Information processing apparatus, information processing method, and information processing program

Abstract

To support efficient derivation of an optimum facility capacity.SOLUTION: The information processor 10A includes a processing unit 20. The facility-capacity optimizing unit 20A of the processing unit 20 searches for the provisional facility capacities of the supplying facilities 32 in descending order of priority from a narrower search range as the priority becomes lower, based on the priority of the supplying facilities 32 that are included in the energy system 30 and supply energy, the input search range of the facility capacities, the number of searches, and the target energy introduction ratio of the energy system 30. The simulation execution unit 20B of the processing unit 20 derives the introduction cost of the supply facility 32 having the searched temporary facility volume and the energy introduction ratio of the energy system 30 including the supply facility 32 having the temporary facility volume every time the temporary facility volume is searched. The processing unit 20 repeatedly executes the search for the provisional facility capacity and the derivation of the introduction cost and the energy introduction rate every time the target energy introduction rate is changed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] It is common to derive the optimal equipment capacity of a supply facility that supplies energy such as renewable energy depending on the environment of an energy system such as a factory that demands energy such as electricity. For example, a user manually inputs various equipment capacities in sequence and executes an operation plan simulation each time a new equipment capacity is input, thereby deriving the optimal equipment capacity. Also, a system has been disclosed that calculates the optimal equipment capacity by applying a linear programming method or an integer programming solver using a mathematical model.

[0003] However, manually inputting facility capacities places a heavy burden on users, making it difficult to efficiently derive optimal facility capacities. Furthermore, in methods that apply linear programming or integer programming solvers using mathematical models, the amount of calculations increases as the number of target supply facilities increases, making it difficult to complete the process within the expected calculation time or resulting in a huge processing load. In other words, it has been difficult for conventional technologies to support efficient derivation of optimal facility capacities. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7047547 [Patent Document 2] Patent No. 7313300 [Patent Document 3] Japanese Patent Publication No. 2023-122401 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide an information processing device, an information processing method, and an information processing program that can support efficient derivation of optimal equipment capacity. [Means for solving the problem]

[0006] An information processing device according to an embodiment includes a processing unit that searches for a provisional equipment capacity of a supply equipment included in an energy system that supplies energy, in descending order of priority, based on an input search range for equipment capacity, the number of searches, and a target energy introduction rate of the energy system, with the search range narrowing as the priority decreases, and each time the provisional equipment capacity is searched for, derives an introduction cost for the supply equipment of the searched provisional equipment capacity and an energy introduction rate of the energy system that includes the supply equipment of the provisional equipment capacity, and repeatedly executes the search for the provisional equipment capacity and the derivation of the introduction cost and the energy introduction rate each time the target energy introduction rate is changed. [Brief explanation of the drawings]

[0007] [Figure 1] Schematic diagram of an information processing system. [Figure 2A] Schematic diagram of the data structure of optimization information. [Figure 2B] Schematic diagram of the data structure of the target energy introduction rate DB. [Figure 2C] FIG. 10 is a schematic diagram illustrating an example of the data configuration of introduction rate search range correspondence information. [Figure 2D] FIG. 3 is a schematic diagram illustrating an example of a data configuration of facility information. [Figure 2E] Schematic diagram of the data structure of annual electricity demand data. [Figure 3] FIG. 4 is an explanatory diagram of a method for setting a search range. [Figure 4A] 10 is a schematic diagram of processing result information when the target energy introduction rate is level 4. [Figure 4B] 10 is a schematic diagram of processing result information when the target energy introduction rate is level 3. [Figure 5]1 is a flowchart of the flow of information processing executed by an information processing device. [Figure 6] Schematic diagram of an information processing system. [Figure 7] 1 is a flowchart of the flow of information processing executed by an information processing device. [Figure 8] Hardware configuration diagram. DETAILED DESCRIPTION OF THE INVENTION

[0008] The information processing method, information processing device, and information processing program according to the present embodiment will be described in detail below with reference to the accompanying drawings.

[0009] (First embodiment) 1 is a schematic diagram of an example of an information processing system 1A according to the present embodiment. The information processing system 1A is an example of the information processing system 1.

[0010] The information processing system 1 includes an information processing device 10A and a terminal device 40. The information processing device 10A and the terminal device 40 are connected to each other so as to be able to communicate with each other via a network NW or the like.

[0011] The terminal device 40 is an information processing device operated by a user such as a business operator that manages the energy system 30. The terminal device 40 is configured by one or more dedicated or general-purpose computers.

[0012] The energy system 30 is a system in which a demand facility that demands energy and a facility 31 that supplies energy to the demand facility have been introduced or are scheduled to be introduced. The energy system 30 is, for example, a factory, but is not limited to a factory. Hereinafter, the facility 31 that has been introduced or is scheduled to be introduced into the energy system 30 may be simply referred to as the facility 31 included in the energy system 30, the facility 31 of the energy system 30, etc.

[0013] The facility 31 is a facility for supplying energy to demand facilities. The facility 31 is, for example, a power generation facility that generates and supplies electricity using renewable energy such as solar, wind, geothermal, tidal, or wave power, a power storage facility that stores and supplies electricity purchased from renewable energy power generation companies, a hydrogen production machine, or the like. Specifically, the facility 31 is, for example, a solar power generation facility, a storage battery, a hydrogen production machine, a fuel cell, a wind power generation device, or the like, but is not limited to these. In this embodiment, the description will be made assuming that the facility 31 to be introduced into the energy system 30 is a solar power generation facility, a storage battery, a fuel cell, and a hydrogen production machine.

[0014] Priorities are set for the multiple pieces of equipment 31 included in the energy system 30 by a user such as a business operator who manages the energy system 30.

[0015] The priority is determined in the order in which users, such as businesses that manage the energy system 30, consider introducing the equipment 31 into the energy system 30. Furthermore, the highest priority is assigned to the equipment 31 that has already been introduced into the energy system 30.

[0016] For example, a user operating the terminal device 40 assigns a desired priority to each of the plurality of pieces of equipment 31 included in the energy system 30. A plurality of pieces of equipment 31 may belong to the same priority level.

[0017] In this embodiment, the equipment 31 set with the highest priority "1" will be described as the first supply equipment 32A. The equipment 31 set with the next highest priority "2" will be described as the second supply equipment 32B. That is, in this embodiment, the multiple equipment 31 that has already been introduced or is scheduled to be introduced into the energy system 30 will be described as being pre-classified into multiple supply equipment 32 according to priority, and each supply equipment 32 will be given a name indicating the Nth priority (N is an integer equal to or greater than 1) that indicates the priority.

[0018] 1 shows an example in which the supply facility 32 includes a first supply facility 32A and a second supply facility 32B. The first supply facility 32A indicates that the supply facility 32 has the highest priority of "1," and the second supply facility 32B indicates that the supply facility 32 has the current priority of "2." The number of supply facilities 32 included in the energy system 30 may be three or more. In this case, it is sufficient that a priority is assigned to each of the three or more supply facilities 32.

[0019] In this embodiment, an example of a configuration will be described in which a solar power generation facility and a storage battery, which are examples of facility 31, belong to a first supply facility 32A with a priority of "1," and a fuel cell and a hydrogen production machine, which are examples of facility 31, belong to a second supply facility 32B with a priority of "2."

[0020] Information regarding the priority of each supply facility 32 of the energy system 30 is transmitted from the terminal device 40 to the information processing device 10A (described in detail later). Note that a user such as a business operator managing the energy system 30 may input information regarding the priority of each supply facility 32 by directly operating and instructing a UI unit 14 of the information processing device 10A (described later).

[0021] Furthermore, the information processing device 10A acquires facility information and annual power demand data of the energy system 30, and uses the acquired information for processing described later. The facility information and annual power demand data will be described in detail later.

[0022] Next, the information processing device 10A will be described.

[0023] The information processing device 10A is an example of the information processing device 10.

[0024] The information processing device 10A is an information processing device that executes a process of deriving an optimal facility capacity of the facility 31 included in the supply facility 32 for the energy system 30. The information processing device 10A is configured by one or more dedicated or general-purpose computers.

[0025] The information processing device 10A includes a communication unit 12, a UI (user interface) unit 14, a storage unit 16, and a processing unit 20. The communication unit 12, the UI unit 14, the storage unit 16, and the processing unit 20 are communicatively connected via a bus 18 or the like.

[0026] The communication unit 12 communicates with an external information processing device via a network NW etc. In this embodiment, the communication unit 12 communicates with a terminal device 40 via a network NW etc.

[0027] The UI unit 14 has a display function for displaying various information and an input function for receiving user input. The display function is, for example, a display, a projection device, etc. The input function is, for example, a pointing device such as a mouse or a touchpad, a keyboard, etc. The UI unit 14 may be a touch panel that integrates the display function and the input function.

[0028] The UI unit 14 may be configured to be communicably connected to the processing unit 20 via a wired or wireless connection. The UI unit 14 may be configured to be provided outside the information processing device 10A, and the UI unit 14 and the processing unit 20 may be connected via a network or the like.

[0029] The storage unit 16 stores various types of data. The storage unit 16 may be provided outside the information processing device 10A. Alternatively, the storage unit 16 and at least one of one or more functional units included in the processing unit 20 (described later) may be mounted on an external information processing device communicatively connected to the information processing device 10A via a network NW or the like.

[0030] In this embodiment, the storage unit 16 stores optimization information 16A, a target energy introduction rate DB (database) 16B, introduction rate search range correspondence information 16C, facility information 16D, annual power demand data 16E, etc. The optimization information 16A, facility information 16D, and annual power demand data 16E are information relating to the energy system 30, which is received from the terminal device 40 or input by a user operating the UI unit 14, etc., and stored in the storage unit 16. Details of the information stored in the storage unit 16 will be described later.

