WO2020059741A1 - Appareil, procédé et programme de planification - Google Patents

Appareil, procédé et programme de planification Download PDF

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Publication number
WO2020059741A1
WO2020059741A1 PCT/JP2019/036524 JP2019036524W WO2020059741A1 WO 2020059741 A1 WO2020059741 A1 WO 2020059741A1 JP 2019036524 W JP2019036524 W JP 2019036524W WO 2020059741 A1 WO2020059741 A1 WO 2020059741A1
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period
model
unit
operation plan
power generation
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PCT/JP2019/036524
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Japanese (ja)
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豪秀 奈木野
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旭化成株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present invention relates to a planning device, a method, and a program.
  • an electrolysis apparatus that generates hydrogen by electrolyzing water has been known.
  • a power supply unit a power generation device that generates power using renewable energy, or a power system in which an electricity rate fluctuates according to weather or power supply cost is known.
  • a first aspect of the present invention provides a planning device.
  • the planning device uses a power generation forecasting model that predicts a change in the amount of renewable energy generated by the power generating device during the target period based on the value of the first factor that is available before the target period.
  • a power generation amount prediction unit for predicting a transition of the power generation amount may be provided.
  • the planning device prepares an operation plan of the electrolysis device that satisfies the usage plan of the product of the electrolysis device in the first period in the future, based on the predicted change in the amount of generated renewable energy and the electricity rate of the power system.
  • An operation plan generation unit that generates the operation plan may be provided.
  • the power generation amount prediction model calculates a transition of the renewable energy power generation amount in the target period based on the value of the first factor including at least one of the renewable energy power generation amount of the power generation device and the weather information before the target period. You can predict.
  • the planning device may include a first model updating unit that updates the power generation prediction model by learning based on the value of the first factor in the past period and the actual change in the amount of renewable energy generated in the past period.
  • the operation plan generation unit during the first period, in order to satisfy the use plan of the product of the electrolysis device, the operation plan to operate the electrolysis device using the electricity from the power generation device in preference to the electricity from the power system. May be generated.
  • the use plan of the product of the electrolyzer is at least one of a plan to maintain the storage amount of the product of the electrolyzer in the first period within the reference range and a plan to satisfy the demand for the product of the electrolyzer in the first period. May be included.
  • the operation plan generation model includes a value of a second factor including at least one of operation data of the electrolysis apparatus, a demand amount of a product of the electrolysis apparatus, and a storage amount of a product of the electrolysis apparatus before the target period, An operation plan of the electrolysis device in the target period may be generated based on the prediction result of the change in the amount of generated renewable energy in the target period and the electricity rate.
  • the planning device calculates the value of the second factor in the past period, the change in the amount of renewable energy generated in the past period or the predicted result of the change in the amount of renewable energy generated in the past period, the change in the electricity rate after the past period, and the change in the past period.
  • a second model updating unit that updates the operation plan generation model by learning based on the target operation plan of the electrolysis apparatus may be provided.
  • the planning device uses a power rate prediction model that predicts a change in the power rate of the power system in the target period based on the value of the third factor available before the target period, and calculates a future change in the power rate.
  • An electricity rate prediction unit for predicting the electricity rate may be provided.
  • the operation plan generation unit generates a use plan of the product of the electrolytic device in the first period in the future based on the predicted future change in the amount of renewable energy generated and the predicted future change in the electricity rate.
  • An operation plan for the electrolyzer to be filled may be generated.
  • the electricity rate prediction model calculates at least one of the electricity rate, the power demand, the power supply, the renewable energy generation, the predicted value of the renewable energy generation, and the weather information in the power system before the target period. Based on the value of the third factor included, the transition of the electricity rate of the power system in the target period may be predicted.
  • a method includes using a power generation forecasting model that predicts a change in the amount of renewable energy generated by a power generator during a target period based on a value of a first factor that is available before the target period.
  • a step of predicting a change in the amount may be provided.
  • the method generates an operation plan of an electrolyzer that satisfies an electrolyzer product usage plan for a first period in the future, based on a predicted future change in renewable energy generation and an electricity rate of a power system.
  • the method may include a step of:
  • FIG. 1 shows a system 10 according to the present embodiment.
  • 4 shows a detailed configuration example of a planning device 40 according to the present embodiment.
  • 5 shows an example of an operation flow of the planning device 40 according to the present embodiment.
  • 14 illustrates an example of a computer 1900 in which aspects of the embodiments can be wholly or partially embodied.
  • Electrolysis device 20 is connected to power generation device 30, planning device 40, and power system 50.
  • the electrolytic device 20 may be a device that generates a product using electric energy.
  • the electrolysis device 20 is, for example, a hydrogen generation device that generates hydrogen as a product by electrolysis.
  • the electrolysis device 20 operates according to the operation plan generated by the planning device 40.
  • the electrolysis device 20 operates by being supplied with power from the power generation device 30 and the power system 50.
  • the planning device 40 generates an operation plan for the electrolysis device 20.
  • the planning device 40 may be a computer such as a personal computer, a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system to which a plurality of computers are connected.
