Server, system, and management method

Abstract

To provide a server capable of easily performing a VPP control using a battery provided in a battery replacement device.SOLUTION: A server 100 is provided with communication units 103 (a first communication unit and a second communication unit) that receive information on an adjustment request of power supply and consumption in a power grid PG. The server 100 is provided with a processor 101 (a control unit) that predicts a schedule for replacing a battery 11 (a first battery) in a battery station 110 (a battery replacement device). The processor 101 identifies a battery 111 (a second battery) capable of being used for charging and discharging with respect to the adjustment request on the basis of the schedule, and determines whether or not to respond to the adjustment request on the basis of the identified battery 111.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a server, a system, and a management method. [Background technology]

[0002] International Publication No. 2019-159475 (Patent Document 1) discloses replacing the batteries of an electric vehicle at a battery station. Although not described in Patent Document 1, there are cases where VPP (Virtual Power Plant) control is performed using the batteries provided in the battery station. Note that VPP control refers to adjusting the power supply and demand of a power grid based on the power generation and consumption by each power adjustment resource. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2019-159475 Summary of the Invention [Problem to be solved by the invention]

[0004] However, as described above, because the batteries provided in the battery station are used to exchange for the batteries of electric vehicles, the type, number, and total stored power of the batteries provided in the battery station vary. This can make it difficult to determine whether VPP control is possible using the batteries provided in the battery station. Therefore, a server and system that can easily determine whether VPP control is possible using the batteries provided in a battery station (battery exchange device) are desired.

[0005] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a server, system, and management method that can easily determine whether VPP control is possible using a battery provided in a battery exchange device. [Means for solving the problem]

[0006] A server according to a first aspect of the present disclosure is a server that manages a battery exchange device equipped with at least one second battery replaceable with a first battery mounted on at least one electric vehicle, and includes a first communication unit that receives information regarding a request for adjustment of power supply and demand in a power grid, and a control unit that predicts a schedule for replacing the first battery in the battery exchange device. The control unit identifies a second battery that can be used for charging or discharging in response to the adjustment request based on the schedule, and determines whether to respond to the adjustment request based on the identified second battery.

[0007] In the server according to the first aspect of the present disclosure, as described above, a second battery that can be used for charging or discharging in response to the adjustment request is identified based on the replacement schedule for the first battery, and whether or not to respond to the adjustment request is determined based on the identified second battery. This allows the control unit to easily grasp the charging or discharging capability (VPP control capability) of the battery replacement device based on the schedule. As a result, it is possible to easily determine whether or not VPP control can be performed using the battery provided in the battery replacement device.

[0008] In the server according to the first aspect, as described above, the control unit detects the first number of electric vehicles around the battery exchange device and predicts a schedule for replacing the first battery based on the first number. With this configuration, it is possible to easily predict the schedule based on the first number of electric vehicles around the battery exchange device.

[0009] In this case, preferably, the system further includes a first memory storing a first vehicle number estimation model. The first vehicle number estimation model is a trained model that receives the first number as an input and outputs a value based on a second number of electric vehicles that use a battery exchange device among the first number. The control unit predicts the schedule based on the first vehicle number estimation model and the first number. With this configuration, the schedule can be accurately predicted based on the first vehicle number estimation model.

[0010] The server that predicts the schedule based on the number of electric vehicles around the battery exchange device preferably includes a second communication unit that receives position information of the electric vehicles. The control unit detects a first number of electric vehicles around the battery exchange device based on the position information. With this configuration, information on the first number of electric vehicles can be easily obtained based on the position information of the electric vehicles obtained through communication.

[0011] As described above, the server according to the first aspect includes a second memory in which a second vehicle number estimation model is stored. The second vehicle number estimation model is a trained model that receives information related to a date and time as input and outputs a value based on a third number of electric vehicles using the battery exchange apparatus at the date and time. The control unit predicts the schedule based on the second vehicle number estimation model and the information related to the date and time. With this configuration, the schedule can be predicted without detecting the number of electric vehicles around the battery exchange apparatus.

