Adjustable load multi-layer polymerization scheduling potential analysis method, system, device and medium

Abstract

The application discloses a method, system, device and medium for analyzing adjustable load multi-layer aggregation scheduling potential, which divides multi-element adjustable load into three levels of equipment layer, user layer and aggregator layer; considers electricity price, incentive mechanism and user willingness, calculates adjustable potential layer by layer for the equipment layer, the user layer and the aggregator layer, and transfers constraint boundaries layer by layer, and finally obtains an aggregation scheduling potential evaluation curve of the multi-element adjustable load. The application enables each subject of the multi-element load resource to obtain adjustment compensation by participating in grid regulation and operation, thereby increasing the income. In addition, the multi-element load resource is cooperatively regulated to improve the distributed new energy consumption level and reduce carbon emissions of power production, which is conducive to achieving the low-carbon target and realizing a win-win situation.

Description

Technical Field

[0001] This invention relates to the field of multi-level load regulation, and specifically to a method, system, equipment, and medium for analyzing the potential of multi-level aggregated scheduling of adjustable loads. Background Technology

[0002] With the deepening of energy transition, the power system has entered a new era. The scale of ultra-high-voltage AC / DC hybrid power grids is rapidly expanding, high-penetration new energy sources are developing rapidly, and the proportion of new loads such as distributed power sources and energy storage is rising rapidly. A new generation of power systems characterized by extensive interconnection, intelligent interaction, flexibility, and safety and controllability is taking shape, posing new requirements for the technical support capabilities of regulation and control. In addition to conventional power source-side regulation resources, some new regulation resources have emerged in the power system, especially some distributed flexible regulation resources, including: residential temperature-controlled loads (such as air conditioners, water heaters, electric heating, etc.), electric vehicles, commercial central air conditioning, and industrial production lines. Users are no longer merely end-point electricity loads; they can interact with dispatching agencies through load-side management. Compared to conventional power sources, these load-side resources are vast in number and diverse in type, and simultaneously play the dual roles of producer and consumer, resulting in complex and variable operating characteristics. For example, the charging load of large-scale electric vehicles will have a significant impact on urban power grids, inevitably exacerbating prominent contradictions such as peak-valley differences, voltage deviations, and local congestion in the power system. On the other hand, the distributed energy storage characteristics of electric vehicles will provide abundant dispatchable resources for grid peak shaving, voltage regulation, and new energy consumption. Therefore, it is urgent to effectively incorporate diverse loads into the grid's controllable resources and achieve data connectivity to enhance the grid's optimal allocation capabilities.

[0003] The relevant existing technologies are described below:

[0004] 1) Prior Art 1: A method and system for evaluating the aggregation response potential of temperature-controlled loads considering response uncertainty, patent application number: 201710425592.5

[0005] A method and system for assessing the aggregation response potential of temperature-controlled loads, taking into account response uncertainty, are disclosed. The method includes calculating the power consumption of the aggregated TCLs based on a pre-established approximate aggregation model of TCLs; calculating the reduceability of the aggregated TCLs based on the rate of change of power consumption, and determining the controllability and acceptability of the aggregated TCLs; and calculating the expected value and distribution characteristics of the aggregation response potential of the aggregated TCLs based on the reduceability, controllability, and acceptability. This prior art 1, based on temperature-controlled load aggregation modeling, can effectively assess the aggregation response potential and distribution characteristics of TCLs under a given demand response mechanism, laying the foundation for temperature-controlled loads to participate in grid regulation and operation or to formulate reasonable demand response policies.

[0006] 2) Prior Art 2: Method and Device for Assessing the Day-ahead Dispatch Potential of a Thermal Storage Electric Heating Virtual Power Plant, Patent Application No.: 202011346151.4

[0007] A method and apparatus for assessing the day-ahead dispatchable potential of a virtual power plant for thermal storage electric heating are disclosed. The method includes obtaining demand response incentive periods and subsidy prices; acquiring user heat load data based on historical data; obtaining thermal storage electric heating equipment model parameters based on equipment parameters; constructing a day-ahead optimized dispatch model for individual thermal storage electric heating systems to determine the output of individual thermal storage electric heating systems; constructing a dispatchable potential assessment model for individual thermal storage electric heating systems based on the output of individual thermal storage electric heating systems, and calculating the dispatchable power of individual thermal storage electric heating systems; and constructing a day-ahead dispatchable potential assessment model for a virtual power plant for thermal storage electric heating systems based on the dispatchable power of thermal storage electric heating systems. This prior art 2 can respond to electricity price incentives while ensuring user comfort, and better characterizes the demand response potential of thermal storage electric heating systems.

[0008] 3) Prior Art 3: A method for assessing the aggregation potential of load participation in demand response, Patent Application No.: 201710834914.1

[0009] A method for assessing the aggregation potential of loads in demand response is disclosed. This method includes input-output physical models based on three types of household loads: air conditioners, water heaters, and electric vehicles. It proposes a user comfort characterization index to calculate the comfort index value for each load. Considering the influence of load operating characteristics, user comfort, user travel plans, and demand response principles, a load aggregation response model is established. Finally, it proposes using the equivalent response power of the load group within a given time period to characterize the aggregation response potential for that period. This prior art 3 can effectively assess the aggregation response capability of intelligent load groups, thereby fully tapping the response potential of loads when the power grid experiences an emergency power shortage, and reducing the pressure on generator primary and secondary frequency regulation reserves.

[0010] The above technical solution physically models the air conditioning load, electric heating load, and household loads of air conditioners, water heaters, and electric vehicles. It assesses the load adjustment potential through single-type resource aggregation or household load group aggregation. On the one hand, air conditioning and electric heating loads are single, and modeling and clustering individual loads cannot accurately reflect the actual electricity consumption of residents. On the other hand, household loads that take into account air conditioners, water heaters, and electric vehicles account for a relatively small proportion of user-side loads, making them impractical. It is necessary to fully consider the adjustment potential of residential loads, commercial building loads, and large industrial loads. Summary of the Invention

[0011] The purpose of this invention is to provide a method, system, device, and medium for analyzing the potential of multi-level aggregated scheduling of adjustable loads, so as to overcome the defects of the existing technology. This invention enables various stakeholders of multiple load resources to obtain adjustment compensation by participating in the grid regulation and operation, thereby increasing their benefits. In addition, by coordinating and regulating multiple load resources, the level of distributed new energy consumption is improved, and the carbon emissions of power production are reduced, which is conducive to promoting the achievement of low-carbon goals and realizing a win-win situation.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] A method for analyzing the potential of adjustable load multi-level aggregation scheduling, the method comprising:

[0014] The diverse adjustable load is divided into three layers: equipment layer, user layer, and aggregator layer.

