Autonomous Support Control Method for Distribution Network Based on Adjustable Load Spring Effect

Abstract

The present application provides a method for autonomous support control of a distribution network based on the adjustable load spring effect, which relates to the technical field of power system control and includes: monitoring the grid load, collecting load overload information, optimizing the load spring parameters to obtain an optimal load spring parameter array, analyzing the adjustable load spring effect to obtain the remaining load overload information, constructing the distribution of power consumption load parameters, optimizing the energy storage support to obtain the optimal energy storage support parameter distribution, and using the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid. Through the present application, the technical problem in the prior art that the operation efficiency of the distribution network is low due to the lack of an effective grid load overload monitoring and response mechanism can be solved. By combining distributed power sources and energy storage systems, optimizing the load spring parameters and the energy storage support parameters, the autonomous support control of the distribution network is realized, and the technical effect of improving the operation efficiency of the distribution network is achieved.

Description

Technical Field

[0001] This application relates to the technical field of power system control, and particularly to a distribution network autonomous support control method based on the spring effect of adjustable loads. Background Art

[0002] With the development of social economy and the increase of population, the power demand is constantly growing, and the grid load is also becoming increasingly heavy. Especially during peak hours, the phenomenon of grid load overload occurs frequently, posing a serious challenge to the stable operation of the grid. However, in the existing technologies, the control methods of distribution networks often have some limitations. The existing grid control methods lack an effective monitoring and response mechanism for load overload. During peak load hours, the grid is prone to overload, while the existing methods cannot detect the overload information in a timely and accurate manner and take corresponding measures for adjustment. To sum up, there is a technical problem in the existing technologies that the operation efficiency of the distribution network is low due to the lack of an effective grid load overload monitoring and response mechanism. Summary of the Invention

[0003] The purpose of this application is to provide a distribution network autonomous support control method based on the spring effect of adjustable loads to solve the technical problem in the existing technologies that the operation efficiency of the distribution network is low due to the lack of an effective grid load overload monitoring and response mechanism.

[0004] In view of the above problems, this application provides a distribution network autonomous support control method based on the spring effect of adjustable loads. Among them, the distribution network autonomous support control method based on the spring effect of adjustable loads includes: during the operation of the target grid, monitoring the grid load and collecting the load overload information when the grid load is overloaded; within the adjustable load space of multiple adjustable power consumption terminals in the target grid, according to the load overload information, aiming at reducing the load overload of the target grid and improving the working quality of multiple adjustable power consumption terminals, optimizing the load spring parameters to obtain an optimal load spring parameter array; according to the optimal load spring parameter array, conducting an analysis of the adjustable load spring effect to obtain the remaining load overload information and distributing it to multiple ordinary power consumption terminals to construct the distribution of power consumption load parameters; according to the photovoltaic and energy storage device array in the area where the target grid is located, combining the distribution of power consumption load parameters, conducting an optimization of energy storage support to obtain an optimal energy storage support parameter distribution, where the optimization is carried out according to the matching degree between the energy storage support parameter distribution and the power consumption load parameter distribution; using the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target grid.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] During the operation of the target power grid, monitor the grid load and collect load overload information when the grid load is overloaded; within the adjustable load space of multiple adjustable power consumption terminals in the target power grid, based on the load overload information, optimize the load spring parameters with the aim of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, and obtain an optimal load spring parameter array; according to the optimal load spring parameter array, conduct an analysis of the adjustable load spring effect, obtain the remaining load overload information, and allocate it to multiple ordinary power consumption terminals to construct the distribution of power consumption load parameters; according to the photovoltaic and energy storage device array in the area where the target power grid is located, combined with the distribution of power consumption load parameters, conduct an optimization of energy storage support to obtain an optimal energy storage support parameter distribution, where the optimization is carried out according to the matching degree between the energy storage support parameter distribution and the power consumption load parameter distribution; use the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid. That is to say, by combining distributed power sources and energy storage systems, optimizing load spring parameters and energy storage support parameters, realizing the autonomous support control of the distribution network, and achieving the technical effect of improving the operation efficiency of the distribution network.

[0007] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0009] Figure 1 It is a flowchart of the distribution network autonomous support control method based on the adjustable load spring effect of this application;

[0010] Figure 2 It is a flowchart of obtaining the optimal energy storage support parameter distribution in the distribution network autonomous support control method based on the adjustable load spring effect of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] By providing a distribution network autonomous support control method based on the adjustable load spring effect, this application solves the technical problem in the prior art that the operation efficiency of the distribution network is low due to the lack of an effective power grid load overload monitoring and response mechanism. Combining distributed power sources and energy storage systems, optimizing the load spring parameters and energy storage support parameters, realizing the autonomous support control of the distribution network, and achieving the technical effect of improving the operation efficiency of the distribution network.

