Virtual power plant scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses a virtual power plant scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a first function according to first data; solving the first function to obtain second data; converting the second data into a first instruction; and performing electric energy scheduling on the station according to the first instruction. According to the method, the first function is constructed through the first data, the first function is solved, the obtained second data is converted into the first instruction, and electric energy scheduling is performed on the station according to the first instruction, so that the economic benefit of a power grid can be improved while the adjustment efficiency can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, and in particular to a virtual power plant scheduling method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the large-scale development of electric vehicles and charging and swapping facilities, they have become important new loads and potential flexible resources in the power grid. The virtual power plant (VPP) technology is a key means of aggregating such distributed resources, participating in power grid interaction, and improving system operation flexibility and new energy consumption capacity. However, the current technical research on the participation of charging and swapping stations in virtual power plants is single and cannot adapt to multi-source heterogeneous scenarios. The existing method uses a "one-size-fits-all" control mode and cannot perform differentiated control according to the emergency level of the power grid demand and the business characteristics of different types of charging and swapping stations (bus stations, logistics stations, and public stations), resulting in insufficient response and excessive impact on operation. In addition, the resource coordination efficiency of the current charging and swapping stations is low, and the economy and experience are difficult to balance. The existing method lacks clear resource calling priority, often adjusts the charging power too early and too much, resulting in a significant extension of the user charging time, or fails to preferentially call the lower-cost energy storage and photovoltaic resources, affecting the overall economy. SUMMARY

[0003] The present application provides a virtual power plant scheduling method, device, electronic device and storage medium to solve the problem of low efficiency of the scheduling scheme.

[0004] According to one aspect of the present application, a virtual power plant scheduling method is provided, comprising:

[0005] constructing a first function according to first data; the first data is used to represent the ability of the field station to adjust power; the first function is used to represent the resource consumption demand of the field station in the power grid under the constraint condition;

[0006] solving the first function to obtain second data; the second data is the peak shaving power of the field station under different peak shaving modes;

[0007] converting the second data into a first instruction;

[0008] performing electric energy scheduling on the field station according to the first instruction.

[0009] According to another aspect of the present application, a virtual power plant scheduling device is provided, comprising:

[0010] a first function determination module, configured to construct a first function according to first data; the first data is used to represent the ability of the field station to adjust power; the first function is used to represent the resource consumption demand of the field station in the power grid under the constraint condition;

[0011] a second data determination module configured to solve the first function to obtain second data, wherein the second data is the peak shaving power of the field station under different peak shaving modes;

[0012] a first instruction determination module configured to convert the second data into a first instruction;

[0013] an electric energy scheduling module configured to schedule electric energy of the field station according to the first instruction.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected to the at least one processor in communication; wherein

[0017] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the virtual power plant scheduling method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the virtual power plant scheduling method according to any one of the embodiments of the present application when executed.

[0019] The technical solution of the embodiments of the present application can construct a first function according to first data, the generation of the first data can quickly aggregate scattered charging and battery swapping station resources to form considerable and reliable upward / downward adjustment capability, the construction of the first function according to the first data can enable the constructed first function to describe the scheduling task of the power grid with the lowest resource consumption, the solving of the first function can enable the obtained second data to complete the power grid adjustment task with the lowest marginal resource consumption, and the overall economic benefit of the charging and battery swapping station assets can be improved, the conversion of the second data into a first instruction can enable the generated numerical data to be converted into adjustment instructions recognizable by the field station, thereby improving the adjustment efficiency, and the electric energy scheduling of the field station according to the first instruction can enable the precise scheduling of electric energy. The method can improve the adjustment efficiency and the economic benefit of the power grid by constructing a first function according to first data, solving the first function, converting the obtained second data into a first instruction, and scheduling electric energy of the field station according to the first instruction.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings should be within the protection scope of the present application.

[0022] Figure 1 A flow chart of a virtual power plant scheduling method provided by the embodiment of the present application;

[0023] Figure 2 A structural schematic diagram of a virtual power plant scheduling device provided by the embodiment of the present application;

[0024] Figure 3 A structural schematic diagram of an electronic device for implementing the virtual power plant scheduling method of the embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the technical personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should be within the protection scope of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0027] Figure 1 A flow chart of a virtual power plant scheduling method provided by the embodiment of the present application, the present embodiment can be applicable to the case of scheduling electric energy of a virtual power plant, the method can be executed by a virtual power plant scheduling device, the virtual power plant scheduling device can be realized in the form of hardware and / or software, and the virtual power plant scheduling device can be configured in any electronic device with network communication function. For example, Figure 1As shown, the method comprises:

[0028] S110, constructing a first function according to first data; the first data is used to represent the ability of the field station to adjust power; the first function is used to represent the resource consumption demand of the field station in the power grid under the constraint condition.

[0029] Wherein, the first data includes: first adjustment capability data and second adjustment capability data. The first adjustment capability data is the maximum adjustable power (unit: kW) of the field station i at time t, which is used for peak shaving. The second adjustment capability data is the maximum adjustable power (unit: kW) of the field station i at time t, which is used for valley filling.

[0030] Wherein, the field station is a physical place with specific energy production, conversion, storage or dispatching function, and complete control, monitoring and communication system, which is the core hub connecting distributed energy and power grid.

[0031] Specifically, the to-be-solved variable is determined. The constraint function is determined according to the to-be-solved variable, the fourth data and the first data. The to-be-solved variable is matched with the corresponding preset parameter, and the product sum is obtained to obtain the second function. The second function and the constraint function are combined to obtain the first function.

