Power distribution network voltage fluctuation cooperative governance method and device, computer equipment, medium and product

Abstract

This application relates to a method, device, computer equipment, medium, and product for collaborative management of voltage fluctuations in distribution networks. The method includes: obtaining the available reactive power adjustment margin of distributed photovoltaic (PV) nodes under current operating conditions, and then obtaining the reactive power margin deficit to determine candidate configuration nodes and compensation capacity ranges for static var compensators (SVCs); constructing a multi-objective optimization function; selecting reactive power compensation nodes from the candidate configuration nodes; obtaining the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range; using distributed PV nodes, reactive power compensation nodes, PV active power capacity, PV reactive power capacity, and reactive power compensation capacity as initial configuration parameters; solving the multi-objective optimization function in multiple rounds to obtain target configuration parameters; updating the current distribution network configuration according to the target configuration parameters; and collaboratively managing voltage fluctuations in the distribution network based on distributed PV nodes and reactive power compensation nodes. This method can improve the accuracy of management.

Description

Technical Field

[0001] This application relates to the field of power distribution network configuration technology, and in particular to a method, device, computer equipment, medium and product for collaborative management of voltage fluctuations in power distribution networks. Background Technology

[0002] With the large-scale integration of distributed photovoltaic (PV) power into distribution networks, traditional single-source distribution networks are gradually evolving into multi-source distribution networks. Changes in system power flow and voltage distribution characteristics can easily lead to node voltage fluctuations and increase line active power losses, thereby affecting the safe and stable operation of the distribution network. Therefore, the key to managing voltage fluctuations in distribution networks under distributed PV integration conditions lies in the rational optimization of the integration nodes and capacity of distributed PV systems to improve voltage quality.

[0003] In traditional methods, static var compensators (SVCs) are usually included in the optimization model as independent compensation units. However, current methods often use fixed or subjective weighting when determining the weights of the objective function, which makes it difficult to dynamically adjust according to changes in the reactive power support requirements of nodes and the operating status of the system. They also lack a linkage mechanism with the available reactive power regulation capabilities of distributed photovoltaic nodes, resulting in low accuracy in managing voltage fluctuations in the distribution network. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, medium, and product for collaborative management of voltage fluctuations in distribution networks that can improve the accuracy of management, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a collaborative management method for voltage fluctuations in distribution networks, including:

[0006] Obtain the available reactive power adjustment margin of the distributed photovoltaic nodes under the current operating state, and obtain the reactive power margin deficit based on the available reactive power adjustment margin;

[0007] Based on the reactive power margin deficit, determine the candidate configuration nodes and compensation capacity range of the static var compensator;

[0008] A multi-objective optimization function is constructed, which includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function. Among them, the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by combining weights.

[0009] Obtain reactive power compensation nodes from the candidate configuration nodes, and obtain the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range;

[0010] Using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity as initial configuration parameters, the multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain the target configuration parameters;

[0011] The current distribution network configuration is updated according to the target configuration parameters, and the voltage fluctuation of the distribution network is managed collaboratively based on distributed photovoltaic nodes and reactive power compensation nodes.

[0012] In one embodiment, the step of obtaining the reactive power margin deficit based on the available reactive power adjustment margin includes:

[0013] Obtain the target voltage of the distributed photovoltaic node, and obtain the voltage deviation between the current voltage of the distributed photovoltaic node and the target voltage;

[0014] The reactive power conversion coefficient is obtained based on the current grid parameters of the distribution network, and the voltage deviation is converted into the required reactive power compensation amount based on the reactive power conversion coefficient.

[0015] The total available reactive power regulation margin of the distributed photovoltaic nodes is determined based on the number of photovoltaic nodes connected to the grid.

[0016] Based on the required reactive power compensation and the total available reactive power adjustment margin, obtain the reactive power margin deficit.

[0017] In one embodiment, the process of determining the combined weights includes:

[0018] Based on the reactive power margin deficit and the required reactive power compensation, obtain the reactive power deficit severity coefficient;

[0019] For each objective function among the network loss objective function, voltage fluctuation objective function, and governance cost objective function, the demand correction coefficient of the objective function is obtained according to the reactive power deficit severity coefficient, and the function dispersion is obtained according to the current function value of the objective function;

[0020] The combined weights are obtained based on the demand correction coefficient and the function dispersion.

[0021] In one embodiment, the step of determining the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit includes:

[0022] Distributed photovoltaic nodes with reactive power margin deficits greater than a preset threshold are selected as candidate configuration nodes for static var compensators.

[0023] For each candidate configuration node, the compensation capacity range is determined based on the reactive power margin deficit and the reactive power compensation threshold; wherein, the upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit and the reactive power compensation threshold corresponding to the candidate configuration node, and the lower limit of the compensation capacity range is 0.

[0024] In one embodiment, the step of solving the multi-objective optimization function in multiple rounds based on initial configuration parameters to obtain the target configuration parameters includes:

[0025] In the process of solving the multi-objective optimization function based on the initial configuration parameters in the current round, the continuous variables in the initial configuration parameters are updated by differential evolution, and the discrete node variables in the initial configuration parameters are updated by mapping.

[0026] Candidate configuration parameters are obtained based on the updated continuous variables, and feasible domain repair is performed on the candidate configuration parameters.

[0027] Obtain the multi-objective optimization function value of the repaired candidate configuration parameters, and determine the initial configuration parameters for the next round of solving from the initial configuration parameters and candidate configuration parameters based on the multi-objective optimization function value.

[0028] In one embodiment, the step of performing feasible domain repair on candidate configuration parameters includes:

[0029] The value range of each configuration parameter is obtained based on the distribution network parameters;

[0030] The parameters to be repaired that do not meet the parameter configuration constraints among the candidate configuration parameters are obtained, and the parameters to be repaired are corrected based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints.

[0031] Secondly, this application also provides a distribution network voltage fluctuation collaborative management device, comprising:

[0032] The deficit acquisition module is used to obtain the available reactive power adjustment margin of the distributed photovoltaic nodes under the current operating state, and to obtain the reactive power deficit based on the available reactive power adjustment margin.

[0033] The compensation acquisition module is used to determine the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit.