[0012] In the server according to the first aspect, the control unit preferably determines whether to respond to the adjustment request based on the SOC of the identified second battery. With this configuration, the control unit can more accurately grasp the charging or discharging capacity of the battery exchange apparatus based on the SOC of the identified second battery.

[0013] In this case, the control unit preferably calculates the total chargeable capacity or the total dischargeable capacity of the second battery based on the SOC of the identified second battery, and determines whether to respond to the adjustment request based on the total chargeable capacity or the total dischargeable capacity. With this configuration, the control unit can more accurately grasp the charging or discharging capacity of the battery exchange device based on the total chargeable capacity or the total dischargeable capacity of the second battery.

[0014] A system according to a second aspect of the present disclosure includes a battery exchange device equipped with at least one second battery replaceable with a first battery installed in at least one electric vehicle, and a server managing the battery exchange device. The server includes a communication unit that receives information related to a request for adjustment of power supply and demand in a power grid, and a control unit that predicts a schedule for replacing the first battery in the battery exchange device. The control unit identifies a second battery that can be used for charging or discharging in response to the adjustment request based on the schedule, and determines whether to respond to the adjustment request based on the identified second battery.

[0015] In the system according to the second aspect of the present disclosure, as described above, a second battery that can be used for charging or discharging in response to the adjustment request is identified based on the replacement schedule of the first battery, and whether or not to respond to the adjustment request is determined based on the identified second battery. This makes it possible to provide a system that can easily determine whether or not VPP control can be performed using a battery provided in a battery exchange device.

[0016] A management method according to a third aspect of the present disclosure is a management method for managing a battery exchange device equipped with at least one second battery replaceable with a first battery installed in at least one electric vehicle, and includes the steps of receiving information regarding a request for adjustment of power supply and demand in a power system, predicting a schedule for replacing the first battery in the battery exchange device, identifying the number of second batteries that can be used for charging or discharging in response to the adjustment request based on the schedule, and determining whether or not to respond to the adjustment request based on the identified second batteries.

[0017] In the management method according to the third aspect of the present disclosure, as described above, a second battery that can be used for charging or discharging in response to the adjustment request is identified based on the replacement schedule of the first battery, and whether or not to respond to the adjustment request is determined based on the identified second battery. This provides a management method that can easily determine whether or not VPP control can be performed using a battery provided in a battery exchange device. [Effects of the Invention]

[0018] According to the present disclosure, it is possible to easily determine whether or not VPP control can be performed using a battery provided in a battery exchange device. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a system according to an embodiment. [Figure 2] FIG. 1 is a diagram illustrating an example of a configuration of a battery station according to an embodiment. [Figure 3] FIG. 2 illustrates functional features of a processor according to one embodiment. [Figure 4] FIG. 1 illustrates a method by which a processor predicts a schedule for battery replacement according to one embodiment. [Figure 5] FIG. 10 is a sequence diagram illustrating sequence control of a server according to an embodiment. [Figure 6] FIG. 10 is a diagram illustrating a method in which a processor predicts a battery replacement schedule according to a variation of an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0021] FIG. 1 is a diagram showing the configuration of a system 1 according to this embodiment. The system 1 includes a server 100, a battery station 110, a power system management server 200, and a power system PG. The server 100 manages the battery station 110. The server 100 may be provided in the battery station 110. The battery station 110 is an example of a "battery exchange device" of the present disclosure.

[0022] The battery station 110 is equipped with a plurality of batteries 111. At the battery station 110, the battery 11 mounted on the electric vehicle 10 is replaced with the battery 111. The battery 11 and the battery 111 are examples of the "first battery" and the "second battery" of the present disclosure, respectively.

[0023] Electrically powered vehicles 10 include, for example, plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs). Electrically powered vehicles 10 may include a data communication module (DCM) or a communication interface compatible with 5G (fifth generation mobile communication system).