[0032] Wherein, the to-be-solved variable includes: The preset parameters include: energy storage discharge resource consumption , V2G discharge resource consumption , charging service resource consumption , controllable load compensation resource consumption .

[0033] Further, the first function can be expressed as:

[0034]

[0035] ;

[0036] Wherein, is the first adjustment capability data; is the total peak shaving power (unit: kWh) allocated to the field station i; is the total peak shaving power (unit: kW) required by the power grid; is the preset control start and end time (h); N is the total number of field stations participating in control.

[0037] Further, the determination process of the first data is: generating first capability data according to the discharge characteristic data of the energy storage system and the lower limit of energy storage discharge safety; generating second capability data according to the discharge characteristic data of the vehicle; determining first adjustment capability data according to the first capability data, the second capability data, the adjustment capability data of the charging pile and the active power of the charging pile under the control instruction value; generating third capability data according to the charging characteristic data of the energy storage system and the upper limit of energy storage charging safety; generating fourth capability data according to the charging characteristic data of the vehicle; and determining second adjustment capability data according to the third capability data, the fourth capability data, the adjustment capability data of the charging pile and the active power of the charging pile under the photovoltaic device.

[0038] S120, solving the first function to obtain second data; the second data is the peak shaving power of the field station under different peak shaving modes.

[0039] The peak shaving mode includes: energy storage discharge mode; controllable load adjustment mode; charging pile power reduction mode; discharge mode.

[0040] Specifically, for the field station assigned to the task, the maximum value of the callable power of each field station under different peak shaving modes is set in advance to obtain a third function. The third function and the first function are solved according to the preset rule to obtain the second data.

[0041] The preset rule is to use the energy storage with a resource consumption less than a first preset threshold first, then use the load adjustment with a resource consumption between the first preset threshold and a second preset threshold, then use the charging pile power reduction between the second preset threshold and a third preset threshold, and finally use the V2G greater than the third preset threshold. The first preset threshold is less than the second preset threshold, and the second preset threshold is less than the third preset threshold.

[0042] The third function can be expressed as:

[0043] ;

[0044] Wherein, is the maximum callable power under the energy storage discharge mode; is the maximum callable power under the controllable load adjustment mode; is the maximum callable power under the charging pile power reduction mode; is the maximum callable power under the V2G discharge mode; is the active power of the charging pile under the control instruction value; is the first capability data; is the second capability data; is the total charging power of all charging piles of the field station i at time t (unit: kW); is the charging pile power adjustment coefficient, which is determined by the control mode.

[0045] wherein the second data can be represented as:

[0046] .

[0047] Further, before solving the first function, the marginal resource consumption of each field station under a preset control mode is determined according to a preset parameter , that is . According to the obtained marginal resource consumption corresponding to each field station, the field stations are sorted to obtain a field station queue Q in the order of low to high resource consumption. Let the total demand power of all field stations be ; and the remaining demand power be . Each field station i is traversed in the order of the queue Q to obtain the maximum allocatable power, the actual allocated power and the updated remaining demand power of the field station. When , the allocation ends.

[0048] wherein the preset control mode can be: full guarantee mode, balanced scheduling mode and minimum guarantee mode.

[0049] wherein the full guarantee mode: a sequence of interrupting non-core charging and load, maximizing calling energy storage discharge and finally scheduling V2G vehicle discharge is executed.

[0050] wherein the balanced scheduling mode: a sequence of full photovoltaic power generation, energy storage on-demand discharge, adjustable controllable load and finally flexible adjustment of charging pile power is executed. The charging pile power adjustment can adopt dynamic average distribution, first-come-first-charge and other strategies.

[0051] wherein the minimum guarantee mode: only the strategy of calling photovoltaic and energy storage is executed, and in principle, the charging pile and the load affecting business are not adjusted.

[0052] wherein the preset control mode is determined according to the day-ahead peak shaving instruction issued by the power grid dispatching system to the virtual power plant. For example, assuming that the power grid dispatching system issues a day-ahead peak shaving instruction to the virtual power plant: please reduce the aggregated load by 1000 kW during T day XX:00:00 to XX+1:00:00, the instruction is a regular peak shaving, and the response delay requirement is <15 minutes. The central platform instruction analysis module automatically analyzes the keywords: regular peak shaving, 1000 kW, response delay <15 minutes. The selection logic module determines that it is a regular peak shaving scene, and selects the balanced scheduling mode.

[0053] S130, converting the second data into the first instruction.

[0054] Specifically, the fourth data is divided by the difference between the preset regulation start time and the preset regulation end time to obtain fifth data. Each data in the second data is divided by the difference between the preset regulation start time and the preset regulation end time to obtain sixth data. The fifth data, the preset regulation mode, the sixth data and the preset regulation time are generated into a structured instruction package to obtain a first instruction.

[0055] Further, the sixth data can be represented as:

[0056] .

[0057] Wherein, is the total peak shaving power under the energy storage discharge mode (unit: kW); is the total peak shaving power under the controllable load regulation mode (unit: kW); is the total peak shaving power under the charging pile power reduction mode (unit: kW); is the total peak shaving power under the V2G discharge mode (unit: kW).

[0058] S140, according to the first instruction, the electric energy of the station is scheduled.

[0059] Specifically, the total peak shaving power under the energy storage discharge mode and the total peak shaving power under the controllable load regulation mode are obtained from the sixth data. The total peak shaving power under the energy storage discharge mode and the total peak shaving power under the controllable load regulation mode are summed to obtain a total peak shaving power. The fifth data is subtracted from the total peak shaving power to obtain seventh data. The seventh data is divided by the first quantity to obtain eighth data. The eighth data is subtracted from the current power of the charging pile to obtain the corresponding reduced power of the charging pile as ninth data. The ninth data is compared with the preset power. If the ninth data is less than the preset power, the first result is that the charging pile can complete the power reduction, and the power of the charging pile is adjusted according to the eighth data. If the ninth data is greater than or equal to the preset power, the first result is that the charging pile cannot complete the power reduction, and the charging pile is reduced according to the preset power. The excess power is allocated to the remaining charging piles for adjustment in the second round of power reduction.