[0031] The processing unit 20 executes information processing in the information processing device 10 A. The processing unit 20 includes an equipment capacity optimization unit 20A, a simulation execution unit 20B, a target energy introduction rate change unit 20C, and an output control unit 20D.

[0032] The facility capacity optimization unit 20A, the simulation execution unit 20B, the target energy introduction rate change unit 20C, and the output control unit 20D are realized, for example, by one or more processors. For example, each of the above units may be realized by having a processor such as a CPU (Central Processing Unit) execute a program, i.e., by software. Each of the above units may be realized by a processor such as a dedicated IC or circuit, i.e., by hardware. Each of the above units may be realized by a combination of software and hardware. When multiple processors are used, each processor may realize one of the units, or may realize two or more of the units.

[0033] The facility capacity optimization unit 20A searches for a tentative facility capacity of the supply facility 32 of the energy system 30.

[0034] Each time the equipment capacity optimization unit 20A searches for a tentative equipment capacity, the simulation execution unit 20B derives the introduction cost of the supply equipment 32 with the searched tentative equipment capacity and the energy introduction rate of the energy system 30 equipped with the supply equipment 32 with the tentative equipment capacity.

[0035] The target energy introduction rate changing unit 20C changes the target energy introduction rate to a lower value in a stepwise manner.

[0036] The processing unit 20 repeatedly executes the facility capacity optimization process, which includes searching for a tentative facility capacity and deriving the introduction cost and the energy introduction rate, every time the target energy introduction rate is changed.

[0037] The tentative equipment capacity is an equipment capacity extracted by the search process performed by the equipment capacity optimization unit 20A, and is an equipment capacity that is a candidate for the optimum equipment capacity to be determined.

[0038] Searching for a tentative equipment capacity means extracting one tentative equipment capacity by searching within a search range for equipment capacity.

[0039] The facility capacity is the amount of energy that represents the energy supply capability of the supply facility 32. The facility capacity may be referred to as, for example, rated power.

[0040] The process executed by the processing unit 20, which includes searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, may be referred to as an equipment capacity optimization process. The equipment capacity optimization process is a process that includes a series of processes, namely, searching for a tentative equipment capacity by the equipment capacity optimization unit 20A and deriving the introduction cost and energy introduction rate by the simulation execution unit 20B. As will be described in detail later, the processing unit 20 executes the equipment capacity optimization process sequentially in descending order of priority.

[0041] The equipment capacity optimization unit 20A searches for the tentative equipment capacity of the supply equipment 32 in order of priority, with the search range becoming narrower as the priority decreases, based on the priority of the supply equipment 32 included in the energy system 30, the input search range for the equipment capacity, the number of searches, and the target energy introduction rate of the energy system 30.

[0042] In this embodiment, the equipment capacity optimization unit 20A includes multiple equipment capacity optimization units that perform search processes corresponding to the priorities. As described above, in this embodiment, an example will be described in which there are two priorities, priority "1" and priority "2." Therefore, in this embodiment, an example will be described in which the equipment capacity optimization unit 20A includes a first equipment capacity optimization unit 20A1 and a second equipment capacity optimization unit 20A2. The first equipment capacity optimization unit 20A1 is an equipment capacity optimization unit 20A that performs search processes for equipment capacities for the first supply equipment 32A with priority "1." The second equipment capacity optimization unit 20A2 is an equipment capacity optimization unit 20A that performs search processes for equipment capacities for the second supply equipment 32B with priority "2" and for the first supply equipment 32A with a higher priority.

[0043] Hereinafter, a series of processes, including searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, performed by the first equipment capacity optimization unit 20A1 and the simulation execution unit 20B, which execute the search process and derivation process for priority "1," may be referred to as the first equipment capacity optimization process. Also, a series of processes, including searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, performed by the second equipment capacity optimization unit 20A2 and the simulation execution unit 20B, which execute the search process and derivation process for priority "2," may be referred to as the second equipment capacity optimization process. The first equipment capacity optimization process and the second equipment capacity optimization process are examples of equipment capacity optimization processes.

[0044] The priority, the input search range for the equipment capacity, and the number of searches are input by a user, such as a business operator, who is considering deriving the optimal equipment capacity for the supply equipment 32 of the energy system 30, via an operation instruction on the terminal device 40 or the UI unit 14. The equipment capacity optimization unit 20A acquires the priority, the input search range for the equipment capacity, and the number of searches by receiving the optimization information 16A and the equipment information 16D from the UI unit 14 or the terminal device 40. The equipment capacity optimization unit 20A may store the received optimization information 16A and the equipment information 16D in the storage unit 16. In the present embodiment, a form in which the equipment capacity optimization unit 20A stores the received optimization information 16A and the equipment information 16D in the storage unit 16 will be described as an example.

[0045] 2A is a schematic diagram illustrating an example of the data configuration of the optimization information 16A. The optimization information 16A includes information indicating the priority of the supply facilities 32 included in the energy system 30, the number of searches for the supply facilities 32, and the input search range for the facility capacity of the facility 31 belonging to the first supply facility 32A with the highest priority.

[0046] Figure 2A illustrates an example in which optimization information 16A includes values ​​for the number of searches during the first optimization process, the number of searches during the second optimization process, the input search range of the solar power generation equipment included in the first supply equipment 32A, the input search range of the storage batteries included in the first supply equipment 32A, and the equipment belonging to each of the first supply equipment 32A and the second supply equipment 32B.

[0047] As described above, the "first" in the first supply facility 32A indicates that it is a supply facility 32 with a priority of "1." As described above, the "second" in the second supply facility 32B indicates that it is a supply facility 32 with a priority of "2." In addition, in this embodiment, the smaller the value of the number representing the priority, the higher the priority.

[0048] The number of searches is information indicating the number of times the equipment capacity optimization unit 20A searches for tentative equipment capacities. In this embodiment, the number of searches is set for each equipment capacity optimization process for each priority level executed by the processing unit 20. Therefore, the optimization information 16A includes the number of searches during the first equipment capacity optimization process, which is the number of searches by the first equipment capacity optimization unit 20A1, and the number of searches during the second equipment capacity optimization process, which is the number of searches by the second equipment capacity optimization unit 20A2.

[0049] The input search range is information representing the search range for searching for a tentative installation capacity, and is information representing the search range input by a user such as a business operator through an operation instruction on the terminal device 40 or the UI unit 14, etc.

[0050] The optimization information 16A only needs to include the input search range of the facility 31 belonging to the first supply facility 32A, which has the highest priority. Therefore, as shown in Fig. 2A, in this embodiment, a form will be described as an example in which the optimization information 16A includes the input search range of each of the photovoltaic power generation facility and the storage battery, which are the facilities 31 belonging to the first supply facility 32A.

[0051] The optimization information 16A also includes information representing the facilities 31 belonging to each of the first supply facility 32A and the second supply facility 32B. Fig. 2A shows that the optimization information 16A includes information representing that the facilities 31 belonging to the first supply facility 32A, which has the highest priority of "1," are a photovoltaic power generation facility and a storage battery, and information representing that the facilities 31 belonging to the second supply facility 32B, which has the next highest priority of "2," are a fuel cell and a hydrogen production machine.

[0052] Returning to Figure 1, we continue the explanation.

[0053] The target energy introduction rate is a target energy introduction rate for the energy system 30. "Targeted" means that it is tentatively set.

[0054] The energy introduction rate is the total value of the supply energy supplied by the supply facility 32 when the supply facility 32 operates at a specified provisional facility capacity relative to the energy demand of the energy system 30 for a predetermined period. The predetermined period is, for example, one year. In this embodiment, an example in which the predetermined period is one year will be described. When the energy supplied by the supply facility 32 is renewable energy, the energy introduction rate may be referred to as the renewable energy introduction rate or the renewable energy introduction rate.

[0055] In this embodiment, the equipment capacity optimization unit 20A searches for tentative equipment capacities of the supply equipment 32 in descending order of priority. Then, the processing unit 20 repeatedly executes the search for tentative equipment capacities by the equipment capacity optimization unit 20A and the execution of a simulation by the simulation execution unit 20B, which will be described later, every time the target energy introduction rate is changed. In detail, the target energy introduction rate change unit 20C changes the target energy introduction rate to a lower range in a stepwise manner.

[0056] In this embodiment, the processing unit 20 pre-classifies the range of the target energy introduction rate into a plurality of levels from a high introduction rate range to a low introduction rate range, and stores the range of the target energy introduction rate corresponding to each level in the memory unit 16 in advance.

[0057] For example, in this embodiment, the storage unit 16 stores a target energy introduction rate DB16B in advance.

[0058] FIG. 2B is a schematic diagram showing an example of the data configuration of the target energy introduction rate DB 16B.

[0059] The target energy introduction rate DB 16B is a database in which levels are associated with ranges of target energy introduction rates. The data format of the target energy introduction rate DB 16B is not limited to a database.

[0060] In the target energy introduction rate DB 16B, a lower target energy introduction rate range is registered in advance in association with a lower level value. The target energy introduction rate value associated with each level may be registered in the target energy introduction rate DB 16B as a target energy introduction rate value that is progressively lower as the level becomes lower. Furthermore, the range of the target energy introduction rate registered in the target energy introduction rate DB 16B may be appropriately changeable by a user's operation instruction on the UI unit 14, etc., within a range that satisfies the condition that the range becomes narrower as the level becomes lower.