[0034] The function construction module is used to construct multi-objective optimization functions. These functions include network loss objective functions, voltage fluctuation objective functions, governance cost objective functions, and voltage penalty functions. The network loss objective functions, voltage fluctuation objective functions, and governance cost objective functions are weighted by combining weights.

[0035] The capacity acquisition module is used to acquire reactive power compensation nodes from the candidate configuration nodes and acquire the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range.

[0036] The function solving module is used to solve the multi-objective optimization function in multiple rounds based on the initial configuration parameters, including distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity, to obtain the target configuration parameters.

[0037] The collaborative governance module is used to update the current distribution network configuration according to the target configuration parameters and to collaboratively manage the voltage fluctuations of the distribution network based on distributed photovoltaic nodes and reactive power compensation nodes.

[0038] 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 any one of the first aspects.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps of any one of the first aspects.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps of any one of the first aspects.

[0041] The aforementioned method, device, computer equipment, medium, and product for collaborative management of distribution network voltage fluctuations obtains the available reactive power adjustment margin of distributed photovoltaic nodes under the current operating state, and obtains the reactive power margin deficit based on the available reactive power adjustment margin. Based on the reactive power margin deficit, candidate configuration nodes and compensation capacity ranges of static var compensators (SVCs) are determined, a multi-objective optimization function is constructed, reactive power compensation nodes are obtained from the candidate configuration nodes, and the reactive power compensation capacity of the reactive power compensation nodes is obtained based on the compensation capacity range. Using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity as initial configuration parameters, the multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain target configuration parameters. The current distribution network configuration is updated according to the target configuration parameters. Based on distributed photovoltaic nodes and reactive power compensation nodes, collaborative management of distribution network voltage fluctuations is achieved. This enables on-demand collaborative configuration of distributed photovoltaics and SVCs, improves the accuracy of distribution network voltage fluctuation management, and thus improves the voltage stability level of the distribution network. Attached Figure Description

[0042] 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.

[0043] Figure 1 This is an application environment diagram of the collaborative management method for voltage fluctuations in a distribution network in one embodiment;

[0044] Figure 2 This is a flowchart illustrating a collaborative management method for voltage fluctuations in a distribution network in one embodiment.

[0045] Figure 3 This is a flowchart illustrating the collaborative management method for voltage fluctuations in a distribution network in another embodiment;

[0046] Figure 4 This is a structural block diagram of a distribution network voltage fluctuation collaborative management device in one embodiment;

[0047] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] 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.

[0049] With the large-scale integration of distributed photovoltaic (PV) power into distribution networks, traditional single-source distribution networks are gradually evolving into multi-source distribution networks. Changes in system power flow and voltage distribution characteristics can easily lead to node voltage fluctuations and increased line active power losses, thereby affecting the safe and stable operation of the distribution network. Therefore, the key to mitigating voltage fluctuations in distribution networks under distributed PV integration conditions lies in the rational optimization of the integration nodes and capacity of distributed PV to improve voltage quality. Existing research mainly focuses on optimizing the configuration of distributed PV integration locations and capacities to improve distribution network voltage quality while also considering system operational economics.

[0050] With the increasing demand for voltage fluctuation mitigation in distribution networks, related research has further incorporated reactive power compensation devices into optimization models to enhance the voltage regulation capability and operational economy of distribution networks. However, existing joint optimization methods for distributed photovoltaic (PV) systems and reactive power compensation devices typically treat Static Var Compensators (SVCs) as independent compensation units and include them in the optimization model in parallel. This lacks a linkage mechanism between SVCs and the available reactive power regulation capabilities of distributed PV nodes, making it difficult to reflect the on-demand synergistic relationship between the two. Furthermore, existing methods often employ fixed or subjective weighting methods when determining the objective function weights, making it difficult to dynamically adjust them based on changes in node reactive power support requirements and system operating conditions. Therefore, it is necessary to propose a distribution network voltage fluctuation mitigation method that can determine the configuration requirements of SVCs based on the available reactive power regulation capabilities of distributed PV nodes and rationally determine the objective function weights in conjunction with the system operating conditions.

[0051] Based on this, this application provides a method for managing voltage fluctuations in distribution networks based on the coordinated configuration of distributed photovoltaic (PV) and SVC (Supply-Voltage Controller). By comprehensively considering the coordinated optimization configuration of distributed PV and SVC, the reactive power support demand and reactive power margin deficit index of each PV node are constructed based on the available reactive power adjustment margin of the PV nodes under the current operating state. The configuration nodes and compensation capacity of the SVC are then determined based on the reactive power margin deficit. Simultaneously, the weights of network loss, voltage fluctuation, and management cost in the objective function are dynamically determined by combining the node reactive power deficit and the dispersion of operating indicators. A differential evolution optimization method oriented towards discrete-continuous hybrid decision variables is used to solve for the distributed PV access nodes, access capacity, and SVC compensation capacity, achieving on-demand coordinated configuration of distributed PV and SVC and improving the accuracy of distribution network voltage fluctuation management.

[0052] The distribution network voltage fluctuation collaborative management 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 placed on a cloud or other network server. Terminal 102 is used to obtain the available reactive power adjustment margin of distributed photovoltaic nodes in their current operating state, and to obtain the reactive power margin deficit based on the available reactive power adjustment margin. Based on the reactive power margin deficit, it determines the candidate configuration nodes and compensation capacity range of static var compensators (SVCs), constructs a multi-objective optimization function, obtains reactive power compensation nodes from the candidate configuration nodes, and obtains the reactive power compensation capacity of the reactive power compensation nodes based on the compensation capacity range. Using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity as initial configuration parameters, the multi-objective optimization function is solved multiple times based on the initial configuration parameters to obtain the target configuration parameters. The current distribution network configuration is updated according to the target configuration parameters, and the voltage fluctuations of the distribution network are collaboratively managed based on distributed photovoltaic nodes and reactive power compensation nodes. 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.

[0053] In one exemplary embodiment, such as Figure 2 As shown, a collaborative management method for voltage fluctuations in distribution networks is provided, which is then applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0054] S202: Obtain the available reactive power adjustment margin of the distributed photovoltaic node under the current operating state, and obtain the reactive power margin deficit based on the available reactive power adjustment margin.