[0012] Next, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. Additionally, it should be noted that for the sake of description, only the parts related to this application are shown in the drawings rather than all.

[0013] Embodiment, please refer to the attached Figure 1 , this application provides a distribution network autonomous support control method based on the adjustable load spring effect. Among them, the distribution network autonomous support control method based on the adjustable load spring effect specifically includes the following steps:

[0014] Step 1: During the operation of the target power grid, monitor the power grid load and collect the load overload information when the power grid load is overloaded.

[0015] Specifically, by installing monitoring devices and sensors, the target power grid is monitored in real time to track the load changes of the power grid. When the power grid load exceeds the set safety threshold, the system will automatically identify and collect relevant overload information. This overload information includes key data such as the time, location, duration, and overload amount of the overload occurrence. For example, if the power grid load in a certain area suddenly rises to exceed the carrying capacity of the power grid in that area at 6 pm, the monitoring system will immediately record this overload event and collect relevant data. Real-time monitoring and timely information collection help quickly identify problem areas in the power grid.

[0016] Step 2: In the adjustable load spaces of multiple adjustable power consumption terminals in the target power grid, based on the load overload information, for the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, optimize the load spring parameters to obtain an optimal load spring parameter array.

[0017] Specifically, there are multiple adjustable power consumption terminals in the target power grid. Within the range where these power consumption terminals can adjust their load response characteristics in the power grid, according to the load overload information, the load spring parameters are optimized, including response time, adjustment range, maximum and minimum loads, etc. While reducing the load overload of the power grid, the working quality of the power consumption terminals is ensured. For example, assume there are multiple adjustable power consumption terminals in a power grid, such as vehicle charging stations. These charging stations can adjust their charging loads according to the needs of the power grid, forming an adjustable load spring effect. When the power grid is overloaded, these charging stations reduce their power consumption loads by reducing the charging rate or shortening the charging time, thereby helping the power grid relieve pressure. By adjusting the working parameters of the charging stations, such as charging rate, maximum charging power, etc., the optimal distribution of the power grid load is achieved, while improving the working quality of the power consumption terminals. However, reducing the power consumption load of the adjustable power consumption terminals may affect their working quality. For example, reducing the charging rate may affect the endurance of electric vehicles. Therefore, it is necessary to balance the reduction of load overload and the improvement of the working quality of the power consumption terminals during the optimization process. Continuously adjust the load spring parameters and adjust the parameters according to the results until the optimal solution is found. After finding the optimal solution, verification is required to ensure that this set of parameters can not only reduce the load overload of the power grid, but also will not significantly affect the working quality of the power consumption terminals. Finally, the optimization algorithm will output a set of optimal load spring parameter arrays, which can enable the power grid to reduce the load overload while maintaining the working quality of the power consumption terminals. Real-time monitoring of the power grid load, analysis of the load overload information, and adjustment of the load response characteristics of the power consumption terminals accordingly can effectively reduce the load overload of the power grid.

[0018] Step 3: According to the optimal load spring parameter array, conduct an analysis of the adjustable load spring effect, obtain the remaining load overload information, and allocate it to multiple ordinary power consumption terminals to construct a power consumption load parameter distribution.

[0019] Specifically, first, calculate the remaining load overload information of the target power grid according to the reduced power consumption loads of multiple adjustable power consumption terminals. If the optimized power grid load still exceeds the power supply capacity of the power grid, then there is remaining load overload. This part of the overload needs to be identified and allocated. Next, according to the magnitudes of the historical average power consumption loads of the ordinary power consumption terminals, allocate the remaining load overload information to the ordinary power consumption terminals with non-adjustable power consumption loads. The allocation method can be simple proportional allocation. For example, the remaining load overload information can be divided by the sum of the power consumption loads of all ordinary power consumption terminals to obtain an allocation coefficient, and then this coefficient is multiplied by the historical average power consumption load of each ordinary power consumption terminal to obtain the load overload amount allocated to each power consumption terminal. Finally, according to the load overload amount allocated to each ordinary power consumption terminal and combined with their location information, construct a power consumption load parameter distribution. By constructing the power consumption load parameter distribution, the load condition of the power grid can be understood more accurately, and appropriate load allocation can be provided for each power consumption terminal.

[0020] Step 4: Based on the energy storage device array in the area where the target power grid is located, and in combination with the distribution of the power consumption load parameters, optimize the energy storage support to obtain the optimal energy storage support parameter distribution, where the optimization is carried out according to the matching degree between the energy storage support parameter distribution and the power consumption load parameter distribution.

