Auxiliary decision-making system and method for load transfer of power distribution network

Abstract

The invention discloses an auxiliary decision-making system and method for load transfer of a power distribution network, and aims to improve the operation efficiency and reliability of the power distribution network. The method comprises the following steps: S10, acquiring node voltage and branch current data in a power distribution network in real time, and calculating a voltage fluctuation index to evaluate the operation state of the power distribution network. And S20, identifying an overload line by using a direct-current power flow calculation method, obtaining a load unbalance factor through calculation, and identifying a key area in which load transfer needs to be carried out. And S30, comprehensively evaluating the transfer capacity of each feeder line through a weighted average method in combination with the voltage fluctuation index and the load imbalance factor, and calculating the transfer capacity coefficient of each feeder line as a basis for optimizing load distribution. And S40, adjusting the switching state by adopting an intelligent optimization algorithm to realize optimal load transfer path planning, and maximizing the transfer capacity coefficient, thereby improving the power supply reliability and economy. The operation efficiency and the power supply quality of the power distribution network can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation and control, and in particular to an auxiliary decision-making system and method for load transfer in a distribution network. Background Art

[0002] With the development of social economy and the advancement of technology, the power system is facing higher and higher reliability and flexibility requirements. As an important part of the power system, the distribution network is responsible for distributing electric energy from the transmission network to end users. In the distribution network, due to various reasons, the line will be temporarily unavailable. At this time, load transfer is needed to ensure the continuity of power supply. Traditional load transfer mainly relies on manual decision-making, which is not only inefficient, but also prone to misjudgment, affecting the quality and safety of power supply.

[0003] In recent years, with the development of distribution automation and information technology, decision-making support systems based on intelligent algorithms and big data analysis have gradually been applied to the operation and management of distribution networks. Such systems can quickly and accurately obtain the real-time operating status of the distribution network, and automatically recommend the best load transfer plan based on the topological structure and operation mode of the distribution network.

[0004] In addition, in order to cope with various complex situations in the distribution network, researchers have also developed a variety of specialized load transfer auxiliary decision-making methods and systems. For example, the "distribution network load transfer auxiliary decision-making method and system" covers multiple key steps such as obtaining distribution network structure information, obtaining fault line information, and selecting alternative lines. It can effectively improve the accuracy of load forecasting and reduce the workload of dispatchers. For another example, the "distribution network load transfer auxiliary decision-making based on dynamic analysis of surplus capacity" dynamically adjusts the load transfer strategy by analyzing the surplus capacity of the power grid to ensure the safe and stable operation of the power grid.

[0005] However, the existing distribution network load transfer auxiliary decision-making system and method still have some shortcomings, such as insufficient adaptability to complex network structures and insufficient response speed to emergencies. Therefore, it is of great theoretical significance and practical value to develop a more efficient, flexible and adaptable load transfer auxiliary decision-making system and method for different distribution network operating environments. Summary of the invention

[0006] The present invention provides an auxiliary decision-making method for load transfer in a distribution network, comprising:

[0007] S10. By collecting node voltage and branch current data in the distribution network in real time, the voltage fluctuation index of each node is calculated to evaluate the operation status of the power grid.

[0008] S20. Use the DC power flow calculation method to identify overloaded lines, obtain the load unbalance factor through calculation, and identify the key areas where load transfer is required.

[0009] S30. Combining the voltage fluctuation index and the load imbalance factor, the transfer capacity of each feeder is comprehensively evaluated through the weighted average method, and the transfer capacity coefficient of each feeder is calculated as the basis for optimizing load distribution.

[0010] S40. Use intelligent optimization algorithms to adjust the switch status to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network.

[0011] The auxiliary decision-making system and method for load transfer in a distribution network as described above, and the auxiliary decision-making method for load transfer in a distribution network are characterized in that the operation status of the power grid is evaluated by real-time data collection and calculation of the voltage fluctuation index, and the method comprises the following sub-steps:

[0012] Configure the data acquisition system to ensure that voltage and branch current data at all key nodes in the distribution network can be obtained;

[0013] Start the data acquisition program, record voltage and current data regularly, and store the measurement sequence;

[0014] Determine the rated voltage of the node and calculate the voltage fluctuation for each sampling point;

[0015] The voltage fluctuation amount at each sampling point is weighted by applying the time decay function, and the square sum of all weighted voltage fluctuation amounts is calculated, and the square root is taken to obtain the voltage fluctuation index.

