing the application effect of the method of the present invention with the application effect of the traditional PID control method, the technical effect of the method of the present invention in improving the voltage and frequency stability of the distribution network system is clearly demonstrated, indicating that the distribution network control method based on graph theory in an embodiment of the present invention has outstanding potential value and broad application prospects in actual application scenarios.

[0213] The test results of this embodiment are as follows Figure 7 As shown, Figure 7 The present invention demonstrates the performance of a traditional distribution network post-disaster recovery method and a distribution network control method based on graph theory in an embodiment of the present invention in simulating the recovery of the distribution network power supply capacity after a disaster when the method is applied to the distribution network system.

[0214] Depend on Figure 7 The test results show that when the traditional distribution network post-disaster recovery method restores the power supply capacity of the distribution network, the power supply recovery rate of the distribution network rises slowly, indicating that its efficiency in restoring the power supply of the distribution network is low. When the method of the present invention restores the power supply capacity of the distribution network, the power supply recovery rate of the distribution network rises significantly faster than the traditional recovery method, and most of the distribution network power supply can be restored in a short time. This result shows that the distribution network control method based on graph theory in the embodiment of the present invention has a significant effect on the recovery ability of the distribution network power supply after a disaster.

[0215] The test results of this embodiment are as follows Figure 8a , Figure 8b , Figure 8c and Figure 8d As shown, it shows the comparison of different optimization methods in four aspects: transmission loss, load fluctuation adaptability, senseless expansion effect and operating cost.

[0216] Depend on Figure 8a It can be seen from the test results that the efficiency of reducing the power transmission loss of the distribution network by applying the traditional distribution network optimization method is significantly lower than the efficiency of reducing the power transmission loss of the distribution network by applying the method of the present invention.

[0217] Depend on Figure 8bThe test results show that when the traditional method is used to control the distribution network, the load of the distribution network changes greatly; especially during the peak load period of the distribution network, the load fluctuates significantly, indicating that the system adaptability of the traditional control method is poor. When the distribution network is controlled by a distribution network control method based on graph theory according to an embodiment of the present invention, the load of the distribution network changes more smoothly, indicating that the method of the present invention can better adapt to the load fluctuation of the distribution network than the traditional method, and improves the stability and response speed of the distribution network system.

[0218] Depend on Figure 8c The test results show that when adding nodes and power transmission paths to the distribution network system using the traditional expansion method, the distribution network system is affected by the expansion process and the resource utilization rate decreases significantly. However, when adding nodes and power transmission paths to the distribution network system using a senseless expansion method according to an embodiment of the present invention, the resource utilization rate of the distribution network decreases less than when the traditional expansion method is used, indicating that the method of the present invention can effectively reduce the disturbance to the existing system during the system expansion process and maintain the stability of the system during the expansion process.

[0219] Depend on Figure 8d The test results show that when the traditional method is used to control the distribution network, the operating cost of the distribution network increases greatly over time, indicating that the operating cost of the system is high when the traditional method is used to control the distribution network. When the distribution network control method based on graph theory in the embodiment of the present invention is used to control the distribution network, the operating cost of the distribution network increases less over time than the traditional control method, indicating that the method of the present invention can significantly reduce the operating cost of the distribution network system compared with the traditional distribution network control method.

[0220] In summary, the distribution network control method based on graph theory in the embodiment of the present invention has significant progress and technical effects in reducing power transmission losses in the distribution network, reducing load fluctuations in the distribution network, maintaining stability during distribution network expansion, and reducing operating costs of the distribution network compared with traditional distribution network control methods and optimization methods. This shows that the distribution network control method and system based on graph theory in the present invention have outstanding potential value and broad application prospects in actual application scenarios.

[0221] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A distribution network control method based on graph theory, characterized in that: The following steps are involved: Obtain the current operating data of the preset distribution network; Constructing a network topology model of the power distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand; Based on the current operation data and the initial load demand, the initial power transmission path is iteratively optimized by a network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan; Based on the current power transmission path and the current power distribution plan, the initial power transmission path and the corresponding initial power distribution plan of the power distribution network are updated, thereby achieving control over the operating state of the power distribution network.

