Method, device and medium for optimizing distribution of low and medium voltage flexible network equipment

Abstract

The application discloses a method and equipment for optimizing distribution of low-voltage flexible network equipment and a medium, and belongs to the technical field of flexible interconnection, comprising the following steps: collecting power distribution network data, preprocessing the power distribution network data, and obtaining typical day superimposed load data through typical day load data algorithm processing; obtaining AC / DC power distribution network power flow through tide flow equation calculation according to the typical day superimposed load data and the power distribution network data, and calculating voltage value; calculating a first voltage index according to the voltage value, and determining a second voltage index based on the first voltage index; calculating a power distribution network elasticity comprehensive index according to the second voltage index and a load bearing margin index; and outputting an optimal distribution scheme of low-voltage flexible network equipment through a particle swarm algorithm under the constraints of the typical day load data algorithm and the power distribution network elasticity comprehensive index. The application solves the problems of single target, poor data quality and insufficient consideration of actual constraints in traditional power distribution network flexible network equipment distribution optimization.

Description

Technical Field

[0001] This invention relates to the field of flexible interconnection technology, specifically to a method, equipment, and medium for optimizing the deployment of medium- and low-voltage flexible network equipment. Background Technology

[0002] With the large-scale grid connection of renewable energy and the widespread application of power electronic equipment, modern distribution networks are developing towards AC / DC hybrid, highly resilient, and intelligent directions. Medium- and low-voltage flexible grid construction equipment (such as flexible interconnection devices and power electronic transformers) are key devices for improving the flexibility and reliability of distribution networks, and their rational placement is of great significance for optimizing grid operation and improving power supply quality. However, existing placement optimization methods often suffer from the following technical bottlenecks: First, traditional distribution network planning mainly relies on static load forecasting and single-objective optimization (such as considering only economic efficiency), making it difficult to cope with random fluctuations on both the source and load sides and the complex power flow characteristics of AC / DC hybrid grids. Especially in scenarios with high penetration of distributed energy, problems such as voltage exceeding limits and line overload occur frequently, necessitating the establishment of a multi-dimensional resilience evaluation system to quantify the grid's recovery capability and steady-state performance under disturbances. Second, existing placement optimization models do not adequately consider actual operational constraints, such as ignoring equipment control capacity limitations and transformer and line capacity constraints, resulting in poor engineering applicability of the optimization results. Meanwhile, data quality directly affects the optimization effect, but historical operating data of distribution networks often contain outliers and missing values. Traditional methods lack effective data cleaning mechanisms and it is difficult to extract representative daily load curves.

[0003] To address the aforementioned issues, this invention proposes an optimization method for the deployment of medium- and low-voltage flexible power distribution network equipment. By integrating data cleaning, AC / DC power flow calculation, multi-objective optimization, and adaptive intelligent algorithms, it provides an optimal deployment scheme that balances economy and flexibility for differentiated distribution networks. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to optimize the layout of medium and low voltage flexible grid equipment by using multi-dimensional elastic evaluation and multi-objective constraints. Typical daily superimposed load data is obtained by preprocessing the collected data, voltage values ​​are calculated by power flow calculation and voltage index is determined, elastic index is calculated by combining load margin, and the optimal layout scheme is output by particle swarm algorithm under constraints.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for optimizing the layout of medium and low voltage flexible grid equipment, which includes the following steps.

[0007] Collect data on the distribution network structure, source-grid-load-storage operation data, information on medium and low voltage flexible network construction equipment, the operating capacity of the distribution network, and the upper and lower thresholds of the voltage at distribution network nodes;

[0008] The source-grid-load-storage operation data is preprocessed, and then processed using a typical daily load data algorithm to obtain typical daily load data. This data is then superimposed to obtain typical daily superimposed load data. Based on the typical daily superimposed load data and the distribution network structure data, the AC / DC distribution network power flow is calculated using power flow equations. Voltage values ​​are calculated based on the AC / DC distribution network power flow, and a first voltage index is calculated based on these voltage values. A second voltage index is determined based on the first voltage index. A distribution network resilience comprehensive index is calculated based on the second voltage index and the load carrying capacity margin index. Under the constraints of the typical daily load data algorithm and the distribution network resilience comprehensive index, the optimal deployment scheme for medium- and low-voltage flexible network equipment is output using a particle swarm optimization algorithm.

[0009] As a preferred embodiment of the method for optimizing the deployment of medium- and low-voltage flexible grid equipment described in this invention, the following steps are taken: Collecting distribution network structure data, source-load-storage operation data, medium- and low-voltage flexible grid equipment information, distribution network operating capacity, and distribution network node voltage thresholds. This includes: collecting distribution network structure data, source-load-storage resource operation data, medium- and low-voltage flexible grid equipment information, and other equipment information; the distribution network structure data includes network node numbers and network topology, i.e., the relationships between nodes and the length and impedance data of each segment of the network line; the source-load-storage resource operation data refers to the historical operating load data of the source, load, storage, and overall distribution network at each distribution network node; the other equipment information includes the distribution network operating capacity and distribution network node voltage thresholds.

