Driver support system, driver support program, driver support method, design support system, design support program, design support method, and plant

Abstract

This technology enables the proper coordination of raw material and energy sources with plant operations. [Solution] The operation support device 100 includes a supplier information acquisition unit that acquires the current or predicted value of the supply amount at a supplier that supplies raw materials or energy used in the plant to the plant; a plant information acquisition unit that acquires the current or predicted value of the raw materials or energy used in the plant; an adjustment parameter determination unit that determines a supply amount adjustment parameter for adjusting the supply amount or a usage amount adjustment parameter for adjusting the usage amount in accordance with fluctuations in the supply amount and usage amount, based on the current or predicted value of the supply amount, the current or predicted value of the usage amount, a time interval at which the supply amount can be adjusted at the supplier, and a time interval at which the usage amount can be adjusted in the plant; and an output unit that outputs the determined supply amount adjustment parameter or usage amount adjustment parameter.

Description

Technical Field

[0001] The present disclosure relates to a technology for assisting the operation and design of a plant.

Background Art

[0002] Technologies for converting electric power into some form of energy for storage and utilization (Power-to-X) have attracted attention. For example, in Patent Document 1, a technology is disclosed for calculating the capacity and operation plan of each facility, the transportation volume and transportation schedule for maximizing the profit of the supply side, consumers, or the entire gas energy supply-demand system by referring to a power generation amount DB storing power generation amount information, a demand amount DB storing demand amount information, a transportation means DB storing transportation means information, a facility DB storing facility information, and an energy price DB storing energy unit price information, and using an optimization method for an energy flow considering power generation using renewable energy, generation, transportation, and demand timing of gas energy.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the supply amounts of electric power, raw materials, etc. and the demand amount of products can vary from moment to moment, the suppliers of electric power and raw materials and the plants that manufacture products using electric power and raw materials need to adjust their operations according to fluctuations in supply and demand amounts. However, when the time constants for operation adjustment are different between the supplier and the plant, it is not easy to appropriately adjust the operation of the entire system.

[0005] An object of the present disclosure is to provide a technology that enables appropriate adjustment of the operations of raw material and energy suppliers and plants. [Means for solving the problem]

[0006] To solve the above problems, an operation support device in one aspect of the present disclosure includes: a supplier information acquisition unit that acquires current or predicted values ​​of the amount of raw materials or energy supplied to a supplier that supplies raw materials or energy used in the plant; a plant information acquisition unit that acquires current or predicted values ​​of the amount of raw materials or energy used in the plant; an adjustment parameter determination unit that determines at least one of the supply amount adjustment parameters for adjusting the amount of raw materials or energy supplied at the supplier in accordance with fluctuations in the amount of raw materials or energy supplied and used in the plant, based on the current or predicted values ​​of the amount of raw materials or energy supplied acquired by the supplier information acquisition unit, the current or predicted values ​​of the amount of raw materials or energy used acquired by the plant information acquisition unit, a time interval at which the amount of raw materials or energy supplied at the supplier can be adjusted, and a usage adjustment parameter for adjusting the amount of raw materials or energy used in the plant; and an output unit that outputs the supply amount adjustment parameter determined by the adjustment parameter determination unit to the supplier, or outputs the usage adjustment parameter to the plant.

[0007] Another aspect of this disclosure is a method for assisting operations. This method causes a computer to perform the following steps: acquire current or predicted values ​​of the amount of raw materials or energy supplied to a supplier that supplies raw materials or energy used in the plant; acquire current or predicted values ​​of the amount of raw materials or energy used in the plant; determine at least one of a supply adjustment parameter for adjusting the amount of raw materials or energy supplied at the supplier and a usage adjustment parameter for adjusting the amount of raw materials or energy used in the plant, based on the acquired current or predicted values ​​of the amount of raw materials or energy supplied, the acquired current or predicted values ​​of the amount of raw materials or energy used, a time interval at which the amount of raw materials or energy supplied at the supplier can be adjusted, and a time interval at which the amount of raw materials or energy used in the plant can be adjusted, in accordance with fluctuations in the amount of raw materials or energy supplied and used; and output the determined supply adjustment parameter to the supplier or output the usage adjustment parameter to the plant.

[0008] A further aspect of this disclosure is a design support device. This device includes a design unit that, in order to enable the plant to operate in accordance with fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of raw materials or energy used in the plant, designs at least one of the following: the configuration and specifications of the plant or supplier, the control mode of the plant or supplier, the configuration and specifications of the storage means for storing raw materials or energy, and conditions relating to the supply of raw materials or energy from a supplier of raw materials or energy other than the supplier, based on a predicted range of fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of raw materials or energy used in the plant, and a time interval in which the amount of raw materials or energy supplied to the supplier can be adjusted.

[0009] A further aspect of this disclosure is a design support method. This method causes a computer to perform the steps of designing at least one of the following: the configuration and specifications of a plant or a supplier, the control mode of a plant or a supplier, the configuration and specifications of a storage means for storing raw materials or energy, and conditions relating to the supply of raw materials or energy from a supplier other than the supplier, based on a predicted range of fluctuations in the supply of raw materials or energy at a supplier and time intervals in which the supply of raw materials or energy at the supplier can be adjusted, in order to enable the plant to operate in accordance with fluctuations in the supply of raw materials or energy from a supplier of raw materials or energy used in the plant.

[0010] Another aspect of this disclosure is a plant. This plant is a plant that manufactures products using raw materials or energy, and is a supplier that supplies raw materials or energy to the plant, and in order to operate the plant in accordance with fluctuations in the amount of raw materials or energy supplied from a supplier whose time intervals for adjusting the amount of raw materials or energy supplied are different from the time intervals for adjusting the amount of raw materials or energy used in the plant, the plant comprises at least one of the following: (1) storage means for storing raw materials or energy; (2) operation adjustment means for adjusting the control mode of the plant based on a predicted value of the range of fluctuations in the amount of raw materials or energy supplied at the supplier and the time intervals for adjusting the amount of raw materials or energy supplied at the supplier; and (3) supply adjustment means for adjusting the amount of raw materials or energy supplied from a supplier of raw materials or energy other than the supplier.

[0011] Furthermore, any combination of the above components, as well as any conversion of the expressions of this disclosure between methods, apparatus, systems, recording media, computer programs, etc., are also valid forms of this disclosure. [Effects of the Invention]

[0012] This disclosure provides technology that enables the proper coordination of raw material and energy sources with plant operations. [Brief explanation of the drawing]

[0013] [Figure 1] This diagram schematically shows the configuration of the product manufacturing system according to the first embodiment. [Figure 2] This diagram schematically shows the functional configuration of the driver assistance system. [Figure 3] This flowchart shows the procedure for the driving assistance method according to the first embodiment. [Figure 4] This diagram schematically shows the functional configuration of the design support device according to the second embodiment. [Figure 5] This flowchart shows the procedure for the design support method according to the second embodiment. [Figure 6] This diagram schematically shows the functional configuration of a product manufacturing plant. [Modes for carrying out the invention]

[0014] First, as a first embodiment of this disclosure, we will describe a technology for responding to fluctuations in the supply of raw materials and energy supplied from a supplier to a plant when the time interval (time constant) at which the supply amount can be adjusted at the supplier and the time interval (time constant) at which the plant can adjust the amount of raw materials and energy used are different. Next, as a second embodiment of this disclosure, we will describe a technology for designing a plant that can respond to differences in time constants using the technology of the first embodiment.

[0015] (First Embodiment) Figure 1 schematically shows the configuration of a product manufacturing system 1 according to the first embodiment of this disclosure. In the figure, solid arrows indicate the flow of power, raw materials, etc., and dashed arrows indicate the flow of control signals and information.

[0016] The product manufacturing system 1 includes an operation support device 100, an energy management system 2, a plant control system 3, a renewable energy power supply source 10, a power grid 11, a power supply system 20, a power storage unit 30, a power consumption system 12, a hydrogen supply system 40, a hydrogen storage unit 50, a hydrogen production system 60, a product manufacturing plant 70, a product storage unit 80, and a product manufacturing system 90.

[0017] The renewable energy power supply source 10 supplies power generated using renewable energy. The power grid 11 is a system that integrates power generation, transformation, transmission, and distribution facilities. The power supply system 20 levels out the fluctuations in the power supplied from the renewable energy power supply source 10 using the power supplied from the power grid 11 and supplies it to the hydrogen supply system 40.

[0018] The hydrogen supply system 40 produces hydrogen by electrolyzing water using the power supplied from the power supply system 20 and supplies the produced hydrogen to the product manufacturing plant 70.

[0019] The product manufacturing plant 70 manufactures products using hydrogen and the like supplied from the hydrogen supply system 40 as raw materials.