[0015] Simultaneously considering electricity prices, incentive mechanisms, and user willingness, the adjustable potential is calculated layer by layer at the equipment layer, user layer, and aggregator layer, and the constraint boundaries are passed layer by layer to finally obtain the aggregation and dispatch potential evaluation curve of multi-variable adjustable loads.

[0016] Furthermore, the calculation of adjustable potential at the equipment layer specifically involves calculating the adjustable potential of a single typical device at the equipment layer, wherein the single typical device includes residential air conditioners, water heaters, electric vehicles, commercial central air conditioning systems, and industrial production lines.

[0017] Furthermore, the adjustability potential of the residential air conditioner is calculated as follows:

[0018] p AC,t =P AC ×S AC,t

[0019]

[0020]

[0021] In the formula, p AC,t P represents the actual power of the air conditioner during time period t. AC S represents the rated power of the air conditioner in cooling mode. AC,t The air conditioner's operating status during time period t, T AC,min T is the minimum room temperature setting. AC,max T is the maximum room temperature setting. AC,t Let T be the room temperature during time period t. AC,t+1 T AC,t G represents the room temperature at time t+1 and time t, respectively. t Δc represents the heat exchange value between the outdoor and indoor environments during time period t, and C represents the indoor temperature coefficient. AC This indicates the air conditioner's heat capacity in cooling mode. This represents the effect of the air conditioner on the change in room temperature under cooling conditions, where Δt represents the time interval.

[0022] Furthermore, the adjustability potential of the water heater is calculated as follows:

[0023] p WH,t =P WH ×S WH,t

[0024]

[0025]

[0026] In the formula, p WH,t P represents the actual power of the water heater during time period t. WH S represents the rated power of the water heater under heating conditions. WH,t The water heater's operating status during time period t, T WH,s T is the maximum water temperature setting for the water heater. WH,t Let T be the water temperature of the water heater during time period t. WH,t+1 T WH,t These represent the water temperature of the water heater during time period t+1 and time period t, respectively. in V represents the temperature of the cold water entering the water heater at the inlet, flt represents the hot water flow rate of the water heater during the time period t, and V represents the flow rate of the hot water entering the water heater at the inlet. WH The volume of the water heater is represented by P, Δt represents the time interval, α represents the heating temperature coefficient of the water heater, and P represents the volume of the water heater. WH S represents the rated power of the water heater under heating conditions. WH,t Let t represent the working state of the water heater during time period t, and ξ represent the decrease in the self-cooling temperature of the hot water inside the water heater within a unit time period under normal room temperature.

[0027] Furthermore, the adjustability potential of the electric vehicle is calculated as follows:

[0028] p EV,t =P EV ·S EV,t

[0029]

[0030]

[0031]

[0032] In the formula, p EV,t P represents the actual charging power of the electric vehicle during time period t. EV Rated charging power for electric vehicles; S EV,t The SOC represents the state of charge of the electric vehicle battery during time period t; SOC0 represents the initial charge of the electric vehicle battery; SOC tThe remaining battery capacity of the electric vehicle during time period t; SOC t+1 The remaining battery capacity of the electric vehicle at time t+1; SOC max Indicates the maximum battery capacity of an electric vehicle when it reaches full charge; η is the charging efficiency; C batt The total capacity of the electric vehicle battery; Δt represents the time interval; B EV Limits on electric vehicle battery capacity; L represents the travel distance of the electric vehicle; E EV For driving efficiency.

[0033] Furthermore, the adjustability potential of the commercial central air conditioning system is calculated as follows:

[0034]

[0035] In the formula, T in (t) represents the room temperature at time t; and These represent the room temperature at time t during the central air conditioning cooling period and at the initial time, respectively. and Q represents the room temperature at time t during the central air conditioning shutdown period and at the initial time, respectively; p This indicates the rated cooling capacity of the chiller, and 'a' represents the temperature variation parameters of the chilled water. Parameters θ1, θ2, θ3, and θ4 are determined by the building parameters.

[0036]

[0037] In the formula, ρ a c represents air density. a V represents the specific heat capacity of air. k Indicates indoor volume; k s Indicates the heat storage coefficient of the interior wall surface. Indicates the area of ​​the interior walls; k roof and k wall S represents the thermal conductivity coefficients of the roof and walls, respectively; roof and S wall Q represents the area of ​​the roof and the area of ​​the walls, respectively; er This represents the total cooling load of indoor equipment, lighting, and occupants; T out Indicates outdoor temperature; m z Indicates the mass of chilled water; c w T represents the specific heat capacity of chilled water. w-in and T w-out These represent the inlet and outlet temperatures of the chilled water, respectively.

[0038] Indoor temperature setting value is T set The range of indoor temperature variation is The operating power of commercial central air conditioning is within the rated power p eThe duration of commercial central air conditioning systems in cooling and shutdown states, respectively, is t, which is the transition between zero and t. cooling and t standby The average cooling power p of the central air conditioning system within a cycle t1 to t2 is then... av for:

[0039]

[0040] Furthermore, the adjustability potential of the industrial production line is determined by n processes, where the maximum adjustability potential of process i is related to the basic parameters of the industrial user's production:

[0041]

[0042] Among them, P ei For process i, the power of a single machine; n ei t represents the number of production equipment for process i; ai t represents the unit working hours of process i; ti The cumulative working hours for process i; t aj t represents the unit time for the subsequent process j; tj Let A be the cumulative working time of the successor process j; let A be the relationship matrix between process i and the successor process j, a ij Let i be the elements of relation matrix A, i = 1, 2, ..., n; j = 1, 2, ..., n; Δt mi The maximum interruptible duration for the next process i

[0043] The maximum adjustability potential of an industrial production line is:

[0044]

[0045] Furthermore, the calculation of adjustable potential for the user layer specifically includes: calculating the adjustable potential for the residential user layer, calculating the adjustable potential for the commercial user layer, and calculating the adjustable potential for the industrial user layer.