[0016] The auxiliary decision-making method for load transfer in a distribution network as described above is characterized in that the overloaded line is detected by using a DC power flow calculation method, and the key area for load transfer is determined by calculating a load imbalance factor, including the following sub-steps:

[0017] Collect branch current and voltage data and prepare for calculations;

[0018] Use DC power flow to calculate actual load power and evaluate load conditions;

[0019] Calculate the standard deviation of the ratio of actual load to rated load to obtain the load imbalance factor;

[0020] Identify overloaded lines and critical areas and determine load transfer requirements.

[0021] The auxiliary decision-making method for load transfer in a distribution network as described above is characterized in that the voltage fluctuation and load imbalance data are integrated by a weighted average method to calculate the feeder transfer capacity coefficient to optimize load distribution, and includes the following sub-steps:

[0022] Calculate the average values ​​of feeder voltage fluctuation index and load unbalance factor to evaluate feeder condition;

[0023] Determine the weight coefficients of voltage fluctuation index and load imbalance factor to reflect the importance of different factors;

[0024] The weighted average method is used to calculate the transfer capacity coefficient of each feeder, taking into account voltage stability and load balance;

[0025] The transfer capacity coefficient is used as the basis for optimizing load distribution and guiding load transfer strategies.

[0026] The auxiliary decision-making method for load transfer in a distribution network as described above is characterized in that the switch state is adjusted by using an intelligent optimization algorithm, the optimal load transfer path is planned, the transfer capacity coefficient is maximized, and the power supply reliability and economy of the distribution network are improved, and the following sub-steps are included:

[0027] Initialize the population of the genetic algorithm, define the switch state encoding rules, and randomly generate the initial switch state combination;

[0028] Perform selection, crossover and mutation operations to iterate the evolution process to find the best switch state combination;

[0029] Calculate the transfer capacity coefficient of each individual in each generation, select the maximum value as the fitness function, and maximize the transfer capacity coefficient as the goal;

[0030] The above process is repeated until the preset number of iterations is reached or the convergence condition is met, and finally the optimal load transfer path is obtained.

[0031] The auxiliary decision-making method for load transfer in a distribution network as described above comprises the following sub-steps:

[0032] Monitor and avoid overload during load transfer;

[0033] After implementing the above steps, continuously monitor the operating status of the distribution network to ensure that all measures are effectively implemented;

[0034] Adjust strategies through real-time data feedback to ensure safe and stable operation of the system;

[0035] Through continuous monitoring, any issues causing overload can be detected and resolved promptly.

[0036] The auxiliary decision-making method for load transfer in a distribution network as described above comprises the following sub-steps:

[0037] Providing a user interface to display load transfer results and recommendations;

[0038] Design a friendly user interface to intuitively display the process and results of load transfer;

[0039] The user interface displays key indicators such as changes in transfer capacity coefficients and load transfer paths;

[0040] Presenting data in a graphical way helps operation and maintenance personnel quickly understand the system status.

[0041] The present invention also provides an auxiliary decision-making system for load transfer in a distribution network, comprising a data acquisition and analysis module, a load analysis and balancing module, a transfer capacity evaluation module, and an intelligent optimization and load transfer module. The system comprises the following sub-steps:

[0042] Data acquisition and analysis module: collects node voltage and branch current data in the distribution network in real time, calculates the voltage fluctuation index of each node, and evaluates the operation status of the power grid;

[0043] Load analysis and balancing module: Uses DC power flow calculation methods to identify overloaded lines, calculates load imbalance factors, and identifies key areas where load transfer is required;

[0044] Transfer capacity assessment module: Combined with the voltage fluctuation index and load imbalance factor, the transfer capacity of each feeder is comprehensively assessed through the weighted average method, and the transfer capacity coefficient of each feeder is calculated as the basis for optimizing load distribution;

[0045] Intelligent optimization and load transfer module: Use intelligent optimization algorithm to adjust the switch status to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network.

