Distribution network voltage regulation methods, devices, computer equipment and storage media

Abstract

This application relates to a method, apparatus, computer equipment, and storage medium for voltage regulation in a distribution network. The method includes: acquiring operational data of each node in the distribution network; constructing a power flow model and a voltage distribution state matrix based on the operational data; obtaining the voltage regulation contribution of each regulating resource connected to the distribution network to each node based on the power flow model and the voltage distribution state matrix; selecting a set of key regulating resources from among the regulating resources based on the voltage regulation contribution and a preset contribution threshold; establishing a source-load collaborative optimization scheduling model based on the set of key regulating resources, with the optimization objective of minimizing node voltage penalties and regulating resource operating costs; solving the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulating resource in each scheduling period; and using the optimal output scheme to regulate the voltage of each node in the distribution network, thereby improving voltage regulation efficiency.

Description

Technical Field

[0001] This application relates to the field of power system and distribution network operation control technology, and in particular to a distribution network voltage regulation method, device, computer equipment and computer-readable storage medium. Background Technology

[0002] During the operation of a distribution network, node voltage is affected by factors such as power distribution and network structure. The effects of different locations and types of regulating resources on voltage vary significantly. With the integration of various resources such as distributed power sources, energy storage, and adjustable loads, voltage regulation is gradually shifting from a centralized control mode relying on a small number of devices to a collaborative regulation mode involving multiple resources.

[0003] However, the voltage regulation methods in related technologies for power distribution networks do not accurately regulate resource utilization, resulting in low voltage regulation efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a distribution network voltage regulation method, device, computer equipment, and computer-readable storage medium that can accurately utilize regulation resources and improve voltage regulation efficiency in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for voltage regulation in a distribution network, the method comprising:

[0006] Obtain operational data from each node of the distribution network, and construct a power flow model and a voltage distribution state matrix based on the operational data.

[0007] Based on the power flow model of the distribution network and the voltage distribution state matrix of the distribution network, the contribution of each regulating resource connected to the distribution network to the voltage regulation of each node of the distribution network is obtained.

[0008] Based on the voltage regulation contribution and the preset contribution threshold, a set of key regulation resources is selected from each regulation resource; the set of key regulation resources includes multiple key regulation resources.

[0009] Based on the set of key regulating resources, a source-load collaborative optimization scheduling model is established with the optimization objective of minimizing the node voltage penalty and the operating cost of regulating resources in the distribution network. The node voltage penalty is used to characterize the node voltage deviation and the degree of exceeding the limit at each node in the distribution network. The operating cost of regulating resources is used to characterize the sum of the operating costs of each key regulating resource.

[0010] The source-load coordinated optimization scheduling model is solved to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network.

[0011] In one embodiment, based on the distribution network power flow model and the distribution network voltage distribution state matrix, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained, including:

[0012] The power flow model of the distribution network is linearized to obtain the response relationship between the node voltage of each node in the distribution network and the power changes of each regulating resource.

[0013] Based on the voltage distribution state matrix and response relationship of the distribution network, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained.

[0014] In one embodiment, before establishing a source-load collaborative optimization scheduling model based on a set of key regulation resources, with the optimization objective of minimizing node voltage penalties and regulation resource operating costs in the distribution network, the method further includes:

[0015] Obtain the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period;

[0016] The node voltage penalty is obtained based on the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period.

[0017] In one embodiment, key regulation resources include photovoltaic units, energy storage units, and adjustable loads. Before establishing a source-load collaborative optimization scheduling model based on the set of key regulation resources, with the optimization objective of minimizing the node voltage penalty of the distribution network and the operating cost of regulation resources, the method further includes:

[0018] The operating cost of each photovoltaic unit is obtained based on the actual active power output and reactive power regulation of each photovoltaic unit during each scheduling period.

[0019] The operating cost of each energy storage unit is obtained based on its charging power, discharging power, and reactive power regulation during each scheduling period.

[0020] The operating cost of adjustable loads is obtained based on the outflow and inflow of each adjustable load during each scheduling period;

[0021] The operating cost of the adjustable resource is obtained by summing the operating costs of the photovoltaic unit, the energy storage unit, and the adjustable load.

[0022] In one embodiment, the source-load collaborative optimization scheduling model also includes constraints, including system power balance constraints, energy storage unit operation constraints, photovoltaic unit operation constraints, adjustable load operation constraints, and power flow balance constraints.

[0023] In one embodiment, after solving the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period, the method further includes:

[0024] Based on the optimal power output scheme, the operating parameters of each key regulating resource are adjusted to obtain the first voltage optimization control results of each node in the distribution network;

[0025] The first voltage optimization control result is verified. If the verification of the first voltage optimization control result fails, the candidate regulation resources are added to the key regulation resource set to obtain a new key regulation resource set. The candidate regulation resources are a preset number of regulation resources with the highest voltage regulation contribution among the regulation resources.

[0026] Based on the new set of key regulation resources, return to the steps of establishing a source-load collaborative optimization scheduling model with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network, until the first voltage optimization regulation result is verified.

[0027] In one embodiment, the verification of the first voltage optimization control result includes:

[0028] Based on the results of the first voltage optimization and control, the first key voltage index is obtained;

[0029] Obtain the reference output scheme of each regulating resource in each scheduling period, adjust the operating parameters of each regulating resource according to the reference output scheme, and obtain the second voltage optimization control result of each node of the distribution network; obtain the second key voltage index according to the second voltage optimization control result.

[0030] The verification result of the first voltage optimization control result is obtained based on the first key voltage index, the second key voltage index, and the preset voltage index threshold.