[0061] As shown in FIG. 2B, this embodiment shows an example in which the target energy introduction rate is classified into four levels: level 4 (95% to 100%), level 3 (80% to 95%), level 2 (70% to 80%), and level 1 (60% to 70%). However, the target energy introduction rate may be classified into two or more levels, and is not limited to a four-level classification. Furthermore, the range of the target energy introduction rate belonging to each level is not limited to the above range.

[0062] Returning to FIG. 1, the description will continue. As described above, in this embodiment, the equipment capacity optimization unit 20A includes a first equipment capacity optimization unit 20A1 and a second equipment capacity optimization unit 20A2. The equipment capacity optimization unit 20A then executes a process of searching for tentative equipment capacities of supply equipment 32 in descending order of priority. Also, as described above, the target energy introduction rate change unit 20C changes the target energy introduction rate to a lower range in stages.

[0063] In detail, first, the facility capacity optimization unit 20A sets the target energy introduction rate to level 4, which is the highest introduction rate.

[0064] Then, the first equipment capacity optimization unit 20A1, which executes a search process for the first supply equipment 32A having the highest priority, reads the number of searches performed by the first equipment capacity optimization unit 20A1 during the first equipment capacity optimization process, and the input search range for the first supply equipment 32A from the optimization information 16A.

[0065] Then, the first facility capacity optimization unit 20A1 sets a search range for the tentative facility capacity of the first supply facility 32A based on the set target energy introduction rate and the read input search range.

[0066] The lower the set target energy introduction rate, the narrower the search range that the processing unit 20 sets. For example, the processing unit 20 stores introduction rate search range correspondence information 16C in the storage unit 16 in advance.

[0067] 2C is a schematic diagram of an example of the data configuration of the introduction rate search range correspondence information 16C. The introduction rate search range correspondence information 16C is a database that associates target energy introduction rate levels with methods for determining search ranges. The data format of the introduction rate search range correspondence information 16C is not limited to a database.

[0068] 2C, the introduction rate search range correspondence information 16C pre-registers information for deriving a lower search range as the target energy introduction rate becomes lower. For example, the introduction rate search range correspondence information 16C registers that when the set target energy introduction rate level is level 4, which represents the highest introduction rate, the input search range is used as the search range. The introduction rate search range correspondence information 16C also registers that as the set target energy introduction rate level becomes lower, such as level 3, level 2, and level 1, 1 / 2, 1 / 3, and 1 / 4 of the search range derived at level 4 are used as the search range for each level.

[0069] Therefore, the processing unit 20 can identify the method for determining the search range corresponding to the set target energy introduction rate from the introduction rate search range correspondence information 16C, and set the search range for the tentative equipment capacity using the identified method for determining the search range.

[0070] More specifically, the first equipment capacity optimization unit 20A1 reads from the introduction rate search range correspondence information 16C a method for determining a search range that corresponds to the level of the set target energy introduction rate. The introduction rate search range correspondence information 16C registers that the input search range is to be used as a method for determining a search range that corresponds to Level 4. Therefore, in this case, the first equipment capacity optimization unit 20A1 sets the input search range of the first supply facility 32A read from the optimization information 16A as the search range for the tentative equipment capacity of the first supply facility 32A.

[0071] Then, the first equipment capacity optimization unit 20A1 searches for a tentative equipment capacity that is within the set search range and is different from the previous search at the same level 4.

[0072] For example, as shown in Figure 2A, it is assumed that the input search range of the photovoltaic power generation equipment belonging to the first supply equipment 32A is 3,000,000 kW, and the input search range of the storage battery belonging to the first supply equipment 32A is 1,000,000 kW. It is also assumed that a target energy penetration rate of Level 4 is set.

[0073] In this case, first equipment capacity optimization unit 20A1 sets the search range for photovoltaic power generation facilities belonging to first supply facility 32A to the input search range of 0 to 3,000,000 kW, and sets the search range for storage batteries belonging to first supply facility 32A to the input search range of 0 to 1,000,000 kW. Then, first equipment capacity optimization unit 20A1 sets values ​​within the search ranges set for each of the photovoltaic power generation facilities and the storage batteries, which are different from those used in the previous search, as the tentative equipment capacities of the photovoltaic power generation facilities and the storage batteries. Therefore, a value of 1 within the range of 0 to 3,000,000 kW is extracted by search as the tentative equipment capacity for the photovoltaic power generation facilities, and a value of 1 within the range of 0 to 1,000,000 kW is extracted by search as the tentative equipment capacity for the storage batteries.

[0074] Returning to Figure 1, we continue the explanation.

[0075] The first facility capacity optimization unit 20A1 outputs the tentative facility capacity found for each facility 31 belonging to the first supply facility 32A to the simulation execution unit 20B.

[0076] As described above, each time the equipment capacity optimization unit 20A searches for a tentative equipment capacity, the simulation execution unit 20B derives the introduction cost of the supply equipment 32 with the searched tentative equipment capacity and the energy introduction rate of the energy system 30 equipped with the supply equipment 32 with the tentative equipment capacity.

[0077] When the simulation execution unit 20B receives the tentative equipment capacity of the first supply equipment 32A from the first equipment capacity optimization unit 20A1, the simulation execution unit 20B executes the following process.

[0078] The simulation execution unit 20B acquires facility information 16D and annual power demand data 16E of the energy system 30. The facility information 16D and annual power demand data 16E are input by a user, such as a business operator, operating the terminal device 40 or the UI unit 14, or by other means. The simulation execution unit 20B acquires the facility information 16D and annual power demand data 16E by receiving the facility information 16D and annual power demand data 16E from the UI unit 14 or the terminal device 40. The simulation execution unit 20B may store the acquired facility information 16D and annual power demand data 16E in the storage unit 16. In the present embodiment, a form in which the simulation execution unit 20B stores the received facility information 16D and annual power demand data 16E in the storage unit 16 will be described as an example.

[0079] FIG. 2D is a schematic diagram of an example of the data configuration of the facility information 16D.

[0080] The facility information 16D is, for example, information in which an item name is associated with a value. For example, the facility information 16D includes information indicating the price of each facility 31 included in the supply facility 32, the operating cost of the supply facility 32, and the construction cost of the supply facility 32.

[0081] FIG. 2E is a schematic diagram of an example of the data configuration of annual power demand data 16E. Annual power demand data 16E is information that represents the amount of power demanded by the energy system 30 over a predetermined period. In this embodiment, as described above, an example will be described in which the predetermined period is one year. For example, annual power demand data 16E is data that records hourly power demand for one year. As shown in FIG. 2E, if the energy system 30 includes equipment 31 that has already been installed, the data may include actual measured values ​​or predicted values ​​for the installed equipment 31 over one year.

[0082] Returning to Figure 1, we continue the explanation.

[0083] The simulation execution unit 20B calculates the installation cost of the first supply facility 32A having the tentative facility capacity found by the first facility capacity optimization unit 20A1, using the facility information 16D.

[0084] Specifically, the simulation execution unit 20B calculates the set price of the photovoltaic power generation facility by multiplying the price per unit power of the photovoltaic power generation facility belonging to the first supply facility 32A, which was found by the first facility capacity optimization unit 20A1, by the provisional facility capacity found for the photovoltaic power generation facility. Similarly, the simulation execution unit 20B calculates the set price of the storage battery by multiplying the price per unit power of the storage battery, which was found by the first facility capacity optimization unit 20A1, by the provisional facility capacity found for the storage battery.

[0085] Then, the simulation execution unit 20B determines the total equipment price to be the sum of the set price of the solar power generation equipment and the set price of the storage battery, and calculates the construction costs and operating costs using the calculation formulas (30%, 10%) for the construction costs and operating costs shown in the equipment information 16D.

[0086] Then, the simulation executing unit 20B derives the total value of the total facility price, construction costs, and operation costs as the introduction cost of the first supply facility 32A.

[0087] Furthermore, the simulation executing unit 20B derives the amount of power generated when the first supply facility 32A having the tentative facility capacity found by the first facility capacity optimizing unit 20A1 operates for a predetermined period (one year in this embodiment) by simulating using a known simulation method. Then, the simulation executing unit 20B derives the derived amount of power generated relative to the annual energy demand of the energy system 30 represented by the annual power demand data 16E as the energy introduction rate.

[0088] Through these processes, the simulation execution unit 20B derives the introduction cost and energy introduction rate of the first supply facility 32A of the tentative facility capacity found by the first facility capacity optimization unit 20A1.

[0089] Then, the simulation execution unit 20B stores in the memory unit 16 the set target energy level, the number of searches during the first equipment capacity optimization process, the tentative equipment capacity obtained by the search by the first equipment capacity optimization unit 20A1, and the energy introduction rate and introduction cost obtained by the simulation execution unit 20B in association with each other.

[0090] The processing unit 20 repeatedly executes the first equipment capacity optimization process, which is a series of processes in which the first equipment capacity optimization unit 20A1 searches for a tentative equipment capacity and the simulation execution unit 20B derives the energy introduction rate and introduction cost, until the number of searches for the first equipment capacity optimization process with priority "1" read by the first equipment capacity optimization unit 20A1 reaches the number. The first equipment capacity optimization process is an example of equipment capacity optimization process, and is equipment capacity optimization process with the highest priority "1."

[0091] When the number of searches is reached, the second equipment capacity optimization unit 20A2 and the simulation execution unit 20B, which are used to execute the search process for the next priority, repeatedly execute the second equipment capacity optimization process, which is a series of processes for searching for a tentative equipment capacity and deriving the energy introduction rate and introduction cost by the simulation execution unit 20B, until the number of searches for the second equipment capacity optimization process for priority "2" is reached.