[0060] Wherein, the seventh data can be represented as:

[0061] ;

[0062] Wherein, is ; is ; is .

[0063] Wherein, the eighth data can be represented as:

[0064] ;

[0065] wherein M is a first number.

[0066] Further, after the power of all charging piles in the station is reduced, the new power of each charging pile is determined, and the total reduced power is determined according to the new power and the original power of each charging pile. If the total reduced power is less than the seventh data, the second round of average allocation is performed.

[0067] wherein the total reduced power can be represented as: wherein, is the new power of the charging pile j; is the original power of the charging pile j.

[0068] wherein, can be represented as: wherein, is the minimum guaranteed power of the charging pile.

[0069] Further, the new parameters contained in the second round of average allocation are: ; .

[0070] Further, after the electric energy scheduling is completed, the running state curve of the equipment in the station after the electric energy scheduling is obtained. The running state curve is compared with the target running state curve, and if the deviation exceeds a threshold value, such as 5%, the rolling optimization is started, the task allocation not executed in the subsequent period is re-adjusted, and the total target is finally ensured to be achieved.

[0071] wherein the running state curve can be: an actual power curve, a SOC change curve.

[0072] Further, the rolling optimization triggering condition is: when the duration exceeds 5 minutes, the rolling optimization is triggered. The triggering rule is: when the deviation rate > 5% and the duration > 5 minutes, the rolling optimization is triggered; the rolling optimization re-calculates the task allocation of the remaining period.

[0073] wherein, is the total cluster power actually measured (unit: kW), i.e. the running state curve; is the target power (unit: kW), i.e. the target running state curve.

[0074] Further, obtained by aggregating the power data reported by each station in real time; is the target power curve generated according to the control instruction.

[0075] For example, assume the time interval is from T-1 to XX:YY on day T; the virtual power plant platform has connected to the data interfaces of the following five stations: Station A: Public fast charging station; Station B: Logistics park (including photovoltaic and energy storage); Station C: Bus station (including energy storage and V2G); Station D: Public fast charging station; Station E: Public fast charging station; The peak shaving instruction is to reduce the aggregated load by 1000kW from XX:00:00 to XX+1:00:00 on day T. This instruction is a regular peak shaving instruction, and the response delay requirement is <15 minutes. The central platform establishes a load forecasting model for each station and runs it on the morning of day T, generating baseline load forecast curves for each station during the time period XX:00-XX+1:00. The central system continuously receives equipment status updates from each station: Station B: Energy storage SOC = 99%, PV inverter online, 3 charging piles charging at 90kW. Station C: Energy storage SOC = 97%, 1 V2G pile idle, 5 piles charging. Stations A, D, and E: Charging pile occupancy rates are 60%, 50%, and 30%, respectively. Based on the peak shaving command and preset control mode, a balanced scheduling mode is selected. The resource fine-grained aggregation module immediately starts, calculating the future status of each station. The first adjustment capacity data within the hour. Sum the first adjustment capacity data of each station to obtain... To meet the peak shaving command requirements, the cluster-level optimization scheduler is started, inputting preset parameters and constructing constraint and objective functions. A two-level priority allocation method is used to solve this, i.e., the solution method in steps C1-C2, to obtain the optimal task decomposition scheme, i.e., the total peak shaving power that each station needs to bear. The total peak shaving power that each station needs to bear yields the total peak shaving power, i.e., .

[0076] Furthermore, structured instructions, namely the first instruction, are issued to each station's EMS. Taking Bilibili as an example, the instruction content is: Preset control mode: Task: Reduce net load Recommended strategy sequence: Full power generation of solar PV; Energy storage as... Discharge; adjust the air conditioner to 26℃, which is the estimated temperature drop. ; Implement dynamic power allocation for charging piles, which requires reducing After receiving the instruction, the B-site site-level strategy executor executes the following steps in sequence: confirming that the photovoltaic system has no output, meaning there is no room for further power generation; and issuing a constant power output to the energy storage system. Discharge command; The building automation system will raise the air conditioning temperature to 26℃; The dynamic power allocation algorithm for charging piles will be executed: M=3 charging piles are currently charging at the station, and each pile needs to reduce its power on average. The power of each charging station will be increased from the original Set as Each station collects actual power data every second and uploads it to the central platform every minute. The platform's communication and closed-loop feedback modules monitor the total load curve of the five stations in real time. Assume that at XX:TT, a sudden vehicle access at station D causes the cluster's total peak load reduction to only 950kW, deviating from the target by 50kW. The platform detects that the deviation exceeds 5% for 5 minutes and immediately initiates rolling optimization. The optimizer, using the remaining 30 minutes as a window, recalculates the allocation of the remaining 50kW task, resulting in an additional plan: 20kW for station B and 30kW for station C. The platform sends additional instructions to stations B and C. The EMS systems at stations B and C receive the instructions and fine-tune the energy storage discharge power or charging pile power, ultimately bringing the cluster's total peak load reduction back to near the 1000kW target around XX:ZZ. At XX+1:00:00, the central platform sends a command to all stations to end control and restore normal operation. Each station's EMS sequentially: restores the charging piles to rated power, stops energy storage discharge, and adjusts the air conditioning back to its original temperature setting.