[0031] Secondly, this application also provides a power distribution network voltage regulation device, the device comprising:

[0032] The operation data acquisition module is used to acquire the operation data of each node in the distribution network, and to construct the power flow model and voltage distribution state matrix of the distribution network based on the operation data.

[0033] The voltage regulation contribution acquisition module is used to obtain the voltage regulation contribution of each regulation resource connected to the distribution network to each node of the distribution network based on the distribution network power flow model and the distribution network voltage distribution state matrix.

[0034] The key regulation resource screening module is used to screen a set of key regulation resources from various regulation resources based on the voltage regulation contribution and a preset contribution threshold; the set of key regulation resources includes multiple key regulation resources.

[0035] The model building module is used to establish a source-load collaborative optimization scheduling model based on the set of key regulating resources, with the optimization objective of minimizing the node voltage penalty and the operating cost of regulating resources in the distribution network. The node voltage penalty is used to characterize the node voltage deviation and the degree of exceeding the limit at each node of the distribution network; the operating cost of regulating resources is used to characterize the sum of the operating costs of each key regulating resource.

[0036] The optimal output scheme acquisition module is used to solve the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network.

[0037] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps of the first aspect.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps of the first aspect.

[0039] The aforementioned distribution network voltage regulation method, device, computer equipment, and computer-readable storage medium acquire operational data from each node of the distribution network. Based on this data, they construct a distribution network power flow model and a distribution network voltage distribution state matrix. Using the power flow model and voltage distribution state matrix, they obtain the voltage regulation contribution of each regulating resource connected to the distribution network to each node. Based on the voltage regulation contribution and a preset contribution threshold, they select a set of key regulating resources. This set includes multiple key regulating resources. Based on this set, and with the optimization objective of minimizing node voltage penalties and regulating resource operating costs, they establish a source-load collaborative optimization scheduling model. Point voltage penalty is used to characterize the node voltage deviation and limit exceedance degree of each node in the distribution network; regulation resource operating cost is used to characterize the sum of the operating costs of each key regulation resource; the source-load collaborative optimization scheduling model is solved to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network; thus, based on the voltage regulation contribution of each regulation resource, this application realizes the quantitative evaluation of the ability of different regulation resources to affect node voltage, and screens key regulation resources. Based on the screened key regulation resources, the optimal output scheme is determined, avoiding the blind use of regulation resources in the voltage regulation process, and enabling precise use of regulation resources, thereby improving the efficiency of voltage regulation. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an application environment diagram of the power distribution network voltage regulation method in one embodiment;

[0042] Figure 2 This is a flowchart illustrating a power distribution network voltage regulation method in one embodiment;

[0043] Figure 3 This is a schematic diagram of a power distribution network voltage regulation method in one embodiment;

[0044] Figure 4 This is a simplified architectural diagram of a power distribution network system in one embodiment;

[0045] Figure 5 This is a schematic diagram of the node voltage during the most unfavorable period in one embodiment;

[0046] Figure 6 This is a schematic diagram of the minimum node voltage over 24 hours in one embodiment;

[0047] Figure 7 This is a schematic diagram of the 24-hour average voltage deviation in one embodiment;

[0048] Figure 8 This is a schematic diagram of the number of voltage over-limit nodes in one embodiment;

[0049] Figure 9 This is a schematic diagram of the 24-hour voltage penalty cost in one embodiment;

[0050] Figure 10 This is a spatiotemporal distribution diagram of node voltages without voltage regulation in one embodiment;

[0051] Figure 11 This is a spatiotemporal distribution diagram of node voltage under the voltage regulation method described in one embodiment;

[0052] Figure 12 This is a graph showing the resource contribution and screening results during the most unfavorable period in one embodiment.

[0053] Figure 13 This is a schematic diagram of the actual adjustment amounts of each resource during the most unfavorable period in one embodiment.

[0054] Figure 14 This is a schematic diagram of the 24-hour net adjustment cost in one embodiment;

[0055] Figure 15 This is a structural block diagram of a power distribution network voltage regulation device in one embodiment;

[0056] Figure 16 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0059] The power distribution network voltage regulation method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server. Terminal 102 acquires operational data from each node of the distribution network. Based on this data, it constructs a power flow model and a voltage distribution state matrix for the distribution network. Using these data, it obtains the voltage regulation contribution of each regulating resource connected to the distribution network to each node. Based on the voltage regulation contribution and a preset contribution threshold, it selects a set of key regulating resources. This set includes multiple key regulating resources. Based on this set, and with the optimization objective of minimizing node voltage penalties and regulating resource operating costs, it establishes a source-load collaborative optimization scheduling model. Node voltage penalties characterize the node voltage deviation and exceedance limits of each node in the distribution network. Regulating resource operating costs characterize the total operating costs of each key regulating resource. The source-load collaborative optimization scheduling model is solved to obtain the optimal output scheme for each key regulating resource during each scheduling period. The optimal output scheme is used to regulate the voltage of each node in the distribution network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0060] In one embodiment, such as Figure 2 As shown, a method for voltage regulation in a distribution network is provided. This embodiment applies this method to... Figure 1 Taking terminal 102 as an example, the method includes the following steps:

[0061] Step S210: Obtain the operating data of each node in the distribution network, and construct the power flow model and voltage distribution state matrix of the distribution network based on the operating data.

[0062] The operational data of each node in the distribution network includes, but is not limited to, the voltage amplitude of each node in the distribution network under its current operating state. Active power reactive power Photovoltaic unit output, energy storage operation status, and network topology parameters.

