A power grid regulation method and device based on multi-form resources and electronic equipment

Abstract

The application relates to a power grid regulation method and device based on multi-form resources and electronic equipment, wherein the power grid regulation method based on multi-form resources comprises the following steps: based on preset sensing constraints and physical characteristics of source grid load resources, classifying all source grid load resources in two dimensions, and constructing a multi-form resource library according to the classification results in two dimensions; combining the classification results in different dimensions in the multi-form resource library to obtain two-dimensional combinations, and constructing a regulation model according to the physical characteristics in the two-dimensional combinations; correcting the regulation model according to actual operation parameters of a single source grid load side monomer resource, obtaining a corrected regulation model, determining a net regulation capacity of a network partition based on the corrected regulation model and a preset monomer-aggregation-network hierarchical evaluation mechanism; and in response to a dispatching instruction of the power grid, determining a regulation scheme of the power grid for different network partitions within the net regulation capacity. Through the application, the dispatching capacity of the power grid is improved.

Description

Technical Field

[0001] This application relates to the field of power grid regulation, and in particular to a power grid regulation method, apparatus and electronic equipment based on multi-form resources. Background Technology

[0002] In the past, electricity consumption by users typically included fixed-use electricity for residential, industrial, commercial, and agricultural purposes. This electricity usage followed a predictable pattern each year, exhibiting a concentrated distribution characteristic. Traditional grid regulation relied on centralized dispatching to adjust power supply according to different industries and regions. However, with socio-economic and technological development, residents' living standards have continuously improved, and their needs have evolved. Electricity consumption by users has increased the use of renewable energy sources, including electric vehicle charging stations, user-side energy storage systems, and various electrical equipment. This has altered the previously concentrated distribution, giving the grid a more decentralized and diverse character. Therefore, how the power grid can adapt to the current resource distribution and regulate power supply has become a pressing issue. Summary of the Invention

[0003] This application provides a power grid regulation method, apparatus, and electronic device based on multi-form resources, to at least solve the problem in the related art of how the power grid adapts to the current resource distribution and performs power regulation.

[0004] In a first aspect, embodiments of this application provide a power grid regulation method based on multi-form resources, including:

[0005] Based on preset sensing constraints and the physical characteristics of source-grid-load resources, all source-grid-load resources are classified in two dimensions, and a multi-form resource library is constructed based on the classification results of the two dimensions.

[0006] A two-dimensional combination is obtained by combining the classification results of different dimensions in the multi-form resource library, and an adjustment model is constructed based on the physical characteristics in the two-dimensional combination.

[0007] The regulation model is modified according to the actual operating parameters of individual source-grid-load side resources to obtain the modified regulation model. Based on the modified regulation model and the preset individual-aggregate-network hierarchical evaluation mechanism, the net regulation capacity of the network partition is determined.

[0008] In response to the dispatch instructions of the power grid, the regulation scheme of the power grid for different network zones is determined within the net regulation capacity.

[0009] In one embodiment, the preset sensing constraints include a first sensing constraint, a second sensing constraint, a third sensing constraint, and a fourth sensing constraint. The step of classifying all source-grid-load resources in two dimensions based on the preset sensing constraints and the physical characteristics of the source-grid-load resources, and constructing a multi-form resource library based on the classification results of the two dimensions, includes:

[0010] The source-network-load resources that satisfy the first perception constraint are classified as non-sensory resources, the source-network-load resources that satisfy the second perception constraint are classified as micro-sensory resources, the source-network-load resources that satisfy the third perception constraint are classified as sensory resources, and the source-network-load resources that satisfy the fourth perception constraint are classified as strong-sensory resources, thereby obtaining the classification result of the first dimension.

[0011] Based on the physical characteristics of the source-grid-load resources, all the source-grid-load resources are classified in a second dimension. The source-grid-load resources that meet the physical characteristics of no energy storage power regulation are classified as power-type resources, the source-grid-load resources that meet the physical characteristics of energy state storage are classified as energy-type resources, and the resources that meet the physical characteristics of multi-energy flow coupling conversion are classified as multi-energy conversion-type resources, thus obtaining the classification results in the second dimension.

[0012] The multi-form resource library is constructed based on the results of the first dimension classification and the results of the second dimension classification.

[0013] In one embodiment, determining the net regulation capacity of a network partition based on the modified regulation model and the preset monomer-aggregate-network hierarchical evaluation mechanism includes:

[0014] Based on the modified regulation model and the preset individual time scale parameter regulation mechanism, the individual regulation capacity of the source-grid-load side individual resources is determined.

[0015] Aggregate the same type of source-grid-load side individual resources within the energy access area to obtain an aggregated group. Based on the individual regulation capacity, preset participation constraints, and preset aggregation time scale, the aggregation regulation capacity of the aggregated group is determined.

[0016] The power grid is divided into different network zones according to the physical topology of the distribution network. Within any network zone, the regional capacity and internal consumption capacity are determined based on all the aggregated regulation capacity and all the aggregated groups within the network zone.

[0017] The net regulation capacity is determined based on the total capacity of the region and the internal consumption capacity.

[0018] In one embodiment, the parametric adjustment mechanism of the preset single-unit timescale includes:

[0019] Based on the modified adjustment model, the modified operating characteristics of the individual resources on the source-grid-load side are determined;

[0020] Based on the modified operating characteristics and individual unit operating constraints, the upper limit of power increase and the lower limit of power decrease for the source-grid-load side individual resources at different time scales are obtained. The upper limit of power increase and the lower limit of power decrease constitute the individual unit regulation capacity. The individual unit operating constraints characterize the physical constraints, comfort constraints, and environmental constraints that the source-grid-load side individual resources must meet during operation.

[0021] In one embodiment, the parametric adjustment mechanism of the preset aggregation timescale includes:

[0022] At each time scale, the subset of source-grid-load side individual resources participating in power adjustment in the aggregate group is determined according to the upper limit of power adjustment, the lower limit of power adjustment, and the preset participation constraint.

