Power distribution network autonomous support control method based on adjustable load spring effect

Abstract

The invention provides a power distribution network autonomous support control method based on an adjustable load spring effect, and relates to the technical field of power system control, and the method comprises the steps: monitoring a power grid load, collecting load overload information, optimizing load spring parameters, obtaining an optimal load spring parameter array, carrying out the adjustable load spring effect analysis, and obtaining residual load overload information. And constructing electrical load parameter distribution, carrying out energy storage support optimization, obtaining optimal energy storage support parameter distribution, and carrying out target power grid control by adopting the optimal load spring parameter array and the optimal energy storage support parameter distribution. The technical problem of low operation efficiency of the power distribution network due to lack of an effective power grid overload monitoring and response mechanism in the prior art can be solved, the distributed power supply and the energy storage system are combined, the load spring parameters and the energy storage support parameters are optimized, autonomous support control of the power distribution network is realized, and the power distribution network operation efficiency is improved. The technical effect of improving the operation efficiency of the power distribution network is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of power system control, and in particular to a distribution network autonomous support control method based on an adjustable load spring effect. Background Art

[0002] With the development of social economy and the increase of population, the demand for electricity continues to grow, and the load on the power grid is also increasing. Especially during peak hours, power grid overload occurs from time to time, which brings serious challenges to the stable operation of the power grid. However, in the prior art, the control method of the distribution network often has some limitations. The existing power grid control method lacks an effective monitoring and response mechanism for load overload. During peak load periods, the power grid is prone to overload, and the existing methods are unable to detect overload information in a timely and accurate manner, and take corresponding measures to adjust it. In summary, there is a technical problem in the prior art that the distribution network has low operating efficiency due to the lack of an effective power grid load overload monitoring and response mechanism. Summary of the invention

[0003] The purpose of this application is to provide an autonomous support control method for a distribution network based on an adjustable load spring effect, so as to solve the technical problem in the prior art that the distribution network has low operating efficiency due to the lack of an effective grid load overload monitoring and response mechanism.

[0004] In view of the above problems, the present application provides a distribution network autonomous support control method based on an adjustable load spring effect, wherein the distribution network autonomous support control method based on the adjustable load spring effect includes: during the operation of the target power grid, monitoring the power grid load and collecting load overload information when the power grid load is overloaded; in the adjustable load space of multiple adjustable power terminals in the target power grid, according to the load overload information, with the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power terminals, optimizing the load spring parameters and obtaining an optimal load spring parameter array; according to the optimal load spring parameter array, performing adjustable load spring effect analysis to obtain residual load overload information, and distributing it to multiple common power terminals to construct a power load parameter distribution; according to the array of photovoltaic storage devices in the area where the target power grid is located, combined with the power load parameter distribution, performing energy storage support optimization to obtain an optimal energy storage support parameter distribution, wherein the optimization is performed according to the matching degree of the energy storage support parameter distribution and the power load parameter distribution; and using the optimal load spring parameter array and the optimal energy storage support parameter distribution to control the target power 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, the load of the power grid is monitored and the load overload information when the power grid load is overloaded is collected; in the adjustable load space of multiple adjustable power terminals in the target power grid, according to the load overload information, the load spring parameters are optimized to reduce the load overload of the target power grid and improve the working quality of multiple adjustable power terminals, and the optimal load spring parameter array is obtained; according to the optimal load spring parameter array, the adjustable load spring effect is analyzed to obtain the remaining load overload information, and the information is distributed to multiple common power terminals to construct the power load parameter distribution; according to the array of photovoltaic storage devices in the area where the target power grid is located, combined with the power load parameter distribution, the energy storage support is optimized to obtain the optimal energy storage support parameter distribution, wherein the optimization is performed according to the matching degree of the energy storage support parameter distribution and the power load parameter distribution; the target power grid is controlled by using the optimal load spring parameter array and the optimal energy storage support parameter distribution. In other words, the distributed power source and the energy storage system are combined to optimize the load spring parameters and the energy storage support parameters, realize the autonomous support control of the distribution network, and achieve 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 the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or 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 in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