[0063] Among them, the distribution network power flow model is a mathematical model used in power system analysis to calculate the voltage of each node, the power flow of branches, and the network loss of the distribution network under specific operating conditions.

[0064] Among them, the voltage distribution state matrix of the distribution network is used to characterize the voltage distribution characteristics under the current operating state of the distribution network.

[0065] In the embodiments of this application, a power flow model of the distribution network is established based on operational data. Existing technologies can be referenced, and will not be described in detail here.

[0066] Calculate the voltage deviation at each node. ,in, The preset reference voltage value is used. Based on the voltage deviation of each node, a voltage distribution state matrix of the distribution network is constructed.

[0067] Step S220: Based on the power flow model of the distribution network and the voltage distribution state matrix of the distribution network, obtain the voltage regulation contribution of each regulating resource connected to the distribution network to each node of the distribution network.

[0068] Among them, the regulating resources include, but are not limited to, photovoltaic units, energy storage units, and adjustable loads.

[0069] Among them, the voltage regulation contribution comprehensively considers the impact of node voltage deviation, electrical distance and power changes on voltage, and is used to quantify the voltage regulation capability of different regulation resources under the current operating conditions.

[0070] In this embodiment, the response relationship between the voltage of each node and the power change of each regulating resource can be obtained based on the power flow model of the distribution network. The voltage regulation contribution of each regulating resource is then obtained based on the response relationship and the voltage distribution state matrix of the distribution network.

[0071] Step S230: Based on the voltage regulation contribution and the preset contribution threshold, select a set of key regulation resources from each regulation resource; the set of key regulation resources includes multiple key regulation resources.

[0072] The preset contribution threshold can be set according to actual needs.

[0073] In this embodiment, regulation resources whose voltage regulation contribution is greater than or equal to a preset contribution threshold are identified as key regulation resources, thus obtaining a set of key regulation resources. Specifically, the set of key regulation resources can be represented as follows:

[0074]

[0075] in, This represents the voltage regulation contribution of the j-th regulating resource. This indicates the preset contribution threshold.

[0076] Step S240: Based on the set of key regulating resources, establish a source-load collaborative optimization scheduling model with the optimization objective of minimizing the node voltage penalty and regulating resource operating cost of the distribution network; the node voltage penalty is used to characterize the node voltage deviation and over-limit degree of each node in the distribution network; the regulating resource operating cost is used to characterize the total operating cost of each key regulating resource.

[0077] In this embodiment of the application, the expression for the optimization objective is:

[0078]

[0079] in, The node voltage penalty is used as the primary optimization objective. This indicates that adjusting resource operating costs is used as an auxiliary optimization objective. This represents the weighting coefficient, used to adjust the weighting relationship between voltage optimization and operating costs.

[0080] Step S250: Solve the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network.

[0081] The optimal power output scheme includes, but is not limited to, the active power output and reactive power regulation of the photovoltaic unit, the charging power, discharging power and reactive power regulation of the energy storage unit, and the transfer-out and transfer-in of the adjustable load.

[0082] In the embodiments of this application, the particle swarm optimization algorithm can be used to find the optimal solution for the source-load collaborative optimization scheduling model and obtain the optimal output scheme of each key regulation resource in each scheduling period.

[0083] The aforementioned distribution network voltage regulation method acquires operational data from each node of the distribution network, and constructs a distribution network power flow model and a distribution network voltage distribution state matrix based on this data. Using the power flow model and voltage distribution state matrix, it obtains the voltage regulation contribution of each regulating resource connected to the distribution network to each node. Based on the voltage regulation contribution and a preset contribution threshold, it selects a set of key regulating resources from among these resources. This set includes multiple key regulating resources. Based on this set, and with the optimization objective of minimizing node voltage penalties and regulating resource operating costs, it establishes a source-load collaborative optimization scheduling model. Node voltage penalties are used to characterize the distribution network's voltage regulation. The application analyzes the voltage deviation and limit exceedance of each node in the power grid; the operating cost of regulating resources is used to characterize the total operating cost of each key regulating resource; the source-load collaborative optimization scheduling model is solved to obtain the optimal output scheme of each key regulating resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network; thus, based on the voltage regulation contribution of each regulating resource, this application realizes the quantitative evaluation of the ability of different regulating resources to affect node voltage, and screens key regulating resources. Based on the screened key regulating resources, the optimal output scheme is determined, avoiding the blind use of regulating resources in the voltage regulation process, and enabling precise utilization of regulating resources, thereby improving the efficiency of voltage regulation.

[0084] In one embodiment, based on the distribution network power flow model and the distribution network voltage distribution state matrix, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained, including:

[0085] The power flow model of the distribution network is linearized to obtain the response relationship between the node voltage of each node in the distribution network and the power changes of each regulating resource.

[0086] Based on the voltage distribution state matrix and response relationship of the distribution network, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained.

[0087] In this embodiment of the application, the expression for the response relationship is:

[0088]

[0089] in, This indicates the active power of resource j being adjusted. This represents the reactive power of the adjusted resource j.

[0090] The expression for the voltage regulation contribution is:

[0091]

[0092] in, For the target set of control nodes; The node weight coefficient is used to characterize the importance of different nodes in the voltage regulation process. It is set according to the degree of node voltage deviation, the node's position in the network, or its power supply importance. The electrical distance between node i and the regulating resource j is represented by network impedance distance.

[0093] In one embodiment, before establishing a source-load collaborative optimization scheduling model based on a set of key regulation resources, with the optimization objective of minimizing node voltage penalties and regulation resource operating costs in the distribution network, the method further includes:

[0094] Obtain the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period;

[0095] The node voltage penalty is obtained based on the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period.