[0055] Optionally, a distributed photovoltaic (PV) node refers to a node connected to a distributed PV power source in the distribution network. The current operating status refers to the actual operating conditions such as current sunlight, load, voltage, and power flow. Available reactive power adjustment margin refers to the reactive power that the PV inverter can still generate or absorb under its current active power output. Reactive power margin deficit refers to the reactive power support required to achieve the node voltage target, minus the reactive power provided by the PV itself, resulting in insufficient reactive power. Under the current operating status, by calculating the upper limit of reactive power that each PV node can currently adjust, and based on the voltage target, calculating how much reactive power the node needs, if the PV itself is insufficient, a reactive power margin deficit will occur. In this case, SVC (Supply-Controlled PV) needs to supplement it, thereby accurately determining which nodes need compensation and avoiding blind configuration.

[0056] S204: Determine the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit.

[0057] Optionally, a Static Var Compensator (SVC) is a power electronic device used for rapid reactive power regulation and voltage stabilization. Candidate configuration nodes refer to the set of nodes suitable for SVC installation selected based on reactive power deficit. The compensation capacity range refers to the reactive power compensation interval required to meet voltage regulation needs. For nodes with reactive power margin deficits, SVCs are needed to supplement them; therefore, these nodes are considered as candidate configuration nodes for SVC installation locations. Simultaneously, the upper and lower limits of the SVC capacity are limited according to the size of the deficit to achieve on-demand configuration.

[0058] S206: Construct a multi-objective optimization function; the multi-objective optimization function includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function; among which, the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by combining weights.

[0059] Optionally, to balance the effectiveness of voltage fluctuation mitigation and compensation costs in the distribution network, a multi-objective optimization function is constructed, including network loss objectives, voltage fluctuation objectives, and mitigation cost objectives, combined with a voltage exceedance penalty term to form the overall objective function. Specifically, the network loss objective function minimizes the total active power loss of the distribution network lines, the voltage fluctuation objective function minimizes the deviation of node voltage from its rated value, and the mitigation cost objective function minimizes the active power configuration cost of distributed photovoltaic (PV) systems, the reactive power regulation cost of PV systems, the installation cost of SVC systems, and the operating cost of SVC compensation. The voltage penalty function limits node voltage, adding a penalty term when voltage exceeds the limit to force the optimization result to meet voltage safety constraints. The network loss objective function, voltage fluctuation objective function, and mitigation cost objective function are weighted by a combination of weights, which can adaptively adjust according to changes in node reactive power support requirements and operating status.

[0060] S208: Obtain reactive power compensation nodes from the candidate configuration nodes, and obtain the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range.

[0061] Optionally, during initialization, distributed photovoltaic access nodes are randomly generated from all accessible nodes, SVC nodes are randomly generated from candidate configuration nodes, and the active and reactive power capacities of distributed photovoltaics and SVC capacities are randomly generated within their respective allowable ranges.

[0062] S210: Using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity as initial configuration parameters, the multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain the target configuration parameters.

[0063] Optionally, a discrete-continuous hybrid differential evolution algorithm is used for collaborative optimization. Considering the characteristics of both discrete node location variables and continuous equipment capacity variables in the collaborative configuration of distributed photovoltaic and SVC, differential evolution mutation and crossover operations are used for continuous variables, while discrete node variables are processed by combining candidate node mapping and feasible region repair mechanisms. During the iteration process, candidate SVC configuration nodes are screened according to the reactive power margin deficit until the maximum number of iterations or the objective function convergence condition is reached, and the optimal target configuration parameters are output.

[0064] S212: Update the current distribution network configuration according to the target configuration parameters, and coordinate the management of distribution network voltage fluctuations based on distributed photovoltaic nodes and reactive power compensation nodes.

[0065] Optionally, the current distribution network configuration can be updated according to the target configuration parameters, which include the access nodes, active power capacity, reactive power capacity of distributed photovoltaics, and the configuration nodes and compensation capacity of SVCs, so as to realize the coordinated optimization configuration of distributed photovoltaics and SVCs, improve the voltage distribution of distribution network nodes, and reduce system voltage fluctuations.

[0066] In the aforementioned collaborative governance method for distribution network voltage fluctuations, the available reactive power adjustment margin of distributed photovoltaic (PV) nodes under the current operating state is obtained, and the reactive power margin deficit is obtained based on the available reactive power adjustment margin. Based on the reactive power margin deficit, candidate configuration nodes and compensation capacity ranges for static var compensators (SVCs) are determined, a multi-objective optimization function is constructed, reactive power compensation nodes are obtained from the candidate configuration nodes, and the reactive power compensation capacity of the reactive power compensation nodes is obtained based on the compensation capacity range. Distributed PV nodes, reactive power compensation nodes, PV active power capacity, PV reactive power capacity, and reactive power compensation capacity are used as initial configuration parameters. The multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain target configuration parameters. The current distribution network configuration is updated according to the target configuration parameters. Based on the distributed PV nodes and reactive power compensation nodes, the distribution network voltage fluctuations are collaboratively governed. This enables on-demand collaborative configuration of distributed PV and SVCs, improves the accuracy of distribution network voltage fluctuation governance, and thus improves the voltage stability level of the distribution network.

[0067] In an exemplary embodiment, the step of obtaining the reactive power margin deficit based on the available reactive power adjustment margin includes: obtaining the target voltage of the distributed photovoltaic (PV) node, obtaining the voltage deviation between the current voltage of the distributed PV node and the target voltage; obtaining the reactive power conversion coefficient based on the current grid parameters of the distribution network, and converting the voltage deviation into the required reactive power compensation amount based on the reactive power conversion coefficient; determining the total available reactive power adjustment margin of the distributed PV node based on the number of PV nodes connected to the distributed PV node; and obtaining the reactive power margin deficit based on the required reactive power compensation amount and the total available reactive power adjustment margin.

[0068] Optionally, in the process of coordinated optimization configuration of distributed photovoltaic (PV) and SVC, the available reactive power adjustment margin of the distributed PV nodes under the current operating state is first calculated. For the access nodes... The reactive power regulation margin of the m-th distributed photovoltaic system at location m is defined as follows:

[0069]

[0070] in, Let m be the rated capacity of the m-th distributed photovoltaic device; The active power capacity of distributed photovoltaic (PV) equipment; This refers to reactive power capacity.