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

[0011] This application solves the technical problem of low distribution network operation efficiency due to the lack of effective grid load overload monitoring and response mechanism in the prior art by providing a distribution network autonomous support control method based on adjustable load spring effect. By combining distributed power supply and energy storage system, optimizing load spring parameters and energy storage support parameters, autonomous support control of distribution network is realized, achieving the technical effect of improving the operation efficiency of distribution network.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0013] For examples, please see the attached Figure 1 The present application provides a distribution network autonomous support control method based on an adjustable load spring effect, wherein the distribution network autonomous support control method based on an adjustable load spring effect specifically comprises the following steps:

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

[0015] Specifically, by installing monitoring equipment and sensors, the target power grid is monitored in real time to track changes in the load of the power grid. When the load on the power grid 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 amount of overload. 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 the overload event and collect relevant data. Real-time monitoring and timely information collection help to quickly identify problem areas in the power grid.

[0016] Step 2: In the adjustable load space of multiple adjustable electric terminals in the target power grid, according to the load overload information, the load spring parameters are optimized to obtain the optimal load spring parameter array with the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable electric terminals.

[0017] Specifically, there are multiple adjustable power terminals in the target power grid. These power terminals can adjust their load corresponding characteristics in the power grid. According to the load overload information, the load spring parameters are optimized, including response time, adjustment amplitude, maximum and minimum loads, etc. While reducing the load overload of the power grid, the working quality of the power terminal is ensured. For example, suppose there are multiple adjustable power 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 load by reducing the charging rate or reducing the charging time, thereby helping the power grid to relieve pressure. Adjusting the working parameters of the charging station, such as charging rate, maximum charging power, etc., can achieve the optimal distribution of the power grid load and improve the working quality of the power terminal. However, reducing the power load of the adjustable power terminal may affect its 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 terminal in 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 overload of the power grid, but also will not significantly affect the working quality of the power consumption end. Finally, the optimization algorithm will output a set of optimal load spring parameter arrays, which can enable the power grid to maintain the working quality of the power consumption end while reducing the overload. Real-time monitoring of the power grid load, analysis of the overload information, and adjustment of the load response characteristics of the power consumption end can effectively reduce the overload of the power grid.

[0018] Step 3: According to the optimal load spring parameter array, the adjustable load spring effect analysis is performed to obtain the residual load overload information, and the residual load overload information is distributed to multiple common power consumption ends to construct the power load parameter distribution.

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

[0020] Step 4: Based on the photovoltaic storage device array in the target power grid area, combined with the power load parameter distribution, energy storage support optimization is performed to obtain the optimal energy storage support parameter distribution, wherein the optimization is performed based on the matching degree between the energy storage support parameter distribution and the power load parameter distribution.

[0021] Specifically, the energy storage parameters of the photovoltaic storage device array in the target power grid area are obtained, including the power generation capacity of the photovoltaic panel, the capacity of the energy storage battery, the charging and discharging efficiency, etc. The historical power consumption data of each common power consumption terminal in the power grid are analyzed to determine the power load parameter distribution, including the average power consumption of each power consumption terminal, the peak and valley power consumption characteristics, etc. The energy storage support parameter distribution is initialized according to the energy storage parameters and the power load parameters. Considering the possible losses in the power transmission process, the transmission loss is calculated according to the distance between the energy storage device and the power consumption terminal. The matching degree between the energy storage support parameter distribution and the power load parameter distribution is evaluated, and the energy storage support parameters, such as the charging and discharging rate, the energy storage capacity, etc., are adjusted to optimize the matching degree. Repeat the above steps and perform iterative optimization until the predetermined matching degree target is reached or other optimization criteria are met. Finally, the optimal energy storage support parameter distribution is obtained, which is the parameter configuration that achieves the best matching between the energy storage device and the power consumption terminal under the condition of considering the transmission loss and the power load parameter distribution. By optimizing the energy storage support parameter distribution, the overall efficiency of the energy storage system can be improved and the power transmission loss can be reduced.

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

[0023] Specifically, the load spring parameters and energy storage support parameters obtained through optimization are used to adjust the load distribution and energy management in the power grid. According to the optimized load spring parameter array, the operating state of the adjustable electric terminal is adjusted. For example, if the working quality parameters of a certain adjustable electric terminal need to be improved, this can be achieved by adjusting the load spring parameters. According to the optimized energy storage support parameter distribution, the operating strategy of the energy storage system is adjusted. For example, if a certain energy storage device needs to participate in power grid support more frequently, this can be achieved by adjusting its energy storage support parameters. According to the long-term operation data and predictions of the power grid, the load spring parameter array and the energy storage support parameter distribution are regularly evaluated and adjusted. By adopting 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 achieved, and the operation efficiency and reliability of the power grid can be improved.

