Schedule table output program, schedule table output method, and information processing device

Abstract

An information processing device according to the present invention: acquires an operation schedule for a vehicle that uses electricity; acquires information pertaining to carbon dioxide emission caused by the generation of electric power used for charging; determines, as a charging timing for the vehicle, a time period in which the vehicle is not in operation and the information pertaining to carbon dioxide emission is relatively low; and outputs a vehicle schedule table that specifies the operation schedule and the charging timing for the vehicle.

Description

Schedule output program, schedule output method, and information processing device

[0001] The present invention relates to a schedule output program, a schedule output method, and an information processing device.

[0002] In recent years, carbon dioxide (CO ２ Efforts to reduce CO2 emissions are being made in countries around the world. One of these efforts is the development and provision of electric vehicles (EVs), which do not use fossil fuels such as gasoline. For example, delivery companies are switching from gasoline-powered vehicles to electric vehicles and creating vehicle schedules that take into account vehicle dispatch plans, which are vehicle operation schedules, and charging plans, which indicate charging timings.

[0003] JP 2022-189319 A JP 2023-13713 A JP 2016-226091 A

[0004] However, vehicle allocation plans change daily, and it is necessary to grasp the status of all vehicles and charging facilities before implementing CO ２ It is difficult to create an appropriate vehicle schedule that can reduce CO2 emissions. Currently, vehicle schedules are generally created by managers or other personnel, which is a heavy burden, and the created vehicle schedule is difficult to ２ However, it is difficult to say that CO2 emissions have been reduced.

[0005] In one aspect, CO ２ The present invention aims to provide a schedule output program, a schedule output method, and an information processing device that can generate a vehicle schedule that can reduce CO2 emissions.

[0006] In the first proposal, the planning table output program is characterized by causing a computer to execute a process of acquiring an operation plan for a vehicle that uses electricity, acquiring carbon dioxide emission information associated with the generation of electricity used for charging, determining a time period when the vehicle is not operating and when the carbon dioxide emission information is relatively low as the timing for charging the vehicle, and outputting a vehicle planning table that specifies the operation plan for the vehicle and the charging timing.

[0007] According to one embodiment, CO２ It is possible to generate a vehicle itinerary that can reduce emissions.

[0008] FIG. 1 is a diagram illustrating an example of the overall configuration of a system according to a first embodiment. FIG. 2 is a diagram illustrating the creation of a general vehicle planning table. FIG. 3 is a functional block diagram illustrating the functional configuration of an information processing device according to a first embodiment. FIG. 4 is a diagram illustrating an example of information stored in an operation plan DB. FIG. 5 is a diagram illustrating an example of power CO ２ FIG. 6 is a diagram illustrating an example of information stored in an information DB. FIG. 6 is a diagram illustrating optimization of an operation plan and a charging plan. FIG. 7 is a flowchart illustrating a process for generating a vehicle plan table according to the first embodiment. FIG. 8 is a diagram illustrating consideration of power capacity. FIG. 9 is a diagram illustrating consideration of power costs. FIG. 10 is a diagram illustrating consideration of the number of chargers. FIG. 11 is a diagram illustrating consideration of vehicle SoC power costs. FIG. 12 is a diagram illustrating a simulation using a digital twin. FIG. 13 is a diagram illustrating an example hardware configuration.

[0009] Hereinafter, a detailed description will be given of an embodiment of a schedule output program, a schedule output method, and an information processing device according to the present invention with reference to the accompanying drawings. However, the present invention is not limited to the embodiment.

[0010] (Overall Configuration) Fig. 1 is a diagram illustrating an example of the overall configuration of a system according to Example 1. The system illustrated in Fig. 1 is an example of a system in which a delivery company that uses electric vehicles (EVs) as delivery vehicles generates a vehicle schedule including a vehicle allocation plan and a charging plan, taking into consideration various constraints and restrictions.

[0011] The vehicle dispatch plan is created taking into consideration constraints on delivery, including shipment volume, designated delivery time, loading and unloading time, loading restrictions, vehicle size restrictions, operating time restrictions, driving distance restrictions, etc., and constraints on EV operation, including EV following distance, route charging, and the need for charging facilities at the destination, etc. A vehicle schedule is created that specifies the times for operation (delivery) and charging, taking into consideration such vehicle dispatch plan and constraints on EV charging, including the number of chargers, charger type, charging time, power capacity, electricity cost, SoC (State of charge), etc.