[0096] The voltage squared value is the square of the node voltage amplitude. The undervoltage relaxation variable represents the degree to which the node voltage falls below the lower voltage limit. The overvoltage relaxation variable represents the degree to which the node voltage exceeds the upper voltage limit. An additional undervoltage soft constraint relaxation variable allows for slight voltage deviations within a certain range, used to quantify the degree of voltage quality degradation.

[0097] In this embodiment, the expression for the node voltage penalty is:

[0098]

[0099] in, Let be the squared voltage value of the i-th node during time period t; Let be the undervoltage relaxation variable of the i-th node during time period t; Let be the overpressure relaxation variable of the i-th node during time period t; Let be the additional undervoltage soft constraint relaxation variable for the i-th node during time period t; This is the voltage deviation penalty coefficient; This is the voltage over-limit relaxation penalty coefficient; This is an additional undervoltage soft constraint penalty coefficient.

[0100] In one embodiment, key regulation resources include photovoltaic units, energy storage units, and adjustable loads. Before establishing a source-load collaborative optimization scheduling model based on the set of key regulation resources, with the optimization objective of minimizing the node voltage penalty of the distribution network and the operating cost of regulation resources, the method further includes:

[0101] The operating cost of each photovoltaic unit is obtained based on the actual active power output and reactive power regulation of each photovoltaic unit during each scheduling period.

[0102] The operating cost of each energy storage unit is obtained based on its charging power, discharging power, and reactive power regulation during each scheduling period.

[0103] The operating cost of adjustable loads is obtained based on the outflow and inflow of each adjustable load during each scheduling period;

[0104] The operating cost of the adjustable resource is obtained by summing the operating costs of the photovoltaic unit, the energy storage unit, and the adjustable load.

[0105] In this embodiment, the expression for adjusting resource operating costs is:

[0106]

[0107] Among them, the operating cost of photovoltaic units The expression is:

[0108]

[0109] in, This refers to the cost coefficient for limited generation of photovoltaic units. Let be the maximum active power output of the k-th photovoltaic unit at time t; This represents the actual active power output of the k-th photovoltaic unit at time t. The reactive power regulation weighting coefficient for photovoltaic units; Let be the reactive power regulation of the k-th photovoltaic unit at time t.

[0110] Energy storage unit operating costs The expression is:

[0111]

[0112] in, Adjust the cost coefficient for energy storage units; , These are the charging power and discharging power of the k-th energy storage unit at time t, respectively. The reactive power regulation weighting coefficient for energy storage units; Let be the reactive power regulation of the k-th energy storage unit at time t.

[0113] Adjustable load operating costs The expression is:

[0114]

[0115] in, This is the load adjustment cost coefficient; , These represent the outflow and inflow of the k-th load at time t, respectively.

[0116] The process of establishing a source-load coordinated optimization scheduling model also includes setting constraints for the model. These constraints include system power balance constraints, energy storage unit operation constraints, photovoltaic unit operation constraints, adjustable load operation constraints, and power flow balance constraints.

[0117] The expression for the system power balance constraint is:

[0118]

[0119] in, , These are the net injected active power and reactive power of the i-th node at time t, respectively. , These represent the original loads of the i-th node at time t; , These represent the active power and reactive power of the energy storage system at time t, respectively, for the k-th node.

[0120] To ensure the supply and demand balance of the distribution network during each dispatch period, the system power balance constraint must be met. That is, during any given period, the load demand of each node is supplied by the output of distributed power sources and the discharge of energy storage. At the same time, energy storage charging and adjustable load replenishment consume some electrical energy, while the reduction of adjustable load effectively reduces the system load demand, thereby achieving power balance under the synergistic effect of source and load.

[0121] The expression for the operating constraints of the energy storage unit is:

[0122]

[0123]

[0124] in, Let be the state of charge of the k-th energy storage unit at time t; This represents the state-of-charge limit of the k-th energy storage unit; This represents the upper limit of the state of charge of the k-th energy storage unit; , The charging and discharging efficiencies of the k-th energy storage unit are respectively ( ); This represents the upper limit of the charging power of the k-th energy storage unit; This represents the upper limit of the discharge power of the k-th energy storage unit; , This represents the charge / discharge state of the k-th energy storage unit.

[0125] The charging and discharging power of the energy storage unit is constrained by the upper limit of the rated power, and charging and discharging are not allowed to occur simultaneously within the same period. At the same time, the state of charge of the energy storage unit evolves dynamically with the charging and discharging process. Its change process must satisfy the energy conservation relationship and is subject to upper and lower limits to avoid overcharging or over-discharging. In addition, in order to ensure the sustainability of energy storage operation within the dispatch cycle, it is usually required that its initial state of charge and the final state of charge remain consistent.

[0126] The expression for the photovoltaic unit's operating constraints is:

[0127]

[0128]

[0129] in, Let be the maximum reactive power output of the k-th photovoltaic unit at time t; Let be the rated apparent capacity of the k-th photovoltaic unit inverter.

[0130] The actual active power output of a photovoltaic unit is limited by available sunlight conditions and must not exceed its maximum available output. At the same time, constrained by the rated capacity of the inverter, its active and reactive power outputs meet the apparent power capacity limit, thereby enabling the reactive power regulation capability to be dynamically adjusted with changes in active power output. In addition, photovoltaic units can release reactive power regulation margin by appropriately reducing active power output in order to participate in distribution network voltage regulation.

[0131] The expression for the adjustable load operation constraint is:

[0132]

[0133] in, This represents the upper limit of the output power per unit time for the kth adjustable load; The unit time power limit for the kth adjustable load.