  • the planning device 40 may generate the operation plan by a process in a CPU, a GPU (Graphics Processing Unit), and / or a TPU (Tensor Processing Unit) of the computer.
  • the planning device 40 may perform various processes on a cloud provided by a server computer.
  • the operation plan generated by the planning device 40 may be a table or data describing a state in which the electrolysis device 20 should be operated in the first period.
  • the operation plan includes, for example, a time period in which the electrolysis device 20 is operated (or not operated), a time period in which the electrolysis device 20 is operated, and a time period in which the electrolysis device 20 is operated with the power from the power generation device 30 and the operation from the power system 50. It may be a table or data or the like that defines a time zone for powering, a time zone for selling the power generated by the power generation device 30 to the power system 50, and / or an operation rate of the electrolysis device 20 for each time zone.
  • the planning device 40 includes an acquisition unit 100, a storage unit 110, a model generation unit 120, a learning processing unit 130, a prediction unit 140, an operation plan generation unit 150, and a control unit 160.
  • the storage unit 110 is connected to the model generation unit 120, the learning processing unit 130, the prediction unit 140, and the operation plan generation unit 150, and stores information acquired by the acquisition unit 100.
  • the storage unit 110 may store data processed by the planning device 40.
  • the storage unit 110 may store intermediate data, calculation results, parameters, and the like calculated (or used) in the process of generating the operation plan by the planning device 40. Further, the storage unit 110 may supply the stored data to a request source in response to a request from each unit in the planning device 40.
  • the storage unit 110 supplies the stored data to the model generation unit 120 in response to a request from the model generation unit 120, for example.
  • the ⁇ model generation unit 120 is connected to the learning processing unit 130, and generates a learning model that the planning device 40 learns.
  • the model generation unit 120 generates a learning model according to the first factor, the second factor, and the third factor stored in the storage unit 110.
  • the model generation unit 120 may generate one or a plurality of learning models.
  • the model generation unit 120 supplies the generated learning model to the learning processing unit 130.
  • the learning processing unit 130 is connected to the prediction unit 140 and learns the generated learning model based on the learning data acquired by the acquisition unit 100.
  • the learning processing unit 130 may execute the reinforcement learning to update the learning model.
  • the learning processing unit 130 may update one or a plurality of learning models.
  • the learning processing unit 130 supplies the updated learning model to the prediction unit 140.
  • the operation plan generation unit 150 is connected to the control unit 160, and generates an operation plan of the electrolysis device 20 in the first period in the future.
  • the operation plan generation unit 150 generates, for example, an operation plan that minimizes or reduces the product manufacturing cost while satisfying the product use plan of the electrolytic device 20 in the first period in the future.
  • the operation plan generation unit 150 supplies the generated operation plan to the control unit 160.
  • the use plan of the product of the electrolyzer 20 includes a plan to maintain the storage amount of the product of the electrolyzer 20 within the reference range and a plan to satisfy the demand or supply of the product of the electrolyzer 20. May be included.
  • the demand amount or supply amount of the product may be, for example, the total amount of the product to be supplied by the electrolysis device 20 in the first period or the amount per predetermined time.
  • the storage amount may be the amount of the product stored inside or outside the electrolysis device 20.
  • Such a reference range of the storage amount, the demand amount of the product, or the supply amount is determined based on data input from the outside to the planning device 40, past data, or past data in the planning device 40 in the future first period. May be expected.
  • the control unit 160 controls the operation of the electrolysis device 20 using the operation plan of the electrolysis device 20 in the first period.
  • the control unit 160 may operate the electrolysis device 20 by selectively using the electric power from the power generation device 30 and the electric power from the power system 50 according to a time zone according to an operation plan.
  • the control unit 160 may operate each of the plurality of electrolyzers 20.
  • Control unit 160 may instruct stop and start of operation of electrolysis apparatus 20 when the operation of electrolysis apparatus 20, the storage amount of the product, and the like fall outside the expected ranges.
  • the control unit 160 may control the power generated by the power generation device 30 to be sold to the power system 50 using the operation plan of the electrolysis device 20 in the first period.
  • the planning device 40 of the present embodiment described above it is possible to generate the operation plan of the electrolysis device 20 according to the fluctuation of the renewable energy power generation amount of the power generation device 30 and the fluctuation of the electricity rate of the power system 50,
  • the product can be produced at a lower production cost than the amount predetermined by the usage plan.
  • a more specific configuration example of such a planning device 40 will be described below.
  • the planning device 40 includes a first model generation unit 200, a first model updating unit 210, and a power generation amount prediction unit 220, and predicts a renewable energy power generation amount of the power generation device 30 in the future.
  • the planning device 40 includes a second model generation unit 230 and a second model update unit 240, and the operation plan generation unit 150 generates a future operation plan of the electrolytic device 20.
  • the planning device 40 includes a third model generation unit 250, a third model update unit 260, and an electricity rate prediction unit 270, and predicts a future electricity rate of the power system 50.
  • the second factor may include information on the operation of the electrolysis device 20 or the usage plan of the product of the electrolysis device 20.
