Power distribution network regulation and control method based on multi-objective optimization dynamic aggregation strategy

Abstract

The invention provides a power distribution network regulation and control method based on a multi-objective optimization dynamic aggregation strategy, and relates to the technical field of power system automation and control, and the method comprises the steps: collecting the voltage, current, power, active and reactive power, and harmonic operation data of each key node of a power distribution network in real time, and carrying out the preprocessing of the operation data, so as to obtain the processed data; according to the processed data, constructing an initial optimization model including a plurality of optimization targets; and according to the running state of the current power distribution network and each optimization target, adjusting and determining the weight and priority of each optimization target, so as to obtain a multi-target optimization model subjected to weight optimization. According to the invention, by dynamically constructing the weight optimized multi-target model, generating and executing the accurate regulation and control instruction, and performing real-time evaluation and closed-loop adjustment, the voltage quality of the power distribution network is comprehensively improved, the network loss is reduced, the consumption of renewable energy sources is promoted, the power supply reliability is enhanced, and the carbon emission is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation and control, and in particular to a distribution network control method based on a multi-objective optimization dynamic aggregation strategy. Background Art

[0002] Traditional control strategies seek to reduce energy losses in the transmission and distribution of electric energy by adjusting voltage levels and optimizing power flow distribution, thereby improving overall energy efficiency. In order to ensure power supply quality and system safety, traditional methods pay close attention to voltage fluctuations and ensure that voltage levels are stable within the allowable range by adjusting reactive power compensation and transformer tap positions. For fault recovery and power supply reliability, traditional strategies focus on quickly locating fault points, isolating fault areas, and restoring power supply to reduce the time users are affected.

[0003] However, in the actual operation of distribution networks, the optimization of these single objectives has been unable to meet the increasingly complex and changing challenges, including:

[0004] With the changes in user electricity consumption behavior, the impact of seasonal climate and the fluctuation of industrial production, the load of the distribution network has shown a high degree of uncertainty and dynamism, which has put forward higher requirements for the flexibility and response speed of the power grid. Some equipment in the distribution network has gradually aged due to long-term use, which not only affects the reliability of power supply, but also may increase the failure rate and maintenance costs, posing a threat to the overall performance and security of the power grid. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network control method based on a multi-objective optimization dynamic aggregation strategy, which improves the comprehensive benefits of distribution network operation by dynamically adjusting and optimizing the weights and relationships between multiple control objectives.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a distribution network control method based on a multi-objective optimization dynamic aggregation strategy is provided, the method comprising:

[0008] Collect voltage, current, power, active and reactive power, and harmonic operation data of key nodes of the distribution network in real time, and pre-process the operation data to obtain processed data;

[0009] Based on the processed data, an initial optimization model including multiple optimization objectives is constructed; based on the current operating status of the distribution network and each optimization objective, the weight and priority of each optimization objective are adjusted and determined to obtain a multi-objective optimization model with weight optimization;

[0010] Solve the multi-objective optimization model to obtain the distribution network control plan;

[0011] Generate control instructions according to the distribution network control plan, and send the control instructions to the corresponding control device; wherein the control instructions include adjusting the output power of the distributed power source, switching capacitor banks, adjusting transformer taps, and optimizing load distribution;

[0012] Conduct real-time evaluation of the distribution network after executing the control instructions, monitor and analyze the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtain evaluation results;

[0013] According to the evaluation results, the parameters and weights of the multi-objective optimization model are adjusted to match the current operating status of the distribution network.

[0014] Furthermore, based on the processed data, an initial optimization model including multiple optimization objectives is constructed, including:

[0015] Integrate the processed electrical parameter data with related data to form a data set; wherein the related data includes load forecast, equipment status, and environmental factors;

[0016] Analyze the data set, identify key indicators and influencing factors related to the optimization objectives, and determine multiple optimization objectives;

[0017] Through the initial optimization model, multiple objectives are optimized and constraints are set, including voltage range, current limit, and equipment capacity.

[0018] Furthermore, according to the current operating state of the distribution network and each optimization objective, the weight and priority of each optimization objective are adjusted and determined to obtain a weight-optimized multi-objective optimization model, including:

[0019] For each optimization goal, define the corresponding objective function to quantify the effect of achieving each optimization goal;

[0020] According to the current operating status of the distribution network, the urgency and importance of each optimization target are evaluated to obtain the evaluation results;

[0021] According to the evaluation results, the weight of each optimization objective is adjusted, and the optimization objectives are prioritized according to the adjusted weights to obtain a multi-objective optimization model with weight optimization.

[0022] Furthermore, the weight-optimized multi-objective optimization model is solved to obtain a distribution network control scheme, including:

[0023] The multi-objective optimization model after weight optimization is converted into a format, and the parameters of the linear programming solver are configured, wherein the parameters of the linear programming solver include solution accuracy, iteration number limit, and time limit;

[0024] The linear programming solver iteratively solves the transformed multi-objective optimization model, traverses the feasible solution space, and obtains the solution result until the preset number of iterations is reached.

[0025] Furthermore, the weight-optimized multi-objective optimization model is solved to obtain a distribution network control plan, which also includes:

[0026] The linear programming solver outputs the solution results, analyzes the solution results, extracts the final values ​​of the optimization variables, and corresponds the final values ​​to the operation instructions in the distribution network control plan;

[0027] A distribution network control scheme is generated according to the final value, wherein the distribution network control scheme includes adjusting the output power of distributed power sources, switching capacitor banks, adjusting transformer taps, and optimizing load distribution.

[0028] Furthermore, the distribution network after executing the control instructions is evaluated in real time, and the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions are monitored and analyzed to obtain evaluation results, including:

[0029] Using smart meters and voltage monitoring systems, the voltage level data of each node in the distribution network is monitored in real time, and it is determined whether the voltage is within the range and whether there is overvoltage or undervoltage.

[0030] The power loss of the distribution network is calculated based on the voltage level data and related parameters, and the impact of the control instructions on the network loss is evaluated; the related parameters include current data, line parameters, transformer parameters, and load data; based on renewable energy and its absorption in the distribution network, the utilization rate and grid-connected performance of renewable energy are evaluated, wherein the renewable energy includes the power generation of solar energy and wind energy; the key indicators of the number of power outages and the duration of power outages are counted to evaluate the reliability and stability of the power supply system; according to the operating data and energy consumption of the distribution network, the carbon emissions are calculated, and the impact of the distribution network operation on the environment is evaluated, so as to obtain the monitoring and analysis results of the distribution network performance, renewable energy utilization, power supply reliability and environmental impact.

