JP7237500B2 - Planning device, method and program - Google Patents

Description

The present invention relates to a planning device, method and program.

2. Description of the Related Art Conventionally, an electrolyzer that generates hydrogen by electrolyzing water is known. Also, as power supply means, a power generation device that generates power using renewable energy, or a power system in which the electricity rate fluctuates according to the weather and the cost of supplying power is known.

When power is supplied to the electrolyzer from such a generator and power system, it is desirable to reduce the cost of operating power and reduce the cost of producing hydrogen. However, there are cases where the amount of hydrogen to be supplied by the electrolytic device is determined. In this case, if the amount of power generated by the power generation device is not sufficient during the hydrogen supply period, the electrolysis device must be operated with power from the power system during periods of high electricity rates.

In order to solve the above problems, in the first aspect of the present invention, the transition of the renewable energy power generation amount of the power generation device in the target period is predicted based on the value of the first factor that is available before the target period Based on the power generation prediction unit that predicts the future transition of renewable energy power generation using the power generation prediction model that predicts future renewable energy power generation and the electricity rate of the power system, a planning apparatus, method, and program comprising: an operation plan generator for generating an operation plan for the electrolyzer that satisfies a plan for using the product of the electrolyzer for a first period in the future.

It should be noted that the above summary of the invention does not list all the necessary features of the invention. Subcombinations of these feature groups can also be inventions.

A system 10 according to the present embodiment is shown. A detailed configuration example of the planning device 40 according to the present embodiment is shown. An example of the operational flow of the planning device 40 according to the present embodiment is shown. 19 illustrates an example computer 1900 in which aspects of the present embodiments may be implemented in whole or in part.

Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution of the invention.

FIG. 1 shows a system 10 according to this embodiment. The system 10 generates an operation plan for the electrolyzer 20 according to the electricity rate and the amount of renewable energy generated, and operates the electrolyzer 20 according to the operation plan. System 10 comprises an electrolyzer 20 , a power generator 30 and a scheduling device 40 .

The electrolyzer 20 is connected to the generator 30 , the scheduling device 40 and the power system 50 . Electrolyzer 20 may be a device that uses electrical energy to produce a product. The electrolytic device 20 is, for example, a hydrogen generating device that generates hydrogen as a product by electrolysis. The electrolyzer 20 operates according to the operation plan generated by the planning device 40 . Electrolyzer 20 operates by being supplied with power from power generator 30 and power system 50 .

Here, the electric power system 50 supplies electric power to a large number of consumers via a transmission network from one or a plurality of power plants that generate power using renewable energy such as nuclear power, thermal power, or wind power, for example. It is a system that In the electric power system 50, the electricity charges (for example, the charge for selling electricity and the charge for purchasing electricity) can fluctuate depending on the amount of power generation and the amount of demand, for example, every predetermined hour, every day, or every month.

The power generation device 30 is connected to the electrolyzer 20, the planning device 40, and the power system 50, and generates electric power using renewable energy such as wind power, solar power, heat, geothermal power, hydraulic power, and/or biomass. It may be a local power source that supplies the electrolytic device 20 . Power generator 30 may power electrolyser 20 directly without power grid 50 and/or via the power grid of power grid 50 . The power generator 30 may sell the generated power to the power grid 50 .

The planning device 40 generates an operation plan for the electrolytic device 20 . The planning device 40 may be a computer such as a personal computer, a tablet computer, a smart phone, a workstation, a server computer, or a general-purpose computer, or may be a computer system in which multiple computers are connected. The planning device 40 may generate an operation plan by processing in a computer's CPU, GPU (Graphics Processing Unit), and/or TPU (Tensor Processing Unit). Also, 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, data, or the like describing the state in which the electrolytic device 20 should operate in the first period. The operation plan includes, for example, a time zone in which the electrolyzer 20 is operated (or not operated), a time zone in which the electrolyzer 20 is operated with power from the power generation device 30, and a time zone in which the electrolyzer 20 is operated with power from the power system 50. It may be a table, data, or the like that defines the time period during which the power is generated, the time period during which the power generated by the power generation device 30 is sold to the power system 50, and/or the operating rate of the electrolysis device 20 for each time period. 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 acquisition unit 100 may be connected to the electrolysis device 20, the power generation device 30, and the storage unit 110 to acquire parameters used for learning, learning data, and the like. The acquisition unit 100 may be connected to a network or the like and acquire data via the network. If at least part of the data to be acquired is stored in an external database or the like, the acquisition unit 100 may access the database or the like to acquire the data. The acquisition unit 100 may supply the acquired data to the storage unit 110 .

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 the information acquired by the acquisition unit 100. FIG. The storage unit 110 may store data processed by the planning device 40 . The storage unit 110 may store intermediate data calculated (or used) in the process of generating the operation plan by the planning device 40, calculation results, parameters, and the like. In addition, the storage unit 110 may supply the stored data to the request source in response to requests from each unit in the planning device 40 . As an example, 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 .

The model generator 120 is connected to the learning processor 130 and generates a learning model for the planning device 40 to learn. Model generator 120 generates a learning model according to the first, second, and third factors stored in storage 110 . The model generator 120 may generate one or more 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 reinforcement learning to update the learning model. The learning processing unit 130 may update one or more learning models. The learning processing unit 130 supplies the updated learning model to the prediction unit 140 .

The prediction unit 140 is connected to the operation plan generation unit 150 . Based on the learning model, the prediction unit 140 predicts future transitions of various data used when the planning device 40 generates an operation plan. The prediction unit 140 predicts, for example, the transition of the future renewable energy power generation amount in the power generation device 30 and/or the transition of the electricity rate of the power system 50 . The prediction unit 140 supplies the predicted data to the operation plan generation unit 150 .