[0046] The present invention also provides a computer storage medium, characterized in that it comprises: at least one memory and at least one processor;

[0047] The storage medium stores computer program instructions, and when the instructions are executed by at least one processor, the at least one processor executes an auxiliary decision-making method for load transfer in a distribution network as described in any one of the above.

[0048] The beneficial effects achieved by the present invention are as follows:

[0049] (1) Improved efficiency and reliability: The present invention significantly improves the efficiency of load transfer and the reliability of power supply of the distribution network by adopting advanced data analysis technology and intelligent algorithms. This not only speeds up the speed of load transfer and reduces the duration and frequency of power outages, but also improves the quality of power and enhances the anti-interference ability of the system.

[0050] (2) Reduce costs and improve user experience: With the support of intelligent decision-making assistance, the present invention can effectively reduce operation and maintenance costs, reduce energy waste, and significantly improve the power consumption experience of end users. This includes reducing the need for manual intervention, reducing operating costs, and improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a flow chart of an auxiliary decision-making system and method for load transfer in a distribution network provided in an embodiment of the present application.

[0053] Figure 2 It is a schematic diagram of an auxiliary decision-making system and method for load transfer in a distribution network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1

[0056] like Figure 1 As shown, an auxiliary decision-making method for load transfer in a distribution network according to an embodiment of the present application includes:

[0057] Step S10: Calculate the voltage fluctuation index of each node and evaluate the operation status of the power grid by collecting node voltage and branch current data in the power distribution network in real time;

[0058] By collecting node voltage and branch current data in the distribution network in real time and calculating the voltage fluctuation index, the operation status of the power grid can be effectively evaluated, aiming to monitor the health of the power grid, improve the power supply quality, optimize the operation of the power grid, quickly detect and locate faults, support predictive maintenance and the application of smart grid technology, thereby ensuring the safe and stable operation of the distribution network and improving the service level of the power system. Real-time collection of node voltage and branch current data in the distribution network and calculation of the voltage fluctuation index of each node include the following sub-steps:

[0059] Step S11, configuring the data acquisition system to ensure that the voltage and branch current data of all key nodes in the distribution network can be obtained;

[0060] First, select voltage sensors and current transformers suitable for the distribution network and install them at each key node and branch. Then connect these sensors to the data acquisition unit (DAQ) and configure the relevant parameters of the DAQ to ensure the accuracy and stability of data acquisition. Finally, perform preliminary tests to verify that all sensors are working properly and communicating correctly with the DAQ.

[0061] Step S12, start the data acquisition program, regularly record voltage and current data, and store the measurement sequence;

[0062] Start the data acquisition program, set the data acquisition frequency, and record the voltage and branch current data of key nodes in the distribution network through the data acquisition software, and then store these data in the database or file system for subsequent analysis.

[0063] Step S13, determining the rated voltage of the node, and calculating the voltage fluctuation for each sampling point;

[0064] First, we need to determine the rated voltage V of each node. rated , then for each sampling point i, calculate the actual measured voltage V i With rated voltage V rated The difference between the voltage fluctuation and the Wherein f can be a function to reflect the influence of other factors on voltage fluctuation.

[0065] Step S14: Apply the time decay function to weight the voltage fluctuation amount at each sampling point, calculate the square sum of all weighted voltage fluctuation amounts, and obtain the voltage fluctuation index by taking the square root.

[0066] By applying time-decay weights to the voltage fluctuations over a period of time, the importance of recent data can be more accurately reflected, thereby calculating the voltage fluctuation index to evaluate the operating status of the power grid. Specifically, the voltage fluctuation index is calculated using the following formula:

[0067]

[0068] Among them, σ V Represents the standard deviation of the voltage, which measures how dispersed the voltage samples are relative to their mean. V nom is the rated voltage of node i, n is the number of sampling points, V i，k represents the voltage value of i at the kth sampling point, t0 represents the time point when sampling starts, and τ represents the time decay constant, which determines the speed at which the voltage drops.

