Operation support device, operation support program, and operation support method

Abstract

An operation support device 100 comprises: a supply source information acquiring unit that acquires a current value or a forecast value of a supply amount at a supply source that supplies a plant with a raw material or energy to be used in the plant; a plant information acquiring unit that acquires a current value or a forecast value of a usage amount of the raw material or energy at the plant; an adjustment parameter determining 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 variations in the supply amount and the usage amount on the basis of the current value or the forecast value of the supply amount, the current value or the forecast value of the usage amount, a time interval at which the supply amount can be adjusted at the supply source, and a time interval at which the usage amount can be adjusted at the plant; and an output unit that outputs the determined supply amount adjustment parameter or usage amount adjustment parameter.

Description

Driving Support Device, Driving Support Program, and Driving Support Method

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

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

[0003] Japanese Patent Application Laid-Open No. 2019-144897

[0004] Since the supply amounts of power, raw materials, etc. and the demand amounts of products can vary from moment to moment, the power supply source and the plant that manufactures products using power and raw materials need to adjust their operations according to the fluctuations in the supply and demand amounts. However, when the time constants for operation adjustment are different between the supply source 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 the raw material or energy supply source and the plant.

[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 conversions of the expressions of this disclosure between methods, apparatus, systems, recording media, computer programs, etc., are also valid as aspects of this disclosure.

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

[0013] This is a schematic diagram showing the configuration of the product manufacturing system according to the first embodiment. This is a schematic diagram showing the functional configuration of the operation support device. This is a flowchart showing the procedure of the operation support method according to the first embodiment. This is a schematic diagram showing the functional configuration of the design support device according to the second embodiment. This is a flowchart showing the procedure of the design support method according to the second embodiment. This is a schematic diagram showing the functional configuration of the product manufacturing plant.

[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 supplying raw materials and energy to the plant differs from the time interval (time constant) at which the plant can adjust the amount of raw materials and energy used. 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 the present 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 comprises 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 source 10 supplies electricity generated using renewable energy. The power grid 11 is a system that integrates power generation, substation, transmission, and distribution facilities. The power supply system 20 levels out fluctuations in the electricity supplied from the renewable energy power source 10 using electricity 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 electricity 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 other materials 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 also 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, or 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 the 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 operation support device 100 of this embodiment comprehensively adjusts the operation of multiple components with different time constants mainly by the following three methods: (1) Absorbing differences in time constants by providing a storage unit for storing energy, raw materials, and products. (2) Absorbing differences in time constants by controlling the operation of each component. (3) Absorbing differences in time constants by coordinating with external suppliers and manufacturers of the 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 level 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. The operation support device 100 stores excess hydrogen in the hydrogen storage unit 50 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 also replenishes the product manufacturing plant 70 with hydrogen from the hydrogen storage unit 50 when the amount of hydrogen supplied from the hydrogen supply system 40 is insufficient to meet the hydrogen usage.

[0033] Product manufacturing system 1 includes a product storage unit 80 for storing products in order 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 shortage to be replenished from the product storage unit 80.

[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 section 50 or the product storage section 80. The power system 11 may constitute part or all of the power storage section 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 slowing down the rate at which the product manufacturing plant 70 accepts hydrogen from the hydrogen supply system 40, or 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 the hydrogen supply is predicted in the short term, but a recovery in the hydrogen supply is predicted in the medium term, the operation support device 100 may replenish the hydrogen shortage during the period of the decrease in 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 value or predicted value of the supply amount of raw materials or energy and the current value or predicted value of the usage amount of raw materials or energy, and optimizes the operations of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70 within a range capable of coping with fluctuations in the supply amount and usage amount of raw materials or energy. The operation support device 100 comprehensively optimizes the operations of the power supply system 20, the hydrogen supply system 40, and the product manufacturing plant 70, taking into account constraint conditions such as carbon intensity, with economic indicators such as the cost related to product manufacturing and the product manufacturing cost itself as the objective function.

[0041] (3) The case of absorbing the difference in time constants by collaborating with suppliers and manufacturers outside the product manufacturing system 1 will be described.