The operation plan generation unit 150 is connected to the control unit 160 and generates an operation plan for the electrolytic 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 production cost of the product while satisfying the product usage 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 .

Here, the plan for using the product of the electrolysis device 20 is a plan to maintain the storage amount of the product of the electrolysis device 20 within the reference range, or a plan to satisfy the demand amount or the supply amount of the product of the electrolysis device 20. may include at least one of The amount of demand or the amount of supply of the product may be, for example, the cumulative amount of the product to be supplied by the electrolytic device 20 in the first period or the amount for each predetermined time period. The storage volume may be the amount of product stored inside or outside the electrolyzer 20 . Such a reference range of the storage amount, the demand amount of the product, or the supply amount of the product can be obtained from data externally input to the planning device 40, past data, or past data in the planning device 40 for a first period in the future. may be predicted for

The control unit 160 performs control to operate the electrolysis device 20 using the operation plan for the electrolysis device 20 in the first period. For example, the control unit 160 may selectively use the power from the power generation device 30 and the power from the power system 50 according to the operation plan to operate the electrolysis device 20 . The control unit 160 may operate each of the plurality of electrolytic devices 20 . Further, the control unit 160 may instruct the stop and start of the operation of the electrolysis device 20 when the operation of the electrolysis device 20 and the storage amount of the product and the like are in different ranges than expected. The control unit 160 may control to sell the power generated by the power generation device 30 to the power system 50 using the operation plan of the electrolysis device 20 in the first period.

According to the planning device 40 of the present embodiment described above, it is possible to generate an operation plan for the electrolysis device 20 according to fluctuations in the renewable energy power generation amount of the power generation device 30 and fluctuations in the electricity rate of the power system 50, More than a predetermined amount of product can be produced at lower manufacturing costs depending on the usage plan. A more specific configuration example of such a planning device 40 will be described below.

FIG. 2 shows a configuration example of the planning device 40 according to this embodiment. In the planning apparatus 40 of FIG. 2, the same reference numerals are assigned to the operations that are substantially the same as those of the planning apparatus 40 according to the present embodiment shown in FIG. 1, and the description thereof is omitted. FIG. 2 shows in more detail the model generation unit 120, the learning processing unit 130, and the prediction unit 140 of the planning device 40 in FIG.

The planning device 40 includes a first model generation unit 200, a first model update unit 210, and a power generation amount prediction unit 220, and predicts the renewable energy power generation amount of the power generation device 30 in the future. The planning device 40 includes a second model generating section 230 and a second model updating section 240 , and an operating plan generating section 150 generates a future operating plan for 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 future electricity rates of the electric power system 50. FIG.

The storage unit 110 stores the first factor, the second factor, and the third factor acquired by the acquisition unit 100 . The first factor (power generation amount prediction factor) may include information that affects the renewable energy power generation amount of the power generation device 30 . The first factor includes, for example, at least one of the renewable energy power generation amount of the power generation device 30 and weather information before the target period for predicting the transition of the renewable energy power generation amount of the power generation device 30 . The first factor may include information acquired by the acquisition unit 100 from the power generation device 30 . For example, the weather information may include at least one of wind speed, wind direction, clear weather, rain, temperature, wave height, sunshine hours, and the like. The weather information may be the weather information of the area where the power generator 30 is installed. The renewable energy power generation amount of the power generation device 30 may include at least one of the power generation amount at substantially constant time intervals, the total power generation amount, the average value, the upper limit value, and the lower limit value in the past predetermined period.

A second factor (operational predictor) may include information regarding the operation of electrolyzer 20 or the planned use of the product of electrolyzer 20 . The second factor is at least one of the operation data of the electrolysis device 20, the demand amount 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 also include an operation plan for the electrolytic device 20 generated by the planning device 40 in the past. Also, the second factor may include virtual data calculated from a physical model of the electrolytic device 20 . Also, the second factor may include information acquired by the acquisition unit 100 from the electrolytic device 20 . For example, the operational data may be the amount of product produced per unit of time and/or the amount of product produced per unit of power in electrolyzer 20 . The demand may be the amount of product that needs to be delivered by the electrolyzer 20 . The storage amount may be the amount stored in a tank or the like that stores the product of the electrolytic device 20 .

A third factor (electricity price predictor) may include information that affects fluctuations in the electricity price of power system 50 . The third factor is the electricity rate, the amount of power demand, the amount of power supply, the amount of renewable energy generated, the predicted value of the amount of renewable energy generated, and weather information in the electric power system 50 before the target period for predicting the electricity rate. including at least one of The electricity price in the power grid 50 is the actual electricity price for the power supplied to the electrolyzer 20 from the power grid 50 at the location where the electrolyzer 20 is installed, and/or the power generated by the generator 30 selling power to the power grid 50. It may be the actual power selling fee. The weather information may be the weather information of the area where the electrolytic device 20 is installed. The amount of power demand, the amount of power supply, the amount of power generated by renewable energy, and the predicted value of the amount of power generated by renewable energy in the power system 50 are used to supply power to the area where the electrolysis device 20 is installed via the power grid of the power system 50. Information such as power plants that The power plant or the like may be different from the power generator 30 , which is the local power source connected to the electrolyzer 20 .

The information of the first factor, the second factor, and the third factor may be time-series information at approximately constant time intervals. The first factor, second factor, and third factor information may be added or updated, respectively, over time. For example, the acquiring unit 100 may acquire and update each piece of information for each predetermined period. Further, the acquiring unit 100 may acquire information in substantially the same or different periods according to the information to be acquired, and add or update the information. The first factor, second factor, and third factor information may include information supplied from an external device or the like.