[0031] In a second aspect, a distribution network control system based on a multi-objective optimization dynamic aggregation strategy is applied to the distribution network control method based on a multi-objective optimization dynamic aggregation strategy, including:

[0032] The acquisition module is used to collect the voltage, current, power, active and reactive power, and harmonic operation data of each key node of the distribution network in real time, and pre-process the operation data to obtain processed data; based on the processed data, an initial optimization model including multiple optimization objectives is constructed; based on the current operation status of the distribution network and each optimization objective, the weight and priority of each optimization objective are adjusted and determined to obtain a multi-objective optimization model after weight optimization; the multi-objective optimization model is solved to obtain a distribution network control plan;

[0033] The processing module is used to generate control instructions according to the distribution network control plan, and send the control instructions to the corresponding control device; wherein the control instructions include adjusting the output power of distributed power sources, switching capacitor banks, adjusting transformer taps, and optimizing load distribution; performing real-time evaluation on the distribution network after executing the control instructions, monitoring and analyzing changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtaining evaluation results; according to the evaluation results, adjusting the parameters and weights of the multi-objective optimization model to match the current operating status of the distribution network.

[0034] According to a third aspect, a computing device includes:

[0035] one or more processors;

[0036] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0037] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0038] The above solution of the present invention includes at least the following beneficial effects:

[0039] By collecting and processing the operating data of key nodes in the distribution network in real time, we can accurately grasp the real-time status of the power grid, and then build and solve the multi-objective optimization model to generate targeted control solutions. This helps to quickly respond to challenges such as load fluctuations and equipment aging, maintain voltage stability, reduce power outages, and thus improve power supply quality and user satisfaction.

[0040] Considering the renewable energy consumption rate as one of the optimization objectives in the optimization model and dynamically adjusting its weight according to the current operating status can effectively promote the access and consumption of renewable energy. This helps to reduce dependence on fossil energy, reduce carbon emissions, and promote the development of distribution networks in a green and low-carbon direction. By constructing a multi-objective optimization model including network loss minimization, this method can effectively reduce the energy loss of the power grid during the transmission and distribution of electric energy while ensuring the quality of power supply. This helps to improve overall energy efficiency, reduce energy waste, and provide strong support for energy conservation and consumption reduction in distribution networks.

[0041] A dynamic aggregation strategy is adopted to flexibly adjust the weight and priority of each target according to the current operating status of the distribution network and the importance of each optimization target. This makes the control scheme more in line with the actual operation needs and enhances the adaptability and flexibility of the distribution network to changes such as load fluctuations and renewable energy access. By conducting real-time evaluation of the distribution network after executing the control command and adjusting the optimization model and aggregation strategy according to the evaluation results, this method can form a closed-loop optimization mechanism. This helps to continuously improve the control effect and enhance the overall performance and optimization level of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a distribution network control method based on a multi-objective optimization dynamic aggregation strategy provided by an embodiment of the present invention.

[0043] Figure 2 It is a schematic diagram of a distribution network control system based on a multi-objective optimization dynamic aggregation strategy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0045] like Figure 1 As shown, an embodiment of the present invention proposes a distribution network control method based on a multi-objective optimization dynamic aggregation strategy, and the method includes the following steps:

[0046] Step 11, collecting the voltage, current, power, active and reactive power, and harmonic operation data of each key node of the distribution network in real time, and preprocessing the operation data to obtain processed data;

[0047] Step 12: construct an initial optimization model including multiple optimization objectives based on the processed data; adjust and determine the weight and priority of each optimization objective based on the current operating state of the distribution network and each optimization objective, so as to obtain a multi-objective optimization model after weight optimization;

[0048] Step 13, solving the multi-objective optimization model to obtain a distribution network control plan;

[0049] Step 14, generating a control instruction according to the distribution network control plan, and sending the control instruction to the corresponding control device; wherein the control instruction includes adjusting the output power of the distributed power source, switching the capacitor bank, adjusting the transformer tap, and optimizing the load distribution;

[0050] Step 15, conduct real-time evaluation of the distribution network after executing the control command, monitor and analyze the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtain evaluation results;

[0051] Step 16: According to the evaluation results, the parameters and weights of the multi-objective optimization model are adjusted to match the current operating status of the distribution network.

[0052] In an embodiment of the present invention, by collecting data in real time, building an optimization model and solving it, the operating state of the distribution network can be accurately controlled, voltage stability can be improved, network losses can be reduced, and overall operating efficiency can be improved. Promoting renewable energy consumption: The optimization model considers the renewable energy consumption rate as one of the optimization goals, which helps to maximize the use of renewable energy and reduce dependence on traditional energy. By real-time monitoring and analysis of key indicators, potential problems can be discovered and solved in a timely manner to ensure the stable operation and reliable power supply of the distribution network. The optimization model considers carbon emissions as one of the optimization goals, which helps to reduce carbon emissions during the operation of the distribution network and achieve environmental protection and sustainable development goals. By real-time evaluation and adjustment of model parameters and weights, it is possible to dynamically adapt to changes in the operating state of the distribution network to ensure the continued effectiveness and accuracy of the optimization and control strategy.

[0053] In a preferred embodiment of the present invention, the above step 11, real-time collection of voltage, current, power, active and reactive power, and harmonic operation data of each key node of the distribution network, and pre-processing of the operation data to obtain processed data, may include:

[0054] Install high-precision and high-reliability sensors and measuring devices at key nodes of the distribution network (such as substations, feeder terminals, important users, etc.). These devices can collect electrical parameters such as voltage, current, power, and power quality indicators such as harmonics in real time. The collected data is transmitted to the data center via wired or wireless means. Encryption and compression technology are used during the transmission process to ensure data security and transmission efficiency. The database has high concurrent read and write capabilities and can support real-time storage and query of large amounts of data. Clean the collected data to remove outliers, duplicate values, and missing values. Outliers are caused by equipment failures, data transmission errors, etc., and need to be identified and eliminated through statistical methods or machine learning algorithms.

