A multi-time-state main and auxiliary micro-grid resource collaborative regulation method and system

Abstract

The application discloses a kind of multi-temporal main and microgrid resource collaborative control method and system, based on the main and micro all application scene main demand, research station line variable house topology multi-scene resource intelligent aggregation technology, support mass resource classification aggregation and multi-scene flexible aggregation, combined with the main and micro integration model and each level regulation resource and regulation capacity, establish the main and micro integration balance analysis model, for the local adequacy in main and microgrid, new energy consumption and other application scenes, research main and micro multi-level collaborative optimization decision technology, establish the main and micro collaborative forward-looking optimization model, propose main and micro ultra-short-term real-time correction strategy, provide accurate decision support for power grid dispatching, it has important significance in the main and micro integration balance analysis and early warning capability.

Description

Technical Field

[0001] This invention relates to the field of main and distribution microgrid balance analysis and resource coordinated regulation technology, and in particular to a multi-temporal main and distribution microgrid resource coordinated regulation method and system. Background Technology

[0002] With the development of power grids, new power systems possess fundamental characteristics such as cleanliness and low carbon emissions, safety and controllability, flexibility and efficiency, intelligence and user-friendliness, and openness and interaction. With the widespread integration of diverse entities such as distributed power sources, new energy storage, electric vehicles, and various loads, microgrids are developing on a large scale, and the power grid is exhibiting a new multi-level collaborative structure involving main, distribution, and microgrids.

[0003] In regions with a high proportion of renewable energy installations, if each distribution area or dedicated transformer is connected to various distributed resources to form a microgrid, the number of regional microgrids will reach millions, the number of connected devices will reach tens of millions, and the amount of data generated will reach hundreds of millions. With the widespread integration of distributed power sources, new energy storage, electric vehicles, and diverse loads, the large-scale development of microgrids is leading to a new multi-level collaborative structure of the power grid, encompassing main, distribution, and microgrids. However, power grid operation and control face challenges such as difficulty in state perception, decision analysis, operation control, and market development, and collaborative analysis has not yet been systematically developed. Summary of the Invention

[0004] Purpose of the invention: The present invention aims to provide a multi-temporal main and distribution microgrid resource collaborative control method for application scenarios such as local insufficient sufficiency and renewable energy consumption in the main and distribution microgrid. Another purpose of the present invention is to provide a multi-temporal main and distribution microgrid resource collaborative control system.

[0005] Technical solution: The multi-temporal main-distribution microgrid balance analysis and resource coordinated control method of the present invention includes...

[0006] Based on the dynamic identification of the station-line-transformer-customer topology of distribution microgrid resources, according to the adjustable resource control characteristics and control scenario requirements, a Boolean matrix is ​​used to determine the matching degree between the adjustable resource control characteristics and multiple control scenarios, and an adjustable resource pool for aggregation of multiple control scenarios is dynamically generated.

[0007] Based on the matching degree between the adjustable resource regulation characteristics and multiple regulation scenarios, and combined with the grid topology relationship, the main and distribution topology analysis method based on breadth-first search is used to obtain the evaluation results of the spatiotemporal aggregation regulation capability from the feeder to the whole network, and determine the feasible domain of resource regulation capability.

[0008] A multi-level main-distribution-micro power grid balance analysis is conducted. Based on the results of the main-distribution-micro power grid balance analysis and the feasible region of resource regulation capacity, with the goal of maximizing the reserve balance margin, the transmission margin of the river-crossing channel, and the regional power supply margin, and considering the resource regulation safety margin, a multi-dimensional integrated main-distribution-micro power grid balance margin assessment model is constructed for the whole network, local areas, and regional areas. The balance margin of the main-distribution-micro power grid at multiple time scales, including real-time, intraday, and day-ahead, is calculated respectively.

[0009] Based on the balance margin of the main, distribution, and micro-level power grid and the adjustable resource pool aggregated for multiple control scenarios, a main, distribution, and micro-level multi-level coordinated control model is constructed to carry out integrated multi-level coordinated control of the main, distribution, and micro-level power grid.