[0069] Step S20: using a DC power flow calculation method to identify overloaded lines, and obtaining load unbalance factors by calculation to identify key areas where load transfer is required;

[0070] The DC power flow calculation method is used to identify overloaded lines, and the load imbalance factor is calculated to identify the key areas where load transfer is required. This process aims to optimize the operating status of the distribution network, improve power supply reliability and efficiency, prevent equipment damage, and reasonably allocate grid resources. By establishing a DC power flow model for the distribution network and calculating the power flow of each branch, it is possible to effectively identify those lines that are close to or exceed their capacity limits, thereby avoiding potential overload problems. At the same time, by calculating the load imbalance factor, it is possible to further determine which areas have serious load imbalances, which helps dispatchers identify key areas where load transfer is required to achieve a more balanced load distribution and ensure the safe and stable operation of the power grid. The specific steps include the following:

[0071] Step S21, collecting branch current and voltage data and preparing for calculation;

[0072] By using sensors or instruments to measure and record branch current and voltage data, the data is then preprocessed, including data cleaning, format conversion, and time alignment, in order to prepare for subsequent calculations.

[0073] Step S22, using DC power flow to calculate actual load power and evaluate load conditions;

[0074] A DC power flow model is established to calculate the actual load power of each branch in the power grid, and the load of the entire power system is evaluated accordingly. Specifically, this step first calculates the power flow of each branch based on the voltage phase angle difference and branch conductance, and then obtains the actual load power of each node. Finally, the actual load power is compared with the rated load power to evaluate whether the power grid is in a safe and stable working state.

[0075] Step S23, calculating the standard deviation of the ratio of the actual load to the rated load to obtain a load imbalance factor;

[0076] First, calculate the ratio of the actual load of each branch to the rated load, and then find the standard deviation of these ratios to quantify the degree of load imbalance in the power grid. This indicator can be used to evaluate the balance of load distribution in the power system and provide a basis for subsequent optimization measures. Specifically, the load imbalance factor is calculated using the following formula:

[0077]

[0078] Among them, σ L represents the load imbalance factor, P j，krepresents the actual load power of branch j at time point k, P nom，j represents the rated power of the jth branch, m represents the number of branches involved in the calculation, S j represents the apparent power of branch j, S nom Indicates the rated apparent power of the entire system.

[0079] Step S24: Identify overloaded lines and key areas, and determine load transfer requirements.

[0080] This includes checking which lines are actually loaded beyond their rated capacity, marking them as overloaded lines, and analyzing the critical areas where these lines are located. Based on this information, load transfer plans can be developed to relieve the pressure on overloaded lines and improve the stability of the entire system.

[0081] Step S30: Combine the voltage fluctuation index and the load imbalance factor, comprehensively evaluate the transfer capacity of each feeder by weighted average method, and calculate the transfer capacity coefficient of each feeder as the basis for optimizing load distribution;

[0082] This step aims to ensure the stability and reliability of the power system. By quantitatively analyzing the carrying capacity and adaptability of different feeders, the load can be distributed more reasonably to avoid overload and voltage quality problems, thereby improving the efficiency and safety of the entire system. It includes the following sub-steps:

[0083] Step S31, calculating a feeder operation status value for evaluating the feeder status;

[0084] Combine the feeder voltage fluctuation index with the load unbalance factor, and use the voltage fluctuation index and load unbalance factor formula to calculate the feeder operation status value:

[0085]

[0086] Among them, σ represents the dispersion degree of feeder voltage fluctuation index and load unbalance factor, V i，k represents the voltage value measured at the i-th feeder node k times, V nom represents the rated voltage value of the feeder, therefore, represents the deviation of the voltage measured at the i-th feeder node k times relative to the rated voltage, P j，k represents the power value measured at the jth feeder node k times, P nom，j represents the rated power value of the feeder. Therefore, S represents the deviation of the power measured at the jth feeder node k times relative to the rated power, j represents the system error of the jth feeder node, S nom represents the rated system error of the feeder. Therefore, represents the magnification of the actual system error of the jth feeder node relative to the rated system error, N represents the number of feeder nodes, and M represents the number of feeders. Therefore, and Represent the weight of each feeder node and each feeder respectively.

[0087] Step S32: determining the weight coefficients of the voltage fluctuation index and the load imbalance factor to reflect the importance of different factors;

[0088] Different weights are assigned to these two indicators according to actual conditions in order to more accurately evaluate the comprehensive status of the feeder and thus guide the decision to optimize load distribution.