[0020] The energy management system 2 comprehensively manages the power supply system 20 and the hydrogen supply system 40.

[0021] The plant control system 3 controls the operation of the product manufacturing plant 70.

[0022] The operation support device 100 comprehensively supports the overall operation of the product manufacturing system 1. The operation support device 100 outputs adjustment parameters for adjusting the operation of each component of the product manufacturing system 1 via the energy management system 2 and the plant control system 3. The operation support device 100 may directly output adjustment parameters to each component of the product manufacturing system 1 without going through the energy management system 2 and the plant control system 3.

[0023] Since the amount of electricity generated by renewable energy fluctuates moment by moment due to external factors such as weather, the amount of electricity supplied from the renewable energy power source 10 fluctuates. If the amount of electricity supplied from the renewable energy power source 10 cannot be absorbed by the electricity supplied from the power grid 11, the amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40 fluctuates. Along with the fluctuation in the amount of electricity supplied to the hydrogen supply system 40, the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70 may also fluctuate.

[0024] The market demand for the products manufactured in the product manufacturing plant 70 fluctuates due to external factors such as market prices. Optimizing the operation of the product manufacturing plant 70 in accordance with the demand for the products will cause fluctuations in the amount of raw materials used in the product manufacturing plant 70. As the amount of hydrogen used in the product manufacturing plant 70 fluctuates, the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70 may also fluctuate.

[0025] Thus, the supply and use of raw materials and energy in each component of the product manufacturing system 1 can fluctuate in response to changes in the supply of raw materials and energy from upstream components, as well as changes in the use of raw materials and energy in downstream components.

[0026] If the time intervals during which the supply of raw materials and energy can be adjusted in the upstream components are shorter than the time intervals during which the use of raw materials and energy can be adjusted in the downstream components, then it is possible to adjust the supply of raw materials and energy in the upstream components in response to fluctuations in the use of raw materials and energy in the downstream components. However, if the time intervals during which the supply of raw materials and energy can be adjusted in the upstream components are longer than the time intervals during which the use of raw materials and energy can be adjusted in the downstream components, then the supply of raw materials and energy in the upstream components may not be able to keep up with fluctuations in the use of raw materials and energy in the downstream components. If the upstream components cannot supply the amount of raw materials and energy required by the downstream components, optimal operation of the downstream components cannot be achieved.

[0027] Thus, if the time constants of each component of product manufacturing system 1 are different, and the supply and use of raw materials and energy fluctuate, the other components may adjust to the operation of the component with the longest time constant, potentially reducing the overall operating efficiency. Conversely, if an attempt is made to adjust a component with a long time constant to one with a short time constant, frequent changes in operation and control may occur, potentially reducing operating efficiency.

[0028] To solve these problems, the driving support device 100 of this embodiment comprehensively adjusts the operation of multiple components with different time constants mainly by the following three methods. (1) Differences in time constants are absorbed by providing a storage section for storing energy, raw materials, and products. (2) Differences in time constants are absorbed by the operation control of each component. (3) Differences in time constants are absorbed by coordinating with external suppliers and manufacturers of product manufacturing system 1.

[0029] The driver assistance device 100 may employ only one of (1) to (3), or it may employ a combination of two or more of (1) to (3). In other words, the driver assistance device 100 may employ only (1), only (2), only (3), (1) and (2), (1) and (3), (2) and (3), or (1), (2), and (3) and comprehensively adjust their operation to absorb the differences in the time constants of multiple components.

[0030] (1) We will explain the case in which differences in time constants are absorbed by providing a storage section for storing energy, raw materials, and products.

[0031] Product manufacturing system 1 includes a power storage unit 30 for storing electricity in order to smooth out fluctuations in the electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power storage unit 30 is composed of a battery or the like. When the amount of electricity used in the hydrogen supply system 40 falls below the amount of electricity supplied from the power supply system 20, the operation support device 100 stores the surplus electricity in the power storage unit 30. When the amount of electricity used in the hydrogen supply system 40 is insufficient with the amount of electricity supplied from the power supply system 20, the operation support device 100 replenishes the deficit with electricity from the power storage unit 30.

[0032] Product manufacturing system 1 includes a hydrogen storage unit 50 for storing hydrogen in order to smooth out fluctuations in the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. When the amount of hydrogen used in the product manufacturing plant 70 falls below the amount of hydrogen supplied from the hydrogen supply system 40, the operation support device 100 causes the surplus hydrogen to be stored in the hydrogen storage unit 50. When the amount of hydrogen used in the product manufacturing plant 70 is insufficient with the amount of hydrogen supplied from the hydrogen supply system 40, the operation support device 100 causes the shortage to be replenished from the hydrogen storage unit 50.

[0033] Product manufacturing system 1 includes a product storage unit 80 for storing products to respond to fluctuations in product demand. When the amount of products manufactured in the product manufacturing plant 70 exceeds the market demand for products, the operation support device 100 causes the surplus products to be stored in the product storage unit 80. When the market demand for products is insufficient to meet the production volume of products manufactured in the product manufacturing plant 70, the operation support device 100 causes the product storage unit 80 to replenish the shortage.

[0034] The capacity of warehouses or tankers used to transport raw materials and products such as hydrogen may constitute part or all of the hydrogen storage unit 50 or the product storage unit 80. The power system 11 may constitute part or all of the power storage unit 30.

[0035] The capacities of the power storage unit 30, hydrogen storage unit 50, and product storage unit 80 may be optimized using objective functions such as CAPEX, OPEX, product manufacturing costs, and the product manufacturing costs themselves (including CAPEX, Fixed OPEX, and Variable OPEX considering a predetermined project period and discount rate, or NPV and IRR based on cash flow), under constraints such as the maximum / minimum operating load of each facility, the maximum load fluctuation rate of each facility, carbon dioxide emissions (carbon intensity), site area, and grid capacity. Alternatively, they may be optimized using LCOX (Levelized Cost of X, where X may be products manufactured from green hydrogen and blue hydrogen) as the objective function.

[0036] If method (1) is not adopted, the product manufacturing system 1 does not need to include at least one of the power storage unit 30, the hydrogen storage unit 50, and the product storage unit 80.

[0037] (2) We will now explain the case in which differences in time constants are absorbed by the operational control of each component.

[0038] The operation support device 100 absorbs fluctuations in the supply and use of raw materials and energy by adjusting the operation of each component of the product manufacturing system 1. The operation support device 100 acquires the current or predicted values ​​of the supply and use of raw materials or energy, and adjusts the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 in accordance with fluctuations in the supply and use of raw materials or energy. For example, if the operation support device 100 acquires information indicating that the voltage of the power supplied from the power supply system 20 is decreasing, it will respond to fluctuations in the supply and use of raw materials or energy by controlling the operation, such as by releasing stored power in the power storage unit 30 of downstream components or by reducing the production rate of products in the product manufacturing plant 70, in order to cover the voltage drop. When the operation support device 100 receives information indicating that the amount of hydrogen supplied from the hydrogen supply system 40 is decreasing, it responds to fluctuations in the hydrogen supply by controlling the operation, such as by reducing the rate at which the product manufacturing plant 70 accepts hydrogen from the hydrogen supply system 40, or by temporarily suspending or slowing down processes that use hydrogen in the product manufacturing plant 70 to reduce the amount of hydrogen used. The operation support device 100 may further replenish the decrease in the hydrogen supply by accepting hydrogen stored in the hydrogen storage unit 50 or by accepting hydrogen from the hydrogen production system 60.

[0039] The operation support device 100 may adjust the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 based on short-term, medium-term, and long-term forecasts of fluctuations in the supply and use of raw materials and energy. For example, if a decrease in hydrogen supply is predicted in the short term, but a recovery in hydrogen supply is predicted in the medium term, the operation support device 100 may replenish the hydrogen shortage during the period of decreased hydrogen supply from the hydrogen storage unit 50 or the hydrogen production system 60 without reducing the production volume of products in the product manufacturing plant 70.

[0040] The operation support device 100 acquires the current or predicted values ​​of the raw material or energy supply and the current or predicted values ​​of the raw material or energy consumption, and optimizes the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 within a range that can accommodate fluctuations in the raw material or energy supply and consumption. The operation support device 100 comprehensively optimizes the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70, taking into account constraints such as carbon intensity, and using economic indicators such as the cost of product manufacturing and the product manufacturing cost itself as objective functions.

[0041] (3) We will now explain how to absorb differences in time constants by coordinating with external suppliers and manufacturers of product manufacturing system 1.