  • the second factor is at least one of the operation data of the electrolysis device 20, the demand of the product of the electrolysis device 20, and the storage amount of the product of the electrolysis device 20, which are available before the target period of the operation plan to be generated. Including one.
  • the second factor may include an operation plan of the electrolysis device 20 generated by the planning device 40 in the past.
  • the second factor may include virtual data calculated from a physical model of the electrolysis device 20.
  • the second factor may include information obtained by the obtaining unit 100 from the electrolysis device 20.
  • the operation data may be a product generation amount per unit time in the electrolysis device 20 and / or a product generation amount per unit power.
  • the demand may be the amount of product that needs to be supplied by the electrolyzer 20.
  • the storage amount may be an amount stored in a tank or the like that stores a product of the electrolysis device 20.
  • the third factor may include information that affects fluctuations in the electricity rate of the power system 50.
  • the third factor is the electricity price, the power demand, the power supply, the renewable energy power generation, the predicted value of the renewable energy power generation, and the weather information in the power system 50 before the target period for predicting the electricity price.
  • At least one of The electricity rate in the power system 50 is the actual electricity rate for the power supplied from the power system 50 to the electrolysis device 20 at the place where the electrolysis device 20 is installed, and / or the power generation device 30 sells the power to the power system 50. It may be the power sale price at the time.
  • the weather information may be weather information of a region where the electrolysis device 20 is installed.
  • the information of the first factor, the second factor, and the third factor may be time-series information at approximately constant time intervals.
  • the information of the first factor, the second factor, and the third factor may be added or updated over time, respectively.
  • the acquisition unit 100 may acquire and update each piece of information at predetermined intervals.
  • the acquisition unit 100 may acquire the information at substantially the same or different periods according to the information to be acquired, and may add or update each.
  • the information of the first factor, the second factor, and the third factor may include information supplied from an external device or the like.
  • the first model generation unit 200 is connected to the first model update unit 210.
  • the first model generation unit 200 generates a power generation prediction model that predicts a change in the amount of renewable energy generated by the power generation device 30 in the target period based on a value of a first factor available before the target period. .
  • the first model generation unit 200 may generate a power generation amount prediction model by a process called pre-learning or off-line learning using information past the target period.
  • the first model generation unit 200 generates a power generation prediction model using, for example, regression analysis, Bayesian inference, a neural network, a Gaussian mixture model, a hidden Markov model, or the like.
  • the first model generation unit 200 supplies the generated power generation prediction model to the first model update unit 210 as a first model.
  • the first model updating unit 210 is connected to the power generation amount prediction unit 220.
  • the first model updating unit 210 updates the power generation prediction model by learning based on the value of the first factor in the past period and the actual change in the amount of renewable energy generated in the past period.
  • the first model updating unit 210 has a first model learning unit 215, and updates the power generation amount prediction model according to the learning result of the first model learning unit 215.
  • the first model update unit 210 may update the power generation amount prediction model learned by the first model learning unit 215 as a new power generation amount prediction model every predetermined first update period.
  • the first model updating unit 210 may respond to various conditions such as that the first model learning unit 215 has learned a predetermined number of times, or that an error difference due to learning is below a predetermined threshold.
  • the power generation amount prediction model may be updated.
  • the power generation amount prediction unit 220 is connected to the operation plan generation unit 150.
  • the power generation amount prediction unit 220 predicts a future transition of renewable energy power generation of the power generation device 30 using a power generation amount prediction model.
  • the power generation amount prediction unit 220 predicts, for example, for each predetermined period, the amount of renewable energy generated by the power generation device 30 in the future for the predetermined period.
  • the power generation amount prediction unit 220 predicts the power generation amount using the power generation amount prediction model and the information of the first factor.
  • the power generation amount prediction unit 220 applies, for example, the information of the first factor in the period immediately before the period in which the renewable energy power generation is to be predicted to the power generation prediction model to calculate the renewable energy power generation Predict.
  • the power generation amount prediction unit 220 supplies the prediction result to the operation plan generation unit 150.
  • the second model generation unit 230 is connected to the second model update unit 240.
  • the second model generation unit 230 generates the operation plan generation model based on the value of the second factor before the target period, the transition of the amount of renewable energy generated by the power generation device 30, and the transition of the electricity rate of the power system 50. Generate.
  • the operation plan generation model by learning, calculates the operation plan in the target period, the value of the second factor available before the target period, and the prediction result of the transition of the renewable energy power generation amount of the power generator 30 in the target period. , A model generated based on the power rate of the power system 50.
  • the second model generation unit 230 may use the value of the first factor as the past transition of renewable energy power generation, and may use the value of the third factor as the past transition of electricity rates.
  • the second model generation unit 230 may generate an operation plan generation model by a learning process called pre-learning or offline learning using information past the target period.
  • the second model generating unit 230 generates an operation plan by executing reinforcement learning using an arbitrary machine learning model such as a regression analysis, Bayesian inference, a neural network, a Gaussian mixture model, and a hidden Markov model as an identification model. Generate a model.
  • the second model generation unit 230 supplies the generated operation plan generation model as a second model to the second model update unit 240.