[0055] Calibrate the cleaned data to ensure data accuracy and consistency. The calibration process includes operations such as time synchronization and value correction to eliminate measurement differences between different devices. For example, convert analog signals into digital signals, or format data from different devices in a unified manner. Extract useful feature information from the converted data, such as voltage fluctuations, current harmonic content, etc. The data acquisition and preprocessing process needs to be carried out in real time to ensure that the control center can obtain the current status of the distribution network in a timely manner.

[0056] When specifically applied, the above content specifically includes:

[0057] At key locations of the substation, such as incoming and outgoing lines, busbars, transformers, etc., voltage sensors, current sensors, power factor meters and other equipment are installed to collect electrical parameters such as voltage, current, and power in real time. At the end of the feeder, voltage monitors, current monitors and other equipment are installed to monitor the voltage and current conditions at the end of the feeder. At the incoming line of important users, power quality monitors are installed to monitor power quality indicators such as voltage fluctuations and harmonics in real time. These devices should all use high-precision and high-reliability products to ensure the accuracy of the collected data. According to actual needs, set a suitable collection frequency, such as collecting once per minute or multiple times per second, including electrical parameters and power quality indicators such as voltage, current, power, active and reactive power, harmonics, etc. The collected data is stored in the memory of the device or transmitted to the data center in real time via wired or wireless methods. Data is transmitted to the data center by wired (such as optical fiber) or wireless (such as wireless private network). Optical fiber transmission has the characteristics of high speed, stability, and strong anti-interference, and is suitable for long-distance and large data transmission; wireless private networks are suitable for occasions where it is difficult to lay optical fibers, such as remote areas or mobile devices. During data transmission, encryption technology (such as the AES encryption algorithm) is used to encrypt data to ensure that the data is not stolen or tampered with during transmission. Specifically, the plaintext data is grouped into 128 bits (16 bytes). If the plaintext length is not an integer multiple of 16, a padding operation is required. The initial key is expanded into multiple round keys for encryption together with the plaintext. The AES algorithm supports three key lengths of 128 bits, 192 bits, and 256 bits. For keys of different lengths, a 128-bit key requires 10 rounds of expansion, a 192-bit key requires 12 rounds of expansion, and a 256-bit key requires 14 rounds of expansion. Each round of encryption includes four operations: SubBytes, ShiftRows, MixColumns, and AddRoundKey.

[0058] SubBytes (byte substitution), replace the input 16 bytes with new 16 bytes by looking up the table. The table used is called S-box, which is a fixed-size table consisting of 256 bytes. ShiftRows (row shift), divide the plaintext into four rows, and then shift each row cyclically. The first row remains unchanged, the second row is cyclically shifted left by one byte, the third row is cyclically shifted left by two bytes, and the fourth row is cyclically shifted left by three bytes. MixColumns (column mixing), confuse each column to change the structure of the plaintext. Use a fixed matrix for transformation to replace each column with another column. AddRoundKey (round key addition), perform an XOR operation on the expanded round key and the plaintext to obtain the encrypted intermediate result. After multiple rounds of encryption, the final encryption result (ciphertext) is obtained. If the plaintext has been padded, the padded part needs to be removed during decryption to restore the original plaintext. The use of encryption technology should comply with relevant security standards and specifications to ensure the security of data transmission. In order to improve transmission efficiency, compression technology can be used to compress data. Compression technology can reduce the amount of data, reduce transmission delay and bandwidth occupancy, and improve the real-time and reliability of data transmission.

[0059] In the data center, the received data is preprocessed, including data cleaning, format conversion, etc. Data cleaning refers to removing noise and irrelevant information from the data and retaining useful information; in the process of data preprocessing, outliers need to be identified and eliminated. Outliers may be caused by equipment failure, data transmission errors, etc. Statistical methods (such as the 3σ principle) are used to identify and eliminate outliers. Specifically, for a given data set, its mean and standard deviation are first calculated. According to the 3σ principle, the range of outliers is determined to be data values ​​outside [μ-3σ, μ+3σ], where μ represents the mean. That is, if a data value is less than μ-3σ or greater than μ+3σ, it is considered an outlier. Traverse the data set and for each data value, determine whether it belongs to the outlier range. If it belongs to the outlier range, the data value is eliminated from the data set. After eliminating the outliers, update the data set.

[0060] Suppose there is a data set containing 10 data values:

[0061] [10, 12, 13, 13, 12, 10, 104, 11, 12, 15].

[0062] Average value = (10+12+13+13+12+10+104+11+12+15) / 10 = 19.6;

[0063] (keep two decimal places);

[0064] Outlier range = [19.6-329.96, 19.6+329.96]≈[-70.28, 119.56] (keep two decimal places). We can see that 104 is not in this range, but since we are in the example, it is obvious that 104 is far from the data value, so it should be considered an outlier. We traverse the data set and find that 104 is an outlier, so we remove it. The updated data set is: [10, 12, 13, 13, 12, 10, 11, 12, 15].

[0065] For example, when the voltage data of a node suddenly deviates from the normal range, it can be judged as an outlier and eliminated.

[0066] Taking a city's distribution network as an example, the distribution network has installed high-precision and high-reliability sensors and measuring equipment at locations such as substations, feeder terminals, and important users. These devices collect electrical parameters such as voltage, current, power, and power quality indicators such as harmonics in real time, and transmit the data to the data center through optical fiber and wireless private networks. During the data transmission process, the AES encryption algorithm is used to encrypt the data to ensure the security of data transmission. At the same time, in order to improve the transmission efficiency, compression technology is used to compress the data. In the data center, the received data is preprocessed and outliers are eliminated to obtain accurate and reliable data support. These data are used to optimize decision-making, fault diagnosis, energy efficiency evaluation, and load forecasting, providing a strong guarantee for the intelligent management of the distribution network.

[0067] Calibrate the cleaned data to ensure data accuracy and consistency. The calibration process includes operations such as time synchronization and value correction to eliminate measurement differences between different devices. Convert the calibrated data into a format suitable for subsequent processing and analysis. For example, convert analog signals into digital signals, or format data from different devices in a unified manner. Extract useful feature information from the converted data, such as voltage fluctuations, current harmonic content, etc.

