A method and system for calculating adjustable potential depth based on participation of charging and battery swapping stations

Abstract

The application discloses a kind of adjustable potential depth calculation method and system based on participation of charging station, it is related to charging station resource adjustable potential depth calculation and dispatching technical field, including: the historical data information of target charging station is collected;Historical data information includes: based on the preset historical data cleaning filtering model, data cleaning filtering is carried out to historical data information;Based on the historical data information after cleaning filtering, charging station baseline load calculation model and charging station predicted load calculation model are constructed;Adjustable potential depth calculation model is constructed;Adjustable potential depth full-time domain matrix calculation model is constructed;Based on adjustable potential depth full-time domain matrix calculation model, the adjustable potential depth heat map of target charging station is calculated and generated.The application provides observable and measurable full-time domain adjustable potential depth data for power grid dispatching center, assists power grid operation control decision.

Description

Technical Field

[0001] This invention relates to the field of deep calculation and scheduling technology of adjustable potential of charging and battery swapping station resources, specifically a method and system for deep calculation of adjustable potential based on the participation of charging and battery swapping stations. Background Technology

[0002] With the development of the electric vehicle and pure electric heavy-duty truck industries and the increasing penetration rate of electric vehicles and pure electric heavy-duty trucks, the number of charging stations and battery swapping stations is constantly increasing. The disorderly nature of electric vehicle and pure electric heavy-duty truck charging and the complex cash flow management will greatly reduce the operating efficiency of charging and battery swapping systems. Virtual power plants and load aggregators can act as resource aggregators and operators of electric vehicles, pure electric heavy-duty trucks, and charging and battery swapping stations, effectively guiding the charging of electric vehicles and pure electric heavy-duty trucks and efficiently managing dispersed or clustered electric vehicles and pure electric heavy-duty trucks. Through economic incentive signals and control measures, charging and battery swapping stations participate in demand response and virtual power plant business scenarios, realizing peak shaving and valley filling, frequency regulation ancillary services, and voltage regulation ancillary services. Under the premise of meeting the electricity demand, they can minimize the growth of system peak load or the deepening of valley load, effectively improve the grid load characteristics, efficiently utilize grid assets, reduce grid system operation risks, and at the same time, allow charging and battery swapping stations to obtain certain economic subsidies in the process of participating in grid regulation.

[0003] However, when existing charging and battery swapping stations are included in intraday demand response, the lack of calculation of adjustable potential depth makes it impossible for users of charging and battery swapping station resources to accurately declare response capacity, which in turn affects the grid dispatch center's precise control of grid operation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for calculating adjustable potential depth based on the participation of charging and swapping stations in order to solve at least one of the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a method for calculating the adjustable potential depth based on the participation of charging and battery swapping stations, comprising: collecting historical data information of a target charging and battery swapping station; the historical data information including: historical power consumption data information of the charging and battery swapping station, historical electricity consumption data information of the charging and battery swapping station, historical demand response date and historical orderly electricity consumption execution date data information of the charging and battery swapping station, and charging and battery swapping station ledger data information; cleaning and filtering the historical data information based on a preset historical data cleaning and filtering model to obtain cleaned and filtered historical data information; constructing a baseline load calculation model and a predicted load calculation model for the charging and battery swapping station based on the cleaned and filtered historical data information; constructing an adjustable potential depth calculation model based on the baseline load calculation model and the predicted load calculation model for the charging and battery swapping station; constructing an adjustable potential depth full-time domain matrix calculation model based on the adjustable potential depth calculation model; and calculating and generating an adjustable potential depth heatmap of the target charging and battery swapping station based on the adjustable potential depth full-time domain matrix calculation model.