[0024] The power system PG is a power grid constructed by power plants and power transmission and distribution facilities (not shown). In this embodiment, an electric power company serves as both a power generation business operator and a power transmission and distribution business operator. The electric power company corresponds to a general power transmission and distribution business operator, and maintains and manages the power system PG. The electric power company corresponds to a manager of the power system PG.

[0025] The grid management server 200 manages the supply and demand of power in the power grid PG (power network). The grid management server 200 belongs to a power company. The grid management server 200 transmits to the server 100 a request (supply and demand adjustment request) to adjust the power demand of the power grid PG based on the power generated and consumed by each power adjustment resource managed by the grid management server 200. Specifically, when the power generated or consumed by the power adjustment resource is expected to be higher than normal (or is currently higher), the grid management server 200 transmits to the server 100 a request to increase or decrease the power demand compared to normal, respectively.

[0026] The server 100 is a server managed by an aggregator, which is an electric utility that provides energy management services by bundling multiple power adjustment resources in a region, a predetermined facility, or the like.

[0027] As one means for increasing or decreasing the power demand of the power system PG, the server 100 uses the battery 111 in the battery station 110 to supply power to the power system PG (external power supply) and charge from the power system PG (external charging).

[0028] The server 100 is also configured to manage information on a plurality of registered electric vehicles 10 (hereinafter also referred to as "vehicle information") and information on each registered user (hereinafter also referred to as "user information"). The user information and vehicle information are distinguished by identification information (ID) and stored in a memory 102 described below.

[0029] The vehicle ID is identification information for identifying the electric vehicle 10. The vehicle ID may be a license plate or a VIN (Vehicle Identification Number). The vehicle information includes the travel schedule of each electric vehicle 10.

[0030] The server 100 includes a processor 101, a memory 102, and a communication unit 103. The processor 101 controls the communication unit 103. The memory 102 stores programs executed by the processor 101 as well as information used by the programs (for example, maps, mathematical formulas, and various parameters). The memory 102 is an example of a "first memory" in the present disclosure.

[0031] The communication unit 103 of the server 100 communicates with the grid management server 200 and each of the multiple electric vehicles 10. The communication unit 103 includes various communication I / Fs. The communication unit 103 is an example of the "first communication unit" and the "second communication unit" of the present disclosure.

[0032] The communication unit 103 of the server 100 receives information relating to a request for adjusting the supply and demand of power in the power system PG from the power system management server 200. The communication unit 103 of the server 100 also receives position information of each of the plurality of electric vehicles 10 by communicating with each of the plurality of electric vehicles 10.

[0033] The memory 102 also stores information (SOC, deterioration level, etc.) about each of the multiple batteries 111 provided in the battery station 110. The memory 102 also stores information (SOC, deterioration level, etc.) about the battery 11 stored in the battery station 110 from the electric vehicle 10 after battery replacement.

[0034] FIG. 2 is a diagram showing a detailed configuration of the battery station 110. In the example shown in FIG. 2, external charging or external power feeding is performed using one of a plurality of batteries 111 (ten batteries in FIG. 2) provided in the battery station 110. In the battery station 110, external charging or external power feeding is performed for each battery 111. In the battery station 110, it is assumed that AkW of power can be exchanged between the battery 111 and the power grid PG in one hour. Note that in the battery station 110, a plurality of batteries 111 may be used for external power feeding or external charging simultaneously.

[0035] 3, the processor 101 includes a detection unit 101a, a prediction unit 101b, an identification unit 101c, and a determination unit 101d. Each of the detection unit 101a, the prediction unit 101b, the identification unit 101c, and the determination unit 101d represents software that blocks functional features of the processor 101.

[0036] The processor 101 (detection unit 101a) detects the number of electric vehicles 10 around the battery station 110 based on the position information of the electric vehicles 10 acquired by the communication unit 103. Specifically, the processor 101 (detection unit 101a) detects the number of electric vehicles 10 in an area S (see FIG. 1) within a predetermined radius (for example, 10 km) centered on the battery station 110.