[0068] In a preferred embodiment of the present invention, the above step 12, constructing an initial optimization model including multiple optimization objectives based on the processed data, may include:

[0069] Step 121, integrating the processed electrical parameter data with related data to form a data set; wherein the related data includes load forecast, equipment status, and environmental factors;

[0070] Step 122, analyzing the data set, identifying key indicators and influencing factors related to the optimization target, and determining multiple optimization targets;

[0071] Step 123, optimizing multiple objectives through the initial optimization model and setting constraints, wherein the constraints include voltage range, current limit, and equipment capacity.

[0072] In an embodiment of the present invention, the processed electrical parameter data includes operating data such as voltage, current, power, active power, reactive power, harmonics, etc. The data is collected in real time by sensors and is cleaned and calibrated. Based on historical load data and weather forecasts, the time series analysis method is used to predict the load and obtain the load demand for a period of time in the future, including the status of equipment such as transformers, switches, and lines (such as operation, failure, and maintenance status) and their historical performance records. Environmental factor data, such as meteorological data such as temperature, humidity, and wind speed, which affect the load and equipment performance of the distribution network.

[0073] Specifically, collect power load data for a period of time, record it in hours or minutes, including total load, regional load, etc. Collect weather data related to load, such as temperature, humidity, precipitation, wind speed, etc., from the meteorological station or weather API. Use chart tools to draw time series graphs of load and weather data to observe the trend, seasonality, and periodicity characteristics of the data. Calculate the correlation coefficient between load and weather variables (such as temperature, humidity, etc.) to identify key weather factors that affect load.

[0074] According to the data characteristics, the autoregressive integrated moving average model is determined, and the data set is divided into a training set and a test set in chronological order (for example, the first 80% of the data is used as the training set, and the last 20% is used as the test set). Determine the model input characteristics, including historical load values ​​and related weather data. Use the autoregressive integrated moving average model and the training set data to fit the model, and adjust the model parameters to optimize the prediction effect. Use the trained model to predict the load demand of the test set. Calculate the prediction performance indicators of the model, including the root mean square error. The calculation formula for the mean square error is: Where MSE stands for mean square error; n stands for the number of samples; represents the predicted value; y i Indicates actual value.

[0075] For each sample point i, calculate the predicted value With the actual value y i The difference between This difference represents the prediction error of the model at this sample point. Each error is squared, that is The purpose of squaring is to eliminate the impact of negative errors on the total and make large errors contribute more to the total, thereby highlighting the performance of the model on large errors. The squared errors of all sample points are summed, that is, This step obtains the sum of the squared errors of all sample points. Divide the sum of the squared errors by the number of samples n, that is, This step is to obtain the average square error of each sample point, so as to more objectively evaluate the overall prediction performance of the model. According to the evaluation results, the model is tuned, such as adjusting model parameters, adding features, changing model structure, etc.

[0076] Repeat the training and evaluation until the final model is found. Deploy the trained model to actual applications for load forecasting. Monitor the model's forecasting effect and update the model in time to adapt to data changes. Use the final model combined with the latest weather forecast data to forecast load demand for a period of time in the future. Use charts to show the comparison between actual load and forecast load to facilitate observation of the model's forecasting effect. Apply load forecasting results to power dispatching, load management, demand response and other fields to optimize the allocation of power resources. Horizontally integrate the processed electrical parameter data with load forecast, equipment status and environmental factor data to form a multidimensional data set. Perform preliminary analysis of the data, observe the relationship between the parameters, and identify trends and patterns. Determine the correlation between electrical parameters, load demand, equipment status and environmental factors, and identify important factors affecting power grid operation.

[0077] According to the analysis results, multiple optimization goals are clarified, such as: maximizing the renewable energy consumption rate, minimizing voltage fluctuations, minimizing network losses, improving power supply reliability, and reducing carbon emissions. Prioritize the optimization goals according to the actual application scenarios and business needs. Construct the initial optimization model based on multiple optimization goals. For example: f(x) = w1f1(x) + w2f2(x) + w3f3(x) + w4f4(x) + w5f5(x); where f(x) represents the goal of the entire optimization model; w1 represents the weight coefficient of renewable energy power generation; f1(x) represents renewable energy power generation; w2 represents the weight coefficient of voltage fluctuation range; f2(x) represents voltage fluctuation range; w3 represents the weight coefficient of transmission loss; f3(x) represents transmission loss; w4 represents the weight coefficient of the number of power supply interruptions; f4(x) represents the number of power supply interruptions; w5 represents the weight coefficient of system carbon emissions; f5(x) represents system carbon emissions. Ensure that the voltage of each node in the distribution network is within the specified range. For example, set the voltage range to [0.95,1.05]pu. Ensure that the current of each line does not exceed the rated capacity. Set the current upper limit of each line, such as I≤I max Consider the capacity limitations of transformers, switches and other equipment to ensure that the control plan is within the equipment carrying capacity, that is: Among them, V min Represents the minimum constraint value of the node voltage; V c Represents the voltage value of the cth node in the distribution network; V max represents the maximum value constraint of the node voltage; I represents the current value of any line in the distribution network; I maxIndicates the maximum current of the line; P indicates the power value transmitted through the equipment (such as transformer, switch, etc.); P y Indicates the maximum capacity constraint of the device.

[0078] By integrating the processed electrical parameter data with related data (such as load forecast, equipment status, and environmental factors), a data set containing multi-dimensional information is formed. This data integration enables the optimization model to more comprehensively consider the operating status and external conditions of the distribution network, thereby improving the accuracy and practicality of the optimization results. After in-depth analysis of the data set, the problems and potential optimization space in the operation of the distribution network can be more clearly identified. This helps to determine multiple optimization goals with clear directions, such as improving voltage stability, reducing network losses, and increasing the renewable energy consumption rate. These clear goals provide a clear direction for the construction of the optimization model. The initial optimization model constructed based on multiple optimization goals comprehensively considers all aspects and needs of the distribution network. This scientific model construction method can ensure that the optimization scheme can achieve the optimization of multiple goals as much as possible while satisfying the constraints, thereby improving the overall performance of the distribution network. The constraints set during the optimization process (such as voltage range, current limit, equipment capacity, etc.) ensure the feasibility and practicality of the optimization scheme. Optimizing multiple goals through the initial optimization model can improve the operating efficiency, stability, and sustainability of the distribution network. For example, after optimization, voltage stability is improved, network losses are reduced, renewable energy consumption rate is increased, power supply reliability is enhanced, and carbon emissions are reduced. These optimization effects help to achieve efficient and stable distribution networks.