[0012] In recent years, CO2 emissions on a global scale have increased ２ Efforts are being made to reduce CO emissions, and each delivery company is also ２ Creating a vehicle planning schedule that can reduce emissions is an important factor in improving corporate value, promoting corporate policies, and taking concrete measures to combat global warming.

[0013] However, under the above constraints, especially CO ２ Creating a vehicle scheduling schedule that reduces vehicle emissions is not an easy task. FIG. 2 is a diagram illustrating the creation of a typical vehicle scheduling schedule. As shown in FIG. 2, for each of a plurality of vehicles (EVs), the administrator identifies operating hours during which the vehicle is making deliveries and non-operating hours during which the vehicle is not making deliveries and can be charged. Meanwhile, for each of a plurality of chargers, the administrator identifies usage hours during which the vehicle is scheduled to be used for charging and non-usage hours during which charging is not being performed and the chargers can be used.

[0014] In this situation, the administrator must allocate chargers that are not in use during non-operating times for each vehicle. However, vehicle schedules vary from vehicle to vehicle on a daily basis, making a charging plan is like a puzzle, placing a heavy burden on the administrator. Simply creating a charging plan is difficult, and on top of that, CO ２ It is extremely difficult to create a vehicle schedule that can reduce CO2 emissions. ２ However, there are no indicators for generating a charging plan that reduces emissions, and this can be complicated and take a long time to process.

[0015] Therefore, as shown in FIG. 1, the information processing device 10 according to the first embodiment acquires an EV operation plan and calculates CO 2 emissions associated with the generation of power used for charging. ２ Then, the information processing device 10 acquires the emission information of the EV during the time period when the EV is not in operation and the CO ２ The timing for charging the EV is determined to be a time period when the emission information is relatively low, and a vehicle schedule table specifying the operation plan and charging timing of the EV is output.

[0016] In this way, the information processing device 10 can process CO ２ By generating a charging plan using the CO ２ It is possible to generate a vehicle itinerary that can reduce emissions.

[0017] 3 is a functional block diagram showing the functional configuration of the information processing device 10 according to Example 1. As shown in FIG. 3, the information processing device 10 includes a communication unit 11, a storage unit 12, and a control unit 20.

[0018] The communication unit 11 is a processing unit that executes communication with other devices, and is realized by, for example, a communication interface. For example, the communication unit 11 receives a power CO2 signal from an external device (not shown). ２ The information is received and the vehicle schedule is sent to an administrator terminal (not shown) or the like.

[0019] The storage unit 12 is a processing unit that stores various data and programs executed by the control unit 20, and is realized by, for example, a memory or a hard disk. The storage unit 12 stores an operation plan DB 13, a charger information DB 14, a power CO ２ It stores an information DB 15, a vehicle planning table 16, etc.

[0020] The operation plan DB 13 is a database that stores operation schedules for EVs, which are delivery vehicles. Specifically, the operation plan DB 13 stores operation plans that are generated by an administrator or an external device and that specify the operating and non-operating times of each EV. Figure 4 is a diagram showing an example of information stored in the operation plan DB 13.

[0021] 4, the operation plan DB 13 stores an operation plan in which operating hours and non-operating hours are registered for each of the vehicles 1001, 1002, 1003, 1004, and 1005. For example, in the case of the vehicle 1001, it is registered that "non-operating hours are from midnight to 2:00," "operating hours are from 2:00 to 12:00," "non-operating hours are from 12:00 to 20:00," and "operating hours are from 20:00 to midnight." In other words, it is indicated that for the vehicle 1001, "from midnight to 2:00" and "from 12:00 to 20:00" are times when no deliveries are being made and charging is possible.

[0022] The charger information DB 14 is a database that stores information about EV chargers. For example, the charger information DB 14 stores the number of chargers, the charger usage schedule, the time periods when charger operators can plug and unplug the chargers, and the like.

[0023] Electricity CO ２ The information DB 15 stores information on CO2 generated by the power used for charging. ２ Specifically, it is a database that stores information on the emission of electricity CO ２ The information DB 15 stores information on the CO emitted during charging, which varies depending on the region and time of day. ２ The discharge information is stored.