[0021] Specifically, obtain the energy storage parameters of the energy storage device array in the area where the target power grid is located, including the power generation capacity of the photovoltaic panels, the capacity of the energy storage battery, the charge and discharge efficiency, etc. Analyze the historical power consumption data of each ordinary power consumption end in the power grid to determine the distribution of the power consumption load parameters, including the average power consumption of each power consumption end, the peak-valley power consumption characteristics, etc. Initialize the energy storage support parameter distribution according to the energy storage parameters and the power consumption load parameters. Considering the possible losses during the power transmission process, calculate the transmission losses according to the distance between the energy storage device and the power consumption end. Evaluate the matching degree between the energy storage support parameter distribution and the power consumption load parameter distribution, and adjust the energy storage support parameters, such as the charge and discharge rate, the energy storage capacity, etc., to optimize the matching degree. Repeat the above steps for iterative optimization until the predetermined matching degree target is reached or other optimization criteria are met. Finally, obtain the optimal energy storage support parameter distribution, which is the parameter configuration that realizes the best matching between the energy storage device and the power consumption end considering the transmission losses and the distribution of the power consumption load parameters. By optimizing the energy storage support parameter distribution, the overall efficiency of the energy storage system can be improved, and the power transmission losses can be reduced.

[0022] Step 5: Use the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid.

[0023] Specifically, use the optimized load spring parameters and energy storage support parameters to adjust the load distribution and energy management in the power grid. According to the optimized load spring parameter array, adjust the operating state of the adjustable power consumption end. For example, if the working quality parameter of a certain adjustable power consumption end needs to be improved, it can be achieved by adjusting the load spring parameters. According to the optimized energy storage support parameter distribution, adjust the operating strategy of the energy storage system. For example, if a certain energy storage device needs to participate in power grid support more frequently, it can be achieved by adjusting its energy storage support parameters. Regularly evaluate and adjust the load spring parameter array and the energy storage support parameter distribution according to the long-term operating data and predictions of the power grid. By using the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid, the optimal distribution of the power grid load and energy management can be realized, and the operating efficiency and reliability of the power grid can be improved.

[0024] Furthermore, Step 1 of this application includes:

[0025] During the operation of the target power grid, monitor and collect the power grid load within a preset time period, and process it to obtain the average power grid load; calculate the load overload information based on the average power grid load and the power of the target power grid.

[0026] Specifically, install sensors and monitoring equipment to collect power grid load data in real time. The preset time period can be set according to the operating characteristics and requirements of the power grid. For example, it can be every hour, every day, etc. Transmit the collected data to the data processing center for processing, including steps such as data cleaning, removing outliers, and calculating the average value, and calculate the average value within this time period to obtain the average power grid load. Based on factors such as the design parameters of the power grid, equipment performance, and operating experience, determine the power capacity of the power grid, that is, the maximum power that the power grid can withstand under safe and stable operating conditions. According to the calculated average power grid load and the power capacity of the target power grid, compare the two values to calculate the load overload information. If the average power grid load is close to or exceeds the power capacity of the power grid, it indicates that there is a risk of overload in the power grid. The overload information usually includes the degree of overload, the occurrence time, the duration, and the possible impacts, etc. Through precise calculation and evaluation, equipment damage, power outages, or more serious power grid accidents caused by overload can be avoided, ensuring the continuity of power supply.

[0027] Further, step two of this application includes:

[0028] Obtain the adjustable load spaces of multiple adjustable power consumption terminals within the target power grid. Among them, each adjustable load space includes multiple load spring parameters; for the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, construct a load spring function as follows: where LSF is the spring fitness, w1 and w2 are weights, M is the number of multiple adjustable power consumption terminals, T i is the load spring parameter of the i-th adjustable power consumption terminal in the load spring parameter array, P i is the working quality parameter of the i-th adjustable power consumption terminal after setting according to the load spring parameter, is the preset working quality parameter of the i-th adjustable power consumption terminal; according to the load spring function, optimize the load spring parameters of multiple adjustable power consumption terminals within the adjustable load space to obtain the optimal load spring parameter array.