[0024] Furthermore, step one of this application includes:

[0025] During the operation of the target power grid, the power grid load within a preset time period is monitored and collected, and the average power grid load is obtained through processing; and the load overload information is calculated based on the average power grid load and the power of the target power grid.

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

[0027] Further, step 2 of this application includes:

[0028] The adjustable load space of multiple adjustable electric terminals in the target power grid is obtained, wherein each adjustable load space includes multiple load spring parameters; in order to reduce the load overload of the target power grid and improve the working quality of multiple adjustable electric terminals, a load spring function is constructed as follows: Where LSF is the spring fitness, w1 and w2 are weights, M is the number of multiple callable electrical terminals, T i is the load spring parameter of the i-th callable electrical end in the load spring parameter array, P i is the working quality parameter of the ith adjustable electric end after setting according to the load spring parameters, is the preset working quality parameter of the ith adjustable electrical terminal; according to the load spring function, the load spring parameters of multiple adjustable electrical terminals are optimized in the adjustable load space to obtain the optimal load spring parameter array.

[0029] Specifically, there are multiple adjustable power terminals in the target power grid, and each power terminal has certain load spring parameters, which determine the power terminal's response ability when the power grid load changes. The adjustable power terminals include industrial equipment with variable load characteristics, smart home appliances, energy storage systems, or other facilities that can adjust their power consumption according to the needs of the power grid. The load spring parameter is the power supply adjustment range of the adjustable power terminal, such as the adjustment range of the power grid to the adjustable power terminal. First, it is necessary to obtain the adjustable load space of each adjustable power terminal in the target power grid, including all load spring parameters of the power terminal, such as response time, adjustment range, maximum and minimum load, etc., which determine the behavior of the power terminal when the power grid load changes. Next, with the purpose of reducing the load overload of the target power grid and improving the working quality of multiple adjustable power terminals, a load spring function is constructed as follows: Among them, LSF is the spring adaptability, which is the key indicator to measure the performance of the load spring function; w1 and w2 are weight coefficients, which adjust the influence of load spring parameters and working quality parameters on LSF; M is the number of multiple adjustable electrical terminals; T i is the load spring parameter of the ith callable electrical terminal in the load spring parameter array; P i is the working quality parameter of the ith adjustable electric end after setting according to the load spring parameters; is the preset working quality parameter of the ith adjustable power terminal. The higher the spring adaptability, the better the power terminal plays in the power grid load management.

[0030] The load spring function is a mathematical model that describes the role of these power terminals in the load management of the power grid. By optimizing these parameters, the load spring function can reduce the load overload of the power grid and improve the working quality of the power terminals. The load spring parameters of multiple adjustable terminals are optimized in the adjustable load space, and optimization algorithms such as genetic algorithms and particle swarm optimization are used 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, thereby reducing the load overload of the power grid and improving the working quality of each adjustable terminal. 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, the present application also includes the following steps:

[0032] In multiple adjustable load spaces, multiple first load spring parameters of multiple adjustable electrical ends are generated to construct a first load spring parameter array; multiple first working quality parameters of the multiple adjustable electrical ends adjusted according to the multiple first load spring parameters are analyzed, and the first spring fitness of the first load spring parameter array is calculated according to the load spring parameters; and the load spring parameters of the multiple adjustable electrical ends are continuously optimized 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 terminal can adjust its load response characteristics in the power grid, including a series of load spring parameters, such as response time, adjustment amplitude, maximum and minimum loads, etc. Generate multiple first load spring parameters for multiple adjustable power terminals, that is, determine a set of initial load spring parameter values ​​for each power terminal within these adjustable load spaces. Organize these parameters into a matrix or array to construct a first load spring parameter array, in which each element represents a load spring parameter of an adjustable power terminal. Clarify how the working quality parameters of each power terminal are affected by its load spring parameters. Working quality parameters may include the efficiency, response time, stability, energy consumption, etc. of the power terminal, which reflect the actual performance of the power terminal in power grid load management.