[0024] FIG. 5 shows the power CO ２ 5 is a diagram illustrating an example of information stored in the information DB 15. As shown in FIG. ２ The information DB 15 stores the CO ２ In the example of FIG. 5, the CO ２ The time series change in emissions is shown, and at "0:00" and "24:00" the CO ２ The amount of CO emissions is high between 12:00 and 16:00. ２ It is clear that emissions are low.

[0025] The vehicle schedule table 16 is generated by the control unit 20 (to be described later). ２ The vehicle schedule that can reduce emissions is stored.

[0026] The control unit 20 is a processing unit that controls the information processing device 10, and is realized by, for example, a processor. The control unit 20 has a plan acquisition unit 21, an information acquisition unit 22, an optimization unit 23, and an output control unit 24. The plan acquisition unit 21, the information acquisition unit 22, the optimization unit 23, and the output control unit 24 are realized by electronic circuits included in the processor, processes executed by the processor, etc.

[0027] The plan acquisition unit 21 is a processing unit that acquires an EV operation plan. For example, the plan acquisition unit 21 acquires an operation plan generated by a manager or an external server (not shown), and stores the operation plan in the operation plan DB 13.

[0028] The information acquisition unit 22 acquires information about the charger and ２ For example, the information acquisition unit 22 acquires information about the charger and CO discharge information from an administrator or an external server (not shown). ２ The discharge information is acquired and stored in the charger information DB 14.

[0029] The optimization unit 23 is configured to optimize the time period when the EV is not in operation and the CO ２ Specifically, the optimization unit 23 determines the time period in which the CO 2 emission information shown in FIG. 5 is relatively low for each EV during the "non-operation time" period in the operation plan shown in FIG. 4 as the charging timing for the EV, and generates a vehicle schedule table. ２ By prioritizing charging times during which emissions are low, CO ２ The optimization unit 23 generates a vehicle planning table that optimizes the reduction of CO emissions. Then, the optimization unit 23 stores the generated vehicle planning table in the vehicle planning table 16. ２ A time period with low emissions is, for example, CO ２ A time period when emissions are below a threshold value can be used.

[0030] Here, optimization will be described. FIG. 6 is a diagram for explaining optimization of an operation plan and a charging plan. As shown in FIG. 6, the optimization unit 23 ２ From the emission information, CO ２ The optimization unit 23 then identifies a time period in which CO emissions are relatively low (small). ２ A time period when emissions are relatively low (small) is selected to determine the timing of charging.

[0031] For example, in the case of the vehicle 1001 shown in FIG. 4, there are two non-operating time periods, "from midnight to 2 o'clock" and "from 12 o'clock to 20 o'clock." ２ The charging timing is determined to be the time period from 12:00 to 20:00, which corresponds to the "time period when emissions are relatively low." The charging time is determined based on the known time required to fully charge an EV.

[0032] In another example, the optimization unit 23 may use continuous carbon intensity data (i.e., CO２ CO emissions) ２ It is possible to identify "time periods when emissions are relatively low."

[0033] For example, "CO ２ An example will be described in which there are two chargeable time periods that fall under the "time period with relatively low (small) emissions" category: "8:00 to 9:00" and "11:00 to 12:00." In this case, the optimization unit 23 calculates an average of 180 g CO2 from "8:00 to 9:00." ２ / kWh (wind: 30%, gas: 40%, oil: 5%, other: 25%) and an average of 78gCO from 11:00 to 12:00 ２ / kWh (wind: 60%, gas: 15%, oil: 1%, other: 24%), of which CO ２ The time period "11:00 to 12:00" when the amount of discharged energy is small is determined as the best charging timing.

[0034] The optimization unit 23 ２ The time periods when emissions are above the threshold or relatively high are determined as "time periods when fossil fuel-derived electricity is relatively high" and CO ２ The optimization unit 23 determines a time period when the amount of emissions is less than the threshold or is relatively small as a “time period when the amount of clean energy-derived electricity is relatively large.” The optimization unit 23 can then select a time period determined to be a “time period when the amount of clean energy-derived electricity is relatively large” from among multiple non-operating times included in the operation plan of each EV, and determine the time period as the charging timing.