[0046] Furthermore, the adjustment potential calculation for the residential user layer is as follows:

[0047]

[0048] In the formula, n1 represents the number of air conditioners; P pot,AC,i S represents the response capacity of the i-th residential air conditioner; i (t) represents the state of the i-th air conditioner; n2 represents the number of water heaters; P pot,WH,j S is the response power of the j-th water heater; j (t) represents the state of the j-th water heater; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k(t) represents the state of the kth electric vehicle.

[0049] Furthermore, the adjustable potential calculation for the business user layer is as follows:

[0050]

[0051] In the formula, n1 represents the number of air conditioners; P pot,HVAC,i S represents the response capacity of the i-th commercial central air conditioning unit; i (t) represents the state of the i-th air conditioner; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle.

[0052] Furthermore, the adjustment potential calculation for the industrial user layer specifically involves: the adjustment potential of the industrial user layer is determined by the potential of each production line. For industrial production lines that can be modeled, their theoretical adjustment potential is analyzed through modeling. For industrial production lines that cannot be modeled, their adjustment potential is obtained by analyzing their process flow statistics.

[0053] Furthermore, the computational physical adjustment potential of the aggregation quotient layer specifically refers to:

[0054] Meteorological data, equipment scale, and incentive strategies passed from the user layer are input into a machine learning-based aggregation characteristic prediction model to obtain an aggregation scheduling potential evaluation curve.

[0055] The adjustable load multi-level aggregation scheduling potential analysis system includes a hierarchy partitioning module and an aggregation schedulable potential analysis module, wherein:

[0056] Hierarchical partitioning module: used to divide diverse adjustable loads into three levels: equipment layer, user layer, and aggregator layer;

[0057] Aggregated dispatchable potential analysis module: Simultaneously considering electricity price, incentive mechanism and user willingness, it calculates the adjustable potential layer by layer at the equipment layer, user layer and aggregator layer, and passes the constraint boundary layer by layer, and finally obtains the aggregated dispatchable potential of multi-dimensional adjustable load.

[0058] Furthermore, the calculation of adjustable potential at the equipment layer specifically involves calculating the adjustable potential of a single typical device at the equipment layer, wherein the single typical device includes residential air conditioners, water heaters, electric vehicles, commercial central air conditioning systems, and industrial production lines.

[0059] Furthermore, the calculation of adjustable potential for the user layer specifically includes: calculating the adjustable potential for the residential user layer, calculating the adjustable potential for the commercial user layer, and calculating the adjustable potential for the industrial user layer.

[0060] Furthermore, the computational physical adjustment potential of the aggregation quotient layer specifically refers to:

[0061] Meteorological data, equipment scale, and incentive strategies passed from the user layer are input into a machine learning-based aggregation characteristic prediction model to obtain an aggregation scheduling potential evaluation curve.

[0062] A computer device 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 adjustable load multi-level aggregation scheduling potential analysis method.

[0063] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the adjustable load multi-level aggregation scheduling potential analysis method.

[0064] Compared with the prior art, the present invention has the following beneficial technical effects:

[0065] This invention employs a bottom-up evaluation method, dividing the overall structure into three layers: "equipment layer - user layer - aggregator layer," based on the evaluation object and scope. The adjustable potential of users is calculated layer by layer, and constraint boundaries are passed down layer by layer, ultimately obtaining the physical dispatchable potential of a massive number of diverse users. This invention enables various stakeholders of diverse load resources to obtain adjustment compensation through participation in grid regulation and operation, increasing their revenue. Furthermore, by coordinating and regulating diverse load resources, the invention improves the absorption level of distributed renewable energy, reduces carbon emissions from power production, and contributes to achieving low-carbon goals, resulting in a win-win situation.

[0066] Furthermore, at the equipment level: analyze the power consumption characteristics of individual typical equipment (residential air conditioners, water heaters, electric vehicles, commercial central air conditioning, industrial production lines), or perform equipment-level modeling based on their historical power consumption data. The role of equipment-level modeling is reflected in: ① providing a foundation for layer-by-layer potential analysis; ② evaluating the adjustable potential of typical equipment for a given control strategy.

[0067] Furthermore, for the user layer: the equipment of individual users (residential users, commercial users, industrial users) is classified, and the response timing and power consumption correlation between different equipment are considered. Based on the equipment layer adjustable potential assessment, the adjustable potential of individual users is evaluated.

[0068] Furthermore, for the aggregator layer: potential assessment is performed on aggregators of multiple load types. Based on the adjustable potential assessment of the equipment layer and the user layer, and considering the balance between calculation speed and the accuracy of assessment results, an adjustable potential assessment method for the aggregator layer is proposed to realize the adjustable potential assessment of massive equipment or users after aggregation.

[0069] By conducting a detailed analysis of the physical regulation performance, economic characteristics, and actual operating features of diverse adjustable load resources, the diverse load resources are optimized and integrated, thereby improving the power grid balancing capacity and balancing means, enhancing the power grid dispatching department's control over regional and multi-regional interconnected distributed flexible regulation resources, and fully supporting the participation of distributed flexible regulation resources in power balancing. Attached Figure Description

[0070] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0071] Figure 1 A block diagram of the physical model of air conditioning load;

[0072] Figure 2 A block diagram of the physical model of a water heater;

[0073] Figure 3 This is a curve showing the change in indoor temperature.

[0074] Figure 4 This refers to the operating status and power of the central air conditioning system.

[0075] Figure 5 This is a potential assessment process based on process flow statistics;

[0076] Figure 6 This is a machine learning-based model for predicting aggregate characteristics.