[0010] As a preferred embodiment of the method for optimizing the deployment of medium- and low-voltage flexible power grid equipment described in this invention, the following steps are performed: Preprocessing the source-grid-load-storage operation data, calculating typical daily load data, and establishing power flow equations include: selecting load data from the source-grid-load-storage operation data, comparing the difference with the average load data, and determining abnormal data if the absolute value of the difference is greater than a set load value, and replacing the abnormal data with the average load data. Based on the preprocessed source-grid-load-storage operation data, calculating the typical daily active power and typical daily reactive power, summing the active power and reactive power respectively, and combining the summed active power and reactive power to form typical daily superimposed load data.

[0011] Based on the aforementioned distribution network data, a power flow equation is established according to the typical daily superimposed load data:

[0012] ;

[0013] In the formula, They are nodes i No. tThe active and reactive power injected at all times, They are nodes i To the node j The conductance and susceptance of the line, if the nodes i and nodes j If they are not directly connected, then both values ​​are 0; For nodes i and nodes j The phase angle difference; For nodes i No. t The voltage value at that moment; For nodes j No. t The voltage value at that moment; This represents the number of nodes in the distribution network.

[0014] As a preferred embodiment of the method for optimizing the layout of medium- and low-voltage flexible grid equipment described in this invention, the calculation of the second voltage index and the load bearing margin index includes:

[0015] The voltage value is obtained by solving the power flow equation. A first voltage index is calculated based on the voltage value; the first voltage index includes a voltage exceedance severity index and a voltage balance index.

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, This is an indicator of the severity of voltage exceedance at time t in the distribution network. For nodes i No. t Voltage deviation at any given time; The number of nodes in the distribution network; These are the upper and lower safety thresholds for voltage; The voltage balance index of the distribution network at time t; The reference voltage; The average voltage across all nodes;

[0021] The second voltage index is calculated based on the voltage exceedance severity index and the voltage balance index. Specifically, the second voltage index is a voltage stability index.

[0022] ;

[0023] In the formula, For the distribution network t Voltage stability index at any time; These are the weights for voltage exceedance severity and voltage balance, respectively, satisfying... ; The first adjustment sensitivity; This is the second adjustment sensitivity; This is an indicator of the severity of voltage exceedance at time t in the distribution network. The voltage balance index of the distribution network at time t;

[0024] The overall load factor of the distribution network is calculated based on the rated capacity of the distribution network and the typical daily superimposed load data.

[0025] ;

[0026] In the formula, For typical days of distribution network t Load rate at any given time; For the operating capacity of the distribution network; For the typical day of the distribution network t Active power at any given moment;

[0027] Load fluctuation data is calculated based on the typical daily superimposed load data:

[0028] ;

[0029] In the formula, For the load fluctuation of the distribution network; These are the typical daily maximum load and the typical daily average load of the entire distribution network, respectively.

[0030] The load carrying capacity margin index is calculated based on the overall load rate of the distribution network and the load fluctuation data.

[0031] ;

[0032] In the formula, For typical days of distribution network t Load capacity margin index at any given time; The weights for the impact of distribution network load rate and volatility are respectively, satisfying the following conditions. ; The third adjustment is the sensitivity. For the load fluctuation of the distribution network; For typical days of distribution network t Load rate at any given time.

[0033] As a preferred embodiment of the method for optimizing the deployment of medium- and low-voltage flexible power grid equipment described in this invention, the following is included: The comprehensive resilience index of the distribution network is calculated based on the second voltage index and the load carrying capacity index:

[0034] ;

[0035] In the formula, For distribution network t The elasticity comprehensive index at any given time; the voltage stability index is calculated based on the voltage exceedance severity index and the voltage balance index. The load carrying capacity margin index is calculated based on the overall load rate of the distribution network and the load fluctuation data. ; The influence weights of voltage stability index and load capacity margin are respectively, satisfying the requirements. .