[0023] By aggregating all the upper limit of power increase and the lower limit of power decrease of the individual resource subsets on the source, grid, and load sides, the aggregation adjustment capacity of the aggregated group at different time scales is obtained.

[0024] In one embodiment, after determining the grid regulation scheme for different network zones within the net regulation capacity, the method further includes:

[0025] Using the supply equipment of the source-grid-load resources as decision variables and the comprehensive cost of the supply equipment as the objective function, a resource cost model is determined;

[0026] Based on the resource requirements of the network partition, several initial configuration schemes for resource partitioning are determined;

[0027] Based on the different initial configuration schemes, solve the resource cost model for each initial configuration scheme.

[0028] In one embodiment, the objective function is specifically configured as follows:

[0029] The comprehensive cost includes the economic cost of the energy supply equipment, the carbon trading cost, and the energy supply reliability cost. The economic cost, carbon trading cost, and energy supply reliability cost are added together as the objective function, wherein the energy reliability is determined by the energy supply reliability function of the energy supply equipment.

[0030] The objective function satisfies the constraints of power grid operation, power grid security, power grid reliability, and power grid economy.

[0031] Secondly, embodiments of this application provide a power grid regulation device based on multi-form resources, comprising:

[0032] The multi-form resource library construction module is used to classify all source-grid-load resources in two dimensions based on preset sensing constraints and the physical characteristics of source-grid-load resources, and to construct a multi-form resource library based on the classification results of the two dimensions.

[0033] The model building module is used to combine the classification results of different dimensions in the multi-form resource library to obtain a two-dimensional combination, and to build an adjustment model based on the physical characteristics in the two-dimensional combination;

[0034] The net regulation capacity determination module is used to modify the regulation model according to the actual operating parameters of individual source-grid-load side resources, obtain the modified regulation model, and determine the net regulation capacity of the network partition based on the modified regulation model and the preset individual-aggregate-network hierarchical evaluation mechanism.

[0035] The regulation scheme determination module is used to determine the regulation scheme of the power grid for different network zones within the net regulation capacity in response to the dispatching instructions of the power grid.

[0036] Thirdly, embodiments of this application provide a computer device, including 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 power grid regulation method based on multi-form resources as described in the first aspect above.

[0037] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid regulation method based on multi-form resources as described in the first aspect above.

[0038] The power grid regulation method, apparatus, and electronic equipment based on multi-form resources provided in this application have at least the following technical effects:

[0039] Based on preset sensing constraints and the physical characteristics of source-grid-load resources, all source-grid-load resources are classified in two dimensions, and a regulation model for different source-grid-load resources is constructed according to the two-dimensional combination. The power regulation model is initially determined through the physical attribute dimension and the user-side sensing dimension, allowing for direct determination of the regulation model based on the classification of unknown resources across two dimensions, thus adapting to different power demands. The regulation model is then modified, and the net regulation capacity of network partitions is determined based on the modified model and the preset individual-aggregate-network hierarchical evaluation mechanism. Individual units, individual aggregations forming groups, and network partitions constitute a transmission chain from micro-resources to the macro-grid. The final net regulation capacity ensures the flexibility and controllability of regulating source-grid-load resources with different distributions, thereby determining the grid's regulation scheme for different network partitions within the net regulation capacity. This regulation scheme enables the grid to perform appropriate power regulation under different resource distributions, improving the grid's power regulation capability.

[0040] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a power grid regulation method based on multi-morphological resources according to an exemplary embodiment;

[0043] Figure 2 This is a schematic diagram of a power grid regulation device based on multi-form resources according to an exemplary embodiment;

[0044] Figure 3 This is a block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0046] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0047] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0049] Multi-form resources refer to various resources in a power system that can provide power supply, regulate power load, or store electrical energy. These multi-form resources include traditional centralized power plants, distributed generation equipment, electric vehicle charging stations, and heat pumps. Multi-form resources possess different operating characteristics and regulation capabilities. With the integration of emerging energy sources into the grid, the number and complexity of multi-form energy sources will increase, disrupting the original grid regulation methods. This raises the question of how the grid can adapt to the changes in multi-form resources and regulate the grid accordingly.

[0050] Based on the above, this application provides a power grid regulation method based on multi-form resources.

[0051] Firstly, embodiments of this application provide a power grid regulation method based on multi-form resources. Figure 1 This is a flowchart illustrating a power grid regulation method based on multi-morphological resources according to an exemplary embodiment, such as... Figure 1 As shown, the power grid regulation method based on multi-form resources includes:

[0052] Step S101: Based on the preset sensing constraints and the physical characteristics of source-grid-load resources, classify all source-grid-load resources in two dimensions, and construct a multi-form resource library based on the classification results of the two dimensions.

[0053] The preset perception constraints include a first perception constraint, a second perception constraint, a third perception constraint, and a fourth perception constraint. The first and second perception thresholds are determined by the amount of environmental change that is acceptable to the user, and the third perception threshold is determined by the amount of environmental change that is unacceptable to the user. The perception constraints are then divided according to the different perception thresholds.

[0054] The first sensing constraint is that the change in the environment is less than the first sensing threshold, meaning that when the source, grid, and load resources are adjusted, the user side cannot perceive the corresponding change and it does not affect the user's electricity experience.

[0055] The second sensing constraint is that the environmental change is greater than or equal to the first sensing threshold and less than the second sensing threshold. When the source, grid, and load resources are adjusted, the user side can sense the corresponding change, but the user side can accept the environmental change and it does not affect the user's electricity experience.

[0056] The third sensing constraint is that the change in the environment is greater than or equal to the second sensing threshold and less than the third sensing threshold. When the source, grid and load resources are adjusted, the user side can sense the corresponding change and accept the change in the environment, but it affects the user's electricity experience.

[0057] The fourth perception constraint is that the amount of environmental change is greater than the third perception threshold, that is, when the source, grid and load resources are adjusted, the user side can perceive the corresponding change, but the user side cannot accept the change in the environment.