[0134] The transfer power of adjustable loads cannot exceed their upper limit, and to ensure the supply and demand balance of the power grid, the system must ensure that the amount of electricity transferred out equals the amount of electricity transferred in within a cycle; that is, the transferred-out electricity must be restored through transfer-in in subsequent periods. These constraints ensure the feasibility of load regulation and energy balance, while avoiding excessive reduction or over-compensation in load regulation.

[0135] The expression for the power flow balance constraint is:

[0136]

[0137] Where m is the downstream node of the i-th node; n is the upstream node of the i-th node; , These represent the active and reactive power flowing from node i to node m, respectively. , These represent the active and reactive power flowing from node n to node i, respectively. Line current; , These are the line resistance and reactance, respectively.

[0138] The active and reactive power of each node i should be determined by the power flow of adjacent nodes and line losses, and must satisfy power flow balance constraints. Power flow balance constraints ensure that power transmission in the power grid conforms to physical laws and that voltage and power are rationally distributed among the nodes.

[0139] In one embodiment, after solving the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period, the method further includes:

[0140] Based on the optimal power output scheme, the operating parameters of each key regulating resource are adjusted to obtain the first voltage optimization control results of each node in the distribution network;

[0141] The first voltage optimization control result is verified. If the verification of the first voltage optimization control result fails, the candidate regulation resources are added to the key regulation resource set to obtain a new key regulation resource set. The candidate regulation resources are a preset number of regulation resources with the highest voltage regulation contribution among the regulation resources.

[0142] Based on the new set of key regulation resources, return to the steps of establishing a source-load collaborative optimization scheduling model with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network, until the first voltage optimization regulation result is verified.

[0143] Among them, the candidate regulation resources are selected from the remaining regulation resources that have not been selected as key regulation resources, sorted according to their contribution to voltage regulation.

[0144] The preset quantity can be set according to actual needs. For example, there can be one or more preset quantities.

[0145] In this embodiment of the application, the control command corresponding to the optimal power output scheme is constructed. According to the mapping relationship between the control resources and the distribution network nodes, various control commands are mapped to the corresponding nodes and control resources, generating control signals for specific control resources. These signals are then uniformly distributed to the corresponding control resources through the dispatch master station or energy management system. After the control signals are executed, the voltage and power of each node are collected to obtain the first voltage optimization control result.

[0146] The obtained first voltage optimization control result is subjected to closed-loop verification, and the set of key regulation resources participating in the control is dynamically corrected when the voltage regulation effect is insufficient. Specifically, when the verification of the first voltage optimization control result fails, it indicates that the voltage regulation effect is insufficient. The remaining unselected regulation resources are sorted according to their voltage regulation contribution from high to low, and the top-ranked candidate regulation resources are introduced into the set of key regulation resources one by one. Each time a candidate regulation resource is added, the source-load collaborative optimization scheduling model is re-executed until the verification of the first voltage optimization control result passes.

[0147] This application embodiment introduces voltage regulation effect constraints, realizing the transformation of voltage regulation from open-loop control to closed-loop control, enhancing the reliability and stability of regulation results, thereby significantly improving the precision and intelligence level of voltage regulation at the distribution network terminal.

[0148] In one embodiment, verifying the first voltage optimization control result includes:

[0149] Based on the results of the first voltage optimization and control, the first key voltage index is obtained;

[0150] Obtain the reference output scheme of each regulating resource in each scheduling period, adjust the operating parameters of each regulating resource according to the reference output scheme, and obtain the second voltage optimization control result of each node of the distribution network; obtain the second key voltage index according to the second voltage optimization control result.

[0151] The verification result of the first voltage optimization control result is obtained based on the first key voltage index, the second key voltage index, and the preset voltage index threshold.

[0152] The first key voltage indicator includes, but is not limited to, the minimum node voltage. Average voltage deviation Number of voltage over-limit nodes .

[0153] The reference output scheme refers to the approach of not screening the voltage contribution of each regulating resource, but establishing a source-load collaborative optimization scheduling model based on all regulating resources, with the optimization objective of minimizing the node voltage penalty of the distribution network and the operating cost of regulating resources. The source-load collaborative optimization scheduling model is then solved to obtain the optimal output scheme (i.e., the reference output scheme) of each regulating resource in each scheduling period.

[0154] The second key voltage indicator includes, but is not limited to, the reference minimum node voltage. Reference average voltage deviation Number of reference voltage over-limit nodes .

[0155] In this embodiment, the optimal power output scheme and the reference power output scheme under the current control method are comprehensively evaluated. If any criterion is not met, it is determined that the current control method has not achieved the expected voltage control effect, and the adjustable resource set needs to be expanded or the voltage control strategy needs to be modified. Specifically, the expression for the criterion is:

[0156]

[0157] in, , These are the preset first deviation threshold and second deviation threshold, which can be set according to actual needs.

[0158] In an optional embodiment, the closed-loop verification process is terminated when the voltage regulation effect reaches the preset requirements, all adjustable resources participate in the optimization scheduling, or the optimization result converges and no longer significantly improves.

[0159] To facilitate understanding of the above method embodiments, as follows: Figure 3 As shown, the distribution network voltage regulation method includes distribution network operation data acquisition and status perception, heterogeneous adjustable resource modeling, voltage regulation contribution calculation, resource screening and classification based on voltage regulation contribution, establishment and solution of source-load collaborative scheduling model, closed-loop verification of regulation effect. After the verification criteria are met, regulation commands are generated and executed according to the optimal output scheme. If the verification criteria are not met, key regulation resources are expanded and the source-load collaborative scheduling model is re-solved until the verification criteria are met.