[0006] Furthermore, the preset historical data cleaning and filtering model includes:

[0007]

[0008] in,

[0009]

[0010] In the formula: These are the average active power of charging and battery swapping stations over the time period t of the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. These are the electricity consumption of the charging and swapping stations during time period t, determined by the charging and swapping station ledger data for the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. The previous consecutive normal working days exclude response days and orderly electricity consumption execution days. DR and OEC are the historical response day set and the historical orderly electricity consumption execution day set, respectively. These represent the maximum and minimum average active power of the charging and battery swapping station over the five consecutive normal working days prior to the demand response invitation date, respectively; max{*} is the function for finding the maximum value; min{*} is the function for finding the minimum value; Δh is the time period step; and D is the demand response invitation date.

[0011] Furthermore, the baseline load calculation model for the charging and swapping station includes:

[0012]

[0013] In the formula: The baseline load of the charging and battery swapping station on the demand response invitation date for time period t;

[0014] The predicted load calculation model for the charging and swapping station includes:

[0015]

[0016] In the formula: The predicted load for the charging and battery swapping station during time period t on the demand response invitation date.

[0017] Furthermore, the adjustable potential depth calculation model includes a lower adjustable potential depth calculation model and an upper adjustable potential depth calculation model; wherein, the lower adjustable potential depth calculation model includes:

[0018]

[0019] In the formula: To ensure the daily demand response of charging and battery swapping stations, it is necessary to make reservations 1q hours in advance and continuously adjust the potential depth for Jcx hours. To ensure the daily demand response of charging and battery swapping stations, itq hours in advance, and to execute the invitation. Baseline load for each time period; To ensure the daily demand response of charging and battery swapping stations, itq hours in advance, and to execute the invitation. Forecasted load for a given period; To ensure the daily demand response of charging and battery swapping stations, it is necessary to make advance reservations 1q hours in advance, adjust continuously for Jcx hours, and execute the reservations. The load adjustment difficulty coefficient for a given time period; Itq and Jcx are integers; The time point for inviting customers to the charging and battery swapping station's intraday demand response is 1q hours in advance; the adjustable potential depth calculation model includes:

[0020]

[0021] In the formula: R kW The installed capacity of charging and battery swapping stations; To ensure the daily demand response of charging and battery swapping stations, it is necessary to make reservations 1q hours in advance and continuously adjust the potential depth for Jcx hours. To ensure the daily demand response of charging and battery swapping stations, it is necessary to make advance reservations 1q hours in advance, continuously adjust 10 hours in advance, and execute the reservations. The difficulty coefficient of load regulation during different time periods.

[0022] Furthermore, the adjustable potential depth full-time domain matrix calculation model includes an upper adjustable potential depth full-time domain matrix calculation model and a lower adjustable potential depth full-time domain matrix calculation model; wherein, the lower adjustable potential depth full-time domain matrix calculation model includes:

[0023]

[0024] In the formula: The adjustable potential depth full-time domain matrix; To enable charging and battery swapping stations to respond to intraday demand in advance, a Hang-hour advance booking is required, with a Lie-hour adjustable potential depth; Hang is an integer ranging from 2 to 8; Lie is an integer ranging from 2 to 6; the full-time domain matrix calculation model for the adjustable potential depth includes:

[0025]

[0026] In the formula: The adjustable potential depth full-time domain matrix; To ensure the daily demand response of charging and battery swapping stations, advance booking by one hour is required, and the adjustable potential depth can be maintained for one hour.

[0027] Furthermore, the adjustable potential depth heatmap includes an upper adjustable potential depth heatmap and a lower adjustable potential depth heatmap; wherein, the horizontal axis of the lower adjustable potential depth heatmap represents the duration of downward adjustment at the current lower adjustable potential depth value, and the vertical axis of the lower adjustable potential depth heatmap represents the advance invitation time for daily demand response at the charging / swapping station; the values ​​at the intersection of the horizontal and vertical axes in the lower adjustable potential depth heatmap correspond to the matrix element values ​​of the lower adjustable potential depth full-time domain matrix, and are represented by different colors; the horizontal axis of the upper adjustable potential depth heatmap represents the duration of upward adjustment at the current upper adjustable potential depth value, and the vertical axis of the upper adjustable potential depth heatmap represents the advance invitation time for daily demand response at the charging / swapping station; the values ​​at the intersection of the horizontal and vertical axes in the upper adjustable potential depth heatmap correspond to the matrix element values ​​of the upper adjustable potential depth full-time domain matrix, and are represented by different colors.