[0029] Specifically, within the target power grid, there are multiple adjustable power consumption terminals. Each power consumption terminal has certain load spring parameters, which determine the response ability of the power consumption terminal when the power grid load changes. The adjustable power consumption terminals include industrial equipment with variable load characteristics, smart household appliances, energy storage systems, or other facilities that can adjust their power consumption according to the power grid demand. The load spring parameter is the power supply adjustment range of the adjustable power consumption terminal, such as the adjustment range given by the power grid to the adjustable power consumption terminal. First, it is necessary to obtain the adjustable load space of each adjustable power consumption terminal within the target power grid, which contains all the load spring parameters of the power consumption terminal, such as response time, adjustment range, maximum and minimum loads, etc., and determines the behavior of the power consumption terminal when the power grid load changes. Next, with the aim of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, a load spring function is constructed as follows: where LSF is the spring fitness, which is a key indicator for measuring the performance of the load spring function; w1 and w2 are weight coefficients that adjust the influence degrees of the load spring parameters and the working quality parameters on the LSF; M is the number of multiple adjustable power consumption terminals; T i is the load spring parameter of the i-th adjustable power consumption terminal within the load spring parameter array; P i is the working quality parameter of the i-th adjustable power consumption terminal after setting according to the load spring parameters; is the preset working quality parameter of the i-th adjustable power consumption terminal. The higher the spring fitness, the better the role of the power consumption terminal in the power grid load management.

[0030] The load spring function is a mathematical model used to describe the role of these power consumption terminals in the power grid load management. By optimizing these parameters, the load spring function can reduce the load overload of the power grid while improving the working quality of the power consumption terminals. Optimize the load spring parameters of multiple adjustable power consumption terminals within the adjustable load space, and use optimization algorithms such as genetic algorithms and particle swarm optimization to find the optimal combination of load spring parameters. The goal of the optimization algorithm is to maximize the spring fitness while meeting the safe operation requirements of the power grid, so as to improve the working quality of each adjustable power consumption terminal while reducing the load overload of the power grid. By optimizing the load spring parameters, the optimal distribution of the power grid load can be achieved, and the stability and reliability of the power grid can be improved.

[0031] Furthermore, this application also includes the following steps:

[0032] In multiple adjustable load spaces, generate multiple first load spring parameters for multiple adjustable power consumption terminals, and construct a first load spring parameter array; analyze multiple first working quality parameters of the multiple adjustable power consumption terminals after being adjusted according to the multiple first load spring parameters, and calculate and obtain a first spring fitness of the first load spring parameter array according to the load spring parameters; continue to optimize the load spring parameters of the multiple adjustable power consumption terminals in the multiple adjustable load spaces to obtain an optimal load spring parameter array with the maximum spring fitness.

[0033] Specifically, the adjustable load space refers to the range in which each power consumption terminal can adjust its load response characteristics in the power grid, including a series of load spring parameters, such as response time, adjustment range, maximum and minimum loads, etc. Generating multiple first load spring parameters for multiple adjustable power consumption terminals means determining a set of initial load spring parameter values for each power consumption terminal within these adjustable load spaces. Organize these parameters into a matrix or array to construct a first load spring parameter array, where each element represents a load spring parameter of an adjustable power consumption terminal. Clearly understand how the working quality parameters of each power consumption terminal are affected by its load spring parameters. The working quality parameters may include the efficiency, response time, stability, energy consumption, etc. of the power consumption terminal, and these parameters reflect the actual performance of the power consumption terminal in power grid load management.

[0034] Calculate and obtain a first spring fitness of the first load spring parameter array according to the load spring parameters. The spring fitness is a comprehensive index measuring the performance of the power consumption terminal in power grid load management, usually calculated comprehensively from multiple working quality parameters. Continue to optimize the load spring parameters of the multiple adjustable power consumption terminals in the multiple adjustable load spaces to obtain an optimal load spring parameter array with the maximum spring fitness. Use the spring fitness (LSF) as the objective function of the optimization problem. The LSF reflects the overall performance of the power grid, including the reduction of load overload and the guarantee of the working quality of the power consumption terminal. Set the constraint conditions in the optimization process according to the safe operation requirements of the power grid and the equipment performance limitations.

[0035] Select appropriate optimization algorithms, such as genetic algorithms, particle swarm optimization, gradient descent, etc., to search for the optimal solution in the parameter space. Through the iterative process, the optimization algorithm will continuously adjust the load spring parameters. In each iteration, a new LSF value will be calculated, and the parameters will be adjusted according to the results until the parameter combination that maximizes the LSF is found. After finding the optimal solution, verification is required to ensure that this set of parameters can not only maximize the LSF but also meet the actual operating requirements of the power grid. Finally, the optimization algorithm will output an array of optimal load spring parameters, which can enable the power grid to reduce load overload while improving the working quality of each adjustable power consumption end. Through the optimization process, the load response characteristics of each adjustable power consumption end in the power grid are precisely adjusted, thereby more effectively managing the power grid load and improving the stability and reliability of the power grid.