The first model generator 200 is connected to the first model updater 210 . The first model generation unit 200 generates a power generation amount prediction model that predicts the transition of the renewable energy power generation amount of the power generation device 30 in the target period based on the value of the first factor that is available before the target period. . The first model generating unit 200 may generate the power generation amount prediction model by using information from the past than the target period through a process called prior learning or offline learning. The first model generation unit 200 generates a power generation prediction model using, for example, regression analysis, Bayesian inference, neural network, Gaussian mixture model, hidden Markov model, and the like. The first model generating unit 200 supplies the generated power generation amount prediction model to the first model updating unit 210 as the 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 amount prediction model by learning based on the value of the first factor in the past period and the actual transition of the renewable energy power generation amount after 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 updating 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, for example, every predetermined first update period. Alternatively, the first model updating unit 210 may be updated according to various conditions, such as the fact that the first model learning unit 215 has learned a predetermined number of times, or the error difference due to learning is below a predetermined threshold value. The power generation amount prediction model may be updated accordingly.

The first model learning unit 215 may learn the power generation amount prediction model by a process called adaptive learning, online learning, or the like. The first model learning unit 215 performs reinforcement learning using any machine learning model such as regression analysis, Bayesian inference, neural network, Gaussian mixture model, and hidden Markov model as a discriminative model, thereby predicting power generation amount. learn the model. By performing such machine learning, the first model learning unit 215 can predict the renewable energy power generation amount according to the first factor with the accuracy according to the model to be applied, with the first factor as input. become able to. Also, as the first model, for example, LSTM (Long short-term memory), RNN (Recurrent Neural Network), and other models with memories are used, the renewable energy power generation amount is calculated from the time series of the first factor It can also be predicted.

It is preferable that the first model learning unit 215 learns by further using information temporally later than the information of the first factor used by the first model generating unit 200 to generate the power generation amount prediction model. The first model learning unit 215 learns the power generation amount prediction model using the information of the first factor updated according to the transition of the actual renewable energy power generation amount. The first model learning unit 215 may learn the power generation amount prediction model in response to the update of the information of the first factor. As an example, the first model learning unit 215 learns the power generation amount prediction model based on the value of the first factor in the past period and the actual transition of the renewable energy power generation amount after the past period. The first model learning unit 215 may perform learning one or more times during the first update period of the first model updating unit 210 . The first model updating unit 210 supplies the updated power generation amount prediction model to the power generation amount prediction unit 220 .

The power generation amount prediction unit 220 is connected to the operation plan generation unit 150 . The power generation amount prediction unit 220 predicts the transition of the future renewable energy power generation amount of the power generation device 30 using the power generation amount prediction model. For example, for each predetermined period, the power generation amount prediction unit 220 predicts the renewable energy power generation amount of the power generation device 30 for the predetermined period in the future. 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. For example, the power generation amount prediction unit 220 applies the information of the first factor in the period immediately before the period in which the renewable energy power generation amount should be predicted to the power generation amount prediction model to estimate the renewable energy power generation amount of the power generation device 30. Predict. The power generation amount prediction unit 220 supplies the prediction result to the operation plan generation unit 150 .

The second model generator 230 is connected to the second model updater 240 . The second model generation unit 230 generates an operation plan generation model based on the value of the second factor before the target period, the transition of the renewable energy power generation amount of the power generation device 30, and the transition of the electricity rate of the power system 50. Generate. The operation plan generation model learns the operation plan for the target period based on the value of the second factor that can be obtained before the target period and the prediction result of the transition of the renewable energy power generation amount of the power generation device 30 during the target period. , and the electricity rate of the power system 50 . Note that the second model generation unit 230 may use the value of the first factor as the past transition of the renewable energy power generation amount, and may use the value of the third factor as the past transition of the electricity rate. The second model generation unit 230 may generate the operation plan generation model by learning processing called pre-learning, offline learning, or the like, using information past the target period.

The second model generation unit 230 generates an operation plan by executing reinforcement learning using arbitrary machine learning models such as regression analysis, Bayesian inference, neural networks, Gaussian mixture models, and hidden Markov models as discriminative models. Generate a model. The second model generation unit 230 supplies the generated operation plan generation model to the second model update unit 240 as a second model.

The second model updater 240 is connected to the operation plan generator 150 . The second model update unit 240 updates the value of the second factor in the past period, the transition of the renewable energy power generation amount of the power generation device 30 after the past period, or the prediction result of the transition of the renewable energy power generation amount of the power generation device 30, The operation plan generation model is updated by learning based on the transition of the electricity rate of the electric power system 50 after the past period and the target operation plan of the electrolysis device 20 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 operating plan generation model learned by the second model learning unit 245 as a new operating plan generation model, for example, every predetermined second update period. Alternatively, the second model updating unit 240 may update the operation plan generation model in response to the fact that the second model learning unit 245 has learned a predetermined number of times.

The second model learning unit 245 may learn the operation plan generation model by processing called adaptive learning, online learning, or the like. The second model learning unit 245 performs reinforcement learning using arbitrary machine learning models such as regression analysis, Bayesian inference, neural networks, Gaussian mixture models, and hidden Markov models as discriminative models, thereby generating an operation plan. learn the model. By performing such machine learning, the second model learning unit 245 can use the second factor as an input and predict a value corresponding to the second factor with an accuracy corresponding to the model to be applied. . In addition, as the second model, for example, LSTM (Long short-term memory), RNN (Recurrent Neural Network), and other models having memories are used, the operating state of the electrolytic device 20 can be obtained from the time series of the second factor can also be predicted.