[0092] In this embodiment, the processing unit 20 sequentially selects priorities to be processed in descending order of priority, and each time a priority is selected, the processing unit 20 identifies the supply facilities 32 with the selected priority and other supply facilities 32 with priorities higher than the selected priority as a group of supply facilities 32 to be processed. Then, the processing unit 20 searches for the tentative facility capacities of the supply facilities 32 belonging to the identified group of supply facilities 32.

[0093] Therefore, the second equipment capacity optimization unit 20A2 identifies the second supply equipment 32B with a priority of "2" and the first supply equipment 32A with a priority of "1" higher than the priority of "2" as a group of supply equipment 32 to be processed. Then, the second equipment capacity optimization unit 20A2 searches for tentative equipment capacities of the identified second supply equipment 32B and first supply equipment 32A.

[0094] First, the second equipment capacity optimization unit 20A2 reads the number of searches during the second equipment capacity optimization process, which is the number of searches performed by the second equipment capacity optimization unit 20A2, from the optimization information 16A.

[0095] Then, the processing unit 20 identifies the optimal equipment capacity, which is identified by an equipment capacity optimization process including searching for a tentative equipment capacity and deriving the installation cost and energy installation rate, for another priority selected previously that is higher than the priority selected this time.

[0096] In detail, the second equipment capacity optimization unit 20A2 identifies the optimal equipment capacity in the first equipment capacity optimization process from a list of tentative equipment capacities derived by the first equipment capacity optimization process for priority "1" which is higher than priority "2" of the second equipment capacity optimization unit 20A2.

[0097] The optimal installed capacity is a provisional installed capacity among the searched multiple tentative installed capacities, for which the corresponding derived energy introduction rate is equal to or greater than a first threshold and the corresponding derived introduction cost is equal to or less than a second threshold. The first threshold and the second threshold may be determined in advance. The first threshold and the second threshold may be changeable as appropriate by a user or the like operating the UI unit 14. The optimal installed capacity is preferably a provisional installed capacity for a combination of a higher energy introduction rate and a lower introduction cost among the searched multiple tentative installed capacities.

[0098] In this embodiment, the second equipment capacity optimization unit 20A2 identifies, from among multiple tentative equipment capacities extracted by multiple searches in the first equipment capacity optimization process, the tentative equipment capacities of each of the photovoltaic power generation equipment and the storage battery belonging to the first supply equipment 32A that correspond to a combination of a higher energy introduction rate and a lower introduction cost as the optimal equipment capacities of each of the photovoltaic power generation equipment and the storage battery.

[0099] Then, the second equipment capacity optimization unit 20A2 sets the search range for each of the first supply equipment 32A and the second supply equipment 32B, which are the targets of the second equipment capacity optimization, to a range equal to or smaller than the identified optimal equipment capacity.

[0100] Therefore, the lower the priority, the narrower the search range that is set, and the processing unit 20 can search for the tentative equipment capacity from a search range that is gradually narrower, thereby enabling an efficient search for the tentative equipment capacity.

[0101] FIG. 3 is an explanatory diagram of an example of a method for setting the search ranges of each of the first supply facility 32A and the second supply facility 32B, which are targets of the second facility capacity optimization process, by the second facility capacity optimization unit 20A2.

[0102] For example, the second equipment capacity optimization unit 20A2 sets a range of 0 to 1 times the optimal equipment capacity of the photovoltaic power generation equipment obtained by the first equipment capacity optimization process as the search range for the photovoltaic power generation equipment included in the first supply equipment 32A, which is the target of the second equipment capacity optimization process.

[0103] Furthermore, for example, the second equipment capacity optimization unit 20A2 sets a range of 0 to 1 times the optimal equipment capacity of the storage battery obtained by the first equipment capacity optimization process as the search range for the storage battery included in the first supply equipment 32A, which is the target of the second equipment capacity optimization process.

[0104] Furthermore, for example, the second equipment capacity optimization unit 20A2 sets the range of 0 to the maximum power demand of the hydrogen production machine as the search range for the storage batteries included in the second supply equipment 32B, which is the target of the second equipment capacity optimization process. The maximum power demand refers to the maximum power demand [kW] among all the times in the power demand data within a predetermined period. As described above, in this embodiment, the predetermined period is described assuming one year. Therefore, in this embodiment, the maximum power demand of the energy system 30 represents the maximum power demand among the hourly power demands of the energy system 30 in one year, as shown in the annual power demand data 16E in FIG. 2E. Furthermore, the maximum power demand of the hydrogen production machine represents the maximum power demand among the hourly power demands of the hydrogen production machine in one year.

[0105] Furthermore, for example, the second equipment capacity optimization unit 20A2 sets a range of 0 to 1 times the optimal equipment capacity of the photovoltaic power generation equipment obtained by the first equipment capacity optimization process as the search range for the fuel cells included in the second supply equipment 32B, which is the target of the second equipment capacity optimization process.

[0106] Returning to Figure 1, we continue the explanation.

[0107] Then, for each of the facilities 31 belonging to the first supply facility 32A and the second supply facility 32B to be processed, the second equipment capacity optimization unit 20A2 searches for a tentative equipment capacity that is within the search range set for each facility 31 and is different from the value at the previous search at the same level 4.

[0108] The second facility capacity optimization unit 20A2 outputs the tentative facility capacity found for each facility 31 belonging to each of the first supply facility 32A and the second supply facility 32B to the simulation execution unit 20B.

[0109] As described above, each time the equipment capacity optimization unit 20A searches for a tentative equipment capacity, the simulation execution unit 20B derives the introduction cost of the supply equipment 32 with the searched tentative equipment capacity and the energy introduction rate of the energy system 30 equipped with the supply equipment 32 with the tentative equipment capacity.

[0110] When the simulation execution unit 20B receives the provisional equipment capacities of the first supply equipment 32A and the second supply equipment 32B from the second equipment capacity optimization unit 20A2, it derives the installation costs and energy installation rates of the first supply equipment 32A and the second supply equipment 32B for the provisional equipment capacities searched for by the second equipment capacity optimization unit 20A2 in the same manner as described above.

[0111] Then, the simulation execution unit 20B stores in the memory unit 16 the set target energy level, the number of searches during the second equipment capacity optimization process, the tentative equipment capacity obtained by the search by the second equipment capacity optimization unit 20A2, and the energy introduction rate and introduction cost obtained by the simulation execution unit 20B in association with each other.

[0112] The processing unit 20 repeatedly executes the second equipment capacity optimization process, which is a series of processes in which the second equipment capacity optimization unit 20A2 searches for a tentative equipment capacity and the simulation execution unit 20B derives the energy introduction rate and introduction cost, until the number of searches for the second equipment capacity optimization process with priority "2" read by the second equipment capacity optimization unit 20A2 reaches the number of searches.

[0113] As described above, in this embodiment, it is assumed that there are two types of priorities, "1" and "2." However, as described above, there may be three or more types of priorities, including "3" or higher. When there are priorities including "3" or higher, the processing unit 20 may execute the equipment capacity optimization process for priorities equal to or lower than priority "3" in the same manner as the second equipment capacity optimization unit 20A2 and the simulation execution unit 20B.

[0114] That is, the processing unit 20 sequentially selects the priority of the processing target in descending order of priority, and each time a priority is selected, the processing unit 20 identifies the supply equipment 32 of the selected priority and other supply equipment 32 of higher priorities as a group of supply equipment 32 to be processed.

[0115] Furthermore, as described above, the processing unit 20 sets a search range for the group of identified supply facilities 32 of the currently selected priority based on the optimal equipment capacity identified by the equipment capacity optimization process, which includes searching for a tentative equipment capacity and deriving the installation cost and energy installation rate, for another priority selected previously that is higher than the currently selected priority.

[0116] Then, the processing unit 20 repeatedly executes the equipment capacity optimization process, which is a series of processes of searching the search range of the tentative equipment capacity of the supply equipment 32 belonging to the identified group of supply equipment 32 and deriving the energy introduction rate and introduction cost by the simulation execution unit 20B, until the number of searches reaches the number of times.

[0117] When the equipment capacity optimization process for each of all priorities has been repeatedly executed until the respective search counts have been reached, the target energy introduction rate change unit 20C changes the target energy introduction rate to a stepwise lower value. For example, if level 4 was set, the processing unit 20 changes it to level 3. Also, for example, if level 3 was set, the processing unit 20 changes it to level 2. Also, for example, if level 2 was set, the processing unit 20 changes it to level 1. Through this process, the previously set target energy introduction rate is changed to a value lower than the previously set value and is newly set.

[0118] The processing unit 20 repeatedly executes the above-described equipment capacity optimization process for each of all priorities each time the target energy introduction rate is changed until the number of searches reaches the specified number. The processing unit 20 also repeatedly executes the equipment capacity optimization process for each of all priorities until the set target energy introduction rate reaches level 1, which is the lowest level.

[0119] In detail, in this embodiment, the processing unit 20 repeatedly executes each of the first equipment capacity optimization process and the second equipment capacity optimization process until the number of searches for each process is reached, and then determines whether the set target energy introduction rate is at Level 1, the lowest level. If the set target energy introduction rate is higher than Level 1, the lowest level, the target energy introduction rate change unit 20C lowers the set target energy introduction rate by one level. The processing unit 20 then repeatedly executes the above-mentioned equipment capacity optimization process according to the set level until the number of searches for each process is reached. The processing unit 20 repeatedly executes the above-mentioned equipment capacity optimization process until the number of searches for each process is reached, until the set target energy introduction rate reaches Level 1, the lowest level.