[0042] The operation support device 100, when the amount of hydrogen used in the product manufacturing plant 70 is insufficient with the amount of hydrogen supplied by the hydrogen supply system 40, will supply the remaining hydrogen from a hydrogen production system 60, which is a hydrogen supply source separate from the hydrogen supply system 40. The hydrogen supplied from the hydrogen production system 60 does not have to be green hydrogen derived from renewable energy.

[0043] If the amount of products manufactured in the product manufacturing plant 70 falls short of the market demand for those products, the operation support device 100 will have the remaining products supplied from a product manufacturing system 90, which is a separate product supplier from the product manufacturing plant 70. The products supplied from the product manufacturing system 90 do not necessarily have to be green products derived from renewable energy.

[0044] When used in combination with method (1), the driving support device 100 may preferentially replenish any insufficient hydrogen or product from the hydrogen storage unit 50 or the product storage unit 80.

[0045] If the hydrogen supplied from the hydrogen supply system 40 is green hydrogen derived from renewable energy, and the hydrogen supplied from the hydrogen production system 60 is blue hydrogen, which is produced by separating and recovering carbon dioxide during extraction from fossil fuels and storing it underground to reduce the release of carbon dioxide into the atmosphere, the operation support device 100 may further consider the carbon dioxide emissions required by the product manufacturing system 1 and the product to determine the amount of hydrogen supplied from the hydrogen supply system 40 and the amount of hydrogen supplied from the hydrogen production system 60.

[0046] If the products manufactured in the product manufacturing plant 70 are green products derived from renewable energy, and the products manufactured in the product manufacturing system 90 are blue products that reduce the release of carbon dioxide into the atmosphere during the manufacturing process, the operation support device 100 may determine the amount of products to be supplied from the product manufacturing plant 70 and the amount of products to be supplied from the product manufacturing system 90, taking into further consideration the carbon dioxide emissions required for the product manufacturing system 1 and the products.

[0047] A portion of the power supplied from the power supply system 20 or the power storage unit 30 may be supplied to the power consumption system 12. An example of a power consumption system is a data center.

[0048] Figure 2 schematically shows the functional configuration of the driver assistance system 100. These functions may be implemented in a circuit or processing circuitry, including a general-purpose processor, application-specific processor, integrated circuit, ASIC (Application Specific Integrated Circuit), CPU (Central Processing Unit), conventional circuitry, and / or a combination thereof, configured or programmed to implement the functions described herein. A processor is considered to be a circuit or processing circuitry that includes transistors and other circuits. A processor may also be a programmed processor that executes a program stored in memory.

[0049] The driver assistance system 100 includes a communication device 101, a display device 102, an input device 103, a processing device 120, and a storage device 160.

[0050] The communication device 101 controls wireless or wired communication. The communication device 101 sends and receives data to and from other devices via the communication network. The display device 102 displays the display image generated by the processing device 120. The input device 103 inputs instructions to the processing device 120.

[0051] The storage device 160 stores data and computer programs used by the processing device 120. The storage device 160 stores the power supply forecasting model 161, the raw material supply forecasting model 162, the plant forecasting model 164, the power supply optimization model 165, the raw material supply optimization model 166, the grid power price forecasting model 167, the plant optimization model 168, and the system information holding unit 169.

[0052] The power supply prediction model 161 is a model for predicting the amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power supply prediction model 161 may also be a simulator or a proxy model thereof that simulates the operation of components such as the power storage unit 30 based on current and predicted values ​​of the amount of electricity supplied from the renewable energy power source 10, current and predicted values ​​of state variables representing the state of components such as the power storage unit 30, and current and predicted values ​​of control variables for controlling components such as the power storage unit 30. The power supply prediction model 161 may be learned by utilizing measured values ​​of the amount of electricity supplied from the renewable energy power source 10 to the power supply system 20, measured values ​​of the amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40, measured values ​​of the amount of electricity stored in the power storage unit 30, and simulated values ​​of these simulated by a simulator. The power supply prediction model 161 may be the same as the one used in the power supply system 20, or it may be provided from the power supply system 20 to the operation support device 100.

[0053] The raw material supply prediction model 162 is a model for predicting the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. The raw material supply prediction model 162 may also be a simulator or a proxy model that simulates the operation of the hydrogen supply system 40 and the hydrogen storage unit 50 based on current and predicted values ​​of the amount of electricity supplied from the power supply system 20, current and predicted values ​​of the amount of hydrogen to be supplied to the product manufacturing plant 70, current and predicted values ​​of state variables representing the state of the hydrogen storage unit 50, and current and predicted values ​​of control variables for controlling the hydrogen supply system 40 and the hydrogen storage unit 50. The raw material supply prediction model 162 may be trained using measured values ​​of the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70, measured values ​​of the amount of hydrogen supplied from the hydrogen production system 60 to the product manufacturing plant 70, measured values ​​of the amount of hydrogen stored in the hydrogen storage unit 50, and simulated values ​​of these simulated by a simulator. The raw material supply prediction model 162 may be the same as the one used in the hydrogen supply system 40, or it may be provided from the hydrogen supply system 40 to the operation support device 100.

[0054] The plant prediction model 164 is a model for predicting the operation of the product manufacturing plant 70. The plant prediction model 164 may also be a physical model related to the operating rate, material heat balance, and reaction performance of the product manufacturing plant 70. The plant prediction model 164 may also be a combination of simulators that individually predict the operation of each component of the hydrogen storage unit 50 and the product manufacturing plant 70. For example, the simulator for predicting the operation of the reaction unit may be a model that combines a reaction model for predicting chemical reactions, a model for predicting catalyst degradation, and a computational fluid dynamics (CFD) model for simulating the state of the fluid inside the reactor. The plant prediction model 164 may also be a surrogate model that has been trained using actual data from when each component of the hydrogen storage unit 50 and the product manufacturing plant 70 were operated, or simulation data calculated by simulators for predicting the operation of each component, as training data. The plant prediction model 164 may take input information such as the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70, and output output information such as product yield, yield rate, specifications (physical properties, such as density, viscosity, flash point, or any physical properties that determine the product specifications), costs of the product manufacturing plant 70, and revenue. The costs of the product manufacturing plant 70 may include the costs required to manufacture products in the product manufacturing plant 70, such as raw material procurement costs, electricity procurement costs, equipment procurement costs, construction costs, equipment maintenance costs, labor costs, catalyst replacement costs, and administrative costs.

[0055] The power supply optimization model 165 is a model for optimizing the operation of the power supply system 20. The power supply optimization model 165 takes as input the current and predicted values ​​of the amount of electricity supplied from the renewable energy power supply source 10, the current and predicted values ​​of state variables representing the state of components such as the power storage unit 30, and the current and predicted values ​​of control variables for controlling components such as the power storage unit 30, and outputs set values ​​for the control variables that optimize the revenue, cost, and operating rate of the power supply system 20. The power supply optimization model 165 may be the same as the one used in the power supply system 20, or it may be provided by the power supply system 20.

[0056] The grid power price forecasting model 167 is a model for forecasting the price of grid power supplied to the power supply system 20. The operation support device 100 optimizes the amount of grid power purchased using the grid power price forecasting model 167 as the objective function, taking into account the constraints of carbon intensity, and using CAPEX, OPEX, the cost of manufacturing the product or the manufacturing cost of the product itself (including CAPEX, Fixed OPEX, Variable OPEX considering a predetermined project period and discount rate, etc., or NPV and IRR based on cash flow), and LCOX (Levelized Cost of X, where X may be a product manufactured from green hydrogen and blue hydrogen). Of course, carbon intensity itself can also be used as the objective function.

[0057] The raw material supply optimization model 166 is a model for optimizing the operation of the hydrogen supply system 40. The raw material supply optimization model 166 takes current and predicted values ​​of the amount of electricity supplied from the power supply system 20, current and predicted values ​​of the amount of hydrogen to be supplied to the product manufacturing plant 70, current and predicted values ​​of state variables representing the configuration state of the hydrogen supply system 40, and current and predicted values ​​of control variables for controlling the configuration of the hydrogen supply system 40 as inputs, and outputs set values ​​for control variables that optimize the revenue, cost, operating rate, etc., of the hydrogen supply system 40. The raw material supply optimization model 166 may be the same as the one used in the hydrogen supply system 40, or it may be provided by the hydrogen supply system 40.

[0058] The plant optimization model 168 is a model for optimizing the operation of the product manufacturing plant 70. The plant optimization model 168 takes inputs such as the current and predicted values ​​of the amount of electricity supplied from the power supply system 20, the current and predicted values ​​of the amount of hydrogen supplied from the hydrogen supply system 40, the current and predicted values ​​of the type, specifications, demand, and unit price of the products to be manufactured, the current and predicted values ​​of state variables representing the state of each component of the product manufacturing plant 70, and the current and predicted values ​​of control variables for controlling each component of the product manufacturing plant 70, and outputs set values ​​for control variables that optimize the revenue, cost, operating rate, product yield, and product yield of the product manufacturing plant 70.