[0058] Continuing with step S101, based on preset perception constraints, all source-grid-load resources are classified in the first dimension. Source-grid-load resources that meet the first perception constraint are classified as non-sensory resources, source-grid-load resources that meet the second perception constraint are classified as slightly-sensory resources, source-grid-load resources that meet the third perception constraint are classified as sensory resources, and source-grid-load resources that meet the fourth perception constraint are classified as strongly-sensory resources.

[0059] It should be noted that non-sensitive resources have no impact on the user's electricity experience and possess extremely high adjustability. Slightly sensitive resources have an impact on the user but do not affect the electricity experience, and possess considerable adjustability. Sensitive resources have an impact on the user, affecting the electricity experience, and possess some adjustability. Strongly sensitive resources have an unacceptable impact on the user and possess extremely low adjustability.

[0060] In one embodiment, insensible resources include distributed power sources (such as rooftop photovoltaics), energy storage, charging piles, renewable energy generation (Power to Gas, P2G), and heat pumps. Slightly insensible resources include air conditioning loads and public lighting. Sensible resources include campus loads and regular residential electricity loads (such as refrigerators, washing machines, and televisions). Highly insensible resources include loads from manufacturing industries, such as raw material insulation, chip processing, equipment manufacturing, transportation, and steel manufacturing.

[0061] Based on the physical characteristics of source-grid-load resources, all source-grid-load resources are classified in a second dimension. Source-grid-load resources that meet the physical characteristics of power regulation without energy storage are classified as power-type resources, source-grid-load resources that meet the physical characteristics of energy state storage are classified as energy-type resources, and resources that meet the physical characteristics of multi-energy flow coupling and conversion are classified as multi-energy conversion-type resources.

[0062] Power-type resources are defined as source-grid-load resources that meet the physical characteristics of power regulation without energy storage. These resources possess strong power output capabilities, enabling them to deliver high power in short periods and meet peak electricity demand. Optionally, power-type resources include wind power, photovoltaic power, heat pumps, gas boilers, and diesel generators.

[0063] Energy-type resources are defined as source-grid-load resources that meet the physical characteristics of energy state storage. In other words, energy-type resources refer to energy storage devices that can store a large amount of energy and are difficult to provide high power output within a preset time period.

[0064] Multi-energy conversion resources are defined as those that meet the physical characteristics of multi-energy flow coupling and conversion. In other words, multi-energy conversion resources include energy conversion devices that can achieve coupling between different energy sources.

[0065] The multi-form resource is constructed based on the results of the first dimension classification and the results of the second dimension classification.

[0066] All resources were classified along two dimensions, with each source-grid-load resource corresponding to a classification in both dimensions. In other words, each source-grid-load resource carries two-dimensional classification results. A multi-format resource repository was constructed based on these two classification results.

[0067] By classifying source, network, and load resources in two dimensions, a multi-form resource library is constructed to provide a foundation for subsequent model building.

[0068] Step S102: Combine the classification results of different dimensions in the multi-form resource library to obtain a two-dimensional combination, and construct an adjustment model based on the physical characteristics of the two-dimensional combination.

[0069] Each source-grid-load resource carries two-dimensional classification results. By combining the classification results from different dimensions in the multi-form resource library, a two-dimensional combination is obtained. Each two-dimensional combination is a combination of the first and second-dimensional classification results of a source-grid-load resource.

[0070] Based on the physical characteristics of the two-dimensional combination, a regulation model corresponding to source-grid-load resources is constructed. The regulation model has generalization ability, enabling source-grid-load resources with the same classification result combination to adapt to the regulation model.

[0071] Specifically, methods for obtaining the regulation model include:

[0072] The classification results of the first dimension are combined with the classification results of the second dimension to obtain several two-dimensional combinations.

[0073] For the same source-grid-load resource, there are both first-dimensional and second-dimensional classification results. Combining these two classification results for the same source-grid-load resource forms a two-dimensional combination. Since the multi-form resource database deals with various source-grid-load resources, the two-dimensional classification results form several two-dimensional combinations.

[0074] For any two-dimensional combination, determine the physical properties of the two-dimensional combination and construct the corresponding adjustment model.

[0075] In any two-dimensional combination, a corresponding regulation model is constructed based on the physical characteristics determined in the two-dimensional combination. The regulation model is a mathematical model of how the source-grid-load resources corresponding to the current two-dimensional combination change according to the regulation command.

[0076] In one embodiment, if the source-grid-load resource is wind power generation and is classified as a power-type resource, then the wind power generation regulation model is determined based on the operating physical characteristics of wind power generation. If the source-grid-load resource is photovoltaic (PV) power generation and is classified as a power-type resource, then the PV power generation regulation model is determined based on the operating physical characteristics of PV power generation. If the source-grid-load resource is a micro gas turbine and is classified as a power-type resource, then the micro gas turbine regulation model is determined based on the operating physical characteristics of the micro gas turbine.

[0077] In another embodiment, if the source-grid-load resource is an electric energy storage system and is classified as an energy-type resource, then the adjustment model is determined based on the physical operating characteristics of the electric energy storage system. The physical characteristics of the electric energy storage system include the adjustable mechanism and physical operating characteristics of lithium-ion batteries, and the adjustable mechanism and physical operating characteristics of thermal energy storage.

[0078] In another embodiment, if the source-grid-load resource is a multi-energy conversion device and is classified as a multi-energy conversion resource, then the adjustment model is determined based on the physical characteristics of multi-energy flow coupling conversion. These physical characteristics include the P2G adjustable mechanism and physical operating characteristics, and the CHP adjustable mechanism and physical operating characteristics.

[0079] In the multi-form resource library, different source-grid-load resources construct corresponding regulation models based on their own two-dimensional classification results and corresponding physical characteristics to provide generalized mathematical models. This allows for the rapid determination of regulation models based on classification results and corresponding physical characteristics when new energy sources are connected, and the direct invocation of regulation models, thereby improving the grid's ability to adapt to different resource distributions.

[0080] Step S103: Modify the regulation model according to the actual operating parameters of individual source-grid-load side resources to obtain the modified regulation model. Based on the modified regulation model and the preset individual-aggregate-network hierarchical evaluation mechanism, determine the net regulation capacity of the network partition.

