Information processing device, production plan creation method, and production plan creation program

Abstract

To achieve creation of production plan for supporting RE maximization.SOLUTION: An information processing device 10 includes: a first accepting unit which accepts a predicted supply amount of clean energy; a second accepting unit which accepts a predicted usage amount of energy; and a simulation unit which implements a simulation for optimizing a production plan in which a manufacturing time band of a product is scheduled for each product for which production is scheduled on the basis of the predicted supply amount of the clean energy and the predicted usage amount of the energy.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a production planning method, and a production planning program. [Background technology]

[0002] RE100 (Renewable Energy 100%) is an international initiative that aims for companies to cover 100% of the electricity used in their operations with renewable energy, and is attracting increasing attention.

[0003] In order to maximize RE, companies and other organizations may supply all of their electricity consumption with electricity derived from clean energy sources, or purchase attribute value separated from the electricity generated by clean energy. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-151174 [Patent Document 2] Japanese Patent Application Publication No. 2022-92898 Summary of the Invention [Problem to be solved by the invention]

[0005] However, product production plans are created based on facilities such as production lines and manufacturing equipment, manufacturing orders including orders for product types, quantities, and delivery dates, and inventory levels. As a result, priority is given to efficiency and delivery dates, and production plans are sometimes created that put RE maximization on the back burner.

[0006] The present invention aims to realize the creation of a production plan that supports RE maximization. [Means for solving the problem]

[0007] An information processing device according to one aspect of the present invention includes a first receiving unit that receives a predicted supply amount of clean energy, a second receiving unit that receives a predicted amount of energy usage, and a simulation unit that executes a simulation to optimize a production plan in which manufacturing time periods for each product to be produced are planned based on the predicted supply amount of clean energy and the predicted amount of energy usage.

[0008] In a production plan creation method according to one aspect of the present invention, a computer executes a process of accepting a predicted supply amount of clean energy, accepting a predicted amount of energy usage, and performing a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted amount of energy usage.

[0009] A production plan creation program according to one aspect of the present invention causes a computer to execute a process of accepting a predicted supply of clean energy, accepting a predicted amount of energy usage, and performing a simulation to optimize a production plan in which manufacturing time periods for each product to be produced are planned based on the predicted supply of clean energy and the predicted amount of energy usage. [Effects of the Invention]

[0010] According to one embodiment, it is possible to create a production plan that supports RE maximization. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a production planning system. [Figure 2] FIG. 2 is a flowchart showing the procedure of the data reception process. [Figure 3] FIG. 3 is a schematic diagram showing a power supply system. [Figure 4] FIG. 4 is a schematic diagram showing an example of purchasing a certificate. [Figure 5]FIG. 5 is a flowchart showing the procedure of the simulation process. [Figure 6] FIG. 6 is a flowchart showing the procedure of the RE degree calculation process. [Figure 7] FIG. 7 is a flowchart showing the procedure of the RE degree calculation process. [Figure 8] FIG. 8 is a diagram illustrating an example of a production plan. [Figure 9] FIG. 9 is a diagram illustrating an example of manufacturing process information. [Figure 10] FIG. 10 is a diagram showing an example of a display on a user terminal. [Figure 11] FIG. 11 is a diagram showing an example of a display on a user terminal. [Figure 12] FIG. 12 is a flowchart showing the procedure of the request issuing process. [Figure 13] FIG. 13 is a flowchart showing the procedure of the request generation process. [Figure 14] FIG. 14 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, with reference to the accompanying drawings, a description will be given of an information processing device, a production planning method, and a production planning program (hereinafter referred to as "embodiments") according to the present application. Each embodiment merely illustrates examples and aspects, and does not limit the range of values, functions, or usage scenarios. Each embodiment can be adaptively combined within the scope of not causing inconsistencies in the processing content. [Example]

[0013] <Overall structure> First, we will explain the overall configuration of a production planning system 1 according to this embodiment. Fig. 1 is a diagram showing an example of the configuration of the production planning system 1. The production planning system 1 shown in Fig. 1 provides a production planning function for creating a production plan for a product in production facilities such as a plant.

[0014] This technology is distinguished from existing conventional technologies in that it creates production plans that support RE maximization as part of the production planning function. "RE maximization" here refers to maximizing the supply of electricity derived from renewable energy up to a specific target value, which is not necessarily limited to the upper limit of 100%, but could also be 80% or 90%.

[0015] Hereinafter, "energy" described in this embodiment may include clean energy and non-clean energy. Of these, "clean energy" is also called green energy, and may include, for example, so-called renewable energy. Furthermore, "non-clean energy" refers to energy that is not clean energy, and may include, for example, fossil energy. Hereinafter, renewable energy will be cited as an example of clean energy, and this renewable energy may be referred to as "renewable energy" or "RE."

[0016] 1, the production planning system 1 may include a production management system 3, an energy trading system 5, various devices 7, an information processing device 10, and a user terminal 30. The production management system 3, the energy trading system 5, the various devices 7, and the user terminal 30 may be communicably connected to the information processing device 10 via any network NW. The network NW may be realized by any technology, whether wired or wireless, such as Internet technology, industrial communication standards, or low-power wireless communication standards for the Internet of Things (IoT).

[0017] The production management system 3 is a system that collectively manages operations related to production carried out at production facilities such as plants. The electricity trading system 5 is a system that trades environmental values ​​such as green power certificates, J-credits, and non-fossil fuel certificates. The electricity trading system will be described later using Figure 4.

[0018] The various devices 7 are various devices that can be connected to the information processing device 10. For example, the various devices 7 may include a smart meter that measures the amount of power consumption in demand facilities owned by a user who receives the production plan creation function. Such demand facilities may include production facilities such as a production line that is a facility of a manufacturing factory and manufacturing equipment included in the production line, and may also include office facilities.

[0019] The information processing device 10 is an example of a computer that provides the above-mentioned production planning function. For example, the information processing device 10 may be realized as a server that provides the above-mentioned production planning function on-premise. Alternatively, the information processing device 10 may be realized as a PaaS (Platform as a Service) or SaaS (Software as a Service) application, thereby providing the above-mentioned production planning function as a cloud service.

[0020] The user terminal 30 is a terminal device used by a user who receives the production plan creation function. The "user" here may include, for example, not only an organization such as a company, but also a related party of the organization. For example, the user terminal 30 may be realized by any computer, such as a personal computer, a smartphone, a tablet terminal, or a wearable terminal.