[0059] The system information storage unit 169 stores information related to the renewable energy power source 10, the power grid 11, the power supply system 20, the hydrogen supply system 40, the product manufacturing plant 70, the power storage unit 30, the hydrogen storage unit 50, the product storage unit 80, the hydrogen production system 60, and the product manufacturing system 90. For example, the system information storage unit 169 stores information such as the time intervals at which the amount of raw materials and energy used and supplied in the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 can be adjusted, the capacity of the power storage unit 30, the hydrogen storage unit 50, and the product storage unit 80, the price, carbon intensity, supply amount, and delivery date of hydrogen supplied from the hydrogen production system 60, and the price, carbon intensity, supply amount, and delivery date of products supplied from the product manufacturing system 90.

[0060] The processing unit 120 includes a power information acquisition unit 121, a power supply forecasting unit 122, a power supply optimization unit 123, a raw material information acquisition unit 124, a raw material supply forecasting unit 125, a raw material supply optimization unit 126, a product information acquisition unit 130, a plant information acquisition unit 131, a plant forecasting unit 132, a plant optimization unit 133, a power supply operation control information output unit 134, a raw material supply operation control information output unit 135, a plant operation control information output unit 137, an adjustment parameter determination unit 138, and a power consumption information acquisition unit 140. These configurations can be realized using hardware components such as arbitrary circuits, computer CPUs and GPUs, memory, and programs loaded into memory. However, here we are describing functional blocks realized through the cooperation of these components, including virtual computers for web services. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways using hardware alone, software alone, or a combination thereof.

[0061] The power information acquisition unit 121 acquires current and predicted information regarding the electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power information acquisition unit 121 acquires from the power supply system 20 the current and predicted values ​​of the amount of electricity supplied from the renewable energy power supply source 10, the current and predicted values ​​of the price of electricity, the current and predicted values ​​of state variables representing the configuration state of the power supply system 20, the current and predicted values ​​of control variables for controlling the configuration of the power supply system 20, and the current and predicted values ​​of the electricity supplied from the power supply system 20 to the hydrogen supply system 40.

[0062] The power consumption information acquisition unit 140 acquires information regarding power consumption in the power consumption system 12. The power consumption information acquisition unit 140 acquires current values ​​and predicted values ​​of power consumption in the power consumption system 12 from the power consumption system 12.

[0063] The power supply forecasting unit 122 uses the power supply forecasting model 161 to predict the power that will be supplied from the power supply system 20 to the hydrogen supply system 40 in the future.

[0064] The power supply optimization unit 123 uses the power supply optimization model 165 to calculate set values ​​for control variables that can optimize the operation of the power supply system 20.

[0065] The power supply operation control information output unit 134 outputs the set value of the control quantity calculated by the power supply optimization unit 123 to the power supply system 20 as the optimal operation control mode. The power supply system 20 controls its operation based on the set value of the control quantity obtained from the operation support device 100. If the power supply system 20 is configured not to accept the set value of the control quantity from the operation support device 100, the power supply operation control information output unit 134 may not be provided.

[0066] If the power information acquisition unit 121 acquires information about power when the power supply system 20 is operated in a manner optimized by the power supply system 20, then the power supply prediction unit 122 and the power supply optimization unit 123 do not need to be provided.

[0067] The raw material information acquisition unit 124 acquires current and predicted information regarding the hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. The raw material information acquisition unit 124 acquires from the hydrogen supply system 40 the current and predicted values ​​of the amount of electricity supplied from the power supply system 20, the current and predicted values ​​of the amount of hydrogen to be supplied to the product manufacturing plant 70, the current and predicted values ​​of state variables representing the configuration state of the hydrogen supply system 40, the current and predicted values ​​of control variables for controlling the configuration of the hydrogen supply system 40, and the current and predicted values ​​of the amount, price, and carbon intensity of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70.

[0068] The raw material supply forecasting unit 125 uses the raw material supply forecasting model 162 to predict the amount of hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70 in the future.

[0069] The raw material supply optimization unit 126 uses the raw material supply optimization model 166 to calculate set values ​​for control quantities that can optimize the operation of the hydrogen supply system 40. The raw material supply optimization unit 126 optimizes the operation of the hydrogen supply system 40 based on the amount of electricity that is predicted to be supplied from the power supply system 20 to the hydrogen supply system 40 when the power supply system 20 is operated as optimized by the power supply optimization unit 123.

[0070] The raw material supply operation control information output unit 135 outputs the set value of the control amount calculated by the raw material supply optimization unit 126 to the hydrogen supply system 40 as the optimal operation control mode. The hydrogen supply system 40 controls its operation based on the set value of the control amount obtained from the operation support device 100. If the hydrogen supply system 40 is configured not to accept the set value of the control amount from the operation support device 100, the raw material supply operation control information output unit 135 may not be provided.

[0071] If the raw material information acquisition unit 124 acquires information about hydrogen when the hydrogen supply system 40 is operated in a manner optimized by the hydrogen supply system 40, then the raw material supply prediction unit 125 and the raw material supply optimization unit 126 do not need to be provided.

[0072] The product information acquisition unit 130 acquires information such as the type of product to be manufactured, specifications, demand quantity, price, and current or predicted values.

[0073] The plant information acquisition unit 131 acquires current and forecast information regarding the product manufacturing plant 70. The plant information acquisition unit 131 acquires current and forecast values ​​of the amount of electricity supplied from the power supply system 20, current and forecast values ​​of the amount of hydrogen supplied from the hydrogen supply system 40, current and forecast values ​​of the type, specifications, demand quantity, and unit price of the products to be manufactured, current and forecast values ​​of state quantities representing the state of each component of the product manufacturing plant 70, current and forecast values ​​of control quantities for controlling each component of the product manufacturing plant 70, and current and forecast values ​​of the yield, yield rate, and quality of the products to be manufactured from the product manufacturing plant 70.

[0074] The plant prediction unit 132 uses the plant prediction model 164 to predict the status, operating rate, product yield, product yield rate, product specifications, etc., of the product manufacturing plant 70. The plant prediction unit 132 outputs the prediction results to the product manufacturing plant 70.

[0075] The plant optimization unit 133 uses the plant optimization model 168 to calculate set values ​​for control variables that can optimize the operation of the product manufacturing plant 70. The plant optimization unit 133 optimizes the operation of the product manufacturing plant 70 based on the amount of electricity that is predicted to be supplied from the power supply system 20 to the hydrogen supply system 40 when the power supply system 20 is operated as optimized by the power supply optimization unit 123, and the amount of hydrogen that is predicted to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70 when the hydrogen supply system 40 is operated as optimized by the raw material supply optimization unit 126.

[0076] The plant operation control information output unit 137 outputs the set values ​​of the control variables calculated by the plant optimization unit 133 as recommended values ​​to the product manufacturing plant 70.

[0077] The plant optimization unit 133 may integrate and optimize the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 based on current and predicted values ​​of product type, specifications, demand quantity, price, etc., acquired by the product information acquisition unit 130, and current information and predicted information regarding the product manufacturing plant 70 acquired by the plant information acquisition unit 131, using the power supply optimization model 165, the raw material supply optimization model 166, and the plant optimization model 168. In this case, the power supply operation control information output unit 134 outputs the calculated control amount setting value to the power supply system 20 as the optimal operation control mode. The raw material supply operation control information output unit 135 outputs the calculated control amount setting value to the hydrogen supply system 40 as the optimal operation control mode. The plant operation control information output unit 137 outputs the calculated control amount setting value to the product manufacturing plant 70 as a recommended value for the optimal operation control mode.

[0078] In the product manufacturing system 1, optimizing all parameters, such as fluctuating state variables, at once would result in an enormous computational load, making it difficult to optimize operation in real time. However, the operation support device 100 of this embodiment optimizes the entire product manufacturing system 1 comprehensively while locally optimizing the operation of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70, thereby efficiently optimizing the entire product manufacturing system 1 while suppressing the computational load. As a result, the operation of the product manufacturing system 1 can be optimized in real time at predetermined time intervals, thereby maximizing the productivity of the product manufacturing system 1.

[0079] If the supply and use of raw materials and energy, or the demand for products, fluctuates significantly during the operation of the product manufacturing system 1, the adjustment parameter determination unit 138 determines adjustment parameters to adjust the supply or use in accordance with the fluctuations in the supply and use of raw materials and energy, using the method described above.