[0037] The processor 101 (prediction unit 101b) predicts a schedule for replacing the battery 11 based on the detected number of electric vehicles 10. For the prediction process, for example, a trained model generated by machine learning technology such as deep learning can be used.

[0038] FIG. 4 is a diagram illustrating an example of a trained model used for predicting the schedule. Estimation model 310, which is a pre-trained model, includes, for example, neural network 311 and parameters 312. Neural network 311 is a well-known neural network used for processing by deep learning. Examples of such neural networks include a convolutional neural network (CNN) and a recurrent neural network (RNN). Parameters 312 include weighting coefficients used in calculations by neural network 311. Note that estimation model 310 is an example of a "first vehicle number estimation model" in the present disclosure.

[0039] A large amount of training data is prepared in advance by a developer. The training data includes example data and correct answer data. The example data is data on the number of electric vehicles 10 around the battery station 110 (an example of the "first number" in the present disclosure). The correct answer data is data on the number of electric vehicles 10 that use the battery station 110 out of the number of electric vehicles 10 around the battery station 110. The learning system 300 trains the estimation model 310 using the example data and the correct answer data.

[0040] As described above, the estimation model 310 is trained, and the trained estimation model 310 is stored in the memory 102. Then, the processor 101 (prediction unit 101b) outputs the number of electric vehicles 10 that use the battery station 110 based on the estimation model 310 and the number of electric vehicles 10 around the battery station 110. In this way, the schedule is predicted. Note that even after the estimation model 310 is stored in the memory 102, the estimation model 310 may be trained continuously based on the prediction result by the prediction unit 101b.

[0041] Because the batteries installed in the battery station are used for replacement, the type, number, and total storage capacity of the batteries installed in the battery station vary. For this reason, in conventional systems, it can be difficult to determine whether VPP control is possible using the batteries installed in the battery station. Therefore, a server and system that can easily determine whether VPP control is possible using the batteries installed in the battery station is desired.

[0042] Therefore, in this embodiment, the processor 101 (the identifying unit 101c) identifies a battery 111 that can be used for external charging or external power supply in response to a request for adjusting the power supply and demand of the power grid PG, based on the replacement schedule of the battery 11. Then, the processor 101 (the determining unit 101d) determines whether or not to respond to the adjustment request, based on the identified battery 111.

[0043] Here, in the battery station 110, the battery 111 with an SOC of 100% is used for exchange with the electric vehicle 10. Therefore, the battery station 110 needs to charge the battery 111 before the battery exchange. Therefore, the processor 101 (the identifying unit 101c) preferentially selects the battery 111 with the highest SOC from among the multiple batteries 111 provided in the battery station 110 for exchange with the electric vehicle 10. This makes it possible to prevent an increase in the amount of power used for charging the battery 111. Then, the processor 101 (the identifying unit 101c) identifies the batteries 111 other than the battery 111 selected for exchange with the electric vehicle 10 as batteries 111 that can be used for external charging or external power supply.

[0044] The method for identifying the battery 111 that can be used for external charging or external power supply is not limited to the above example. For example, the processor 101 (identification unit 101c) may preferentially select a battery 111 with a low level of deterioration from among the plurality of batteries 111 provided in the battery station 110 to be used to replace the battery 11 of the electric vehicle 10.

[0045] Furthermore, the processor 101 (determination unit 101d) determines whether or not to respond to a request for adjusting the power supply and demand of the power system PG, based on the SOC of the battery 111 identified as a battery 111 that can be used for external charging or external power feeding. Specifically, the processor 101 (determination unit 101d) calculates the total chargeable amount or the total dischargeable amount (power supplyable) by the battery 111 based on the SOC of the identified battery 111, and determines whether or not to respond to the adjustment request, based on the total chargeable amount or the total dischargeable amount (power supplyable). The total chargeable amount or the total dischargeable amount is calculated based on SOC information of the battery 111 stored in the memory 102. The determination method will be described later with reference to the sequence diagram of FIG. 5.