[0077] Among them, the first adjustment capacity data for each station is as follows: Station A Bilibili Station C Station D E-Station .

[0078] The preset parameters are: energy storage discharge resource consumption. Controllable load regulation of resource consumption Charging service resource consumption V2G discharge resource consumption .

[0079] The total peak shaving power to be borne by each station is as follows: Station A Bilibili Station C Station D E-Station .

[0080] Optionally, a first function is constructed based on the first data, including steps A1-A4:

[0081] Step A1: Determine the third data and generate the first data based on the third data; the first data is used to characterize the status data of energy storage equipment and vehicles in the site.

[0082] Specifically, third data is obtained from the database and real-time monitoring equipment. Discharge characteristic data and the lower limit of energy storage discharge are obtained from the third data, and first capacity data is generated based on these. Discharge characteristic data and charging characteristic data of the vehicle are obtained from the third data. Second capacity data is generated based on the vehicle's discharge characteristic data. The first capacity data, second capacity data, charging pile regulation capacity data, and the active power of the charging pile under control command values ​​are used to determine the first regulation capacity data. Third capacity data is generated based on the charging characteristic data and the upper limit of energy storage charging safety. Fourth capacity data is generated based on the vehicle's charging characteristic data. Second regulation capacity data is determined based on the third capacity data, fourth capacity data, charging pile regulation capacity data, and the active power of the charging pile under photovoltaic devices.

[0083] Step A2: Construct a constraint function based on the first data, the fourth data, and the variable to be determined; the fourth data is the total peak-shaving power allocated to the power station; the constraint function is used to constrain the peak-shaving power allocated to the power station.

[0084] Specifically, determine the variable to be solved. Determine the constraint function based on the variable to be solved, the fourth data, and the first data.

[0085] The variables to be determined include: .in, Peak-shaving power achieved through energy storage discharge (unit: kWh); Peak-shaving power achieved through V2G discharge (unit: kWh); Peak-shaving power achieved through power reduction via charging piles (unit: kWh); Peak shaving power generated through controllable load regulation (unit: kWh).

[0086] Furthermore, the constraint function can be expressed as:

[0087] ;

[0088] in, This is the data for the first regulatory capacity; The total peak-shaving power allocated to station i (unit: kWh); The total peak-shaving power required by the power grid (unit: kW); The preset start and end times (h) of the control are derived from the power grid command.

[0089] in, It is given directly through power grid dispatch instructions.

[0090] Step A3: Construct a second function based on the variables to be determined and preset parameters; the second function represents the resource consumption requirements that the power grid stations need to meet.

[0091] Among them, the preset parameters include at least: energy storage discharge resource consumption. V2G discharge resource consumption Charging service resource consumption Controllable load compensation resource consumption The preset parameters are satisfied. .

[0092] Furthermore, the energy storage discharge resource consumption can be set. V2G discharge resource consumption Charging service resource consumption Controllable load compensation resource consumption .

[0093] The second function is used to characterize the minimum total consumption of all types of resources for regulating resources at all stations.

[0094] Specifically, the variable to be determined is matched with the corresponding preset parameters, and the products are summed to obtain the second function.

[0095] Furthermore, the second function can be expressed as:

[0096] ;

[0097] Where N represents the total number of stations participating in regulation.

[0098] Step A4: Generate the first function based on the second function and the constraint function.

[0099] Specifically, the second function and the constraint function are combined to obtain the first function.

[0100] Optionally, determine the third data, and generate the first data based on the third data, including steps B1-B5:

[0101] Step B1: Generate first capability data and second capability data based on the third data; the first capability data is used to characterize the discharge capability of the energy storage device; the second capability data is used to characterize the discharge capability of the vehicle.

[0102] The first capability data is the maximum dischargeable power of the energy storage system at time t (unit: kW). The second capability data is the total dischargeable power of all V2G vehicles at site i at time t (unit: kW).

[0103] Specifically, third-party data is obtained from databases and real-time monitoring equipment. Discharge characteristic data and the lower limit of energy storage discharge are obtained from the third-party data, and first-party capability data is generated based on these data. Discharge characteristic data of the vehicle is obtained from the third-party data, and second-party capability data is generated based on this data.

[0104] The discharge characteristic data of the energy storage system shall include at least the following: the rated discharge power of the energy storage system; the state of charge of the energy storage system; the lower limit of the safe discharge of the energy storage system; the length of the grid preset control period; and the rated capacity of the energy storage system.

[0105] The state of charge of the energy storage system is reported in real time by the energy storage BMS every 15 seconds; the rated discharge power and rated capacity of the energy storage system are obtained by querying the technical parameter files of the energy storage equipment; the lower limit of energy storage discharge safety is obtained by querying the safety specifications of the battery manufacturer; and the length of the grid preset control period is set in advance according to the working status of the grid.

[0106] Furthermore, technical parameter files for energy storage devices and safety specifications from battery manufacturers are stored in the database.

[0107] Furthermore, the primary capability data can be represented as:

[0108] ;

[0109] in, This is the primary capability data; Rated discharge power of the energy storage system (unit: kW); The state of charge of the energy storage system at time t, ranging from 0 to 1; The lower limit for safe discharge of stored energy is typically 0.3 (i.e., 30%). Preset the control period length (unit: hours) for the power grid. Rated capacity of the energy storage system (unit: kWh).

[0110] For example, assuming a 125kW / 250kWh energy storage system, its ; .

[0111] The discharge characteristic data includes at least: the total dischargeable power of V2G vehicles; data on the collection of vehicles supporting V2G functionality at the depot; and the maximum V2G discharge power of the vehicles. Discharge capacity coefficient function.