[0120] As a result of the processing unit 20 executing the above processing, the memory unit 16 obtains processing result information including the provisional equipment capacity, energy introduction rate, and introduction cost of each of the searched facilities 31 for each level of the target energy introduction rate.

[0121] FIG. 4A is a schematic diagram of an example of processing result information when the target energy introduction rate is at level 4. As shown in FIG.

[0122] As shown in FIG. 4A, the processing result information when the target energy introduction rate is level 4 is, for example, a list of correspondences between the tentative equipment capacity, the energy introduction rate, and the introduction cost of each of the equipment 31 included in the supply equipment 32 corresponding to the number of searches obtained by executing each of the first equipment capacity optimization process and the second equipment capacity optimization process.

[0123] FIG. 4B is a schematic diagram of an example of processing result information when the target energy introduction rate is at level 3. In FIG.

[0124] As shown in FIG. 4B, the processing result information when the target energy introduction rate is at level 3 is, for example, a list of correspondences between the tentative equipment capacity, the energy introduction rate, and the introduction cost of each of the facilities 31 included in the supply facilities 32 corresponding to the number of searches obtained by executing each of the first equipment capacity optimization process and the second equipment capacity optimization process.

[0125] Similarly, when the target energy introduction rate is level 2 or level 1, a list of correspondences between the provisional equipment capacity, energy introduction rate, and introduction cost of each of the equipment 31 included in the supply equipment 32 corresponding to the search number is obtained.

[0126] Furthermore, the processing unit 20 may identify the optimal equipment capacity of each of the tentative equipment capacities of the first supply equipment 32A and the second supply equipment 32B when the target energy introduction rate is set to each level.

[0127] When the processing result information shown in FIG. 4A is obtained, the processing unit 20 specifies, as the optimal equipment capacities for the first supply facility 32A and the second supply facility 32B, the provisional equipment capacities of the solar power generation facility and the storage battery belonging to the first supply facility 32A, and the hydrogen production machine and the fuel cell belonging to the second supply facility 32B obtained when the search count in the second facility capacity optimization process was "3," which corresponds to the combination of a higher energy introduction rate of 93% and a lower introduction cost of 107.8 billion yen, as the optimal equipment capacities for the target energy introduction rate of level 4.

[0128] When the processing result shown in FIG. 4B is obtained, the processing unit 20 specifies, as the optimal equipment capacity for the target energy introduction rate of level 3, the provisional equipment capacities of the solar power generation equipment and storage battery belonging to the first supply facility 32A, and the hydrogen production machine and fuel cell belonging to the second supply facility 32B obtained when the number of searches for the second facility capacity optimization was "1", which corresponds to the combination of a higher energy introduction rate of 82% and a lower introduction cost of 43.5 billion yen.

[0129] Furthermore, the processing unit 20 may further identify, from among the optimal equipment capacities identified for each level of the target energy introduction rate, the optimal equipment capacity with a higher energy introduction rate and a lower introduction cost as the most preferable optimal equipment capacity for the first supply facility 32A and the second supply facility 32B.

[0130] Returning to Figure 1, we continue the explanation.

[0131] The output control unit 20D outputs processing result information including the searched tentative equipment capacity, the introduction cost of the supply equipment 32 with the searched tentative equipment capacity, and the energy introduction rate of the energy system 30 including the supply equipment 32 with the searched tentative equipment capacity. The processing result information may further include at least one of the optimal equipment capacity identified by the processing unit 20 for each level of the target energy introduction rate and the most preferable optimal equipment capacity for the supply equipment 32 identified by the processing unit 20.

[0132] The output control unit 20D outputs the processing result information by performing at least one of displaying the processing result information on the UI unit 14, storing it in the memory unit 16, and transmitting it to an external information processing device such as a terminal device 40 via the communication unit 12.

[0133] Next, an example of the flow of information processing executed by the information processing device 10 of this embodiment will be described.

[0134] FIG. 5 is a flowchart showing an example of the flow of information processing executed by the information processing device 10A of this embodiment.

[0135] The facility capacity optimization unit 20A sets the target energy introduction rate to level 4, which is the highest introduction rate (step S100). Then, the processing unit 20 selects the highest priority "1" (step S102).

[0136] The first equipment capacity optimization unit 20A1, which is used to perform a search process for the first supply equipment 32A with priority "1" selected in step S102, reads the number of searches during the first equipment capacity optimization process and the input search range for the first supply equipment 32A from the optimization information 16A (step S104).

[0137] The first equipment capacity optimization unit 20A1 sets the search range for the first supply equipment 32A based on the input search range read in step S104 and the set target energy introduction rate level (step S106). For example, the first equipment capacity optimization unit 20A1 reads from the introduction rate search range correspondence information 16C a method for determining the search range that corresponds to the set target energy introduction rate level. The introduction rate search range correspondence information 16C registers that the input search range is used to determine the search range that corresponds to level 4. Therefore, in this case, the first equipment capacity optimization unit 20A1 sets the input search range for the first supply equipment 32A read from the optimization information 16A as the search range for the first supply equipment 32A.

[0138] Then, the first equipment capacity optimization unit 20A1 searches for a tentative equipment capacity that is within the search range set in step S106 and is different from the previous search at the same level (step S108).The first equipment capacity optimization unit 20A1 outputs the tentative equipment capacity searched for for each facility 31 belonging to the first supply facility 32A to the simulation execution unit 20B.

[0139] The simulation executing unit 20B derives, by executing a simulation or the like, the introduction cost of the supply facility 32 of the provisional facility capacity found in step S108 and the energy introduction rate of the energy system 30 including the supply facility 32 of the provisional facility capacity (step S110). The simulation executing unit 20B derives the introduction cost and energy introduction rate of the first supply facility 32A of the provisional facility capacity found in step S108 using the facility information 16D and annual power demand data 16E of the energy system 30.

[0140] Then, the simulation executing unit 20B associates the set target energy level, the number of searches during the first equipment capacity optimization process, the tentative equipment capacity obtained by the search by the first equipment capacity optimization unit 20A1, and the energy introduction rate and introduction cost obtained by the simulation executing unit 20B, and stores them in the memory unit 16 (step S112).

[0141] The processing unit 20 determines whether the first equipment capacity optimization process, which is the process of steps S108 to S112, has been executed the number of times to search for the first equipment capacity optimization process with priority "1" read in step S104 (step S114). If the determination in step S114 is negative (step S114: No), the process returns to step S108. If the determination in step S114 is positive (step S114: Yes), the process proceeds to step S116.

[0142] In step S116, the processing unit 20 selects a value obtained by incrementing the priority by 1. In this embodiment, the processing unit 20 selects a priority of "2" which is incremented by 1 from the previously selected priority of "1" (step S116).

[0143] The second equipment capacity optimization unit 20A2 for executing the search process for the priority "2" selected in step 116 reads the number of searches during the second equipment capacity optimization process from the optimization information 16A (step S118).

[0144] Then, the processing unit 20 identifies the optimal equipment capacity by the first equipment capacity optimization process with priority "1" (step S120). The second equipment capacity optimization unit 20A2 identifies, as the optimal equipment capacity, the provisional equipment capacity for the combination with the higher energy introduction rate and the lower introduction cost from among the multiple provisional equipment capacities for the first supply equipment 32A searched for by repeating the first equipment capacity optimization process of steps S108 to S112 the search number of times.

[0145] Then, the second equipment capacity optimization unit 20A2 sets the search range for each of the first supply equipment 32A and the second supply equipment 32B, which are targets of the second equipment capacity optimization, in accordance with the optimal equipment capacities identified in step S120 (step S122). For example, the second equipment capacity optimization unit 20A2 sets the search range for each of the first supply equipment 32A and the second supply equipment 32B, which are targets of the second equipment capacity optimization, to a range equal to or less than the optimal equipment capacities identified in step S120.

[0146] Therefore, the lower the priority, the narrower the search range that is set, and the processing unit 20 can search for the tentative equipment capacity from a search range that is gradually narrower, thereby enabling an efficient search for the tentative equipment capacity.

[0147] Then, for each of the facilities 31 belonging to the first supply facility 32A and the second supply facility 32B to be processed, the second facility capacity optimization unit 20A2 searches for a tentative facility capacity that is within the search range set for each facility 31 and that is different from the previous search at the same level (step S124).The second facility capacity optimization unit 20A2 outputs the tentative facility capacity searched for for each of the facilities 31 belonging to the first supply facility 32A and the second supply facility 32B to the simulation execution unit 20B.

[0148] When the simulation execution unit 20B receives the tentative equipment capacities of the first supply equipment 32A and the second supply equipment 32B from the second equipment capacity optimization unit 20A2, it derives the installation costs and energy installation rates of the first supply equipment 32A and the second supply equipment 32B for the tentative equipment capacities searched for in step S124 (step S126).

[0149] Then, the simulation executing unit 20B associates the set target energy level, the number of searches during the second equipment capacity optimization process, the tentative equipment capacity obtained by the search by the second equipment capacity optimization unit 20A2, and the energy introduction rate and introduction cost obtained by the simulation executing unit 20B, and stores them in the memory unit 16 (step S128).

[0150] The processing unit 20 determines whether the second equipment capacity optimization process, which is the process of steps S124 to S128, has been executed the number of times for searching the second equipment capacity optimization process with priority "2" read in step S118 (step S130). If the determination in step S130 is negative (step S130: No), the process returns to step S124. If the determination in step S130 is positive (step S130: Yes), the process proceeds to step S132.