[0079] In another preferred embodiment of the present invention, the above step 12, according to the current operating state of the distribution network and each optimization target, adjusts and determines the weight and priority of each optimization target to obtain a multi-objective optimization model after weight optimization, and may also include:

[0080] Step 124, for each optimization target, define a corresponding objective function to quantify the effect of achieving each optimization target;

[0081] Step 125, evaluating the urgency and importance of each optimization target according to the current operating status of the distribution network, and obtaining an evaluation result;

[0082] Step 126, according to the evaluation results, the weights of the optimization objectives are adjusted, and the optimization objectives are prioritized according to the adjusted weights to obtain a multi-objective optimization model after weight optimization.

[0083] In the embodiment of the present invention, a quantitative objective function is established for each optimization target so that it can be analyzed and optimized through a mathematical model. These objective functions can effectively reflect the effect of achieving each target. For example: Where f1(x) represents the amount of electricity generated by renewable energy; P r Represents the total power generated by renewable energy (such as solar energy, wind energy, hydropower, etc.); P t It indicates the power that the distribution network can provide in a specific time period. f2(x)=max(V c )-min(V c ); where f2(x) represents the voltage fluctuation range; min(V c ) represents the minimum constraint value of the node voltage; V c Indicates the voltage value of the cth node in the distribution network; max(V c ) represents the maximum value constraint of the node voltage. f3(x)=∑P s ; Where f3(x) represents the transmission loss; P s Represents the lost power. f4(x)=R; where f4(x) represents the number of power supply interruptions; R represents the frequency of power outages, the average duration of power outages, and the number of power supply interruptions. f5(x)=∑CO2; where f5(x) represents the system carbon emissions; ∑CO2 represents the total amount of carbon dioxide emitted by each power generation source in the distribution network during the power generation process. According to the actual operating status of the distribution network, analyze and evaluate the urgency and importance of each optimization goal. According to the evaluation method, score each optimization goal to form an evaluation result. For example, a scoring system of 1-5 is used, where 5 means very important and 1 means not important:

[0084] The optimization objectives are: maximize the renewable energy consumption rate, minimize voltage fluctuations, minimize network losses, and improve power supply reliability. The corresponding importance scores are: 4, 5, 3, 5, 4. According to the evaluation results, the weights of each optimization objective are adjusted. The weight setting should reflect the importance and urgency of each objective. The adjustment steps include: normalize the evaluation results to ensure that all weights are compared under the same standard. According to the importance of each objective, assign corresponding weights to each objective to ensure that the overall weight sum is 1. After the weight adjustment is completed, prioritize the optimization objectives. You can sort them from high to low by weight to form a priority list. For example: if the weight of minimizing voltage fluctuations is 0.25, the priority is 1; if the weight of improving power supply reliability is 0.25, the priority is 2. The weight of maximizing the renewable energy consumption rate is 0.20; the priority is 3. The weight of reducing carbon emissions is 0.15, the priority is 4. The weight of minimizing network losses is 0.15, the priority is 5. Integrate the weighted optimized objective function into a unified multi-objective optimization model. This model needs to consider the mutual influence of each objective and the comprehensive objective after weight allocation.

[0085] By obtaining a multi-objective optimization model with weight optimization, we can use renewable energy more efficiently, reduce dependence on traditional energy, and reduce environmental impact. Improve the stability of grid voltage, reduce voltage fluctuations, and ensure power quality. Reduce power loss in the grid, improve energy efficiency, and reduce operating costs. By giving priority to the optimization of power supply reliability, reduce the frequency and duration of power outages, and ensure continuous and stable power supply to users. Reduce carbon dioxide emissions and help the distribution network develop in a green and low-carbon direction, in line with environmental protection and energy-saving goals. Adjust the optimization goals in a timely manner according to the current operating status of the distribution network, give priority to solving the most pressing problems (such as voltage instability or load overload), and improve the system's emergency response capabilities.

[0086] In a preferred embodiment of the present invention, the above step 13, solving the weight-optimized multi-objective optimization model to obtain a distribution network control scheme, may include:

[0087] Step 131, converting the multi-objective optimization model after weight optimization, and configuring the parameters of the linear programming solver, wherein the parameters of the linear programming solver include solution accuracy, iteration number limit, and time limit;

[0088] Step 132, the linear programming solver iteratively solves the converted multi-objective optimization model, traverses the feasible solution space, and obtains a solution result until a preset number of iterations is reached.

[0089] In the embodiment of the present invention, ensure that the objective function and constraints can adapt to the form of linear programming. Convert all objective functions into linear form. Ensure that all constraints are linear expressions. Linearize nonlinear constraints. Define the meaning, unit and upper and lower bounds of each decision variable. All variables must be within the feasible range. Configure appropriate parameters in the solver to ensure that the solution process is efficient and accurate. Determine the convergence criteria of the solution, such as setting the tolerance of relative error and absolute error. Set to 10 -6, to ensure the accuracy of the solution. Set an upper limit on the number of iterations. For example, the maximum number of iterations can be set to 1000 times, or an appropriate value can be set based on historical solution experience. In order to prevent excessive solution time, the maximum allowable solution time (such as 300 seconds) can be set to ensure that the solution can be completed within a reasonable time. Input the converted multi-objective optimization model into the linear programming solver and initialize all variables. The solver starts iterative solution and traverses the feasible solution space. In each iteration, the solver will optimize the objective function based on the current solution state and verify the feasibility according to the constraints. In each iteration, the solver needs to check whether the current solution meets the accuracy requirements. If the maximum number of iterations is reached, the iteration is stopped. During the iteration process, the solver needs to record the solution and objective function value of each step and monitor the optimization progress. After the solution is completed, the final optimization result is extracted, including the optimal value of each decision variable and the corresponding objective function value. Check whether the solution meets all constraints. If not, analysis is required to determine whether the model needs to be adjusted or the parameters need to be reset. Based on the solution results, specific distribution network control measures are proposed. For example, adjusting the access amount of renewable energy, optimizing load distribution, improving voltage control strategies, etc. The solution process, results and corresponding distribution network control schemes are compiled into a report for reference by decision makers.