[0112] Furthermore, The vehicle's BMS reports the information via the charging station every 30 seconds. The power rating is determined by the vehicle's technical parameter file or set to the rated power of the charging pile; the vehicle's on-site status is obtained through the charging pile connection status monitoring system.

[0113] Furthermore, the second capability data can be represented as:

[0114] ;

[0115] in, This is data related to the second capability. Let be the set of vehicles at depot i at time t that support V2G functionality; The maximum V2G discharge power of vehicle v (unit: kW); This is a function of the discharge capacity coefficient based on the battery's state of charge.

[0116] For example, the V2G charging station supports 350kW, and the vehicle battery supports 250kW; the smaller value will be used. =250kW.

[0117] Furthermore, the discharge capability coefficient function can be expressed as:

[0118] .

[0119] Furthermore, in addition to generating the first and second capacity data, it is also necessary to generate the total baseline load power, which is determined based on the total number of charging piles in the station, the indicator function of the charging piles, and the rated power of the charging piles.

[0120] The indicator function characterizes whether charging pile j is busy at time t. This indicates that charging station j is charging the vehicle; This indicates that charging pile j is in an idle state.

[0121] Furthermore, the total baseline load power can be expressed as: ;

[0122] in, t represents the total baseline load power of the charging station at time t (unit: kW); k represents the total number of charging piles in the charging station. For indicator functions; The rated power of charging pile j (unit: kW).

[0123] Furthermore, Data is acquired through a real-time monitoring system for charging piles, with a data collection frequency of once per minute. The charging pile nameplate parameters are stored in the equipment file database.

[0124] Step B2: Determine the first adjustment capability data based on the first capability data and the second capability data; the first adjustment capability data is used to characterize the degree to which the power of the power station can be reduced.

[0125] The first regulation capability data is the maximum adjustable power (unit: kW) of power station i at time t. The first regulation capability data is used for peak shaving.

[0126] Specifically, the first adjustment capability data is determined based on the first capability data, the second capability data, the charging pile adjustment capability data, and the active power of the charging pile under the control command value.

[0127] The charging pile adjustment capacity data can be expressed as follows: .

[0128] Furthermore, the first regulatory capacity data can be expressed as:

[0129] ;

[0130] in, This is the data for the first regulatory capacity; This refers to the active power of the charging pile under the control command value. This is the primary capability data; This is data related to the second capability. The total charging power of all charging piles at station i at time t (unit: kW); This is the power adjustment coefficient for the charging pile, determined by the preset control mode.

[0131] Furthermore, the relationship between the preset control mode and the control coefficient can be expressed as:

[0132] .

[0133] Step B3: Generate third capability data and fourth capability data based on the third data; the third capability data is used to characterize the energy storage capacity of the energy storage device; the fourth capability data is used to characterize the energy storage capacity of the vehicle.

[0134] The third capability data is the maximum rechargeable power of the energy storage system at time t (unit: kW). The fourth capability data is the total rechargeable power of all V2G vehicles at site i at time t (unit: kW).

[0135] Among them, V2G vehicles are electric vehicles that support Vehicle-to-Grid (V2G) technology. They can achieve bidirectional energy and information interaction with the power grid through bidirectional charging and discharging equipment. They are both transportation tools and dispatchable mobile energy storage units, and can participate in power grid peak shaving and valley filling and ancillary services.

[0136] Specifically, third-party data is obtained from databases and real-time monitoring equipment. Discharge characteristic data and charging safety limits of the energy storage system are obtained from this third-party data, and third-party capability data is generated based on these data. Vehicle charging characteristic data is obtained from the third-party data, and fourth-party capability data is generated based on this data.

[0137] The discharge characteristic data of the energy storage system shall include at least: the rated discharge power of the energy storage system; the state of charge of the energy storage system; the length of the grid's preset control period; and the rated capacity of the energy storage system.

[0138] Furthermore, the third capability data can be represented as:

[0139] ;

[0140] in, This is data related to third-party capabilities; Rated discharge power of the energy storage system (unit: kW); The state of charge of the energy storage system at time t, ranging from 0 to 1; The upper limit for safe discharge of stored energy is usually 0.9 (i.e., 90%). Preset the control period length (unit: hours) for the power grid. Rated capacity of the energy storage system (unit: kWh).

[0141] The charging characteristic data includes at least: data on the set of vehicles that can be guided to charge; the maximum charging power of the vehicles; and the battery capacity of the vehicles.

[0142] Furthermore, the fourth capability data can be represented as:

[0143] ;

[0144] in, This is data for the fourth capability; Let t be the set of vehicles at station i that are available for guided charging; Maximum charging power for vehicle v (unit: kW); The battery capacity of vehicle v (unit: kWh); The upper limit for safe discharge of stored energy is usually 0.9 (i.e., 90%).

[0145] Step B4: Determine the second adjustment capability data based on the third and fourth capability data; the second adjustment capability data is used to characterize the extent to which the power station can be increased.

[0146] The second regulation capacity data is the maximum adjustable power (unit: kW) of station i at time t. The second regulation capacity data is used for valley filling.

[0147] Specifically, the second regulation capability data is determined based on the third capability data, the fourth capability data, the charging pile regulation capability data, and the active power of the charging pile under the photovoltaic device.

[0148] Furthermore, the second regulatory capacity data can be expressed as:

[0149] ;

[0150] in, This is the second regulatory capacity data; This refers to the active power of the charging pile under the photovoltaic device. This is data related to third-party capabilities; This is data for the fourth capability; This represents the total baseline load power.