It is desirable that the second model learning unit 245 learns using information temporally later than the second factor information used by the second model generation unit 230 to generate the operation plan generation model. The second model learning unit 245 includes the first factor information updated according to the transition of the actual renewable energy power generation amount of the power generation device 30, the second factor information updated according to the actual operation of the electrolysis device 20, and The operation plan generation model is learned using the information of the third factor updated by the actual transition of the electricity rate. As for the transition of the renewable energy power generation amount, for example, the prediction result of the power generation amount prediction unit 220 may be used instead of the actual transition of the renewable energy power generation amount. Also, for the transition of the electricity bill, for example, instead of the actual transition of the electricity bill, the prediction result of the electricity bill prediction unit 270 may be used. That is, the second model learning unit 245 calculates the value of the second factor in the past period, the transition of the renewable energy power generation amount and the electricity price after the past period, or the prediction result of the renewable energy power generation amount and the transition of the electricity price. Based on, learn the operation plan generation model.

The second model learning unit 245 may learn the operation plan generation model in response to the update of the second factor information. The second model learning unit 245 performs learning one or more times during the second update period of the second model updating unit 240 . The second model updating unit 240 supplies the updated operating plan generation model to the operating plan generation unit 150 .

The third model generator 250 is connected to the third model updater 260 . The third model generation unit 250 generates an electricity rate prediction model that predicts changes in the electricity rate of the electric power system 50 during the target period based on the value of the third factor available before the target period. The third model generating unit 250 may generate an electricity bill prediction model by using information from the past than the target period through a process called pre-learning, offline learning, or the like. The third model generation unit 250 generates an electricity bill prediction model using, for example, regression analysis, Bayesian inference, neural network, Gaussian mixture model, hidden Markov model, and the like. The third model generating unit 250 supplies the generated electricity rate prediction model to the third model updating unit 260 as a third model.

The third model updating unit 260 is connected to the electricity bill prediction unit 270 and updates the electricity bill prediction model by learning. The third model updating unit 260 has a third model learning unit 265 and updates the electricity bill prediction model according to the learning result of the third model learning unit 265 . The third model update unit 260 may update the electricity bill prediction model learned by the third model learning unit 265 as a new electricity bill prediction model, for example, every predetermined third update period. Alternatively, the third model updating unit 260 may update the electricity rate prediction model according to the fact that the third model learning unit 265 has learned a predetermined number of times.

The third model learning unit 265 may learn the electricity bill prediction model through a process called adaptive learning, online learning, or the like. The third model learning unit 265 performs reinforcement learning using, for example, any machine learning model such as regression analysis, Bayesian inference, neural network, Gaussian mixture model, and hidden Markov model as a discriminative model, thereby predicting electricity bills. learn the model. By performing such machine learning, the third model learning unit 265 uses the third factor as an input and predicts the electricity bill according to the third factor with accuracy according to the model to be applied. Become. Also, as the third model, for example, LSTM (Long short-term memory), RNN (Recurrent Neural Network), and other models with memories are used to predict the electricity price from the time series of the third factor can also

As for the 3rd model learning part 265, it is desirable to learn further using the information temporally later than the information of the 3rd factor which the 3rd model generation part 250 used for the generation of the electricity rate prediction model. The third model learning unit 265 learns an electricity bill prediction model using the information of the third factor updated by the actual transition of the electricity bill of the electric power system 50 . The third model learning unit 265 may learn the electricity rate prediction model in response to the update of the third factor information. As an example, the third model learning unit 265 learns an 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 updating unit 260 . The third model update unit 260 supplies the updated electricity bill prediction model to the electricity bill prediction unit 270 .

Electricity rate prediction unit 270 is connected to operation plan generation unit 150 . The electricity rate prediction unit 270 predicts future changes in the electricity rate of the power system 50 using the updated electricity rate prediction model. For example, for each predetermined period, the electricity rate prediction unit 270 predicts the future electricity rate for the predetermined period. The electricity bill prediction unit 270 predicts the electricity bill using the electricity bill prediction model and information on the third factor. The electricity bill prediction unit 270 predicts the electricity bill by applying the information of the third factor in the period immediately before the period in which the electricity bill is to be predicted, to the electricity bill prediction model, for example. The electricity bill prediction unit 270 supplies the prediction result to the operation plan generation unit 150 .

The operation plan generation unit 150 generates the electrolysis device 20 in the first period in the future based on the predicted transition of the renewable energy power generation amount of the power generation device 30 in the future and the predicted future electricity rate of the power system 50. to generate an operation plan for the electrolyzer 20 that satisfies the product usage plan of . The operation plan generation unit 150 may generate an operation plan for the electrolytic device 20 in the first future period using the operation plan generation model. During the first period, the operation plan generation unit 150 uses the electricity from the power generation device 30 preferentially over the electricity from the power system 50 so as to satisfy the plan for using the product of the electrolysis device 20 to operate the electrolysis device 20. You may generate an operation plan to operate. The operation plan generation unit 150 generates an operation plan for the electrolytic device 20, for example, with a period of several days, ten and several days, one or several weeks as the first period. As an example, the operation plan generation unit 150 generates an operation plan for N days.

The control unit 160 operates the electrolyzer 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, and includes the amount of power supplied from the power generation device 30 to the electrolysis device 20, the time period of supply, the amount of power to sell the power generated by the power generation device 30, and/or You may control the time zone etc. to do.

The planning device 40 according to the present embodiment described above predicts the renewable energy power generation amount of the power generation device 30 and the electricity rate of the power system 50 by learning, and generates the operation plan of the electrolysis device 20 . The operation of such a planning device 40 will now be described.

FIG. 3 shows an example of the operation flow of the planning device 40 according to this embodiment. The planning device 40 may execute the operation flow shown in FIG. 3 to operate the electrolytic device 20 .

The acquisition unit 100 acquires information on the renewable energy power generation amount of the power generation device 30, the electricity rate of the power system 50, and the past trends of the first factor, the second factor, and the third factor of the electrolysis device 20 ( S310). For example, the acquisition unit 100 acquires information on the first factor, the second factor, and the third factor from time t0 to time t1. Here, the period from time t0 to time t1 is a second period that precedes 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. Also, the acquisition unit 100 may directly supply the information on the first factor, the second factor, and the third factor to the model generation unit 120 .