  • the second model updating unit 240 is connected to the operation plan generating unit 150.
  • the second model updating unit 240 calculates the value of the second factor in the past period and the prediction result of the transition of the renewable energy generation amount of the power generation device 30 or the transition of the renewable energy generation amount of the power generation device 30 after the past period,
  • the operation plan generation model is updated by learning based on the transition of the electricity rate of the power system 50 after the past period and the operation plan of the electrolytic device 20 to be targeted after the past period.
  • the second model updating unit 240 has a second model learning unit 245, and updates the operation plan generation model according to the learning result of the second model learning unit 245.
  • the second model updating unit 240 may update the operation plan generation model learned by the second model learning unit 245 as a new operation plan generation model every predetermined second update period.
  • the second model updating unit 240 may update the operation plan generation model in response to the second model learning unit 245 learning a predetermined number of times.
  • the second model learning unit 245 may learn the operation plan generation model by a process called adaptive learning or online learning.
  • the second model learning unit 245 executes an operation plan generation by executing reinforcement learning using an arbitrary machine learning model such as a regression analysis, Bayesian inference, a neural network, a Gaussian mixture model, and a hidden Markov model as an identification model. Learn the model. By performing such machine learning, the second model learning unit 245 can predict a value corresponding to the second factor with an accuracy corresponding to a model to be applied, using the second factor as an input. .
  • the second model learning unit 245 further learns using information that is temporally later than the information of the second factor used by the second model generation unit 230 to generate the operation plan generation model.
  • the second model learning unit 245 includes information on the first factor updated by the change in the actual amount of renewable energy generated by the power generation device 30, information on the second factor updated by the actual operation of the electrolysis device 20,
  • the operation plan generation model is learned using the information on the third factor updated according to the actual transition of the electricity rate.
  • the prediction result of the power generation amount prediction unit 220 may be used instead of the actual renewable energy power generation amount transition.
  • the prediction result of the electricity rate prediction unit 270 may be used instead of the transition of the actual electricity rate. That is, the second model learning unit 245 calculates the value of the second factor in the past period and the transition result of the renewable energy generation amount and the electricity rate or the prediction result of the transition of the renewable energy generation amount and the electricity rate in the past period and thereafter. Based on, the operation plan generation model is learned.
  • the second model learning unit 245 may execute the learning of the operation plan generation model in accordance with the update of the information of the second factor.
  • the second model learning unit 245 executes learning one or more times during the second update period of the second model update unit 240.
  • the second model updating unit 240 supplies the updated operation plan generation model to the operation plan generation unit 150.
  • the third model generation unit 250 is connected to the third model update unit 260.
  • the third model generation unit 250 generates an electricity rate prediction model that predicts a change in the electricity rate of the power system 50 in the target period based on a value of a third factor available before the target period.
  • the third model generation unit 250 may generate an electricity price prediction model by using a process called pre-learning or off-line learning using information past the target period.
  • the third model generation unit 250 generates an electricity bill prediction model using, for example, regression analysis, Bayesian inference, a neural network, a Gaussian mixture model, a hidden Markov model, or the like.
  • the third model generation unit 250 supplies the generated electricity price prediction model as a third model to the third model update unit 260.
  • the third model learning unit 265 may learn the electricity bill prediction model by a process called adaptive learning or online learning.
  • the third model learning unit 265 executes, for example, an electric charge prediction by executing reinforcement learning using an arbitrary machine learning model such as a regression analysis, Bayesian inference, a neural network, a Gaussian mixture model, and a hidden Markov model as an identification model. Learn the model.
  • the third model learning unit 265 can input the third factor and predict the electricity rate according to the third factor with accuracy according to the model to be applied. Become. Further, if a model having LSTM (Long short-term memory), RNN (Recurrent Neural Network), and other storages is used as the third model, for example, the electricity rate can be predicted from the time series of the third factor. Can also.
  • LSTM Long short-term memory
  • RNN Recurrent Neural Network
  • the third model learning unit 265 learns further using information that is temporally later than the information of the third factor used by the third model generation unit 250 to generate the electricity rate prediction model.
  • the third model learning unit 265 learns the electricity rate prediction model using the information on the third factor updated based on the actual transition of the electricity rate of the power system 50.
  • the third model learning unit 265 may execute the learning of the electricity rate prediction model according to the update of the information of the third factor.
  • the third model learning unit 265 learns the electricity rate prediction model based on the value of the third factor in the past period and the actual transition of the electricity rate after the past period.
  • the third model learning unit 265 performs learning one or more times during the third update period of the third model update unit 260.
  • the third model updating unit 260 supplies the updated electricity bill prediction model to the electricity bill prediction unit 270.
  • the electricity rate prediction unit 270 is connected to the operation plan generation unit 150.
  • the electricity rate prediction unit 270 predicts a future transition of the electricity rate of the power system 50 using the updated electricity rate prediction model.
  • the electricity rate prediction unit 270 for example, predicts the electricity rate in the future for the predetermined period for each predetermined period.
  • the electricity rate prediction unit 270 predicts the electricity rate using the electricity rate prediction model and the information of the third factor.