[0034] According to the load spring parameters, the first spring fitness of the first load spring parameter array is calculated. Spring fitness is a comprehensive indicator to measure the performance of the power end in the power grid load management, which is usually calculated by a combination of multiple work quality parameters. Continue to optimize the load spring parameters of multiple adjustable power ends in multiple adjustable load spaces to obtain the optimal load spring parameter array with the largest spring fitness. The spring fitness (LSF) is used as the objective function of the optimization problem. LSF reflects the overall performance of the power grid, including the reduction of load overload and the guarantee of the work quality of the power end. According to the safe operation requirements of the power grid and the performance limitations of the equipment, set the constraints in the optimization process.

[0035] Select a suitable optimization algorithm, such as genetic algorithm, 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. Each iteration will calculate a new LSF value and adjust the parameters according to the results until the parameter combination that maximizes the LSF is found. After finding the optimal solution, it is necessary to verify to ensure that this set of parameters can not only maximize the LSF but also meet the actual operation requirements of the power grid. Finally, the optimization algorithm will output a set of optimal load spring parameter arrays, which can improve the working quality of each adjustable power terminal while reducing the load overload. Through the optimization process, the load response characteristics of each adjustable power terminal in the power grid are accurately adjusted, so as to more effectively manage the power grid load and improve the stability and reliability of the power grid.

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

[0037] According to the optimal load spring parameter array, the total load reduction parameter is calculated, and combined with the load overload information, the remaining load overload information is calculated; according to the historical average load of multiple ordinary power terminals in the target power grid, the remaining load overload information is distributed and calculated to obtain multiple power load parameters; according to the location information of the multiple ordinary power terminals, combined with the multiple power load parameters, a power load parameter distribution is constructed.

[0038] Specifically, the total load reduction parameter is calculated based on the optimal load spring parameter array obtained by optimization, 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 parameters of a certain power consumption end result in a load reduction of 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, the overload 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 that still exists in the power grid after adjusting the power consumption end.

[0039] According to the historical average load of multiple common power consumers in the target power grid, the contribution of each power consumer to the power grid load is analyzed. The historical average load refers to the average amount of electrical energy consumed by each power consumer within a certain time range. According to the historical average load of each power consumer, the remaining load overload information is allocated to these power consumers. The principles of allocation include fairness, load characteristics, geographical location and other factors. Based on the allocated load overload information, the new load parameters of each power consumer are calculated to reflect the role of each power consumer in the adjusted power grid load management. The location information of each common power consumer is collected, including the specific geographical location of the power consumer, the line connected to the power grid, the position relationship in the power grid, etc. For example, one power consumer may be located in the central area of ​​the city, while another may be located in the suburbs.

[0040] Combine multiple power load parameters to analyze the load characteristics of each power end and understand the load contribution of each power end in the power grid. Construct a power load parameter distribution to analyze the location and load characteristics of the power ends in the power grid in order to more accurately understand the load status of the power grid and achieve optimal load distribution. It reflects the load characteristics and location information of each power end in the power grid, which helps to more accurately understand the load status of the power grid. For example, the power load parameter distribution can show which power ends are located in the high-load area of ​​the power grid and which power ends have greater load adjustment capabilities. By calculating the total load reduction parameters, obtaining the remaining load overload information, allocating the remaining load overload information, and constructing the power load parameter distribution, a comprehensive power grid load distribution diagram can be obtained, which helps to identify weak links in the power grid and formulate more effective load management strategies.

[0041] Further, if Figure 2 As shown, step 4 of this application includes:

[0042] Obtain an energy storage parameter array of the photovoltaic storage device array in the area where the target power grid is located, and construct an energy storage support parameter space; randomly generate a first energy storage support parameter array in the energy storage support parameter space according to the residual load overload information, and construct a first energy storage support parameter distribution in combination with the location information of multiple photovoltaic storage devices in the photovoltaic storage device array, wherein the sum of multiple first energy storage support parameters in the first energy storage support parameter array is equal to the residual load overload information; analyze the matching degree of the first energy storage support parameter distribution and the power 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, determine to obtain a temporary optimization result; continue to optimize until convergence, output the final temporary optimization result, and obtain the optimal energy storage support parameter distribution.