[0046] (Server sequence control) Next, with reference to FIG. 5, a sequence control for determining whether or not the VPP control by the server 100 can be executed will be described.

[0047] In step S1, the communication unit 103 of the server 100 receives from the grid management server 200 a request for adjusting the power supply and demand of the power grid PG.

[0048] In step S2, the communication unit 103 of the server 100 receives the position information of the electric vehicles 10 from each of the plurality of electric vehicles 10.

[0049] In step S3, the processor 101 (detection unit 101a) detects the number of electric vehicles 10 in the area S (see FIG. 1) around the battery station 110 based on the location information received in step S2.

[0050] In step S4, the processor 101 (prediction unit 101b) predicts a schedule for battery replacement of the battery 11 by the electric vehicle 10 based on the detection result in step S3. The processor 101 (prediction unit 101b) predicts the schedule based on the learning result by machine learning, as described above.

[0051] In step S5, the processor 101 (the identifying unit 101c) identifies the battery 111 that can be used for VPP control (external charging or external power supply) based on the schedule predicted in step S4.

[0052] In step S6, the processor 101 (determination unit 101d) determines whether or not there is (one or more) batteries 111 available for VPP control (external charging or external power supply) in the battery station 110. If there is a battery 111 available for VPP control (Yes in S6), the process proceeds to step S7. If there is no battery 111 available for VPP control (No in S6), the process proceeds to step S8.

[0053] In step S7, the processor 101 (determination unit 101d) determines whether the power supply and demand adjustment request received in step S1 is a request for external charging. If the power supply and demand adjustment request is a request for external charging (Yes in S7), the process proceeds to step S9. If the power supply and demand adjustment request is not a request for external charging (No in S7), the process proceeds to step S13. Note that the power supply and demand adjustment request is not a request for external charging means that the power supply and demand adjustment request is a request for external power feeding.

[0054] In step S8, the processor 101 (determination unit 101d) determines that VPP control in accordance with the power supply and demand adjustment request is not possible, and then the process ends.

[0055] In step S9, the processor 101 (determination unit 101d) determines whether the total chargeable amount of the battery 111 identified in step S5 is equal to or greater than the amount of power (requested value for external charging) that satisfies the power supply and demand adjustment request in step S1. Specifically, the processor 101 (determination unit 101d) determines whether the total value of available power capacity in the identified battery 111 is equal to or greater than the requested value. If the total chargeable amount is equal to or greater than the requested value (Yes in S9), the process proceeds to step S10. If the total chargeable amount is smaller than the requested value (No in S9), the process proceeds to step S12.

[0056] In step S10, the processor 101 (determination unit 101d) determines whether the time required to charge the amount of power (requested amount, X) that satisfies the power supply and demand adjustment request will be completed within the time required to satisfy the power supply and demand adjustment request (requested time, T1). Specifically, the processor 101 (determination unit 101d) determines whether the value (X / A) obtained by dividing the requested amount by the chargeable amount per hour (AkW, see FIG. 2) is equal to or less than the requested time (X / A≦T1). If the divided value is equal to or less than the requested time (Yes in S10), the process proceeds to step S11. If the divided value is greater than the requested time (No in S10), the process proceeds to step S12. Note that the time required for charging may include the time required to replace the battery 111 to be charged within the battery station 110 (the time required to transport the battery 111).

[0057] In step S11, the processor 101 (determination unit 101d) determines that external charging that satisfies the power supply and demand adjustment request is possible, and then the process ends.

[0058] In step S12, the processor 101 (determination unit 101d) determines that external charging that satisfies the power supply and demand adjustment request is not possible, and then the process ends.