[0036] As a preferred embodiment of the method for optimizing the layout of medium- and low-voltage flexible grid construction equipment described in this invention, the optimization of the layout of medium- and low-voltage flexible grid construction equipment includes:

[0037] Based on the preset optimization objectives, the comprehensive resilience index of the distribution network is transformed from multi-objective optimization to single-objective optimization:

[0038] ;

[0039] ;

[0040] ;

[0041] In the formula, To synthesize the optimal objective function; This refers to the total investment cost of medium and low voltage flexible grid construction equipment. This represents the maximum operating investment cost for low-voltage flexible network equipment in the power distribution network. For nodes i Construction costs of connecting to medium and low voltage flexible network equipment; For nodes i The decision factor for accessing medium- and low-voltage flexible network equipment is 1, indicating that the node... i Access, a value of 0 indicates a node i No connection; This represents the minimum value of the comprehensive resilience index of the distribution network at all times. Weights are assigned to both economic efficiency and distribution network resilience to meet the requirements. ; The number of nodes in the distribution network;

[0042] Based on the constraints of the typical daily load data algorithm and the distribution network resilience comprehensive index, the single-objective optimization of the distribution network resilience comprehensive index is solved to obtain the decision factors of the medium and low voltage flexible network equipment, and the decision factors are incorporated into the selection of the layout of the medium and low voltage flexible network equipment.

[0043] As a preferred embodiment of the method for optimizing the deployment of medium and low voltage flexible grid equipment described in this invention, the constraints include: voltage safety constraints after connection, controllability constraints of medium and low voltage flexible grid equipment, safe operation constraints of node transformers, and line capacity constraints.

[0044] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the method for optimizing the deployment of medium and low voltage flexible grid equipment.

[0045] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method for optimizing the deployment of medium- and low-voltage flexible grid equipment.

[0046] The beneficial effects of this invention are as follows: By diagnosing and cleaning abnormal data, typical daily data is constructed to ensure data accuracy and completeness, providing reliable data support for subsequent optimization; by establishing a comprehensive distribution network resilience index, based on indicators such as voltage stability and load carrying capacity margin, combined with AC / DC power flow calculations, a precise quantitative assessment of distribution network resilience is achieved, comprehensively reflecting the steady-state performance and anti-disturbance capability of the power grid; by adopting a multi-objective constrained optimization method, multi-objective optimization is transformed into single-objective optimization, achieving the optimal balance between cost and performance; by dynamically adjusting the inertia weight through an adaptive particle swarm optimization algorithm, the convergence speed and global search capability are improved, achieving efficient adaptation to distribution networks of different scales. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the overall process of optimizing the layout of medium- and low-voltage flexible grid equipment according to an embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for optimizing the deployment of medium and low voltage flexible grid equipment, including:

[0051] S100: Collects data on the distribution network structure, source-grid-load-storage operation data, information on medium and low voltage flexible network equipment, distribution network operating capacity, and upper and lower voltage thresholds of distribution network nodes.

[0052] S200: Preprocess the source-grid-load-storage operation data, process the preprocessed source-grid-load-storage operation data through the typical daily load data algorithm to obtain typical daily load data, and then superimpose the data to obtain typical daily superimposed load data.

[0053] S300: Based on typical daily superimposed load data and distribution network structure data, the power flow of AC / DC distribution networks is calculated using power flow equations.

[0054] S400: Calculates voltage values ​​based on AC / DC power flow, calculates a first voltage index based on the voltage values, and determines a second voltage index based on the first voltage index.

[0055] S500: Calculate the comprehensive resilience index of the distribution network based on the second voltage index and the load carrying capacity margin index.

[0056] S600: Under the constraints of typical daily load data algorithm and distribution network resilience comprehensive index, the optimal layout scheme of medium and low voltage flexible network equipment is output through particle swarm algorithm.

[0057] It should be noted that existing distribution network optimization methods rely on static load forecasting and single-objective optimization, which are difficult to adapt to the power flow characteristics of AC / DC hybrid power grids. For example, in scenarios with high penetration of distributed energy resources, problems such as voltage exceeding limits and line overload occur frequently, necessitating the establishment of a multi-dimensional resilience evaluation system to quantify the power grid's recovery capability and steady-state performance under disturbances. Secondly, existing distribution network optimization models do not adequately consider actual operational constraints, such as ignoring equipment control capacity limitations and transformer and line capacity constraints, resulting in poor engineering applicability of the optimization results. Furthermore, data quality directly affects the optimization effect, but historical operating data of distribution networks often contains outliers and missing values. Traditional methods lack effective data cleaning mechanisms, making it difficult to extract representative typical daily load curves.

[0058] Therefore, addressing the aforementioned issues of reliance on static prediction, single-objective optimization, and lack of effective data cleaning, a method for optimizing the deployment of medium- and low-voltage flexible power grid equipment is constructed through steps S100-S600. First, by establishing a comprehensive distribution network resilience index, and combining indicators such as voltage stability and load capacity margin with AC / DC power flow calculations, a precise quantitative assessment of distribution network resilience is achieved. Second, by employing a multi-objective constrained optimization method, multi-objective optimization is transformed into single-objective optimization, achieving an optimal balance between cost and performance. Finally, anomaly diagnosis and cleaning are performed on the collected data to extract accurate typical daily load data, providing reliable data support for subsequent optimization.