[0081] The regulation model is an idealized theoretical mathematical model, while any individual resource on the source-grid-load side is affected by external factors during actual operation. Therefore, it is necessary to obtain the actual operating parameters of the individual resources on the source-grid-load side and revise the regulation model based on these parameters to obtain a revised regulation model. The revised regulation model, based on the original model, incorporates external factors as a consideration, thereby determining the actual regulation power capacity of the individual resources on the source-grid-load side and avoiding overestimation or underestimation of their regulation capabilities.

[0082] In one embodiment, the electric bus load (EBL) is taken as the object to be regulated. The EBL is easily affected by traffic conditions, weather conditions, and pedestrian flow. Therefore, the operating characteristics of the individual EBL are modified according to the traffic conditions, weather conditions, and pedestrian flow along the route of the EBL to obtain a modified regulation model for the EBL.

[0083] In another embodiment, the central air conditioning load (CACL) is taken as the object to be regulated. CACL is easily affected by traffic conditions, weather conditions, and pedestrian flow. Therefore, the operating characteristics of the individual CACL are modified based on the indoor-outdoor temperature difference, indoor heat dissipation equipment, and human body heat dissipation to obtain a modified regulation model for CACL.

[0084] Based on the revised regulation model and the pre-defined individual-aggregate-network hierarchical evaluation mechanism, the evaluation is conducted sequentially at three levels: source-grid-load side individual resources, aggregated individual resources, and network partitions, to ultimately determine the net regulation capacity within each network partition. The net regulation capacity is the amount of electricity that the current network partition can use for external regulation.

[0085] Among them, based on the modified regulation model and the preset single-unit-aggregate-network hierarchical evaluation mechanism, the net regulation capacity of the network partition is determined, including:

[0086] Step S131: Based on the modified regulation model and the preset individual time scale parameter regulation mechanism, determine the individual regulation capacity of individual source-grid-load side resources.

[0087] The pre-defined single-unit timescale method specifically includes:

[0088] Based on the modified regulation model, the modified operating characteristics of individual resources on the source-grid-load side are determined, and the modified operating characteristics can be used to determine the actual operating state of individual resources close to the source-grid-load side.

[0089] Based on the modified operating characteristics and individual unit operating constraints, the upper limit of power increase and the lower limit of power decrease for individual resources on the source-grid-load side are obtained at different time scales. The upper limit of power increase and the lower limit of power decrease constitute the individual unit regulation capacity. The individual unit operating constraints characterize the physical constraints, comfort constraints and environmental constraints that individual resources on the source-grid-load side must meet during operation.

[0090] The single-unit operation constraints characterize the physical constraints, comfort constraints, and environmental constraints that the single-unit resources on the source-grid-load side must meet during operation. They are a set of mathematical expressions of the physical laws, user service requirements, and response requirements that the single-unit resources on the source-grid-load side must meet during operation.

[0091] Based on the modified operating characteristics and individual unit operating constraints, the feasibility of adjusting individual resources on the source-grid-load side at each moment is determined. Based on the adjustment feasibility at each moment, a sequence of feasible operating states for the source-grid-load side resources within a day is determined, along with the upper limit and lower limit of power adjustment for individual resources on the source-grid-load side at each moment. The upper limit and lower limit of power adjustment constitute the individual unit adjustment capacity.

[0092] Based on the feasible operational state sequence and the individual unit regulation capacity at each moment, the individual unit regulation capacity of the source-grid-load side resources at different time scales is determined. The time scales are divided as follows:

[0093] The system operates on a 24-hour cycle, with a first preset duration as the day-ahead time scale, a second preset duration as the intraday time scale (using rolling corrections for the next few hours during daily operation), and a third preset duration as the online time scale. The first preset duration is greater than or equal to the second preset duration, and the second preset duration is greater than the third preset duration.

[0094] Continuing with step S131, the individual adjustment capacity under the day-ahead time scale, intraday time scale, and online time scale was determined by the modified adjustment model and the preset individual time scale parameter adjustment mechanism, thus determining the individual adjustment capacity of each source-grid-load side individual resource to support the subsequent aggregation capacity.

[0095] Step S132: Aggregate the same type of source-grid-load side individual resources within the energy access area to obtain an aggregated group. Based on the individual regulation capacity, preset participation constraints, and preset aggregation time scale, determine the aggregation regulation capacity of the aggregated group.

[0096] Aggregating similar source-grid-load side individual resources within an energy access area creates an aggregated group. For example, using a bus stop as the power access area, aggregating all EBLs within the bus stop yields an aggregated group for those EBLs.

[0097] The aggregation regulation capacity of the aggregation group is determined based on the individual regulation capacity of each source-grid-load side resource in the aggregation group and the parameter regulation mechanism of the preset aggregation time scale. The parameter regulation mechanism of the preset aggregation time scale includes:

[0098] At each time scale, based on the upper limit of power adjustment, the lower limit of power adjustment, and the preset participation constraints of the individual resources on the source, grid, and load sides, the subset of individual resources on the source, grid, and load sides participating in power adjustment in the aggregated population is determined.

[0099] At each time scale, based on the preset participation constraints of individual resources on the source-grid-load side, it is determined whether these individual resources participate in grid regulation and their capacity for participation. This determines the subset of source-grid-load side resources participating in power regulation within the aggregated group at each time scale. The preset participation constraints include loss costs and economic benefits. For example, if the source-grid-load side resource is an EBL (Extended Baseline Resource), the participation constraints include charging losses, discharging losses, and incentive prices. If the source-grid-load side resource is a CACL (Continuous Access Linked Resource), the participation constraints include start-up losses, shutdown losses, and incentive prices.

[0100] By aggregating all the upper limit of power increase and the lower limit of power decrease of the individual resource subsets on the source, grid, and load sides, the aggregation adjustment capacity of the aggregated group at different time scales is obtained.

[0101] The aggregation power upper limit is obtained by adjusting the upper limit of the power of the individual resource sub-units on the source-grid-load side at each time scale, and the aggregation power lower limit is obtained by adjusting the lower limit of the power of the individual resource sub-units on the source-grid-load side. Based on the aggregation power upper and lower limits at different time scales, the aggregation regulation capacity of the aggregation population at different times is obtained.