[0160] To verify the effectiveness of the above-mentioned distribution network voltage regulation method, this embodiment uses... Figure 4 Taking a distribution network system with multiple types of heterogeneous regulating resources as an example, the voltage contribution of heterogeneous regulating resources and the voltage quality optimization of the distribution network are performed according to the distribution network voltage regulation method of this application. The parameter settings of the distribution network system are shown in Table 1 below:

[0161]

[0162] Table 1

[0163] To verify the voltage regulation effect of the distribution network voltage regulation method proposed in this application, four scenarios were set up for comparison. The specific scenario settings and verification dimensions are as follows:

[0164] Scenario 1: No control benchmark scenario. Without considering any control resources, the node voltage distribution is determined solely by the original operating state of the distribution network, serving as a comparison benchmark.

[0165] Scenario 2: Optimization and control scenario involving all resources. No voltage regulation contribution screening is performed; all photovoltaic units, energy storage units, and adjustable loads participate in optimized scheduling. This scenario characterizes the voltage regulation effect and resource utilization level of traditional full-scale control methods.

[0166] Scenario 3: Control scenario based on voltage regulation contribution. This scenario filters control resources based on their voltage regulation contribution, selecting only high-contribution resources for optimized scheduling. This is used to verify the impact of the proposed voltage regulation contribution-based resource selection mechanism on voltage control effectiveness and resource utilization efficiency.

[0167] Scenario 4: Control scenario based on voltage regulation contribution and closed-loop verification correction (the scenario corresponding to this application). Based on Scenario 3, a closed-loop verification mechanism for the control effect is introduced. When the voltage regulation effect is insufficient, some candidate regulation resources are dynamically supplemented, forming a closed-loop control process of "screening - optimization - verification - correction".

[0168] The voltage regulation effect is shown in Table 2 below. Figures 5-11 As shown, in the scenario without a control baseline, the system exhibits a significant undervoltage problem, with a minimum node voltage of 0.8803 pu. Furthermore, the number of nodes exceeding voltage limits and the voltage deviation are substantial throughout the day, indicating poor voltage quality under the initial operating conditions. Scenario 2, by adjusting the minimum voltage of the photovoltaic unit, energy storage unit, and adjustable load to 0.9380 pu, significantly reduces the number of low-voltage nodes, substantially improves the node voltage level, and effectively suppresses the system voltage deviation. Based on this, Scenario 3 introduces an optimization method based on voltage regulation contribution to screen regulation resources, retaining only high-contribution resources for regulation. The minimum node voltage is slightly lower than in Scenario 2, but the overall voltage deviation is still small and meets the voltage operation constraints. Moreover, the number of nodes exceeding voltage limits does not increase significantly, indicating that the proposed voltage regulation contribution optimization method can maintain a good voltage level while reducing resource usage. Further, Scenario 4 uses a closed-loop verification and correction mechanism for the regulation effect to dynamically compensate for periods of insufficient voltage regulation, resulting in a certain improvement in the system voltage level. This demonstrates that the proposed closed-loop correction mechanism can achieve optimal resource allocation while ensuring voltage regulation effectiveness.

[0169]

[0170] Table 2

[0171] The results of the heterogeneous resource contribution screening were verified, including: the voltage regulation contribution of each resource during the most unfavorable period and the comparison between the screening results and the actual regulation amounts. Figure 12 , Figure 13As shown in the diagram, in Scenario 2, all 10 adjustable resources participate in voltage regulation at different times, with some resources experiencing only minor adjustments, indicating a degree of redundant resource allocation during the full resource optimization process. Therefore, while the optimization method in Scenario 2 achieves good voltage regulation, the utilization rate of adjustable resources still needs improvement. Scenario 3, by introducing a voltage regulation contribution factor to filter resources, retains only adjustable resources with higher contributions for voltage regulation, reducing the average number of resources used to four. Figure 13 It is evident that the number of resources involved in regulation has significantly decreased, and energy storage and photovoltaic units at key nodes (such as end-of-line nodes) have undertaken the main voltage regulation tasks, demonstrating a clear characteristic of prioritizing efficient resources. Based on this, the closed-loop correction mechanism proposed in Scenario 4 appropriately introduces some suboptimal resources to participate in voltage regulation when the voltage regulation effect is insufficient, enabling the system to achieve the optimal combination of resources while ensuring voltage quality, thus avoiding performance degradation caused by over-selection. Furthermore, the distribution of resource regulation during the most unfavorable period shows that the proposed method can adaptively adjust the degree of resource participation, making the regulation behavior more concentrated and efficient, verifying the rationality of the contribution evaluation and selection mechanism.

[0172] Economic verification of the distribution network system includes: Figure 14 It can be seen that during peak load periods, all adjustable resources in Scenario 2 participate in voltage regulation, resulting in a net system regulation cost of 12,095 yuan and significant fluctuations in the overall cost curve. Combining the indicators in Table 2, while the full resource optimization method effectively improves voltage levels, its unreasonable resource allocation leads to relatively high system operating costs. In Scenario 3, the voltage regulation contribution screening method reduces the participation of low-contribution resources, significantly lowering the net system regulation cost to 5,571 yuan. Figure 14 As can be seen, the adjustment cost in all time periods is lower than in Scenario 2, with a more significant cost reduction during peak load periods. However, the voltage penalty cost increases to 48,216 yuan, indicating that the voltage regulation effect was not fully considered when reducing resource allocation, leading to an increase in total cost. Based on this, Scenario 4, through closed-loop verification and correction of the regulation effect, appropriately supplements some adjustable resources while ensuring the voltage regulation effect, resulting in a net system adjustment cost of 6,767 yuan. In this scenario, the overall adjustment cost is still lower than in Scenario 2, although it is higher than in Scenario 3, the fluctuation is more gradual, and the total system cost is reduced to 49,347 yuan, the lowest among all scenarios. This shows that the closed-loop correction mechanism can achieve a more reasonable balance between adjustment cost and voltage penalty cost, further optimizing the overall economic efficiency of the system.