[0010] Furthermore, an adjustable resource pool is dynamically generated for aggregation across multiple control scenarios, as detailed below:

[0011] Based on real-time distribution micro-model data and multi-temporal resource up / down adjustment capabilities and power prediction data reported by operators before and during the day, resources are aggregated and monitored according to grid access and power supply path, forming aggregated data of different voltage levels, different grids and regional grids. The full path result of resource station-line-transformer-customer information is generated. Based on the adjustable resource control characteristics and the matching degree between adjustable resource control characteristics and multiple control scenarios, an adjustable resource pool aggregated for multiple control scenarios is dynamically generated.

[0012] Furthermore, the adjustable resource regulation characteristics include at least one of the following: adjustable resource regulation period, regulation capacity, and response speed.

[0013] Furthermore, multiple control scenarios include at least one of the following: exceeding the limit of the interruption surface, reserve gap, and peak shaving.

[0014] Furthermore, the resource regulation safety margin includes considerations of line power constraints, critical section power flow constraints, unit operation safety constraints, and reserved reserve constraints from the perspective of the main grid, and considerations of line power constraints, equipment operation safety constraints, and adjustable resource regulation margin constraints from the perspective of the distribution microgrid.

[0015] Furthermore, in the multi-level coordinated control model of main grid, distribution network, and microgrids, the upper layer performs network-wide optimization, generating adjustment decision instructions for adjustable resources of the main grid, distribution network, and microgrids. The lower layer involves coordinated optimization performed by the distribution and microgrids, dynamically reporting balance adjustment needs based on their adjustable resource models and forecast data. Simultaneously, based on the adjustment decision instructions generated by the upper layer, the internal adjustment instructions of the distribution and microgrids are determined. That is, following the main logic of main grid priority and distribution-microgrid coordination, the main grid performs network-wide optimization, aggregating adjustable resources into virtual machine groups and calculating aggregated adjustment amounts. The distribution and microgrids perform coordinated optimization, dynamically reporting balance adjustment needs based on resource models and forecast data. The main grid, combining security constraints and adjustment capabilities, collaborates with the local dispatch center to conduct rotational security checks and power flow calculations, achieving efficient resource allocation and ultimately realizing multi-level coordinated optimization of main grid, distribution network, and microgrids to ensure stable grid operation.

[0016] The multi-temporal primary and secondary microgrid balance analysis and resource coordinated control system of the present invention includes:

[0017] The adjustable resource pool generation unit for micro-distribution resources adaptive scenarios is used for dynamic identification of the station-line-transformer-customer topology based on micro-distribution network resources. According to the adjustable resource control characteristics and control scenario requirements, it uses a Boolean matrix to determine the matching degree between the adjustable resource control characteristics and multiple control scenarios, and dynamically generates an adjustable resource pool for aggregation of multiple control scenarios.

[0018] The main distribution micro-level integrated balance margin assessment model construction and solution unit is used to construct multi-dimensional main distribution micro-level integrated balance margin assessment models for the whole network, local and regional levels, based on the balance analysis results of the main distribution micro-level multi-level power grid and the feasible domain of resource regulation capacity. With the goal of maximizing the reserve balance margin, the transmission margin of the river-crossing channel and the regional power supply margin, and considering the resource regulation safety margin, the unit constructs the main distribution micro-level integrated balance margin assessment models for the whole network, local and regional levels, and calculates the balance margin of the main distribution micro-level multi-level power grid at multiple time scales such as real time, intraday and day-ahead.

[0019] The integrated multi-level coordinated control decision unit for main distribution and micro-level grids is used to construct a multi-level coordinated control model for main distribution and micro-level grids based on the balance margin of the main distribution and micro-level grids and the adjustable resource pool aggregated for multiple control scenarios, and to carry out integrated multi-level coordinated control of main distribution and micro-level grids.

[0020] Furthermore, in the multi-level coordinated control model of main grid, distribution grid, and microgrid, the upper layer is optimized by the main grid to generate adjustment decision instructions for the adjustable resources of the main grid, distribution grid, and microgrid; the lower layer is coordinated optimization by the distribution and microgrid, dynamically reporting the balance adjustment demand based on the adjustable resource model and prediction data of the distribution and microgrid, and determining the internal adjustment instructions of the distribution and microgrid according to the adjustment decision instructions generated by the upper layer.

