Self-balancing capability planning method and system for active power distribution network grid

Abstract

The invention discloses a self-balancing capability planning method and system for an active power distribution network grid, and the method comprises the steps: generating a network supply load curve based on the output data of a distributed power supply and the load data of a diversified user, and calculating a self-balancing deviation coefficient of the network supply load curve; calculating a source-to-load ratio and a source-to-load characteristic matching degree of the target power supply grid based on the output data of the distributed power supply and the load data of the diversified users; calculating a grid mutual aid coefficient based on the power grid topology and operation data of the target power supply grid and the adjacent grid; and according to the self-balancing deviation coefficient, the source-to-load ratio, the source-to-load characteristic matching degree and the grid mutual aid coefficient, generating a differential planning optimization scheme for the target power supply grid. The closed loop from accurate evaluation to targeted optimization is realized, and the new energy consumption capability, the operation toughness and the planning scientificity of the power distribution network are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of active power distribution network planning, and particularly relates to a self-balancing capability planning method and system for an active power distribution network grid. BACKGROUND

[0002] With high proportion of distributed energy access and diversification of load types, the power distribution network is transforming from the traditional unidirectional power supply network to the multi-directional power flow "active power distribution network". Grid planning is a key technical means to improve the reliability, flexibility and economy of the power distribution network, and its core lies in dividing the power distribution network service area into relatively independent "grid units" and conducting differentiated planning.

[0003] The existing power distribution network power supply grid evaluation system is mainly established around four dimensions of power supply capability, network structure, equipment level and digitalization level. However, this system has obvious deficiencies: first, its evaluation perspective is still limited to meeting unidirectional power supply, is not associated with "source-load-storage" self-balancing, and does not consider the reconstruction needs of multi-directional power flow on evaluation indicators, and cannot adapt to the new features of active power distribution network; second, the existing system takes independent evaluation of a single grid as the core, lacks attention to the dynamic coordination effect of "source-load-storage" within the grid, and has no standard for quantifying the mutual aid capability between grids, making it difficult to meet the requirements of new power systems for flexibility and resilience.

[0004] Therefore, there is an urgent need in the art for a new method and system that can scientifically quantify the self-balancing capability of the active power distribution network grid and generate accurate planning schemes accordingly. SUMMARY

[0005] The purpose of the present application is to overcome the deficiencies of the prior art, provide a self-balancing capability planning method and system for an active power distribution network grid, and scientifically evaluate the self-balancing capability of the grid under diversified new elements, and generate differentiated planning optimization schemes based on the evaluation results to improve the local self-consistency and global coordination capability of the power distribution network.

[0006] In a first aspect, the present application provides a self-balancing capability planning method for an active power distribution network grid, comprising: obtaining output data of distributed power sources, load data of diversified users and adjustment potential data of adjustable resources within a target power supply grid; generating a grid-load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculating a self-balancing deviation coefficient of the grid-load curve; calculating a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources, the load data of the diversified users and the adjustment potential data of the adjustable resources; a grid mutual aid coefficient is calculated based on the power grid topology and operation data of the target power supply grid and adjacent grids; According to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree, and the grid mutual aid coefficient, a differentiated planning optimization scheme for the target power supply grid is generated.

[0007] In a second aspect, the present application provides a self-balancing capability planning system for a grid of an active power distribution network, comprising: An acquisition module is configured to acquire output data of distributed power sources, load data of diversified users, and adjustment potential data of adjustable resources in a target power supply grid; A first calculation module is configured to generate a grid-load curve based on the output data of the distributed power sources and the load data of the diversified users, and to calculate a self-balancing deviation coefficient of the grid-load curve; A second calculation module is configured to calculate a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources, the load data of the diversified users, and the adjustment potential data of the adjustable resources; A third calculation module is configured to calculate a grid mutual aid coefficient based on the power grid topology and operation data of the target power supply grid and adjacent grids; A generation module is configured to generate a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree, and the grid mutual aid coefficient.

[0008] In a third aspect, an electronic device is provided, comprising at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the self-balancing capability planning method for a grid of an active power distribution network according to any one of the embodiments of the present application.