[0173] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0174] Based on the same inventive concept, this application also provides a distribution network voltage regulation device for implementing the above-mentioned distribution network voltage regulation method. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more embodiments of the distribution network voltage regulation device provided below can be found in the limitations of the distribution network voltage regulation method described above, and will not be repeated here.

[0175] In one exemplary embodiment, please refer to Figure 15 A voltage regulation device for a power distribution network is provided, the device comprising:

[0176] The operation data acquisition module 1510 is used to acquire the operation data of each node in the distribution network, and to construct the power flow model and voltage distribution state matrix of the distribution network based on the operation data.

[0177] The voltage regulation contribution acquisition module 1520 is used to obtain the voltage regulation contribution of each regulation resource connected to the distribution network to each node of the distribution network based on the distribution network power flow model and the distribution network voltage distribution state matrix.

[0178] The key regulation resource screening module 1530 is used to screen a set of key regulation resources from various regulation resources based on the voltage regulation contribution and a preset contribution threshold; the set of key regulation resources includes multiple key regulation resources.

[0179] The model building module 1540 is used to establish a source-load collaborative optimization scheduling model based on the set of key regulation resources, with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network; the node voltage penalty is used to characterize the node voltage deviation and the degree of exceeding the limit of each node in the distribution network; the regulation resource operating cost is used to characterize the sum of the operating costs of each key regulation resource;

[0180] The optimal output scheme acquisition module 1550 is used to solve the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node in the distribution network.

[0181] In one embodiment, based on the distribution network power flow model and the distribution network voltage distribution state matrix, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained, including:

[0182] The power flow model of the distribution network is linearized to obtain the response relationship between the node voltage of each node in the distribution network and the power changes of each regulating resource.

[0183] Based on the voltage distribution state matrix and response relationship of the distribution network, the contribution of each regulating resource to the voltage regulation of each node in the distribution network is obtained.

[0184] In one embodiment, before establishing a source-load collaborative optimization scheduling model based on a set of key regulation resources, with the optimization objective of minimizing node voltage penalties and regulation resource operating costs in the distribution network, the method further includes:

[0185] Obtain the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period;

[0186] The node voltage penalty is obtained based on the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period.

[0187] In one embodiment, key regulation resources include photovoltaic units, energy storage units, and adjustable loads. Before establishing a source-load collaborative optimization scheduling model based on the set of key regulation resources, with the optimization objective of minimizing the node voltage penalty of the distribution network and the operating cost of regulation resources, the method further includes:

[0188] The operating cost of each photovoltaic unit is obtained based on the actual active power output and reactive power regulation of each photovoltaic unit during each scheduling period.

[0189] The operating cost of each energy storage unit is obtained based on its charging power, discharging power, and reactive power regulation during each scheduling period.

[0190] The operating cost of adjustable loads is obtained based on the outflow and inflow of each adjustable load during each scheduling period;

[0191] The operating cost of the adjustable resource is obtained by summing the operating costs of the photovoltaic unit, the energy storage unit, and the adjustable load.

[0192] In one embodiment, the source-load collaborative optimization scheduling model also includes constraints, including system power balance constraints, energy storage unit operation constraints, photovoltaic unit operation constraints, adjustable load operation constraints, and power flow balance constraints.

[0193] In one embodiment, after solving the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period, the method further includes:

[0194] Based on the optimal power output scheme, the operating parameters of each key regulating resource are adjusted to obtain the first voltage optimization control results of each node in the distribution network;

[0195] The first voltage optimization control result is verified. If the verification of the first voltage optimization control result fails, the candidate regulation resources are added to the key regulation resource set to obtain a new key regulation resource set. The candidate regulation resources are a preset number of regulation resources with the highest voltage regulation contribution among the regulation resources.

[0196] Based on the new set of key regulation resources, return to the steps of establishing a source-load collaborative optimization scheduling model with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network, until the first voltage optimization regulation result is verified.

[0197] In one embodiment, the verification of the first voltage optimization control result includes:

[0198] Based on the results of the first voltage optimization and control, the first key voltage index is obtained;

[0199] Obtain the reference output scheme of each regulating resource in each scheduling period, adjust the operating parameters of each regulating resource according to the reference output scheme, and obtain the voltage optimization control results of each node of the distribution network; obtain the second key voltage index according to the second voltage optimization control results.

[0200] The verification result of the first voltage optimization control result is obtained based on the first key voltage index, the second key voltage index, and the preset voltage index threshold.

[0201] Each module in the aforementioned power distribution network voltage regulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0202] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 16As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power distribution network voltage regulation method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0203] Those skilled in the art will understand that Figure 16 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the aforementioned power distribution network voltage regulation method. The steps of the power distribution network voltage regulation method described here may be steps from one of the power distribution network voltage regulation methods in the various embodiments described above.

[0204] In one embodiment, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, causes the processor to perform the steps of the aforementioned power distribution network voltage regulation method. The steps of this power distribution network voltage regulation method may be those steps from one of the power distribution network voltage regulation methods described in the various embodiments above.