[0021] The computer device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0022] The computer-readable storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0023] Beneficial Effects: Compared with existing technologies, the significant advantages of this invention are: 1. Based on the main needs of all application scenarios of the main distribution microgrid, this invention studies the intelligent aggregation technology of multi-scenario resources in the substation-line-transformer-customer topology, supports the classification and aggregation of massive resources and flexible aggregation in multiple scenarios, and establishes an integrated balance analysis model of the main distribution microgrid by combining the integrated model of the main distribution microgrid and the regulation resources and regulation capabilities at each level. For application scenarios such as insufficient local sufficiency and new energy consumption in the main distribution microgrid, this invention studies the multi-level collaborative optimization decision-making technology of the main distribution microgrid, establishes a collaborative forward-looking optimization model of the main distribution microgrid, and proposes an ultra-short-term real-time correction strategy for the main distribution microgrid, providing accurate decision support for grid dispatch. It is of great significance in the integrated balance analysis and early warning capabilities of the main distribution microgrid; 2. By constructing a collaborative technical architecture system of the main distribution microgrid, this invention realizes the multi-dimensional integration of source, grid, load, storage, and new energy (horizontal) and the efficient collaborative operation of the main distribution microgrid (vertical), guiding the orderly development of multi-resource access to the grid. It is of great significance in improving power supply capacity, promoting new energy consumption, promoting smart energy use in society, assisting enterprises in low-carbon transformation, and improving the quality of customer services. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method of the present invention;

[0025] Figure 2 A flowchart for dynamically generating an adjustable resource pool that aggregates across multiple control scenarios;

[0026] Figure 3 The flowchart shows the balance margin assessment of the main and auxiliary components. Detailed Implementation

[0027] The multi-temporal primary and secondary microgrid balance analysis and resource coordinated control method of the present invention includes:

[0028] S1: Based on the dynamic identification of the station-line-transformer-customer topology using micro-resources, and combined with the adjustment characteristics of adjustable resources and the requirements of control scenarios, a Boolean matrix is ​​used to determine the matching degree between the adjustable resource adjustment period, adjustment capacity, response speed, etc., and the scenario. This enables the dynamic generation of available resource pools for control scenarios such as cross-section over-limit, reserve gap, and peak shaving. It should be noted that:

[0029] For adjustable resources, aggregated monitoring is performed according to dimensions such as grid connection and power supply path. For microgrid-side resources, after obtaining the grid connection point information of the main grid based on the minimum control unit, the resources are aggregated layer by layer. The power supply path is dynamically identified according to the grid connection point of the resource and its power supply topology relationship. Aggregation is performed from bottom to top to form aggregated data at various levels such as 35kV main transformer, 110kV main transformer, 220kV main transformer, grid, and regional and local grids. The full path result of resource station-line-transformer-customer information is generated. Then, Boolean matrix is ​​used to determine the matching degree between the adjustable resource adjustment period, adjustment capacity, response speed and other aspects with the scenario, so as to realize the dynamic generation of available resource pools that are self-adaptive to control scenarios such as cross-section over-limit, reserve gap, and peak shaving.

[0030] S2: Combining the results of the main-distribution-micro multi-level power grid balance analysis with resource regulation capacity constraints, and aiming to maximize reserve balance margin, cross-river transmission margin, and regional power supply margin, while considering resource regulation safety margin, a multi-dimensional integrated main-distribution-micro balance margin assessment model and solution method are constructed for the entire network, local areas, and regional areas. It should be noted that:

[0031] Considering the interactions and influences of the main grid, distribution network, and microgrids, a power receiving analysis model is constructed for each level. The main grid, as the primary power supply center, provides power support to the distribution network and microgrids, and its power supply capacity is affected by the load demands of these networks. The distribution network, as the intermediate level connecting the main grid and microgrids, plays a role in power transmission and distribution; its operating status affects the power interaction between the main grid and microgrids. Microgrids can connect to both the main and distribution networks, meeting their own load demands while also participating in the power balance regulation of the main and distribution networks. These three elements are interconnected, forming a complex power system network.