Next, the model generator 120 generates a learning model (S320). The model generator 120 generates a learning model based on the values of the first factor, second factor, and third factor in the second period. For example, the first model generation unit 200 generates the power generation amount prediction model using the value of the first factor including at least one of the renewable energy power generation amount of the power generation device 30 and the weather information in the second period. The third model generating unit 250 generates a third model including at least one of electricity rate, power demand, power supply, renewable energy power generation, renewable energy power generation forecast value, and weather information in the second period. Generate an electricity rate forecast model using the factor values.

Also, 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 determines the amount of renewable energy generated by 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 At least one of the virtual data of the operation plan of the electrolyzer 20 is used to generate the operation plan generation model.

In addition, the second model generating unit 230 uses virtual data based on the physical model of the electrolysis device 20 as target prediction data, and compares the prediction data with the actual data obtained by the past operation of the electrolysis device 20. By doing so, an operation plan generation model may be generated. For example, the second model generation unit 230 executes reinforcement learning to generate an operation plan generation model so that the difference between target prediction data and past actual data is 0 or less than a predetermined value. .

As an example, the second model generation unit 230 sets a period of M days in the second period as a virtual prediction period. Note that M days may be, for example, a period of several days, ten and several days, or one or several weeks. It is desirable that M days coincide with the first period (N days). Then, the second model generation unit 230 generates the prediction result of the operating motion 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, and the prediction period Reinforcement learning is performed so that the error with real data or virtual data is minimized.

In this case, the second model generation unit 230 satisfies the plan for using the product of the electrolyzer 20 (first condition), and preferentially uses the electricity from the power generation device 30 over the electricity from the power system 50 to Reinforcement learning may be performed so as to reduce the operating cost while satisfying the condition of operating 20 (second condition). Here, the first condition that the plan for using the product of the electrolytic device 20 is satisfied is, for example, that the fluctuation range of the storage amount of the product of the electrolytic device 20 is set to a range from 0 to the maximum storage amount, or The condition may be such that the electrolyzer 20 produces more products than the sum of the amount demanded or supplied. In addition, the second condition is that all of the cumulative renewable energy power generation amount of 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 that the power generation device 30 It may be a condition such as supplying more electric power for operation of the electrolytic device 20 than the above.

Note that the learning model generation by the model generation unit 120 may be executed before the planning device 40 acquires the actual data of the electrolysis device 20 as the electrolysis device 20 starts operating.

Next, the learning processing unit 130 adaptively learns the generated learning model (S330). Here, the acquisition unit 100 may further acquire information on the first factor, the second factor, and the third factor. For example, the acquisition unit 100 acquires information on the first factor, the second factor, and the third factor from time t1 to time t2. Note that the period from time t1 to time t2 is the third period between the first period and the second period. The learning processing unit 130 may perform adaptive learning using the information of the first factor, the second factor, and the third factor newly acquired by the acquisition unit 100 .

For example, the first model learning unit 215 adaptively learns the power generation amount prediction model based on the value of the first factor. The first model learning unit 215 may adaptively learn the power generation amount prediction model using at least one of the renewable energy power generation amount of the power generation device 30 and the weather information in the third period. The first model learning unit 215 performs reinforcement learning so that the result of predicting the renewable energy power generation amount in the third period using the power generation amount prediction model matches the acquired renewable energy power generation amount in the third period. good.

As an example, the first model learning unit 215 sets a period of M days in the third period as a virtual prediction period. Note that M days may be, for example, a period of several days, ten and several days, or one or several weeks. It is desirable that M days coincide with the first period (N days). As an example, the first model learning unit 215 determines that the difference between the prediction result in the prediction period based on the value of the first factor in the period preceding the prediction period in the third period and the actual data in the prediction period is 0 or predetermined Reinforcement learning is performed so that it is less than the set value.

Also, 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, the second model learning unit 245, in the third period, 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 electrolyzer At least one of the operational data of 20, the product storage or demand of the electrolyzer 20, and the actual data of the operational plan may be used to train the operational plan generation model. 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. Reinforcement learning may be performed such that

As an example, the second model learning unit 245 sets a period of M days in the third period as a virtual prediction period. Note that M days may be, for example, a period of several days, ten and several days, or one or several weeks. It is desirable that M days coincide with the first period (N days). Then, the second model learning unit 245 obtains the prediction result of the operating motion 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 prediction period Reinforcement learning is performed so that the difference from actual data is 0 or less than a predetermined value.

In this case, the second model learning unit 245 may similarly use the first condition, the second condition, etc. 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 reinforcement learning of the operation plan generation model so as to reduce the manufacturing cost of the product while satisfying the two conditions.

Also, the third model learning unit 265 adaptively learns an electricity rate prediction model based on the value of the third factor. The third model learning unit 265 learns at least one of the electricity rate, power demand, power supply, renewable energy power generation, renewable energy power generation prediction value, and weather information for the power system 50 in the third period. may be used to adaptively learn the electricity price prediction model. The third model learning unit 265 may perform reinforcement learning so that the result of predicting the electricity bill and the like in the third period using the electricity bill prediction model matches the obtained electricity bill in the third period.

As an example, the third model learning unit 265 sets a period of M days in the third period as a virtual prediction period. Note that M days may be, for example, a period of several days, ten and several days, or one or several weeks. It is desirable that M days coincide with the first period (N days). As an example, the third model learning unit 265 determines that the difference between the prediction result in the prediction period based on the value of the third factor in the period preceding the prediction period in the third period and the actual data in the prediction period is 0 or predetermined Reinforcement learning is performed so that it is less than the set value.