[0043] Specifically, the energy storage parameter array of the photovoltaic storage device array in the area where the target power grid is located is obtained, including the capacity, charging and discharging efficiency, charging and discharging speed, service life, etc. of the energy storage device. For example, a photovoltaic storage device array may include multiple solar panels and battery energy storage systems, each of which has its own 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 residual load overload information, a first energy storage support parameter array is randomly generated in the energy storage support parameter space, including the charging and discharging strategy, operating mode, response time, etc. of the energy storage device. For example, an energy storage device array may include multiple battery energy storage systems, each of which has its own specific parameters. By randomly generating these parameters, different energy storage strategies can be explored to find the best energy storage solution.

[0044] Combined with the location information of multiple photo-storage devices in the photo-storage device array, a first energy storage support parameter distribution is constructed, and the sum of multiple first energy storage support parameters in the first energy storage support parameter array is equal to the residual load overload information. According to the first energy storage support parameter distribution and the power load parameter distribution, the first energy storage support parameter of each photo-storage device is sequentially distributed to multiple common power consumption ends from near to far. According to the power supply and the transmission distance, the distribution loss ratio can be calculated. The distribution loss ratio refers to the ratio of energy loss caused by factors such as lines and equipment to the power supply power during the transmission of electric energy from the energy storage device to the power consumption end. According to the 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 end, the second energy storage support parameter distribution can be randomly generated. On the basis of the existing energy storage parameters, by randomly adjusting certain parameters, different energy storage strategies are explored to find a better matching solution. For example, the charging and discharging strategy, operation mode, response time and other parameters of the energy storage device can be randomly adjusted. Similar to the analysis of the first matching degree, the second matching degree is obtained by analyzing the second energy storage support parameter distribution.

[0045] Determine 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 means 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 a 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 to calculate the ratio of the second matching degree to the first matching degree, which is used 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, and according to the ratio of the two, correct the basic optimization probability to obtain a new optimization probability. According to the optimization probability, select 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 make adjustments until the optimal energy storage support parameter distribution is approached. When the matching degree no longer changes significantly, the final temporary optimization result, that is, the optimal energy storage support parameter distribution, is output. This result is the parameter configuration that achieves the best match between the energy storage device and the power consumption end under the current conditions. Through continuous iteration and optimization, it can be ensured that the support capacity provided by the energy storage system is highly matched with the actual needs of the power grid, thereby improving the operating efficiency and reliability of the power grid.

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

[0048] According to the first energy storage support parameter distribution and the power load parameter distribution, the first energy storage support parameter of each photovoltaic storage device is sequentially distributed to multiple common power consumption ends from near to far, until the power load parameters of the multiple common power consumption ends are equal to the distributed energy storage support parameter, so as to obtain a first distribution result; according to the energy storage distribution loss of the first distribution result, a first distribution loss parameter is obtained; according to the first distribution loss parameter, a first matching degree is calculated.

[0049] Specifically, the first energy storage support parameter of each photovoltaic storage device is determined according to the first energy storage support parameter distribution and the power load parameter distribution. According to the location information of the photovoltaic storage device, the first energy storage support parameter of each photovoltaic storage device is sequentially allocated to multiple ordinary power terminals with distances from near to far. For example, if a photovoltaic storage device has a 10-megawatt energy storage support capacity and is relatively close to a power terminal, then the 10-megawatt energy storage support capacity can be allocated to this power terminal. Each photovoltaic storage device gives priority to supplying power to the nearest power terminal, and then supplies power to the next one when the power load parameter is reached, and gradually distributes to more distant power terminals until the power load parameters of multiple ordinary power terminals are equal to the allocated energy storage support parameters.

[0050] According to the first allocation result, the distribution losses that may be generated in the process of the energy storage device supplying power to each power consumer are analyzed, including line losses, transformer losses, switch equipment losses, etc. The distribution loss ratio can be calculated based on the power supplied and the transmission distance. The distribution loss ratio refers to the ratio of energy loss caused by factors such as lines and equipment to the power supply power in the process of transmitting electric energy from the energy storage device to the power consumer. By comparing the ability of the energy storage device to supply power to each power consumer and the required power supply of each power consumer, the first matching degree can be calculated. The first matching degree reflects the degree of matching between the energy storage device and the power consumer, usually expressed as a percentage or fraction. By calculating the first matching degree, the load distribution of the power grid can be controlled more accurately to ensure the safe and efficient operation of the power grid.

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

[0052] Determine whether the second matching degree is greater than the first matching degree. If so, use the second energy storage support parameter distribution as a 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 the basic optimization probability and obtain the optimization probability; according to the optimization probability, select the second energy storage support parameter distribution or the first energy storage support parameter distribution as the temporary optimization result.