[0071] If multiple distributed photovoltaic systems are connected to node i, then the total available reactive power regulation margin of that node is:

[0072]

[0073] Furthermore, the required reactive power compensation for node i is calculated based on the node voltage regulation requirements. Assume the target voltage for node i is... Then the reactive power compensation required by node i can be expressed as:

[0074]

[0075] in, The conversion factor between node voltage deviation and reactive power compensation can be determined based on the current power flow status, branch parameters, or node voltage response relationship. Let be the voltage amplitude at node i.

[0076] Therefore, the reactive power margin deficit at the node is:

[0077]

[0078] In this embodiment, by obtaining the target voltage of the distributed photovoltaic node, obtaining the voltage deviation between the current voltage of the distributed photovoltaic node and the target voltage, obtaining the reactive power conversion coefficient based on the current grid parameters of the distribution network, converting the voltage deviation into the required reactive power compensation amount based on the reactive power conversion coefficient, determining the total available reactive power adjustment margin of the distributed photovoltaic node based on the number of photovoltaic nodes connected, and obtaining the reactive power margin deficit based on the required reactive power compensation amount and the total available reactive power adjustment margin, the static var compensator can be accurately configured, avoiding resource waste.

[0079] In an exemplary embodiment, the process of determining the combined weights includes: obtaining a reactive power deficit severity coefficient based on the reactive power margin deficit and the required reactive power compensation amount; for each objective function among the network loss objective function, voltage fluctuation objective function, and governance cost objective function, obtaining a demand correction coefficient for the objective function based on the reactive power deficit severity coefficient, and obtaining the function dispersion based on the current function value of the objective function; and obtaining the combined weights based on the demand correction coefficient and the function dispersion.

[0080] Optionally, to balance the effectiveness of voltage fluctuation mitigation and compensation costs in the distribution network, a multi-objective optimization function is constructed, including network loss target, voltage fluctuation target, and mitigation cost target.

[0081] Among them, active power loss in the distribution network system Represented as:

[0082]

[0083] Where L is the total number of branches, For the first Branch resistance, For the first Branch current.

[0084] Normalized network loss objective function for:

[0085]

[0086] in, This represents the initial active power loss without distributed photovoltaic and SVC configuration.

[0087] Furthermore, for the voltage fluctuation objective function, a reactive power margin deficit contribution rate is introduced based on the node sensitivity to construct the node comprehensive control weights. Let the voltage sensitivity factor of node i be... Then its normalization result is:

[0088]

[0089] Here, j represents a distinct node.

[0090] Let the reactive power deficit contribution rate of node i be:

[0091]

[0092] in, , This refers to the reactive power margin deficit at the node. To prevent extremely small positive numbers with a denominator of zero.

[0093] The node comprehensive regulation weight is defined as follows:

[0094]

[0095] in, This is the adjustment coefficient.

[0096] The objective function for voltage fluctuation is then expressed as:

[0097]

[0098] in, The voltage amplitude at node i; The target voltage for node i; This is the initial voltage fluctuation index.

[0099] Furthermore, the governance cost includes the active power configuration cost of distributed photovoltaic (PV) systems, the reactive power regulation cost of distributed PV systems, the installation cost of SVC systems, and the operating cost of SVC compensation. Therefore, the normalized governance cost objective function is expressed as:

[0100]

[0101] in, , , , These are the active power configuration cost coefficient, the reactive power regulation cost coefficient, the SVC installation cost coefficient, and the SVC compensation operation cost coefficient, respectively. The active power capacity of distributed photovoltaic (PV) equipment; Reactive power capacity; The actual number of SVCs configured; This is the normalized benchmark cost.

[0102] Furthermore, to avoid the inadequacy of fixed-weight or subjective weighting methods in adapting to different operating states, a dynamic combination weighting method based on the severity of reactive power deficit and the dispersion of the indicators is adopted. The reactive power deficit severity coefficient is expressed as:

[0103]

[0104] in, This refers to the reactive power margin deficit at the node. This is the amount of reactive power compensation required. To prevent extremely small positive numbers with a denominator of zero.

[0105] The requirement correction coefficients for each objective function are expressed as follows:

[0106]

[0107] in, , , These correspond to the network loss objective function, voltage fluctuation objective function, and governance cost objective function, respectively. When the reactive power deficit of the distribution network system is large, the weight of the voltage fluctuation objective automatically increases; when the reactive power deficit of the distribution network system is small, the weights of the network loss and cost objectives relatively increase.

[0108] Furthermore, in each iteration of the differential evolution algorithm, the function dispersion of the three objective function values ​​in the current feasible individual population is statistically analyzed:

[0109]

[0110] in, For the normalized objective function The standard deviation in the current set of feasible individuals.

[0111] The final combined weights, obtained based on the demand correction coefficient and the function dispersion, are expressed as follows:

[0112]

[0113] In this embodiment, by obtaining the reactive power deficit severity coefficient based on the reactive power margin deficit and the required reactive power compensation amount, and for each objective function among the network loss objective function, voltage fluctuation objective function, and governance cost objective function, the demand correction coefficient of the objective function is obtained based on the reactive power deficit severity coefficient, and the function dispersion is obtained based on the current function value of the objective function. Based on the demand correction coefficient and the function dispersion, the combined weight is obtained, which can accurately determine the weight of different objective functions, improve the matching degree between the multi-objective optimization function and the voltage governance requirements, and thus improve the accuracy of the target configuration parameters.

[0114] In an exemplary embodiment, the step of determining candidate configuration nodes and compensation capacity range of a static var compensator (SVC) based on reactive power margin deficit includes: designating distributed photovoltaic nodes with reactive power margin deficits greater than a preset threshold as candidate configuration nodes for the SVC; and for each candidate configuration node, determining a compensation capacity range based on the reactive power margin deficit and the reactive power compensation threshold; wherein the upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit and the reactive power compensation threshold corresponding to the candidate configuration node, and the lower limit of the compensation capacity range is 0.