[0035] Furthermore, if the EV is not fully charged during non-operating hours that fall under the "time period when electricity derived from clean energy is relatively high," the optimization unit 23 determines the charging timing for a time period that will ensure the amount of charge required for the subsequent driving distance. Here, if the optimization unit 23 cannot ensure the amount of charge required for the subsequent driving distance, the optimization unit 23 can also use the "time period when electricity derived from fossil fuels is relatively high" as the charging timing.

[0036] Here, the optimization unit 23 can also determine the charging timing in consideration of the constraints on the number of chargers that can be used.

[0037] Specifically, when reducing the number of chargers to be used, the optimization unit 23 optimizes the chargers to be used by allocating one of a plurality of chargers to each vehicle. For example, for vehicle 1002 in Fig. 6, charger #01 is in use by vehicle 1001 during non-operating hours from 4:00 PM onwards, so the optimization unit 23 determines to use charger #01 for charging between "midnight and 8:00 AM" when charger #01 is not in use. Furthermore, for vehicle 1003, charger #01 is in use during non-operating hours from 10:00 AM onwards, so the optimization unit 23 determines to use charger #02.

[0038] On the other hand, for example, if there is no restriction on the number of chargers to be used, the optimization unit 23 actively allocates available chargers from among a plurality of chargers. For example, the optimization unit 23 allocates the vehicle 1002 in FIG. 6 to the chargers that are available during the non-operating times of "0:00 to 8:00" and "16:00 and after." ２ It is decided to use unused charger #02 for the period "after 4 pm" when the amount of discharge is small.

[0039] The output control unit 24 is a processing unit that outputs the vehicle schedule table. For example, the output control unit 24 displays the optimized vehicle schedule table 16 stored in the storage unit 12 on a display or transmits it to a device designated by an administrator or the like.

[0040] 7 is a flowchart showing the flow of the process of generating a vehicle schedule table according to the first embodiment. As shown in FIG. 7, when the information processing device 10 receives an instruction to start the process of generating a vehicle schedule table (S101: Yes), the information processing device 10 acquires an operation plan (S102). Subsequently, the information processing device 10 acquires information on each charger and the power CO ２ The information is acquired (S103).

[0041] Then, the information processing device 10 ２ For information, CO ２ The information processing device 10 then identifies the time periods when the amount of CO emissions is relatively low for each EV (S104). ２ A time period when the amount of discharged fuel is relatively low is determined as the timing for charging (S105).

[0042] Then, the information processing device 10 determines the charging timing for each EV, generates a vehicle planning table in which the operating time, non-operating time, and charging timing for each EV are determined (S106), and outputs the generated vehicle planning table (S107).

[0043] (Effect) As described above, the information processing device 10 can calculate the power CO₂ that varies depending on the region and time. ２ Collect information and calculate the time when EVs are not in operation and CO ２ The timing of charging is determined during times when CO emissions are low. ２ Furthermore, the information processing device 10 can generate a vehicle schedule that can reduce CO emissions. ２ It can reduce vehicle schedule processing time, which can reduce emissions.

[0044] In the first embodiment, the power CO ２ In the above example, the vehicle planning table 16 is generated using the information, but the optimization unit 23 of the information processing device 10 can generate the vehicle planning table 16 using various other information. ２ An example in which the vehicle schedule table 16 is generated using information other than the above information will be described.

[0045] (Power Capacity) For example, the information processing device 10 can determine a time period for charging the vehicle so that the amount of power usage during charging does not exceed a threshold value that is set arbitrarily (for example, a contracted power capacity) or so that the amount of power usage is relatively low. In other words, the information processing device 10 generates a charging plan that is leveled so that the amount of power usage during charging does not exceed a threshold value in combination with the amount of power usage in the delivery company's building.

[0046] Fig. 8 is a diagram illustrating consideration of power capacity. For example, assume that the information processing device 10 acquires a building power usage plan as shown by X in Fig. 8 from an external server, an administrator, or the like. In this case, the information processing device 10 generates a leveled EV power usage plan as shown by Z in Fig. 8 without generating an inappropriate EV power usage plan (charging plan) that exceeds the upper limit of power usage as shown by Y in Fig. 8. For example, ２When combined with the information, the information processing device 10 can calculate the power usage upper limit and CO ２ The timing of charging each EV is determined by solving a mathematical optimization model with the CO2 emissions as parameters. As a result, the information processing device 10 determines the charging timing of each EV within a range that does not exceed the upper limit of power usage. ２ A vehicle itinerary that reduces emissions can be generated.