[0077] Figure 7 The variable load aggregation characteristics are shown in Figure 1; where (a) is the prediction curve of the variable load aggregation characteristics, (b) is the dynamic time of the aggregation decrease, and (c) is the dynamic time of the aggregation increase.

[0078] Figure 8 This is a flowchart of the method of the present invention;

[0079] Figure 9 This is a system structure diagram of the present invention. Detailed Implementation

[0080] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0081] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0082] This invention proposes a method for analyzing the potential of multi-level aggregation and dispatching of adjustable loads. This method quantifies the potential of different types of users to participate in power grid regulation and adopts a bottom-up evaluation approach. It considers factors such as electricity price, incentive mechanism, and user willingness, and calculates the adjustable potential at the equipment layer, user layer, and aggregator layer layer by layer. It also passes the constraint boundary layer by layer to finally obtain the aggregated dispatchable potential of multi-loads.

[0083] like Figure 8 As shown, the specific steps include:

[0084] (1) Assessment of the scheduling potential at the equipment level

[0085] 1) The physical model of residential air conditioning load participating in electricity demand response is attached. Figure 1 As shown. The model's input variables include the demand response control signal for time period t, the room temperature for time period t, the air conditioning temperature setpoint and temperature set range, and the outdoor temperature; the output variables include the air conditioning operating status or actual power for time period t, and the room temperature for time period t+1. The room temperature for time period t+1 is fed back to the input terminal as an input variable for the calculation of variables in the next time period. Other variables in the model include the air conditioning equipment's own parameters, room volume, and indoor thermal conductivity, which are considered as constants based on statistical data during the specific calculations. The actual power of the air conditioning for time period t is calculated as follows:

[0086] p AC,t =P AC ×S AC,t (1)

[0087]

[0088] In the formula, p AC,t P represents the actual power of the air conditioner during time period t. AC This refers to the rated power of the air conditioner in cooling mode; S AC,t The value represents the air conditioner's operating status during time period t (0 indicates power off; 1 indicates power on); TAC,min The minimum room temperature setting; T AC,t The room temperature during time period t.

[0089] The room temperatures during time period t+1 and time period t are as follows:

[0090]

[0091] In the formula, T AC,t+1 T AC,t G represents the room temperature at time t+1 and time t, respectively; t The value of heat exchange between the outdoor and indoor environments during time period t is represented by Δc, which represents the indoor temperature coefficient, i.e., the amount of heat required to increase the room temperature by 10°C; C AC This indicates the air conditioner's heat capacity in cooling mode. This represents the effect of the air conditioner on the change in room temperature under cooling conditions; Δt represents the time interval, which is taken as 1 minute here.

[0092] 2) The physical model of water heater load participating in electricity demand response is attached. Figure 2 As shown. The model's input variables include the demand response control signal for time period t, the water temperature inside the water heater for time period t, the cold water temperature flowing into the water heater, the water heater's temperature setting range, and the room temperature; the output variables include the water heater's operating status or actual power for time period t, and the water heater's temperature for time period t+1. The water heater's temperature for time period t+1 is fed back to the input terminal as an input variable for the calculation of variables in the next time period. Other variables in the model include the water heater's own parameters, hot water consumption, and hot water flow rate, which are determined based on user lifestyle statistics during the specific calculation. The actual power of the water heater for time period t is calculated as follows:

[0093] p WH,t =P WH ×S WH,t (4)

[0094]

[0095] In the formula, p WH,t P represents the actual power of the water heater during time period t. WH This refers to the rated power of the water heater under heating conditions; S WH,t The value represents the working status of the water heater during time period t (0 indicates power failure and heating stop; 1 indicates power on and heating stop); T WH,s This is the maximum water temperature setting for the water heater; T WH,t The water temperature in the water heater during time period t.

[0096] The water temperature of the water heater during time period t+1 and time period t is calculated as follows:

[0097]

[0098] In the formula, T WH,t+1 T WH,t These represent the water temperature of the water heater during time period t+1 and time period t, respectively; T in Indicates the temperature of the cold water entering the water heater inlet; f lt V represents the hot water flow rate of the water heater during time period t, which is related to residents' living habits; WH P represents the volume of the water heater; Δt represents the time interval, taken as 1 minute; α represents the heating temperature coefficient of the water heater, that is, the increase in water temperature per unit time under the rated heating power of the water heater; WH This refers to the rated power of the water heater under heating conditions; S WH,t t represents the working status of the water heater during time period (a value of 0 indicates that the power is off and heating has stopped; a value of 1 indicates that the power is on and heating has started); ξ represents the decrease in the self-cooling temperature of the hot water inside the water heater within a unit time period under normal room temperature. ξ is related to the volume and surface area of ​​the water heater, room temperature, and the temperature of the hot water inside the water heater.

[0099] 3) The electric vehicle charging load model is related to the initial charging time, the rated charging power of the on-board battery, the initial state of charge (SOC) of the battery, and the final full charge requirement. In the absence of a demand response control signal, when the electric vehicle's SOC has not reached the required charge level, it will be in a continuous charging phase until the full charge requirement is reached. The actual charging power of the electric vehicle during time period t is calculated as follows:

[0100] p EV,t =P EV ·S EV,t (7)

[0101]

[0102]

[0103]

[0104] In the formula, P EV Rated charging power for electric vehicles; S EV,t The charging state of the electric vehicle battery during time period t (a value of 0 indicates power failure and charging has stopped; a value of 1 indicates power is on and charging is in progress); SOC0 represents the initial charge of the electric vehicle battery; SOC t The remaining battery capacity of the electric vehicle during time period t; SOC max State of Charge (SOC) indicates the maximum battery capacity of an electric vehicle when it reaches full charge. t+1 η represents the remaining battery capacity of the electric vehicle at time t+1; η represents the charging efficiency; C batt B represents the total capacity of the electric vehicle's battery; Δt represents the time interval; EVLimits on electric vehicle battery capacity; L represents the travel distance of the electric vehicle; E EV For driving efficiency.