2. A 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 consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein the node set includes a power station, a substation, a distribution room and a load node, and the edge set includes an initial power transmission path; The network topology model further includes a load sub-model, which is used to represent the power demand of each load node in the network topology model and the overall load demand, 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 distribution plan.

3. A distribution network control method based on graph theory according to claim 2, characterized in that: The following steps are also included: When the power supply of the distribution network fails, the current operation data of the distribution network and the current data of the node set and edge set in the network topology model are obtained, so as to obtain the nodes with abnormal or interrupted data and identify the fault location of the distribution network.

4. A distribution network control method based on graph theory according to claim 2, characterized in that: The steps of realizing the control of the operating state of the distribution network specifically include: Processing the current operation data and current load demand of the distribution network through a preset state estimation algorithm, thereby obtaining an estimated value of the current state of the distribution network; Based on the state estimation value, a control law is designed, and then based on the control law, the operating parameters of the distribution network are adjusted to achieve control of the operating state of the distribution network.

5. A distribution network control method based on graph theory according to claim 4, characterized in that: The following steps are also included: Obtain historical operation data and historical load demand of distribution network; Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand; Based on the future load demand, acquiring new nodes and new power transmission paths in the distribution network; When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and part of the load is gradually transferred to the newly added nodes, thereby realizing a seamless expansion of the network topology model.

6. A distribution network control system based on graph theory, characterized in that: include: A data acquisition module is used to obtain the current operating data of the preset power distribution network; A model building module, used to build a network topology model of the distribution network, wherein the network topology model includes an initial power transmission path, an initial power distribution plan, and an initial load demand; an iterative optimization module, wherein the module is preset with a network flow reconstruction algorithm in graph theory, and is used to iteratively optimize the initial power transmission path based on the current operation data and the initial load demand through the network flow reconstruction algorithm in graph theory to obtain a current power transmission path and a corresponding current power distribution plan; The distribution network control module is used to update the initial power transmission path and the corresponding initial power distribution plan of the distribution network based on the current power transmission path and the current power distribution plan, thereby realizing the control of the operating state of the distribution network.

7. A distribution network control system based on graph theory according to claim 6, characterized in that: In the model building module, the network topology model of the distribution network is a directed graph consisting of a node set and an edge set, which is used to represent the network topology structure of the distribution network; wherein the node set includes a power station, a substation, a distribution room and a load node, and the edge set includes an initial power transmission path; The network topology model further includes a load sub-model, which is used to represent the power demand of each load node in the network topology model and the overall load demand, 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 distribution plan.

8. A distribution network control system based on graph theory according to claim 6, characterized in that: Also includes: The fault node identification module is used to obtain the current operation data of the distribution network and the current data of the node set and edge set in the network topology model when the power supply of the distribution network fails, so as to obtain the nodes with abnormal data or data interruption, and realize the identification of the fault location of the distribution network.

9. A distribution network control system based on graph theory according to claim 6, characterized in that: The distribution network control module also includes an adaptive feedback control submodule, which is preset with a state estimation algorithm for implementing the following steps: Processing the current operation data and current load demand of the distribution network by the state estimation algorithm, thereby obtaining a current state estimation value of the distribution network; Based on the state estimation value, a control law is designed, and then based on the control law, the operating parameters of the distribution network are adjusted to achieve control of the operating state of the distribution network.

10. A distribution network control system based on graph theory according to claim 6, characterized in that: It also includes a non-sensing expansion module, which is used to perform the following steps: Obtain historical operation data and historical load demand of distribution network; Predict future load demand of the distribution network based on historical operation data, historical load demand, current operation data and current load demand; Based on the future load demand, acquiring new nodes and new power transmission paths in the distribution network; When the current load of the distribution network meets the preset requirements, the newly added nodes and the newly added power transmission paths are gradually updated to the network topology model, and part of the load is gradually transferred to the newly added nodes, thereby realizing a seamless expansion of the network topology model.

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