[0042] When the amount of hydrogen used in the product manufacturing plant 70 is insufficient for the supply amount of hydrogen supplied from the hydrogen supply system 40, the operation support device 100 causes the hydrogen manufacturing system 60, which is a hydrogen supply source different from the hydrogen supply system 40, to supply the insufficient hydrogen. The hydrogen supplied from the hydrogen manufacturing system 60 does not have to be green hydrogen derived from renewable energy.

[0043] When the production volume of the product manufactured in the product manufacturing plant 70 is insufficient for the demand volume of the product in the market, the operation support device 10 causes the product manufacturing system 90, which is a product supply source different from the product manufacturing plant 70, to supply the insufficient product. The product supplied from the product manufacturing system 90 does not have to be a green product derived from renewable energy.

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

[0045] When 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 with reduced carbon dioxide emissions into the atmosphere, such as separating and recovering carbon dioxide during extraction from fossil fuels and storing it underground, the operation support device 100 may further consider the carbon dioxide emissions required by the product manufacturing system 1 or the product, etc., and determine the supply amount of hydrogen from the hydrogen supply system 40 and the supply amount of hydrogen from the hydrogen production system 60.

[0046] When the product manufactured in the product manufacturing plant 70 is a green product derived from renewable energy and the product manufactured in the product manufacturing system 90 is a blue product with reduced carbon dioxide emissions into the atmosphere during the manufacturing process, the operation support device 100 may further consider the carbon dioxide emissions required by the product manufacturing system 1 or the product, etc., and determine the supply amount of the product from the product manufacturing plant 70 and the supply amount of the product from the product manufacturing system 90.

[0047] A part of the electric power supplied from the electric power supply system 20 or the electric power storage unit 30 may be supplied to the electric power consumption system 12. Examples of the electric power consumption system include a data center, etc.

[0048] Figure 2 schematically shows the functional configuration of the operation support device 100. These functions may be realized in a circuit (circuitry) or a processing circuit (processing circuitry) including a general-purpose processor, a specific-purpose processor, an integrated circuit, ASICs (Application Specific Integrated Circuits), a CPU (Central Processing Unit), a conventional circuit, and / or a combination thereof, which is configured or programmed to realize the functions described in this specification. The processor may be regarded as a circuit (circuitry) or a processing circuit (processing circuitry) including transistors and other circuits. The processor may be a programmed processor that executes a program stored in a memory.

[0049] The driver assistance device 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 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 quantities representing the state of components such as the power storage unit 30, and current and predicted values ​​of control quantities 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 the 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 was 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 as input, 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 management 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 quantities representing the state of components such as the power storage unit 30, and the current and predicted values ​​of control quantities for controlling components such as the power storage unit 30, and outputs set values ​​for control quantities that optimize the revenue, cost, operating rate, etc., of the power supply optimization model 165. 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 prediction model 167 is a model for predicting 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 prediction values ​​of the grid power price prediction 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 as input 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, and the current and predicted values ​​of control variables for controlling the configuration of the hydrogen supply system 40, 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 prediction unit 122, a power supply optimization unit 123, a raw material information acquisition unit 124, a raw material supply prediction unit 125, a raw material supply optimization unit 126, a product information acquisition unit 130, a plant information acquisition unit 131, a plant prediction 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 a virtual computer for a Web service. 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 quantities representing the configuration state of the power supply system 20, the current and predicted values ​​of control quantities 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 quantities representing the configuration state of the hydrogen supply system 40, the current and predicted values ​​of control quantities 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 a set value for a control amount 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 value of the control quantity calculated by the plant optimization unit 133 as a recommended value 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 the 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 reducing the computational load and efficiently optimizing the entire product manufacturing system 1. 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, the power supply forecasting unit 122, the raw material information acquisition unit 124, and the 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 during which the supply amount of raw material or energy can be adjusted in the power supply system 20 and the hydrogen supply system 40, and the time intervals during 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 the 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 a power 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 related 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 related 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 supply source, 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 prediction unit 122, a raw material information acquisition unit 124, and a raw material supply prediction unit 125, acquires the current or predicted value of the raw material or energy supply amount (S10). The plant information acquisition unit 131 acquires the current or predicted value 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 usage, 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 usage of raw materials or energy can be adjusted in the product manufacturing plant 70, in accordance with fluctuations in the supply or usage of raw materials or energy: a supply adjustment parameter for adjusting the supply of raw materials or energy in the power supply system 20 and hydrogen supply system 40, and a usage adjustment parameter for adjusting the usage of raw materials or energy in the product manufacturing plant 70 (S14).