Next, 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, after starting adaptive learning, the learning processing unit 130 continues adaptive learning for an initial update period required for updating, performs the first update of the learning model, and then performs updates at regular intervals. repeat. Here, it is desirable that the initial update period is N days or longer, which is the period of the operation plan to be generated. Also, the fixed period for repeating updating may be several hours, ten and several hours, one day, several tens of hours, several days, or the like.

For example, the first model update unit 210 updates the power generation amount prediction model every first update period after the initial update period. After the initial update period, the second model update unit 240 updates the operation plan generation model every second update period. Also, the third model updating unit 260 updates the electricity bill prediction model every third update period after the initial update period. The first update period, the second update period, and the third update period may be different periods, or alternatively may be substantially the same period. The first update period, the second update period, and the third update period are one day, for example.

Next, the prediction unit 140 predicts the renewable energy power generation amount of the power generation device 30 and the electricity rate of the power system 50 using the updated learning model (S350). For example, the power generation amount prediction unit 220 uses the updated power generation amount prediction model and the value of the first factor to predict the transition of the renewable energy power generation amount of the power generation device 30 in the first period. As an example, 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 data for N days after the initial update period. Predict trends in renewable energy power generation.

In addition, the electricity bill prediction unit 270 predicts the transition of the electricity bill in the first period using the updated electricity bill 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 calculates the electricity rate for N days after the initial update period. Predict price trends.

Next, the operation plan generator 150 uses the updated learning model to generate an operation plan for the electrolytic device 20 for the first period (S360). The operation plan generation unit 150 uses the updated operation plan generation model, the prediction result of the renewable energy power generation amount of the power generation amount prediction unit 220, the electricity price prediction result of the electricity price prediction unit 270, and the value of the second factor. to generate the operation plan for the first period. As an example, the operation plan generation unit 150 includes the value of the second factor for N days acquired by the acquisition unit 100 during the initial update period, the prediction result of the renewable energy power generation amount for N days after the initial update period, and By applying the prediction result of the transition of the electricity rate to the operation plan generation model, an operation plan for N days after the initial update period is generated.

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.

For example, in the first period, the operation plan generation unit 150 creates an operation plan including a period in which the electrolysis device 20 is operated by the power from the power generation device 30 and a period in which the electrolysis device 20 is operated by the power from the power system 50. Generate. As an example, in the operation plan, the operation plan generation unit 150 uses the electric power from the power generation device 30 to operate the electrolytic device so that the renewable energy power generation amount predicted by the power generation device 30 in the first period is used up within the first period. 20 may be set. Furthermore, when the power from the power generation device 30 is insufficient to satisfy the plan for using the product of the electrolysis device 20 in the first period, the operation plan generation unit 150 generates electrolysis in the first period in the operation plan. The electrolyzer 20 may be set to operate with power from the utility grid 50 during periods when the electricity rate is below the threshold to meet the shortfall in the planned use of the product of the device 20 .

In the first period, the operation plan generation unit 150 may generate an operation plan including a period during which the electrolytic device 20 is operated and a period during which the electrolysis apparatus 20 is not operated. In addition, the operation plan generation unit 150 may generate an operation plan that indicates the period in which the electrolytic device 20 is to be operated together with the operation rate. The operation plan generation unit 150 desirably generates an operation plan in which the operation rate changes in time series. The operation plan generation unit 150 generates, for example, an operation plan for each fixed time period. The operation plan generation unit 150 may generate an operation plan every several tens of minutes, one hour, or several hours.

Moreover, when the control unit 160 controls a plurality of electrolysis devices 20 , the operation plan generation unit 150 may generate an operation plan for each of the plurality of electrolysis devices 20 . When the plurality of electrolysis devices 20 are substantially the same electrolysis device 20, the operation plan generation unit 150 may generate substantially the same operation plan. In addition, the operation plan generating unit 150 controls a plurality of electrolyzers 20 including different types of electrolyzers 20 from the control unit 160, electrolyzers 20 purchased at different times, electrolyzers 20 from different manufacturers, or combinations thereof. In this case, different operation plans may be generated for each electrolytic device 20 .

In this case, the second model generation unit 230 may generate a plurality of operation plan generation models corresponding to each number of operating electrolysis devices 20 or each combination of electrolysis devices 20 . In addition, the second model learning unit 245 may learn each of the plurality of operation generation models, and the second model updating unit 240 may update each of the plurality of operation generation models. The operation plan generation unit 150 uses the operation plan generation model according to the usage plan of the products of the plurality of electrolysis devices 20 in the first period, among the plurality of operation plan generation models, to generate the operation plan of the electrolysis device 20 in the first period. A working plan may be generated. In addition, the second model generation unit 230 generates one operation plan generation model corresponding to a plurality of electrolysis devices 20, and the second model update unit 240 updates the operation plan generation model learned by the second model learning unit 245. may be updated. In this case, the operation plan generation model may be a model for generating an operation plan for cooperatively operating the plurality of electrolysis devices 20. As an example, the operation start timing and operation period of each of the plurality of electrolysis devices 20, etc. may be a model that generates an optimized operation plan.

The control unit 160 operates the electrolytic device 20 for N days using the operation plan generated by the operation plan generation unit 150 (S370). Thereby, the electrolyzer 20 can be operated so as to reduce the production cost of the product while satisfying the predetermined supply plan of the product in the first period. Further, the control unit 160 may use the operation plan to control the power generation device 30 , supply power from the power generation device 30 to the electrolysis device 20 , and sell power from the power generation device 30 to the power system 50 .