[0032] Based on multi-temporal power system models and cross-sectional data, this study comprehensively analyzes the main grid power balance level from multiple perspectives, including the overall positive reserve margin, the overall negative reserve margin, local critical channel overload, and regional power supply adequacy. It also analyzes the balance margin of distribution microgrids from the perspective of the safe and stable operation reliability of equipment such as regional main transformers and feeders. Through rolling analysis at multiple time scales, including day-ahead, intraday, and real-time, a multi-dimensional integrated main grid, distribution, and microgrid balance margin assessment model is constructed. This model considers constraints such as line power constraints, critical cross-section power flow constraints, unit operation safety constraints, and reserved reserve constraints from the main grid, and line power constraints, equipment operation safety constraints, and adjustable resource adjustment margin constraints from the distribution microgrid, thus achieving a multi-dimensional balance margin assessment of the power grid.

[0033] S3: Construct a multi-level collaborative and interactive two-layer optimization model for the main network, distribution network, and microgrids. Combined with the dynamic available resource pool of the distribution and microgrids, the upper layer performs network-wide optimization, generating adjustment decision instructions for adjustable resources in the main network, distribution network, and microgrids. The lower layer generates internal adjustment instructions for the distribution and microgrids. It should be noted that:

[0034] Based on the balance analysis results, the positive reserve gap value of the system at time t is obtained. and negative reserve gap value With the goal of minimizing the total positive and negative reserve gap, the objective function is established as follows:

[0035]

[0036] In the formula: T is the future optimization period; , These represent the upward and downward adjustment powers of the main and auxiliary microsystems at time t, respectively, satisfying... , ,in , The power limit can be adjusted upwards or downwards respectively; This represents the total positive and negative reserve gap value during the future optimization cycle.

[0037] For the mainnet to operate securely, the following AC power flow equations must be satisfied:

[0038]

[0039] In the formula: , These represent the active and reactive power outputs of the generator unit at node i at time t, respectively. , These represent the active and reactive load values ​​of the power user at node i at time t; Let be the voltage amplitude at node i at time t; Let the phase angle difference of the branch formed by nodes i and j at time t be given by the following formula: = - ,in , Let be the phase angles of nodes i and j at time t, respectively; Let i be the set of adjacent nodes of node i.

[0040] The distribution network follows the principle of closed-loop design and open-loop operation. Its power flow constraints can be regarded as radial operation power flow constraints. The power flow constraints of the radial operation of the distribution network are:

[0041]

[0042] In the formula: i, j, and h represent the node numbers, ij represents the branch with i and j as the two endpoints, and jh represents the branch with j and h as the two endpoints; , These are the sets of parent and child nodes of node j, respectively. It belongs to the symbol; , , Let represent the active power, reactive power, and current of branch ij at time t, respectively; , Let represent the active power and reactive power of branch jh at time t, respectively; , These represent the resistance and reactance values ​​of branch ij, respectively. , Let represent the active power and reactive power of node j at time t, respectively; Let be the voltage magnitude of node i at time t; Let be the voltage amplitude of node j at time t.

[0043] The capacity constraints of both the main and distribution network branches must be met:

[0044]

[0045] In the formula: , It can represent the active and reactive power of both the main grid branch and the distribution network branch; Let be the maximum permissible actual power of branch ij.

[0046] The node voltage amplitude constraints of the main and distribution networks satisfy:

[0047]

[0048] In the formula: , These are the upper and lower limits of the voltage amplitude at node i, respectively.

[0049] The output constraints of conventional generator units within the main and distribution networks must be met:

[0050]

[0051] In the formula: , and , These are the upper and lower limits of the unit's active power output and reactive power output, respectively.

[0052] Conventional generator sets meet the following climbing constraints:

[0053]

[0054] In the formula: , These represent the maximum upward increase rate of output and the maximum downward decrease rate of output for the conventional generator set at node i, respectively.