[0080] The adjustment parameter determination unit 138 determines at least one of the following based on the current or predicted values ​​of the raw material or energy supply amount obtained by the power information acquisition unit 121, power supply forecasting unit 122, raw material information acquisition unit 124, and raw material supply forecasting unit 125, the current or predicted values ​​of the raw material or energy usage amount obtained by the plant information acquisition unit 131, the time intervals in which the supply amount of raw material or energy can be adjusted in the power supply system 20 and hydrogen supply system 40, and the time intervals in which the usage amount of raw material or energy can be adjusted in the product manufacturing plant 70: a supply amount adjustment parameter for adjusting the supply amount of raw material or energy in the power supply system 20 and hydrogen supply system 40, and a usage amount adjustment parameter for adjusting the usage amount of raw material or energy in the product manufacturing plant 70, in accordance with fluctuations in the supply amount of raw material or energy.

[0081] The adjustment parameter determination unit 138 may determine supply adjustment parameters and usage adjustment parameters to optimize the supply of raw materials or energy at the source and the operation of the plant, within a range that can accommodate fluctuations in the supply and usage of raw materials or energy. The adjustment parameter determination unit 138 may determine the optimal supply adjustment parameters and usage adjustment parameters using the power supply optimization model 165, the raw material supply optimization model 166, and the plant optimization model 168.

[0082] The adjustment parameter determination unit 138 may determine supply adjustment parameters and usage adjustment parameters to optimize the supply of raw materials or energy at the source and the operation of the plant, to the extent that constraints relating to the supply or use of raw materials or energy, or to the products produced in the product manufacturing plant 70, are met. Constraints relating to the product may include manufacturing costs, carbon strength, delivery time, etc. The adjustment parameter determination unit 138 may determine the optimal supply adjustment parameters and usage adjustment parameters using the power supply optimization model 165, the raw material supply optimization model 166, and the plant optimization model 168.

[0083] The adjustment parameter determination unit 138 may determine supply adjustment parameters or usage adjustment parameters when the power supply system 20, hydrogen supply system 40, product manufacturing plant 70, energy management system 2, or plant control system 3 requests adjustment of the supply or usage amount of raw materials or energy, or when fluctuations exceeding a predetermined amount in the supply or usage amount of raw materials or energy are predicted. The adjustment request may be received directly from the power supply system 20, hydrogen supply system 40, or product manufacturing plant 70, or it may be received via the energy management system 2 or plant control system 3.

[0084] The predicted supply of raw materials or energy may be calculated based on an electricity supply forecasting model 161, a raw material supply forecasting model 162, or external information regarding raw materials or energy, for predicting the supply of raw materials or energy at the source. The external information may include weather, the price of raw materials or energy, demand, etc.

[0085] The predicted values ​​for raw material or energy consumption may be calculated based on a consumption prediction model for predicting raw material or energy consumption in the plant, or on external information regarding the products produced in the plant. The consumption prediction model may be a plant prediction model 164, for example. The external information may include the price and demand for the products manufactured in the product manufacturing plant 70.

[0086] The adjustment parameter determination unit 138 may determine the supply quantity adjustment parameter and the usage quantity adjustment parameter based on the current or predicted value of the product demand obtained by the product information acquisition unit 130.

[0087] When adopting the method described in (1) above, the usage adjustment parameters may include parameters relating to the amount and capacity of raw materials or energy stored in the hydrogen storage unit 50.

[0088] When adopting the method described in (2) above, the supply adjustment parameters may include parameters relating to the generation of raw materials or energy in the raw material or energy generation means provided in the power supply system 20 or the hydrogen supply system 40. In addition, the usage adjustment parameters may include parameters relating to the operation of the product manufacturing plant 70.

[0089] When adopting the method described in (3) above, the supply adjustment parameter may include a parameter relating to the amount of raw materials or energy supplied from a raw material or energy supplier other than the hydrogen supply system 40. In addition, the usage adjustment parameter may include a parameter relating to the amount of products supplied from a product supplier other than the product manufacturing plant 70.

[0090] The output unit 139 outputs the supply amount adjustment parameter determined by the adjustment parameter determination unit 138 to the supplier, or outputs the usage amount adjustment parameter to the plant.

[0091] Figure 3 is a flowchart showing the procedure of the operation support method according to the first embodiment. The operation support device 100, consisting of a power information acquisition unit 121, a power supply forecasting unit 122, a raw material information acquisition unit 124, and a raw material supply forecasting unit 125, acquires the current or forecast values ​​of the raw material or energy supply amount (S10). The plant information acquisition unit 131 acquires the current or forecast values ​​of the raw material or energy consumption amount (S12). The adjustment parameter determination unit 138 determines at least one of the following based on the current or predicted values ​​of the acquired raw material or energy supply, the current or predicted values ​​of the acquired raw material or energy consumption, the time intervals during which the supply of raw materials or energy can be adjusted in the power supply system 20 and hydrogen supply system 40, and the time intervals during which the consumption of raw materials or energy can be adjusted in the product manufacturing plant 70, in accordance with fluctuations in the supply or consumption of raw materials or energy (S14).

[0092] (Second Embodiment) This section describes a technique for designing a plant that can respond to fluctuations in the supply of raw materials and energy from their suppliers using the technology of the first embodiment.

[0093] The design support device according to the second embodiment assists in the design of multiple components with different time constants in initial studies, laboratory-scale studies, bench-scale studies, and commercial-scale studies when constructing a plant, in accordance with the technologies (1) to (3) described above. The design support device may assist in the design of a plant alone, or it may assist in the overall design of multiple components including a plant.

[0094] Figure 4 schematically shows the functional configuration of the design support device 200 according to the second embodiment. These functions may be realized in a circuit or processing circuitry, including a general-purpose processor, application-specific processor, integrated circuit, ASICs (Application Specific Integrated Circuits), CPU (Central Processing Unit), conventional circuit, and / or a combination thereof, configured or programmed to realize the functions described herein. A processor is considered to be a circuit or processing circuitry including transistors and other circuits. A processor may also be a programmed processor that executes a program stored in memory.

[0095] The design support device 200 assists in the design of the product manufacturing system 1. The design support device 200 may assist in the design of the entire product manufacturing system 1, or it may assist in the design of some of the components included in the product manufacturing system 1. The design target may be a product manufacturing plant 70 in which all components included in the product manufacturing system 1 are newly constructed, or it may be a product manufacturing plant 70 that includes existing components.

[0096] The design support device 200 includes a communication device 201, a display device 202, an input device 203, a processing device 220, and a storage device 260.

[0097] The communication device 201 controls wireless or wired communication. The communication device 201 sends and receives data with other devices via the communication network. The display device 202 displays the display image generated by the processing device 220. The input device 203 inputs instructions to the processing device 220.

[0098] The storage device 260 stores data and computer programs used by the processing device 220. The storage device 260 stores the power supply forecasting model 261, the raw material supply forecasting model 262, the plant forecasting model 264, the power supply optimization model 265, the raw material supply optimization model 266, the grid power price forecasting model 267, the plant optimization model 268, and the system information holding unit 269.

[0099] The power supply prediction model 261 is a model for predicting the amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power supply prediction model 261 may be similar to the power supply prediction model 161 of the driver assistance device 100.

[0100] The raw material supply prediction model 262 is a model for predicting the amount of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. The raw material supply prediction model 262 may be the same as the raw material supply prediction model 162 of the operation support device 100.

[0101] The plant prediction model 264 is a model for predicting the operation of the product manufacturing plant 70. The plant prediction model 264 may be similar to the plant prediction model 164 of the operation support device 100.

[0102] The power supply optimization model 265 is a model for optimizing the operation of the power supply system 20. The power supply optimization model 265 may be similar to the power supply optimization model 165 of the operation support device 100.

[0103] The grid power price prediction model 267 is a model for predicting the price of grid power supplied to the power supply system 20. The grid power price prediction model 267 may be similar to the grid power price prediction model 167 of the operation support device 100.

[0104] The raw material supply optimization model 266 is a model for optimizing the operation of the hydrogen supply system 40. The raw material supply optimization model 266 may be similar to the raw material supply optimization model 166 of the operation support device 100.

[0105] The plant optimization model 268 is a model for optimizing the operation of the product manufacturing plant 70. The plant optimization model 268 may be similar to the plant optimization model 168 of the operation support device 100.

[0106] The system information holding unit 269 holds information regarding the renewable energy power source 10, power grid 11, power supply system 20, hydrogen supply system 40, product manufacturing plant 70, power storage unit 30, hydrogen storage unit 50, product storage unit 80, hydrogen production system 60, and product manufacturing system 90 that are under design. The system information holding unit 269 may be the same as the system information holding unit 169 of the operation support device 100.