[0115] Optionally, when reactive power margin deficit When >0, it indicates that the existing reactive power regulation capability of distributed photovoltaic power at node i is insufficient to meet the node voltage control requirements, and SVC needs to be introduced for compensation; when When the value is 0, it indicates that the distributed photovoltaic system at this node can already meet the voltage regulation requirements, and there is no need to configure an SVC. Based on this, the set of candidate SVC configuration nodes can be represented as follows:

[0116]

[0117] in, This is the preset threshold for reactive power margin deficit, which is usually 0.

[0118] Furthermore, for candidate configuration node i, its SVC compensation capacity range is expressed as:

[0119]

[0120] in, For SVC compensation capacity; This represents the maximum SVC compensation capacity.

[0121] In this embodiment, distributed photovoltaic nodes with reactive power margin deficits greater than a preset threshold are selected as candidate configuration nodes for static var compensators (SVCs). For each candidate configuration node, the compensation capacity range is determined based on the reactive power margin deficit and the reactive power compensation threshold. This enables on-demand configuration of SVCs and improves the accuracy of power distribution network fluctuation management.

[0122] In an exemplary embodiment, the step of solving a multi-objective optimization function in multiple rounds based on initial configuration parameters to obtain target configuration parameters includes: during the current round of solving the multi-objective optimization function based on initial configuration parameters, performing differential evolution updates on continuous variables in the initial configuration parameters and mapping updates on discrete node variables in the initial configuration parameters; obtaining candidate configuration parameters based on the updated continuous variables and performing feasible region repair on the candidate configuration parameters; obtaining the multi-objective optimization function values ​​of the repaired candidate configuration parameters, and determining the initial configuration parameters for the next round of solving from the initial configuration parameters and candidate configuration parameters based on the multi-objective optimization function values.

[0123] Optionally, to ensure that the node voltage meets the constraints, a voltage over-limit penalty function is introduced, expressed as:

[0124]

[0125] in, , This is the penalty coefficient; The voltage amplitude at node i; and These represent the maximum and minimum node voltages, respectively.

[0126] Furthermore, the multi-objective optimization function is expressed as: .

[0127] Optionally, when solving the multi-objective optimization function, the initial configuration parameters are individually encoded, and a candidate solution can be represented as:

[0128]

[0129] in, These are distributed photovoltaic access nodes, which are discrete variables; and The active and reactive power capacities of distributed photovoltaic systems are continuous variables. The candidate configuration nodes for SVC are discrete variables. The capacity for SVC compensation is a continuous variable.

[0130] Furthermore, during initialization, the distributed photovoltaic access node Randomly generated from all accessible nodes; SVC node In the candidate set The active and reactive power capacities of distributed photovoltaic systems and the SVC capacity are randomly generated within their respective allowable ranges. A power flow calculation is performed once for each initial individual, and its available reactive power margin, reactive power deficit, and objective function value are calculated.

[0131] For continuous variables, differential evolution mutation is used for updating:

[0132]

[0133] in, , , Each of these is a unique, random individual ID that is different from i. Let this be the current iteration algebra; It is a variable factor.

[0134] Furthermore, for discrete node variables, after mutation, they are first rounded and then mapped to the nearest valid node; for SVC nodes, they also need to be further mapped to the candidate set.

[0135] Among them, for target individuals With variant individuals Crossover is performed to generate experimental individuals. , is represented as:

[0136]

[0137] in, The crossover probability; The dimension numbers are randomly selected to ensure that at least one dimension comes from the variant individual.

[0138] Furthermore, feasible region repair is performed on the experimental individuals obtained after crossover. For individuals with parameter configuration constraints, their node positions and capacity parameters are modified to meet the constraints of the distributed photovoltaic and SVC collaborative optimization configuration model. Then, power flow calculations are performed on the repaired experimental individuals to obtain their target indicators such as network loss, voltage fluctuation, and mitigation costs. Dynamic combination weights are determined based on node reactive power margin deficit and the dispersion of each target indicator to calculate the objective function value of the experimental individual. The objective function value is compared with the objective function value of the corresponding target individual, and the better individual is retained for the next generation of the population. This iterative update process is repeated until the maximum number of iterations or the objective function convergence condition is reached, at which point the target parameter configuration corresponding to the optimal individual is output.

[0139] In this embodiment, during the current round of solving the multi-objective optimization function based on the initial configuration parameters, the continuous variables in the initial configuration parameters are updated by differential evolution, and the discrete node variables in the initial configuration parameters are updated by mapping. Candidate configuration parameters are obtained based on the updated continuous variables, and feasible region repair is performed on the candidate configuration parameters to obtain the multi-objective optimization function value of the repaired candidate configuration parameters. The initial configuration parameters for the next round of solving are determined from the initial configuration parameters and candidate configuration parameters based on the multi-objective optimization function value. This enables on-demand collaborative configuration of distributed photovoltaic and static var compensator devices, improves the accuracy of voltage fluctuation management in the distribution network, and thus improves the voltage stability level of the distribution network.

[0140] In an exemplary embodiment, the step of performing feasible domain repair on candidate configuration parameters includes: obtaining the value range of each configuration parameter based on the distribution network parameters; obtaining the parameters to be repaired among the candidate configuration parameters that do not meet the parameter configuration constraints, and correcting the parameters to be repaired based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints.

[0141] Optionally, the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints. For a distribution network with distributed photovoltaic (PV) access, let the total number of distribution network nodes be N, the maximum number of distributed PV installations be M, and the maximum number of static var compensators (SVCs) be K. Let the m-th distributed PV access node be... Its active capacity and reactive capacity are respectively and Let the access node of the k-th SVC be... Its compensation capacity is .

[0142] Each decision variable satisfies the following constraints:

[0143]

[0144] in, This represents the maximum active power capacity of the distributed photovoltaic (PV) system. This represents the maximum reactive power capacity of distributed photovoltaic (PV) equipment. Let m be the rated capacity of the m-th distributed photovoltaic device; This represents the maximum compensation capacity of the SVC.

[0145] The power flow of the distribution network must satisfy the following equality constraints:

[0146]

[0147] in, , These represent the active and reactive power of the conventional power supply at node i, respectively. , These represent the active and reactive power of the load at node i, respectively. The voltage amplitude at node i; , These are the real and imaginary parts of the nodal admittance matrix, respectively. For the distributed photovoltaic collection connected to node i; For the set of SVCs accessed at node i.