[0092] (Second Embodiment) This section describes a technique for designing a plant that can respond to fluctuations in the amount of raw materials and energy supplied from the raw material and energy suppliers to the plant, 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 described in (1) to (3) 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, an application-specific processor, an integrated circuit, an ASIC (Application Specific Integrated Circuit), a CPU (Central Processing Unit), a 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 overall design of the 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 to and from 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 the same as 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 the same as 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 the same as the power supply optimization model 165 for 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 the same as 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 the same as 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 the same as 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 the subject of 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 prediction unit 122, a power supply optimization unit 123, a raw material information acquisition unit 124, a raw material supply prediction unit 125, a raw material supply optimization unit 126, a product information acquisition unit 130, a plant information acquisition unit 131, a plant prediction 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 a virtual computer for a Web service. 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 power 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 power supplied from the renewable energy power source 10, predictive values ​​of the price of power 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 power 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 power 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 values ​​representing the configuration state of the power supply system 20, the predicted control values ​​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 predict 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 predicted values ​​of the amount of electricity supplied from the power supply system 20, the predicted amount of hydrogen to be supplied to the product manufacturing plant 70, the predicted values ​​of state quantities representing the configuration state of the hydrogen supply system 40, the predicted values ​​of control quantities for controlling the configuration of the hydrogen supply system 40, and the predicted values ​​of the amount, price, and carbon intensity of hydrogen to be supplied from the hydrogen supply system 40 to the product manufacturing plant 70, the raw material supply forecasting unit 225 predicts 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 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 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 amount of raw materials and energy from upstream of the design target, based on predicted values ​​of the range of fluctuations and distribution of fluctuations in the supply amount of raw materials and energy supplied from upstream 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 amount and consumption of raw materials and energy can be adjusted upstream of 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 in such a way that differences in time constants between components are absorbed, using the technologies (1) to (3) described in the first embodiment.

[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 supply quantity for 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 that allows for adjustment of the raw material or energy supply amount (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 for the range of fluctuations 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 amount 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, a case was described 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. 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 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.

[0140] This disclosure relates to technologies that support the operation and design of plants.

[0141] 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, 134 135 Power supply operation control information output unit, 137 Raw material supply operation control information output unit, 138 Plant operation control information output unit, 139 Adjustment parameter determination unit, 140 Output 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