[0036] Furthermore, step three of this application includes:

[0037] According to the array of optimal load spring parameters, calculate the total load reduction parameter, and combine the load overload information to calculate the remaining load overload information; according to the historical average load of multiple ordinary power consumption ends in the target power grid, perform distribution calculation on the remaining load overload information to obtain multiple power consumption load parameters; according to the location information of the multiple ordinary power consumption ends, combine the multiple power consumption load parameters to construct a power consumption load parameter distribution.

[0038] Specifically, according to the array of optimal load spring parameters obtained by optimization, calculate the total load reduction parameter, that is, the total load that can be reduced by adjusting the load spring parameters of the power consumption end. For example, if the optimal load spring parameter of a certain power consumption end causes its load to decrease by 50 kilowatts, then this value is included in the total load reduction parameter. Compare the total load of the power grid with the power capacity of the power grid. If the total load exceeds the power capacity, then the overload amount is the difference between the two. By subtracting the total load reduction parameter, the remaining load overload information can be obtained, that is, the load overload amount that still exists in the power grid after adjusting the power consumption end.

[0039] Analyze the contribution of each power consumption end to the power grid load according to the historical average load of multiple ordinary power consumption ends in the target power grid. The historical average load refers to the average amount of electrical energy consumed by each power consumption end within a certain time range. According to the historical average load of each power consumption end, distribute the remaining load overload information to these power consumption ends. The distribution principles include factors such as fairness, load characteristics, and geographical location. According to the distributed load overload information, calculate the new load parameters of each power consumption end, reflecting the role of each power consumption end in the adjusted power grid load management. Collect the location information of each ordinary power consumption end, including the specific geographical location of the power consumption end, the line connected to the power grid, and the positional relationship in the power grid. For example, one power consumption end may be located in the city center area, while another may be located in the suburbs.

[0040] Combining multiple electrical load parameters, analyzing the load characteristics of each power consumption end, and understanding the load contribution of each power consumption end in the power grid. Constructing the distribution of electrical load parameters to analyze the location and load characteristics of power consumption ends in the power grid, so as to more accurately understand the load situation of the power grid, achieve optimal load distribution, reflect the load characteristics and location information of each power consumption end in the power grid, and help to more accurately understand the load situation of the power grid. For example, the distribution of electrical load parameters can show which power consumption ends are located in the high-load areas of the power grid and which power consumption ends have greater load adjustment capabilities. By calculating the total load reduction parameters, obtaining the remaining load overload information, distributing the remaining load overload information, and constructing the distribution of electrical load parameters, a comprehensive power grid load distribution map can be obtained, which helps to identify the weak links in the power grid and formulate more effective load management strategies.

[0041] Furthermore, as Figure 2 shown, step four of this application includes:

[0042] Obtaining the energy storage parameter array of the energy storage device array in the area where the target power grid is located, and constructing an energy storage support parameter space; according to the remaining load overload information, randomly generating a first energy storage support parameter array within the energy storage support parameter space, and combining the position information of multiple energy storage devices in the energy storage device array to construct a first energy storage support parameter distribution, where the sum of multiple first energy storage support parameters in the first energy storage support parameter array is equal to the remaining load overload information; analyzing the matching degree between the first energy storage support parameter distribution and the electrical load parameter distribution to obtain a first matching degree; continuing to randomly generate and construct a second energy storage support parameter distribution and analyzing to obtain a second matching degree; judging according to the second matching degree and the first matching degree to obtain a temporary optimization result; continuing to optimize until convergence, outputting the final temporary optimization result, and obtaining the optimal energy storage support parameter distribution.

[0043] Specifically, obtaining the energy storage parameter array of the energy storage device array in the area where the target power grid is located includes the capacity, charge-discharge efficiency, charge-discharge speed, service life, etc. of the energy storage device. For example, an energy storage device array may include multiple solar panels and battery energy storage systems, and each device has its specific parameters. Based on these energy storage parameters, an energy storage support parameter space is constructed, which contains all possible combinations of energy storage support parameters. According to the remaining load overload information, within the energy storage support parameter space, a first energy storage support parameter array is randomly generated, including the charge-discharge strategies, operating modes, response times, etc. of the energy storage devices. For example, an energy storage device array may include multiple battery energy storage systems, and each system has its specific parameters. By randomly generating these parameters, different energy storage strategies can be explored to find the best energy storage solution.