[0021] <Configuration of information processing device 10> Next, an example of the functional configuration of the information processing device 10 according to this embodiment will be described. Fig. 1 shows a block diagram related to the production plan creation function of the information processing device 10. As shown in Fig. 1, the information processing device 10 has a communication control unit 11, a storage unit 13, and a control unit 15. Note that Fig. 1 only shows a selection of functional units related to the above-mentioned production plan creation function, and the information processing device 10 may also be provided with functional units other than those shown.

[0022] The communication control unit 11 is a functional unit that controls communications with other devices such as the production management system 3, the energy trading system 5, the various devices 7, and the user terminal 30. For example, the communication control unit 11 can be realized by a network interface card.

[0023] The storage unit 13 is a functional unit that stores various types of data. For example, the storage unit 13 is realized by an internal, external, or auxiliary storage of the information processing device 10. For example, the storage unit 13 stores data such as a predicted supply amount 13A, setting information 13B, a predicted usage amount 13C, and production specification information 13D. Note that the data such as the predicted supply amount 13A, setting information 13B, a predicted usage amount 13C, and production specification information 13D will be described together with the situations where reference, generation, or registration is performed.

[0024] The control unit 15 is a functional unit that performs overall control of the information processing device 10. For example, the control unit 15 can be realized by a hardware processor. As shown in FIG. 1, the control unit 15 has a reception unit 15A, a simulation unit 15B, an output unit 15C, and a request unit 15D. Note that the control unit 15 may also be realized by hardwired logic or the like.

[0025] The reception unit 15A is a processing unit that receives various types of information. The reception unit 15A corresponds to an example of a first reception unit, a second reception unit, and a third reception unit. In one embodiment, the reception unit 15A receives data such as a predicted supply amount, setting information, a predicted usage amount, and production specification information via the production management system 3, the energy trading system 5, various devices 7, or the user terminal 30.

[0026] 2 is a flowchart showing the procedure for the data reception process. As shown in Fig. 2, the production management system 3, the energy trading system 5, the various devices 7, or the user terminal 30 transmits data such as a predicted supply amount, setting information, a predicted usage amount, and production specification information (step S101).

[0027] When the data is transmitted in this manner, the receiving unit 15A receives data such as the predicted supply amount, setting information, predicted usage amount, and production specification information via the network NW (step S102).

[0028] Then, the reception unit 15A registers data such as the predicted supply amount, setting information, predicted usage amount, and production specification information as predicted supply amount 13A, setting information 13B, predicted usage amount 13C, and production specification information 13D in the storage unit 13 (step S103). Note that the predicted supply amount 13A, setting information 13B, predicted usage amount 13C, and production specification information 13D may be stored in the storage unit 13 in any data format, such as a relational database.

[0029] In one aspect, the reception unit 15A can receive the predicted supply amount of energy by time period that the user will receive from the power generator in the above step S102, in order to realize power tracking at the scheduled production time when the production of the product is determined in the production plan.

[0030] "Power tracking" here refers to proving that the electricity consumed by a consumer originates from a specific power generation source. However, since electricity flowing through the power system (transmission and distribution network) is considered physically indistinguishable from its source of generation, physical identification and tracking of electricity flowing through the power system is not performed.

[0031] The basic concept of tracking is that when the amount of electricity generated by a power source connected to the same power grid is equal to the amount consumed by the consumer facility (same amount of electricity at the same time), and it can be confirmed that the electricity generated from that power source is not consumed elsewhere, the electricity from a specific power source is considered to have been consumed by a specific consumer.

[0032] Here, energy supply from power generators to consumers can be implemented in the following ways. The term "power generator" used herein refers to an example of a supplier that has power generation facilities that generate electricity, including clean energy and fossil energy sources. The term "consumer" refers to an example of a consumer that has power generation facilities that receive and consume electricity. These power generators and consumers may be businesses, such as individuals or corporations, or public organizations, such as local or national governments. The first scenario involves a combination of specific consumers and specific power generation sources that is defined in advance. Figure 3 is a schematic diagram of a power supply system. As shown in Figure 3, the power supply system can be divided into a power generation sector, a transmission and distribution sector, and a retail sector. For example, the power generation sector includes thermal power plants, which use fossil energy, as well as power generation sources that use renewable energy, such as solar power, wind power, and hydroelectric power. Furthermore, the transmission sector involves a power transmission and distribution network connecting power plants to consumers. The transmission and distribution network may include transmission lines connecting power plants to transmission substations, such as extra-high voltage substations, primary substations, and secondary substations, as well as distribution lines connecting distribution substations to general consumers. Furthermore, the retail sector may include consumers such as large factories and large buildings that receive extra-high voltage power, consumers such as medium-sized factories that receive high voltage power, and general consumers that receive low voltage power.

[0033] For example, in the example of simultaneous balancing in the above-mentioned power supply system, if a solar power generation system generates 1 kWh and at the same time a user who has a contract with this solar power generation company consumes 1 kWh, this 1 kWh is considered to be solar-generated electricity. Regardless of the type of power generation source, not just solar power generation, the same transmission and distribution lines are used to supply electricity regardless of the type of power generation source you contract with, so simultaneous balancing can be achieved by matching the power company (supply side) with the user (demand side).

[0034] In such a power supply system, power is supplied based on a contract between a power generation company and a consumer. For example, the receiving unit 15A receives contract information between the power generation company and the user organization from the user terminal 30, thereby acquiring the amount of power defined in each contract information as a predicted energy supply amount for the user organization. The "predicted supply amount" here may be, for example, the average power generated over a standard interval, such as 30 minutes, for simultaneous simultaneous supply. By acquiring the predicted supply amount for each contract information in this manner, the predicted supply amount for each time period for each type of energy from the power generation source owned by the power generation company can be acquired. For example, the predicted supply amount can be acquired not only for classifications such as fossil energy and renewable energy, but also for each type of renewable energy, such as solar, wind, hydroelectric, geothermal, solar thermal, and biomass. Instead of automatically extracting the predicted energy supply amount from the contract information, the user organization may input the predicted energy supply amount for the user organization via a graphical user interface (GUI).

[0035] The second is when a certificate is issued after electricity generation that represents an attribute value separated from the non-fossil energy electricity, and the value is determined only through the purchase of the certificate. For example, a typical example of attribute value is environmental value. Environmental value can include "Green Power Certificates," "J Credits," and "Non-Fossil Energy Certificates." Of these, Green Power Certificates separate the value of renewable energy electricity into "the value of the electricity itself" and "environmental value," and the "environmental value" is certifiable.