[0059] Example 2, refer to Figure 1 This is the second embodiment of the present invention, which provides a method for optimizing the layout of medium and low voltage flexible network equipment.

[0060] In this embodiment of the invention, step S100 involves collecting data on the distribution network structure, source-grid-load-storage operation data, information on medium and low voltage flexible network equipment, the operating capacity of the distribution network, and the upper and lower thresholds of the voltage at distribution network nodes.

[0061] It should be noted that the source-load-storage resource operation data refers to the overall historical operating load data of the source, load, storage, and distribution network at each node, with a collection point interval of 15 minutes and a sampling time span of one month; the information on medium and low voltage flexible network equipment includes the construction cost of medium and low voltage flexible network equipment.

[0062] In this embodiment of the invention, in step S200, the source-grid-load-storage operation data is preprocessed, and the preprocessed source-grid-load-storage operation data is processed by the typical daily load data algorithm to obtain typical daily load data, and then superimposed to obtain typical daily superimposed load data.

[0063] Specifically, the preprocessing of source-grid-load-storage operation data involves anomaly diagnosis and cleaning.

[0064] It should be noted that the anomaly diagnosis and cleaning of data specifically involves diagnosing erroneous data, empty data, and excessive data.

[0065] The specific forms of diagnostic error data are as follows:

[0066] ;

[0067] In the formula, For the i-th node of the distribution network, the m-th type of main body history k Heavenly t The workload of the moment; For the first i The m-th type of main body of the node has all historical measurement days. t Average load at time, Average load With load The normal fluctuation threshold constitutes this.

[0068] The specific form of diagnostic empty data is as follows:

[0069] ;

[0070] In the formula, The load of the i-th node of the distribution network at time t on day k of the history of the m-th main body; The average load at time t for all measurement days of the history of the i-th node and the m-th subject. The number of days for which historical data was collected.

[0071] It should be noted that when historical data meets this formula, the data at that moment is diagnosed as outlier, and the outlier data is then adjusted to the average load at the corresponding moment. The data after data diagnosis, cleaning, and correction is as follows: .

[0072] The specific forms of diagnostic excess data are as follows:

[0073] ;

[0074] In the formula, For the distribution network i The node m Class Subject History k Heavenly t The workload of the moment; For the first i The node m All measurement days of the main body of the category t Average load at any given time; For the first i The node m All measurement days of the main body of the category t Load variance at time points; The number of days for which historical data was collected.

[0075] It should be noted that, m =1, 2, 3, 4 correspond to the four categories of source, load, storage, and distribution network as a whole, respectively. Since there are no nodes in the overall distribution network, therefore when... m Remove subscript when =4 i That is, the overall history of the power distribution network k Heavenly t Time for .

[0076] Based on the revised historical data of the power generation, load, storage, and distribution networks, the typical daily load data is calculated as follows:

[0077] ;

[0078] In the formula, For the first i The node m Typical historical day of the main body t The load of time, For the number of days of historical data collection, k For the number of days, To complete the data diagnosis, cleaning, and correction.

[0079] It should be noted that the typical daily load data is a combination of typical daily active power data and typical daily reactive power data; and the above typical daily load data are all typical daily active power data, and the typical daily reactive power data are also calculated according to this method to obtain the [number missing]. i The node m Typical historical day of the main body t reactive power at any moment .

[0080] Specifically, the calculation of the superimposed active power of each node is as follows:

[0081] ;

[0082] In the formula, For nodes i No. t Typical daily superimposed active power at any given time; They are nodes i No. t Historical typical daily active power data of source, load, and storage at specific times can be used to calculate the node's active power. i No. t Typical daily superimposed reactive power at any given time.

[0083] It should be noted that the typical daily superimposed load data is formed by combining the typical daily superimposed active power data and the typical daily superimposed reactive power data; and the typical daily superimposed reactive power data and the typical daily superimposed active power data are calculated in the same way.

[0084] In this embodiment of the invention, in step S300, the AC / DC power flow is calculated by power flow equation based on typical daily superimposed load data and distribution network structure data.

[0085] The power flow equation is specifically represented as follows:

[0086] ;

[0087] In the formula, They are nodes i No. t The active and reactive power injected at all times, They are nodes i To the node j The conductance and susceptance of the line, if the nodes i and nodes j If they are not directly connected, then the two values ​​are 0; For nodes i and nodes j The phase angle difference; For nodes i No. t The voltage value at that moment; Let be the voltage value of node j at time t; This represents the number of nodes in the distribution network.

[0088] It should be noted that the voltage values ​​at different nodes are obtained by solving the power flow equations. This provides data support for subsequent calculations of voltage stability indicators.

[0089] In this embodiment of the invention, in step S400, the voltage value is calculated based on the AC / DC power flow of the distribution network, a first voltage index is calculated based on the voltage value, and a second voltage index is determined based on the first voltage index.