[0107] The processing unit 220 includes a power information acquisition unit 121, a power supply forecasting unit 122, a power supply optimization unit 123, a raw material information acquisition unit 124, a raw material supply forecasting unit 125, a raw material supply optimization unit 126, a product information acquisition unit 130, a plant information acquisition unit 131, a plant forecasting unit 132, a plant optimization unit 133, a power supply operation control information output unit 134, a raw material supply operation control information output unit 135, a plant operation control information output unit 137, an adjustment parameter determination unit 138, and a power consumption information acquisition unit 140. These configurations can be realized by hardware components such as arbitrary circuits, computer CPUs and GPUs, memory, and programs loaded into memory, but here we are describing functional blocks realized by the cooperation of these, including virtual computers for web services. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways by hardware alone, software alone, or a combination thereof.

[0108] The power information acquisition unit 221 acquires predictive information regarding the electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power information acquisition unit 221 acquires predictive values ​​of the amount of electricity supplied from the renewable energy power source 10, predictive values ​​of the price of electricity predicted by the grid power price prediction model 267, predictive values ​​of state variables representing the configuration state of the power supply system 20, predictive values ​​of control variables for controlling the configuration of the power supply system 20, and predictive values ​​of the electricity supplied from the power supply system 20 to the hydrogen supply system 40. The power information acquisition unit 221 acquires time intervals in the power supply system 20 that allow for adjustment of the amount of electricity supplied.

[0109] The power consumption information acquisition unit 240 acquires information regarding the amount of power consumed in the power consumption system 12. The power consumption information acquisition unit 240 also acquires predicted values ​​of the amount of power consumed in the power consumption system 12.

[0110] The power supply forecasting unit 222 uses the power supply forecasting model 261 to predict the amount of electricity to be supplied from the power supply system 20 to the hydrogen supply system 40 in the future. Based on the predicted values ​​of the amount of electricity supplied from the renewable energy power source 10, the predicted price of electricity, the predicted state variables representing the configuration state of the power supply system 20, the predicted control variables for controlling the configuration of the power supply system 20, the predicted amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40, and the predicted amount of electricity consumption in the power consumption system 12, which are acquired by the power information acquisition unit 221 and the power consumption information acquisition unit 240, the power supply forecasting unit 222 predicts the amount of electricity to be supplied from the power supply system 20 to the hydrogen supply system 40 in the future. The power supply forecasting unit 222 changes the above predicted values ​​within the range predicted during the operation of the product manufacturing system 1 to predict the range of fluctuations and the distribution of fluctuations in the amount of electricity supplied from the power supply system 20 to the hydrogen supply system 40.

[0111] The raw material information acquisition unit 224 acquires predictive information regarding the hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. The raw material information acquisition unit 224 acquires predictive values ​​for the amount of electricity supplied from the power supply system 20, the amount of hydrogen to be supplied to the product manufacturing plant 70, predictive values ​​for state variables representing the configuration state of the hydrogen supply system 40, predictive values ​​for control variables for controlling the configuration of the hydrogen supply system 40, and predictive values ​​for the amount, price, and carbon intensity of hydrogen supplied from the hydrogen supply system 40 to the product manufacturing plant 70. The raw material information acquisition unit 224 acquires time intervals in the hydrogen supply system 40 during which the amount of hydrogen supplied can be adjusted.

[0112] The raw material supply forecasting unit 225 uses the raw material supply forecasting model 262 to forecast the amount of hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70 in the future. Based on the raw material information acquisition unit 224, which uses the following to forecast the amount of hydrogen to be supplied from the power supply system 20, the amount of hydrogen to be supplied to the product manufacturing plant 70, the state variables representing the configuration state of the hydrogen supply system 40, the control variables for controlling the configuration of the hydrogen supply system 40, and the hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70, the raw material supply forecasting unit 225 forecasts the amount of hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70 in the future. The raw material supply forecasting unit 225 changes the above forecast values ​​within the range expected during the operation of the product manufacturing system 1 to forecast the range and distribution of fluctuations in the amount of electricity used in the hydrogen supply system 40, and the range and distribution of fluctuations in the amount of hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70 in the future.

[0113] The product information acquisition unit 230 acquires information such as the type of product to be manufactured, specifications, demand volume, and predicted values ​​such as price.

[0114] The plant information acquisition unit 231 acquires predictive information regarding the product manufacturing plant 70. The plant information acquisition unit 231 acquires predictive values ​​for the amount of electricity supplied from the power supply system 20, predictive values ​​for the amount of hydrogen supplied from the hydrogen supply system 40, predictive values ​​for the type, specifications, demand, and unit price of the products to be manufactured, predictive values ​​for state variables representing the state of each component of the product manufacturing plant 70, predictive values ​​for control variables for controlling each component of the product manufacturing plant 70, and predictive values ​​for the yield, yield rate, and quality of the products to be manufactured.

[0115] The plant forecasting unit 232 uses the plant forecasting model 264 to predict the state, operating rate, product yield, product yield rate, product specifications, etc. of the product manufacturing plant 70. Based on the predicted values ​​obtained by the plant information acquisition unit 231, such as the predicted amount of electricity supplied from the power supply system 20, the predicted amount of hydrogen supplied from the hydrogen supply system 40, the predicted types, specifications, demand quantities, and unit prices of products to be manufactured, the predicted values ​​of state quantities representing the state of each component of the product manufacturing plant 70, the predicted values ​​of control quantities for controlling each component of the product manufacturing plant 70, and the predicted values ​​of the yield, yield rate, and quality of the products to be manufactured, the plant forecasting unit 232 predicts the state, operating rate, product yield, product yield rate, product specifications, etc. of the product manufacturing plant 70. The plant forecasting unit 232 changes the above predicted values ​​within the range predicted during the operation of the product manufacturing system 1 to predict the range and distribution of fluctuations in the amount of hydrogen used in the product manufacturing plant 70, as well as the range and distribution of fluctuations in the state, operating rate, product yield, product yield rate, product specifications, etc. of the product manufacturing plant 70.

[0116] The power supply system design unit 234, the raw material supply system design unit 235, and the plant design unit 237 design the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 based on the prediction results from the power supply forecasting unit 222, the raw material supply forecasting unit 225, and the plant forecasting unit 232. The power supply system design department 234, the raw material supply system design department 235, and the plant design department 237 design at least one of the following to enable adjustment of the supply and consumption of raw materials and energy from sources other than the upstream side of the design target: the configuration and specifications of the design target, the control mode of the design target, the configuration and specifications of the storage means for storing raw materials and energy, and conditions relating to the supply of raw materials and energy from sources other than the upstream side of the design target. These conditions are based on predicted values ​​of the range of fluctuations and distribution of fluctuations in the supply amount of raw materials and energy supplied from the upstream side of the design target, predicted values ​​of the range of fluctuations and distribution of fluctuations in the consumption amount of raw materials and energy used downstream of the design target, and time intervals during which the supply and consumption amounts of raw materials and energy can be adjusted upstream of the design target and downstream of the design target.

[0117] The power supply system design department 234, the raw material supply system design department 235, and the plant design department 237 design the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 using the technologies (1) to (3) described in the first embodiment, so as to absorb the differences in time constants between the components.

[0118] If method (1) is adopted, the power supply system design department 234, the raw material supply system design department 235, and the plant design department 237 design whether the power storage unit 30, the hydrogen storage unit 50, and the product storage unit 80 are necessary, their number, capacity, performance, etc.

[0119] If method (2) is adopted, the power supply system design unit 234, the raw material supply system design unit 235, and the plant design unit 237 design the configuration, specifications, and operation control model of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70. The operation control model may be artificial intelligence that takes the history, current values, and predicted values ​​of state variables and control variables in the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 as input and outputs recommended control variables and operation adjustment parameters for the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70. The power supply system design unit 234, the raw material supply system design unit 235, and the plant design unit 237 may learn the operation control model based on the learning data generated using the power supply prediction model 261, the raw material supply prediction model 262, and the plant prediction model 264.

[0120] If method (3) is adopted, the power supply system design department 234, the raw material supply system design department 235, and the plant design department 237 design whether or not to accept raw materials, energy, and products from the hydrogen production system 60 and the product manufacturing system 90, and the contents of the supply contract. The supply contract may include the price, specifications, delivery date, maximum and minimum values ​​of the raw materials, energy, and products.

[0121] The optimization unit 238 optimizes the design target using economic indicators such as the cost of product manufacturing and the product manufacturing cost itself as objective functions, while taking into account constraints such as carbon intensity when the power supply system design unit 234, the raw material supply system design unit 235, and the plant design unit 237 design the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70. The optimization unit 238 searches for candidates with superior economic indicators from among candidates designed so that the differences in time constants are absorbed by the technologies (1) to (3) described in the first embodiment.