[0009] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the self-balancing capability planning method for a grid of an active power distribution network according to any one of the embodiments of the present application.

[0010] The self-balancing capability planning method and system of the active power distribution grid mesh of the application, by constructing a "three-level progressive" evaluation integrating distributed power supply, diversified load and adjustable resources, for the first time realizes scientific quantification and accurate description of the self-balancing capability of the active power distribution grid mesh, which not only accurately diagnoses the deep-seated problems such as "spatial and temporal mismatch of source and load" and "insufficient implicit balancing capability" that are difficult to find in traditional evaluation by means of innovative indicators such as self-balancing deviation coefficient and source and load characteristic matching degree, but also fills the gap in quantitative evaluation of cross-mesh coordination capability by introducing a mesh mutual aid coefficient; on this basis, the system can automatically generate highly differentiated planning schemes according to the evaluation results, directly guiding the optimal configuration of distributed power supply, the scientific sizing of energy storage systems and the accurate construction of tie channels, thereby forming a complete technical closed loop from "accurate evaluation" to "targeted optimization", effectively improving the consumption capability of the power distribution grid for high proportion of new energy and the operation resilience in complex conditions, and providing reliable technical support for accurate planning of the power distribution grid under the background of new-type power system. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0012] Figure 1 A flow chart of a self-balancing capability planning method of an active power distribution grid mesh provided by an embodiment of the application is shown in Figure 2 A structural block diagram of a self-balancing capability planning system of an active power distribution grid mesh provided by an embodiment of the application is shown in Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the application is shown in DETAILED DESCRIPTION

[0013] In order to make the objects, technical solutions and advantages of the embodiments of the application clearer, the following will combine the drawings in the embodiments of the application to clearly and completely describe the technical solutions in the embodiments of the application. Obviously, the described embodiments are some embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0014] Please refer to Figure 1 which shows a flow chart of a self-balancing capability planning method of an active power distribution grid mesh of the application.

[0015] As Figure 1As shown, the self-balancing capability planning method of the active power distribution grid specifically comprises the following steps: Step S101, obtaining the output data of the distributed power supply in the target power supply grid, the load data of the diversified users and the adjustment potential data of the adjustable resources.

[0016] In this step, the original output data of the distributed power supply in the target power supply grid is obtained; For photovoltaic power supply, the original output data is corrected according to a preset first correction strategy to obtain the output data of the distributed power supply in the final target power supply grid, wherein the expression of the first correction strategy is: , In the formula, is the output data of the distributed power supply in the final target power supply grid, is the original output data of the distributed power supply in the target power supply grid, is the real-time light intensity, is the light intensity under standard test conditions; For wind power supply, the original output data is corrected according to a preset second correction strategy to obtain the output data of the distributed power supply in the final target power supply grid, wherein the expression of the second correction strategy is: , In the formula, is the real-time wind speed, is the cut-in wind speed, is the rated wind speed.

[0017] Further, the users are accurately classified based on industry attributes and electricity consumption rules; Based on the smart meter data integration, a standardized hourly load data set is formed; Based on the historical load data and the user classification results, the load density method is used to obtain the load data of the diversified users, and the expression is: , , , In the formula, is the total load predicted for a certain grid in the yth year, is the load density of the kth user in the yth year, is the load correlation base of the kth user in the yth year, is the load density of the kth user in the reference year, is the average annual growth rate of the load density of the kth user, is the predicted year, is the reference year, is the current total load of the kth type of user, is the load correlation base of the kth type of user, is the load density of the kth type of user, is the number of user types in the unit.

[0018] The rated parameters and real-time state of the energy storage facility are obtained, and the real-time available charging potential and real-time available discharging potential of the energy storage facility are calculated, and the expression is: , In the formula, is the available charging potential in the t period, is the rated capacity of the energy storage, is the maximum allowed state of charge, is the predicted state of charge in the t period, is the charging efficiency; , In the formula, is the available discharging potential in the t period, is the state of charge in the t period, is the minimum allowed state of charge, is the discharging efficiency; Based on the classification of interruptible load, the capacity, allowed interruption time and online rate of interruptible load are counted, and the total interruptible potential of interruptible load is calculated, and the expression is: , In the formula, is the total interruptible potential of the interruptible load, is the interruptible load capacity of the kth level, is the maximum allowed interruption time of the interruptible load of the kth level, is the online rate of the interruptible load of the kth level.