[0055] The power interaction constraints between the microgrid and the distribution network during time period t satisfy:

[0056]

[0057] In the formula: The power limit for interaction between the microgrid and the distribution network. This represents the interaction power between the microgrid and the distribution network during time period t.

[0058] The operational constraints of energy storage devices within a microgrid must meet:

[0059]

[0060] In the formula: Let be the available capacity of the energy storage device at time t; , , respectively, represent the charging and discharging power at time t; For charge and discharge efficiency; This refers to a time interval, i.e., a scheduling period. , These represent the upper and lower limits of the usable capacity of the energy storage device, respectively. , This refers to the upper limit of the charging and discharging power of the energy storage device.

[0061] Combining the dynamic available resource pool of the distribution microgrid, the upper layer uses the interior-point method for full-network optimization. After generating adjustment decision instructions for each adjustable resource in the main network and distribution microgrid within the optimization period T of the previous day, the distribution microgrid solves the aggregated resource optimization model with the goal of minimizing the adjustment amount.

[0062]

[0063] In the formula: The set of regulating resources within the microgrid is defined as follows: x represents the xth regulating resource, and its operating constraints correspond to the operating constraints of conventional generating units and energy storage. To determine the adjustment amount for the xth regulating resource in the microgrid, The day-ahead microgrid aggregation adjustment value given by the main network is used as a reference value.

[0064] After generating adjustment instructions for the main network, distribution network, and microgrid, the intraday optimization model with a short time period is as follows:

[0065]

[0066] In the formula: T m For short optimization cycles within the day; This is the x-th resource adjustment instruction value generated on the main network recently, and this value is compared with the resource adjustment instruction value generated on the distribution microgrid recently. These values, together, serve as reference values ​​for intraday optimization, generating adjustment instructions for mainnet resources during the intraday period. Adjustment instructions for microgrid resources within the day .

[0067] Both day-ahead and intraday optimization are optimization decisions based on the look-ahead optimization cycle; the only difference is the length of the optimization cycle. To further reduce the deviation between look-ahead optimization decisions and actual operation, a main-distribution-micro ultra-short-term real-time correction strategy is proposed to continuously correct look-ahead optimization decision commands. This, combined with control effect evaluation and safety verification, further ensures control accuracy and guarantees the real-time reliable operation of the power grid. Therefore, the main-distribution-micro rolling optimization model for the real-time stage, with the objective of minimizing the adjustment deviation, is constructed as follows:

[0068]

[0069] In the formula: the adjustment instructions for main network resources in the real-time phase are as follows: Adjustment commands for microgrid resources in real time. .

[0070] The aforementioned master-slave micro-optimization models for the day-ahead, intraday, and real-time phases can all use the interior point method to generate adjustment instructions for each resource.

[0071] By combining the dynamic available resource pool of the distribution microgrid, the upper layer performs network-wide optimization, generates adjustment decision instructions for the main grid, distribution grid, and microgrid adjustable resources, and adopts conditional quantile regression technology to strictly capture the source-load distribution characteristics, reducing the deviation between forward optimization decisions and actual operation. A main grid, distribution, and microgrid ultra-short-term real-time correction strategy is proposed to continuously correct forward optimization decision instructions. Combined with regulation effect evaluation and safety verification, control accuracy is further guaranteed, ensuring the real-time reliable operation of the power grid.

[0072] The multi-temporal primary and secondary microgrid balance analysis and resource coordinated control system of the present invention includes:

[0073] Equipped with a micro-resource adaptive scenario adjustable resource pool generation unit, based on the provincial and regional hierarchical access of various resources such as sources, loads, storage, and new energy to the power system and the multi-temporal resource up / down adjustment capabilities and power prediction data reported by operators, resources are aggregated and monitored according to dimensions such as grid access and power supply path, generating full-path results of resource station-line-transformer-customer information. Based on the adjustable resource adjustment characteristics and control scenario requirements, combined with the matching degree of adjustable resource adjustment time period, adjustment capacity, response speed, etc. with the scenario, the unit realizes the dynamic generation of self-adaptive available resource pools for control scenarios such as cross-section over-limit, reserve gap, and peak shaving.