[0047] (Electricity Cost) For example, the information processing device 10 may ２ The timing of charging the EV can be determined by prioritizing time periods when the unit price of the electricity used for charging is below a threshold among time periods when the emission information is relatively low. In other words, the information processing device 10 generates a charging plan that prioritizes time periods when the unit price of electricity is low and minimizes the electricity cost associated with charging.

[0048] FIG. 9 is a diagram illustrating consideration of power costs. For example, assume that the information processing device 10 acquires the power costs of a region in which a delivery company is located, as shown in FIG. 9, from an external server, an administrator, or the like. In this case, the information processing device 10 generates a power usage plan (charging plan) for an EV that prioritizes the use of the time periods "0:00 to 6:00" and "20:00 to 24:00", where the power cost is less than the threshold, over "6:00 to 20:00", where the power cost is greater than or equal to the threshold. For example, ２ When combined with the information, the information processing device 10 calculates the electricity unit price and CO ２ The timing of charging each EV is determined by solving a mathematical optimization model with the CO2 emissions as parameters. As a result, the information processing device 10 can reduce charging costs while minimizing CO2 emissions. ２ A vehicle itinerary that reduces emissions can be generated.

[0049] (Number of Chargers) For example, when charging multiple EVs using multiple chargers, the information processing device 10 can determine the charging timing for each of the multiple EVs under the constraint of using as few chargers as possible. In other words, the information processing device 10 matches EVs with chargers and generates a charging plan that maximizes charger usage efficiency.

[0050] FIG. 10 is a diagram illustrating consideration of the number of chargers. For example, the information processing device 10 assigns chargers that can charge EVs during non-operating times. In the example of FIG. 10, the information processing device 10 assigns an unused charger CH_1 to non-operating time a of EV_1, and assigns an unused charger CH_2 to non-operating time d of EV_2, which is the same time slot as non-operating time a. Similarly, the information processing device 10 assigns an unused charger CH_1 to non-operating time b of EV_1, assigns an unused charger CH_2 to non-operating time c of EV_2, which time slot partially overlaps with non-operating time b, and assigns an unused charger CH_1 to non-operating time e of EV_3, which time slot partially overlaps with non-operating time c. For example, when the power CO ２ When combined with the information, the information processing device 10 can calculate the number of chargers and CO ２ The timing of charging each vehicle is determined by solving a mathematical optimization model with the CO2 emissions as parameters. As a result, the information processing device 10 can reduce fixed costs by further reducing the number of chargers. ２ A vehicle itinerary that reduces emissions can be generated.

[0051] (Vehicle Soc) For example, the information processing device 10 may ２ The timing for charging the EV can be determined to be a time period in which the EV's state of charge does not fall below a threshold among time periods in which the emission information is relatively low. In other words, the information processing device 10 generates a charging plan for charging an amount of power that will prevent the estimated value of the vehicle SoC from falling below a threshold.

[0052] FIG. 11 is a diagram illustrating consideration of vehicle SoC power costs. For example, assume that the information processing device 10 acquires information as shown in FIG. 11 for the vehicle SoC of a certain EV from an external server, administrator, or the like. In this case, the information processing device 10 generates a charging plan for charging before the vehicle SoC falls below a threshold. That is, since the information processing device 10 estimates that the vehicle SoC will decrease from around 6:00 as the EV operates, it determines that charging is necessary around 12:00 and generates a charging plan. After charging, the information processing device 10 estimates that the vehicle SoC will decrease again from around 13:00 as the EV operates, so it determines that charging is necessary between 20:00 and 23:00 and generates a charging plan. For example, if the power CO ２ When combined with the information, the information processing device 10 calculates the vehicle SoC estimate and CO ２ The timing of charging each EV is determined by solving a mathematical optimization model with the CO2 emissions as parameters. As a result, the information processing device 10 can efficiently operate and charge the EVs while minimizing CO2 emissions. ２ A vehicle itinerary that reduces emissions can be generated.