[0090] By configuring power output, load distribution and equipment adjustment, the operating efficiency and reliability of the distribution network can be effectively improved. Accurate control schemes can reduce unnecessary energy loss, reduce operating costs and bring economic benefits. By increasing the utilization rate of renewable energy and reducing dependence on fossil fuels, the goal of sustainable development can be supported. Effective control measures can help stabilize the operation of the power grid, reduce the risk of failures and improve the safety of power supply. By analyzing the solution results, a data-driven decision-making basis can be formed to improve the scientificity and effectiveness of the control strategy.

[0091] In another preferred embodiment of the present invention, the above step 13, solving the weight-optimized multi-objective optimization model to obtain a distribution network control scheme, may also include:

[0092] Step 133, the linear programming solver outputs the solution result, analyzes the solution result, extracts the final value of the optimization variable, and corresponds the final value to the operation instruction in the distribution network control plan;

[0093] Step 134, generating a distribution network control plan according to the final value, wherein the distribution network control plan includes adjusting the output power of the distributed power source, switching capacitor banks, adjusting transformer taps, and optimizing load distribution.

[0094] In an embodiment of the present invention, after the solution is completed, the solver will generate a solution result, including the final value of each decision variable and the optimal value of the objective function (such as minimum loss or maximum economic benefit). The final value of each decision variable is extracted from the solution result, and these values ​​will directly affect the regulation and control operation of the distribution network. For example: the output power of the distributed power source, the number and type of the switched capacitor group, the tap position of the transformer, and the specific strategy of load distribution. The extracted optimization variables are sorted into an understandable format. The state and final result of the solution process are recorded. According to the optimization results, specific operation instructions are generated. The instructions should clearly specify the specific output power value of each distributed power source, adjust the switching state of the capacitor group according to the demand, ensure the optimization of the system power factor, indicate the specific tap position and adjustment range to adapt to the load change, formulate a specific load distribution strategy, and ensure the reasonable distribution of the load at each node. The operation instructions are sorted into a distribution network regulation plan document, which includes the regulation objectives and expected effects, operation steps and execution schedule, and the expected effects and monitoring indicators of various regulation measures. Implement distribution network control measures according to the generated operating instructions, continuously monitor the operating status of the distribution network, and evaluate the effectiveness of the control measures, including voltage stability, changes in system losses, and improvements in economic benefits.

[0095] Reduce energy loss by optimizing power output and load distribution. Adjusting transformer taps and switching capacitor banks can effectively improve system voltage stability and prevent the impact of excessive or low voltage on equipment. By adjusting the output power of distributed power sources, the use of renewable energy can be maximized, dependence on fossil fuels can be reduced, and environmental protection goals can be supported. Use the optimization results to form a data-driven control plan to improve the scientificity and effectiveness of the control strategy. After implementing the control plan, continuously monitor the operation of the distribution network and collect data to evaluate the control effect. According to the gap between the actual operation results and the expected results, timely adjust the parameters of the optimization model to improve the solution accuracy and practicality.

[0096] In a preferred embodiment of the present invention, the above step 14 generates a control instruction according to the distribution network control scheme, and sends the control instruction to the corresponding control device; wherein the control instruction includes adjusting the output power of the distributed power source, switching the capacitor bank, adjusting the transformer tap, optimizing the load distribution, and may include:

[0097] The control scheme includes adjusting the output power of distributed generation, switching capacitor banks, and adjusting transformer taps. Parameters are collected from the solution results of the multi-objective optimization model. These parameters include the target output power of distributed generation, the state of the capacitor bank (on or off), the tap position of the transformer, and the specific situation of load distribution. For example, the optimization results may indicate:

[0098] The output power of the wind farm is set at 150kw;

[0099] Switch on capacitor bank 1 and turn off capacitor bank 2;

[0100] Transformer A is adjusted to tap position 2.

[0101] Transfer 20kw of load from node 1 to node 2. Prepare control instructions based on the optimization results. The instructions should clearly indicate the type of distributed power source that needs to be adjusted and the specific output power. For example, the instruction content is "Adjust the output power of the wind farm to 150kW." For the switching status of the capacitor group, the instruction should indicate the number and status (on or off) of the specific capacitor group. For example, the instruction content is "Capacitor group 1 is switched on and capacitor group 2 is closed." The instruction should include the specific transformer identification and the new tap position. For example, the instruction content is "The tap of transformer A is adjusted to position 2." For load redistribution, the instruction should clearly state the source node, target node and the amount of load to be transferred. For example, the instruction content is "Transfer the 20kW load of node 1 to node 2."

[0102] Arrange the control instructions into a complete control instruction package to ensure that it contains all necessary instructions. The format of this package should be easy to parse and can be executed immediately after sending. According to the architecture of the distribution network control system, determine the way to send the control instructions. Send the assembled control instructions to the corresponding control device in a determined way. After receiving the control instruction, the control device will return feedback information to confirm the execution status of the instruction. The feedback information includes whether the instruction is executed successfully or not. If it fails, return specific error information. If the feedback information shows that the instruction is executed successfully, record the execution result and update the system status. If the instruction fails to execute, analyze the cause of the failure and take corresponding measures, such as retrying to send the instruction or adjusting the strategy. After the instruction is executed, the status information of each node and device is obtained in real time through the monitoring system of the distribution network to ensure the normal operation of the system, including the monitoring of key parameters such as voltage, power, and load. According to the monitoring data, adjust the original control strategy and instructions when necessary to deal with possible emergencies and ensure the stability and efficiency of the system. Save the execution process of the control instruction, including the sending time, execution status, feedback information, etc.

[0103] In a preferred embodiment of the present invention, the above step 15 performs real-time evaluation on the distribution network after executing the control instruction, monitors and analyzes the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtains evaluation results, which may include:

[0104] Step 151, using a smart meter and a voltage monitoring system to monitor the voltage level data of each node of the distribution network in real time, and determine whether the voltage is within a range, and identify whether there is an overvoltage or undervoltage situation;

[0105] Step 152, based on the voltage level data and related parameters, the power loss of the distribution network is calculated, and the impact of the control instructions on the network loss is evaluated; the related parameters include current data, line parameters, transformer parameters, and load data; based on renewable energy and its absorption in the distribution network, the utilization rate and grid-connected performance of renewable energy are evaluated, wherein the renewable energy includes the power generation of solar energy and wind energy; the key indicators of the number of power outages and the duration of power outages are counted to evaluate the reliability and stability of the power supply system; according to the operating data and energy consumption of the distribution network, the carbon emissions are calculated, and the impact of the distribution network operation on the environment is evaluated, so as to obtain the monitoring and analysis results of the distribution network performance, renewable energy utilization, power supply reliability and environmental impact.