[0102] Continuing with step S132, individual resources on the source-grid-load side are aggregated into aggregate groups. At each time scale, the aggregated regulation capacity of the aggregate group is determined based on the individual regulation capacity and participation constraints of each individual source-grid-load side unit. On the one hand, by introducing participation and individual regulation capacity, a conservative correction can be made to the actual aggregated regulation capacity of the aggregate group, avoiding situations where individual source-grid-load side units cannot be scheduled during grid dispatch. On the other hand, the aggregated regulation capacity enables coordinated operation across different time scales. It integrates individual resources on the source-grid-load side and provides input boundaries for subsequent network partitioning scheduling.

[0103] Step S133: Divide the power grid into different network zones according to the physical topology of the distribution network. Within any network zone, determine the zone capacity and internal consumption capacity based on the total aggregated regulation capacity and the total aggregated groups within the network zone.

[0104] The entire power grid is divided into different network zones based on factors such as the differences in adjustable resources, geographical location, and voltage level. For example, a 10kV feeder may be used as the basis for dividing the network zones. Each network zone includes multiple aggregation groups.

[0105] The current network partition capacity is determined based on all aggregate groups and all aggregate regulation capacities within the network partition. The partition capacity is the power consumption required to meet the operational needs of the network partition.

[0106] Internal power consumption capacity is the amount of power consumed, which is affected by factors such as voltage safety, line capacity, and power balance within a given zone.

[0107] Step S134: Determine the net adjustment capacity based on the partition capacity and internal consumption capacity.

[0108] Subtracting the internal power consumption capacity from the partition capacity yields the net regulating capacity of the network partition. The net regulating capacity determines whether the current network partition, while meeting its internal power needs, can provide additional power to the outside world.

[0109] Continuing with step S103, the parameters of each individual resource on the source-grid-load side are determined based on the revised regulation model to ascertain the actual regulation capacity of each individual resource, i.e., the individual regulation capacity, ensuring the authenticity of the individual resource regulation capacity. Based on the individual regulation capacity and participation constraints, the aggregated regulation capacity is determined to achieve large-scale resource utilization and ensure the availability of grid dispatch. Based on the aggregated group, the partition capacity and internal consumption capacity of the network partition are determined to determine the final net regulation capacity, ensuring the feasibility of grid dispatch. Therefore, the pre-defined individual-aggregate-network hierarchical evaluation mechanism can determine the net regulation capacity according to different resource types, avoiding grid dispatch command failures and operational risks, and ensuring the feasibility and security of grid dispatch. In addition, different time scales are involved in the measurement of individual resources, aggregated groups, and network partitions on the source-grid-load side. The time scales in the three different levels are interrelated, ensuring the dynamic matching of grid dispatch at different time scales.

[0110] Step S104: In response to the grid's dispatch instructions, determine the grid's regulation scheme for different network zones within the net regulation capacity.

[0111] In response to the grid's dispatch instructions, the regulation scheme for each network zone is determined based on the net regulation capacity of each zone and the content of the dispatch instructions.

[0112] After determining the power grid's dispatching schemes for different network zones, the economic benefits of these schemes can be further evaluated to optimize the regulation plan. This includes:

[0113] The resource cost model is determined by taking the supply equipment of source-grid-load resources as the decision variable and the comprehensive cost of the supply equipment as the objective function.

[0114] The objective function is specifically configured as follows:

[0115] The objective function is defined by the economic cost of the energy supply equipment, the carbon trading cost, and the energy supply reliability cost, where energy reliability is determined by the energy supply equipment's reliability function. The objective function satisfies grid operation constraints, grid security constraints, grid reliability constraints, and grid economic constraints. Specifically, it satisfies the following formula:

[0116] ;

[0117] in, It is the overall objective function of the power grid across all scenario sets; N Y Indicates the number of scene sets generated; The annual investment cost of the system in the y-th scenario set is This refers to the annual operation and maintenance cost for the y-th scenario set. It is the annual cost of purchasing electricity from the grid in the y-th scenario set. It is the annual revenue from selling electricity to the grid in the y-th scenario set; It is the device residual value in the y-th scene set; It is the annual carbon trading cost of the system under the y-th scenario set; It is a penalty for insufficient reliability of energy supply (electricity, heat, and cooling) in y scenario sets.

[0118] The economic costs of supply equipment specifically include annual investment costs, annual operation and maintenance costs, annual gas costs, annual costs of purchasing electricity from the grid, annual revenue from selling electricity to the grid, and the residual value of the equipment. Carbon trading costs include annual carbon trading fees, and energy supply reliability costs include penalties for insufficient energy (electricity, heat, and cooling) supply reliability.

[0119] The different costs within economic costs specifically include:

[0120] The annual investment cost satisfies the following formula:

[0121] ;

[0122] in, N is the annual investment cost of the system in the y-th scenario set. eq This indicates the total number of device types that require configured capacity in the system. It is the configuration coefficient of the i-th type of device in the y-th scene set. It is the configuration capacity of the i-th type of device in the y-th scenario set; This represents the unit investment cost of equipment of type i.

[0123] The annual operation and maintenance cost satisfies the following formula:

[0124] ;

[0125] in: N is the annual operation and maintenance cost for the y-th scenario set. T It is the total number of time periods contained in a scene set ( ); This represents the output power of the i-th type of device at time t in the y-th scene set. For devices with constant power... For non-constant power equipment (wind power, photovoltaic power generation); ; It represents the output characteristics of the i-th type of device at time t in the y-th scene set. It is the unit operation and maintenance cost of the i-th type of equipment.

[0126] The annual gas cost satisfies the following formula:

[0127] ;

[0128] in, It is the annual gas cost in the y-th scenario set. It's the price of natural gas; It represents the power generation capacity of the micro gas turbine at time t in the y-th scenario set. It is the heating power of the micro gas turbine at time t in the y-th scenario set; This represents the heating power of the gas-fired boiler at time t; This represents the electrical power consumed by the electro-gas conversion device at time t; , , These are the gas loss coefficients for the corresponding equipment; , , These refer to the efficiency of the corresponding equipment; It has the low calorific value of natural gas combustion; .