[0089] Step S33, applying the weighted average method to calculate the transfer capacity coefficient of each feeder, taking into account voltage stability and load balance;

[0090] The comprehensive condition of the feeder is evaluated by comprehensively considering the importance of the two factors, voltage stability and load balance. This process involves determining the weight coefficients of the voltage fluctuation index and the load imbalance factor to reflect the importance of different factors, and then calculating the transfer capacity coefficient of each feeder to provide a basis for optimizing load distribution. Specifically, the formula is used:

[0091] Given the weight factor ω V and ω L , calculate the transfer capacity coefficient C of feeder f trans，f ;

[0092] C trans，f =ω V ·σ V，f +ω L ·σ L，f

[0093] Among them, σ V，f and σ L，f They represent the average value of the voltage fluctuation index of all nodes contained in the feeder f and the average value of the load unbalance factor of all branches respectively.

[0094] Step S34: Use the transfer capacity coefficient as a basis for optimizing load distribution and guiding the load transfer strategy.

[0095] These coefficients guide load transfer strategies and help optimize load distribution in the power system, ensuring system stability and reliability.

[0096] Step S40: Use an intelligent optimization algorithm to adjust the switch state to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network.

[0097] Based on the transfer capacity coefficient of each feeder, the intelligent algorithm automatically finds the best load transfer solution. The algorithm takes into account two important factors: voltage stability and load balance, and dynamically adjusts the switch state to ensure the stable operation of the power system, reduce energy loss, and improve service quality. In this way, overload and voltage quality problems can be effectively avoided to ensure the efficient and reliable operation of the power system. The specific sub-steps include the following:

[0098] Step S41, initialize the population of the genetic algorithm, define the switch state coding rules, and randomly generate an initial switch state combination.

[0099] First, define the switch state encoding rules, such as using the binary bit "0" to represent open and "1" to represent closed. Then randomly generate a series of switch state combinations as the initial population, and each individual represents a network configuration scheme. For example, for a distribution network containing 10 switches, each individual consists of 10 binary digits, forming a diverse initial solution space as a whole, laying the foundation for subsequent evolutionary iterations.

[0100] Step S42: perform selection, crossover and mutation operations, and iterate the evolution process to find the best switch state combination.

[0101] In genetic algorithms, the population is iterated and evolved through selection, crossover, and mutation operations. Selection evaluates individual performance based on the fitness function and selects individuals that are more adaptable to the environment to enter the next generation; crossover produces new offspring by exchanging some genes of two parents; mutation randomly changes certain gene values ​​to introduce new features. These operations are repeated until the stopping condition is met, and finally a switch state combination with high fitness is obtained as the best solution to the optimization problem.

[0102] Step S43, calculate the transfer capacity coefficient of each individual in each generation, select the maximum value as the fitness function, and maximize the transfer capacity coefficient as the goal.

[0103] In each generation, the transfer capacity coefficient of each individual is calculated, which is obtained by combining the voltage fluctuation index and the load imbalance factor through the weighted average method. The individual with the largest transfer capacity coefficient is selected as the target of the fitness function. Guided by maximizing this coefficient, the optimal switch state combination is found through the iterative evolution process of the genetic algorithm.

[0104] Step S44, repeat the above process until a preset number of iterations is reached or a convergence condition is met, and finally an optimal load transfer path is obtained.

[0105] Embodiment 2

[0106] like Figure 2 As shown, Embodiment 2 of the present application provides an auxiliary decision-making system for load transfer in a distribution network, comprising:

[0107] Data acquisition and analysis module 21: collects node voltage and branch current data in the distribution network in real time, calculates the voltage fluctuation index of each node, and evaluates the operation status of the power grid, including the following submodules:

[0108] 1. Install sensors in the distribution network to collect node voltage and branch current data in real time to ensure the accuracy and timeliness of the data;

[0109] In order to collect node voltage and branch current data in the distribution network in real time and ensure its accuracy and timeliness, high-precision sensors should be selected and data verification mechanisms should be implemented to avoid abnormal values. In addition, reliable communication technologies such as optical fiber or wireless networks should be used to ensure stable and low-latency data transmission. Finally, an efficient data processing system should be built to analyze data in a timely manner to support power grid monitoring, thereby ensuring data quality and availability.