[0053] Specifically, it is determined 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 means that the randomly generated second energy storage support parameter distribution is better than 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 a 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, and a probability is calculated to receive the poor second energy storage support parameter distribution as a temporary optimization result. This ratio reflects the degree of excellence of the second energy storage support parameter distribution. The smaller the ratio, the smaller the probability of the second energy storage support parameter distribution as a temporary optimization result. The second energy storage support parameter distribution is analyzed 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 load parameter distribution. For example, the ratio of multiple energy storage parameters can be calculated, and then the variance of these ratios can be calculated, and the inverse of the variance can be used as the balance information. The smaller the variance, the greater the balance and the more uniform the distribution of energy storage device parameters.

[0054] Similar to the calculation of the second balance information, the first balance information of the first energy storage support parameter distribution is calculated. According to the ratio of the second balance information to the first balance information, multiplied by the basic optimization probability, the correction calculation is completed to ensure that the optimization probability not only considers the matching degree, but also considers 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 better matching and balance, the second energy storage support parameter distribution is selected as the temporary optimization result. If the optimization probability indicates that the first energy storage support parameter distribution provides better matching and balance, the first energy storage support parameter distribution is selected as the temporary optimization result. By obtaining the temporary optimization result, the energy storage equipment can be used more effectively and the operating efficiency and stability of the power grid can be improved.

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

[0056] During the operation of the target power grid, the load of the power grid is monitored and the load overload information when the power grid load is overloaded is collected; in the adjustable load space of multiple adjustable power terminals in the target power grid, according to the load overload information, the load spring parameters are optimized to reduce the load overload of the target power grid and improve the working quality of multiple adjustable power terminals, and the optimal load spring parameter array is obtained; according to the optimal load spring parameter array, the adjustable load spring effect is analyzed to obtain the remaining load overload information, and the information is distributed to multiple common power terminals to construct the power load parameter distribution; according to the array of photovoltaic storage devices in the area where the target power grid is located, combined with the power load parameter distribution, the energy storage support is optimized to obtain the optimal energy storage support parameter distribution, wherein the optimization is performed according to the matching degree of the energy storage support parameter distribution and the power load parameter distribution; the target power grid is controlled by using the optimal load spring parameter array and the optimal energy storage support parameter distribution. In other words, the distributed power source and the energy storage system are combined to optimize the load spring parameters and the energy storage support parameters, realize the autonomous support control of the distribution network, and achieve 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 apparent to those skilled in the art, and the general principles defined herein may 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 conform to 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 belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A distribution network autonomous support control method based on adjustable load spring effect, characterized in that: include During the operation of the target power grid, the load of the power grid is monitored and load overload information is collected when the power grid is overloaded; In the adjustable load space of the multiple adjustable electric terminals in the target power grid, according to the load overload information, the load spring parameters are optimized to obtain the optimal load spring parameter array for the purpose of reducing the load overload of the target power grid and improving the working quality of the multiple adjustable electric terminals; According to the optimal load spring parameter array, an adjustable load spring effect analysis is performed to obtain residual load overload information, and the residual load overload information is distributed to multiple common power consumption terminals to construct a power consumption load parameter distribution; According to the photovoltaic storage device array in the target power grid area, combined with the power load parameter distribution, energy storage support optimization is performed to obtain the optimal energy storage support parameter distribution, wherein the optimization is performed according to the matching degree between the energy storage support parameter distribution and the power load parameter distribution; The target power grid is controlled by using the optimal load spring parameter array and the optimal energy storage support parameter distribution.

2. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 1 is characterized in that: During the operation of the target power grid, the power grid load is monitored and when the power grid is overloaded, load overload information is collected, including: During the operation of the target power grid, the power grid load within a preset time period is monitored and collected, and the average power grid load is obtained through processing; Load overload information is obtained by calculation according to the average grid load and the power of the target grid.