[0036] FIG. 4 is a schematic diagram showing an example of certificate purchase. As shown in FIG. 4, when bidding for non-fossil fuel certificates, the purchase amount (offset) of non-fossil fuel certificates is calculated based on the consumer's electricity consumption. An example of an offset is a purchase amount corresponding to the amount of electricity that is insufficient for RE100. Once the purchase amount is determined in this way, the purchasing side, such as the consumer, submits a bid to the energy trading system 5, specifying the slots they wish to purchase from the 48 slots for one day, with a minimum trading unit of 50 kWh per slot (30 minutes), and the purchase amount. Meanwhile, the selling side, such as power producer a and power producer b, also submits a bid to the energy trading system 5, specifying the slots they wish to sell and the sales amount. Based on this bidding, the energy trading system 5 matches the "buy" and "sell" bids and determines the contract price and contract amount, thereby conducting the transaction and procuring non-fossil fuel certificates. Although an example in which a consumer bids on the energy trading system 5 has been given here, it goes without saying that an agent who manages the environmental value of a consumer can also bid on the energy trading system 5.

[0037] For example, in the case of the simultaneous and equal amount of certificate purchases mentioned above, let's say that Factory A installs solar power generation equipment and consumes it in-house. In this case, if Factory A does not need the environmental value of "not emitting carbon dioxide," it can sell this environmental value as a "green power certificate." If another Factory B purchases this certificate, it will be considered that Factory B has used renewable energy and reduced carbon dioxide emissions.

[0038] In the case of purchasing such certificates, non-fossil fuel certificates are procured via the energy trading system 5. For this purpose, the reception unit 15A receives the purchase amount of non-fossil fuel certificates from the energy trading system 5. For example, the energy trading system 5 can obtain the purchase amount of the non-fossil fuel certificates as a predicted supply amount of renewable energy for the user organization. It goes without saying that the predicted supply amount can also be obtained for each type of renewable energy, such as solar, wind, hydroelectric, geothermal, solar heat, and biomass.

[0039] As another aspect, the receiving unit 15A can receive the user setting information from the user terminal 30 in step S102 above.

[0040] For example, the setting information may include settings such as each power generator's energy type, CO2 emission coefficient, and renewable energy coefficient. Among these, the "CO2 emission coefficient" is an index that indicates how much CO2 is emitted to supply 1 kWh of electricity. For example, the CO2 emission coefficient (kg-CO2 / kWh) is calculated by dividing the amount of CO2 emitted by the amount of electricity sold. Furthermore, the "renewable energy coefficient" is an index that indicates the degree to which the power generator's energy corresponds to clean energy. For example, the renewable energy coefficient may be a value normalized to a numerical range of 0 to 1. In this case, as the renewable energy coefficient approaches 1, it approaches completely clean energy, while as the renewable energy coefficient approaches 0, it approaches pure fossil energy. Such renewable energy coefficients can be set all at once or for each time period.

[0041] Furthermore, the configuration information may include a setting of priorities for energy allocation destinations in power tracking. The term "allocation destination" here may include demand facilities grouped into any unit, such as a product, a manufacturing process, a manufacturing line, or a manufacturing factory. This is not limited to manufacturing-related targets, and may include demand facilities included in offices, etc. For example, if production lines A to Z are used as allocation destinations, the priorities are set in ascending or descending order, starting with the most prioritized production line among production lines A to Z. The configuration information may also include a setting of priorities for facilities owned by the user organization. For example, priority 1 can be set as factory A, priority 2 as factory B, and priority 3 as office. In this case, power is allocated according to the priority order of factory A > factory B > office. Furthermore, the configuration information may include a setting of priorities for power generators or types of energy from power generators to be allocated to the allocation destinations. For example, priority 1 can be set as wind power, priority 2 as solar power, and priority 3 as fossil fuel. In this case, power is allocated according to the priority order of wind power > solar power > fossil fuel. Priorities for allocation destinations, facilities, and energy types can be set all at once or for each time period.

[0042] The setting information may also include a setting for the simultaneous balancing interval. For example, the simultaneous balancing interval can be set to any interval, such as 15 minutes or 60 minutes, in addition to the standard 30 minutes. The setting information may also include settings for the products to be simulated by the simulation unit 15B (described later). The setting information may also include settings for allowing or prohibiting input to the production management system 3, such as input of a production plan. The setting information may also include settings for allowing or prohibiting automatic trading in the energy trading system 5. The settings for the simultaneous balancing interval, target products, input to the production management system 3, automatic trading in the energy trading system 5, etc. can be received, for example, via the user terminal 30.

[0043] As another aspect, the reception unit 15A can receive the user organization's energy usage by time period from the various devices 7 in step S102. For example, the reception unit 15A can receive the power usage measured by each smart meter connected to the user organization's demand facilities, as an example of the various devices 7. Typically, a smart meter reads the average power consumption at 30-minute intervals, which is the standard for simultaneous balancing intervals, that is, the so-called 30-minute demand value. In this case, the target of the smart meter reading may be any unit of the user organization's facilities, such as a product, a manufacturing process, a device that implements a production line, or an entire factory. Note that while the example above illustrates receiving energy usage from a smart meter, input of energy usage from the user terminal 30 may also be received via a GUI or the like. In this case, the reception unit 15A may allow the user to input energy usage for a predetermined period, such as a day's or a week's worth, all at once.

[0044] Based on the thus received actual measured values ​​of the energy usage, the receiving unit 15A can predict the energy usage for each time period. For example, such prediction of the energy usage may be realized by a machine learning model such as a neural network, a support vector machine, or gradient boosting.

[0045] For example, a machine learning model inputs a data sequence of actual usage values ​​obtained as past history and outputs a future predicted usage amount or a data sequence of predicted usage amounts. Training data for training such a machine learning model can be generated from usage history data including a data sequence of actual usage values ​​for a predetermined period of time in the past. For example, the usage history data is alternately divided into segments of a period corresponding to the input size of the machine learning model and segments of correct labels following the segments. This segmentation allows a dataset including training data and its correct labels to be obtained from the usage history data. For example, in the training phase, the training data is used as the explanatory variable of the machine learning model, and the labels are used as the objective variable of the machine learning model, and the machine learning model can be trained according to any machine learning algorithm, such as deep learning. This results in a trained machine learning model. In the prediction phase, a data sequence of actual usage values ​​obtained by going back a period corresponding to the input size of the machine learning model from the time the latest actual usage value was received is input to the trained machine learning model. As a result, the trained machine learning model outputs a predicted usage amount or a data sequence of predicted usage amounts for a specific time after the current time when the latest actual usage value was obtained.