[0148] Node voltages must meet node voltage constraints:

[0149]

[0150] in, and These represent the maximum and minimum node voltages, respectively.

[0151] Distributed photovoltaic (PV) equipment and SVC (Supply Vulcanizing) equipment need to meet configuration quantity constraints:

[0152]

[0153] in, This represents the maximum number of distributed photovoltaic (PV) devices that can be connected to the grid. This represents the maximum number of SVC devices that can be connected.

[0154] In this embodiment, by obtaining the value range of each configuration parameter according to the distribution network parameters, the parameters to be repaired that do not meet the parameter configuration constraints among the candidate configuration parameters are obtained, and the parameters to be repaired are corrected based on the value range of the parameters to be repaired, which can improve the rationality of the configuration parameters and thus improve the accuracy of distribution network voltage fluctuation management.

[0155] In one exemplary embodiment, such as Figure 3 As shown, a collaborative management method for voltage fluctuations in a distribution network is provided, which includes the following steps:

[0156] (1) Calculation of reactive power margin deficit: Obtain the available reactive power adjustment margin of the distributed photovoltaic node under the current operating state, and obtain the target voltage of the distributed photovoltaic node, and obtain the voltage deviation between the current voltage and the target voltage of the distributed photovoltaic node; obtain the reactive power conversion coefficient according to the current grid parameters of the distribution network, and convert the voltage deviation into the required reactive power compensation amount based on the reactive power conversion coefficient; determine the total available reactive power adjustment margin of the distributed photovoltaic node according to the number of photovoltaic nodes connected; obtain the reactive power margin deficit according to the required reactive power compensation amount and the total available reactive power adjustment margin.

[0157] (2) Reactive power compensation determination: Distributed photovoltaic nodes with reactive power margin deficit greater than a preset threshold are selected as candidate configuration nodes for static reactive power compensation devices; For each candidate configuration node, the compensation capacity range is determined based on the reactive power margin deficit and the reactive power compensation threshold; The upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit and the reactive power compensation threshold corresponding to the candidate configuration node, and the lower limit of the compensation capacity range is 0.

[0158] (3) Dynamic weight determination: Obtain the reactive power deficit severity coefficient based on the reactive power margin deficit and the required reactive power compensation amount; for each objective function in the network loss objective function, voltage fluctuation objective function and governance cost objective function, obtain the demand correction coefficient of the objective function based on the reactive power deficit severity coefficient, and obtain the function dispersion based on the current function value of the objective function; obtain the combined weight based on the demand correction coefficient and the function dispersion.

[0159] (4) Construction of multi-objective optimization function: Construct a multi-objective optimization function; the multi-objective optimization function includes network loss objective function, voltage fluctuation objective function, governance cost objective function and voltage penalty function; among them, the network loss objective function, voltage fluctuation objective function and governance cost objective function are weighted by combination weight.

[0160] (5) Discrete-continuous hybrid differential evolution solution: Obtain reactive power compensation nodes from candidate configuration nodes, and obtain the reactive power compensation capacity of reactive power compensation nodes according to the compensation capacity range. Using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active capacity, photovoltaic reactive capacity and reactive power compensation capacity as initial configuration parameters, in the current round of solving the multi-objective optimization function based on the initial configuration parameters, perform differential evolution update on the continuous variables in the initial configuration parameters, and perform mapping update on the discrete node variables in the initial configuration parameters; obtain candidate configuration parameters based on the updated continuous variables, and obtain the value range of each configuration parameter according to the distribution network parameters; obtain the parameters to be repaired in the candidate configuration parameters that do not meet the parameter configuration constraints, and correct the parameters to be repaired based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints and equipment configuration quantity constraints; obtain the multi-objective optimization function value of the repaired candidate configuration parameters, and determine the initial configuration parameters for the next round of solving from the initial configuration parameters and candidate configuration parameters according to the multi-objective optimization function value. The current distribution network configuration is updated according to the target configuration parameters, and the voltage fluctuation of the distribution network is managed collaboratively based on distributed photovoltaic nodes and reactive power compensation nodes.

[0161] In this embodiment, the available reactive power adjustment margin of the distributed photovoltaic (PV) nodes under the current operating state is obtained, and the reactive power margin deficit is obtained based on the available reactive power adjustment margin. Based on the reactive power margin deficit, candidate configuration nodes and compensation capacity ranges of static var compensators (SVCs) are determined, and a multi-objective optimization function is constructed. Reactive power compensation nodes are obtained from the candidate configuration nodes, and the reactive power compensation capacity of the reactive power compensation nodes is obtained based on the compensation capacity range. The distributed PV nodes, reactive power compensation nodes, PV active power capacity, PV reactive power capacity, and reactive power compensation capacity are used as initial configuration parameters. The multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain target configuration parameters. The current distribution network configuration is updated according to the target configuration parameters. Based on the distributed PV nodes and reactive power compensation nodes, the voltage fluctuation of the distribution network is managed collaboratively. This enables the on-demand collaborative configuration of distributed PV and SVCs, improves the accuracy of distribution network voltage fluctuation management, and thus improves the voltage stability level of the distribution network.

[0162] 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.

[0163] Based on the same inventive concept, this application also provides a distribution network voltage fluctuation collaborative management device for implementing the above-mentioned distribution network voltage fluctuation collaborative management method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more distribution network voltage fluctuation collaborative management device embodiments provided below can be found in the limitations of the distribution network voltage fluctuation collaborative management method above, and will not be repeated here.

[0164] In one exemplary embodiment, such as Figure 4 As shown, a distribution network voltage fluctuation collaborative management device is provided, comprising: a deficit acquisition module 10, a compensation acquisition module 20, a function construction module 30, a capacity acquisition module 40, a function solving module 50, and a collaborative management module 60, wherein:

[0165] The deficit acquisition module 10 is used to acquire the available reactive power adjustment margin of the distributed photovoltaic node in the current operating state, and to acquire the reactive power deficit based on the available reactive power adjustment margin.

[0166] The compensation acquisition module 20 is used to determine the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit.

[0167] The function construction module 30 is used to construct a multi-objective optimization function. The multi-objective optimization function includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function. Among them, the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by combining weights.