[0053] (Consideration of all elements) Furthermore, the information processing device 10 can generate a charging plan by using all the information described in the first and second embodiments, and generate a vehicle planning table. For example, the information processing device 10 can generate a charging plan by using all the information described in the first and second embodiments, such as carbon dioxide emission information (CO ２ The timing of charging an EV is determined by solving a mathematical optimization model with the following parameters: amount of fuel consumed during charging (maximum power consumption), amount of electricity consumed during charging (maximum power consumption), unit price of the charged electricity, number of chargers used for charging, and the EV's state of charge (estimated vehicle SoC value).

[0054] In this case, the information processing device 10 converts the above elements into common indices and solves a mathematical optimization model using the converted indices as parameters, thereby determining the timing of charging the EV. In addition, the information processing device 10 can generate a vehicle schedule table tailored to each delivery company by solving a mathematical optimization model in which important elements are weighted. For example, the information processing device 10 can calculate the CO ２ If you care about emissions,２ The CO emissions are multiplied by a weighting coefficient of 2.0. ２ If you prioritize CO emissions and then the upper limit on power consumption, ２ The emission amount is multiplied by a weighting factor of 2.0, and the upper limit of power usage is multiplied by a weighting factor of 1.5. On the other hand, if the information processing device 10 does not consider the number of chargers to be more important than other factors, it multiplies the number of chargers by a weighting factor of 0.5.

[0055] (Digital Twin) The information processing device 10 can also determine charging timing by simulating on a digital twin that virtually reproduces the actual space in which the EVs operate. The information processing device 10 can also confirm the reliability of the vehicle schedule in advance by simulating the operation of each EV on the digital twin based on the vehicle schedule that incorporates the generated charging plan and operation plan.

[0056] Furthermore, the information processing device 10 calculates vehicle operation and CO2 emissions based on an operation plan on a digital twin that virtually reproduces the actual space in which EVs are operated and charged. ２ The results of the simulation are used to calculate the CO emissions during periods when EVs are not in operation. ２ The timing for charging the EV can be determined to be a time period when the emission information is relatively low.

[0057] 12 is a diagram illustrating a simulation using a digital twin. As shown in FIG. 12, the information processing device 10 calculates an EV operation plan and a power CO ２ The information processing device 10 incorporates real-world data, such as traffic congestion information, weather information, and information on employees who drive EVs, into the digital twin, and reproduces the real-world data on the digital twin. More specifically, the information processing device 10 creates a digital twin in a virtual space that is time-synchronized with the real space. The information processing device 10 then performs a simulation of the operation plan to generate a reproducible operation plan, and also calculates the power consumption, CO2, etc. ２ Simulate the information and ２The information processing device 10 then uses the results of each simulation to determine the timing of charging the EV in the same manner as in Example 1. As a result, the information processing device 10 can calculate a highly reproducible, highly reliable, and highly feasible CO ２ A vehicle itinerary that reduces emissions can be generated.

[0058] Although the embodiments of the present invention have been described above, the present invention may be embodied in various different forms other than the above-described embodiments.

[0059] The numerical values, graphs, etc. used in the above embodiments are merely examples and can be changed as desired. The processing flow described in each flowchart can also be changed as appropriate within a consistent range. Similar processing can be applied not only to EVs but also to hybrid vehicles.

[0060] (System) The information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings may be changed arbitrarily unless otherwise specified.

[0061] Furthermore, the specific form of distribution or integration of the components of each device is not limited to that shown in the figure. For example, the optimization unit 23 and the output control unit 24 may be integrated. That is, all or some of the components may be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions of each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

[0062] Furthermore, all or any part of the processing functions performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

[0063] (Hardware) Fig. 13 is a diagram illustrating an example of a hardware configuration. As shown in Fig. 13, an information processing device 10 includes a communication device 10a, a hard disk drive (HDD) 10b, a memory 10c, and a processor 10d. The components shown in Fig. 13 are connected to each other via a bus or the like.

[0064] The communication device 10a is a network interface card or the like, and communicates with other devices. The HDD 10b stores programs and databases that operate the functions shown in FIG.