[0046] Although an example has been given here in which actual measured values ​​of usage are acquired and then predicted from the actual measured values ​​of usage, predicted energy usage by time period can also be received from the user terminal 30 or a usage prediction device (not shown). Furthermore, the reception unit 15A can also predict a predicted supply amount from an actual measured value of the supply amount, in the same way as predicting a predicted usage amount from an actual measured value of the usage amount, by using a machine learning model similar to the above-described machine learning model.

[0047] In another aspect, the receiving unit 15A can receive production specification information from the production management system in step S102. Here, "production specification information" refers to specification information used to create a production plan. For example, the production specification information may include orders from the user organization's business partners, such as manufacturing orders containing product types, quantities, and delivery dates, to enable a computer to identify products to be produced and their delivery dates. Furthermore, the production specification information may include a manufacturing master data set, such as the following, to enable a computer to identify a manufacturing method for the products to be produced. For example, the manufacturing master data may include information correlating the required work time for each production line or each piece of manufacturing equipment included in the production line with each product or manufacturing process, as well as an operating calendar setting the shifts of on-site workers, operators, and other personnel. Additionally, the production specification information may optionally include inventory quantities for each product to enable a computer to identify the quantity of products to be produced. It may also include the renewable energy ratio and CO2 emissions of parts and materials used in manufacturing. Although an example has been given here in which production specification information is received from the production management system 3, production specification information may also be received via a GUI or the like from the user terminal 30. In this case, the receiving unit 15A may allow production specification information for a predetermined period, such as one day's worth, one week's worth, or one month's worth, to be input all at once.

[0048] The simulation unit 15B is a processing unit that executes a simulation to optimize a production plan in which the manufacturing time period of each product to be produced is planned, based on the predicted supply amount of clean energy by time period and the predicted energy usage amount by time period.

[0049] 5 is a flowchart showing the procedure of the simulation process. As shown in FIG. 5, the simulation unit 15B acquires the predicted supply amount 13A, the predicted use amount 13C, and the production specification information 13D stored in the storage unit 13 (step S201).

[0050] Then, the simulation unit 15B receives a designation of the purpose of the simulation, etc. (Step S202). For example, examples of the purpose include maximizing RE for a specific product, maximizing RE for a specific customer, and maximizing RE for a product in a specific time period.

[0051] At this time, if maximizing RE of a specific product is designated as the objective in step S202 (Yes in step S203), the simulation unit 15B acquires the objective function, variables, and constraints corresponding to the objective (step S204).

[0052] Furthermore, if maximizing RE for a specific customer is specified as the objective in step S202 (No in step S203 and Yes in step S205), the simulation unit 15B acquires the objective function, variables, and constraints corresponding to the objective (step S206).

[0053] Furthermore, if maximizing RE of the product in the specific time period is specified as the objective in step S202 (No in step S203 and No in step S205), the simulation unit 15B acquires the objective function, variables, and constraints corresponding to the objective (step S207).

[0054] The objective function, variables, and constraints are acquired through the processing of any one of step S204, step S206, and step S207. Then, the simulation unit 15B executes a simulation to analyze the optimization problem of the production plan to maximize or minimize the objective function (step S208).

[0055] Here, we will cite an example of using linear programming as an algorithm for analyzing the optimization problem of a production plan. In this case, the products to be produced are set as variables based on the production orders included in the production specification information. Furthermore, the production lines or production equipment are set as variables based on the production master included in the production specification information. Furthermore, as constraints restricting the range within which the variables can be manipulated, a linear inequality or other expression is used to define the range within which production can be completed on time and without shift changes based on the operation calendar of the production orders or production master included in the production specification information. Furthermore, for each objective, an objective function corresponding to that objective is formulated using variables. For example, we will cite a case in which a loss function is used as an example of an objective function. In this case, when setting a loss function with the objective of maximizing RE for a specific product, a loss function is formulated using the variables such that the loss increases as the RE degree of the specific product, for example, the difference between the predicted usage and the predicted supply, increases. Here, although the difference between the predicted usage and the predicted supply is used as an example of the ratio between the predicted usage and the predicted supply, the ratio between the predicted usage and the predicted supply may also be used. Note that when setting a loss function corresponding to an objective other than maximizing the RE of the specific product described above, the loss function can be set using the same logic.

[0056] Based on the formulation of these loss functions and constraints, a combination of variables that satisfies the constraints and minimizes the loss function is calculated. The combination of variables calculated in this way determines a production plan in which the manufacturing time period for each product to be produced is defined. While a loss function is used as an example of an objective function here, the combination of variables may also be calculated by maximizing a score function formulated using the variables. Furthermore, any analytical algorithm, not limited to the linear programming method described above, can be applied to the optimization problem of a production plan.

[0057] Such a simulation makes it possible to create a production plan that supports maximizing RE of a product corresponding to a purpose specified by a user.

[0058] Thereafter, the simulation unit 15B executes loop processing 1, which repeats the processing of step S209 a number of times corresponding to the number L of production plans that satisfy a predetermined condition among the production plans obtained as a result of step S208, for example, production plans whose losses are equal to or less than a threshold value.

[0059] That is, the simulation unit 15B executes an RE degree calculation process to calculate the RE degree of the product to be produced under the lth production plan based on the lth production plan and the predicted supply amount and predicted usage amount acquired in step S201 (step S209).

[0060] By repeating this loop process 1, the RE degree for each product is calculated for each of the L production plans.

[0061] Next, the simulation unit 15B outputs the production plans whose RE degrees are among the top predetermined number to the display unit of the user terminal 30 (step S210). Then, the processing from step S202 to step S210 is repeated until a confirmation operation for ending the simulation is received (step S211 No).

[0062] Thereafter, when a confirmation operation for confirming that the simulation is finished is accepted (Yes in step S211), the simulation unit 15B selects one of the production plans whose RE degrees are among the top predetermined number (step S212), and ends the process.

[0063] In step S212, the production plan with the highest RE degree may be automatically selected, or the selection may be manually accepted from the user terminal 30 via a GUI or the like.

[0064] Next, the RE degree calculation process shown in step S209 of Fig. 5 will be described in detail with reference to Fig. 6 and Fig. 7. Fig. 6 and Fig. 7 are flowcharts showing the procedure of the RE degree calculation process. As shown in Fig. 6, the simulation unit 15B acquires the predicted supply amount 13A, setting information 13B, and predicted usage amount 13C stored in the storage unit 13 (step S301).