[0059] In step S13, the processor 101 (determination unit 101d) determines whether the total amount of power that can be supplied (discharged) from the battery 111 identified in step S5 is equal to or greater than the amount of power (requested value for external power supply) that satisfies the power supply and demand adjustment request in step S1. Specifically, the processor 101 (determination unit 101d) determines whether the total amount of power that can be supplied from the battery 111 identified above is equal to or greater than the requested value. If the total amount of power that can be supplied is equal to or greater than the requested value (Yes in S13), the process proceeds to step S14. If the total amount of power that can be supplied is smaller than the requested value (No in S13), the process proceeds to step S16.

[0060] In step S14, the processor 101 (determination unit 101d) determines whether the time required to supply power (requested amount, Y) that satisfies the power supply and demand adjustment request will be completed within the time (requested time, T2) that satisfies the power supply and demand adjustment request. Specifically, the processor 101 (determination unit 101d) determines whether the value (Y / A) obtained by dividing the requested amount by the available power supply amount per hour (AkW, see FIG. 2) is equal to or less than the requested time (Y / A≦T2). If the divided value is equal to or less than the requested time (Yes in S14), the process proceeds to step S15. If the divided value is greater than the requested time (No in S14), the process proceeds to step S16. Note that the time required for power supply may include the time required to replace the battery 111 to be supplied with power within the battery station 110 (the time required to transport the battery 111).

[0061] In step S15, the processor 101 (determining unit 101d) determines that external power supply that satisfies the power supply and demand adjustment request is possible, and then the process ends.

[0062] In step S16, the processor 101 (determining unit 101d) determines that external power supply that satisfies the power supply and demand adjustment request is not possible, and then the process ends.

[0063] As described above, in the above embodiment, the processor 101 predicts a schedule for battery 11 replacement based on the number of electric vehicles 10 around the battery station 110, and identifies a battery 111 that can be used for charging or discharging in response to the power supply and demand adjustment request based on the schedule. Then, the processor 101 determines whether to respond to the power supply and demand adjustment request based on the identified battery 111. This makes it possible to clarify the external charging (power supply) capacity available at the battery station 110 based on the schedule. As a result, it is possible to appropriately determine whether to respond to the power supply and demand adjustment request.

[0064] In the above embodiment, an example is shown in which whether or not to respond to the power supply and demand adjustment request is determined based on the SOC of the batteries 111 identified as being usable for VPP control, but the present disclosure is not limited to this. For example, whether or not to respond to the power supply and demand adjustment request may be determined based on the number of batteries 111 identified as being usable for VPP control.

[0065] In the above embodiment, an example has been shown in which the number of electric vehicles 10 around the battery station 110 is detected based on the position information of the electric vehicles 10 acquired through communication with the battery station 110, but the present disclosure is not limited to this. For example, the number of electric vehicles 10 around the battery station 110 may be detected based on video from a camera installed at or near the battery station 110.

[0066] In the above embodiment, an example is shown in which whether or not to respond to the power supply and demand adjustment request is determined based on the SOC of the battery 111 identified as being usable for VPP control, but the present disclosure is not limited to this. For example, whether or not to respond to the power supply and demand adjustment request may be determined based on the sum of the SOC of the identified battery 111 and the SOC of the battery 11 that is stored in the battery station 110 after replacing the battery 111.

[0067] In the above embodiment, an example has been described in which a trained model generated by machine learning techniques such as deep learning is used to predict the battery exchange schedule, but the present disclosure is not limited to this. The trained model does not have to be used to predict the battery exchange schedule. For example, the ratio of the number of electric vehicles 10 expected to undergo battery exchange at the battery station 110 to the number of electric vehicles 10 around the battery station 110 may be uniformly determined.

[0068] In the above embodiment, an example has been described in which a battery exchange schedule is predicted based on the number of electric vehicles 10 around the battery station 110, but the present disclosure is not limited to this. For example, the schedule may be predicted using a trained model (estimation model) generated by machine learning technology based on date and time information. Specifically, the schedule may be predicted using an estimation model 410 trained by a learning system 400 (see FIG. 6 ) using information related to date and time as example data and the number of electric vehicles 10 that have used the battery station 110 at each date and time as correct answer data. Note that the estimation model 410, which is a pre-trained model, includes, for example, a neural network 411 and parameters 412.