[0065] The processor 10d reads out from the HDD 10b or the like a program that executes the same processes as the respective processing units shown in Fig. 3 and loads it into the memory 10c, thereby operating a process that executes each function described in Fig. 3 or the like. For example, this process executes the same functions as the respective processing units of the information processing device 10. Specifically, the processor 10d reads out from the HDD 10b or the like a program that has the same functions as the plan acquisition unit 21, the information acquisition unit 22, the optimization unit 23, the output control unit 24, etc. Then, the processor 10d executes a process that executes the same processes as the plan acquisition unit 21, the information acquisition unit 22, the optimization unit 23, the output control unit 24, etc.

[0066] In this way, the information processing device 10 operates as an information processing device that executes the schedule output method by reading and executing a program. The information processing device 10 can also realize functions similar to those of the above-described embodiment by reading the program from a recording medium using a media reader and executing the read program. Note that the program in these other embodiments is not limited to being executed by the information processing device 10. For example, the above-described embodiment may also be applied to cases where another computer or server executes the program, or where these execute the program in cooperation with each other.

[0067] This program may be distributed via a network such as the Internet. Alternatively, this program may be recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, a magneto-optical disk (MO), or a digital versatile disk (DVD), and may be read out from the recording medium and executed by a computer.

[0068] 10 Information processing device 11 Communication unit 12 Storage unit 13 Operation plan DB 14 Charger information DB 15 Power CO ２ Information DB 16 Vehicle plan table 20 Control unit 21 Plan acquisition unit 22 Information acquisition unit 23 Optimization unit 24 Output control unit

Claims

1. A schedule output program that causes a computer to execute the following processes: acquire an operation plan for a vehicle that uses electricity; acquire carbon dioxide emission information associated with the generation of electricity used for charging; determine a time period when the vehicle is not in operation and when the carbon dioxide emission information is relatively low as the timing for charging the vehicle; and output a vehicle schedule that specifies the operation plan for the vehicle and the charging timing.

2. The schedule output program according to claim 1, characterized in that the determination process determines the time period for charging the vehicle so that the amount of power usage during charging does not exceed a threshold value or so that the amount of power usage is relatively low.

3. The schedule output program of claim 1, characterized in that the determination process determines the timing of charging the vehicle by prioritizing time periods during which the vehicle is not in operation and the carbon dioxide emission information is relatively low, and during which the unit price of the electricity used to charge is below a threshold.

4. The planning table output program according to claim 1, characterized in that the determination process determines the charging timing for each of the multiple vehicles under the constraint of using fewer chargers when charging multiple vehicles using multiple chargers.

5. The schedule output program of claim 1, characterized in that the determination process determines the timing for charging the vehicle to be a time period during which the vehicle is not in operation and the carbon dioxide emission information is relatively low, and during which the vehicle's state of charge does not fall below a threshold value.

6. The schedule output program of claim 1, characterized in that the determination process determines the timing of charging the vehicle by solving an optimization problem with parameters being the carbon dioxide emission information, the amount of electricity used during charging, the unit price of the electricity to be charged, the number of chargers to be used for charging, and the charging state of the vehicle.

7. The schedule output program according to claim 6, characterized in that the determination process converts the carbon dioxide emission information, the amount of electricity used during charging, the unit price of the electricity to be charged, the number of chargers used for charging, and the charging state of the vehicle into a common index, and determines the timing of charging the vehicle by solving an optimization problem with each index as a parameter.

8. The schedule output program of claim 1, characterized in that the determination process simulates the vehicle operation based on the operation plan and the carbon dioxide emission information on a digital twin that virtually reproduces the actual space in which the vehicle operates and is charged, and uses the results of the simulation to determine the time period when the vehicle is not operating and when the carbon dioxide emission information is relatively low as the timing for charging the vehicle.

9. A schedule output method characterized by executing the following processes: a computer acquires an operation plan for a vehicle that uses electricity; acquires carbon dioxide emission information associated with the generation of electricity used for charging; determines a time period when the vehicle is not in operation and when the carbon dioxide emission information is relatively low as the timing for charging the vehicle; and outputs a vehicle schedule that specifies the operation plan for the vehicle and the charging timing.

10. An information processing device characterized by having a control unit that acquires an operation plan for a vehicle that uses electricity, acquires carbon dioxide emission information associated with the generation of electricity used for charging, determines a time period when the vehicle is not in operation and when the carbon dioxide emission information is relatively low as the timing for charging the vehicle, and outputs a vehicle schedule table that specifies the operation plan for the vehicle and the timing for charging.

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