[0028] Secondly, embodiments of the present invention also provide an adjustable potential depth calculation system based on the participation of charging and battery swapping stations, comprising: a data acquisition module, a cleaning and filtering module, a first model construction module, a second model construction module, a third model construction module, and a calculation and generation module; wherein, the data acquisition module is used to acquire historical data information of the target charging and battery swapping stations; the historical data information includes: historical power consumption data information of the charging and battery swapping stations, historical electricity consumption data information of the charging and battery swapping stations, historical demand response date and historical orderly electricity consumption execution date data information of the charging and battery swapping stations, and charging and battery swapping station ledger data information; the cleaning and filtering module is used to clean the historical data information based on a preset historical data cleaning and filtering model. The system is divided into three modules: a first model building module and a second model building module. The first module is used to build a baseline load calculation model and a predicted load calculation model for the charging and battery swapping station based on the historical data after cleaning and filtering. The second module is used to build an adjustable potential depth calculation model based on the baseline load calculation model and the predicted load calculation model. The third module is used to build an adjustable potential depth full-time domain matrix calculation model based on the adjustable potential depth calculation model. The third module is used to calculate and generate an adjustable potential depth heatmap of the target charging and battery swapping station based on the adjustable potential depth full-time domain matrix calculation model.

[0029] Thirdly, embodiments of the present invention also provide an electronic 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 method described in the first aspect above.

[0030] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described in the first aspect above.

[0031] This invention provides a method and system for calculating the adjustable potential depth based on the participation of charging and battery swapping stations. It proposes a refined model and method for calculating the adjustable potential depth of charging and battery swapping stations participating in intraday demand response, which helps to explore and analyze the adjustable potential of charging and battery swapping station resources. It also helps to improve the accuracy of adjustable potential declarations by load aggregators and power users when assisting charging and battery swapping station resources in participating in intraday demand response. Furthermore, it can provide reference and guidance for the participation of charging and battery swapping station resources in virtual power plants, demand response, direct dispatch, grid-load interaction potential analysis and control, etc., alleviating the technical problem in existing technologies where the lack of adjustable potential depth calculation leads to charging and battery swapping station resource users being unable to accurately declare response capacity, thus affecting the precise control of grid operation by the power grid dispatch center. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 A flowchart of a method for calculating adjustable potential depth based on the participation of charging and swapping stations, provided in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of an adjustable potential depth calculation system based on the participation of charging and swapping stations, provided as an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] Figure 1 This is a flowchart illustrating a method for calculating adjustable potential depth based on the participation of charging and swapping stations, according to an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps:

[0038] Step S102: Collect historical data information of the target charging and battery swapping station; historical data information includes: historical power consumption data of the charging and battery swapping station, historical electricity consumption data of the charging and battery swapping station, historical demand response date and historical orderly electricity consumption execution date data of the charging and battery swapping station, and charging and battery swapping station ledger data.

[0039] Step S104: Based on the preset historical data cleaning and filtering model, the historical data information is cleaned and filtered to obtain the cleaned and filtered historical data information.

[0040] Step S106: Based on the historical data information after cleaning and filtering, construct the baseline load calculation model and the predicted load calculation model of the charging and battery swapping station.

[0041] Step S108: Based on the baseline load calculation model and the predicted load calculation model of the charging and swapping station, construct the adjustable potential depth calculation model.

[0042] Step S110: Based on the adjustable potential depth calculation model, construct the adjustable potential depth full-time domain matrix calculation model.

[0043] Step S112: Based on the adjustable potential depth full-time domain matrix calculation model, calculate and generate the adjustable potential depth heat map of the target charging and swapping station.