[0044] Combining the location information of multiple energy storage and power generation devices in the energy storage and power generation device array, a first energy storage support parameter distribution is constructed. The sum of multiple first energy storage support parameters in the first energy storage support parameter array is equal to the remaining load overload information. According to the first energy storage support parameter distribution and the power consumption load parameter distribution, the first energy storage support parameters of each energy storage and power generation device are sequentially assigned to multiple ordinary power consumption terminals from near to far. According to the power supply power and the transmission distance, the power distribution loss ratio can be calculated. The power distribution loss ratio refers to the ratio of the energy loss caused by factors such as lines and equipment to the power supply power during the process of electric energy transmission from the energy storage device to the power consumption terminal. Based on the power distribution loss ratio, the first matching degree is calculated. Next, in order to further optimize the matching between the energy storage device and the power consumption terminal, a second energy storage support parameter distribution can be randomly generated. Based on the existing energy storage parameters, by randomly adjusting some parameters, different energy storage strategies are explored to find a better matching solution. For example, parameters such as the charge-discharge strategy, operation mode, and response time of the energy storage device can be randomly adjusted. Similar to the analysis of the first matching degree, for the second energy storage support parameter distribution, the second matching degree is analyzed and obtained.

[0045] Judge whether the second matching degree is greater than the first matching degree. If the second matching degree is greater than the first matching degree, it indicates that the randomly generated second energy storage support parameter distribution is better. In this case, the second energy storage support parameter distribution can be used as the temporary optimization result. If not, it means that the randomly generated second energy storage support parameter distribution is not as good as the first matching degree, and further analysis is required. Calculate the ratio of the second matching degree to the first matching degree, and use this as the basic optimization probability. Calculate the second balance information of multiple second energy storage support parameters in the second energy storage support parameter distribution and the first balance information of the first energy storage support parameter distribution. According to the ratio of the two, the basic optimization probability is corrected to obtain a new optimization probability. Based on the optimization probability, choose to use the second energy storage support parameter distribution or the first energy storage support parameter distribution as the temporary optimization result.

[0046] Repeat the above steps, continue to optimize, and continuously adjust until approaching the optimal energy storage support parameter distribution. When the matching degree no longer changes significantly, output the final temporary optimization result, that is, the optimal energy storage support parameter distribution. This result is the parameter configuration that achieves the best matching between the energy storage device and the power consumption terminal under the current conditions. Through continuous iteration and optimization, it can be ensured that the support ability provided by the energy storage system highly matches the actual needs of the power grid, thereby improving the operation efficiency and reliability of the power grid.

[0047] Furthermore, the present application further includes the following steps:

[0048] According to the first energy storage support parameter distribution and the power consumption load parameter distribution, the first energy storage support parameters of each energy storage and photovoltaic device are sequentially assigned to multiple ordinary power consumption terminals from near to far until the power consumption load parameters of the multiple ordinary power consumption terminals are equal to the assigned energy storage support parameters, obtaining a first allocation result; according to the energy storage power distribution loss of the first allocation result, a first power distribution loss parameter is obtained; according to the first power distribution loss parameter, a first matching degree is calculated.

[0049] Specifically, according to the first energy storage support parameter distribution and the power consumption load parameter distribution, the first energy storage support parameter of each energy storage and photovoltaic device is determined. According to the position information of the energy storage and photovoltaic device, the first energy storage support parameter of each energy storage and photovoltaic device is sequentially assigned to multiple ordinary power consumption terminals from near to far. For example, if an energy storage and photovoltaic device has an energy storage support capacity of 10 megawatts and is close to a power consumption terminal, then the 10-megawatt energy storage support capacity can be assigned to this power consumption terminal. Each energy storage and photovoltaic device preferentially supplies power to the nearest power consumption terminal, and when the power consumption load parameter is reached, it supplies power to the next one, gradually distributing to more distant power consumption terminals until the power consumption load parameters of the multiple ordinary power consumption terminals are equal to the assigned energy storage support parameters.

[0050] According to the first allocation result, analyze the possible power distribution losses during the power supply process from the energy storage device to each power consumption terminal, including line losses, transformer losses, switchgear losses, etc. According to the power supplied and the transmission distance, the power distribution loss ratio can be calculated. The power distribution loss ratio refers to the ratio of the energy loss caused by factors such as lines and equipment to the power supply power during the transmission of electrical energy from the energy storage device to the power consumption terminal. By comparing the power supply capacity of the energy storage device to each power consumption terminal and the required power supply amount of each power consumption terminal, a first matching degree can be calculated. The first matching degree reflects the matching degree between the energy storage device and the power consumption terminal, usually expressed in percentage or fraction form. By calculating the first matching degree, the load distribution of the power grid can be controlled more precisely to ensure the safe and efficient operation of the power grid.

[0051] Furthermore, the present application further includes the following steps:

[0052] Judge whether the second matching degree is greater than the first matching degree. If so, use the second energy storage support parameter distribution as the temporary optimization result; if not, calculate the ratio of the second matching degree to the first matching degree to obtain a basic optimization probability; calculate the second balance information of multiple second energy storage support parameters in the second energy storage support parameter distribution, and calculate the first balance information of the first energy storage support parameter distribution. Use the ratio of the second balance information to the first balance information to correct and calculate the basic optimization probability to obtain an optimization probability; select to use the second energy storage support parameter distribution or the first energy storage support parameter distribution as the temporary optimization result according to the optimization probability.