[0110] 2. Preprocess the collected data, including data cleaning and formatting, to ensure data quality and consistency;

[0111] Data cleaning to remove outliers and missing values; formatting data to ensure consistency and readability; applying algorithms to detect and correct erroneous data; ensuring data quality meets analysis requirements. In addition, the data timestamp needs to be verified to maintain the timeliness of the data, and the data format needs to be standardized for subsequent processing and analysis.

[0112] 3. Calculate the standard deviation of the ratio of the actual value of each node voltage to the rated value to obtain the voltage fluctuation index, which is used to evaluate the operating status of the power grid;

[0113] Determine the rated voltage V of each node rated , then for each sampling point i, calculate the actual measured voltage V i With rated voltage V rated The difference between the voltage fluctuation and the Where f can be a function to reflect the impact of other factors on voltage fluctuations and help monitor the stability of the power grid.

[0114] 4. Based on the calculated voltage fluctuation index, evaluate whether the operating status of the power grid is stable and provide a basis for subsequent decision-making.

[0115] Calculate the standard deviation of the ratio of the actual voltage value to the rated value of all monitoring nodes as the voltage fluctuation index; set the threshold of the voltage fluctuation index according to national standards or industry experience;

[0116] By applying time decay weights to the voltage fluctuations over a period of time, the importance of recent data can be more accurately reflected, thereby calculating the voltage fluctuation index to evaluate the operating status of the power grid. Specifically, the formula is used:

[0117]

[0118] Among them, σ V Represents the standard deviation of the voltage, which measures how dispersed the voltage samples are relative to their mean. V nom is the rated voltage of node i, n is the number of sampling points, V i，k represents the voltage value of i at the kth sampling point, t0 represents the time point when sampling starts, and τ represents the time decay constant, which determines the speed at which the voltage drops.

[0119] Compare the calculated voltage fluctuation index with the threshold to determine whether the grid operation status is stable; record the voltage fluctuation index and evaluation results and form a report; provide a basis for grid maintenance and dispatching decisions based on the evaluation results, and take adjustment measures when necessary. These steps help ensure the safe and stable operation of the grid and provide clear operational guidance for maintenance personnel.

[0120] Load analysis and balancing module 22: Use the DC power flow calculation method to identify overloaded lines, obtain load imbalance factors through calculation, and identify key areas where load transfer is required, including the following sub-modules:

[0121] 1. Use DC power flow calculation method to determine potential overload lines in the network;

[0122] Assuming that the line resistance is much larger than the reactance, the AC power flow equation is simplified to a DC model; the node voltage phase angle difference is calculated based on the node injection power and line parameters; the line power flow is calculated using the node voltage phase angle difference and line parameters; the line power flow is compared with the line capacity limit to determine the potential overloaded line.

[0123] 2. Calculate the standard deviation of the ratio of actual load to rated load to obtain the load imbalance factor;

[0124] First, the ratio of the actual load of each branch to the rated load is calculated, and then the standard deviation of these ratios is calculated to quantify the degree of load imbalance in the power grid. Specifically, the formula is used:

[0125]

[0126] Among them, σ L represents the load imbalance factor, P j，k represents the actual load power of branch j at time point k, P nom，j represents the rated power of the jth branch, m represents the number of branches involved in the calculation, Sj represents the apparent power of branch j, S nom Indicates the rated apparent power of the entire system. This indicator can be used to evaluate the balance of load distribution in the power system and provide a basis for subsequent optimization measures.

[0127] 3. Identify critical areas where load transfer is required;

[0128] Monitor the load conditions of each node and branch in real time; calculate the standard deviation of the ratio of actual load to rated load; determine areas with high load imbalance factors; evaluate the load distribution in these areas; identify key areas that require load transfer to balance the grid load.

[0129] 4. Based on the above analysis results, a load analysis report is formed to provide a basis for the next decision.