[0151] The target energy introduction rate changing unit 20C determines whether the level of the set target energy introduction rate is the lowest level, Level 1 (Step S132). If the determination in Step S132 is negative (Step S132: No), the process proceeds to Step S134.

[0152] In step S134, the target energy introduction rate changing unit 20C lowers the level of the set target energy introduction rate by one level (step S134), and then the process returns to step S106.

[0153] As described above, the processing unit 20 changes the target energy introduction rate to a gradually lower range, and sets a narrower search range as the target energy introduction rate becomes lower. Therefore, in the search range setting process of step S106, a narrower search range is set as the target energy introduction rate becomes gradually lower. This allows the processing unit 20 to search for a tentative equipment capacity from a gradually narrower search range, thereby enabling an efficient search for a tentative equipment capacity.

[0154] On the other hand, if the determination in step S132 is affirmative (step S132: Yes), the process proceeds to step S136. In step S136, the output control unit 20D outputs processing result information including the tentative equipment capacity searched for by the processing of steps S100 to S134, the introduction cost of the supply equipment 32 with the searched tentative equipment capacity, and the energy introduction rate of the energy system 30 equipped with the supply equipment 32 with the searched tentative equipment capacity (step S136). The processing result information may further include at least one of the optimal equipment capacity identified by the processing unit 20 for each level of the target energy introduction rate and the most preferable optimal equipment capacity for the supply equipment 32 identified by the processing unit 20. Then, this routine ends.

[0155] As described above, the information processing device 10A of this embodiment includes a processing unit 20. The equipment capacity optimization unit 20A of the processing unit 20 searches for a provisional equipment capacity of the supply equipment 32 in order of priority, with the search range narrower as the priority decreases, based on the priority of the supply equipment 32 that supplies energy and is included in the energy system 30, the input search range for the equipment capacity, the number of searches, and the target energy introduction rate of the energy system 30. Each time a provisional equipment capacity is searched for, the simulation execution unit 20B of the processing unit 20 derives the introduction cost of the supply equipment 32 for the searched provisional equipment capacity and the energy introduction rate of the energy system 30 that includes the supply equipment 32 with the provisional equipment capacity. The processing unit 20 repeatedly searches for the provisional equipment capacity and derives the introduction cost and the energy introduction rate each time the target energy introduction rate is changed.

[0156] In the conventional technology, the user manually inputs various equipment capacities one by one, and an operation plan simulation is executed each time a new equipment capacity is input, thereby deriving the optimal equipment capacity. In addition, in the conventional technology, the optimal equipment capacity is calculated by applying a linear programming method or an integer programming solver using a mathematical model.

[0157] However, manually inputting facility capacities places a heavy burden on users, making it difficult to efficiently derive optimal facility capacities. Furthermore, in methods that apply linear programming or integer programming solvers using mathematical models, the amount of calculations increases as the number of target supply facilities increases, making it difficult to complete the process within the expected calculation time or resulting in a huge processing load. In other words, it has been difficult for conventional technologies to support efficient derivation of optimal facility capacities.

[0158] On the other hand, in the information processing device 10A of this embodiment, the equipment capacity optimization unit 20A searches for a tentative equipment capacity of the supply equipment 32 included in the energy system 30 that supplies energy, in descending order of priority, with the search range narrower as the priority decreases, based on the priority of the supply equipment 32, the input search range for the equipment capacity, the number of searches, and the target energy introduction rate of the energy system 30. Each time a tentative equipment capacity is searched for, the simulation execution unit 20B of the processing unit 20 derives the introduction cost of the supply equipment 32 with the searched tentative equipment capacity, and the energy introduction rate of the energy system 30 including the supply equipment 32 with the tentative equipment capacity. The processing unit 20 repeatedly searches for the tentative equipment capacity and derives the introduction cost and the energy introduction rate each time the target energy introduction rate is changed.

[0159] Therefore, the information processing device 10A of this embodiment can search for tentative equipment capacities of the multiple supply facilities 32 while narrowing the search range according to the priority each time the target energy introduction rate is changed. Therefore, the information processing device 10A of this embodiment can efficiently search for tentative equipment capacities for the multiple supply facilities 32. Furthermore, since the introduction cost of the supply facilities 32 and the energy introduction rate of the energy system 30 for the searched tentative equipment capacities are derived, it becomes possible to easily identify the optimal equipment capacity from the searched tentative equipment capacities based on the introduction cost and the energy introduction rate.

[0160] Therefore, the information processing device 10A of this embodiment can support efficient derivation of the optimum equipment capacity.

[0161] In the present embodiment, there are two types of priorities, "1" and "2," and the equipment capacity optimization unit 20A includes a first equipment capacity optimization unit 20A1 that executes a search process corresponding to priority "1" and a second equipment capacity optimization unit 20A2 that executes a search process corresponding to priority "2." However, as described above, the priority assigned to the supply equipment 32 may be three or more. In this case, the equipment capacity optimization unit 20A may further include an M-th equipment capacity optimization unit (M is an integer equal to or greater than three) that corresponds to each priority from priority "3" onward. The processing unit 20 then executes the equipment capacity optimization process in the same manner as described above, in descending order of priority.

[0162] (Second embodiment) In this embodiment, a form will be described in which, when the evaluation value represented by the optimal equipment capacity determined by the equipment capacity optimization process of the currently selected priority is lower than the evaluation value of the optimal equipment capacity determined by the equipment capacity optimization process of the previously selected higher priority, the information processing device 10 omits the equipment capacity optimization process of the currently selected priority to be executed after changing the target energy introduction rate.

[0163] The same components as those in the above embodiment are given the same reference numerals and detailed description thereof will be omitted.

[0164] 6 is a schematic diagram of an example of an information processing system 1B according to this embodiment. The information processing system 1B is an example of the information processing system 1.

[0165] The information processing system 1 is similar to the information processing system 1A of the above embodiment, except that the information processing system 1 includes an information processing device 10B instead of the information processing device 10A.

[0166] The information processing device 10B is an example of the information processing device 10. The information processing device 10B is similar to the information processing device 10A except that the information processing device 10B includes a processing device 21 instead of the processing device 20.

[0167] The processing unit 21 includes an equipment capacity optimization unit 20A, a simulation execution unit 20B, a target energy introduction rate change unit 20C, an output control unit 20D, and a necessity determination unit 21E. The processing unit 21 is similar to the processing unit 20 of the above embodiment, except that it further includes the necessity determination unit 21E.

[0168] The necessity determination unit 21E determines whether an evaluation value R2 represented by the optimal equipment capacity identified by the equipment capacity optimization process, including a search for a tentative equipment capacity and a derivation of the introduction cost and the energy introduction rate, for the currently selected priority is equal to or less than an evaluation value R1 represented by the optimal equipment capacity identified by the equipment capacity optimization process, including a search for a tentative equipment capacity and a derivation of the introduction cost and the energy introduction rate, for another priority selected previously that is higher than the currently selected priority.

[0169] As in the above embodiment, the following description will be given assuming that the equipment capacity optimization unit 20A includes a first equipment capacity optimization unit 20A1 and a second equipment capacity optimization unit 20A2.

[0170] As in the above embodiment, the first equipment capacity optimization unit 20A1, which executes the first equipment capacity optimization process for priority "1," searches for a tentative equipment capacity, and the simulation execution unit 20B derives the introduction cost and energy introduction rate based on the tentative equipment capacity. The first equipment capacity optimization process is repeated a number of times to derive multiple tentative equipment capacities. Then, as in the above embodiment, the processing unit 21 identifies, from among the multiple tentative equipment capacities, the tentative equipment capacity corresponding to the highest energy introduction rate and the lowest introduction cost as the optimal equipment capacity.

[0171] Then, similarly to the above embodiment, the second equipment capacity optimization unit 20A2, which executes the second equipment capacity optimization process for priority "2," searches for a tentative equipment capacity, and the simulation execution unit 20B derives the introduction cost and energy introduction rate based on the tentative equipment capacity. The second equipment capacity optimization process is repeated a number of searches to derive multiple tentative equipment capacities. Then, similarly to the above embodiment, the processing unit 21 identifies, from among the multiple tentative equipment capacities, the tentative equipment capacity corresponding to the highest energy introduction rate and the lowest introduction cost as the optimal equipment capacity.

[0172] The necessity determination unit 21E calculates an evaluation value R2 of the optimal equipment capacity identified by the second equipment capacity optimization process for the currently selected priority "2." The necessity determination unit 21E calculates a higher evaluation value R2 for the energy introduction rate and introduction cost corresponding to the optimal equipment capacity, and the evaluation value R2 is lower for a lower energy introduction rate.

[0173] Furthermore, the necessity determination unit 21E calculates an evaluation value R1 of the optimal equipment capacity identified by the first equipment capacity optimization process for the previously selected priority "1." The necessity determination unit 21E calculates a higher evaluation value R1 for the energy introduction rate and introduction cost corresponding to the optimal equipment capacity, the higher the energy introduction rate, and the lower the introduction rate, the lower the evaluation value R1.

[0174] The necessity determining unit 21E then determines whether the evaluation value R2 of the optimal equipment capacity with the currently selected priority "2" is equal to or less than the evaluation value R1 of the optimal equipment capacity with the previously selected priority "1".

[0175] For example, assume that the optimal equipment capacity of the solar power generation equipment, which is equipment 31 belonging to the first supply equipment 32A, identified by the first equipment capacity optimization process is 100,000 kW, the optimal equipment capacity of the storage battery, which is equipment 31 belonging to the first supply equipment 32A, is 10,000 kW, the energy introduction rate is 93%, and the introduction cost is 350 million yen.