[0065] Next, the simulation unit 15B executes a pre-processing to match the simultaneous balancing interval set in the setting information 13B with the intervals of the predicted supply amount 13A and the predicted use amount 13C (step S302).

[0066] For example, if the interval between predicted supply amount 13A and predicted usage amount 13C is 30 minutes and the simultaneous balancing interval is set to 1 hour, the two paired predicted supply amounts are added together and the two paired predicted usage amounts are added together.Also, if the measurement interval between predicted supply amount 13A and predicted usage amount 13C is 30 minutes and the simultaneous balancing interval is set to 15 minutes, the 30-minute predicted supply amount and the 30-minute predicted usage amount are each divided by the ratio of the simultaneous balancing interval, for example, 2, to calculate the 15-minute predicted supply amount and the 15-minute predicted usage amount.

[0067] Then, the simulation unit 15B executes loop processing 1, which repeats the processing from step S303 to step S311 described below, the number of times corresponding to the number K of slots for time periods for which clean energy allocation has not yet been processed.

[0068] That is, the simulation unit 15B determines the selection order of the power generators to which clean energy is allocated based on the priorities of the power generators set in the setting information 13B (step S303). For example, when the priorities of the power generators are set as follows: Priority 1: power generator A, Priority 2: power generator C, Priority 3: power generator B, the selection order is determined as power generator A, power generator C, power generator B. Note that, although an example has been given here in which the selection order of the power generators is determined based on the priorities of the power generators, the selection order of the power generators may also be determined based on the priorities of the types of energy of the power generators.

[0069] Then, the simulation unit 15B executes loop process 2 and loop process 3, which repeats the processes from step S304 to step S311 described below, until the selection of M power generators is completed or the selection of N allocation destinations is completed. Note that the "allocation destination" referred to here may be a product, a manufacturing process, a manufacturing line, or a manufacturing device included in the manufacturing process or manufacturing line.

[0070] That is, the simulation unit 15B determines whether the predicted usage amount of the currently selected allocation destination n is greater than "0" (step S304). At this time, if the predicted usage amount of the allocation destination n is not greater than "0" (No in step S304), it is determined that the energy allocation of the allocation destination n has been completed. In this case, the processes from step S305 to step S309 are skipped, and the loop counter n of the allocation destination is incremented to select the next allocation destination. The selection order of such allocation destinations is also determined by the priority of the allocation destinations set in the setting information 13B.

[0071] On the other hand, if the predicted usage amount of the allocation destination n is greater than "0" (Yes in step S304), it is determined that the allocation of energy to the allocation destination n has not been completed. In this case, the simulation unit 15B further determines whether the predicted supply amount of the selected power generator m is greater than "0" (step S305).

[0072] If the predicted supply amount of the power generator m is not greater than "0" (No in step S305), it is determined that the power generator m has no power remaining to allocate to the allocation destination n. In this case, the processes from step S306 to step S311 are skipped, and the loop counter m of the power generator is incremented to select the next power generator.

[0073] Furthermore, if the predicted supply amount of the power generator m is greater than "0" (Yes in step S305), it is determined that the power generator m has room to allocate power to the allocation destination n. In this case, the simulation unit 15B compares the predicted usage amount of the allocation destination n with the predicted supply amount of the power generator m (step S306).

[0074] Here, if the predicted usage of the allocation destination n is less than the predicted supply of the power generator m (No in step S307), it is determined that the remaining predicted supply of the power generator m can complete the allocation of power to the allocation destination n. In this case, the simulation unit 15B updates the predicted usage of the allocation destination n to "0" (step S308), and updates the predicted supply of the power generator m to the latest by subtracting the predicted usage of the allocation destination n from the predicted supply of the power generator m (step S309). Thereafter, the allocation destination loop counter n is incremented to select the next allocation destination.

[0075] By repeating this loop process 3, power generators with higher priorities or types of energy with higher priorities are allocated in order starting from the allocation destination with the highest priority.

[0076] Furthermore, if the predicted usage of the allocation destination n is equal to or greater than the predicted supply of the power generator m (Yes in step S307), it is determined that the allocation of power to the allocation destination n will not be completed even if all of the remaining predicted supply of the power generator m is allocated to the allocation destination n. In this case, the simulation unit 15B updates the predicted usage of the allocation destination n to the latest by subtracting the predicted supply of the power generator m from the predicted usage of the allocation destination n (step S310), and updates the predicted supply of the power generator m to "0" (step S311). Thereafter, the loop counter m of the power generator is incremented to select the next power generator.

[0077] By repeating this loop process 2, energy is allocated to the power generator with the highest priority or to the allocation destination with the highest priority in order of the type of energy with the highest priority.

[0078] Furthermore, by repeating loop process 1, for each time period in which clean energy allocation has not yet been processed, energy is allocated to a power generator with a higher priority, or to a type of energy with a higher priority, in order from the highest priority allocation destination, and energy is allocated to a power generator with a higher priority, or to a type of energy with a higher priority, in order from the highest priority allocation destination.

[0079] 6, an example has been given in which the target renewable energy ratio is RE100 and clean energy of an amount of power corresponding one-to-one to the total predicted usage of the allocation destination n is allocated to the allocation destination, but this is not limiting. For example, the target renewable energy ratio may be any target value less than RE100, such as RE90 or RE80.

[0080] After that, as shown in FIG. 7, the simulation unit 15B acquires the lth production plan and the allocation result corresponding to the lth production plan (step S312).

[0081] Then, the simulation unit 15B executes loop processing 4, which repeats the processing from step S313 to step S315 described below the number of times corresponding to the number P of products included in the l-th production plan.

[0082] That is, the simulation unit 15B refers to the manufacturing time slot of the selected product p in the l-th production plan, and extracts the allocation results of the time slots that overlap with the manufacturing time slot of the product p from the allocation results (step S313).