[0105] 4) The change in indoor heat in commercial buildings can be calculated based on the difference between the heat transferred into the building over a period of time and the heat lost due to the reduction in central air conditioning load. Therefore, the time-varying formula for indoor temperature in buildings with central air conditioning is:

[0106]

[0107] In the formula, T in (t) represents the room temperature at time t; and These represent the room temperature at time t during the central air conditioning cooling period and at the initial time, respectively. and Q represents the room temperature at time t during the central air conditioning shutdown period and at the initial time, respectively; p This indicates the rated cooling capacity of the chiller. 'a' represents the temperature variation parameters of the chilled water; parameters θ1, θ2, θ3, and θ4 are determined by the building parameters.

[0108]

[0109] In the formula, ρ a c represents air density. a V represents the specific heat capacity of air. k Indicates indoor volume; k s Indicates the heat storage coefficient of the interior wall surface. Indicates the area of ​​the interior walls; k roof and k wall S represents the thermal conductivity coefficients of the roof and walls, respectively; roof and S wall Q represents the area of ​​the roof and the area of ​​the walls, respectively; er This represents the total cooling load of indoor equipment, lighting, and occupants; T out Indicates outdoor temperature; m z Indicates the mass of chilled water; c w T represents the specific heat capacity of chilled water. w-in and T w-out These represent the inlet and outlet temperatures of the chilled water, respectively.

[0110] Indoor temperature setting value is T set As attached Figure 3 As shown, the indoor temperature is at the set value T set Fluctuations in the vicinity, with a range of variation. The operating power of the central air conditioning system is within the rated power P e The conversion between zero and t. The duration of the central air conditioning system in cooling and off states are t and t, respectively. cooling and t standby As attached Figure 4 As shown, the average cooling power P of the central air conditioning system during a cycle t1 to t2 is... av for:

[0111]

[0112] 5) The assessable scheduleability of an industrial production line is the sum of the maximum adjustable potential of n processes. The maximum adjustable potential of process i is:

[0113]

[0114] Where n represents the number of processes in the industrial production line, P ei For process i, the power of a single machine; n ei t represents the number of production equipment for process i; ai t represents the unit working hours of process i; ti The cumulative working hours for process i; t aj t represents the unit time for the subsequent process j; tj Let A be the cumulative working time of the successor process j; let A be the relationship matrix between process i and the successor process j, a ij Let i be the elements of relation matrix A, i = 1, 2, ..., n; j = 1, 2, ..., n; Δt mi This represents the maximum interruptible duration of the subsequent process i.

[0115] Maximum adjustable capacity of industrial production lines:

[0116]

[0117] (2) User Layer Adjustable Potential Assessment

[0118] 1) Assessment of the adjustable potential of the residential user layer

[0119] For residential users, the formula for calculating the adjustable potential for multiple users, depending on the type of equipment, is as follows:

[0120]

[0121] In the formula, n1 represents the number of air conditioners; P pot,AC,i S represents the response capacity of the i-th residential air conditioner; i (t) represents the state of the i-th air conditioner; n2 represents the number of water heaters; P pot,WH,j S is the response power of the j-th water heater; j (t) represents the state of the j-th water heater; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle.

[0122] 2) Adjustable potential of the commercial user segment:

[0123] For commercial users, adjustable equipment typically includes central air conditioning, electric vehicles, etc. The formula for calculating the adjustable potential for diverse users is as follows:

[0124]

[0125] In the formula, n1 represents the number of air conditioners; P pot，HVAC，i S represents the response capacity of the i-th commercial central air conditioning unit; i (t) represents the state of the i-th air conditioner; n3 represents the number of electric vehicles; P pot,EV，k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle.

[0126] 3) Adjustable potential at the industrial user level:

[0127] The adjustable potential of industrial user-level production lines is determined by the adjustable potential of each individual production line. For industrial production lines that can be modeled, their theoretical adjustable potential is analyzed through modeling. For most industrial production lines that cannot be modeled, their adjustable potential is determined by analyzing their process flow statistics. The basic process for potential assessment based on process flow statistics is attached. Figure 5 As shown, the process flow of the industrial production line is first analyzed. Based on the load proportion of each process, its power consumption characteristics are analyzed to determine the load response type and response time of each process, thereby assessing the response potential of the industrial production line. Industrial users typically sign interruptible contracts with power companies in advance based on their own adjustment capabilities; the adjustability potential of industrial users can be assessed based on the contract capacity.

[0128] (3) Assessment of the adjustable potential of the polymer layer

[0129] With a large number of adjustable loads, this invention proposes a machine learning-based aggregation characteristic prediction model, the network structure of which is as follows: Figure 6 As shown, this prediction model includes convolutional layers, pooling layers, fully connected layers, and a prediction layer. The prediction layer's role is to transform the information output by the fully connected layers into corresponding class probabilities, thus performing a classification function.

[0130] In this prediction model, the input data consists of one-dimensional data such as meteorological data, equipment scale, and stimulus strategies, with a size of W×1×3. Before being fed into the convolutional neural network for training, this data is preprocessed to remove duplicate information and noisy data, maintain data consistency, and normalize it. The input image size is 224×1×3. Then, the cleaned data is fed into the VGG16 network for training. The VGG16 network contains a total of 16 sub-layers: the first convolutional layer consists of two conv3-64 layers, the second convolutional layer consists of two conv3-128 layers, the third convolutional layer consists of three conv3-256 layers, the fourth convolutional layer consists of three conv3-512 layers, the fifth convolutional layer consists of three conv3-512 layers, followed by two FC4096 fully connected layers and one FC1000 softmax output layer, for a total of 16 layers.

[0131] The convolutional and pooling layers can be divided into 5 blocks. The convolutional layers are separated by maxpooling, and the activation units of all hidden layers use the ReLU function. The processing flow of each block is convolution, ReLU, convolution, ReLU, pooling.

[0132] Block1 consists of two conv3-64 convolutional layers and one max pooling layer. Each convolutional layer has a kernel size of 3×3 and a number of 64 kernels, which means there are 64 output channels. The input of the convolutional layer is 224×1×3 and the output is 224×1×64. The pooling layer has a size of 2×2 and the output is 112×1×64.