[0176] Also, for example, assume that the optimal equipment capacity of the solar power generation equipment, which is equipment 31 belonging to the first supply equipment 31A, identified by the second equipment capacity optimization process is 50,000 kW, the optimal equipment capacity of the storage battery, which is equipment 31 belonging to the first supply equipment 32A, is 10,000 kW, the optimal equipment capacity of the hydrogen production machine, which is equipment 31 belonging to the second supply equipment 32B, is 5,000 kW, the optimal equipment capacity of the fuel cell, which is equipment 31 belonging to the second supply equipment 32B, is 10,000 kW, the energy introduction rate is 90%, and the introduction cost is 500 million yen.

[0177] In this case, the evaluation value R1 of the optimal equipment capacity obtained by the first equipment capacity optimization process, which has a higher energy introduction rate and a lower introduction cost, is higher than the evaluation value R2 of the optimal equipment capacity obtained by the second equipment capacity optimization process, which has a lower energy introduction rate and a higher introduction cost.

[0178] If the evaluation value R2 is equal to or less than the evaluation value R1, i.e., if the evaluation value R1 is higher than the evaluation value R2, the necessity determination unit 21E determines that the power demand of the energy system 30 can be met by only the supply facility 32 (here, the first supply facility 32A) targeted in the equipment capacity optimization process for a priority higher than the currently selected priority. Therefore, the processing unit 20 performs control so as to omit the equipment capacity optimization process for a priority lower than the currently selected priority, which is to be executed after the target energy introduction rate is changed. That is, if the evaluation value R2 is equal to or less than the evaluation value R1, i.e., if the evaluation value R1 is higher than the evaluation value R2, the processing unit 20 omits the second equipment capacity optimization process for priority "2" and executes only the first equipment capacity optimization process for priority "1" in the subsequent processes, even after the target energy introduction rate has been changed to a lower level by the target energy introduction rate change unit 20C.

[0179] Next, an example of the flow of information processing executed by the information processing device 10B of this embodiment will be described.

[0180] FIG. 7 is a flowchart showing an example of the flow of information processing executed by the information processing device 10B of this embodiment.

[0181] The facility capacity optimization unit 20A sets the target energy introduction rate to level 4, which is the highest introduction rate (step S200). Then, the processing unit 21 selects the highest priority "1" (step S202).

[0182] The first equipment capacity optimization unit 20A1, which is used to perform a search process for the first supply equipment 32A with priority "1" selected in step 102, reads the number of searches during the first equipment capacity optimization process and the input search range for the first supply equipment 32A from the optimization information 16A (step S204).

[0183] The processing unit 20 sets the second equipment capacity optimization trigger to 1 (step S206). When the second equipment capacity optimization trigger is 1, it means that the second equipment capacity optimization process is executed. When the second equipment capacity optimization trigger is 0, it means that the second equipment capacity optimization process is omitted.

[0184] The first equipment capacity optimization unit 20A1 sets the search range for the first supply equipment 32A based on the input search range read in step S204 and the set target energy introduction rate level (step S208). For example, the first equipment capacity optimization unit 20A1 reads from the introduction rate search range correspondence information 16C a method for determining the search range that corresponds to the set target energy introduction rate level. The introduction rate search range correspondence information 16C registers that the input search range is used to determine the search range that corresponds to level 4. Therefore, in this case, the first equipment capacity optimization unit 20A1 sets the input search range for the first supply equipment 32A read from the optimization information 16A as the search range for the first supply equipment 32A.

[0185] Then, the first equipment capacity optimization unit 20A1 searches for a tentative equipment capacity that is within the search range set in step S208 and is different from the previous search at the same level (step S210).The first equipment capacity optimization unit 20A1 outputs the tentative equipment capacity searched for for each facility 31 belonging to the first supply facility 32A to the simulation execution unit 20B.

[0186] The simulation executing unit 20B derives, by executing a simulation or the like, the introduction cost of the supply facility 32 of the provisional facility capacity found in step S108 and the energy introduction rate of the energy system 30 including the supply facility 32 of the provisional facility capacity (step S212). The simulation executing unit 20B derives the introduction cost and energy introduction rate of the first supply facility 32A of the provisional facility capacity found in step S210, using the facility information 16D and annual power demand data 16E of the energy system 30.

[0187] Then, the simulation executing unit 20B associates the set target energy level, the number of searches during the first equipment capacity optimization process, the tentative equipment capacity obtained by the search by the first equipment capacity optimization unit 20A1, and the energy introduction rate and introduction cost obtained by the simulation executing unit 20B, and stores them in the memory unit 16 (step S214).

[0188] The processing unit 21 determines whether the first equipment capacity optimization process, which is the process of steps S210 to S214, has been executed the number of times to search for the first equipment capacity optimization process with priority "1" read in step S204 (step S216). If the determination in step S216 is negative (step S216: No), the process returns to step S210. If the determination in step S216 is positive (step S216: Yes), the process proceeds to step S218.

[0189] In step S218, the processing unit 20 determines whether the second equipment capacity optimization trigger is 1 (step S218). If the determination in step S218 is negative (step S218: No), the process proceeds to step S242, which will be described later. If the determination in step S218 is positive (step S218: Yes), the process proceeds to step S220.

[0190] In step S220, the processing unit 20 selects a value obtained by counting up the priority by 1. In this embodiment, the processing unit 21 selects a priority "2" obtained by counting up the immediately preceding selected priority "1" by 1 (step 220).

[0191] The second equipment capacity optimization unit 20A2 for executing the search process for the priority "2" selected in step 220 reads the number of searches during the second equipment capacity optimization process from the optimization information 16A (step S222).

[0192] Then, the processing unit 21 identifies the optimal equipment capacity by the first equipment capacity optimization process with priority "1" (step S224). The second equipment capacity optimization unit 20A2 identifies, as the optimal equipment capacity, the provisional equipment capacity for the combination with the higher energy introduction rate and the lower introduction cost from among the multiple provisional equipment capacities for the first supply equipment 32A searched for by repeating the first equipment capacity optimization process of steps S210 to S214 the search number of times.

[0193] Then, the second equipment capacity optimization unit 20A2 sets the search range for each of the first supply equipment 32A and the second supply equipment 32B, which are targets of the second equipment capacity optimization, in accordance with the optimal equipment capacities identified in step S224 (step S226). For example, the second equipment capacity optimization unit 20A2 sets the search range for each of the first supply equipment 32A and the second supply equipment 32B, which are targets of the second equipment capacity optimization, to a range equal to or smaller than the optimal equipment capacities identified in step S224.

[0194] Therefore, the lower the priority, the narrower the search range that is set, and the processing unit 20 can search for the tentative equipment capacity from a search range that is gradually narrower, thereby enabling an efficient search for the tentative equipment capacity.

[0195] Then, for each of the facilities 31 belonging to the first supply facility 32A and the second supply facility 32B to be processed, the second facility capacity optimization unit 20A2 searches for a tentative facility capacity that is within the search range set for each facility 31 and that is different from the previous search at the same level (step S228).The second facility capacity optimization unit 20A2 outputs the tentative facility capacity searched for for each of the facilities 31 belonging to the first supply facility 32A and the second supply facility 32B to the simulation execution unit 20B.

[0196] When the simulation execution unit 20B receives the tentative equipment capacities of the first supply equipment 32A and the second supply equipment 32B from the second equipment capacity optimization unit 20A2, it derives the installation costs and energy installation rates of the first supply equipment 32A and the second supply equipment 32B for the tentative equipment capacities searched for in step S124 (step S230).

[0197] Then, the simulation execution unit 20B associates the set target energy level, the number of searches during the second equipment capacity optimization process, the tentative equipment capacity obtained by the search by the second equipment capacity optimization unit 20A2, and the energy introduction rate and introduction cost obtained by the simulation execution unit 20B, and stores them in the memory unit 16 (step S232).

[0198] The processing unit 21 determines whether the second equipment capacity optimization process, which is the process of steps S228 to S232, has been searched the number of times for the second equipment capacity optimization process with priority "2" read in step S22 (step S234). If the determination in step S234 is negative (step S234: No), the process returns to step S228. If the determination in step S234 is positive (step S234: Yes), the process proceeds to step S236.

[0199] The necessity determination unit 21E derives an evaluation value R1 of the optimal equipment capacity by the first equipment capacity optimization process identified in step S224, and an evaluation value R2 of the optimal equipment capacity by the second equipment capacity optimization process with priority "2" in steps S228 to S232 (step S238).

[0200] Then, the necessity determination unit 21E determines whether the evaluation value R2 is greater than the evaluation value R1 (step S238). If the necessity determination unit 21E determines that the evaluation value R2 is equal to or less than the evaluation value R1 (step S238: No), it sets the second equipment capacity optimization trigger to 0 (step S240) and proceeds to step S242. If the necessity determination unit 21E makes an affirmative determination in step S238 (step S238: Yes), it proceeds to step S242.

[0201] In step S242, the target energy introduction rate changing unit 20C determines whether the level of the set target energy introduction rate is the lowest level, Level 1 (step S242). If the determination in step S242 is negative (step S242: No), the process proceeds to step S244.

[0202] In step S244, the target energy introduction rate changing unit 20C lowers the level of the set target energy introduction rate by one level (step S244), and then the process returns to step S208.

[0203] As described above, the processing unit 21 changes the target energy introduction rate to a gradually lower range, and sets a narrower search range as the target energy introduction rate becomes lower. Therefore, in the search range setting process of step S208, a narrower search range is set as the target energy introduction rate becomes gradually lower. This allows the processing unit 20 to search for a tentative equipment capacity from a gradually narrower search range, thereby enabling an efficient search for a tentative equipment capacity.