[0129] The annual cost of purchasing electricity from the grid satisfies the following formula:

[0130] ;

[0131] in, It is the annual cost of purchasing electricity from the grid in the y-th scenario set. It is the state of the interaction between power grids at time t in the y-th scenario set. Options are -1, 0, and 1, where, This indicates that the system purchases electricity from the power grid. (This indicates that the system sells electricity to the grid). It is the unit price of electricity purchased from the grid at time t; It refers to the types of electrical energy generating equipment. These are types of electrical energy-consuming equipment; It is the electrical load value at time t in the y-th scene set.

[0132] The annual revenue from selling electricity to the grid satisfies the following formula:

[0133] ;

[0134] in, This refers to the annual revenue from selling electricity to the grid in the y-th scenario set. It is the state of the interaction between power grids at time t in the y-th scenario set. Options are -1, 0, and 1, where, This indicates that the system purchases electricity from the power grid. (This indicates that the system sells electricity to the grid). It is the unit price of electricity purchased from the grid at time t; It refers to the types of electrical energy generating equipment. These are types of electrical energy-consuming equipment; It is the electrical load value at time t in the y-th scene set.

[0135] The residual value of the equipment satisfies the following formula:

[0136] ;

[0137] in, It is the device residual value in the y-th scene set. It is the residual value coefficient of the i-th type of device in the y-th scene set. It is the residual capacity per unit of the i-th type of device in the y-th scene set. It is the configuration capacity of the i-th type of device in the y-th scene set.

[0138] The carbon trading costs satisfy the following formula:

[0139] ;

[0140] in, It is the annual carbon trading cost of the system under the y-th scenario set; It is the carbon trading price in the y-th scenario set. This represents the transaction fee for the y-th scenario set; It is the carbon emission intensity of power generation from the power grid. Indicates the carbon emission intensity of natural gas combustion; Indicates the carbon emission quota for the power grid; , This is the annual cost of purchasing carbon credits. That is the annual cost of burning carbon.

[0141] The cost of energy supply reliability satisfies the following formula:

[0142] ;

[0143] ;

[0144] in, It refers to the penalty for insufficient reliability of energy supply (electricity, heat, and cooling) in y scenario sets. Indicates the penalty coefficient; Let f(x) represent the power supply reliability function of the system at time t under the y-th scenario set; , , , , This indicates the types of equipment used for generating electrical energy, consuming electrical energy, generating heat energy, consuming heat energy, and refrigeration. , , Represents the electrical power, thermal power, and cooling power of the i-th type of device at time t; , and Let represent the electrical load, thermal load, and cooling load values ​​at time t, respectively.

[0145] All formulas for the constructed objective function satisfy grid operation constraints, grid security constraints, grid reliability constraints, and grid economic constraints. Among these, grid operation constraints include electricity balance constraints, thermal balance constraints, cold energy balance constraints, natural gas balance constraints, equipment capacity constraints, equipment power constraints, SoC constraints for electricity storage, SoC constraints for thermal / cold storage, SoC constraints for gas / hydrogen storage, carbon emission intensity constraints, total carbon emission constraints, and carbon trading quota constraints, all designed to ensure the normal operation of the grid.

[0146] Power grid safety constraints include voltage quality constraints, power grid loss constraints, distribution network equipment safety constraints, and reliability index constraints, to ensure the safe operation of the power grid and prevent accidents. Specifically, these include: maximum line voltage drop constraints (the voltage drop of a line cannot exceed the upper limit set by the power grid); bus voltage qualification rate constraints (to ensure the safe operation of the distribution network, the bus voltage qualification rate must be 100%); theoretical line loss rate constraints (the theoretical loss rate of a line must be limited to an acceptable range); distribution transformer loss rate constraints (the loss of distribution transformers must be controlled within certain limits); distribution transformer load rate constraints (the ratio of the actual load to the rated capacity of the transformer must be kept within a reasonable range); and conductor current carrying capacity constraints (the product of the economic current density of the conductor and the cross-sectional area of ​​the conductor must be at least greater than or equal to a certain value). The following constraints apply: current flowing through the conductor; power factor constraint, meaning the power factor is limited to between 0.8 and 0.9; line load factor constraint, meaning the effective load should be controlled between 50% and 80%; power flow imbalance constraint, meaning to avoid excessive imbalance in system power flow; reserve contribution, the reserve contribution of multi-form multi-energy flow resources should not be less than 20%; DREG penetration rate constraint, meaning the static penetration rate of renewable energy should reach 50%, and the effective penetration rate should reach 33%; DREG absorption rate constraint, the lower limit of DREG absorption rate is 90%, and the loss reduction contribution rate should be higher than 10%.

[0147] Based on power grid dispatch instructions, several initial configuration schemes for resource allocation are determined. For each initial configuration scheme, a resource cost model is solved to determine the overall cost. The initial configuration scheme with the lowest overall cost is then selected as the target configuration scheme.

[0148] Based on power grid dispatch instructions, an initial configuration scheme for resource allocation is determined at the initial stage. The comprehensive cost of each scheme is then determined using the aforementioned resource cost model. The initial configuration scheme with the lowest comprehensive cost is selected as the target configuration scheme to optimize the power grid's regulation scheme for different network zones, achieving power grid dispatch that adapts to resource distribution and minimizes cost.

[0149] In summary, the power grid regulation method based on multi-form resources provided in this application classifies multi-form resources in two dimensions. Based on user-perceived constraints, it determines the regulation potential of different resources, constructs a regulation model based on physical characteristics, and modifies the model according to actual parameters. This allows for accurate assessment of the regulation capacity of individual resources on the source-grid-load side. A hierarchical evaluation method of individual-aggregate-individual-aggregate-network is used to determine the regulation capacity and the final net regulation capacity at three different levels. Corresponding to power grid dispatch instructions, a regulation scheme can be determined based on the net regulation capacity. The regulation scheme can be further optimized using a resource cost model. By adapting to different resource distributions and considering economic factors, the optimization of the regulation scheme can be achieved, satisfying power grid dispatch requirements in terms of resource allocation and economy, thereby improving the power grid's regulation capacity.