[0133] Block2 consists of two conv3-128 convolutional layers and one max pooling layer. Each convolutional layer has a kernel size of 3×3 and a number of kernels of 128, which means there are 128 output channels. The input of the convolutional layer is 112×1×64 and the output is 112×1×128. The pooling layer has a size of 2×2 and the output is 56×1×128.

[0134] Block3 consists of three conv3-256 convolutional layers and one max pooling layer. Each convolutional layer has a kernel size of 3×3 and a number of kernels of 256, which means there are 256 output channels. The input of the convolutional layer is 56×1×128 and the output is 56×1×256. The pooling layer has a size of 2×2 and the output is 28×1×256.

[0135] Block4 consists of three conv3-512 convolutional layers and one max pooling layer. Each convolutional layer has a kernel size of 3×3 and a number of kernels of 512, which means the number of output channels is 512. The input of the convolutional layer is 28×1×256 and the output is 28×1×512. The pooling layer has a size of 2×2 and the output is 14×1×512.

[0136] Block5 consists of three conv3-512 convolutional layers and one max pooling layer. Each convolutional layer has a kernel size of 3×3 and a number of kernels of 512, which means the number of output channels is 512. The input of the convolutional layer is 14×1×512 and the output is 14×1×512. The pooling layer has a size of 2×2 and the output is 7×1×512.

[0137] The fully connected layer consists of two FC4096 layers. The processing flow of this layer is FC, ReLU, and Dropout. The role of Dropout is to randomly disconnect the connections of some neurons in the fully connected layer, thereby preventing overfitting by not activating certain neurons.

[0138] The first fully connected layer, FC4096, consists of 4096 neurons. The input to FC is a 7×1×512 one-dimensional vector, and the output is 4096 neurons.

[0139] The second fully connected layer, FC4096, consists of 4096 neurons, with 4096 neurons as inputs and 4096 neurons as outputs.

[0140] The third fully connected layer, FC1000, consists of 1000 neurons, corresponding to the 1000 categories in the ImageNet dataset. The processing flow of this layer is: FC (Fully Connected). The input to FC is 4096 neurons, and the output is 1000 neurons.

[0141] The softmax layer takes the computation results of 1000 neurons and outputs the predicted probability values ​​corresponding to 1000 categories through the softmax function.

[0142] This network, with its deep structure, small convolutional kernels, and pooling sampling domains, can control the number of parameters while acquiring more feature information, avoiding excessive computation and overly complex structures. After completing forward and backward propagation, the model predicts the rising dynamic time, falling dynamic time, delay time, and power recovery time in the aggregation characteristics, as shown in the appendix. Figure 7 As shown, the final evaluation curve for aggregation scheduling potential is obtained.

[0143] This invention also provides an adjustable load multi-level aggregation scheduling potential analysis system, such as... Figure 9 As shown, it includes a hierarchical partitioning module and an aggregate schedulable potential analysis module, wherein:

[0144] Hierarchical partitioning module: used to divide diverse adjustable loads into three levels: equipment layer, user layer, and aggregator layer;

[0145] Aggregated dispatchable potential analysis module: Simultaneously considering electricity price, incentive mechanism and user willingness, it calculates the adjustable potential layer by layer at the equipment layer, user layer and aggregator layer, and passes the constraint boundary layer by layer, and finally obtains the aggregated dispatchable potential of multi-dimensional adjustable load.

[0146] Specifically, calculating the adjustable potential of the equipment layer involves calculating the adjustable potential of a single typical device at the equipment layer, including residential air conditioners, water heaters, electric vehicles, commercial central air conditioning systems, and industrial production lines.

[0147] The calculation of adjustable potential at the user level specifically includes: calculating the adjustable potential at the residential user level, calculating the adjustable potential at the commercial user level, and calculating the adjustable potential at the industrial user level.

[0148] Specifically, calculating the physical regulation potential of the aggregation layer involves inputting meteorological data, equipment scale, and incentive strategies transmitted from the user layer into a machine learning-based aggregation characteristic prediction model to obtain an aggregation scheduling potential evaluation curve.