An operation support device comprising: 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; a plant information acquisition unit that acquires the current or predicted value 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 value of the amount of raw materials or energy supplied acquired by the supplier information acquisition unit, the current or predicted value of the amount of raw materials or energy used acquired by the plant information acquisition unit, a time interval in which the amount of raw materials or energy supplied at the supplier can adjust the amount of raw materials or energy used at the plant; and a usage adjustment parameter for adjusting the amount of raw materials or energy used in the plant. 　 The operation support device according to claim 1, wherein the adjustment parameter determination unit determines the supply amount adjustment parameter and the usage amount adjustment parameter for optimizing the supply of the raw materials or energy at the supply source and the operation of the plant, within a range that can accommodate fluctuations in the supply amount and usage amount of the raw materials or energy. 　 The operation support device according to claim 1 or 2, wherein the adjustment parameter determination unit determines the supply amount adjustment parameter and the usage amount adjustment parameter for optimizing the supply of the raw materials or energy at the supplier and the operation of the plant, within a range where constraints relating to the supply or use of the raw materials or energy, or the products produced in the plant, are met. 　 The operation support device according to any one of claims 1 to 3, wherein the adjustment parameter determination unit determines the supply amount adjustment parameter or the usage amount adjustment parameter when the supplier or the plant requests an adjustment of the supply amount or usage amount of the raw materials or energy, or when a fluctuation of the supply amount or usage amount of the raw materials or energy exceeding a predetermined amount is predicted. 　 The driving support device according to any one of claims 1 to 4, wherein the predicted value of the supply amount of the raw material or energy is calculated based on a supply amount prediction model for predicting the supply amount of the raw material or energy at the supply source, or based on external information relating to the raw material or energy. 　 The operation support device according to any one of claims 1 to 5, wherein the predicted value of the amount of raw materials or energy used is calculated based on a usage prediction model for predicting the amount of raw materials or energy used in the plant, or on external information relating to the products produced in the plant. 　 The operation support device according to any one of claims 1 to 6, further comprising a demand information acquisition unit that acquires the current or predicted demand for the product to be produced in the plant, wherein the adjustment parameter determination unit further determines the supply quantity adjustment parameter and the usage quantity adjustment parameter based on the current or predicted demand for the product acquired by the demand information acquisition unit. 　 The operating support device according to any one of claims 1 to 7, wherein the supply amount adjustment parameter includes parameters relating to the amount and capacity of the raw material or energy stored in the supply source storage means for storing the raw material or energy provided at the supply source. 　 The operation support device according to any one of claims 1 to 8, wherein the 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 operating support device according to any one of claims 1 to 9, wherein the supply amount adjustment parameter includes a parameter relating to the generation of the raw material or energy in the raw material or energy generation means provided at the supply source. 　 The operation support device according to any one of claims 1 to 10, wherein the usage adjustment parameter includes parameters relating to the operation of the plant. 　 The operation support device according to any one of claims 1 to 11, wherein the supply amount adjustment parameter includes a parameter relating to the amount of raw material or energy supplied from a raw material or energy supplier other than the supplier. 　 The operation support device according to any one of claims 1 to 12, wherein the usage adjustment parameter includes a parameter relating to the supply amount of the product supplied from a product supplier other than the plant. 　 An operation support program for a computer to function as: 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 a supply quantity adjustment parameter 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 at the supplier and the amount of raw materials or energy 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 adjust the amount of raw materials or energy used at the plant; and an output unit that outputs the supply quantity adjustment parameter determined by the adjustment parameter determination unit to the supplier, or outputs the amount of usage adjustment parameter to the plant. 　 An operation support method that causes a computer to perform the following steps: acquire 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; acquire the current or predicted value 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 value of the amount of raw materials or energy supplied, the acquired current or predicted value of the amount of raw materials or energy used, a time interval at which the supplier can adjust the amount of raw materials or energy supplied, and a time interval at which the plant can adjust the amount of raw materials or energy used, 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. A design support device comprising 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: 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, based on a predicted value of the 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. 　 The design support device according to claim 16, further comprising an optimization unit that searches for the optimal candidate among a plurality of candidates designed in the design unit. 　 The design support device according to claim 17, wherein 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 to products produced in the plant, are satisfied. 　 The design support device according to any one of claims 16 to 18, wherein the predicted value of the fluctuation range of the supply amount of the raw material or energy is calculated based on a supply amount prediction model for predicting the supply amount of the raw material or energy at the supply source, or based on external information relating to the raw material or energy. 　 A design support program for a computer to function as a design unit that designs at least one of the following: 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 the raw materials or energy from a supplier other than the supplier, 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, and a time interval in which the amount of raw materials or energy supplied to the plant can be adjusted at the supplier. 　 A design support method that causes a computer to perform the step of designing at least one of the following: 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 the raw materials or energy from a supplier other than the supplier, 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, and a time interval in which the amount of raw materials or energy supplied to the plant can be adjusted at the supplier. 　 A plant that manufactures products using raw materials or energy, comprising at least one of the following: (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 fluctuation range of the supply amount of the raw materials or energy at the supply source and the time interval at which the supply amount of the raw materials or energy can be adjusted at the supply source; and (3) supply adjustment means for adjusting the supply amount of the raw materials or energy from a supply source other than the supply source, in order to enable the plant to be operated in accordance with fluctuations in the supply amount of the raw materials or energy from the supply source where the time interval at which the supply amount of the raw materials or energy can be adjusted is different; and (3) supply adjustment means for adjusting the supply amount of the raw materials or energy from a supply source of the raw materials or energy other than the supply source.

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