[0168] The capacity acquisition module 40 is used to acquire reactive power compensation nodes from candidate configuration nodes and acquire the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range.

[0169] The function solving module 50 is used to solve the multi-objective optimization function in multiple rounds based on the initial configuration parameters, using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity and reactive power compensation capacity as initial configuration parameters, to obtain the target configuration parameters.

[0170] The collaborative governance module 60 is used to update the current distribution network configuration according to the target configuration parameters and to collaboratively manage the voltage fluctuations of the distribution network based on distributed photovoltaic nodes and reactive power compensation nodes.

[0171] In an exemplary embodiment, the deficit acquisition module 10 is further configured to acquire the target voltage of the distributed photovoltaic node, acquire the voltage deviation between the current voltage of the distributed photovoltaic node and the target voltage; acquire the reactive power conversion coefficient according to the current grid parameters of the distribution network, convert the voltage deviation into the required reactive power compensation amount based on the reactive power conversion coefficient; determine the total available reactive power adjustment margin of the distributed photovoltaic node according to the number of photovoltaic nodes connected; and acquire the reactive power margin deficit according to the required reactive power compensation amount and the total available reactive power adjustment margin.

[0172] In an exemplary embodiment, the function construction module 30 is further configured to obtain a reactive power deficit severity coefficient based on the reactive power margin deficit and the required reactive power compensation amount; for each objective function among the network loss objective function, voltage fluctuation objective function, and governance cost objective function, obtain a demand correction coefficient for the objective function based on the reactive power deficit severity coefficient, and obtain the function dispersion based on the current function value of the objective function; and obtain a combined weight based on the demand correction coefficient and the function dispersion.

[0173] In an exemplary embodiment, the capacity acquisition module 40 is further configured to select distributed photovoltaic nodes with reactive power margin deficits greater than a preset threshold as candidate configuration nodes for the static var compensator; for each candidate configuration node, the compensation capacity range is determined based on the reactive power margin deficit and the reactive power compensation threshold; wherein, the upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit and the reactive power compensation threshold corresponding to the candidate configuration node, and the lower limit of the compensation capacity range is 0.

[0174] In an exemplary embodiment, the function solving module 50 is further configured to, during the current round of solving the multi-objective optimization function based on the initial configuration parameters, perform differential evolution updates on the continuous variables in the initial configuration parameters and perform mapping updates on the discrete node variables in the initial configuration parameters; obtain candidate configuration parameters based on the updated continuous variables and perform feasible region repair on the candidate configuration parameters; obtain the multi-objective optimization function values ​​of the repaired candidate configuration parameters, and determine the initial configuration parameters for the next round of solving from the initial configuration parameters and candidate configuration parameters based on the multi-objective optimization function values.

[0175] In an exemplary embodiment, the function solving module 50 is further configured to obtain the value range of each configuration parameter based on the distribution network parameters; obtain the parameters to be repaired that do not meet the parameter configuration constraints among the candidate configuration parameters, and correct the parameters to be repaired based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints.

[0176] Each module in the aforementioned power distribution network voltage fluctuation collaborative management 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.

[0177] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As 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 collaborative management method for voltage fluctuations in a power distribution network. 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.

[0178] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does 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 those shown in the figure, or combine certain components, or have different component arrangements.

[0179] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: obtaining the available reactive power adjustment margin of distributed photovoltaic nodes in the current operating state, and obtaining the reactive power margin deficit based on the available reactive power adjustment margin; determining candidate configuration nodes and compensation capacity ranges for static var compensators (SVCs) based on the reactive power margin deficit; constructing a multi-objective optimization function; the multi-objective optimization function includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function; wherein the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by a combination of weights; obtaining reactive power compensation nodes from the candidate configuration nodes, and obtaining the reactive power compensation capacity of the reactive power compensation nodes based on the compensation capacity range; using distributed photovoltaic nodes, reactive power compensation nodes, photovoltaic active power capacity, photovoltaic reactive power capacity, and reactive power compensation capacity as initial configuration parameters, solving the multi-objective optimization function in multiple rounds based on the initial configuration parameters to obtain target configuration parameters; updating the current distribution network configuration according to the target configuration parameters, and performing collaborative governance of distribution network voltage fluctuations based on distributed photovoltaic nodes and reactive power compensation nodes.

[0180] In one embodiment, the process of obtaining a reactive power margin deficit based on available reactive power adjustment margin when the processor executes a computer program includes: obtaining the target voltage of the distributed photovoltaic (PV) node; obtaining the voltage deviation between the current voltage of the distributed PV node and the target voltage; obtaining a reactive power conversion coefficient based on the current grid parameters of the distribution network; converting the voltage deviation into a reactive power compensation amount based on the reactive power conversion coefficient; determining the total available reactive power adjustment margin of the distributed PV node based on the number of PV nodes connected to the grid; and obtaining the reactive power margin deficit based on the reactive power compensation amount and the total available reactive power adjustment margin.

[0181] In one embodiment, the process of determining the combined weights involved when the processor executes the computer program includes: obtaining a reactive power deficit severity coefficient based on the reactive power margin deficit and the required reactive power compensation amount; for each objective function among the network loss objective function, voltage fluctuation objective function, and governance cost objective function, obtaining a demand correction coefficient for the objective function based on the reactive power deficit severity coefficient, and obtaining the function dispersion based on the current function value of the objective function; and obtaining the combined weights based on the demand correction coefficient and the function dispersion.

[0182] In one embodiment, the process of determining candidate configuration nodes and compensation capacity range of a static var compensator (SVC) based on reactive power margin deficit when the processor executes the computer program includes: designating distributed photovoltaic (PV) nodes with reactive power margin deficit greater than a preset threshold as candidate configuration nodes for the SVC; and for each candidate configuration node, determining the compensation capacity range based on the reactive power margin deficit and the reactive power compensation threshold; wherein the upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit and the reactive power compensation threshold corresponding to the candidate configuration node, and the lower limit of the compensation capacity range is 0.