[0019] Step S102, based on the output data of the distributed power supply and the load data of the diversified users, a grid-supply load curve is generated, and a self-balancing deviation coefficient of the grid-supply load curve is calculated.

[0020] In this step, the self-balancing deviation coefficient is determined by calculating the fluctuation amplitude and smoothness of the grid-supply load curve after superimposing the grid-in source load curve. The smaller the fluctuation amplitude and the higher the smoothness, the higher the self-balancing level, that is, the power grid performs better in dynamic matching of power output and load demand, and can better rely on itself to realize supply-demand balance and reduce dependence on external power grid. The expression for calculating the self-balancing deviation coefficient of the grid-supply load curve is: wherein, is a self-balancing bias coefficient, is a number of hours, is a grid-supply load at time i, is a self-balancing target value of the grid-supply load.

[0021] In step S103, based on the output data of the distributed power supply, the load data of the diversified users, and the adjustment potential data of the adjustable resources, the source-load ratio and the source-load characteristic matching degree of the target power supply grid are calculated.

[0022] In this step, the source-load ratio refers to the ratio of the installed capacity of the hierarchical power supply to the maximum load in the period of the hierarchical photovoltaic output. It is used to measure the overall proportional relationship between the distributed photovoltaic power supply capacity and the local load demand. This index can reflect whether the photovoltaic capacity connected to the power grid is suitable for the local load demand. If the source-load ratio is too high, it means that the development of photovoltaic installation is unreasonable compared with the local load level. The expression for calculating the source-load ratio of the target power supply grid is: wherein, is the installed capacity of the distributed power supply, is the load at time i of the i-th distributed power supply.

[0023] The source-load characteristic matching degree is an important index for evaluating the time distribution coordination between distributed photovoltaic power generation and local load. It focuses on measuring the matching degree of power generation output and load demand in time, rather than just the balance in total amount. High matching degree indicates that photovoltaic power generation can be more effectively consumed by local load, thereby reducing the dependence on energy storage and external transmission. Photovoltaic power generation peaks usually occur during the day, while load demand is often concentrated in the morning and evening. Improving the source-load characteristic matching degree is of great significance for optimizing the utilization efficiency of photovoltaic power generation. In the actual discrete time scale, for the new energy output curve X and the load demand curve Y, the expression for the source-load characteristic matching degree of the target power supply grid is: wherein, is the source-load characteristic matching degree of the target power supply grid, is the sample size, is the new energy output at time i, is the load at time i.

[0024] In step S104, based on the power grid topology and operation data of the target power supply grid and adjacent grids, the grid mutual aid coefficient is calculated.

[0025] ​​​In this step, the grid mutual aid coefficient quantifies the power support capability between adjacent grids, filling the gap in the existing evaluation system. Based on the grid topology and line transmission capacity, the power support potential of adjacent grids in the event of failure or insufficient output is calculated to evaluate the mutual aid capability. The expression for calculating the grid mutual aid coefficient is: , wherein, is the grid mutual aid coefficient of grid i and grid j, is the maximum transmission power of the line between grid i and grid j, is the maximum power deficiency of grid i, is the maximum power deficiency of grid j, is the transmission efficiency.

[0026] Step S105, according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree and the grid mutual aid coefficient, a differentiated planning optimization scheme for the target power supply grid is generated.

[0027] In this step, in response to the self-balancing deviation coefficient being higher than a first threshold value, a scheme of configuring energy storage devices and / or optimizing adjustable resource control strategies is generated. In response to the source-load ratio indicating insufficient power supply capacity, a distributed power supply augmentation or layout optimization scheme is generated. In response to the source-load ratio indicating excess power supply capacity, a low-efficiency power supply reduction or excess output export scheme is generated. In response to the source-load characteristic matching degree being lower than a second threshold value, a scheme of configuring energy storage or implementing source-load storage collaborative control is generated. In response to the grid mutual aid coefficient indicating insufficient mutual aid capability, a scheme of constructing, upgrading the tie-in channel or optimizing the dispatching rules is generated.