  • the electricity rate prediction unit 270 predicts the electricity rate by applying, for example, the information of the third factor in the period immediately before the period in which the electricity rate is to be predicted to the electricity rate prediction model.
  • the electricity rate prediction unit 270 supplies the prediction result to the operation plan generation unit 150.
  • the operation plan generation unit 150 determines the electrolysis device 20 for the first period in the future based on the predicted change in the amount of renewable energy generated by the power generation device 30 and the predicted electricity rate of the power system 50 in the future.
  • the operation plan of the electrolysis device 20 that satisfies the use plan of the product of the above is generated.
  • the operation plan generation unit 150 may generate an operation plan of the electrolysis device 20 in the future first period by using the operation plan generation model.
  • the operation plan generation unit 150 uses the electrolysis device 20 by giving priority to the power from the power generation device 30 over the power from the power system 50 so as to satisfy the usage plan of the product of the electrolysis device 20.
  • An operation plan to be operated may be generated.
  • the operation plan generation unit 150 generates an operation plan of the electrolysis apparatus 20 with a period such as several days or ten and several days, one or several weeks as a first period.
  • the operation plan generation unit 150 generates an operation plan for N days, for example.
  • the control unit 160 operates the electrolysis device 20 according to the operation plan generated by the operation plan generation unit 150.
  • the control unit 160 may control the power generation device 30, the amount of power to be supplied from the power generation device 30 to the electrolysis device 20, the time period to be supplied, the amount of power to sell the power generated by the power generation device 30, and / or the power sale.
  • the time period of the operation may be controlled.
  • the planning device 40 generates the operation plan of the electrolysis device 20 while predicting the amount of renewable energy generated by the power generation device 30 and the electricity rate of the power system 50 by learning. The operation of the planning device 40 will be described below.
  • FIG. 3 shows an example of an operation flow of the planning device 40 according to the present embodiment.
  • the planning device 40 may execute the operation flow illustrated in FIG. 3 to operate the electrolysis device 20.
  • the acquisition unit 100 acquires information on the amount of renewable energy generated by the power generation device 30, the electricity rate of the power system 50, and the first factor, the second factor, and the third factor that are past trends of the electrolysis device 20 ( S310).
  • the acquisition unit 100 acquires, for example, information on a first factor, a second factor, and a third factor from time t0 to time t1.
  • the period from time t0 to time t1 is a second period before the first period.
  • the acquiring unit 100 causes the storage unit 110 to store the acquired information of the first factor, the second factor, and the third factor. Further, the acquisition unit 100 may directly supply the information of the first factor, the second factor, and the third factor to the model generation unit 120.
  • the model generation unit 120 generates a learning model (S320).
  • the model generation unit 120 generates a learning model based on the values of the first factor, the second factor, and the third factor in the second period.
  • the first model generation unit 200 generates a power generation amount prediction model using a value of a first factor including at least one of the amount of renewable energy generated by the power generation device 30 and weather information in the second period.
  • the third model generation unit 250 includes a third model that includes at least one of the electricity rate, the power demand, the power supply, the renewable energy generation, the predicted value of the renewable energy generation, and the weather information in the second period. Using the values of the factors, an electricity price prediction model is generated.
  • the second model generation unit 230 generates an operation plan generation model based on the values of the first factor, the second factor, and the third factor. For example, the second model generation unit 230 outputs the renewable energy power generation amount of the power generation device 30, the electricity rate of the power system 50, the operation data of the electrolysis device 20, the storage amount of the product of the electrolysis device 20, and the second period.
  • An operation plan generation model is generated using at least one of the virtual data of the operation plan of the electrolysis device 20.
  • the second model generation unit 230 sets virtual data based on the physical model of the electrolysis device 20 as prediction data to be targeted, and compares the prediction data with actual data obtained by operation of the electrolysis device 20 in the past.
  • an operation plan generation model may be generated.
  • the second model generation unit 230 executes the reinforcement learning to generate the operation plan generation model so that the difference between the target predicted data and the past actual data is 0 or less than a predetermined value. .
  • the second model generation unit 230 sets the period of M days in the second period as a virtual prediction period, for example.
  • the M days may be, for example, a period of several days, ten and several days, one or several weeks. It is desirable that M days coincide with the first period (N days).
  • the second model generation unit 230 calculates the prediction result of the operation operation in the prediction period based on the values of the first factor, the second factor, and the third factor in the period before the prediction period in the second period, The reinforcement learning is performed so that an error between the actual data and the virtual data is minimized.
  • the second model generation unit 230 satisfies the usage plan of the product of the electrolysis device 20 (first condition), and uses the electricity from the power generation device 30 in preference to the electricity from the power system 50 to use the electrolysis device.
  • the reinforcement learning may be performed so as to reduce the operation cost while satisfying the condition of operating 20 (second condition).
  • the first condition that the use plan of the product of the electrolysis device 20 is satisfied is, for example, a range in which the storage amount of the product in the electrolysis device 20 varies from 0 to the maximum storage amount, or The condition may be such that a product equal to or more than the total demand or supply amount of the product is generated in the electrolytic device 20.