[0130] Transfer capacity evaluation module 23: Combined with the voltage fluctuation index and the load imbalance factor, the transfer capacity of each feeder is comprehensively evaluated by the weighted average method, and the transfer capacity coefficient of each feeder is calculated as the basis for optimizing load distribution, including:

[0131] 1. Integrate the data of voltage fluctuation index and load imbalance factor;

[0132] The voltage fluctuation index is obtained by calculating the standard deviation of the ratio of the actual voltage value to the rated value of all nodes. The load imbalance factor is obtained by calculating the standard deviation of the ratio of the actual load to the rated load. These two indicators are associated with geographic information and displayed visually. The two indicators are comprehensively analyzed to identify areas that require special attention and adjustment.

[0133] 2. Perform weighted average calculation on the voltage fluctuation index and load imbalance factor to comprehensively evaluate the transfer capacity of each feeder;

[0134] 3. Calculate the transfer capacity coefficient of each feeder based on the weighted average result;

[0135] The comprehensive condition of the feeder is evaluated by comprehensively considering the importance of the two factors, voltage stability and load balance. This process involves determining the weight coefficients of the voltage fluctuation index and the load imbalance factor to reflect the importance of different factors, and then calculating the transfer capacity coefficient of each feeder to provide a basis for optimizing load distribution. Specifically, the formula is used:

[0136] Given the weight factor ω V and ω L , calculate the transfer capacity coefficient C of feeder f trans，f ;

[0137] C trans，f =ω V ·σ V，f +ωL ·σ L，f

[0138] Among them, σ V，f and σ L，f They represent the average value of the voltage fluctuation index of all nodes contained in the feeder f and the average value of the load unbalance factor of all branches respectively.

[0139] 4. Summarize the calculation results and form a transfer capacity assessment report as an important basis for optimizing load distribution.

[0140] Intelligent optimization and load transfer module 24: uses intelligent optimization algorithm to adjust the switch state to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network, including:

[0141] 1. First, analyze the current switch status and load distribution of the distribution network, and determine the goal of maximizing the transfer capacity coefficient to improve power supply reliability and economy;

[0142] 2. Use intelligent optimization algorithms to simulate and adjust the switch status in the distribution network to find the optimal solution that can maximize the transfer capacity coefficient;

[0143] Define the objective function of the transfer capacity coefficient; set the switch state variables; use intelligent algorithms such as genetic algorithms and particle swarm optimization to search for the optimal switch combination; adjust the switch state through simulation to find the optimal solution that maximizes the transfer capacity coefficient.

[0144] 3. Based on the optimal switch state obtained by the intelligent optimization algorithm, the optimal load transfer path is planned to ensure voltage stability and load balance during the transfer process;

[0145] Adjust the switch status in the distribution network according to the optimization results; plan the optimal load transfer path; ensure voltage stability and load balance during the transfer process; and continuously monitor system performance to verify the improvement effect of power supply reliability and economy.

[0146] 4. Adjust the switch status in the actual distribution network according to the planning results, implement the optimal load transfer path, monitor system performance, and verify the improvement effect of optimized power supply reliability and economy.

[0147] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0148] The memory is used to store one or more program instructions;

[0149] A processor, configured to run one or more program instructions to execute an auxiliary decision-making system for load transfer in a distribution network;

[0150] Corresponding to the above-mentioned embodiments, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer storage medium contains one or more program instructions, and the one or more program instructions are used by a processor to execute an auxiliary decision-making method for load transfer in a distribution network.

[0151] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned auxiliary decision-making method for load transfer in a distribution network.

[0152] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0153] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or a mature storage medium in the art. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0154] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0155] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0156] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus DRAM (DRRAM).

[0157] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0158] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0159] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. An auxiliary decision-making method for load transfer in a distribution network, characterized in that: include: S10, by collecting node voltage and branch current data in the distribution network in real time, calculating the voltage fluctuation index of each node, and evaluating the operation status of the power grid; S20, using the DC power flow calculation method to identify overloaded lines, and obtain the load unbalance factor through calculation to identify the key areas where load transfer is required; S30, combining the voltage fluctuation index and the load imbalance factor, comprehensively evaluating the transfer capacity of each feeder through the weighted average method, and calculating the transfer capacity coefficient of each feeder as the basis for optimizing load distribution; S40. Use intelligent optimization algorithms to adjust the switch status to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network.