[0183] In one embodiment, the process of a processor executing a computer program to solve a multi-objective optimization function based on initial configuration parameters in multiple rounds to obtain target configuration parameters includes: during the current round of solving the multi-objective optimization function based on the initial configuration parameters, performing differential evolution updates on continuous variables in the initial configuration parameters and mapping updates on discrete node variables in the initial configuration parameters; obtaining candidate configuration parameters based on the updated continuous variables and repairing the feasible region of the candidate configuration parameters; obtaining the multi-objective optimization function values ​​of the repaired candidate configuration parameters; and determining the initial configuration parameters for the next round of solving based on the multi-objective optimization function values ​​from the initial configuration parameters and candidate configuration parameters.

[0184] In one embodiment, the feasible domain repair of candidate configuration parameters involved in the processor executing the computer program includes: obtaining the value range of each configuration parameter according to the distribution network parameters; obtaining the parameters to be repaired among the candidate configuration parameters that do not meet the parameter configuration constraints, and correcting the parameters to be repaired based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints.

[0185] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0186] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0187] 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, and when executed, it 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.

[0188] 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.

[0189] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, 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 collaborative management of voltage fluctuations in distribution networks, characterized in that, The method includes: Obtain the available reactive power adjustment margin of the distributed photovoltaic node under the current operating state, and obtain the reactive power margin deficit based on the available reactive power adjustment margin; Based on the aforementioned reactive power margin deficit, determine the candidate configuration nodes and compensation capacity range of the static var compensator; A multi-objective optimization function is constructed; the multi-objective optimization function includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function; wherein, the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by a combination of weights; Obtain reactive power compensation nodes from the candidate configuration nodes, and obtain the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range; Using the distributed photovoltaic nodes, the reactive power compensation nodes, the photovoltaic active power capacity, the photovoltaic reactive power capacity, and the reactive power compensation capacity as initial configuration parameters, the multi-objective optimization function is solved in multiple rounds based on the initial configuration parameters to obtain the target configuration parameters; The current distribution network configuration is updated according to the target configuration parameters, and the voltage fluctuation of the distribution network is managed collaboratively based on the distributed photovoltaic nodes and the reactive power compensation nodes.

2. The method according to claim 1, characterized in that, The step of obtaining the reactive power margin deficit based on the available reactive power adjustment margin includes: Obtain the target voltage of the distributed photovoltaic node, and obtain the voltage deviation between the current voltage of the distributed photovoltaic node and the target voltage; The reactive power conversion coefficient is obtained based on the current grid parameters of the distribution network, and the voltage deviation is converted into the required reactive power compensation amount based on the reactive power conversion coefficient. The total available reactive power regulation margin of the distributed photovoltaic nodes is determined based on the number of photovoltaic nodes connected to the grid. Based on the required reactive power compensation amount and the total available reactive power adjustment margin, the reactive power margin deficit is obtained.

3. The method according to claim 2, characterized in that, The process of determining the combined weights includes: Based on the reactive power margin deficit and the required reactive power compensation, obtain the reactive power deficit severity coefficient; For each objective function among the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function, the demand correction coefficient of the objective function is obtained according to the reactive power deficit severity coefficient, and the function dispersion is obtained according to the current function value of the objective function; The combined weights are obtained based on the demand correction coefficient and the function dispersion.

4. The method according to claim 1, characterized in that, The step of determining the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit includes: Distributed photovoltaic nodes with reactive power margin deficits greater than a preset threshold are selected as candidate configuration nodes for static var compensators. For each candidate configuration node, a compensation capacity range is determined based on the reactive power margin deficit and the reactive power compensation threshold; wherein, the upper limit of the compensation capacity range is the minimum value between the reactive power margin deficit corresponding to the candidate configuration node and the reactive power compensation threshold, and the lower limit of the compensation capacity range is 0.

5. The method according to claim 1, characterized in that, The process of solving the multi-objective optimization function in multiple rounds based on the initial configuration parameters to obtain the target configuration parameters includes: During the current round of solving the multi-objective optimization function based on the initial configuration parameters, the continuous variables in the initial configuration parameters are updated by differential evolution, and the discrete node variables in the initial configuration parameters are updated by mapping. Candidate configuration parameters are obtained based on the updated continuous variables, and feasible domain repair is performed on the candidate configuration parameters. Obtain the multi-objective optimization function value of the repaired candidate configuration parameters, and determine the initial configuration parameters for the next round of solving from the initial configuration parameters and the candidate configuration parameters based on the multi-objective optimization function value.

6. The method according to claim 5, characterized in that, The feasible domain repair of the candidate configuration parameters includes: The value range of each configuration parameter is obtained based on the distribution network parameters; The parameters to be repaired that do not meet the parameter configuration constraints among the candidate configuration parameters are obtained, and the parameters to be repaired are corrected based on the value range of the parameters to be repaired; the parameter configuration constraints include distribution network power flow constraints, node voltage constraints, and equipment configuration quantity constraints.

7. A device for collaborative management of voltage fluctuations in a power distribution network, characterized in that, The device includes: The deficit acquisition module is used to acquire the available reactive power adjustment margin of the distributed photovoltaic node in the current operating state, and to acquire the reactive power margin deficit based on the available reactive power adjustment margin. The compensation acquisition module is used to determine the candidate configuration nodes and compensation capacity range of the static var compensator based on the reactive power margin deficit. A function construction module is used to construct a multi-objective optimization function; the multi-objective optimization function includes a network loss objective function, a voltage fluctuation objective function, a governance cost objective function, and a voltage penalty function; wherein, the network loss objective function, the voltage fluctuation objective function, and the governance cost objective function are weighted by a combination of weights; The capacity acquisition module is used to acquire reactive power compensation nodes from the candidate configuration nodes and acquire the reactive power compensation capacity of the reactive power compensation nodes according to the compensation capacity range. The function solving module is used to solve the multi-objective optimization function in multiple rounds based on the initial configuration parameters, using the distributed photovoltaic node, the reactive power compensation node, the photovoltaic active power capacity, the photovoltaic reactive power capacity and the reactive power compensation capacity as initial configuration parameters, to obtain the target configuration parameters; The collaborative governance module is used to update the current distribution network configuration according to the target configuration parameters and to collaboratively manage the voltage fluctuations of the distribution network based on the distributed photovoltaic nodes and the reactive power compensation nodes.

8. 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 6.

9. 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 6.

10. A computer program product, comprising a computer program, 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 6.

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