  • the second condition is that the total amount of renewable energy generated by the power generation device 30 predicted in a predetermined period (for example, the second period) is used for the operation of the electrolysis device 20 or the power generation device 30
  • the condition may be such that more electric power is supplied for the operation of the electrolysis device 20.
  • the generation of the learning model by the model generation unit 120 may be executed before the planning device 40 acquires the actual data of the electrolysis device 20 with the operation of the electrolysis device 20.
  • the first model learning unit 215 adaptively learns the power generation prediction model based on the value of the first factor.
  • the first model learning unit 215 may adaptively learn the power generation prediction model using at least one of the renewable energy power generation amount of the power generation device 30 and weather information in the third period.
  • the first model learning unit 215 performs reinforcement learning so that the result of predicting the amount of renewable energy generated in the third period using the generated amount prediction model matches the obtained amount of renewable energy generated in the third period. Good.
  • the first model learning unit 215 sets the period of M days in the third period as a virtual prediction period, for example.
  • the M days may be, for example, a period of several days, ten and several days, one or several weeks. It is desirable that M days coincide with the first period (N days).
  • the first model learning unit 215 determines that the difference between the prediction result of the prediction period based on the value of the first factor in the period before the prediction period in the third period and the actual data of the prediction period is 0 or a predetermined value. Reinforcement learning is performed so as to be less than the set value.
  • the second model learning unit 245 may apply and learn the operation plan generation model based on the values of the first factor, the second factor, and the third factor. For example, in the third period, the second model learning unit 245 calculates the transition of the renewable energy power generation amount of the power generation device 30 or the prediction result of the transition, the transition of the electricity rate of the power system 50 or the prediction result of the transition, the electrolysis device.
  • the operation plan generation model may be learned using at least one of the operation data of the operation plan 20, the storage amount or demand amount of the product of the electrolysis device 20, and the actual data of the operation plan.
  • the second model learning unit 245 determines that the difference between the result of predicting the operation of the electrolysis device 20 in the third period using the operation plan generation model and the acquired actual data in the third period is 0 or less than a predetermined value.
  • the reinforcement learning may be performed so that
  • the second model learning unit 245 sets the period of M days in the third period as a virtual prediction period, for example.
  • the M days may be, for example, a period of several days, ten and several days, one or several weeks. It is desirable that M days coincide with the first period (N days).
  • the second model learning unit 245 calculates the prediction result of the operation operation in the prediction period based on the values of the first factor, the second factor, and the third factor in the period before the prediction period in the third period, and The reinforcement learning is performed such that the difference between the actual data and the actual data becomes 0 or less than a predetermined value.
  • the second model learning unit 245 may similarly use the first condition, the second condition, and the like used by the second model generation unit 230 to generate the operation plan generation model. That is, the second model learning unit 245 may perform the reinforcement learning of the operation plan generation model so as to reduce the production cost of the product while satisfying the two conditions.
  • the third model learning unit 265 sets the period of M days in the third period as a virtual prediction period, for example.
  • the M days may be, for example, a period of several days, ten and several days, one or several weeks. It is desirable that M days coincide with the first period (N days).
  • the third model learning unit 265 determines that the difference between the prediction result of the prediction period based on the value of the third factor in the period before the prediction period in the third period and the actual data of the prediction period is 0 or a predetermined value. Reinforcement learning is performed so as to be less than the set value.
  • the learning processing unit 130 updates the learning model (S340).
  • the learning processing unit 130 may update the learning model every predetermined time. For example, the learning processing unit 130 performs the first update of the learning model after continuing the adaptive learning for an initial update period necessary for the update after the start of the adaptive learning, and thereafter updates the learning model at regular intervals. repeat.
  • the initial update period is N days or more, which is the period of the operation plan to be generated.
  • the fixed period for repeating the update may be several hours, ten and several hours, one day, several tens of hours, or several days.
  • the prediction unit 140 predicts the amount of renewable energy generated by the power generation device 30 and the electricity rate of the power system 50 using the updated learning model (S350).
  • the power generation amount prediction unit 220 predicts the transition of the renewable energy power generation amount of the power generation device 30 in the first period using the updated power generation amount prediction model and the value of the first factor.
  • the power generation amount prediction unit 220 applies the value of the first factor for N days acquired by the acquisition unit 100 during the initial update period to the power generation amount prediction model, and reproduces N days after the initial update period. Forecast the transition of available energy generation.
  • the electricity rate prediction unit 270 predicts the transition of the electricity rate in the first period using the updated electricity rate prediction model and the value of the third factor. As an example, the electricity rate prediction unit 270 applies the value of the third factor for N days acquired by the acquisition unit 100 during the initial update period to the electricity rate prediction model, and generates electricity for N days after the initial update period. Predict changes in rates.
  • the operation plan generation unit 150 may similarly use the first condition and the second condition used by the second model generation unit 230 to generate the operation plan generation model. That is, the operation plan generation unit 150 may generate an operation plan that minimizes the manufacturing cost while satisfying the two conditions.