2. The auxiliary decision-making method for load transfer in a distribution network according to claim 1, characterized in that: The grid operation status is evaluated by real-time data collection and calculation of the voltage fluctuation index, including the following sub-steps: Configure the data acquisition system to ensure that voltage and branch current data at all key nodes in the distribution network can be obtained; Start the data acquisition program, record voltage and current data regularly, and store the measurement sequence; Determine the rated voltage of the node and calculate the voltage fluctuation for each sampling point; The voltage fluctuation amount at each sampling point is weighted by applying the time decay function, and the square sum of all weighted voltage fluctuation amounts is calculated, and the square root is taken to obtain the voltage fluctuation index.

3. The auxiliary decision-making method for load transfer in distribution network according to claim 1, characterized in that: Use the DC power flow calculation method to detect overloaded lines and determine the key areas for load transfer by calculating the load unbalance factor, including the following sub-steps: Collect branch current and voltage data and prepare for calculations; Use DC power flow to calculate actual load power and evaluate load conditions; Calculate the standard deviation of the ratio of actual load to rated load to obtain the load imbalance factor; Identify overloaded lines and critical areas and determine load transfer requirements.

4. The auxiliary decision-making method for load transfer in a distribution network according to claim 1, characterized in that: The voltage fluctuation and load imbalance data are integrated by weighted average method to calculate the feeder transfer capacity coefficient to optimize load distribution, including the following sub-steps: Calculate the average values ​​of feeder voltage fluctuation index and load unbalance factor to evaluate feeder condition; Determine the weight coefficients of voltage fluctuation index and load imbalance factor to reflect the importance of different factors; The weighted average method is used to calculate the transfer capacity coefficient of each feeder, taking into account voltage stability and load balance; The transfer capacity coefficient is used as the basis for optimizing load distribution and guiding load transfer strategies.

5. The auxiliary decision-making method for load transfer in distribution network according to claim 1, characterized in that: Using intelligent optimization algorithms to adjust the switch status, plan the optimal load transfer path, maximize the transfer capacity coefficient, and improve the power supply reliability and economy of the distribution network, including the following sub-steps: Initialize the population of the genetic algorithm, define the switch state encoding rules, and randomly generate the initial switch state combination; Perform selection, crossover and mutation operations to iterate the evolution process to find the best switch state combination; Calculate the transfer capacity coefficient of each individual in each generation, select the maximum value as the fitness function, and maximize the transfer capacity coefficient as the goal; The above process is repeated until the preset number of iterations is reached or the convergence condition is met, and finally the optimal load transfer path is obtained.

6. The auxiliary decision-making method for load transfer in a distribution network according to claim 1, characterized in that: Monitor and avoid overload during load transfer; After implementing the above steps, continuously monitor the operating status of the distribution network to ensure that all measures are effectively implemented; Adjust strategies through real-time data feedback to ensure safe and stable operation of the system; Through continuous monitoring, any issues causing overload can be promptly identified and resolved.

7. The auxiliary decision-making method for load transfer in a distribution network according to claim 1, characterized in that: Providing a user interface to display load transfer results and recommendations; Design a friendly user interface to intuitively display the process and results of load transfer; The user interface displays key indicators such as changes in transfer capacity coefficients and load transfer paths; Presenting data in a graphical way helps operation and maintenance personnel quickly understand the system status.

8. An auxiliary decision-making system for load transfer in a distribution network, characterized in that: Data acquisition and analysis module: collects node voltage and branch current data in the distribution network in real time, calculates the voltage fluctuation index of each node, and evaluates the operation status of the power grid; Load analysis and balancing module: Uses DC power flow calculation methods to identify overloaded lines, calculates load imbalance factors, and identifies key areas where load transfer is required; Transfer capacity assessment module: Combined with the voltage fluctuation index and load imbalance factor, the transfer capacity of each feeder is comprehensively assessed through the weighted average method, and the transfer capacity coefficient of each feeder is calculated as the basis for optimizing load distribution; Intelligent optimization and load transfer module: Use intelligent optimization algorithm to adjust the switch status to achieve optimal load transfer path planning and maximize the transfer capacity coefficient, thereby improving the power supply reliability and economy of the entire distribution network.

9. A computer storage medium, characterized in that include: at least one memory and at least one processor; A memory for storing one or more program instructions; A processor is used to run one or more program instructions to execute an auxiliary decision-making method for load transfer in a distribution network.

Images