[0122] The power supply optimization unit 223 optimizes the configuration and control of the power supply system 20 using the power supply optimization model 265 when the optimization unit 238 optimizes the design target.

[0123] The raw material supply optimization unit 226 optimizes the configuration and control of the hydrogen supply system 40 using the raw material supply optimization model 266 when the optimization unit 238 optimizes the design target.

[0124] The plant optimization unit 233 optimizes the configuration and control of the product manufacturing plant 70 using the plant optimization model 268 when the optimization unit 238 optimizes the design target.

[0125] The output unit 239 outputs information about the product manufacturing system 1, which has been designed by the power supply system design unit 234, the raw material supply system design unit 235, and the plant design unit 237, and optimized by the optimization unit 238.

[0126] Figure 5 is a flowchart showing the procedure of the operation support method according to the second embodiment. The raw material information acquisition unit 224 of the design support device 200 acquires a predicted value of the fluctuation range of the raw material or energy supply amount in the hydrogen supply system 40, which is the source of raw materials or energy used in the product manufacturing plant 70 (S20). The raw material information acquisition unit 224 acquires a time interval in the hydrogen supply system 40 during which the raw material or energy supply amount can be adjusted (S22). The plant design department 237 designs at least one of the following conditions for supplying raw materials or energy from a hydrogen production system 60, which is a source of raw materials or energy other than the hydrogen supply system 40: the configuration and specifications of the product manufacturing plant 70 or the hydrogen supply system 40; the control mode of the product manufacturing plant 70 or the hydrogen supply system 40; the configuration and specifications of the power storage unit 30 or the hydrogen storage unit 50, which is a storage means for storing raw materials or energy; and the conditions for supplying raw materials or energy from a hydrogen production system 60, which is a source of raw materials or energy other than the hydrogen supply system 40, based on a predicted value of the range of fluctuation in the amount of raw materials or energy supplied to the product manufacturing plant 70 from a hydrogen supply system 40, which is a source of raw materials or energy used in the product manufacturing plant 70 (S24).

[0127] Figure 6 shows the configuration of a product manufacturing plant 70 designed by the design support device 200 according to the second embodiment.

[0128] The product manufacturing plant 70 comprises a product manufacturing section 71, a hydrogen storage section 50, a product storage section 80, a plant operation control information acquisition section 72, a control section 73, an adjustment parameter acquisition section 74, a storage adjustment section 75, an operation adjustment section 76, and a supply adjustment section 77.

[0129] The product manufacturing unit 71 manufactures products using hydrogen supplied from the hydrogen supply system 40. The hydrogen storage unit 50 temporarily stores the hydrogen supplied from the hydrogen supply system 40. The product storage unit 80 temporarily stores the products manufactured in the product manufacturing unit 71. The hydrogen storage unit 50 and the product storage unit 80 may be located inside the product manufacturing plant 70 or outside the product manufacturing plant 70.

[0130] The plant operation control information acquisition unit 72 acquires plant operation control information from the operation support device 100. The control unit 73 controls the product manufacturing unit 71 based on the plant operation control information acquired by the plant operation control information acquisition unit 72.

[0131] The adjustment parameter acquisition unit 74 acquires supply amount adjustment parameters or usage amount adjustment parameters from the driving support device 100.

[0132] If the usage adjustment parameters obtained by the adjustment parameter acquisition unit 74 include parameters relating to the amount or capacity of raw materials or energy stored in the hydrogen storage unit 50, the storage adjustment unit 75 adjusts the amount or capacity of the hydrogen storage unit 50.

[0133] If the usage adjustment parameters acquired by the adjustment parameter acquisition unit 74 include parameters related to the operation of the product manufacturing plant 70, the operation adjustment unit 76 instructs the control unit 73 to adjust the operation of the product manufacturing unit 71.

[0134] If the supply adjustment parameters acquired by the adjustment parameter acquisition unit 74 include parameters relating to the supply amount of raw materials or energy supplied from the hydrogen production system 60, the supply adjustment unit 77 controls the acceptance of hydrogen from the hydrogen production system 60.

[0135] If the usage adjustment parameters acquired by the adjustment parameter acquisition unit 74 include parameters relating to the supply quantity of the product supplied from the product manufacturing system 90, the supply adjustment unit 77 adjusts the supply of the product from the product manufacturing system 90.

[0136] The present disclosure has been explained above based on examples. These examples are illustrative, and it will be understood by those skilled in the art that various modifications are possible in combinations of their components and processing processes, and that such modifications are also within the scope of the present disclosure.

[0137] The above embodiment describes a case where the operation of a product manufacturing system that produces products using hydrogen produced with electricity as a raw material is supported. However, the technology of this disclosure is also applicable to supporting the operation of any plant that produces products using raw materials or energy. For example, it is applicable to plants that generate products or energy using electricity with hydrogen or carbon dioxide as raw materials, plants that manufacture chemical products using chemical substances as raw materials, plants that generate electricity using fossil fuels, hydroelectric energy, biomass, etc., and systems that generate materials or energy using electricity. Each component that makes up the product manufacturing system 1, such as the power supply system 20 and the hydrogen supply system 40, also generates electricity and hydrogen, which become products, from raw materials or energy, and therefore falls under the category of the plant of this disclosure.

[0138] In the above embodiment, the case in which the operation support device 100 comprehensively adjusts the supply and use of raw materials and energy in the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 has been described. However, the individual power supply system 20, hydrogen supply system 40, and product manufacturing plant 70 may each adjust the supply and use of raw materials and energy in their respective systems. In this case, each component may notify other components of fluctuations in the supply and use of raw materials and energy or request adjustments to the supply and use of raw materials and energy via a higher-level configuration such as the energy management system 2, the plant control system 3, and the operation support device 100. Alternatively, each component may directly notify other components of fluctuations in the supply and use of raw materials and energy or request adjustments to the supply and use of raw materials and energy among themselves.

[0139] The technology of this embodiment can be used not only to optimize plant operation in real time during plant operation, but also as an operator's operational indicator during plant operation, when formulating plant operation plans, and when designing plants. [Explanation of Symbols]

[0140] 1 Product manufacturing system, 2 Energy management system, 3 Plant control system, 10 Renewable energy power supply source, 11 Power grid, 12 Power consumption system, 20 Power supply system, 30 Power storage unit, 40 Hydrogen supply system, 50 Hydrogen storage unit, 60 Hydrogen production system, 70 Product manufacturing plant, 71 Product manufacturing unit, 72 Plant operation control information acquisition unit, 73 Control unit, 74 Adjustment parameter acquisition unit, 75 Storage adjustment unit, 76 Operation adjustment unit, 77 Supply adjustment unit, 80 Product storage unit, 90 Product manufacturing system, 100 Operation support device, 121 Power information acquisition unit, 122 Power supply forecasting unit, 123 Power supply optimization unit, 124 Raw material information acquisition unit, 125 Raw material supply forecasting unit, 126 Raw material supply optimization unit, 130 Product information acquisition unit, 131 Plant information acquisition unit, 132 Plant forecasting unit, 133 Plant optimization unit, 134 Power supply operation control information output unit, 135 Raw material supply operation control information output unit, 137 Plant operation control information output unit, 138 Adjustment parameter determination unit, 139 Output unit, 140 Power consumption information acquisition unit, 161 Power supply prediction model, 162 Raw material supply prediction model, 164 Plant prediction model, 165 Power supply optimization model, 166 Raw material supply optimization model, 167 Grid power price prediction model, 168 Plant optimization model, 169 System information holding unit, 200 Design support device, 221 Power information acquisition unit, 222 Power supply prediction unit, 223 Power supply optimization unit, 224 Raw material information acquisition unit, 225 Raw material supply prediction unit, 226 Raw material supply optimization unit, 230 Product information acquisition unit, 231 Plant information acquisition unit, 232 Plant prediction unit, 233 Plant optimization unit, 234 Power supply system design unit, 235 Raw material supply system design unit, 237 Plant design unit, 238 Optimization unit, 239 Output unit, 240 Power consumption information acquisition unit, 260 Storage device, 261 Power supply forecasting model, 262 Raw material supply forecasting model, 264 Plant forecasting model, 265 Power supply optimization model, 266 Raw material supply optimization model, 267 Grid power price forecasting model, 268 Plant optimization model, 269 System information holding unit.