[0028] In one specific embodiment, based on the calculation result of the source-load ratio coefficient, for the grid with excessively high or low source-load ratio (photovoltaic installation does not match local load), the distributed power supply configuration is adjusted in combination with the load distribution in the grid. For the grid with excessively high source-load ratio, low-efficiency distributed power supply installation is preferentially reduced, or excess output is guided to the grid with concentrated load in the surrounding to avoid wind and light abandonment. For the grid with excessively low source-load ratio, in combination with the load distribution and network topology, distributed power supply is added near the load center to improve the local power supply capacity and reduce the dependence on the main grid.

[0029] Optimization scheme for insufficient source-load ratio: The scheme includes distributed power supply augmentation and distributed power supply layout optimization.

[0030] Distributed power supply augmentation scheme: taking the source-load ratio reaching 2 (typical value of supply-demand balance) in the next 3 years as the target, the distributed power supply capacity to be augmented is back calculated, and the formula is:

[0031] In the formula, is the total capacity of distributed power supply to be added, is the maximum load prediction value of the grid in the third year, is the total installed capacity of the current distributed power supply, is the target value of the source-load ratio.

[0032] Distributed power supply layout optimization scheme: preferentially select nodes with high load density and large line capacity margin to access new power supply, and avoid power output limitation due to access point line overload.

[0033] Optimization scheme for excess source-load ratio The scheme includes low-efficiency power supply reduction and excess output export.

[0034] Low-efficiency power supply reduction scheme: take "abandonment rate ≤ 5%" as the target, calculate the low-efficiency power supply capacity to be reduced, and the formula is:

[0035] In the formula, is the low-efficiency power supply capacity to be reduced (unit: kW); is the total capacity of low-efficiency power supply; is the current abandonment rate; is the target abandonment rate.

[0036] Excess output export scheme: establish a connection channel with the surrounding grid, transmit excess output across the grid through the connection line channel, and reduce abandonment.

[0037] According to the results of source-load characteristic matching degree, for the grid with large load curve fluctuation and poor source-load characteristic matching, the optimization characteristics of energy storage and flexible load regulation are used. For the grid with low source-load characteristic matching degree, configure energy storage devices to smooth power output fluctuation, and fill the peak-valley difference through energy storage charging and discharging to make the grid-load curve smoother; at the same time, with the help of flexible load regulation means, guide the adjustable load to shift to the power output peak period, and improve the space-time coordination of source and load.

[0038] Energy storage configuration optimization: Calculate the energy storage capacity for the purpose of filling the peak-valley difference and smoothing fluctuations, and the formula is:

[0039] In the formula, is the rated capacity of energy storage; is the source-load peak-valley difference value; is the load peak duration; is a standard deviation of distributed power output; is a regulation duration; is a storage charging and discharging efficiency.

[0040] The basic data of "distributed power real-time output, storage SOC, and load real-time data" are integrated to realize real-time data sharing and unified scheduling; the regulation priority rules are formulated according to the order of preferentially consuming local power, then using storage, and finally taking power from the grid.

[0041] According to the evaluation results of the grid mutual aid coefficient, the cross-grid cooperation is strengthened through the construction and capacity improvement of the tie-in channel. For the grids adjacent to each other and complementary in source and load characteristics, the tie-in channel is constructed to realize the complementarity of excess output and load demand; for the grids with insufficient capacity of the existing tie-in channel, the channel facilities are upgraded to improve the power transmission capacity. At the same time, the power transmission limit and scheduling rules of the tie-in channel are clarified to ensure the stability of the grid flow during cross-grid mutual aid, avoid line overload or voltage out-of-limit, and ultimately improve the overall self-balancing ability of the multi-grid.

[0042] Please refer to Figure 2 which shows a structure block diagram of a self-balancing capability planning system of an active power distribution grid of the present application.

[0043] As shown in Figure 2 , the self-balancing capability planning system 200 includes an acquisition module 210, a first calculation module 220, a second calculation module 230, a third calculation module 240, and a generation module 250.