[0044] Specifically, the preset historical data cleaning and filtering model includes:

[0045]

[0046] in,

[0047]

[0048] In the formula: These are the average active power of charging and battery swapping stations over the time period t of the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. These are the electricity consumption of the charging and swapping stations during time period t, determined by the charging and swapping station ledger data for the 1st, 2nd, 3rd, 4th, and 5th consecutive normal working days prior to the demand response invitation date. The historical consecutive normal working days exclude response days and orderly electricity consumption execution days. The value of t is any integer in the range [1, 24]. DR and OEC are the sets of historical response days and the sets of historical orderly electricity consumption execution days, respectively. These represent the maximum and minimum average active power of the charging and battery swapping station over the five consecutive normal working days prior to the demand response invitation date, respectively; max{*} is the function for finding the maximum value; min{*} is the function for finding the minimum value; Δh is the time period step; and D is the demand response invitation date.

[0049] Preferably, Δh is set to 1 hour.

[0050] Specifically, the baseline load calculation model for charging and battery swapping stations includes:

[0051]

[0052] In the formula: The baseline load of the charging and battery swapping station on the demand response invitation date for time period t;

[0053] The predicted load calculation model for charging and battery swapping stations includes:

[0054]

[0055] In the formula: The predicted load for the charging and battery swapping station during time period t on the demand response invitation date.

[0056] Specifically, in this embodiment of the invention, the adjustable potential depth calculation model includes a lower adjustable potential depth calculation model and an upper adjustable potential depth calculation model; wherein,

[0057] The adjustable potential depth calculation model includes:

[0058]

[0059] In the formula: To ensure the daily demand response of charging and battery swapping stations, it is necessary to make reservations 1q hours in advance and continuously adjust the potential depth for Jcx hours. To ensure the daily demand response of charging and battery swapping stations, itq hours in advance, and to execute the invitation. Baseline load for each time period; To ensure the daily demand response of charging and battery swapping stations, itq hours in advance, and to execute the invitation. Forecasted load for a given period; To ensure the daily demand response of charging and battery swapping stations, it is necessary to make advance reservations 1q hours in advance, adjust continuously for Jcx hours, and execute the reservations. The load adjustment difficulty coefficient for a given time period; Itq and Jcx are integers; The timeframe for inviting reservations for charging and battery swapping stations is 1q hours in advance of the daily demand response.

[0060] Preferably, The value range is (0,1), and the larger the value, the greater the difficulty of load adjustment; the adjustment methods include switching from fast charging to slow charging and switching from running state to shutdown state.

[0061] Preferably, Itq is any integer in the range [2, 8]; Jcx is any integer in the range [2, 6].

[0062] Specifically, the adjustable potential depth calculation model includes:

[0063]

[0064] In the formula: R kW The installed capacity of charging and battery swapping stations; To ensure the daily demand response of charging and battery swapping stations, it is necessary to make reservations 1q hours in advance and continuously adjust the potential depth for Jcx hours. To ensure the daily demand response of charging and battery swapping stations, it is necessary to make advance reservations 1q hours in advance, continuously adjust 10 hours in advance, and execute the reservations. The difficulty coefficient of load regulation during different time periods.

[0065] Preferably, The value range is (0,1), and the larger the value, the greater the difficulty of load adjustment; the adjustment methods include slow charging to fast charging and shutdown to operation.

[0066] Specifically, the adjustable potential depth full-time domain matrix calculation model includes an upper adjustable potential depth full-time domain matrix calculation model and a lower adjustable potential depth full-time domain matrix calculation model; among which,

[0067] The full-time domain matrix calculation model for adjustable potential depth includes:

[0068]

[0069] In the formula: The adjustable potential depth full-time domain matrix; For matrix The element in the Hang-th row and Lie-th column represents the adjustable potential depth of the charging and swapping station's daily demand response advance invitation of Hang hours and continuous Lie hours; Hang is an integer from 2 to 8; Lie is an integer from 2 to 6.

[0070] Specifically, the full-time domain matrix calculation model for adjustable potential depth includes:

[0071]

[0072] In the formula: The adjustable potential depth full-time domain matrix; For matrix The elements in the Hang row and Lie column specifically represent the adjustable potential depth of the charging and swapping station's intraday demand response advance Hang hour invitation and continuous Lie hour.