[0090] It should be noted that the first voltage index is the severity of voltage over-limit, and the second voltage index is the voltage balance index.

[0091] It should be noted that both the voltage exceedance severity index and the voltage balance index are dimensionless.

[0092] The specific manifestation of the voltage exceedance severity index is as follows:

[0093] ;

[0094] In the formula, For distribution network t The severity index of voltage exceeding the limit at any given time; For nodes i No. t Voltage deviation at any given time; The reference voltage; This represents the number of nodes in the distribution network.

[0095] It should be noted that, This represents the number of nodes in the distribution network. The distribution network here is the one that needs to be analyzed, and the number of nodes must be at least 2 (at least 2 nodes are needed to form a line segment), meaning there is no case where the number is 0.

[0096] It should be noted that the voltage exceedance severity index reflects the overall situation of the grid voltage deviating from the safe threshold by quantifying the sum of voltage deviations at each node, providing a direct quantitative basis for early warning equipment damage and power outages.

[0097] Among them, the severity index of voltage exceedance follows the voltage deviation constraint, and the specific manifestation of the voltage deviation constraint is as follows:

[0098] ;

[0099] In the formula, These are the upper and lower threshold values ​​for voltage safety. The threshold values ​​are set according to the power grid's regulations for voltage quality management, and the safe voltage range is set according to the benchmark value of 0.93~1.07. For nodes i No. t Voltage deviation at time, For nodes i No. t The voltage value at a given moment.

[0100] The specific form of the average voltage across all nodes is as follows:

[0101] ;

[0102] In the formula, For nodes i No. t The voltage value at that moment; The number of nodes in the distribution network; This represents the average voltage across all nodes.

[0103] The specific manifestations of the voltage balance index are as follows:

[0104] ;

[0105] In the formula, The voltage balance index of the distribution network at time t; Let be the voltage value of node i at time t; The number of nodes in the distribution network; This represents the average voltage across all nodes.

[0106] It should be noted that the voltage balance index quantifies the uniformity of voltage distribution by calculating the dispersion of voltage at each node, providing a basis for reducing line losses and stabilizing load distribution.

[0107] It should be noted that the voltage stability index is calculated based on the voltage exceedance severity index and the voltage balance index.

[0108] ;

[0109] In the formula, For the distribution network t Voltage stability index at any time; These are the weights for voltage exceedance severity and voltage balance, respectively, satisfying... ; The first adjustment sensitivity; This is the second adjustment sensitivity; This is an indicator of the severity of voltage exceedance at time t in the distribution network. This is the voltage balance index of the distribution network at time t.

[0110] It should be noted that the first adjustment sensitivity ranges from [1, 3], and the second adjustment sensitivity ranges from [2, 5].

[0111] It should be noted that both the voltage exceedance severity index and the voltage balance index are dimensionless numerical values.

[0112] It should be noted that the voltage stability index achieves a comprehensive quantification of the voltage stability state by weighted integration of the severity of voltage exceedance and the balance index; the voltage stability index ensures safety and efficiency, and provides a quantitative basis for the resilient assessment of the distribution network and the optimization of equipment deployment.

[0113] In this embodiment of the invention, in step S500, the distribution network resilience index is calculated based on the second voltage index and the load carrying capacity index.

[0114] It should be noted that the load carrying capacity margin index is calculated by weighting the overall load rate and load fluctuation of the distribution network.

[0115] The overall load factor of the distribution network is calculated based on the operating capacity of the distribution network and the typical daily superimposed load data. The specific form of the overall load factor of the distribution network is as follows:

[0116] ;

[0117] In the formula, For typical days of distribution network t Load rate at any given time; For the operating capacity of the distribution network; For the typical day of the distribution network t Active power at any given moment;

[0118] The load fluctuation data is calculated based on the typical daily superimposed load data, and the specific manifestation of load fluctuation is as follows:

[0119] ;

[0120] In the formula, For the load fluctuation of the distribution network; These are the typical daily maximum load and the typical daily average load of the entire distribution network, respectively.

[0121] The load carrying capacity margin index is calculated based on the overall load rate of the distribution network and the load fluctuation data. The load carrying capacity margin index is specifically expressed as follows:

[0122] ;

[0123] In the formula, This is the load carrying capacity margin index for a typical day in a distribution network at time t. The weights for the impact of distribution network load rate and volatility are respectively, satisfying the following conditions. ; The third adjustment is the sensitivity. For the load fluctuation of the distribution network; For typical days of distribution network t Load rate at any given time.

[0124] It should be noted that the third sensitivity adjustment range is [3, 7], and the default setting is 5.

