Control method, control device and power system

Abstract

The control method of the present disclosure includes a step of executing a first planning method for planning an output of a fuel cell device so as to compensate for a difference between a predicted value of a power demand of a power consumer and a predicted value of an output of a photovoltaic power generation device. The control device of the present disclosure includes a memory device that stores the predicted value of a power demand of a power consumer and a predicted value of an output of a photovoltaic power generation device, and a planner that executes the planning method for planning an output of a fuel cell device so as to compensate for a difference between the predicted value of a power demand of the power consumer and the predicted value of an output of the photovoltaic power generation device.

Description

[Technical field]

[0001] The present disclosure relates to a control method, a control device, and a power system. [Background technology]

[0002] Patent Document 1 describes a household distributed power supply system equipped with a solar cell, a fuel cell, and a power storage device. In the household distributed power supply system described in Patent Document 1, a control signal for the output required by the fuel cell is calculated as the difference between the load current and the output current of the solar cell. The power storage device supplies the power shortage caused by the delay in increasing the fuel cell output. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-208436 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional technology, the output of the fuel cell is prone to fluctuation, and the fuel cell is prone to deterioration. [Means for solving the problem]

[0005] The present disclosure provides a control method including a step of executing a first planning method for planning output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of a power consumer and a predicted value of output of a solar power generation device. Effect of the Invention

[0006] According to the present disclosure, deterioration of a fuel cell device can be suppressed. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a configuration diagram of a power system according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a functional block diagram of the control device and the database shown in FIG. [Diagram 3] FIG. 3 is a flowchart showing a process for switching between the first planning method and the second planning method. [Figure 4A] FIG. 4A is a graph showing fluctuations in various types of power and charge rates in the power system of this embodiment when the first planning method is executed. [Figure 4B] FIG. 4B is a graph showing fluctuations in various powers and charge rates in the power system of this embodiment when the second planning method is executed. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments.

[0009] (Embodiment) 1 is a configuration diagram of a power system according to an embodiment of the present disclosure. The power system 200 includes a control device 10, a database 20, a solar power generation device 30, a fuel cell device 40, and a power storage device 50. The solar power generation device 30, the fuel cell device 40, and the power storage device 50 are connected to a load 300 via a power line 102. Power generated by the solar power generation device 30 and the fuel cell device 40 is supplied to the load 300 via the power line 102. As necessary, the power generated by the solar power generation device 30 and the fuel cell device 40 is stored in the power storage device 50, and power is supplied from the power storage device 50 to the load 300.

[0010] A commercial power source 100 is connected to the power line 102. The power system 200 may be interconnected with the commercial power source 100. Power may be purchased from the commercial power source 100, or surplus power may be sold (reverse power flow) to the commercial power source 100. However, according to the power system 200 of the present embodiment, power shortages are unlikely to occur, and surplus power is also unlikely to occur.

[0011] The solar power generation device 30 includes a solar cell module 31, a first PCS 32 (Power Conditioning Subsystem), a first power meter 33, and a first control device 34. The solar power generation device 30 may include a plurality of solar cell modules 31. The solar cell module 31 may be configured by a plurality of solar cell panels. The first PCS 32 converts the power generated by the solar cell module 31 from DC power to AC power and outputs the power to the power line 102. The first power meter 33 detects the power output from the first PCS 32, that is, the power generated by the solar power generation device 30. The first control device 34 is communicably connected to the control device 10. The first control device 34 controls the solar cell module 31 or the first PCS 32 according to an instruction received from the control device 10. The first control device 34 may be a computer including a storage device in which a program required for controlling the solar power generation device 30 is stored, and a processor that reads the program from the storage device and executes it.

[0012] The fuel cell device 40 includes a fuel cell unit 41, a second PCS 42, a second power meter 43, and a second control device 44. The fuel cell device 40 may include a plurality of fuel cell units 41 that can be operated independently of each other. The power generated by the fuel cell device 40 can be changed by adjusting the number of fuel cell units 41 in operation and / or their output. The second PCS 42 converts the power generated by the fuel cell unit 41 from DC power to AC power and outputs it to the power line 102. The second power meter 43 detects the power output from the second PCS 42, that is, the power generated by the fuel cell device 40. The second control device 44 is communicably connected to the control device 10. The second control device 44 controls the fuel cell unit 41 or the second PCS 42 according to instructions received from the control device 10. The second control device 44 may be a computer including a storage device in which a program required for controlling the fuel cell device 40 is stored, and a processor that reads the program from the storage device and executes it.

[0013] The fuel cell in the fuel cell device 40 may be a polymer electrolyte fuel cell, a solid oxide fuel cell, a phosphoric acid fuel cell, a molten carbonate fuel cell, or the like.