[0149] This invention employs a bottom-up evaluation method, dividing the system into three layers—equipment layer, user layer, and aggregator layer—based on the evaluation object and scope. The adjustability potential of users is calculated layer by layer, with constraints passed down through each layer, ultimately yielding the physical schedulable potential of a massive number of diverse users. At the equipment layer, the adjustment potential of individual users such as air conditioning equipment, water heaters, electric vehicles, industrial production lines, and commercial central air conditioning systems is evaluated. At the user layer, the adjustment potential is calculated for residential households, commercial buildings, and large industrial loads. At the aggregator layer, the adjustment potential of diverse users is calculated by aggregating them into multiple layers.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for analyzing the potential of adjustable load multi-level aggregation scheduling, characterized in that, The method includes: The diverse adjustable load is divided into three layers: equipment layer, user layer, and aggregator layer. Simultaneously considering electricity prices, incentive mechanisms, and user willingness, the adjustable potential is calculated layer by layer at the equipment layer, user layer, and aggregator layer, and the constraint boundary is passed layer by layer to finally obtain the aggregation dispatch potential evaluation curve of multiple adjustable loads; The adjustment potential calculation for the equipment layer specifically involves calculating the adjustment potential of a single typical device at the equipment layer, including residential air conditioners, water heaters, electric vehicles, commercial central air conditioning systems, and industrial production lines. The adjustability potential of the residential air conditioner is calculated as follows: p AC,t =P AC ×S AC,t In the formula, p AC,t P represents the actual power of the air conditioner during time period t. AC S represents the rated power of the air conditioner in cooling mode. AC,t The air conditioner's operating status during time period t, T AC,min T is the minimum room temperature setting. AC,max T is the maximum room temperature setting. AC,t+1 T AC,t G represents the room temperature at time t+1 and time t, respectively. t Δc represents the heat exchange value between the outdoor and indoor environments during time period t, and C represents the indoor temperature coefficient. AC This indicates the air conditioner's heat capacity in cooling mode. This represents the effect of the air conditioner on the change in room temperature under cooling conditions, where Δt represents the time interval. The adjustable potential of the water heater is calculated as follows: p WH,t =P WH ×S WH,t In the formula, p WH,t P represents the actual power of the water heater during time period t. WH S represents the rated power of the water heater under heating conditions. WH,t The water heater's operating status during time period t, T WH,t Let T be the water temperature of the water heater during time period t. WH,t+1 T WH,t These represent the water temperature of the water heater during time period t+1 and time period t, respectively. in This indicates the temperature of the cold water entering the water heater's inlet, fl. t V represents the hot water flow rate of the water heater during time period t. WH The volume of the water heater is represented by P, Δt represents the time interval, α represents the heating temperature coefficient of the water heater, and P represents the volume of the water heater. WH S represents the rated power of the water heater under heating conditions. WH,t Let t represent the working state of the water heater during time period t, and ξ represent the decrease in the self-cooling temperature of the hot water inside the water heater within a unit of time period under normal room temperature. The adjustability potential of the electric vehicle is calculated as follows: p EV,t =P EV ·S EV,t In the formula, p EV,t P represents the actual charging power of the electric vehicle during time period t. EV Rated charging power for electric vehicles; S EV,t The SOC represents the state of charge of the electric vehicle battery during time period t; SOC0 represents the initial charge of the electric vehicle battery; SOC t The remaining battery capacity of the electric vehicle during time period t; SOC t+1 The remaining battery capacity of the electric vehicle at time t+1; SOC max Indicates the maximum battery capacity of an electric vehicle when it reaches full charge; η is the charging efficiency; C batt The total capacity of the electric vehicle battery; Δt represents the time interval; B EV Limits on electric vehicle battery capacity; L represents the travel distance of the electric vehicle; E EV For driving efficiency; The adjustability potential of the commercial central air conditioning system is calculated as follows: In the formula, T in (t) represents the room temperature at time t; and These represent the room temperature at time t during the central air conditioning cooling period and at the initial time, respectively. and Q represents the room temperature at time t during the central air conditioning shutdown period and at the initial time, respectively; p This indicates the rated cooling capacity of the chiller, and 'a' represents the temperature variation parameters of the chilled water. Parameters θ1, θ2, θ3, and θ4 are determined by the building parameters. In the formula, ρ a c represents air density. a V represents the specific heat capacity of air. k Indicates indoor volume; k s Indicates the heat storage coefficient of the interior wall surface. Indicates the area of ​​the interior walls; k roof and k wall S represents the thermal conductivity coefficients of the roof and walls, respectively; roof and S wall Q represents the area of ​​the roof and the area of ​​the walls, respectively; er This represents the total cooling load of indoor equipment, lighting, and occupants; T out Indicates outdoor temperature; m z Indicates the mass of chilled water; c w T represents the specific heat capacity of chilled water. w-in and T w-out These represent the inlet and outlet temperatures of the chilled water, respectively. Indoor temperature setting value is T set The range of indoor temperature variation is The operating power of commercial central air conditioning is within the rated power p e The duration of commercial central air conditioning systems in cooling and shutdown states, respectively, is t, which is the transition between zero and t. cooling and t standby The average cooling power p of the central air conditioning system within a cycle t1 to t2 is then... av for: The adjustability potential of the industrial production line is determined by n processes, where the maximum adjustability potential of process i is related to the basic parameters of the industrial user's production: Among them, P ei For process i, the power of a single machine; n ei t represents the number of production equipment for process i; ai t represents the unit working hours of process i; ti The cumulative working hours for process i; t aj t represents the unit time for the subsequent process j; tj Let A be the cumulative working time of the successor process j; let A be the relationship matrix between process i and the successor process j, a ij Let i be the elements of relation matrix A, i = 1, 2, ..., n; j = 1, 2, ..., n; Δt mi The maximum interruptible duration for the next process i; The maximum adjustability potential of an industrial production line is: The calculation of adjustable potential at the user level specifically includes: calculating the adjustable potential at the residential user level, calculating the adjustable potential at the commercial user level, and calculating the adjustable potential at the industrial user level; The adjustment potential calculation for the residential user tier is as follows: In the formula, n1 represents the number of air conditioners; P pot,AC,i S represents the response capacity of the i-th residential air conditioner; i (t) represents the state of the i-th air conditioner; n2 represents the number of water heaters; P pot,WH,j S is the response power of the j-th water heater; j (t) represents the state of the j-th water heater; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle; The adjustable potential calculation for the business user layer is as follows: In the formula, n1 represents the number of air conditioners; P pot,HVAC,i S represents the response capacity of the i-th commercial central air conditioning unit; i (t) represents the state of the i-th air conditioner; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle; The adjustment potential calculation for the industrial user layer is specifically as follows: the adjustment potential of the industrial user layer is determined by the potential of each production line. For industrial production lines that can be modeled, their theoretical adjustment potential is analyzed by modeling. For industrial production lines that cannot be modeled, their adjustment potential is obtained by analyzing their process flow statistics. The specific physical adjustment potential of the aggregation quotient layer is as follows: Meteorological data, equipment scale, and incentive strategies passed from the user layer are input into a machine learning-based aggregation characteristic prediction model to obtain an aggregation scheduling potential evaluation curve.