[0053] Specifically, it is judged whether the second matching degree is greater than the first matching degree. If the second matching degree is higher than the first matching degree, it indicates that the randomly generated second energy storage support parameter distribution is superior to the first energy storage support parameter distribution, and the matching between the energy storage device and the power consumption end is better. Then, the second energy storage support parameter distribution is used as the temporary optimization result. If not, it means that the randomly generated second energy storage support parameter distribution is not as good as the first matching degree. In this case, the ratio of the second matching degree to the first matching degree is calculated to obtain a probability to accept the inferior second energy storage support parameter distribution as the temporary optimization result. This ratio reflects the quality of the second energy storage support parameter distribution. The smaller the ratio, the smaller the probability of the second energy storage support parameter distribution being used as the temporary optimization result. Analyze the second energy storage support parameter distribution to evaluate the balance of the energy storage device parameters. The balance information reflects the distribution of the energy storage device parameters and their matching degree with the power consumption load parameter distribution. For example, the variance can be calculated by calculating the ratios of multiple energy storage parameters and then taking the reciprocal of the variance as the balance information. The smaller the variance, the greater the balance and the more uniform the distribution of the energy storage device parameters.

[0054] Similar to the calculation of the second balance information, calculate the first balance information of the first energy storage support parameter distribution. Multiply the ratio of the second balance information to the first balance information by the basic optimization probability to complete the correction calculation, ensuring that the optimization probability takes into account not only the matching degree but also the balance of the energy storage parameter distribution. Finally, according to the optimization probability, if the optimization probability indicates that the second energy storage support parameter distribution provides a better matching degree and balance, select the second energy storage support parameter distribution as the temporary optimization result. If the optimization probability indicates that the first energy storage support parameter distribution provides a better matching degree and balance, select the first energy storage support parameter distribution as the temporary optimization result. By obtaining the temporary optimization result, the energy storage device can be utilized more effectively, improving the operation efficiency and stability of the power grid.

[0055] In summary, the distribution network autonomous support control method based on the adjustable load spring effect provided by this application has the following technical effects:

[0056] During the operation of the target power grid, monitor the grid load and collect load overload information when the grid load is overloaded; within the adjustable load spaces of multiple adjustable power consumption terminals in the target power grid, optimize the load spring parameters for the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals according to the load overload information, and obtain an optimal load spring parameter array; according to the optimal load spring parameter array, conduct an analysis of the adjustable load spring effect, obtain the remaining load overload information, and distribute it to multiple ordinary power consumption terminals to construct the distribution of power consumption load parameters; according to the photovoltaic and energy storage device array in the area where the target power grid is located, combine the distribution of power consumption load parameters, conduct optimization of energy storage support, and obtain the optimal energy storage support parameter distribution, where the optimization is carried out according to the matching degree between the energy storage support parameter distribution and the power consumption load parameter distribution; use the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid. That is to say, by combining distributed power sources and energy storage systems, optimize the load spring parameters and energy storage support parameters to achieve autonomous support control of the distribution network, achieving the technical effect of improving the operation efficiency of the distribution network.

[0057] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0058] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A distribution network autonomous support control method based on the adjustable load spring effect, characterized in that including During the operation of the target power grid, monitor the grid load and collect load overload information when the grid load is overloaded; In the adjustable load spaces of multiple adjustable power consumption terminals in the target power grid, based on the load overload information, with the aim of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, optimize the load spring parameters to obtain an optimal load spring parameter array; The load spring parameter is the power supply adjustment range of the adjustable power consumption terminal; based on the optimal load spring parameter array, conduct an analysis of the adjustable load spring effect to obtain remaining load overload information and distribute it to multiple ordinary power consumption terminals to construct an electrical load parameter distribution; Based on the photovoltaic and energy storage device array in the area where the target power grid is located, combined with the electrical load parameter distribution, conduct an optimization of energy storage support to obtain an optimal energy storage support parameter distribution, where the optimization is carried out according to the matching degree between the energy storage support parameter distribution and the electrical load parameter distribution; Use the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power grid.

2. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 1, wherein During the operation of the target power grid, monitor the grid load and collect load overload information when the grid load is overloaded, including: During the operation of the target power grid, monitor and collect the grid load within a preset time period, and process it to obtain the average grid load; Calculate the load overload information based on the average grid load and the power of the target power grid.

3. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 1, wherein In the adjustable load spaces of multiple adjustable power consumption terminals in the target power grid, based on the load overload information, with the aim of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, optimize the load spring parameters, including: Obtain the adjustable load spaces of multiple adjustable power consumption terminals in the target power grid; With the aim of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power consumption terminals, construct a load spring function as follows: ； wherein, LSF is the spring fitness, and is the weight, M is the number of multiple adjustable power consumption terminals, is the load spring parameter of the i-th adjustable power consumption terminal in the load spring parameter array, is the working quality parameter of the i-th adjustable power consumption terminal set according to the load spring parameter, is the preset working quality parameter of the i-th adjustable power consumption terminal; Based on the load spring function, optimize the load spring parameters of multiple adjustable power consumption terminals in the adjustable load space to obtain an optimal load spring parameter array.

4. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 3, wherein, Based on the load spring function, optimize the load spring parameters of multiple adjustable power consumption terminals in the adjustable load space, including: In multiple adjustable load spaces, generate multiple first load spring parameters for multiple adjustable power consumption terminals and construct a first load spring parameter array; Analyze multiple first working quality parameters after adjusting multiple adjustable power consumption terminals according to multiple first load spring parameters, and calculate the first spring fitness of the first load spring parameter array according to the load spring function; Continue to optimize the load spring parameters of multiple adjustable power consumption terminals in the multiple adjustable load spaces to obtain an optimal load spring parameter array with the maximum spring fitness.

5. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 1, characterized in that Based on the optimal load spring parameter array, conduct an analysis of the adjustable load spring effect to obtain remaining load overload information and distribute it to multiple ordinary power consumption terminals to construct an electrical load parameter distribution, including: Based on the optimal load spring parameter array, calculate the total load reduction parameter, and combined with the load overload information, calculate the remaining load overload information; According to the magnitudes of the historical average load amounts of multiple ordinary power consumption terminals in the target power grid, perform distribution calculation on the remaining load overload information to obtain multiple power consumption load parameters; According to the location information of the multiple ordinary power consumption terminals, combine the multiple power consumption load parameters to construct a power consumption load parameter distribution.

6. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 1, characterized in that, According to the photovoltaic and energy storage device array in the area where the target power grid is located, combine the power consumption load parameter distribution to perform energy storage support optimization to obtain an optimal energy storage support parameter distribution, including: Obtain the energy storage parameter array of the photovoltaic and energy storage device array in the area where the target power grid is located, and construct an energy storage support parameter space; According to the remaining load overload information, randomly generate a first energy storage support parameter array in the energy storage support parameter space, and combine the location information of multiple photovoltaic and energy storage devices in the photovoltaic and energy storage device array to construct a first energy storage support parameter distribution, wherein the sum of multiple first energy storage support parameters in the first energy storage support parameter array is equal to the remaining load overload information; Analyze the matching degree between the first energy storage support parameter distribution and the power consumption load parameter distribution to obtain a first matching degree; Continue to randomly generate and construct a second energy storage support parameter distribution, and analyze to obtain a second matching degree; According to the second matching degree and the first matching degree, discriminate to obtain a temporary optimization result; Continue to perform optimization until convergence, output the final temporary optimization result, and obtain the optimal energy storage support parameter distribution.

7. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 6, characterized in that, Analyze the matching degree between the first energy storage support parameter distribution and the power consumption load parameter distribution to obtain a first matching degree, including: According to the first energy storage support parameter distribution and the power consumption load parameter distribution, sequentially assign the first energy storage support parameter of each photovoltaic and energy storage device to multiple ordinary power consumption terminals from near to far until the power consumption load parameters of the multiple ordinary power consumption terminals are equal to the assigned energy storage support parameters to obtain a first allocation result; Obtain a first power distribution loss parameter according to the energy storage power distribution loss of the first allocation result; Calculate to obtain a first matching degree according to the first power distribution loss parameter.

8. The autonomous support control method for a distribution network based on the adjustable load spring effect according to claim 6, wherein According to the second matching degree and the first matching degree, discriminate to obtain a temporary optimization result, including: Judge whether the second matching degree is greater than the first matching degree. If so, use the second energy storage support parameter distribution as the temporary optimization result; If not, calculate the ratio of the second matching degree to the first matching degree to obtain a basic optimization probability; Calculate the second balance information of multiple second energy storage support parameters in the second energy storage support parameter distribution, and calculate the first balance information of the first energy storage support parameter distribution. Use the ratio of the second balance information to the first balance information to perform correction calculation on the basic optimization probability to obtain an optimization probability; Select to use the second energy storage support parameter distribution or the first energy storage support parameter distribution as the temporary optimization result according to the optimization probability.

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