[0083] Then, the simulation unit 15B executes loop process 5, which repeats the processing of step S314 described below a number of times corresponding to the number T of slots in the time slots corresponding to the manufacturing time slot of product p. Loop process 5 further includes loop process 6, which repeats the processing of step S314 described below a number of times corresponding to the number K of manufacturing lines or manufacturing equipment for product p operating in the currently selected time slot t. That is, the simulation unit 15B calculates the operation rate of manufacturing line k for product p during time slot t or the operation rate of manufacturing equipment k for product p during time slot t based on the period in which the currently selected time slot t overlaps with the manufacturing time slot of product p, i.e., the actual operation time of manufacturing line k for product p or the actual operation time of manufacturing equipment k (step S314). For example, the operation rate can be calculated by normalizing the actual operation time during time slot t to the actual operation time per unit time. As a calculation example, if the size of the time slot t is 30 minutes, the unit time is 1 hour, and the actual operating time is 10 minutes, the actual operating time per hour is calculated as 10 minutes x (60 minutes / 30 minutes), which gives "20 minutes." Therefore, by dividing this "20 minutes" by the unit time, the operating ratio is calculated as "1 / 3."

[0084] By repeating this loop process 6, the operation rate is calculated for each production line k or production equipment k of product p in the selected time period t. Furthermore, by repeating loop process 5, the operation rate of production line k of product p or the operation rate of production equipment k of product p is calculated for each time period t in which the production time periods of product p overlap.

[0085] Thereafter, the simulation unit 15B calculates the RE degree of product p (step S315). For example, when a renewable energy ratio is calculated as an example of the RE degree, the simulation unit 15B can calculate the renewable energy ratio of product p according to the following formula (1). Here, the "RE degree of production line k or production equipment k in time slot t" in the following formula (1) can be calculated according to the following formula (2). Note that "i" in the following formulas (1) and (2) refers to the number of power generators assigned to production line k or production equipment k.

[0086] Renewable energy ratio = Σ (RE degree of production line k or production equipment k in time period t × operation ratio) / Σ (operation ratio) (1) RE degree of production line k or production equipment k in time period t = Σ(allocation it × renewable energy index i of allocated power) / Σ predicted usage kt (2)

[0087] Furthermore, when CO2 emissions are calculated as an example of the RE degree, the simulation unit 15B can calculate the CO2 emissions of product p according to the following formula (3): "CO2 emissions of production line k or production equipment k in time period t" in the following formula (3) can be calculated according to the following formula (4).

[0088] CO2 emissions = Σ (CO2 emissions of production line k or production equipment k in time period t × operation rate) + Σ (CO2 emissions of materials used in production line k or production equipment k) (3) CO2 emissions of production line k or production equipment k during time period t = Σ(allocation it × CO2 emission coefficient of allocated electricity i) (4)

[0089] By repeating this loop process 4, the RE degree, such as the renewable energy ratio or CO2 emissions, is calculated for each of the P products. Note that while Fig. 7 shows an example in which the RE degree is calculated for each product, by replacing the product with the manufacturing process, manufacturing line, or manufacturing plant, the RE degree can be calculated for each manufacturing process, manufacturing line, or manufacturing plant.

[0090] The calculation results of the RE degree calculated in this way for each product or each manufacturing process can be stored in the storage unit 13. Here, the calculation results of the RE degree do not necessarily have to be stored in a relational database or the like. For example, the calculation results of the RE degree can be recorded in a blockchain network.

[0091] Blockchain is a distributed ledger technology in which multiple nodes that make up a P2P (Peer to Peer) network maintain the same database. In a blockchain, transactions on the P2P network are processed collectively as blocks, and each block is linked by a hash function. Data recorded in a block in a blockchain cannot be retroactively changed unless all subsequent blocks are also changed, making ledger management platforms using blockchain highly secure against alterations.

[0092] The consensus algorithm used between the nodes that form such a blockchain network can be any method such as PoW (Proof of Work) or PoS (Proof of Stake).

[0093] In blockchain, spoofing is prevented by attaching a digital signature using a private key to transaction data. It is not necessary to use public key cryptography to encrypt transaction data. Any encryption algorithm can be used, such as the Advanced Encryption Standard (AES), Secure Hash Algorithm (SHA), Rivest-Shamir-Adleman cryptosystem (RSA), or Elliptic Curve Cryptography (ECC).

[0094] In addition, data on each transaction is made public and shared across the entire blockchain network. Note that depending on the type of P2P database, the entire P2P network does not necessarily need to maintain the same records.

[0095] The consensus algorithm used between the nodes that form such a blockchain network can be any method such as PoW (Proof of Work) or PoS (Proof of Stake).

[0096] When recording the calculation result of the RE degree in the blockchain network in this manner, the simulation unit 15B generates transaction data corresponding to the calculation result of the RE degree for one time period, and sends a request to the blockchain network requesting registration of the transaction data, thereby recording the calculation result of the RE degree in the blockchain.

[0097] Here, an example of the production plan selected in step S212 shown in FIG. 5 is presented. FIG. 8 is a diagram showing an example of a production plan. FIG. 8 shows a production plan for January 23, 2023, but production plans for dates other than January 23 and before or after that date may also be included. Furthermore, FIG. 8 excerpts two products, product A1 and product B1, as an example of the production plan for January 23, 2023. However, other products may also be included. As shown in FIG. 8, the production plan includes a production time slot for each product to be produced. In the example shown in FIG. 8, the production time slot is defined by the start time and required work time for each process corresponding to the manufacturing process included in each of product A1 and product B1. Note that the production plan may also include information other than the product, manufacturing process, production equipment, and manufacturing time slot, such as the delivery date of the product to the customer in the example shown in FIG. 8.

[0098] Fig. 9 is a diagram showing an example of manufacturing process information. Fig. 9 illustrates an example of manufacturing process information from the production plan shown in Fig. 8. As shown in Fig. 9, the manufacturing process information also indicates the manufacturing time period for each product scheduled to be produced. In the example shown in Fig. 9, the manufacturing time period is defined by the start time and end time for each step corresponding to the manufacturing process included in each of product A1 and product B1.

[0099] Returning to the explanation of FIG. 1, the output unit 15C is a processing unit that outputs various types of information. As one aspect, the output unit 15C can output the RE degree for each product included in each production plan obtained as a simulation result by the simulation unit 15B to any output destination including the user terminal 30. Note that the output destinations referred to here may include applications and services executed on a computer of the user organization, as well as applications and services executed on a computer of a third party other than the user organization, such as a computer of a customer of the product.

[0100] 10 and 11 are diagrams showing examples of displays on the user terminal 30. These diagrams show GUI screens displayed on the user terminal 30 in step S210 shown in FIG. 5. Furthermore, these diagrams show a product "wafer 1" as an example of a product designated as a specific product in step S202 shown in FIG. 5 among the products scheduled for production. Furthermore, these diagrams show a manufacturing schedule 1 corresponding to one of two production plans with the top two RE degrees, and a manufacturing schedule 2 corresponding to the other production plan, as candidates for the manufacturing time slot of the product "wafer 1" scheduled for production.