[0074] The integrated balance margin assessment model and solution unit of the main grid, distribution network and microgrid analyze the balance level of the main grid from multiple levels, including the whole grid reserve, local critical channel heavy overload and regional power supply margin, and analyze the balance margin of the distribution network and microgrid from the perspective of regional safe operation. Through multi-time scale rolling analysis, a multi-dimensional integrated balance margin assessment model of the main grid, distribution network and microgrid is constructed. Combined with the grid safety and power flow constraints at each level, the multi-dimensional balance margin assessment of the grid is realized.

[0075] The integrated multi-level collaborative control decision-making unit for main, distribution, and microgrids constructs a multi-level collaborative control model based on balance margin assessment results and the dynamic available resource pool of the distribution and microgrids. The upper layer performs network-wide optimization, generating adjustment decision instructions for adjustable resources in the main network, distribution network, and microgrids. The lower layer, executed by the distribution and microgrids, performs collaborative optimization. Based on the adjustable resource model and predicted data of the distribution and microgrids, it dynamically reports balance adjustment needs and simultaneously optimizes and solves the aggregation allocation model based on the decision instructions generated by the upper layer, generating internal adjustment instructions for the distribution and microgrids to achieve efficient resource allocation.

[0076] Furthermore, in the multi-level coordinated control model of main grid, distribution grid, and microgrid, the upper layer is optimized by the main grid to generate adjustment decision instructions for the adjustable resources of the main grid, distribution grid, and microgrid; the lower layer is coordinated optimization by the distribution and microgrid, dynamically reporting the balance adjustment demand based on the adjustable resource model and prediction data of the distribution and microgrid, and determining the internal adjustment instructions of the distribution and microgrid according to the adjustment decision instructions generated by the upper layer.

[0077] The computer device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0078] The computer-readable storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0079] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0080] This invention analyzes the balancing capacity and resource aggregation and adjustability of regional power grids from both a nationwide and local perspective, enabling dynamic generation of multi-temporal adjustable resource pools and grid balance analysis. It quantifies and assesses the adjustability of primary, distribution, and micro-level resources and the grid's balance margin across multiple time periods and dimensions, thus helping users analyze the grid's balance at multiple time scales. Simultaneously, it allows users to grasp the upstream and downstream adjustability of adjustable resources and corresponding scenarios from various aspects. Based on the balance analysis results and multi-level resource regulation capabilities, it provides optimal auxiliary decision-making schemes for different problem scenarios, as well as the power flow changes after the scheme's pre-implementation.

Claims

1. A method for balance analysis and resource coordinated regulation of multi-temporal master-distributor microgrids, characterized in that, include Based on the dynamic identification of the station-line-transformer-customer topology of distribution microgrid resources, according to the adjustable resource control characteristics and control scenario requirements, a Boolean matrix is ​​used to determine the matching degree between the adjustable resource control characteristics and multiple control scenarios, and an adjustable resource pool for aggregation of multiple control scenarios is dynamically generated. Based on the matching degree between the adjustable resource regulation characteristics and multiple regulation scenarios, and combined with the grid topology relationship, the main and distribution topology analysis method based on breadth-first search is used to obtain the evaluation results of the spatiotemporal aggregation regulation capability from the feeder to the whole network, and determine the feasible domain of resource regulation capability. A multi-level main-distribution-micro power grid balance analysis is conducted. Based on the results of the main-distribution-micro power grid balance analysis and the feasible region of resource regulation capacity, with the goal of maximizing the reserve balance margin, the transmission margin of the river-crossing channel, and the regional power supply margin, and considering the resource regulation safety margin, a multi-dimensional integrated main-distribution-micro power grid balance margin assessment model is constructed for the whole network, local areas, and regional areas. The balance margin of the main-distribution-micro power grid at multiple time scales, including real-time, intraday, and day-ahead, is calculated respectively. Based on the balance margin of the main, distribution, and micro-level power grid and the adjustable resource pool aggregated for multiple control scenarios, a main, distribution, and micro-level multi-level coordinated control model is constructed to carry out integrated multi-level coordinated control of the main, distribution, and micro-level power grid.