2. An adjustable load multi-level aggregation scheduling potential analysis system, characterized in that, It includes a hierarchical partitioning module and an aggregated schedulable potential analysis module, wherein: Hierarchical partitioning module: used to divide diverse adjustable loads into three levels: equipment layer, user layer, and aggregator layer; Aggregated dispatchable potential analysis module: Simultaneously considering electricity price, incentive mechanism and user willingness, it calculates the adjustable potential layer by layer at the equipment layer, user layer and aggregator layer, and passes the constraint boundary layer by layer to finally obtain the aggregated dispatchable potential of multi-variable adjustable load; The adjustment potential calculation for the equipment layer specifically involves calculating the adjustment potential of a single typical device at the equipment layer, including residential air conditioners, water heaters, electric vehicles, commercial central air conditioning systems, and industrial production lines. The adjustability potential of the residential air conditioner is calculated as follows: p AC,t =P AC ×S AC,t In the formula, p AC,t P represents the actual power of the air conditioner during time period t. AC S represents the rated power of the air conditioner in cooling mode. AC,t The air conditioner's operating status during time period t, T AC,min T is the minimum room temperature setting. AC,max T is the maximum room temperature setting. AC,t+1 T AC,t G represents the room temperature at time t+1 and time t, respectively. t Δc represents the heat exchange value between the outdoor and indoor environments during time period t, and C represents the indoor temperature coefficient. AC This indicates the air conditioner's heat capacity in cooling mode. This represents the effect of the air conditioner on the change in room temperature under cooling conditions, where Δt represents the time interval. The adjustable potential of the water heater is calculated as follows: p WH,t =P WH ×S WH,t In the formula, p WH,t P represents the actual power of the water heater during time period t. WH S represents the rated power of the water heater under heating conditions. WH,t The water heater's operating status during time period t, T WH,t Let T be the water temperature of the water heater during time period t. WH,t+1 T WH,t These represent the water temperature of the water heater during time period t+1 and time period t, respectively. in This indicates the temperature of the cold water entering the water heater's inlet, fl. t V represents the hot water flow rate of the water heater during time period t. WH The volume of the water heater is represented by P, Δt represents the time interval, α represents the heating temperature coefficient of the water heater, and P represents the volume of the water heater. WH S represents the rated power of the water heater under heating conditions. WH,t Let t represent the working state of the water heater during time period t, and ξ represent the decrease in the self-cooling temperature of the hot water inside the water heater within a unit of time period under normal room temperature. The adjustability potential of the electric vehicle is calculated as follows: p EV,t =P EV ·S EV,t In the formula, p EV,t P represents the actual charging power of the electric vehicle during time period t. EV Rated charging power for electric vehicles; S EV,t The SOC represents the state of charge of the electric vehicle battery during time period t; SOC0 represents the initial charge of the electric vehicle battery; SOC t The remaining battery capacity of the electric vehicle during time period t; SOC t+1 The remaining battery capacity of the electric vehicle at time t+1; SOC max Indicates the maximum battery capacity of an electric vehicle when it reaches full charge; η is the charging efficiency; C batt The total capacity of the electric vehicle battery; Δt represents the time interval; B EV Limits on electric vehicle battery capacity; L represents the travel distance of the electric vehicle; E EV For driving efficiency; The adjustability potential of the commercial central air conditioning system is calculated as follows: In the formula, T in (t) represents the room temperature at time t; and These represent the room temperature at time t during the central air conditioning cooling period and at the initial time, respectively. and Q represents the room temperature at time t during the central air conditioning shutdown period and at the initial time, respectively; p This indicates the rated cooling capacity of the chiller, and 'a' represents the temperature variation parameters of the chilled water. Parameters θ1, θ2, θ3, and θ4 are determined by the building parameters. In the formula, ρ a c represents air density. a V represents the specific heat capacity of air. k Indicates indoor volume; k s Indicates the heat storage coefficient of the interior wall surface. Indicates the area of ​​the interior walls; k roof and k wall S represents the thermal conductivity coefficients of the roof and walls, respectively; roof and S wall Q represents the area of ​​the roof and the area of ​​the walls, respectively; er This represents the total cooling load of indoor equipment, lighting, and occupants; T out Indicates outdoor temperature; m z Indicates the mass of chilled water; c w T represents the specific heat capacity of chilled water. w-in and T w-out These represent the inlet and outlet temperatures of the chilled water, respectively. Indoor temperature setting value is T set The range of indoor temperature variation is The operating power of commercial central air conditioning is within the rated power p e The duration of commercial central air conditioning systems in cooling and shutdown states, respectively, is t, which is the transition between zero and t. cooling and t standby The average cooling power p of the central air conditioning system within a cycle t1 to t2 is then... av for: The adjustability potential of the industrial production line is determined by n processes, where the maximum adjustability potential of process i is related to the basic parameters of the industrial user's production: Among them, P ei For process i, the power of a single machine; n ei t represents the number of production equipment for process i; ai t represents the unit working hours of process i; ti The cumulative working hours for process i; t aj t represents the unit time for the subsequent process j; tj Let A be the cumulative working time of the successor process j; let A be the relationship matrix between process i and the successor process j, a ij Let i be the elements of relation matrix A, i = 1, 2, ..., n; j = 1, 2, ..., n; Δt mi The maximum interruptible duration for the next process i; The maximum adjustability potential of an industrial production line is: The calculation of adjustable potential at the user level specifically includes: calculating the adjustable potential at the residential user level, calculating the adjustable potential at the commercial user level, and calculating the adjustable potential at the industrial user level; The adjustment potential calculation for the residential user tier is as follows: In the formula, n1 represents the number of air conditioners; P pot,AC,i S represents the response capacity of the i-th residential air conditioner; i (t) represents the state of the i-th air conditioner; n2 represents the number of water heaters; P pot,WH,j S is the response power of the j-th water heater; j (t) represents the state of the j-th water heater; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle; The adjustable potential calculation for the business user layer is as follows: In the formula, n1 represents the number of air conditioners; P pot,HVAC,i S represents the response capacity of the i-th commercial central air conditioning unit; i (t) represents the state of the i-th air conditioner; n3 represents the number of electric vehicles; P pot,EV,k S represents the response power of the k-th electric vehicle; k (t) represents the state of the kth electric vehicle; The adjustment potential calculation for the industrial user layer is specifically as follows: the adjustment potential of the industrial user layer is determined by the potential of each production line. For industrial production lines that can be modeled, their theoretical adjustment potential is analyzed by modeling. For industrial production lines that cannot be modeled, their adjustment potential is obtained by analyzing their process flow statistics. The specific physical adjustment potential of the aggregation quotient layer is as follows: Meteorological data, equipment scale, and incentive strategies passed from the user layer are input into a machine learning-based aggregation characteristic prediction model to obtain an aggregation scheduling potential evaluation curve.

3. 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 the adjustable load multi-level aggregation scheduling potential analysis method as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the adjustable load multi-level aggregation scheduling potential analysis method as described in claim 1.

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