Claims

1. A supplier information acquisition unit that acquires the current or predicted value of the amount of raw materials or energy supplied to a supplier that supplies raw materials or energy used in the plant to the plant, A plant information acquisition unit that acquires current or predicted values ​​of the amount of raw materials or energy used in the plant, Based on the current or predicted value of the raw material or energy supply amount obtained by the supplier information acquisition unit, the current or predicted value of the raw material or energy usage amount obtained by the plant information acquisition unit, the time intervals at which the supplier can adjust the raw material or energy supply amount, and the time intervals at which the plant can adjust the raw material or energy usage amount, an adjustment parameter determination unit determines at least one of the following: a supply amount adjustment parameter for adjusting the raw material or energy supply amount at the supplier in accordance with fluctuations in the supply amount and usage amount of the raw material or energy; and a usage amount adjustment parameter for adjusting the raw material or energy usage amount at the plant. An output unit that outputs the supply amount adjustment parameter determined by the adjustment parameter determination unit to the supply source, or outputs the usage amount adjustment parameter to the plant, A driver assistance system equipped with the following features.

2. The adjustment parameter determination unit determines the supply adjustment parameters and usage adjustment parameters for optimizing the supply of raw materials or energy at the supplier and the operation of the plant, within a range that can accommodate fluctuations in the supply and usage of raw materials or energy. The driving support device according to claim 1.

3. The adjustment parameter determination unit determines the supply amount adjustment parameters and usage amount adjustment parameters for optimizing the supply of raw materials or energy at the supplier and the operation of the plant, to the extent that constraints relating to the supply or use of raw materials or energy, or the products produced at the plant, are met. The driving support device according to claim 1.

4. The adjustment parameter determination unit determines the supply adjustment parameter or the usage adjustment parameter when the supplier or the plant requests an adjustment to the supply or usage amount of the raw materials or energy, or when a fluctuation of more than a predetermined amount in the supply or usage amount of the raw materials or energy is predicted. The driving support device according to claim 1.

5. The predicted quantity of the raw material or energy supply is calculated based on a supply prediction model for predicting the quantity of the raw material or energy supply at the supplier, or on external information relating to the raw material or energy. The driving support device according to claim 1.

6. The predicted values ​​for the use of the raw materials or energy are calculated based on a usage prediction model for predicting the use of the raw materials or energy in the plant, or on external information relating to the products produced in the plant. The driving support device according to claim 1.

7. The plant further comprises a demand information acquisition unit that acquires the current or predicted value of the demand for the products produced in the plant, The adjustment parameter determination unit determines the supply quantity adjustment parameter and the usage quantity adjustment parameter based on the current or predicted demand for the product obtained by the demand information acquisition unit. The driving support device according to claim 1.

8. The supply adjustment parameters include parameters relating to the amount and capacity of the raw materials or energy stored in the supply source storage means provided at the supply source for storing the raw materials or energy. The driving support device according to any one of claims 1 to 7.

9. The aforementioned usage adjustment parameters include parameters relating to the amount and capacity of the raw materials or energy stored in the plant storage means for storing the raw materials or energy provided in the plant. The driving support device according to any one of claims 1 to 7.

10. The supply adjustment parameters include parameters relating to the generation of the raw materials or energy in the raw material or energy generation means provided at the supply source. The driving support device according to any one of claims 1 to 7.

11. The usage adjustment parameters include parameters relating to the operation of the plant. The driving support device according to any one of claims 1 to 7.

12. The supply adjustment parameters include parameters relating to the amount of raw materials or energy supplied from a different supplier of raw materials or energy than the aforementioned supplier. The driving support device according to any one of claims 1 to 7.

13. The aforementioned usage adjustment parameters include parameters relating to the supply quantity of the product supplied from a supplier other than the plant. The driving support device according to any one of claims 1 to 7.

14. Computers, A supplier information acquisition unit that acquires the current or predicted value of the amount of raw materials or energy supplied to a supplier that supplies raw materials or energy used in the plant to the plant, A plant information acquisition unit that acquires current or predicted values ​​of the amount of raw materials or energy used in the plant, Based on the current or predicted value of the raw material or energy supply amount obtained by the supplier information acquisition unit, the current or predicted value of the raw material or energy usage amount obtained by the plant information acquisition unit, the time intervals at which the supplier can adjust the raw material or energy supply amount, and the time intervals at which the plant can adjust the raw material or energy usage amount, an adjustment parameter determination unit determines at least one of the following: a supply amount adjustment parameter for adjusting the raw material or energy supply amount at the supplier in accordance with fluctuations in the supply amount and usage amount of the raw material or energy; and a usage amount adjustment parameter for adjusting the raw material or energy usage amount at the plant. An output unit that outputs the supply amount adjustment parameter determined by the adjustment parameter determination unit to the supply source, or outputs the usage amount adjustment parameter to the plant, A driver assistance program designed to function as such.

15. On the computer, A step of obtaining the current or predicted value of the amount of raw materials or energy supplied to the plant by a supplier that supplies the raw materials or energy used in the plant, The steps include obtaining the current or predicted value of the amount of raw materials or energy used in the plant, A step of determining at least one of the following, based on the current or predicted value of the acquired raw material or energy supply, the current or predicted value of the acquired raw material or energy consumption, the time interval at which the supply source can adjust the raw material or energy supply, and the time interval at which the plant can adjust the raw material or energy consumption, a supply adjustment parameter for adjusting the raw material or energy supply at the supply source in accordance with fluctuations in the supply and consumption of the raw material or energy, and a consumption adjustment parameter for adjusting the raw material or energy consumption at the plant. The steps include outputting the determined supply adjustment parameter to the supply source, or outputting the usage adjustment parameter to the plant, A driver assistance method that enables the execution of a driving action.

16. The plant is equipped with a design unit that, in order to enable the plant to be operated in accordance with fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of raw materials or energy used in the plant, designs at least one of the following based on a predicted value of the range of fluctuations in the amount of raw materials or energy supplied to the plant from the supplier and a time interval in which the amount of raw materials or energy supplied to the supplier can be adjusted: the configuration and specifications of the plant or the supplier, the control mode of the plant or the supplier, the configuration and specifications of the storage means for storing the raw materials or energy, and conditions relating to the supply of raw materials or energy from a supplier of raw materials or energy other than the supplier. Design support equipment.

17. The design unit includes an optimization unit that searches for the optimal candidate from among multiple candidates designed in the design unit. The design support device according to claim 16.

18. The optimization unit searches for the optimal candidate among the plurality of candidates within the range that constraints relating to the supply or use of raw materials or energy, or the products produced in the plant, are met. The design support device according to claim 17.

19. The predicted range of fluctuations in the supply of the raw materials or energy is calculated based on a supply forecasting model for predicting the supply of the raw materials or energy at the supplier, or on external information relating to the raw materials or energy. A design support device according to any one of claims 16 to 18.

20. Computers, A design department designs at least one of the following based on a predicted value of the range of fluctuation in the amount of raw materials or energy supplied to the plant from a supplier of raw materials or energy used in the plant: the configuration and specifications of the plant or the supplier, the control mode of the plant or the supplier, the configuration and specifications of the storage means for storing the raw materials or energy, and the conditions relating to the supply of raw materials or energy from a supplier of raw materials or energy other than the supplier, in order to enable the plant to be operated in accordance with fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of raw materials or energy used in the plant. A design support program to enable it to function as such.

21. On the computer, To enable the plant to operate in accordance with fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of those raw materials or energy used in the plant, the procedure involves designing at least one of the following based on a predicted range of fluctuations in the amount of raw materials or energy supplied to the plant from the supplier, the time intervals at which the supplier can adjust the amount of raw materials or energy supplied to the plant, the configuration and specifications of the plant or the supplier, the control mode of the plant or the supplier, the configuration and specifications of the storage means for storing the raw materials or energy, and the conditions relating to the supply of raw materials or energy from a supplier other than the supplier, based on a predicted range of fluctuations in the amount of raw materials or energy supplied to the plant from a supplier of those raw materials or energy used in the plant, and the time intervals at which the supplier can adjust the amount of raw materials or energy supplied to the plant. Design support method.

22. A plant that manufactures products using raw materials or energy, A supplier that supplies the raw materials or energy to the plant, wherein the time interval at which the amount of raw materials or energy supplied can be adjusted is different from the time interval at which the amount of raw materials or energy used in the plant can be adjusted, in order to enable the plant to operate in accordance with fluctuations in the amount of raw materials or energy supplied from the supplier, (1) Storage means for storing the raw materials or energy, (2) Operation adjustment means for adjusting the control mode of the plant based on a predicted value of the range of fluctuation in the amount of raw materials or energy supplied at the supplier and a time interval at which the amount of raw materials or energy supplied at the supplier can be adjusted, (3) Supply adjustment means for adjusting the amount of raw materials or energy supplied from a source of raw materials or energy other than the aforementioned supplier, It comprises at least one of the following: plant.

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