[0151] Step B5: Determine the first data based on the first regulation capability data and the second regulation capability data.

[0152] Specifically, the first regulatory capacity data and the second regulatory capacity data are combined to obtain the first data.

[0153] Optionally, the first function can be solved to obtain the second data, including steps C1-C2:

[0154] Step C1: Preset the third function; the third function is the amount of electricity that each station can call up under different peak shaving methods.

[0155] Specifically, for the power stations assigned to tasks, the maximum amount of electricity that each power station can call up under different peak-shaving methods is preset, resulting in the third function.

[0156] The third function can be expressed as:

[0157] .

[0158] Furthermore, before setting the third function, the marginal resource consumption of each station under the preset control mode is determined based on preset parameters. ,Right now Based on the marginal resource consumption of each power station, the power stations are sorted to obtain a queue Q of power stations with increasing resource consumption. Let the total power demand of all power stations be... Remaining electricity demand Iterate through each station i in queue Q order to obtain the maximum allocable power, the actual allocated power, and the updated remaining power demand for that station. The allocation process ends at that time.

[0159] The maximum allocatable power capacity of this station can be expressed as:

[0160] ;

[0161] The actual allocated electricity can be expressed as:

[0162] ;

[0163] The update of remaining power demand can be represented as:

[0164] .

[0165] The above steps ensure that the station resources with the lowest marginal resource consumption are prioritized for use.

[0166] Step C2: Determine the second data based on the third function and the first function.

[0167] Specifically, the third function and the first function are solved according to preset rules to obtain the second data.

[0168] The preset rules are as follows: first, energy storage with resource consumption less than a first preset threshold is used; then, load regulation with resource consumption between the first and second preset thresholds is used; next, charging piles with power reduction between the second and third preset thresholds are used; finally, V2G with power consumption greater than the third preset threshold is used. The first preset threshold is less than the second preset threshold, which is less than the third preset threshold.

[0169] The second data can be represented as:

[0170] .

[0171] Optionally, the second data is converted into the first instruction, including steps D1-D3:

[0172] Step D1: Determine the fifth data based on the fourth data and the preset control time; the fifth data is used to characterize the power reduction required by the station within the preset control time.

[0173] The preset control time includes: preset control start time. and preset control termination time .

[0174] Specifically, the fifth data is obtained by dividing the fourth data by the difference between the preset control start time and the preset control end time.

[0175] Furthermore, the fifth data point can be represented as:

[0176] .

[0177] Step D2: Determine the sixth data based on the second data and the preset control time; the sixth data is used to characterize the power change target of the power station under different peak shaving modes.

[0178] Specifically, the difference between each data point in the second data and the preset control start time and preset control end time is divided to obtain the sixth data.

[0179] Furthermore, the sixth data can be represented as:

[0180] .

[0181] Step D3: Package the fifth data, the preset control mode, the sixth data, and the preset control time to obtain the first instruction.

[0182] Specifically, the fifth data, the preset control mode, the sixth data, and the preset control time are used to generate a structured instruction package to obtain the first instruction.

[0183] Optionally, power dispatching is performed on the substation according to the first instruction, including steps E1-E3:

[0184] Step E1: Determine the seventh data based on the fifth and sixth data; the seventh data is the power that needs to be adjusted for each charging pile in the station.

[0185] Specifically, the total peak-shaving power under energy storage discharge mode and the total peak-shaving power under controllable load regulation mode are obtained from the sixth data point. The total peak-shaving power under energy storage discharge mode and the total peak-shaving power under controllable load regulation mode are summed to obtain the total peak-shaving power. The difference between the fifth data point and the total peak-shaving power is taken to obtain the seventh data point.

[0186] Furthermore, the seventh data can be represented as:

[0187] ;

[0188] in, for ; for ; for .

[0189] Step E2: Determine the eighth data based on the seventh data and the first quantity; the eighth data is the average power reduction required for each charging pile in the station; the first quantity is the number of charging piles that are currently charging.

[0190] Specifically, divide the seventh data by the first quantity to obtain the eighth data.

[0191] Furthermore, the eighth data can be represented as:

[0192] ;

[0193] Where M is the first quantity.

[0194] Step E3: Perform power dispatch based on the eighth data.

[0195] Specifically, the difference between the eighth data point and the current power of the charging pile is used to obtain the reduced power of the charging pile, which is then used as the ninth data point. The ninth data point is compared with the preset power. If the ninth data point is less than the preset power, the first result is that the charging pile can complete the power reduction, and the power of the charging pile is adjusted according to the eighth data point. If the ninth data point is greater than or equal to the preset power, the first result is that the charging pile cannot complete the power reduction, and the excess power is allocated to the other charging piles for adjustment during the second round of power reduction.

[0196] Optionally, power dispatch is performed based on the eighth data, including steps F1-F3:

[0197] Step F1: Determine the ninth data based on the eighth data.

[0198] Specifically, the difference between the eighth data point and the current power of the charging pile is used to obtain the reduced power of the charging pile, which is then used as the ninth data point.

[0199] Furthermore, the ninth data point can be represented as: ,in, This represents the current power of the charging station.

[0200] in, Data is collected in real time through the charging pile, with a collection frequency of once per minute.

[0201] Step F2: Compare the ninth data with the preset power to obtain the first result.

[0202] Among them, preset power The minimum guaranteed power of a charging pile (unit: kW) is usually set at 20kW to ensure basic charging functionality.

[0203] Furthermore, the preset power is set through the site operation strategy. The safe lower limit power of each charging pile during power adjustment is usually 20kW to ensure the most basic charging function of the vehicle.