When the planning device 40 continues the operation of the electrolysis device 20 after the first period has elapsed (S380: No), the process returns to S330, and the learning processing unit 130 adaptively learns the learning model. In this case, the acquisition unit 100 sequentially acquires information on the first factor and the third factor in the first period and information on the second factor that changes due to the operation of the electrolysis apparatus 20 in the first period, and the storage unit 110 are stored in order. That is, the planning device 40 includes the information of the first period in the past information, and sets the target period to be predicted to the period after the first period (for example, the fourth period).

Then, the planning device 40 repeats adaptive learning of the model using the information of the first period, updates the model according to the lapse of a certain period of time, and generates an operation plan for the electrolysis device 20 for the fourth period. The electrolytic device 20 is operated according to the operation plan. In this way, the planning device 40 according to the present embodiment repeats the generation of the operation plan for the target period of the electrolysis device 20 and the operation of the target period, thereby continuing the electrolysis device 20 while updating the learning model. can operate

In the operation flow of the planning device 40 described above, an example of operating the planning device 40 in chronological order in the order of the second period, the third period, the first period, and the fourth period has been described. Here, the second period, the third period, the first period, and the fourth period may be consecutive periods in this order. At least the first period and the fourth period are preferably consecutive periods.

The planning device 40 according to the present embodiment predicts the renewable energy power generation amount of the power generation device 30 and the electricity rate of the power system 50 by learning, and the amount of product in accordance with the usage plan in the electrolysis device 20 is produced at low cost. Create a generable operation plan.

Note that the planning device 40 according to the present embodiment does not have to include the third model generation unit 250 , the third model update unit 260 and the electricity bill prediction unit 270 . In this case, the electricity rate of the power system 50 used by the planning device 40 may be, for example, the electricity rate predicted by an external device, the electricity rate provided by the operator of the power system 50, or the past electricity rate. . Also, the electricity rate of the power system 50 may be constant without fluctuation.

FIG. 4 illustrates an example computer 1900 upon which aspects of the invention may be implemented in whole or in part. Programs installed on the computer 1900 may cause the computer 1900 to function as one or more sections of an operation or apparatus associated with an apparatus according to embodiments of the invention, or may Sections may be executed and/or computer 1900 may be caused to execute processes or steps of such processes according to embodiments 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 computer 1900 according to the present embodiment includes a CPU peripheral section having a CPU 2000, a RAM 2020, a graphic controller 2075, and a display device 2080, which are interconnected by a host controller 2082, and an input/output controller 2084, which is connected to the host controller 2082. and a legacy input/output unit having a ROM 2010, a flash memory drive 2050, and an input/output chip 2070 connected to an input/output controller 2084. .

Host controller 2082 connects RAM 2020 to CPU 2000 and graphics controller 2075 that access RAM 2020 at a high transfer rate. The CPU 2000 operates based on programs stored in the ROM 2010 and RAM 2020, and controls each section. The graphic controller 2075 acquires image data generated by the CPU 2000 or the like on a frame buffer provided in the RAM 2020 and displays it on the display device 2080 . Alternatively, the graphics controller 2075 may internally include a frame buffer for storing image data generated by the CPU 2000 or the like.

The input/output controller 2084 connects the host controller 2082 with the communication interface 2030, hard disk drive 2040, and DVD drive 2060, which are relatively high-speed input/output devices. Communication interface 2030 communicates with other devices via a network by wire or wirelessly. Also, the communication interface functions as hardware for communication. Hard disk drive 2040 stores programs and data used by CPU 2000 in computer 1900 . DVD drive 2060 reads programs or data from DVD 2095 and provides them to hard disk drive 2040 via RAM 2020 .

The I/O controller 2084 is also connected to the ROM 2010 , the flash memory drive 2050 and the I/O chip 2070 , which are relatively low-speed I/O devices. The ROM 2010 stores a boot program executed by the computer 1900 at startup and/or a program depending on the hardware of the computer 1900, and the like. Flash memory drive 2050 reads programs or data from flash memory 2090 and provides them to hard disk drive 2040 via 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, parallel ports, serial ports, keyboard ports, mouse ports, and the like. Connect to controller 2084 .

A program provided to the hard disk drive 2040 via the RAM 2020 is stored in a recording medium such as the flash memory 2090, DVD 2095, or IC card and provided by the user. The program is read from the recording medium, installed in hard disk drive 2040 in computer 1900 via RAM 2020, and executed in CPU 2000. FIG. The information processing described in these programs is read by computer 1900 and provides cooperation between the software and the various types of hardware resources described above. An apparatus or method may be configured by implementing the manipulation or processing of information in accordance with the use of computer 1900 .

As an example, when communicating between the computer 1900 and an external device or the like, the CPU 2000 executes a communication program loaded on the RAM 2020, and based on the processing content described in the communication program, the communication interface. 2030 is instructed to perform communication processing. Under the control of the CPU 2000, the communication interface 2030 reads transmission data stored in a transmission buffer area or the like provided on a storage device such as the RAM 2020, the hard disk drive 2040, the flash memory 2090, or the DVD 2095, and transmits the transmission data to the network. Alternatively, the received data received from the network is written into a receive buffer area or the like provided on the storage device. In this way, the communication interface 2030 may transfer data to and from a storage device using a DMA (direct memory access) method. The transmission/reception data may be transferred by reading the data from and writing the data to the communication interface 2030 or storage device of the transfer destination.

The CPU 2000 also DMAs all or necessary portions of files or databases stored in external storage devices such as the hard disk drive 2040, DVD drive 2060 (DVD 2095), and flash memory drive 2050 (flash memory 2090). 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 . Then, the CPU 2000 writes the processed data back to the external storage device by DMA transfer or the like. In such processing, 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 kinds of information such as various programs, data, tables, databases, etc. in this embodiment are stored in such a storage device, and are subject to information processing. Note that the CPU 2000 can also hold part of the RAM 2020 in cache memory and perform reading and writing on the cache memory. Even in such a form, the cache memory is responsible for part of the functions of the RAM 2020, so in this embodiment, unless otherwise indicated, the cache memory is also included in the RAM 2020, memory, and/or storage device. do.