[0101] 10 and 11, Fig. 10 shows an example in which the manufacturing processes for the planned production product "Wafer 1" do not include any manufacturing processes that do not achieve RE100. With this display, the person in charge of production planning or responsible person in the user organization can understand that whether Manufacturing Schedule 1 or Manufacturing Schedule 2 is adopted, RE100 will be achieved for the planned production product "Wafer 1."

[0102] On the other hand, Figure 11 shows an example in which the manufacturing process for the planned production product "Wafer 1" includes a manufacturing process that does not achieve RE100. From this display, the production planner or manager of the user organization can understand that it will be difficult to achieve RE100 for the planned production product "Wafer 1" regardless of whether Manufacturing Schedule 1 or Manufacturing Schedule 2 corresponding to the other production plan is adopted. In the example shown in Figure 1, it can be seen that Manufacturing Schedule 1 will result in a clean energy power shortage for the processing at Equipment 5, which is scheduled to start at 14:15:00 on November 2, 2022. Furthermore, Manufacturing Schedule 2 can be seen that there will be a clean energy power shortage for the processing at Equipment 1, which is scheduled to start at 16:40:00 on November 2, 2022. In addition, it can be seen that the clean energy shortage for the processing at Equipment 5 in Manufacturing Schedule 1 is 3 kW, and that the clean energy shortage for the processing at Equipment 1 in Manufacturing Schedule 2 is also 3 kW.

[0103] The technological significance of being able to grasp the shortage of clean energy in this way is great, because it allows us to grasp the gap between supply and demand of clean energy at the time of planned manufacturing when creating production plans. This makes it possible to detect a shortage of clean energy supply in advance or to purchase the minimum amount of clean energy, which is more expensive than fossil energy.

[0104] Returning to the explanation of FIG. 1, the request unit 15D is a processing unit that issues requests to other systems such as the production control system 3 and the energy trading system 5. Such requests can be issued automatically if the setting information 13B is set to permit input to the production control system 3 or if the setting information 13B is set to permit automatic trading with the energy trading system 5. On the other hand, even if the setting information 13B prohibits input to the production control system 3 or automatic trading with the energy trading system 5, a request can be issued when an operation of a GUI component that permits the issuance of a request is received from the user terminal 30 via a GUI screen such as that shown in FIG.

[0105] 12 is a flowchart showing the procedure of the request issuing process. As shown in FIG. 12, the request unit 15D acquires the simulation result by the simulation unit 15B (step S501).

[0106] Then, the request unit 15D generates a request to another system such as the production management system 3 or the energy trading system 5 based on the simulation results acquired in step S501 (step S502), and transmits the generated request to the other system.

[0107] As an example, in the case of a request to the production management system 3, a request to register the production plan selected in step S212 or a request to register the production plan designated via the GUI components shown in Fig. 10 or 11 is generated. As another example, in the case of a request to the energy trading system 5, a request to purchase non-fossil certificates to cover the shortage of clean energy designated via the GUI component shown in Fig. 11 is generated. Note that a method for generating a request to be issued to the energy trading system 5 will be described later with reference to Fig. 13.

[0108] Meanwhile, other systems such as the production management system 3 and the energy trading system 5 accept the request sent by the request unit 15D (step S503) and execute processing corresponding to the request (step S504).

[0109] 13 is a flowchart showing the procedure of the request generation process, which is the process of generating a request to be issued to the energy trading system 5, among the processes executed in step S502 shown in FIG.

[0110] 13, the request unit 15D identifies a time period of the simultaneous balancing interval that overlaps with a section in which a manufacturing time period of a specific product that does not achieve an RE target value, for example, RE100, or a manufacturing process of a specific product in which the supply of clean energy is insufficient, is scheduled to be executed, from among the time periods divided by the simultaneous balancing interval setting (step S601). Note that in step S601, the request unit 15D may make the selection automatically, or may manually accept the selection from the user terminal 30 via a GUI.

[0111] Then, the request unit 15D selects a supply destination of the clean energy that is in short supply during the time period selected in step S601 (step S602). For example, the request unit 15D may automatically select the type of clean energy that is set to the highest priority in the setting information 13B, or may accept a selection of the type of clean energy from the user terminal 30 via a GUI.

[0112] Next, the request unit 15D identifies the amount of clean energy shortage in the time period identified in step S601 (step S603).

[0113] Thereafter, the request unit 15D generates a trading request including a specification of the time slot corresponding to the time period identified in step S601 among the slots accepted as trading units by the energy trading system 5, a specification of the type of clean energy selected in step S602, and a specification of the purchase amount of environmental value corresponding to the shortfall of clean energy identified in step S603 (step S604).

[0114] By sending the generated trading request to the energy trading system 5, it becomes possible to bid on the environmental value of the desired type of clean energy to purchase the amount that corresponds to the shortage of clean energy. This allows the RE maximization of a specific product to be achieved through the purchase of environmental value even when the supply of clean energy is insufficient. This allows user companies to prove that they are promoting the introduction of renewable energy into their business operations. Furthermore, promoting efforts to achieve social goals such as decarbonization also leads to an increase in corporate value.

[0115] <One aspect of the effect> As described above, the information processing device 10 according to this embodiment executes a simulation to optimize a production plan in which the manufacturing time slots of each product are planned, based on the predicted clean energy supply amount by time slot and the predicted energy consumption amount by time slot. Therefore, the information processing device 10 according to this embodiment can realize the creation of a production plan that supports maximizing RE of products.

[0116] <Numbers, etc.> The matters described in the above embodiment, such as the type of energy, the number of power generators, and the specific examples of the display of the simulation and the display of the RE degree, are merely examples and can be changed. Also, the order of processing in the flowcharts described in the embodiment can be changed within a consistent range.

[0117] <System> The information including the processing procedures, control procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, any one or more of the functional units of the reception unit 15A, the simulation unit 15B, the output unit 15C, and the request unit 15D may be configured as separate devices.

[0118] Furthermore, the components of each device shown in the figure are functional concepts and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown. In other words, all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Note that each configuration may also be a physical configuration.