3. The distribution network autonomous support control method based on adjustable load spring effect according to claim 1 is characterized in that: In the adjustable load space of the multiple adjustable electric terminals in the target power grid, according to the load overload information, the load spring parameters are optimized for the purpose of reducing the load overload of the target power grid and improving the working quality of the multiple adjustable electric terminals, including: Obtaining adjustable load spaces of a plurality of adjustable electrical terminals in a target power grid, wherein each adjustable load space includes a plurality of load spring parameters; In order to reduce the load overload of the target power grid and improve the working quality of multiple callable power terminals, a load spring function is constructed as follows: Where LSF is the spring fitness, w1 and w2 are weights, M is the number of multiple callable electrical terminals, T i is the load spring parameter of the i-th callable electrical end in the load spring parameter array, P i is the working quality parameter of the ith adjustable electric end after setting according to the load spring parameters, is the preset working quality parameter of the i-th adjustable electrical terminal; According to the load spring function, load spring parameters of multiple adjustable electrical terminals are optimized in the adjustable load space to obtain an optimal load spring parameter array.

4. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 3 is characterized in that: According to the load spring function, load spring parameters of a plurality of adjustable electrical ends are optimized in the adjustable load space, including: In a plurality of adjustable load spaces, generating a plurality of first load spring parameters of a plurality of adjustable electrical ends, and constructing a first load spring parameter array; Analyzing a plurality of first working quality parameters of the plurality of adjustable electrical terminals after adjustment according to a plurality of first load spring parameters, and calculating and obtaining a first spring adaptability of the first load spring parameter array according to the load spring parameters; Continue to optimize the load spring parameters of multiple adjustable electrical ends within the multiple adjustable load spaces to obtain an optimal load spring parameter array with the maximum spring adaptability.

5. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 1, characterized in that: According to the optimal load spring parameter array, the adjustable load spring effect analysis is performed to obtain the residual load overload information, and the residual load overload information is distributed to multiple common power consumption ends to construct the power consumption load parameter distribution, including: According to the optimal load spring parameter array, a total load reduction parameter is calculated, and in combination with the load overload information, residual load overload information is calculated; According to the historical average load of multiple common power consumption terminals in the target power grid, the residual load overload information is distributed and calculated to obtain multiple power consumption load parameters; According to the location information of the plurality of common power consumption terminals and in combination with the plurality of power consumption load parameters, a power consumption load parameter distribution is constructed.

6. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 1, characterized in that: According to the photovoltaic storage device array in the target power grid area, combined with the power load parameter distribution, energy storage support optimization is performed to obtain the optimal energy storage support parameter distribution, including: Obtaining an energy storage parameter array of an array of photovoltaic storage devices in an area where the target power grid is located, and constructing an energy storage support parameter space; According to the remaining load overload information, a first energy storage support parameter array is randomly generated in the energy storage support parameter space, and a first energy storage support parameter distribution is constructed in combination with the position information of multiple optical storage devices in the optical storage device array, 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; Analyzing the matching degree between the first energy storage support parameter distribution and the power load parameter distribution to obtain a first matching degree; Continue to randomly generate and construct the second energy storage support parameter distribution, and analyze to obtain the second matching degree; According to the second matching degree and the first matching degree, determining and obtaining a temporary optimization result; Continue the optimization until convergence, output the final temporary optimization results, and obtain the optimal energy storage support parameter distribution.

7. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 6, characterized in that: Analyzing the matching degree between the first energy storage support parameter distribution and the power load parameter distribution to obtain a first matching degree includes: According to the first energy storage support parameter distribution and the power load parameter distribution, the first energy storage support parameter of each photovoltaic storage device is sequentially distributed to a plurality of common power consumption terminals from near to far, until the power load parameters of the plurality of common power consumption terminals are equal to the distributed energy storage support parameter, thereby obtaining a first distribution result; Obtaining a first distribution loss parameter according to the energy storage distribution loss of the first allocation result; A first matching degree is calculated according to the first power distribution loss parameter.

8. The method for controlling the distribution network autonomous support based on the adjustable load spring effect according to claim 6 is characterized in that: According to the second matching degree and the first matching degree, determining and obtaining a temporary optimization result includes: Determine whether the second matching degree is greater than the first matching degree, and if so, use the second energy storage support parameter distribution as a temporary optimization result; If not, then calculating the ratio of the second matching degree to the first matching degree to obtain a basic optimization probability; Calculating second balance information of a plurality of second energy storage support parameters in the second energy storage support parameter distribution, and calculating first balance information of the first energy storage support parameter distribution, and using 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; According to the optimization probability, the second energy storage support parameter distribution or the first energy storage support parameter distribution is selected as a temporary optimization result.

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