  • the operation plan generation unit 150 may generate an operation plan including a period during which the electrolysis device 20 is operated and a period during which the electrolysis device 20 is not operated in the first period. Further, the operation plan generation unit 150 may generate an operation plan indicating a period during which the electrolysis apparatus 20 is operated, together with an operation rate. It is desirable that the operation plan generation unit 150 generates an operation plan in which the operation rate changes in a time series. The operation plan generation unit 150 generates, for example, an operation plan for each fixed time. The operation plan generation unit 150 may generate an operation plan every tens of minutes, every hour, or every several hours.
  • the operation plan generator 150 may generate an operation plan for each of the plurality of electrolyzers 20.
  • the operation plan generator 150 may generate substantially the same operation plan.
  • the operation plan generation unit 150 controls the control unit 160 to control different types of electrolyzers 20, electrolyzers 20 purchased at different times, electrolyzers 20 of different manufacturers, or a plurality of electrolyzers 20 including a combination thereof. In this case, different operation plans may be generated for each of the electrolyzers 20.
  • the second model generation unit 230 generates one operation plan generation model corresponding to the plurality of electrolyzers 20, and the second model update unit 240 generates the operation plan generation model learned by the second model learning unit 245. May be updated.
  • the operation plan generation model may be a model that generates an operation plan for cooperatively operating the plurality of electrolyzers 20.
  • the operation start timing and the operation period of each of the plurality of electrolyzers 20 may be used.
  • the process returns to S330, and the learning processing unit 130 adaptively learns the learning model.
  • the acquiring unit 100 sequentially acquires the information of the first factor and the third factor in the first period and the information of the second factor that changes due to the operation of the electrolysis device 20 in the first period, and stores the information in the storage unit 110.
  • the planning device 40 includes the information of the first period in the past information, and sets the target period to be predicted to be a period later than the first period (for example, a fourth period).
  • the planning device 40 repeats the adaptive learning of the model using the information of the first period, updates the model as the certain period elapses, generates an operation plan of the electrolysis device 20 for the fourth period, and generates The electrolysis device 20 is operated according to the operation plan thus set.
  • the planning device 40 continues the electrolysis device 20 while updating the learning model by repeatedly generating the operation plan of the electrolysis device 20 for the target period and operating the target period. Can operate.
  • the planning device 40 is operated in chronological order in the second period, the third period, the first period, and the fourth period.
  • the second period, the third period, the first period, and the fourth period may be temporally continuous in this order. It is preferable that at least the first period and the fourth period are continuous periods.
  • the planning device 40 predicts the amount of renewable energy generated by the power generation device 30 and the electricity rate of the power system 50 by learning, and generates a quantity of products according to the usage plan in the electrolysis device 20 at low cost. Create a work plan that can be generated.
  • Programmable circuits include logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, memory elements such as flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc. And the like, and may include reconfigurable hardware circuits.
  • Computer readable media may include any tangible device capable of storing instructions for execution by a suitable device, such that computer readable media having instructions stored thereon is specified in a flowchart or block diagram.
  • Product comprising instructions that can be executed to create a means for performing the specified operation.
  • Examples of the computer readable medium may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, and the like.
  • the computer readable instructions may be provided to a processor or programmable circuit of a general purpose computer, special purpose computer, or other programmable data processing device, either locally or over a wide area network (WAN) such as a local area network (LAN), the Internet, or the like. ) May be executed to create means for performing the operations specified in the flowcharts or block diagrams.
  • WAN wide area network
  • LAN local area network
  • processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.
  • FIG. 4 illustrates an example of a computer 1900 in which aspects of the present invention may be wholly or partially implemented.
  • the programs installed on the computer 1900 can cause the computer 1900 to function as one or more sections of the operation or the device associated with the device according to the embodiment of the present invention, or the operation or the one or more of the one or more devices. Sections may be executed and / or computer 1900 may execute a process or steps of a process according to an embodiment of the present invention.
  • Such programs may be executed by CPU 2000 to cause computer 1900 to perform certain operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.
  • the input / output controller 2084 connects the host controller 2082 to the communication interface 2030, the hard disk drive 2040, and the DVD drive 2060, which are relatively high-speed input / output devices.
  • the communication interface 2030 communicates with another device via a network by wire or wirelessly.
  • the communication interface functions as hardware for performing communication.
  • the hard disk drive 2040 stores programs and data used by the CPU 2000 in the computer 1900.
  • the DVD drive 2060 reads a program or data from the DVD 2095 and provides it to the hard disk drive 2040 via the RAM 2020.
  • the ROM 2010, the flash memory drive 2050, and the relatively low-speed input / output device of the input / output chip 2070 are connected to the input / output controller 2084.
  • the ROM 2010 stores a boot program executed by the computer 1900 at the time of startup, and / or a program depending on hardware of the computer 1900.
  • the flash memory drive 2050 reads a program or data from the flash memory 2090 and provides it to the hard disk drive 2040 via the RAM 2020.
  • the input / output chip 2070 connects the flash memory drive 2050 to the input / output controller 2084 and inputs / outputs various input / output devices via, for example, a parallel port, a serial port, a keyboard port, a mouse port, and the like. Connect to controller 2084.