[0106] In an embodiment of the present invention, smart meters and voltage monitoring systems are installed at key nodes and user terminals of the distribution network to ensure that voltage level data can be obtained in real time. The distribution network monitoring system regularly collects voltage data from each node from smart meters. This includes measuring parameters such as instantaneous voltage and average voltage. Set the voltage range (such as ±10% rated voltage range). The monitoring system compares the current voltage data with the standard range in real time to identify whether there is an overvoltage or undervoltage condition. The system records the time, duration and severity of each voltage anomaly. Collect relevant parameters, including real-time current data from smart meters and transformers, line parameters, such as line resistance, length, etc. Transformer parameters, transformer efficiency, capacity and other information,

[0107] The load conditions of each part of the system. The power loss is calculated as: P loss =I 2 ·R; where I represents current and R represents line resistance. Summarize the power losses of different nodes and evaluate the changes in network losses before and after the execution of the control instructions. Collect solar and wind power generation data. These data are obtained through the intelligent monitoring system of photovoltaic power generation systems and wind turbines. Calculate the utilization rate of renewable energy as: Where P represents actual power generation and M represents maximum power generation. According to the utilization rate of renewable energy, evaluate its grid connection performance in the distribution network and analyze whether the grid connection of renewable energy causes voltage fluctuations or load imbalance. Count the number of power outages and the duration of each power outage. Obtain through the fault recording function of smart meters and monitoring systems. Collect energy consumption data of the distribution network through the monitoring system, including the use of various types of energy. Integrate various data into a comprehensive monitoring platform to compare and analyze voltage levels, network losses, renewable energy utilization, and power supply reliability. Generate an evaluation report based on the monitoring and analysis results, including changes in various indicators, analysis of influencing factors, and corresponding suggestions and improvement measures.

[0108] Through real-time monitoring, overvoltage or undervoltage can be detected in time, and corresponding measures can be taken to adjust to ensure stable voltage levels. By comparing the power loss before and after the execution of the control command, the impact of the control command on the network loss can be evaluated. If the network loss is reduced, it means that the control command has effectively optimized the operation of the distribution network. Evaluate the absorption of renewable energy in the distribution network. By collecting the power generation data of solar and wind energy, the utilization rate and grid-connected performance of renewable energy can be calculated. If the utilization rate of renewable energy is improved, it means that the control command has promoted the absorption of renewable energy, which helps to reduce the use of fossil energy and reduce carbon emissions. Evaluate the reliability and stability of the power supply system by counting the number of power outages and the duration of power outages. If the number of power outages is reduced and the duration of power outages is shortened, it means that the control command has improved the reliability of power supply and reduced the power outage time of users.

[0109] In another preferred embodiment of the present invention, an evaluation result is generated based on the monitoring and analysis results of distribution network performance, renewable energy utilization, power supply reliability and environmental impact. The evaluation result includes changes in key indicators, comparisons with expected goals, existing problems and improvement measures, and may include:

[0110] Distribution network performance data includes voltage level, power loss, network loss, etc. Renewable energy utilization data includes actual power generation of solar and wind power, grid connection status, etc. Power supply reliability data includes the number of power outages, the duration of power outages, user complaints, etc. Determine the frequency and degree of voltage anomalies. Generate a voltage fluctuation chart to show whether the voltage is within the expected range. Analyze the reasons for the reduction or increase in network losses. Compare the actual power generation with the expected power generation, and evaluate the grid connection performance of renewable energy in different time periods (such as peak and trough periods). Count the number of power outages and the duration of power outages. Analyze the root causes of power supply reliability problems through user feedback and complaint records. Set expected targets for each key indicator. For example:

[0111] Voltage stability ≥ 95%;

[0112] Network loss rate ≤5%;

[0113] Renewable energy utilization rate ≥30%.

[0114] The average power outage duration is ≤ 1 hour. Compare the monitored key indicators with the expected targets to identify the gaps. Make a comparison chart to show the achievement of each indicator. Based on the comparison results, identify the possible reasons for the failure to meet the target for each indicator: voltage anomalies may be caused by load fluctuations or line aging, increased network losses may be related to insufficient line maintenance or equipment failure, low utilization of renewable energy may be affected by weather conditions, technical problems or grid connection policy restrictions, and increased power outages may be related to equipment aging, insufficient maintenance or emergencies.

[0115] In response to voltage level issues, check and maintain equipment at voltage abnormality nodes, and upgrade the system when necessary. Use intelligent dispatching technology to optimize load distribution and ensure voltage stability. In response to network loss issues, regularly check and maintain distribution network lines and replace aging equipment in a timely manner. Optimize line design to reduce line length and resistance and improve efficiency. In response to renewable energy utilization issues, introduce new technologies to improve the grid connection capacity and power generation efficiency of renewable energy. Strive for policy support, encourage the development and utilization of renewable energy, and promote a higher proportion of renewable energy access. In response to power supply reliability issues, strengthen emergency management, formulate emergency response plans, and improve the ability to respond to emergencies. Strengthen communication and feedback mechanisms with users and solve problems reported by users in a timely manner.

[0116] In a preferred embodiment of the present invention, the above step 16, adjusting the parameters and weights of the multi-objective optimization model according to the evaluation results to match the current operating state of the distribution network, may include:

[0117] According to the evaluation results, adjust the parameters of each objective function. For example, if the power loss is high, increase the weight of the power loss. If the utilization rate of renewable energy is insufficient, increase the weight of the utilization rate of renewable energy. Assign weights to each objective based on the actual operating conditions. Establish a feedback mechanism to monitor the operating status of the distribution network after the optimization is implemented to ensure that the optimization objectives are achieved. Based on new operating data and evaluation results, regularly re-evaluate and adjust model parameters and weights to maintain the timeliness and effectiveness of the model.