[0044] The acquisition module 210 is configured to acquire output data of distributed power sources, load data of diversified users, and regulation potential data of adjustable resources in a target power supply grid; the first calculation module 220 is configured to generate a grid-supply load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculate a self-balancing deviation coefficient of the grid-supply load curve; the second calculation module 230 is configured to calculate a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources and the load data of the diversified users; the third calculation module 240 is configured to calculate a grid mutual aid coefficient based on grid topology and operation data of the target power supply grid and adjacent grids; and the generation module 250 is configured to generate a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree, and the grid mutual aid coefficient.

[0045] It should be understood that Figure 2 the modules described in the specification and the reference Figure 1The various steps in the method described above correspond to each other. Thus, the operations and features described above for the method and the corresponding technical effects apply equally to the modules in Figure 2 in which no further elaboration is given.

[0046] In some embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, causes the processor to perform the method of self-balancing capability planning of active power distribution grid. As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, which are configured to: obtain output data of distributed power sources, load data of diversified users and adjustment potential data of adjustable resources in a target power supply grid; generate a grid-supply load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculate a self-balancing deviation coefficient of the grid-supply load curve; calculate a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources and the load data of the diversified users; calculate a grid mutual aid coefficient based on grid topology and operation data of the target power supply grid and adjacent grids; generate a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree and the grid mutual aid coefficient.

[0047] The computer readable storage medium can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the self-balancing capability planning system of the active power distribution grid, etc. In addition, the computer readable storage medium can include a high-speed random access memory, and can also include a memory such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the computer readable storage medium can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the self-balancing capability planning system of the active power distribution grid through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0048] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, like Figure 3As shown, the device includes a processor 310 and a memory 320. The electronic device can also include an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 can be connected by a bus or other means, Figure 3 The memory 320 is the computer readable storage medium described above. The processor 310 performs various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, implements the active power distribution grid mesh self-balancing capability planning method of the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the active power distribution grid mesh self-balancing capability planning system. The output device 340 can include a display device such as a display screen.

[0049] The electronic device described above can perform the method provided by the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method. Technical details not described in detail in the present embodiment can be referred to the method provided by the embodiments of the present application.

[0050] As an implementation manner, the electronic device described above is applied to the active power distribution grid mesh self-balancing capability planning system, and is used for a client, and includes at least one processor, and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain output data of distributed power sources in a target power supply grid, load data of diversified users and adjustment potential data of adjustable resources; generate a grid load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculate a self-balancing deviation coefficient of the grid load curve; calculate a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources and the load data of the diversified users; calculate a grid mutual aid coefficient based on power grid topology and operation data of the target power supply grid and adjacent grids; generate a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree and the grid mutual aid coefficient.

[0051] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary general hardware platforms through the description of the above embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of the various embodiments or some parts of the embodiments.

[0052] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A self-balancing capability planning method for an active distribution grid mesh, characterized in that, The method comprises the following steps: obtaining output data of distributed power sources in a target power supply grid, load data of diversified users, and adjustment potential data of adjustable resources; generating a grid-supply load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculating a self-balancing deviation coefficient of the grid-supply load curve; calculating a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources, the load data of the diversified users, and the adjustment potential data of the adjustable resources; calculating a grid mutual aid coefficient based on grid topology and operation data of the target power supply grid and adjacent grids; generating a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree, and the grid mutual aid coefficient.

2. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The step of obtaining output data of distributed power sources in a target power supply grid comprises the following steps: obtaining original output data of distributed power sources in a target power supply grid; for photovoltaic power sources, correcting the original output data according to a preset first correction strategy to obtain the output data of the distributed power sources in the target power supply grid, wherein the expression of the first correction strategy is: ， In the formula, is the output data of the distributed power supply in the target power supply grid, is the original output data of the distributed power supply in the target power supply grid, is the real-time light intensity, is the light intensity under standard test conditions; for wind power sources, correcting the original output data according to a preset second correction strategy to obtain the output data of the distributed power sources in the target power supply grid, wherein the expression of the second correction strategy is: ， wherein is the real-time wind speed, is the cut-in wind speed, is the rated wind speed.

3. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The step of obtaining load data of diversified users comprises the following steps: classifying users based on industry attributes and power utilization rules; integrating smart meter data to form a standardized hourly load data set; obtaining load data of diversified users based on historical load data and user classification results using a load density method, and the expression is: ， ， ， In the formula, Total load predicted for a certain grid in year y, Load density of the kth user in year y, Load association base of the kth user in year y, Load density of the kth user in the base year, Annual growth rate of the kth user load density, Predicted year, Base year, Current total load of the kth user, Load association base of the kth user, Load density of the kth user, Number of user types within a unit.

4. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The step of obtaining adjustment potential data of adjustable resources comprises the following steps: obtaining rated parameters and real-time states of energy storage facilities, calculating real-time available charging potential and real-time available discharging potential of the energy storage facilities, and the expression is: ， wherein is the available charge potential for the time period t, is the energy storage rated capacity, is the maximum allowed state of charge, is the predicted state of charge for the time period t, is the charging efficiency; ， wherein is the dischargeable potential for the period t, is the state of charge for the period t, is the minimum allowed state of charge, is the discharge efficiency; based on the classification of interruptible loads, counting the capacity, allowable interruption time, and online rate of interruptible loads, and calculating the total interruptible potential of interruptible loads, and the expression is: ， In the formula, is the total interruptible potential of interruptible loads, is the interruptible load capacity of the first level, is the maximum allowed interruption duration of the first level interruptible load, is the online rate of the first level interruptible load.

5. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The expression for calculating the self-balancing deviation coefficient of the grid-supply load curve is: ， In the formula, is a self-balancing deviation coefficient, is the number of hours, is the grid-supply load at time i, is the self-balancing target value of the grid-supply load.

6. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The expression for calculating the source-load ratio of the target power supply grid is: ， In the formula, is the installed capacity of the distributed power supply, is the power consumption load at the output time of the i-th distributed power supply.

7. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The expression for calculating the source-load characteristic matching degree of the target power supply grid is: ， In the formula, a source and load characteristic matching degree of a target power grid, a sample size, a new energy output at i moment, a load at i moment.

8. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The expression for calculating the grid mutual aid coefficient is: ， wherein, is the grid intertie coefficient of grid i and grid j, is the maximum transmission power of the line between grid i and grid j, is the maximum power deficit of grid i, is the maximum power deficit of grid j, is the transmission efficiency.

9. The method for self-balancing capability planning of an active power distribution grid mesh of claim 1, wherein, The step of generating a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree, and the grid mutual aid coefficient comprises the following steps: in response to the self-balancing deviation coefficient being higher than a first threshold value, generating a scheme of configuring energy storage devices and / or optimizing adjustable resource control strategies; in response to the source-load ratio indicating insufficient power supply capacity, generating a distributed power supply augmentation or layout optimization scheme; in response to the source-load ratio indicating excessive power supply capacity, generating a low-efficiency power supply reduction or surplus output export scheme; in response to the source-load characteristic matching degree being lower than a second threshold value, generating a scheme of configuring energy storage or implementing source-load storage collaborative control; In response to the grid mutual aid coefficient indicating insufficient mutual aid capability, a scheme of constructing, upgrading a contact channel or optimizing a dispatching rule is generated.

10. A self-balancing capability planning system for an active distribution grid mesh, characterized by, The method comprises the steps of: an acquisition module configured to acquire output data of distributed power sources, load data of diversified users and adjustment potential data of adjustable resources in a target power supply grid; a first calculation module configured to generate a grid-load curve based on the output data of the distributed power sources and the load data of the diversified users, and calculate a self-balancing deviation coefficient of the grid-load curve; a second calculation module configured to calculate a source-load ratio and a source-load characteristic matching degree of the target power supply grid based on the output data of the distributed power sources, the load data of the diversified users and the adjustment potential data of the adjustable resources; a third calculation module configured to calculate a grid mutual aid coefficient based on grid topology and operation data of the target power supply grid and adjacent grids; a generation module configured to generate a differentiated planning optimization scheme for the target power supply grid according to the self-balancing deviation coefficient, the source-load ratio, the source-load characteristic matching degree and the grid mutual aid coefficient.

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