[0119] Furthermore, each processing function performed by each device can be realized, in whole or in part, by a CPU (Central Processing Unit) and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0120] <Hardware> Next, an example of the hardware configuration of the computer described in the embodiment will be described. Fig. 14 is a diagram showing an example of the hardware configuration. As shown in Fig. 14, an information processing device 10 includes a communication device 10a, an HDD (Hard Disk Drive) 10b, a memory 10c, and a processor 10d. The components shown in Fig. 14 are connected to each other via a bus or the like.

[0121] The communication device 10a is a network interface card or the like, and communicates with other servers. The HDD 10b stores programs and DBs that operate the functions shown in FIG.

[0122] The processor 10d reads a program that executes the same processing as the processing unit shown in Fig. 1 from the HDD 10b or the like and loads it into the memory 10c, thereby operating a process that executes the functions described in Fig. 1 or the like. For example, this process executes the same functions as the processing unit of the information processing device 10. Specifically, the processor 10d reads a program having the same functions as the reception unit 15A, the simulation unit 15B, the output unit 15C, the request unit 15D, etc. from the HDD 10b or the like. Then, the processor 10d executes a process that executes the same processing as the reception unit 15A, the simulation unit 15B, the output unit 15C, the request unit 15D, etc.

[0123] In this way, the information processing device 10 operates as an information processing device that executes a production plan creation method by reading and executing a program. The information processing device 10 can also realize functions similar to those of the above-described embodiment by reading the program from a recording medium using a medium reading device and executing the read program. Note that the program in these other embodiments is not limited to being executed by the information processing device 10. For example, the present invention can also be applied in the same way to cases where another computer or server executes the program, or where these execute the program in cooperation with each other.

[0124] The above program can be distributed via a network such as the Internet. The above program can also be recorded on any recording medium and executed by a computer by reading it from the recording medium. For example, the recording medium can be a hard disk, a flexible disk (FD), a CD-ROM, a magneto-optical disk (MO), a digital versatile disk (DVD), or the like.

[0125] <Other> Some examples of combinations of the disclosed technical features are set out below.

[0126] (1) a first reception unit that receives a predicted supply amount of clean energy; a second reception unit that receives predicted energy usage; a simulation unit that executes a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted consumption amount of energy; An information processing device comprising:

[0127] (2) the simulation unit optimizes the production plan by maximizing or minimizing an objective function including a difference or ratio between the predicted supply amount and the predicted usage amount; The information processing device according to (1) above.

[0128] (3) further comprising a third reception unit configured to receive a setting of a priority of an allocation destination of the predicted supply amount of clean energy; the simulation unit compares the predicted supply amount of clean energy with the predicted usage amount of the allocation recipient included in the production plan obtained as a result of the simulation, and determines the allocation amount of clean energy to the allocation recipient based on the priority. The information processing device according to (1) or (2) above.

[0129] (4) A request unit is further provided to issue a request to purchase environmental value to make up for a shortage of the allocated amount compared to the predicted usage amount to an electricity trading system that trades the buying and selling of environmental value separated from the non-fossil energy electricity after generation. The information processing device according to (3) above.

[0130] (5) The first reception unit receives the predicted supply amount for each type of clean energy, the third reception unit receives a setting of the priority for each of the types and each of the time periods; the simulation unit determines the allocation amount for each of the types and each of the time periods. The information processing device according to (3) or (4) above.

[0131] (6) Accepting the forecasted supply of clean energy; Accepts predicted energy usage, Executing a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted amount of energy used; A production planning method characterized in that the processing is executed by a computer.

[0132] (7) Accepting the forecasted supply of clean energy; Accepts predicted energy usage, Executing a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted amount of energy used; A production planning program that causes a computer to execute a process. [Explanation of symbols]

[0133] 1. Production planning system 3 Production management system 5. Energy trading system 7. Various devices 10. Information processing equipment 11 Communication control section 13 Storage section 13A Forecasted Supply 13B Setting Information 13C predicted usage 13D Production Specifications 15 Control Unit 15A Reception 15B Simulation Section 15C Output section 15D Request Section 30 User terminals

Claims

1. a first receiving unit that receives a predicted supply amount of clean energy; a second reception unit that receives the predicted energy usage amount; a third receiving unit that receives a setting of a priority of an allocation destination of the predicted supply amount of clean energy; a simulation unit that executes a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted consumption amount of energy, compares the predicted supply amount of clean energy with the predicted consumption amount of the allocation recipient included in the production plan obtained as a result of the simulation, and determines the allocation amount of clean energy to the allocation recipient based on the priority; a request unit that issues a request to purchase environmental value to make up for a shortage of the allocated amount compared to the predicted usage amount, to an electricity trading system that trades the buying and selling of environmental value separated from non-fossil energy electricity after generation; An information processing device comprising:

2. the simulation unit optimizes the production plan by maximizing or minimizing an objective function including a difference or a ratio between the predicted supply amount and the predicted usage amount; 2. The information processing apparatus according to claim 1, wherein:

3. the first reception unit receives the predicted supply amount for each type of clean energy; the third reception unit receives the setting of the priority for each of the types and each of the time periods; the simulation unit determines the allocation amount for each of the types and each of the time periods.

2. The information processing apparatus according to claim 1, wherein:

4. Accepting the predicted supply of clean energy, Accepts predicted energy usage, Accepting a setting of a priority of destinations to which the predicted supply of clean energy is to be allocated; running a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted consumption amount of energy, comparing the predicted supply amount of clean energy with the predicted consumption amount of the allocation recipient included in the production plan obtained as a result of the simulation, and determining the allocation amount of clean energy to the allocation recipient based on the priority; issuing a request to purchase environmental value to make up for the shortfall in the allocated amount compared to the predicted usage amount to an electricity trading system that trades the buying and selling of environmental value separated from the non-fossil energy electricity after generation; A production planning method characterized in that the processing is executed by a computer.

5. Accepting the predicted supply of clean energy, Accepts predicted energy usage, Accepting a setting of a priority of destinations to which the predicted supply of clean energy is to be allocated; running a simulation to optimize a production plan in which a manufacturing time period for each product to be produced is planned based on the predicted supply amount of clean energy and the predicted consumption amount of energy, comparing the predicted supply amount of clean energy with the predicted consumption amount of the allocation recipient included in the production plan obtained as a result of the simulation, and determining the allocation amount of clean energy to the allocation recipient based on the priority; issuing a request to purchase environmental value to make up for the shortfall in the allocated amount compared to the predicted usage amount to an electricity trading system that trades the buying and selling of environmental value separated from the non-fossil energy electricity after generation; A production planning program that causes a computer to execute a process.

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