[0150] Secondly, embodiments of this application provide a power grid regulation device based on multi-form resources. Figure 2 This is a schematic diagram of a power grid regulation device based on multi-form resources, according to an exemplary embodiment, such as... Figure 2 As shown, the power grid regulation device based on multi-form resources includes:

[0151] The multi-form resource library construction module is used to classify all source-grid-load resources in two dimensions based on preset sensing constraints and the physical characteristics of source-grid-load resources, and to construct a multi-form resource library based on the classification results of the two dimensions.

[0152] The model building module is used to combine classification results from different dimensions in the multi-form resource library to obtain a two-dimensional combination, and to build an adjustment model based on the physical characteristics of the two-dimensional combination.

[0153] The net regulation capacity determination module is used to modify the regulation model based on the actual operating parameters of individual source-grid-load side resources, obtain the modified regulation model, and determine the net regulation capacity of the network partition based on the modified regulation model and the preset individual-aggregate-network hierarchical evaluation mechanism.

[0154] The regulation scheme determination module is used to determine the regulation scheme of the power grid for different network zones within the net regulation capacity in response to the grid's dispatch instructions.

[0155] In summary, the power grid regulation device based on multi-form resources provided in this application classifies all source-grid-load resources in two dimensions based on preset sensing constraints and the physical characteristics of source-grid-load resources. A regulation model for different source-grid-load resources is constructed based on this two-dimensional combination. The power regulation model is initially determined through the physical attribute dimension and the user-side sensing dimension, allowing for direct determination of the regulation model based on the classification of unknown resources across two dimensions, thus adapting to different power demands. The regulation model is then modified, and the net regulation capacity of network partitions is determined based on the modified model and a preset individual-aggregate-network hierarchical evaluation mechanism. Individual units, individual aggregations forming groups, and network partitions constitute a transmission chain from micro-resources to the macro-grid. The final net regulation capacity ensures the flexibility and controllability of regulating source-grid-load resources with different distributions. Therefore, the regulation scheme for different network partitions can be determined based on the net regulation capacity, enabling the power grid to adapt to different resource distributions and perform power regulation, thereby improving the power grid's power regulation capability.

[0156] It should be noted that the power grid regulation device based on multi-form resources provided in this embodiment is used to implement the above-described embodiments, and details already described will not be repeated. As used above, terms such as "module," "unit," and "subunit" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the above embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0157] Thirdly, embodiments of this application provide an electronic device, Figure 3 This is a block diagram illustrating an electronic device according to an exemplary embodiment. (e.g.) Figure 3 As shown, the electronic device may include a processor 81 and a memory 82 storing computer program instructions.

[0158] Specifically, the processor 81 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0159] The memory 82 may include a mass storage device for data or instructions. For example, and not limitingly, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include removable or non-removable (or fixed) media. Where appropriate, the memory 82 may be internal or external to a data processing device. In a particular embodiment, the memory 82 is non-volatile memory. In a particular embodiment, the memory 82 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0160] The memory 82 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 81.

[0161] The processor 81 reads and executes computer program instructions stored in the memory 82 to implement any of the power grid regulation methods based on multi-form resources in the above embodiments.

[0162] In one embodiment, the power grid regulation device based on multi-form resources may further include a communication interface 83 and a bus 80. Wherein, as... Figure 3 As shown, the processor 81, memory 82, and communication interface 83 are connected through bus 80 and complete communication with each other.

[0163] The communication interface 83 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication interface 83 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0164] Bus 80, comprising hardware, software, or both, couples together components of a multi-form resource-based power grid regulation device. Bus 80 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, and Local Bus. For example, and not as a limitation, bus 80 may include Accelerated Graphics Port (AGP) or other graphics buses, Extended Industry Standard Architecture (EISA) buses, Front Side Bus (FSB), HyperTransport (HT) interconnects, Industry Standard Architecture (ISA) buses, InfiniBand interconnects, and Low Pin Count (LPC) interconnects. Bus 80 may include a wire, memory bus, MicroChannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0165] Fourthly, embodiments of this application provide a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the power grid regulation method based on multi-form resources provided in the first aspect.

[0166] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0167] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, is used to cause the terminal device to perform steps of implementing the power grid regulation method based on multi-morphological resources provided in the first aspect.

[0168] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A power grid regulation method based on multi-form resources, characterized in that, include: Based on preset sensing constraints and the physical characteristics of source-grid-load resources, all source-grid-load resources are classified in two dimensions, and a multi-form resource library is constructed based on the classification results of the two dimensions; the acceptable or unacceptable environmental changes on the user side are used as the sensing threshold, and sensing constraints are divided according to different sensing thresholds. A two-dimensional combination is obtained by combining the classification results of different dimensions in the multi-form resource library, and an adjustment model is constructed based on the physical characteristics in the two-dimensional combination. The regulation model is modified based on the actual operating parameters of individual source-grid-load side resources to obtain a modified regulation model. Based on the modified regulation model and a preset individual time scale parameter regulation mechanism, the individual regulation capacity of the individual source-grid-load side resources is determined. Individual source-grid-load side resources of the same type are aggregated within the energy access area to obtain an aggregated group. Based on the individual regulation capacity and a preset aggregation time scale parameter regulation mechanism, the aggregated regulation capacity of the aggregated group is determined. The power grid is divided into different network zones according to the physical topology of the distribution network. Within any network zone, the regional capacity and internal consumption capacity are determined based on all the aggregated regulation capacities and all the aggregated groups within the network zone. The net regulation capacity is determined based on the regional capacity and the internal consumption capacity. The preset individual time scale parameter adjustment mechanism includes: determining the modified operating characteristics of the source-grid-load side individual resources based on the modified adjustment model; obtaining the upper limit of power increase and the lower limit of power decrease for the source-grid-load side individual resources at different time scales according to the modified operating characteristics and individual operating constraints, wherein the upper limit of power increase and the lower limit of power decrease constitute the individual adjustment capacity, and the individual operating constraints characterize the physical constraints, comfort constraints, and environmental constraints that the source-grid-load side individual resources must meet during operation; the preset aggregation time scale parameter adjustment mechanism includes: determining the subset of source-grid-load side individual resources participating in power adjustment in the aggregation group according to the upper limit of power increase, the lower limit of power decrease, and preset participation constraints at each time scale; aggregating all the upper limits of power increase and the lower limits of power decrease for the subset of source-grid-load side individual resources respectively to obtain the aggregation adjustment capacity of the aggregation group at different time scales; In response to the dispatch instructions of the power grid, a regulation scheme for different network zones of the power grid is determined within the net regulation capacity.