[0118] like Figure 2 As shown, an embodiment of the present invention further provides a distribution network control system 20 based on a multi-objective optimization dynamic aggregation strategy, comprising:

[0119] The acquisition module 21 is used to collect the voltage, current, power, active and reactive power, and harmonic operation data of each key node of the distribution network in real time, and pre-process the operation data to obtain processed data; construct an initial optimization model including multiple optimization objectives based on the processed data; adjust and determine the weight and priority of each optimization objective based on the current operation status of the distribution network and each optimization objective to obtain a multi-objective optimization model after weight optimization; solve the multi-objective optimization model to obtain a distribution network control plan;

[0120] The processing module 22 is used to generate control instructions according to the distribution network control plan, and send the control instructions to the corresponding control device; wherein the control instructions include adjusting the output power of distributed power sources, switching capacitor banks, adjusting transformer taps, and optimizing load distribution; performing real-time evaluation on the distribution network after executing the control instructions, monitoring and analyzing changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtaining evaluation results; according to the evaluation results, adjusting the parameters and weights of the multi-objective optimization model to match the current operating status of the distribution network.

[0121] The system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0122] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0123] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0124] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distribution network control method based on multi-objective optimization dynamic aggregation strategy, characterized in that: The method comprises: Collect voltage, current, power, active and reactive power, and harmonic operation data of key nodes of the distribution network in real time, and pre-process the operation data to obtain processed data; Based on the processed data, an initial optimization model including multiple optimization objectives is constructed; based on the current operating status of the distribution network and each optimization objective, the weight and priority of each optimization objective are adjusted and determined to obtain a multi-objective optimization model with weight optimization; Solve the multi-objective optimization model to obtain the distribution network control plan; Generate control instructions according to the distribution network control plan, and send the control instructions to the corresponding control device; wherein the control instructions include adjusting the output power of the distributed power source, switching capacitor banks, adjusting transformer taps, and optimizing load distribution; Conduct real-time evaluation of the distribution network after executing the control instructions, monitor and analyze the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtain evaluation results; According to the evaluation results, the parameters and weights of the multi-objective optimization model are adjusted to match the current operating status of the distribution network.

2. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 1 is characterized in that: Based on the processed data, an initial optimization model with multiple optimization objectives is constructed, including: Integrate the processed electrical parameter data with related data to form a data set; wherein the related data includes load forecast, equipment status, and environmental factors; Analyze the data set, identify key indicators and influencing factors related to the optimization objectives, and determine multiple optimization objectives; Multiple objectives are optimized through a linear programming model, and constraints are set, including voltage range, current limit, and equipment capacity.

3. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 2 is characterized in that: According to the current operating status of the distribution network and various optimization objectives, the weights and priorities of various optimization objectives are adjusted and determined to obtain a multi-objective optimization model with weight optimization, including: For each optimization goal, define the corresponding objective function to quantify the effect of achieving each optimization goal; According to the current operating status of the distribution network, the urgency and importance of each optimization target are evaluated to obtain the evaluation results; According to the evaluation results, the weight of each optimization objective is adjusted, and the optimization objectives are prioritized according to the adjusted weights to obtain a multi-objective optimization model with weight optimization.

4. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 3 is characterized in that: The weighted multi-objective optimization model is solved to obtain the distribution network control scheme, including: The multi-objective optimization model after weight optimization is converted into a format, and the parameters of the linear programming solver are configured, wherein the parameters of the linear programming solver include solution accuracy, iteration number limit, and time limit; The transformed multi-objective optimization model is solved by a linear programming solver, and the feasible solution space is traversed until the preset number of iterations is reached to obtain the solution result.

5. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 4 is characterized in that: Solve the weighted multi-objective optimization model to obtain the distribution network control plan, which also includes: The solver outputs the solution result, analyzes the solution result, extracts the final value of the optimization variable, and corresponds the final value to the operation instruction in the distribution network control scheme; A distribution network control scheme is generated according to the final value, wherein the distribution network control scheme includes adjusting the output power of distributed power sources, switching capacitor banks, adjusting transformer taps, and optimizing load distribution.

6. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 5 is characterized in that: Conduct real-time evaluation of the distribution network after executing the control instructions, monitor and analyze the changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, to obtain evaluation results, including: Using smart meters and voltage monitoring systems, the voltage level data of each node in the distribution network is monitored in real time, and it is determined whether the voltage is within the range and whether there is overvoltage or undervoltage. The power loss of the distribution network is calculated based on the voltage level data and related parameters, and the impact of the control instructions on the network loss is evaluated; the related parameters include current data, line parameters, transformer parameters, and load data; based on renewable energy and its absorption in the distribution network, the utilization rate and grid-connected performance of renewable energy are evaluated, wherein the renewable energy includes the power generation of solar energy and wind energy; the key indicators of the number of power outages and the duration of power outages are counted to evaluate the reliability and stability of the power supply system; according to the operating data and energy consumption of the distribution network, the carbon emissions are calculated, and the impact of the distribution network operation on the environment is evaluated, so as to obtain the monitoring and analysis results of the distribution network performance, renewable energy utilization, power supply reliability and environmental impact.

7. The distribution network control method based on multi-objective optimization dynamic aggregation strategy according to claim 6 is characterized in that: Based on the monitoring and analysis results of distribution network performance, renewable energy utilization, power supply reliability and environmental impact, an evaluation result is generated, which includes the changes in key indicators, comparison with expected goals, existing problems and improvement measures.

8. A distribution network control system based on a multi-objective optimization dynamic aggregation strategy, the system implementing the method as described in any one of claims 1 to 7, characterized in that: include: The acquisition module is used to collect the voltage, current, power, active and reactive power, and harmonic operation data of each key node of the distribution network in real time, and pre-process the operation data to obtain processed data; Based on the processed data, an initial optimization model including multiple optimization objectives is constructed; According to the current operating status of the distribution network and various optimization objectives, the weights and priorities of various optimization objectives are adjusted and determined to obtain a multi-objective optimization model with weight optimization; Solve the multi-objective optimization model to obtain the distribution network control plan; The processing module is used to generate control instructions according to the distribution network control plan, and send the control instructions to the corresponding control device; wherein the control instructions include adjusting the output power of distributed power sources, switching capacitor banks, adjusting transformer taps, and optimizing load distribution; performing real-time evaluation on the distribution network after executing the control instructions, monitoring and analyzing changes in key indicators such as voltage level, network loss, renewable energy consumption rate, power supply reliability, and carbon emissions, and obtaining evaluation results; according to the evaluation results, adjusting the parameters and weights of the multi-objective optimization model to match the current operating status of the distribution network.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images