  • the program provided to the hard disk drive 2040 via the RAM 2020 is stored in a recording medium such as a flash memory 2090, a DVD 2095, or an IC card and provided by a user.
  • the program is read from the recording medium, installed on the hard disk drive 2040 in the computer 1900 via the RAM 2020, and executed by the CPU 2000.
  • the information processing described in these programs is read by the computer 1900 and provides for cooperation between the software and the various types of hardware resources described above.
  • An apparatus or method may be configured for implementing manipulation or processing of information according to the use of computer 1900.
  • the communication interface 2030 may transfer the transmission / reception data to / from the storage device by the DMA (Direct Memory Access) method, and instead, the CPU 2000 may use the transfer source storage device or the communication interface 2030.
  • the data may be read from the communication interface 2030 or the data may be written to the communication interface 2030 or the storage device of the transfer destination to transfer the transmission and reception data.
  • DMA Direct Memory Access
  • the CPU 2000 transfers all or a necessary portion from a file or a database stored in an external storage device such as a hard disk drive 2040, a DVD drive 2060 (DVD 2095), or a flash memory drive 2050 (flash memory 2090) to a DMA.
  • the data is read into the RAM 2020 by transfer or the like, and various processes are performed on the data on the RAM 2020.
  • the CPU 2000 writes the processed data back to the external storage device by DMA transfer or the like.
  • the RAM 2020 can be regarded as temporarily holding the contents of the external storage device. Therefore, in this embodiment, the RAM 2020 and the external storage device are collectively referred to as a memory, a storage unit, or a storage device.
  • Various information such as various programs, data, tables, and databases in the present embodiment are stored on such a storage device and are subjected to information processing.
  • the CPU 2000 can also hold a part of the RAM 2020 in a cache memory and perform reading and writing on the cache memory. Even in such a form, the cache memory plays a part of the function of the RAM 2020. Therefore, in the present embodiment, the cache memory is also included in the RAM 2020, the memory, and / or the storage device unless otherwise indicated. I do.
  • the CPU 2000 performs various calculations, information processing, condition determination, information search / replacement, and the like described in the present embodiment on the data read from the RAM 2020, as specified by the instruction sequence of the program. And write it back to the RAM 2020.
  • the CPU 2000 determines whether the various variables described in the present embodiment satisfy conditions such as larger, smaller, greater than, less than, equal to, and the like as compared with other variables or constants. Then, if the condition is satisfied (or not satisfied), a branch is made to a different instruction sequence or a subroutine is called.
  • the CPU 2000 can search for information stored in a file or a database in the storage device. For example, in the case where a plurality of entries in which the attribute value of the second attribute is associated with the attribute value of the first attribute are stored in the storage device, the CPU 2000 determines whether the plurality of entries stored in the storage device By searching for an entry in which the attribute value of the first attribute matches the specified condition and reading the attribute value of the second attribute stored in the entry, the entry is associated with the first attribute satisfying the predetermined condition. The attribute value of the obtained second attribute can be obtained.
  • elements other than the listed elements may be used. For example, if "X performs Y using A, B, and C", X may perform Y using D in addition to A, B, and C.
  • System 20 Electrolysis device 30 Power generation device 40 Planning device 50 Power system 100 Acquisition unit 110 Storage unit 120 Model generation unit 130 Learning processing unit 140 Prediction unit 150 Operation plan generation unit 160 Control unit 200 First model generation unit 210 First model update Unit 215 first model learning unit 220 power generation prediction unit 230 second model generation unit 240 second model update unit 245 second model learning unit 250 third model generation unit 260 third model update unit 265 third model learning unit 270 electricity Charge prediction unit 1900 Computer 2000 CPU 2010 ROM 2020 RAM 2030 Communication interface 2040 Hard disk drive 2050 Flash memory drive 2060 DVD drive 2070 Input / output chip 2075 Graphic controller 2080 Display device 2082 Host controller 2084 Input / output controller 2090 Flash memory 2095 DVD

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Abstract

Il est nécessaire de réduire les coûts pour la puissance de fonctionnement d'un appareil d'électrolyse. L'invention concerne un appareil de planification comprenant : une unité de prédiction de quantité de génération d'énergie qui prédit une tendance pour la quantité future de génération d'énergie renouvelable à l'aide d'un modèle de prédiction de génération d'énergie qui prédit la tendance dans la quantité de génération d'énergie renouvelable d'un appareil de production d'énergie pendant une période cible sur la base de la valeur d'un premier facteur disponible avant la période cible ; et une unité de génération de plan de fonctionnement qui génère un plan de fonctionnement pour l'appareil d'électrolyse qui satisfait un plan d'utilisation pour un produit de l'appareil d'électrolyse dans une première période future, sur la base des débits d'électricité du système électrique et de la tendance prédite de la quantité de génération future d'énergie renouvelable.
PCT/JP2019/036524 2018-09-21 2019-09-18 Appareil, procédé et programme de planification WO2020059741A1 (fr)

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