[0125] It should be noted that the load carrying capacity index quantifies the grid's ability to carry load and its margin to cope with fluctuations by weighted integration of load rate and load volatility; the load carrying capacity index ensures static load and fluctuation, providing a core quantitative basis for assessing the grid's load carrying capacity potential.

[0126] It should be noted that a comprehensive distribution network resilience index is established based on the voltage stability index and the load carrying capacity margin index. The specific form of the comprehensive distribution network resilience index is as follows:

[0127] ;

[0128] In the formula, For distribution network t The elasticity comprehensive index at any given time; the voltage stability index is calculated based on the voltage exceedance severity index and the voltage balance index. The load carrying capacity margin index is calculated based on the overall load rate of the distribution network and the load fluctuation data. ; The influence weights of voltage stability index and load capacity margin are respectively, satisfying the requirements. .

[0129] In this embodiment of the invention, step S600, under the constraints of the typical daily load data algorithm and the distribution network resilience comprehensive index, outputs the optimal deployment scheme of medium and low voltage flexible network equipment through the particle swarm optimization algorithm, including the following steps A1-A3:

[0130] A1: Set constraints.

[0131] A2: The weighted normalization method is used to transform the multi-objective optimization model into a single-objective optimization model.

[0132] A3: Solve single-objective problems using the particle swarm optimization algorithm.

[0133] In step A1, the constraints specifically include voltage safety constraints after connection, control capability constraints of medium and low voltage flexible grid equipment, safe operation constraints of node transformers, and line capacity constraints.

[0134] The specific manifestation of voltage safety constraints after connection is as follows:

[0135] ;

[0136] In the formula, These are the upper and lower voltage safety thresholds, which are set according to the power grid's regulations on voltage quality management, and the safe voltage range is set according to the benchmark value of 0.93~1.07. To connect to medium and low voltage flexible grid equipment nodes i No. t The voltage value at a given moment.

[0137] The specific manifestations of the constraints on the control capabilities of medium- and low-voltage flexible grid-connected equipment are as follows:

[0138] ;

[0139] ;

[0140] In the formula, They are nodes i No. t Active and reactive power injection quantities of low-voltage flexible grid equipment at all times; These are the maximum active and reactive power injection capacities of the interconnected equipment, respectively. These are the minimum active and reactive power injection capacities of the interconnected equipment, respectively.

[0141] It should be noted that after connecting to the medium- and low-voltage flexible grid equipment, the loads of each node are adjusted and modified, including active power and reactive power, as follows:

[0142] ;

[0143] ;

[0144] In the formula, These are the downstream nodes of medium and low voltage flexible grid construction equipment. i No. tActive and reactive power adjusted in real time; They are nodes i No. t Historical active power data for typical daily loads of power sources, loads, and storage at specific times; They are nodes i No. t Historical typical daily load reactive power data for power sources, loads, and storage at specific times; They are nodes i No. t The active and reactive power injection quantities of low-voltage flexible grid equipment are controlled at all times.

[0145] The specific manifestations of the safety operation constraints for node transformers are as follows:

[0146] ;

[0147] In the formula, For nodes i The rated capacity of the transformer; For nodes i The upper limit of the safe operating load rate of a transformer.

[0148] The specific manifestations of line capacity constraints are as follows:

[0149] ;

[0150] In the formula, For nodes i and nodes a The impedance of the line; For the line ia Rated current carrying capacity; For the line ia The maximum safe operating load rate.

[0151] In step A2, a weighted normalization method is used to transform the multi-objective optimization model into a single-objective optimization model, specifically in the following form:

[0152] ;

[0153] ;

[0154] ;

[0155] In the formula, To synthesize the optimal objective function; This refers to the total investment cost of medium and low voltage flexible grid construction equipment. Investment costs for low-voltage flexible network equipment in power distribution networks; This represents the maximum operating investment cost for low-voltage flexible network equipment in the power distribution network. For nodes i Construction costs of connecting to medium and low voltage flexible network equipment; For nodes i Decision factors for accessing medium and low voltage flexible grid construction equipment; This represents the minimum value of the comprehensive resilience index of the distribution network at all times. Weights are assigned to both economic efficiency and distribution network resilience to meet the requirements. ; This represents the number of nodes in the distribution network.

[0156] It should be noted that by transforming multi-objective optimization into single-objective optimization, the trade-off between cost and flexibility is quantified, providing a solvable objective function for the particle swarm optimization algorithm, and ensuring that the solution balances economy and performance.

[0157] In step A3, the particle swarm optimization algorithm is used to solve the single-objective problem, taking the decision factors of the medium- and low-voltage flexible network equipment at each node as an example. As a solution to the requirement, the position vector of the particle at the i-th node in D-dimensional space is: The velocity vector is The specific form of the iterative update calculation of particle velocity and position is as follows:

[0158] ;

[0159] In the formula, No. i The node r+ Velocity vector of generation 1; For the first i The node The inertial weight of the generation; No. i The node r The velocity vector of the generation; and The learning factor is set to 0.5. and A random number in the interval [0,1]. For the first i The node w The first particle The optimal position of the generation, i.e., the optimal solution for an individual; For the first i All particles at node i.e. The optimal position of the generation, i.e., the optimal solution for the population; For the first i The node r The position vector of the corresponding particle i Position vector in D-dimensional space .