In addition, the CPU 2000 performs various operations, including various calculations, information processing, condition determination, information search/replacement, etc., which are specified by the instruction sequence of the program, on the data read from the RAM 2020, and which are described in the present embodiment. and write it back to the RAM 2020 . For example, when performing conditional judgment, the CPU 2000 compares the various variables shown in this embodiment with other variables or constants to determine whether or not the conditions such as greater, lesser, greater than, less than or equal to are satisfied. If the condition is satisfied (or not satisfied), branch to a different instruction sequence or call a subroutine.

The CPU 2000 can also search for information stored in a file, database, or the like in the storage device. For example, when a plurality of entries in which attribute values of a first attribute are respectively associated with attribute values of a second attribute are stored in the storage device, the CPU 2000 performs processing of the plurality of entries stored in the storage device. An entry whose attribute value of the first attribute matches the specified condition is searched from among the entries, and the attribute value of the second attribute stored in that entry is read out to associate it with the first attribute that satisfies the predetermined condition. It is possible to obtain the attribute value of the given second attribute.

Also, when a plurality of elements are listed in the description of the embodiment, elements other than the listed elements may be used. For example, when it is stated that "X performs Y using A, B and C," X may perform Y using D in addition to A, B and C.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the scope of claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

The execution order of each process such as actions, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, the specification, and the drawings is particularly "before", "before etc., and it should be noted that they can be implemented in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if the description is made using "first," "next," etc. for the sake of convenience, it means that it is essential to carry out in this order. not a thing

10 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 amount 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 Fee 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

Claims (12)

Trends in future renewable energy power generation using a power generation prediction model that predicts changes in renewable energy power generation for power generation equipment during the target period based on the value of the first factor that is available prior to the target period. a power generation amount prediction unit that predicts
generating an operation plan for the electrolysis device that satisfies the usage plan for the product of the electrolysis device for a first period in the future based on the predicted transition of the renewable energy power generation amount in the future and the electricity rate of the electric power system; Equipped with an operation plan generation unit and
The operation plan generation unit corresponds to each of a plurality of electrolyzers of different types, a plurality of electrolyzers purchased at different times, a plurality of electrolyzers from different manufacturers, or a combination thereof. , to generate different run plans
planning equipment. The power generation amount prediction model is 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 weather information before the target period, the renewable energy power generation in the target period. 2. The planning device of claim 1, which predicts the evolution of quantities. It further comprises a first model updating unit that updates the power generation amount prediction model by learning based on the value of the first factor in the past period and the actual transition of the renewable energy power generation amount after the past period. 2. Planning device according to claim 2. During the first period, the operation plan generation unit preferentially uses the electricity from the power generation device over the electricity from the power system so as to satisfy the plan for using the product of the electrolysis device. The planning device according to any one of claims 1 to 3, which generates an operation plan for operating the . The plan for using the product of the electrolyzer includes a plan to maintain the storage amount of the product of the electrolyzer during the first period within a reference range, and a demand for the product of the electrolyzer during the first period. 5. A planning device according to any one of claims 1 to 4, comprising at least one quantity filling plan. The operation plan generation unit combines the operation plan for the target period with the value of the second factor that can be obtained before the target period, the prediction result of the transition of the renewable energy power generation amount in the target period, and the electricity rate. The planning apparatus according to any one of claims 1 to 5, wherein an operation plan for the electrolysis device in the first period in the future is generated using an operation plan generation model generated based on. The operation plan generation model includes at least one of the operation data of the electrolysis device, the demand amount of the product of the electrolysis device, and the storage amount of the product of the electrolysis device before the target period. 7. The planning device according to claim 6, wherein an operation plan for the electrolysis device for a target period is generated based on a factor value, a prediction result of the transition of the renewable energy power generation amount during the target period, and the electricity rate. The value of the second factor in the past period, the transition of the renewable energy power generation amount after the past period or the prediction result of the transition of the renewable energy power generation amount, and the electricity price transition after the past period; The planning apparatus according to claim 6 or 7, further comprising a second model updating unit that updates the operation plan generation model by learning based on the target operation plan of the electrolysis apparatus after the past period. Electricity that predicts future changes in electricity prices using an electricity price prediction model that predicts changes in electricity prices in the power system during the target period based on the value of the third factor that is available before the target period. further comprising a charge prediction unit,
The operation plan generation unit generates the electrolysis device in the first period in the future based on the predicted transition of the future renewable energy power generation amount and the predicted future transition of the electricity rate. 9. A planning device according to any one of claims 1 to 8, for generating a run plan for the electrolyzer that meets a product usage plan. The electricity rate prediction model includes at least one of electricity rate, power demand, power supply, renewable energy power generation, forecast value of renewable energy power generation, and weather information in the power system prior to the target period. 10. The planning device according to claim 9, wherein transition of the electricity rate of the power system in the target period is predicted based on the value of the third factor including one. Trends in future renewable energy power generation using a power generation prediction model that predicts changes in renewable energy power generation for power generation equipment during the target period based on the value of the first factor that is available prior to the target period. a computer predicting the
The computer creates an operation plan of the electrolysis device that satisfies the usage plan of the product of the electrolysis device for a first period in the future based on the predicted future transition of the renewable energy power generation amount and the electricity rate of the power system. with a stage generated by and
The step of generating the operation plan corresponds to each of a plurality of electrolyzers including a plurality of different types of electrolyzers, a plurality of electrolyzers purchased at different times, a plurality of electrolyzers from different manufacturers, or a combination thereof. and causing the computer to generate different operating plans
Method. A program that causes a computer to function as the planning device according to any one of claims 1 to 10.

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