[0073] Specifically, the adjustable potential depth heatmap includes an upper adjustable potential depth heatmap and a lower adjustable potential depth heatmap. Step S112 also includes the following steps:

[0074] Step S1121: Based on the full-time domain matrix calculation model of the adjustable potential depth, create a heat map of the adjustable potential depth.

[0075] In the adjustable potential depth heatmap, the horizontal axis represents the duration of downward adjustment under the current adjustable potential depth value, in hours; the vertical axis represents the advance invitation time for daily demand response at the charging and swapping station, in hours; the values ​​at the intersection of the horizontal and vertical axes in the adjustable potential depth heatmap correspond to the matrix element values ​​of the full-time domain matrix of the adjustable potential depth, and are represented by different colors.

[0076] Step S1122: Based on the full-time domain matrix calculation model of the adjustable potential depth, create a heat map of the adjustable potential depth.

[0077] The horizontal axis of the adjustable potential depth heatmap represents the duration of upward adjustment under the current adjustable potential depth value, in hours; the vertical axis represents the advance invitation time for daily demand response at the charging and swapping station, in hours; the intersection of the horizontal and vertical axes in the adjustable potential depth heatmap corresponds to the matrix element value of the adjustable potential depth full-time domain matrix, and is represented by different colors.

[0078] The method provided in this embodiment of the invention further includes: outputting the adjustable potential depth calculation result information of the target charging and battery swapping station. Specifically, the adjustable potential depth calculation result information includes: the upper adjustable potential depth for charging and battery swapping stations to respond to daily demand 2-8 hours in advance and last for 2-6 hours; the lower adjustable potential depth for charging and battery swapping stations to respond to daily demand 2-8 hours in advance and last for 2-6 hours; the full-time domain matrix of the upper adjustable potential depth; the full-time domain matrix of the lower adjustable potential depth; the heat map of the upper adjustable potential depth; and the heat map of the lower adjustable potential depth, etc.

[0079] As described above, this invention provides a method for calculating the depth of adjustable potential based on the participation of charging and swapping stations. It proposes a refined model and method for calculating the depth of adjustable potential of charging and swapping stations participating in intraday demand response, which helps to explore and analyze the adjustable potential of charging and swapping station resources. It also helps to improve the accuracy of adjustable potential declarations by load aggregators and power users when assisting charging and swapping station resources in participating in intraday demand response. Furthermore, it can provide reference and guidance for the participation of charging and swapping station resources in virtual power plants, demand response, direct dispatch, grid-load interaction potential analysis and control, etc., alleviating the technical problem in existing technologies where the lack of adjustable potential depth calculation leads to charging and swapping station resource users being unable to accurately declare response capacity, thus affecting the grid dispatch center's precise control of grid operation.

[0080] Example 2

[0081] Figure 2 This is a schematic diagram of an adjustable potential depth calculation system based on the participation of charging and swapping stations, according to an embodiment of the present invention. Figure 2 As shown, the system includes: a data acquisition module 10, a cleaning and filtering module 20, a first model building module 30, a second model building module 40, a third model building module 50, and a calculation and generation module 60.

[0082] Specifically, the data acquisition module 10 is used to collect historical data information of the target charging and battery swapping station. The historical data information includes: historical power consumption data of the charging and battery swapping station, historical electricity consumption data of the charging and battery swapping station, historical demand response date and historical orderly electricity consumption execution date data of the charging and battery swapping station, and charging and battery swapping station ledger data.

[0083] The cleaning and filtering module 20 is used to clean and filter historical data information based on a preset historical data cleaning and filtering model to obtain the cleaned and filtered historical data information.

[0084] The first model construction module 30 is used to construct a baseline load calculation model and a predicted load calculation model for charging and battery swapping stations based on historical data information after cleaning and filtering.

[0085] The second model construction module 40 is used to construct an adjustable potential depth calculation model based on the baseline load calculation model and the predicted load calculation model of the charging and swapping station.