[0204] Specifically, the ninth data point is compared with the preset power. If the ninth data point is less than the preset power, the first result is that the charging pile can reduce its power. If the ninth data point is greater than or equal to the preset power, the first result is that the charging pile cannot reduce its power.

[0205] Step F3: Perform power dispatch based on the first result.

[0206] Specifically, if the first result is that the charging pile can complete the power reduction, then the power of the charging pile is adjusted according to the eighth data; if the first result is that the charging pile cannot complete the power reduction, then the charging pile is reduced according to the preset power, and the excess power is allocated to the other charging piles for adjustment during the second round of power reduction.

[0207] For example, suppose The current power of charging pile A is 79kW; the current power of charging pile B is 90kW; the current power of charging pile C is 90kW; the preset power is 79kW. =20kW; Eighth data point =79 / 3≈26.33kW. The reduced power for charging pile A is: 90-26.33=63.67kW>20kW, so charging pile A can complete the power reduction. The reduced power for charging pile B is: 90-26.33=63.67kW>20kW, so charging pile A can complete the power reduction. The reduced power for charging pile C is: 90-26.33=63.67kW>20kW, so charging pile A can complete the power reduction.

[0208] For example, suppose The current power of charging pile A is 79kW; the current power of charging pile B is 90kW; the current power of charging pile C is 40kW; the preset power is 79kW. =20kW; Eighth data point =79 / 3≈26.33kW. The reduced power for charging pile A is: 90-26.33=63.67kW>20kW, so charging pile A can complete the power reduction. The reduced power for charging pile B is also: 90-26.33=63.67kW>20kW, so charging pile A can complete the power reduction. The reduced power for charging pile C is: 40-26.33=13.67kW<20kW, so charging pile C can only reduce to 20kW, leaving 20kW of power. The remaining 20kW of power will be shared by charging piles A and B in the next round of power reduction.

[0209] The technical solution of this embodiment constructs a first function based on first data. The generation of the first data can quickly aggregate dispersed charging and battery swapping station resources to form a considerable and reliable upward / downward adjustment capability. Constructing the first function based on the first data enables the constructed first function to describe the grid dispatching task with the lowest resource consumption. Solving the first function yields second data, which enables the second data to complete the grid regulation task with the lowest marginal resource consumption, improving the overall economic efficiency of charging and battery swapping station assets. The second data is converted into a first instruction. The generation of the first instruction can convert the acquired numerical data into adjustment instructions that the station can recognize, thereby improving the efficiency of adjustment. Power dispatching of the station according to the first instruction can achieve precise power dispatching. This method constructs a first function based on the first data, solves the first function, converts the obtained second data into a first instruction, and performs power dispatching of the station according to the first instruction, which can improve the efficiency of regulation and also improve the economic efficiency of the power grid.

[0210] Figure 2 This is a schematic diagram of a virtual power plant dispatching device provided in an embodiment of the present invention. This embodiment is applicable to the dispatching of electrical energy from a virtual power plant. The virtual power plant dispatching device can be implemented in hardware and / or software, and can be configured in any electronic device with network communication capabilities. Figure 2 As shown, the device includes: a first function determination module 210, a second data determination module 220, a first instruction determination module 230, and a power dispatching module 240, wherein:

[0211] First function determination module 210: used to construct a first function based on first data; the first data is used to characterize the adjustable power capability of the power station; the first function is used to characterize the resource consumption demand of the power station in the power grid under constrained conditions;

[0212] Second data determination module 220: used to solve the first function to obtain the second data; the second data is the peak reduction power of the power station under different peak shaving methods;

[0213] First instruction determination module 230: used to convert second data into first instructions;

[0214] Power dispatching module 240: used to dispatch power to the station according to the first instruction.

[0215] Optionally, the first function determination module 210 includes:

[0216] First data determination unit: used to determine third data and generate first data based on the third data; the first data is used to characterize the status data of energy storage equipment and vehicles in the site.

[0217] Constraint function determination unit: used to construct constraint functions based on the first data, the fourth data, and the variables to be determined; the fourth data is the sum of the peak-shaving power allocated to the power station; the constraint function is used to constrain the peak-shaving power allocated to the power station.

[0218] The second function determination unit is used to construct a second function based on the variables to be determined and preset parameters; the second function is the resource consumption requirement that the power grid stations need to meet.

[0219] First function determination unit: used to generate the first function based on the second function and the constraint function.

[0220] Optionally, the first data determining unit includes:

[0221] Capability data determination subunit: used to generate first capability data and second capability data based on third data; the first capability data is used to characterize the discharge capability of the energy storage device; the second capability data is used to characterize the discharge capability of the vehicle;

[0222] First Adjustment Capability Data Determination Subunit: Used to determine first adjustment capability data based on first capability data and second capability data; the first adjustment capability data is used to characterize the degree to which the power station can be reduced.

[0223] Capability data determination subunit: used to generate third capability data and fourth capability data based on the third data; the third capability data is used to characterize the energy storage capacity of the energy storage device; the fourth capability data is used to characterize the energy storage capacity of the vehicle;

[0224] The second regulation capability data determination subunit is used to determine the second regulation capability data based on the third and fourth capability data; the second regulation capability data is used to characterize the extent to which the power station can be increased.

[0225] First data determination subunit: used to determine first data based on first regulation capability data and second regulation capability data.

[0226] Optionally, the second data determination module 220 includes:

[0227] Third function determination unit: used to preset the third function; the third function is the amount of electricity that each station can call up under different peak shaving modes;

[0228] Second data determination unit: used to determine the second data based on the third function and the first function.