2. The multi-temporal primary and secondary microgrid resource coordinated control method according to claim 1, characterized in that, Dynamically generate adjustable resource pools for aggregation across multiple control scenarios, as detailed below: Based on real-time distribution micro-model data and multi-temporal resource up / down adjustment capabilities and power prediction data reported by operators before and during the day, resources are aggregated and monitored according to grid access and power supply path, forming aggregated data of different voltage levels, different grids and regional grids. The full path result of resource station-line-transformer-customer information is generated. Based on the adjustable resource control characteristics and the matching degree between adjustable resource control characteristics and multiple control scenarios, an adjustable resource pool aggregated for multiple control scenarios is dynamically generated.

3. The multi-temporal primary and secondary microgrid resource coordinated control method according to claim 1, characterized in that, Adjustable resource regulation characteristics include at least one of the following: adjustable resource regulation period, regulation capacity, and response speed.

4. The multi-temporal primary and secondary microgrid resource coordinated control method according to claim 1, characterized in that, Multiple control scenarios include at least one of the following: exceeding the limit of the interruption surface, reserve gap, and peak shaving.

5. The multi-temporal primary and secondary microgrid resource coordinated control method according to claim 1, characterized in that, Resource regulation safety margin includes line power constraints, critical section power flow constraints, unit operation safety constraints, and reserved reserve constraints from the perspective of the main grid, and line power constraints, equipment operation safety constraints, and adjustable resource regulation margin constraints from the perspective of the distribution microgrid.

6. The multi-temporal primary and secondary microgrid resource coordinated control method according to claim 1, characterized in that, The upper layer of the multi-level coordinated control model of main grid, distribution grid and microgrid is optimized by the main grid, generating adjustment decision instructions for the adjustable resources of the main grid, distribution grid and microgrid; the lower layer is coordinated optimization by the distribution microgrid, dynamically reporting the balance adjustment demand based on the adjustable resource model and prediction data of the distribution microgrid, and determining the internal adjustment instructions of the distribution microgrid according to the adjustment decision instructions generated by the upper layer.

7. A multi-temporal microgrid balance analysis and resource coordinated control system, characterized in that, include The adjustable resource pool generation unit for micro-distribution resources adaptive scenarios is used for dynamic identification of the station-line-transformer-customer topology based on micro-distribution network resources. According to the adjustable resource control characteristics and control scenario requirements, it uses a Boolean matrix to determine the matching degree between the adjustable resource control characteristics and multiple control scenarios, and dynamically generates an adjustable resource pool for aggregation of multiple control scenarios. The main distribution micro-level integrated balance margin assessment model construction and solution unit is used to construct multi-dimensional main distribution micro-level integrated balance margin assessment models for the whole network, local and regional levels, based on the balance analysis results of the main distribution micro-level multi-level power grid and the feasible domain of resource regulation capacity. With the goal of maximizing the reserve balance margin, the transmission margin of the river-crossing channel and the regional power supply margin, and considering the resource regulation safety margin, the unit constructs the main distribution micro-level integrated balance margin assessment models for the whole network, local and regional levels, and calculates the balance margin of the main distribution micro-level multi-level power grid at multiple time scales such as real time, intraday and day-ahead. The integrated multi-level coordinated control decision unit for main distribution and micro-level grids is used to construct a multi-level coordinated control model for main distribution and micro-level grids based on the balance margin of the main distribution and micro-level grids and the adjustable resource pool aggregated for multiple control scenarios, and to carry out integrated multi-level coordinated control of main distribution and micro-level grids.

8. The multi-temporal main-distribution microgrid balance analysis and resource coordinated control system according to claim 7, characterized in that, The upper layer of the multi-level coordinated control model of main grid, distribution grid and microgrid is optimized by the main grid, generating adjustment decision instructions for the adjustable resources of the main grid, distribution grid and microgrid; the lower layer is coordinated optimization by the distribution microgrid, dynamically reporting the balance adjustment demand based on the adjustable resource model and prediction data of the distribution microgrid, and determining the internal adjustment instructions of the distribution microgrid according to the adjustment decision instructions generated by the upper layer.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of any one of the methods described in claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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