[0086] The third model construction module 50 is used to construct an adjustable potential depth full-time domain matrix calculation model based on the adjustable potential depth calculation module.

[0087] The calculation and generation module 60 is used to calculate and generate a heat map of the adjustable potential depth of the target charging and swapping station based on the adjustable potential depth full-time domain matrix calculation model.

[0088] This invention also provides an electronic 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 method described in Embodiment 1 above.

[0089] This invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described in Embodiment 1 above.

[0090] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0091] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for calculating adjustable potential depth based on the participation of charging and battery swapping stations, characterized in that, include: Collect historical data information of the target charging and battery swapping stations; The historical data information includes: historical power consumption data of charging and battery swapping stations, historical electricity consumption data of charging and battery swapping stations, historical demand response date and historical orderly electricity consumption execution date data of charging and battery swapping stations, and charging and battery swapping station ledger data. Based on a preset historical data cleaning and filtering model, the historical data information is cleaned and filtered to obtain the cleaned and filtered historical data information. Based on the historical data information after cleaning and filtration, a baseline load calculation model and a predicted load calculation model for the charging and battery swapping station are constructed. Based on the baseline load calculation model and the predicted load calculation model of the charging and swapping station, an adjustable potential depth calculation model is constructed. Based on the adjustable potential depth calculation model, a full-time domain matrix calculation model for adjustable potential depth is constructed. Based on the adjustable potential depth full-time domain matrix calculation model, the adjustable potential depth heat map of the target charging and swapping station is calculated and generated. The baseline load calculation model for the charging and swapping station includes: ， In the formula: These are the average active power of charging and battery swapping stations over the time period t of the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. These represent the maximum and minimum average active power of the charging and battery swapping station over the five consecutive normal working days prior to the demand response invitation date. The baseline load of the charging and battery swapping station on the demand response invitation date for time period t; The predicted load calculation model for the charging and swapping station includes: ， In the formula: For the predicted load of the charging and battery swapping station on the demand response invitation date in time period t; The adjustable potential depth calculation model includes a lower adjustable potential depth calculation model and an upper adjustable potential depth calculation model; wherein... The adjustable potential depth calculation model includes: ， In the formula: To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The adjustable potential depth within hours; To advance the intraday demand response of charging and battery swapping stations Invitation and execution of invitation within hours Baseline load for each time period; To advance the intraday demand response of charging and battery swapping stations Invitation and execution of invitation within hours Forecasted load for a given period; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The first adjustment and execution of the invitation within the hour The difficulty coefficient of load regulation during a given time period; and It is an integer; To advance the intraday demand response of charging and battery swapping stations The exact time of the invitation within one hour; The adjustable potential depth calculation model includes: ， In the formula: The installed capacity of charging and battery swapping stations; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The adjustable potential depth within hours; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous Adjusting and executing invitations within hours The difficulty coefficient of load regulation during different time periods.

2. The method according to claim 1, characterized in that: The preset historical data cleaning and filtering model includes: ， in, ， In the formula: , , , , These are the average active power of charging and battery swapping stations over the time period t of the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. These are the electricity consumption of the charging and swapping stations during time period t, determined by the charging and swapping station ledger data for the previous 1, 2, 3, 4, and 5 consecutive normal working days prior to the demand response invitation date. The consecutive normal working days exclude the response date and the orderly electricity consumption execution date. , These are respectively the set of historical response days and the set of historical orderly electricity consumption execution days; , These represent the maximum and minimum average active power of the charging and battery swapping station over the five consecutive normal working days prior to the demand response invitation date; To find the maximum value function; To find the minimum value of the function; D represents the time step; D represents the demand response invitation date.