[0229] Optionally, the first instruction determining module 230 includes:

[0230] Fifth data determination unit: used to determine the fifth data based on the fourth data and the preset control time; the fifth data is used to characterize the power reduction required by the station within the preset control time.

[0231] The sixth data determination unit is used to determine the sixth data based on the second data and the preset control time; the sixth data is used to characterize the power change target of the power station under different peak-shaving modes;

[0232] First instruction determination unit: used to package the fifth data, preset control mode, sixth data and preset control time to obtain the first instruction.

[0233] Optionally, the power dispatch module 240 includes:

[0234] The seventh data determination unit is used to determine the seventh data based on the fifth and sixth data; the seventh data is the power that needs to be adjusted for each charging pile in the station.

[0235] The eighth data determination unit is used to determine the eighth data based on the seventh data and the first quantity; the eighth data is the average power reduction required for each charging pile in the station; the first quantity is the number of charging piles that are currently charging;

[0236] Power dispatching unit: used for power dispatching based on the eighth data.

[0237] Optional, the power dispatch unit includes:

[0238] Ninth Data Determination Subunit: Used to determine the ninth data based on the eighth data;

[0239] First Result Determination Subunit: Used to compare the ninth data with the preset power to obtain the first result;

[0240] Power dispatching subunit: used to perform power dispatching based on the first result.

[0241] The virtual power plant scheduling device provided in the embodiments of the present invention can execute the virtual power plant scheduling method provided in any of the embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the virtual power plant scheduling method. For details, please refer to the relevant operations of the virtual power plant scheduling method in the foregoing embodiments.

[0242] Figure 3This is a schematic diagram of the structure of an electronic device for implementing the virtual power plant scheduling method of this invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0243] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0244] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0245] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as virtual power plant scheduling methods.

[0246] In some embodiments, the virtual power plant scheduling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the virtual power plant scheduling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the virtual power plant scheduling method by any other suitable means (e.g., by means of firmware).

[0247] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0248] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0249] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0250] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0251] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0252] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0253] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0254] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A virtual power plant dispatching method, characterized in that, include: Construct a first function based on the first data; The first data is used to characterize the adjustable power capability of the power station; the first function is used to characterize the resource consumption demand of the power station within the power grid under constrained conditions. Solving the first function yields the second data; the second data represents the peak-shaving power of the power station under different peak-shaving methods. Convert the second data into the first instruction; The power dispatching of the station is carried out according to the first instruction.

2. The method according to claim 1, characterized in that, The construction of the first function based on the first data includes: A third data point is determined, and a first data point is generated based on the third data point; the first data point is used to characterize the status data of energy storage equipment and vehicles within the site. A constraint function is constructed based on the first data, the fourth data, and the variable to be determined; the fourth data is the sum of the peak-shaving power allocated to the power station; the constraint function is used to constrain the peak-shaving power allocated to the power station. A second function is constructed based on the variables to be determined and preset parameters; the second function represents the resource consumption requirements that the power stations within the power grid need to meet. The first function is generated based on the second function and the constraint function.

3. The method according to claim 2, characterized in that, The process of determining the third data and generating the first data based on the third data includes: First capability data and second capability data are generated based on the third data; the first capability data is used to characterize the discharge capability of the energy storage device; the second capability data is used to characterize the discharge capability of the vehicle. The first adjustment capability data is determined based on the first capability data and the second capability data; the first adjustment capability data is used to characterize the degree to which the power station power can be reduced. Third capability data and fourth capability data are generated based on the third data; the third capability data is used to characterize the energy storage capacity of the energy storage device; the fourth capability data is used to characterize the energy storage capacity of the vehicle. The second adjustment capability data is determined based on the third and fourth capability data; the second adjustment capability data is used to characterize the extent to which the power station can be increased. The first data is determined based on the first regulation capability data and the second regulation capability data.

4. The method according to claim 1, characterized in that, Solving the first function to obtain the second data includes: A third function is preset; the third function is the amount of electricity that each power station can call up under different peak-shaving modes. The second data is determined based on the third function and the first function.

5. The method according to claim 1, characterized in that, The step of converting the second data into a first instruction includes: The fifth data is determined based on the fourth data and the preset control time; the fifth data is used to characterize the power reduction required by the station within the preset control time. The sixth data is determined based on the second data and the preset control time; the sixth data is used to characterize the power change target of the power station under different peak-shaving modes. The fifth data, the preset control mode, the sixth data, and the preset control time are packaged together to obtain the first instruction.

6. The method according to claim 1, characterized in that, The step of performing power dispatching on the power station according to the first instruction includes: The seventh data is determined based on the fifth and sixth data; the seventh data is the power that needs to be adjusted for each charging pile in the station. The eighth data is determined based on the seventh data and the first quantity; the eighth data is the average power reduction required for each charging pile in the station; the first quantity is the number of charging piles that are currently charging. Power dispatch is performed based on the eighth data.

7. The method according to claim 6, characterized in that, The power dispatching based on the eighth data includes: The ninth data point is determined based on the eighth data point; The ninth data is compared with the preset power to obtain the first result; Power dispatch is performed based on the first result.

8. A virtual power plant dispatching device, characterized in that, include: The first function determination module is used to construct the first function based on the first data; The first data is used to characterize the adjustable power capability of the power station; the first function is used to characterize the resource consumption demand of the power station within the power grid under constrained conditions. The second data determination module is used to solve the first function to obtain the second data; the second data is the peak reduction power of the power station under different peak shaving methods; The first instruction determination module is used to convert the second data into a first instruction; The power dispatch module is used to perform power dispatch on the power station according to the first instruction.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the virtual power plant scheduling method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the virtual power plant scheduling method according to any one of claims 1-7.

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