[0204] On the other hand, if the determination in step S242 is affirmative (step S242: Yes), the process proceeds to step S246. In step S246, the output control unit 20D outputs processing result information including the tentative equipment capacity searched for by the processing of steps S200 to S244, the introduction cost of the supply equipment 32 with the searched tentative equipment capacity, and the energy introduction rate of the energy system 30 equipped with the supply equipment 32 with the searched tentative equipment capacity (step S246). The processing result information may further include at least one of the optimal equipment capacity identified by the processing unit 21 for each level of the target energy introduction rate and the most preferable optimal equipment capacity for the supply equipment 32 identified by the processing unit 21. Then, this routine ends.

[0205] As described above, the processing unit 21 of the information processing device 10B of this embodiment determines whether the evaluation value represented by the optimal equipment capacity determined by the equipment capacity optimization process, including searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, for the currently selected priority is equal to or less than the evaluation value represented by the optimal equipment capacity determined by the equipment capacity optimization process, including searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, for another priority selected previously that is higher than the currently selected priority. If the determination is affirmative, the processing unit 21 omits the equipment capacity optimization process, including searching for a tentative equipment capacity and deriving the introduction cost and energy introduction rate, for priorities lower than the currently selected priority, which will be executed after the target energy introduction rate is changed.

[0206] Therefore, in addition to the effects of the above-described embodiments, the information processing device 10B of this embodiment can assist in more efficient derivation of the optimum equipment capacity.

[0207] In the present embodiment, there are two types of priorities, “1” and “2,” and the equipment capacity optimization unit 20A includes a first equipment capacity optimization unit 20A1 that executes a search process corresponding to priority “1” and a second equipment capacity optimization unit 20A2 that executes a search process corresponding to priority “2.” However, as in the first embodiment, the priority assigned to the supply facility 32 may be three or more. In this case, the equipment capacity optimization unit 20A may further include an M-th equipment capacity optimization unit (M is an integer equal to or greater than three) corresponding to each of priorities “3” and above. The processing unit 20 may then execute the equipment capacity optimization process sequentially in descending order of priority in the same manner as described above. Furthermore, if the evaluation value represented by the optimal equipment capacity determined by the equipment capacity optimization process for the currently selected priority is equal to or less than the evaluation value represented by the optimal equipment capacity determined by the equipment capacity optimization process for another priority selected previously that is higher than the currently selected priority, the processing unit 21 may perform control such that the equipment capacity optimization process for priorities lower than the currently selected priority, which is to be executed after the target energy introduction rate is changed, is omitted.

[0208] Next, an example of the hardware configuration of the information processing device 10 (information processing device 10A, information processing device 10B) of the above embodiment will be described.

[0209] FIG. 8 is a hardware configuration diagram of an example of the information processing device 10 of the above embodiment.

[0210] The information processing device 10 of the above embodiment includes a control device such as a CPU (Central Processing Unit) 90D, a storage device such as a ROM (Read Only Memory) 90E, a RAM (Random Access Memory) 90F, and a HDD (Hard Disk Drive) 90G, an I / F unit 90B that interfaces with various devices, an output unit 90A that outputs various information, an input unit 90C that accepts user operations, and a bus 90H that connects each unit, and has a hardware configuration that utilizes a normal computer.

[0211] In the information processing device 10 of the above embodiment, the CPU 90D reads out a program from the ROM 90E onto the RAM 90F and executes it, thereby realizing each of the above units on the computer.

[0212] The programs for executing the above processes executed by the information processing device 10 of the embodiment may be stored in the HDD 90G. Also, the programs for executing the above processes executed by the information processing device 10 of the embodiment may be provided by being pre-installed in the ROM 90E.

[0213] Furthermore, the program for executing the above-described processes executed by the information processing device 10 of the above-described embodiment may be stored in an installable or executable file format on a computer-readable storage medium such as a CD-ROM, CD-R, memory card, DVD (Digital Versatile Disc), or flexible disk (FD) and provided as a computer program product. Furthermore, the program for executing the above-described processes executed by the information processing device 10 of the above-described embodiment may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. Furthermore, the program for executing the above-described processes executed by the information processing device 10 of the above-described embodiment may be provided or distributed via a network such as the Internet.

[0214] Although the present embodiment has been described above, the above embodiment is presented as an example and is not intended to limit the scope of the invention. This novel embodiment can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. This embodiment and its modifications are included within the scope and spirit of the invention, and are also included in the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0215] 10, 10A, 10B Information processing device 20, 21 Processing section 20A Equipment Capacity Optimization Department 20A1 First Facility Capacity Optimization Department 20A2 Second Facility Capacity Optimization Department 20B Simulation execution unit 20C Target energy introduction rate change section 20D Output control section 21E Necessity determination section 31 Equipment 32 Supply equipment

Claims

1. searching for a tentative equipment capacity of the supply equipment included in the energy system that supplies energy, based on the priority of the supply equipment, an input search range for equipment capacity, the number of searches, and a target energy introduction rate of the energy system, in descending order of priority, with a search range that becomes narrower as the priority decreases; Each time the provisional equipment capacity is searched for, an introduction cost of the supply equipment of the searched provisional equipment capacity and an energy introduction rate of the energy system including the supply equipment of the provisional equipment capacity are derived; The search for the tentative equipment capacity and the derivation of the introduction cost and the energy introduction rate are repeatedly performed every time the target energy introduction rate is changed. Processing section, An information processing device comprising:

2. The processing unit Among the plurality of provisional equipment capacities, the provisional equipment capacity in which the energy introduction rate is equal to or greater than a first threshold and the introduction cost is equal to or less than a second threshold is identified as an optimal equipment capacity. The information processing device according to claim 1 .

3. The processing unit setting the search range for the provisional equipment capacity based on the target energy introduction rate and the input search range; Searching for the tentative equipment capacity within the set search range; The information processing device according to claim 1 .

4. The processing unit The target energy introduction rate is changed to a lower range in a stepwise manner, The lower the target energy introduction rate, the narrower the search range is set. The information processing device according to claim 3 .

5. The processing unit sequentially selecting the priorities of the processing targets in descending order of priority, and each time a priority is selected, specifying the supply equipment of the selected priority and other supply equipment of a higher priority as a group of the supply equipment of the processing targets; searching for the provisional equipment capacity of the supply equipment belonging to the identified group of supply equipment; The information processing device according to claim 1 .

6. The processing unit setting the search range for the identified group of supply facilities of the currently selected priority based on an optimal equipment capacity identified by an equipment capacity optimization process including searching for the tentative equipment capacity and deriving the introduction cost and the energy introduction rate for another previously selected priority that is higher than the currently selected priority; searching for the tentative equipment capacity of the supply equipment belonging to the identified group of supply equipment within the set search range; The information processing device according to claim 5 .

7. The processing unit a range equal to or less than the optimal equipment capacity identified by the equipment capacity optimization process for another priority selected previously that is higher than the currently selected priority, is set as the search range for the identified group of supply facilities for the currently selected priority; The information processing device according to claim 6 .

8. The processing unit For at least some of the supply facilities, setting the search range to a range equal to or less than a maximum power demand that represents the maximum amount of power demand in the power supply facility over a predetermined period of time; The information processing device according to claim 3 .

9. The processing unit outputting processing result information including the provisional equipment capacity, the introduction cost of the supply equipment of the provisional equipment capacity, and the energy introduction rate of the energy system including the supply equipment of the provisional equipment capacity; The information processing device according to claim 1 .

10. The processing unit a second evaluation value represented by an optimal equipment capacity identified by an equipment capacity optimization process including searching for the tentative equipment capacity and deriving the introduction cost and the energy introduction rate in the currently selected priority, If the optimum equipment capacity is equal to or less than a first evaluation value represented by the optimum equipment capacity identified by an equipment capacity optimization process including searching for the tentative equipment capacity and deriving the introduction cost and the energy introduction rate, for another priority selected previously that is higher than the priority selected this time, omit the equipment capacity optimization process, which is executed after changing the target energy introduction rate, for the priorities equal to or lower than the currently selected priority, including searching for the provisional equipment capacity and deriving the introduction cost and the energy introduction rate; The information processing device according to claim 5 .

11. An information processing method executed by an information processing device, searching for a tentative equipment capacity of the supply equipment included in the energy system that supplies energy, based on the priority of the supply equipment, an input search range for equipment capacity, the number of searches, and a target energy introduction rate of the energy system, in descending order of priority, with a search range that becomes narrower as the priority decreases; Each time the provisional equipment capacity is searched for, an introduction cost of the supply equipment of the searched provisional equipment capacity and an energy introduction rate of the energy system including the supply equipment of the provisional equipment capacity are derived; The search for the tentative equipment capacity and the derivation of the introduction cost and the energy introduction rate are repeatedly performed every time the target energy introduction rate is changed. Information processing methods.

12. On the computer, a search for a tentative equipment capacity of the supply equipment included in the energy system that supplies energy, based on the priority of the supply equipment, an input search range for equipment capacity, the number of searches, and a target energy introduction rate of the energy system, in descending order of priority, with a search range that becomes narrower as the priority decreases; Each time the provisional equipment capacity is searched for, an introduction cost of the supply equipment of the searched provisional equipment capacity and an energy introduction rate of the energy system including the supply equipment of the provisional equipment capacity are derived; repeatedly executing the search for the tentative equipment capacity and the derivation of the introduction cost and the energy introduction rate every time the target energy introduction rate is changed; Information processing program for.

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