[0069] As described above, the estimation model 410 is trained, and the trained estimation model 410 is stored in the memory 202. Then, the processor (prediction unit 201b) outputs the number of electric vehicles 10 that use the battery station 110 based on the estimation model 410 and date and time information. This allows the schedule to be predicted. Note that even after the estimation model 410 is stored in the memory 102, the estimation model 410 may be trained continuously based on the results of the prediction. Note that the memory 202 and the estimation model 410 are examples of a "second memory" and a "second vehicle number estimation model" in the present disclosure, respectively.

[0070] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the claims, not by the description of the above embodiments, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0071] 1 System, 10 Electric vehicle, 11 Battery (first battery), 100 Server, 101 Processor (control unit), 102 Memory (first memory), 103 Communication unit (first communication unit) (second communication unit), 110 Battery station (battery exchange device), 111 Battery (second battery), 202 Memory (second memory), 310 Estimation model (first vehicle number estimation model), 410 Estimation model (second vehicle number estimation model).

Claims

1. A server that manages a battery exchange device that is provided with at least one second battery that can be exchanged for a first battery that is mounted on at least one electric vehicle, a first communication unit that receives information regarding a request for adjustment of power supply and demand in the power grid; a control unit that predicts a schedule for replacement of the first battery in the battery exchange device, The control unit Identifying the second battery that can be used for charging or discharging in response to the adjustment request based on the schedule; The server determines whether or not to respond to the adjustment request based on the identified second battery.

2. The server according to claim 1 , wherein the control unit detects a first number of the electric vehicles around the battery exchange apparatus, and predicts a schedule for the exchange of the first battery based on the first number.

3. further comprising a first memory in which a first vehicle number estimation model is stored; the first vehicle number estimation model is a trained model that receives the first number as an input and outputs a value based on a second number of the electric vehicles that use the battery exchange apparatus among the first number, The server according to claim 2 , wherein the control unit predicts the schedule based on the first vehicle number estimation model and the first number of vehicles.

4. a second communication unit that receives position information of the electric vehicle; The server according to claim 2 , wherein the control unit detects the number of the electric vehicles around the battery exchange device based on the position information.

5. further comprising a second memory in which a second vehicle number estimation model is stored; the second vehicle number estimation model is a trained model that receives information related to a date and time as an input and outputs a value based on a third number of the electric vehicles that use the battery exchange apparatus on the date and time, The server according to claim 1 , wherein the control unit predicts the schedule based on the second vehicle number estimation model and information related to the date and time.

6. The server according to claim 1, wherein the control unit determines whether or not to respond to the adjustment request based on the SOC of the identified second battery.

7. The control unit calculating a total chargeable amount or a total dischargeable amount of the second battery based on the identified SOC of the second battery; The server according to claim 6 , wherein the server determines whether or not to respond to the adjustment request based on the total chargeable amount or the total dischargeable amount.

8. a battery exchange device including at least one second battery that can be exchanged with a first battery mounted on at least one electric vehicle; a server that manages the battery exchange device, The server a communication unit that receives information regarding a request for adjusting power supply and demand in the power grid; a control unit that predicts a schedule for replacement of the first battery in the battery exchange device, The control unit Identifying the second battery that can be used for charging or discharging in response to the adjustment request based on the schedule; The system determines whether or not to respond to the adjustment request based on the identified second battery.

9. A management method for managing a battery exchange device provided with at least one second battery replaceable with a first battery mounted on at least one electric vehicle, the method comprising: receiving information regarding a request for adjusting power supply and demand in the power grid; predicting a schedule for replacement of the first battery in the battery exchange device; determining the number of the second batteries that can be used for charging or discharging in response to the adjustment request based on the schedule; and determining whether or not to respond to the adjustment request based on the identified second battery.

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