3. The method according to claim 1, characterized in that: The adjustable potential depth full-time domain matrix calculation model includes an upper adjustable potential depth full-time domain matrix calculation model and a lower adjustable potential depth full-time domain matrix calculation model; wherein... The adjustable potential depth full-time domain matrix calculation model includes: ， In the formula: The adjustable potential depth full-time domain matrix; To advance the intraday demand response of charging and battery swapping stations Invitation every hour, continuing The adjustable potential depth within hours; For integers ranging from 2 to 8; It is an integer ranging from 2 to 6; The adjustable potential depth full-time domain matrix calculation model includes: ， In the formula: The adjustable potential depth full-time domain matrix; To advance the intraday demand response of charging and battery swapping stations Invitation every hour, continuing The adjustable potential depth within hours.

4. The method according to claim 3, characterized in that: The adjustable potential depth heatmap includes an upper adjustable potential depth heatmap and a lower adjustable potential depth heatmap; among which, The horizontal axis of the adjustable potential depth heatmap represents the duration of downward adjustment at the current adjustable potential depth value, and the vertical axis represents the advance invitation time for the daily demand response of the charging and swapping station. The values ​​at the intersection of the horizontal and vertical axes in the adjustable potential depth heatmap correspond to the matrix element values ​​of the full-time domain matrix of the adjustable potential depth, and are represented by different colors. The horizontal axis of the adjustable potential depth heatmap represents the duration of the upward adjustment under the current adjustable potential depth value, and the vertical axis represents the advance invitation time for the daily demand response of the charging and swapping station. The intersection point of the horizontal and vertical axes in the adjustable potential depth heatmap corresponds to the matrix element value of the full-time domain matrix of the adjustable potential depth, and is represented by different colors.

5. A system for calculating adjustable potential depth based on the participation of charging and battery swapping stations, characterized in that, include: The system comprises a data acquisition module, a cleaning and filtering module, a first model building module, a second model building module, a third model building module, and a calculation and generation module; among which, The data acquisition module is used to collect historical data information of the target charging and battery swapping station; the historical data information includes: historical power consumption data of the charging and battery swapping station, historical electricity consumption data of the charging and battery swapping station, historical demand response date and historical orderly electricity consumption execution date data of the charging and battery swapping station, and charging and battery swapping station ledger data. The cleaning and filtering module is used to clean and filter the historical data information based on a preset historical data cleaning and filtering model to obtain the historical data information after cleaning and filtering. The first model building module is used to build a baseline load calculation model and a predicted load calculation model for the charging and battery swapping station based on the historical data information after cleaning and filtering. The second model building module is used to build an adjustable potential depth calculation model based on the baseline load calculation model of the charging and swapping station and the predicted load calculation model of the charging and swapping station. The third model construction module is used to construct an adjustable potential depth full-time domain matrix calculation model based on the adjustable potential depth calculation module. The calculation and generation module is used to calculate and generate the adjustable potential depth heat map of the target charging and swapping station based on the adjustable potential depth full-time domain matrix calculation model. The baseline load calculation model for the charging and swapping station includes: ， In the formula: The baseline load of the charging and battery swapping station on the demand response invitation date for time period t; The predicted load calculation model for the charging and swapping station includes: ， In the formula: For the predicted load of the charging and battery swapping station on the demand response invitation date in time period t; The adjustable potential depth calculation model includes a lower adjustable potential depth calculation model and an upper adjustable potential depth calculation model; wherein... The adjustable potential depth calculation model includes: ， In the formula: To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The adjustable potential depth within hours; To advance the intraday demand response of charging and battery swapping stations Invitation and execution of invitation within hours Baseline load for each time period; To advance the intraday demand response of charging and battery swapping stations Invitation and execution of invitation within hours Forecasted load for a given period; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The first adjustment and execution of the invitation within the hour The difficulty coefficient of load regulation during a given time period; and It is an integer; To advance the intraday demand response of charging and battery swapping stations The exact time of the invitation within one hour; The adjustable potential depth calculation model includes: ， In the formula: The installed capacity of charging and battery swapping stations; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous The adjustable potential depth within hours; To advance the intraday demand response of charging and battery swapping stations Hourly invitation, continuous Adjusting and executing invitations within hours The difficulty coefficient of load regulation during different time periods.

6. An electronic device, characterized in that, include: 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 method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-4.

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