2. The power grid regulation method based on multi-form resources according to claim 1, characterized in that, The preset sensing constraints include a first sensing constraint, a second sensing constraint, a third sensing constraint, and a fourth sensing constraint. The first sensing constraint is that the environmental change is less than a first sensing threshold; the second sensing constraint is that the environmental change is greater than or equal to the first sensing threshold and less than the second sensing threshold; the third sensing constraint is that the environmental change is greater than or equal to the second sensing threshold and less than the third sensing threshold; and the fourth sensing constraint is that the environmental change is greater than the third sensing threshold. Based on the preset sensing constraints and the physical characteristics of the source-grid-load resources, all source-grid-load resources are classified in two dimensions. A multi-form resource library is constructed based on the classification results of the two dimensions, including: The source-network-load resources that satisfy the first perception constraint are classified as non-sensory resources, the source-network-load resources that satisfy the second perception constraint are classified as micro-sensory resources, the source-network-load resources that satisfy the third perception constraint are classified as sensory resources, and the source-network-load resources that satisfy the fourth perception constraint are classified as strong-sensory resources, thereby obtaining the classification result of the first dimension. The source-grid-load resources that satisfy the physical characteristics of power regulation without energy storage are classified as power-type resources, the source-grid-load resources that satisfy the physical characteristics of energy state storage are classified as energy-type resources, and the resources that satisfy the physical characteristics of multi-energy flow coupling conversion are classified as multi-energy conversion resources, thus obtaining the classification results of the second dimension. The multi-form resource library is constructed based on the results of the first dimension classification and the results of the second dimension classification.

3. The power grid regulation method based on multi-form resources according to claim 1, characterized in that, After determining the grid regulation scheme for different network zones within the net regulation capacity, the method further includes: Using the supply equipment of the source-grid-load resources as decision variables and the comprehensive cost of the supply equipment as the objective function, a resource cost model is determined; Based on the resource requirements of the network partition, several initial configuration schemes for resource partitioning are determined; Based on different initial configuration schemes, the resource cost model of each initial configuration scheme is solved to determine the comprehensive cost, and the initial configuration scheme with the minimum comprehensive cost is taken as the target configuration scheme.

4. The power grid regulation method based on multi-form resources according to claim 3, characterized in that, The objective function is specifically configured as follows: The comprehensive cost includes the economic cost of the energy supply equipment, the carbon trading cost, and the energy supply reliability cost. The economic cost, the carbon trading cost, and the energy supply reliability cost are added together as the objective function, wherein the energy supply reliability is determined by the energy supply reliability function of the energy supply equipment. The objective function satisfies the constraints of power grid operation, power grid security, power grid reliability, and power grid economy.

5. A power grid regulation device based on multi-form resources, characterized in that, include: The multi-form resource library construction module is used to classify all source-grid-load resources in two dimensions based on preset sensing constraints and the physical characteristics of source-grid-load resources, and construct a multi-form resource library based on the classification results of the two dimensions; the acceptable or unacceptable environmental changes on the user side are used as the sensing threshold, and the sensing constraints are divided according to different sensing thresholds. The model building module is used to combine the classification results of different dimensions in the multi-form resource library to obtain a two-dimensional combination, and to build an adjustment model based on the physical characteristics in the two-dimensional combination. The net regulation capacity determination module is used to modify the regulation model based on the actual operating parameters of individual source-grid-load side resources to obtain a modified regulation model; based on the modified regulation model and a preset individual time scale parameter regulation mechanism, determine the individual regulation capacity of the individual source-grid-load side resources; aggregate the same type of source-grid-load side resources within the energy access area to obtain an aggregated group; determine the aggregated regulation capacity of the aggregated group based on the individual regulation capacity and a preset aggregated time scale parameter regulation mechanism; divide the power grid into different network partitions according to the physical topology of the distribution network; and within any network partition, determine the regional capacity and internal consumption capacity based on all the aggregated regulation capacities and all the aggregated groups within the network partition. The net regulation capacity is determined based on the regional capacity and the internal consumption capacity. The preset individual time scale parameter adjustment mechanism includes: determining the modified operating characteristics of the source-grid-load side individual resources based on the modified adjustment model; obtaining the upper limit of power increase and the lower limit of power decrease for the source-grid-load side individual resources at different time scales according to the modified operating characteristics and individual operating constraints, wherein the upper limit of power increase and the lower limit of power decrease constitute the individual adjustment capacity, and the individual operating constraints characterize the physical constraints, comfort constraints, and environmental constraints that the source-grid-load side individual resources must meet during operation; the preset aggregation time scale parameter adjustment mechanism includes: determining the subset of source-grid-load side individual resources participating in power adjustment in the aggregation group according to the upper limit of power increase, the lower limit of power decrease, and preset participation constraints at each time scale; aggregating all the upper limits of power increase and the lower limits of power decrease for the subset of source-grid-load side individual resources respectively to obtain the aggregation adjustment capacity of the aggregation group at different time scales; The regulation scheme determination module is used to determine the regulation scheme of the power grid for different network zones within the net regulation capacity in response to the dispatching instructions of the power grid.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the power grid regulation method based on multi-morphological resources as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the power grid regulation method based on multi-form resources as described in any one of claims 1 to 4.

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