[0160] It should be noted that decision factors With position vector The meanings are the same: position vector. Decision factors One kind of development.

[0161] It should be noted that when using the particle swarm optimization algorithm to solve single-objective problems, the inertia weight needs to be adjusted, specifically in the following form:

[0162] ;

[0163] ;

[0164] In the formula, and The first i The maximum and minimum values ​​of the inertia weight of each node; For the first i The node A measure of population diversity during generation iteration; For the first i The historical maximum value of node diversity; The number of particles in the particle swarm; For the first i The node The generation y The position of each particle;

[0165] It should be noted that the decision factors for the medium- and low-voltage flexible network equipment at each node can be obtained through the above optimization algorithm. , A value of 1 indicates a node. i Access, a value of 0 indicates a node i If not connected, The nodes are then incorporated into the selection of the layout of the medium and low voltage flexible grid construction device, and the optimal layout scheme of the medium and low voltage flexible grid construction equipment is constructed and output.

[0166] It should be noted that the particle swarm optimization algorithm improves the convergence speed by adjusting the inertia weight, ensuring that the optimal point placement scheme is found in single-objective optimization.

[0167] In summary, this invention, through anomaly data diagnosis and cleaning, constructs typical daily data to ensure data accuracy and completeness, providing reliable data support for subsequent optimization; by establishing a comprehensive distribution network resilience index, based on indicators such as voltage stability and load capacity margin, combined with AC / DC power flow calculations, it achieves a precise quantitative assessment of distribution network resilience, comprehensively reflecting the steady-state performance and disturbance resistance capability of the power grid; by adopting a multi-objective constrained optimization method, it transforms multi-objective optimization into single-objective optimization, achieving an optimal balance between cost and performance; and by dynamically adjusting inertia weights through an adaptive particle swarm optimization algorithm, it improves convergence speed and global search capability, achieving efficient adaptation to distribution networks of different scales.

[0168] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0169] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0170] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0171] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination of all three. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the layout of medium and low voltage flexible grid equipment, characterized in that: include, Collect data on the distribution network structure, source-grid-load-storage operation data, information on medium and low voltage flexible network construction equipment, the operating capacity of the distribution network, and the upper and lower thresholds of the voltage at distribution network nodes; The source-grid-load-storage operation data is preprocessed, and the preprocessed source-grid-load-storage operation data is processed by a typical daily load data algorithm to obtain typical daily load data, and then superimposed to obtain typical daily superimposed load data. Based on the typical daily superimposed load data and the distribution network structure data, the AC / DC distribution network power flow is calculated using power flow equations. Calculate the voltage value based on the AC / DC power flow of the distribution network, calculate the first voltage index based on the voltage value, and determine the second voltage index based on the first voltage index. Calculate the comprehensive resilience index of the distribution network based on the second voltage index and the load carrying capacity index; Under the constraints of the typical daily load data algorithm and the distribution network resilience comprehensive index, the optimal deployment scheme of medium and low voltage flexible network equipment is output through particle swarm optimization algorithm. Based on the second voltage index and the load carrying capacity index, calculate the comprehensive resilience index of the distribution network: ROSE t =α·VSI t +β·LCMI t ; In the formula, RESI t The elasticity comprehensive index of the distribution network at time t; the voltage stability index VSI is calculated based on the voltage exceedance severity index and the voltage balance index. t The Load Capacity Margin Index (LCMI) is calculated based on the overall load factor and load volatility data of the distribution network. t α and β are the influence weights of voltage stability index and load bearing margin, respectively, satisfying α+β=1; Optimize the deployment of medium- and low-voltage flexible grid construction equipment, including: Based on the preset optimization objectives, the comprehensive resilience index of the distribution network is transformed from multi-objective optimization to single-objective optimization: RECEIPT min =min{RESI1,RESI2,...,RESI t ,...}; In the formula, F is the comprehensive optimal objective function; C inv C represents the total investment cost of medium- and low-voltage flexible grid construction equipment. inv,max The maximum operating investment cost for low-voltage flexible network equipment in the distribution network; c i Construction cost of connecting node i to medium- and low-voltage flexible network equipment; x i The decision factor for node i to access the medium- and low-voltage flexible network equipment is 1, indicating that node i accesses the network, and 0 indicates that node i does not access the network; RESI min This represents the minimum value of the comprehensive resilience index of the distribution network at all times. ξ1 and ζ2 are the weights for economic considerations and distribution network flexibility, respectively, satisfying ξ1 + ξ2 = 1; N node This refers to the number of nodes in the distribution network. Based on the constraints of the typical daily load data algorithm and the distribution network resilience comprehensive index, the single-objective optimization of the distribution network resilience comprehensive index is solved to obtain the decision factors of the medium and low voltage flexible network equipment, and the decision factors are incorporated into the selection of the layout of the medium and low voltage flexible network equipment.