[0205] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, causes the processor to perform the steps of the aforementioned power distribution network voltage regulation method. The steps of this power distribution network voltage regulation method may be those steps from one of the power distribution network voltage regulation methods described in the various embodiments above.

[0206] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0207] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0208] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0209] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for voltage regulation in a power distribution network, characterized in that, The method includes: Obtain the operating data of each node in the distribution network, and construct the power flow model and voltage distribution state matrix of the distribution network based on the operating data; Based on the power flow model of the distribution network and the voltage distribution state matrix of the distribution network, the contribution of each regulating resource connected to the distribution network to the voltage regulation of each node of the distribution network is obtained. Based on the voltage regulation contribution and a preset contribution threshold, a set of key regulation resources is selected from the various regulation resources; the set of key regulation resources includes multiple key regulation resources. Based on the set of key regulating resources, a source-load collaborative optimization scheduling model is established with the optimization objective of minimizing the node voltage penalty and the operating cost of regulating resources in the distribution network. The node voltage penalty is used to characterize the node voltage deviation and the degree of exceeding the limit at each node of the distribution network. The operating cost of regulating resources is used to characterize the sum of the operating costs of all the key regulating resources. The source-load collaborative optimization scheduling model is solved to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node of the distribution network.

2. The method according to claim 1, characterized in that, The step of obtaining the voltage regulation contribution of each regulating resource to each node of the distribution network based on the distribution network power flow model and the distribution network voltage distribution state matrix includes: The power flow model of the distribution network is linearized to obtain the response relationship between the node voltage of each node in the distribution network and the power changes of each regulating resource. Based on the voltage distribution state matrix of the distribution network and the response relationship, the contribution of each regulating resource to the voltage regulation of each node of the distribution network is obtained.

3. The method according to claim 1, characterized in that, Before establishing a source-load collaborative optimization scheduling model based on the key regulation resource set, with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network, the method further includes: Obtain the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period; The node voltage penalty is obtained based on the squared voltage value, undervoltage relaxation variable, overvoltage relaxation variable, and additional undervoltage soft constraint relaxation variable of each node in the distribution network during each scheduling period.

4. The method according to claim 1, characterized in that, The key regulating resources include photovoltaic units, energy storage units, and adjustable loads. Before establishing a source-load collaborative optimization scheduling model based on the set of key regulating resources, with the optimization objective of minimizing the node voltage penalty of the distribution network and the operating cost of the regulating resources, the method further includes: The operating cost of the photovoltaic unit is obtained based on the actual active power output and reactive power regulation of each photovoltaic unit during each scheduling period. The operating cost of the energy storage unit is obtained based on the charging power, discharging power and reactive power regulation of each energy storage unit during each scheduling period. The operating cost of the adjustable load is obtained based on the outflow and inflow of each adjustable load during each scheduling period. The operating cost of the adjustable resource is obtained by summing the operating costs of the photovoltaic unit, the energy storage unit, and the adjustable load.

5. The method according to claim 1, characterized in that, The source-load collaborative optimization scheduling model also includes constraints, which include system power balance constraints, energy storage unit operation constraints, photovoltaic unit operation constraints, adjustable load operation constraints, and power flow balance constraints.

6. The method according to claim 1, characterized in that, After solving the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period, the method further includes: Based on the optimal power output scheme, the operating parameters of each of the key regulating resources are adjusted to obtain the first voltage optimization control results of each node in the distribution network; The first voltage optimization control result is verified. If the verification of the first voltage optimization control result fails, candidate regulation resources are added to the key regulation resource set to obtain a new key regulation resource set. The candidate regulation resources are a preset number of regulation resources that rank first in terms of voltage regulation contribution among the regulation resources. Based on the new set of key regulation resources, the steps for establishing a source-load collaborative optimization scheduling model with the optimization objective of minimizing the node voltage penalty and regulation resource operating cost of the distribution network are returned until the first voltage optimization regulation result is verified.

7. The method according to claim 6, characterized in that, The verification of the first voltage optimization control result includes: Based on the first voltage optimization and control result, the first key voltage index is obtained; Obtain the reference output scheme of each regulating resource in each scheduling period; adjust the operating parameters of each regulating resource according to the reference output scheme to obtain the second voltage optimization control result of each node of the distribution network; obtain the second key voltage index according to the second voltage optimization control result. The verification result of the first voltage optimization control result is obtained based on the first key voltage index, the second key voltage index, and the preset voltage index threshold.

8. A voltage regulation device for a power distribution network, characterized in that, The device includes: The operation data acquisition module is used to acquire the operation data of each node in the distribution network, and to construct the power flow model and voltage distribution state matrix of the distribution network based on the operation data. The voltage regulation contribution acquisition module is used to obtain the voltage regulation contribution of each regulation resource connected to the distribution network to each node of the distribution network based on the distribution network power flow model and the distribution network voltage distribution state matrix. A key regulation resource screening module is used to screen a set of key regulation resources from the various regulation resources based on the voltage regulation contribution and a preset contribution threshold; the set of key regulation resources includes multiple key regulation resources. The model building module is used to establish a source-load collaborative optimization scheduling model based on the set of key regulating resources, with the optimization objective of minimizing the node voltage penalty and the operating cost of regulating resources in the distribution network; the node voltage penalty is used to characterize the node voltage deviation and the degree of exceeding the limit of each node in the distribution network; the operating cost of regulating resources is used to characterize the sum of the operating costs of all the key regulating resources. The optimal output scheme acquisition module is used to solve the source-load collaborative optimization scheduling model to obtain the optimal output scheme of each key regulation resource in each scheduling period; the optimal output scheme is used to regulate the voltage of each node of the distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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