2. The method for optimizing the layout of medium and low voltage flexible grid equipment as described in claim 1, characterized in that: Collect data on the distribution network structure, source-grid-load-storage operation data, information on medium and low voltage flexible network construction equipment, the operating capacity of the distribution network, and the upper and lower thresholds of the distribution network node voltage, including: Collect data on the distribution network structure, the operation data of source, load and storage resources, information on medium and low voltage flexible network equipment, and other equipment information; The power distribution network data includes the network node number, network topology, i.e. the relationship between each node and the length and impedance data of each network line segment; The source-load-storage resource operation data refers to the historical operating load data of the source, load, storage, and distribution network of each distribution network node. The other equipment information includes the operating capacity of the distribution network and the upper and lower thresholds of the voltage at the distribution network nodes.

3. The method for optimizing the layout of medium and low voltage flexible grid equipment as described in claim 2, characterized in that: The source-grid-load-storage operation data is preprocessed, and typical daily load data is calculated to establish power flow equations, including: Load data is selected from the source-grid-load-storage operation data and compared with the average load data. When the absolute value of the difference is greater than the set load value, it is determined to be abnormal data, and the average load data is used to replace the abnormal data. Based on the preprocessed source-grid-load-storage operation data, the active power and reactive power of a typical day are calculated, and the active power and reactive power are summed respectively. The summed active power and reactive power are combined to form the superimposed load data of a typical day. Based on the aforementioned distribution network data, a power flow equation is established according to the typical daily superimposed load data: In the formula, G represents the active power and reactive power injected into node i at time t, respectively. ij B ij δ represents the conductance and susceptance of the line from node i to node j, respectively. If node i and node j are not directly connected, both values ​​are 0; ij V represents the phase angle difference between nodes i and j; i,t V is the voltage value at node i at time t; j,t Let N be the voltage value of node j at time t; node This represents the number of nodes in the distribution network.

4. The method for optimizing the layout of medium and low voltage flexible grid equipment as described in claim 3, characterized in that: The calculation of the second voltage index and the load capacity margin index includes: The voltage value V is obtained by solving the power flow equation. i,t A first voltage index is calculated based on the voltage value; the first voltage index includes a voltage exceedance severity index and a voltage balance index. In the formula, V dev,t ΔV is the severity index of voltage over-limit in the distribution network at time t. i,t N represents the voltage deviation at node i at time t; node V represents the number of nodes in the distribution network. max V min These are the upper and lower safety thresholds for voltage, respectively; V unbalance,t V is the voltage balance index of the distribution network at time t. benchmark V is the reference voltage. avg,t The average voltage across all nodes; The second voltage index is calculated based on the voltage exceedance severity index and the voltage balance index. Specifically, the second voltage index is a voltage stability index. In the formula, VSI t Let be the voltage stability index of the distribution network at time t; ω1 and ω2 are the weights for the severity of voltage exceedance and voltage balance, respectively, satisfying ω1+ω2=1; k1 is the first regulation sensitivity; k2 is the second regulation sensitivity; The overall load factor of the distribution network is calculated based on the rated capacity of the distribution network and the typical daily superimposed load data. In the formula, η network,t P represents the load factor at time t on a typical day of the distribution network. max For the operating capacity of the distribution network; P 4,t This represents the active power of the entire distribution network at time t on a typical day. Load fluctuation data is calculated based on the typical daily superimposed load data: In the formula, δ network For the load fluctuation of the distribution network; These are the typical daily maximum load and the typical daily average load of the entire distribution network, respectively. The load carrying capacity margin index is calculated based on the overall load rate of the distribution network and the load fluctuation data. In the formula, LCMI t ω3 represents the load carrying capacity margin index at time t on a typical day of the distribution network; ω3 and ω4 are the load rate and volatility impact weights of the distribution network, respectively, satisfying ω3+ω4=1; k3 is the third regulation sensitivity.

5. The method for optimizing the layout of medium and low voltage flexible grid equipment as described in claim 4, characterized in that: The constraints include: The constraints include voltage safety after connection, control capability of medium and low voltage flexible grid equipment, safe operation of node transformers, and line capacity.

6. 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 for optimizing the layout of medium and low voltage flexible network equipment as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for optimizing the layout of medium and low voltage flexible network equipment as described in any one of claims 1 to 5.

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