[0014] The power storage device 50 includes a storage battery 51, a third PCS 52, a third power meter 53, and a third control device 54. The storage battery 51 may be a secondary battery. The power storage device 50 may include a capacitor instead of the storage battery 51. The third PCS 52 is a bidirectional PCS. When power is stored in the storage battery 51, the third PCS 52 converts AC power to DC power. When power is discharged from the storage battery 51, the third PCS 52 converts DC power to AC power. The third power meter 53 detects the power output from the third PCS 52 and the power input to the third PCS 52. That is, the third power meter 53 detects the charging power and discharging power of the power storage device 50. The third control device 54 is connected to the control device 10 so as to be able to communicate with the control device 10. The third control device 54 controls the storage battery 51 or the third PCS 52 according to an instruction received from the control device 10. The third control device 54 may be a computer including a storage device that stores a program required for controlling the power storage device 50, and a processor that reads and executes the program from the storage device.

[0015] The third control device 54 sequentially transmits the state of charge (SOC) of the power storage device 50 to the control device 10. The state of charge (SOC) of the power storage device 50 is stored in the database 20 in chronological order.

[0016] The load 300 represents a power consumer, such as a factory. The fourth power meter 302 detects, for example, the power consumption of the factory. The fourth power meter 302 may be part of the power system 200.

[0017] The control device 10 issues control instructions to each of the first control device 34, the second control device 44, and the third control device 54, and controls the operation of each of the photovoltaic power generation device 30, the fuel cell device 40, and the power storage device 50. Specifically, the control device 10 transmits information required for the respective control operations to each of the first control device 34, the second control device 44, and the third control device 54. The control device 10 is a computer equipped with a storage device in which a program required for the control of these devices is stored, and a processor that reads and executes the program from the storage device. The control device 10 acquires detection values ​​from the first power meter 33, the second power meter 43, the third power meter 53, and the fourth power meter 302 at a predetermined sampling period (for example, every 30 seconds) and transmits them to the database 20. From these detection values, the power generated by the photovoltaic power generation device 30, the power generated by the fuel cell device 40, the charging power of the power storage device 50, the discharging power of the power storage device 50, and the power consumption of the load 300 can be identified.

[0018] The database 20 is constituted by a server computer, and is communicatively connected to the control device 10. The database 20 stores data in chronological order, such as the power generated by the solar power generation device 30, the power generated by the fuel cell device 40, the charging power of the power storage device 50, the discharging power of the power storage device 50, the power consumption of the load 300, and the power storage rate of the power storage device 50. The amount of power (kWh) can be calculated from the time-series data.

[0019] The control device 10 may be a cloud computer or an edge computer. The database 20 may be a database in a cloud server or an edge server. The cloud computer and the cloud server may be located at a location away from the location where the solar power generation device 30, the fuel cell device 40, and the power storage device 50 are provided. The cloud computer as the control device 10 is communicatively connected to the first control device 34, the second control device 44, and the third control device 54 via a communication network such as the Internet. The edge computer and the edge server may be located near the location where the solar power generation device 30, the fuel cell device 40, and the power storage device 50 are provided.

[0020] Fig. 2 is a functional block diagram of the control device 10 and database 20 shown in Fig. 1. The database 20 stores operation data, weather data, power data, and power storage data of the factory. The control device 10 creates a prediction model by machine learning using various data stored in the database 20, and creates operation instructions for the fuel cell device 40 using the prediction model. The prediction model includes a prediction model for predicting the power demand of the factory and a prediction model for predicting the power generation of the solar power generation device 30.

[0021] In the database 20, the factory operation data is data entered by the administrator from the terminal 21. The factory operation data is a combination of data indicating whether the factory is operating or not and date data. The data indicating whether the factory is operating or not is data indicating a holiday or an operating day. The factory operation data makes it possible to identify whether a specific day in the past was a holiday or an operating day. There is a correlation between the power consumption of a factory and whether the factory is operating or not. The power consumption of a factory is high on operating days, but low on holiday days. Therefore, the factory operation data can be used as learning data when creating a prediction model for predicting the power demand of a factory.

[0022] Even when the load 300 (Fig. 1) is a facility other than a factory, there is a correlation between power consumption and whether or not it is operating. Examples of facilities other than factories include commercial facilities, hospitals, schools, and office buildings. For example, when the load 300 is a commercial facility such as a department store, the operation data can be a combination of date data and data indicating one of "holiday," "business day and weekday," and "business day and holiday." A commercial facility consumes more power on business days, but less power on holiday days. Even on the same business day, there is a difference between the power consumption on weekdays and the power consumption on holidays.

[0023] In the database 20, the weather data is data input from the weather information service 22 via a communication network such as the Internet. The weather data may be input from the terminal 21 by an administrator. The weather data is data that combines data indicating the contents of the weather with date data. The data indicating the contents of the weather is, for example, data that combines a time period and the intensity of sunlight. According to the weather data, the intensity of sunlight in each time period in the past can be specified. The intensity of sunlight is expressed in a number of stages, for example, from an intensity corresponding to clear skies to an intensity corresponding to rain. There is a correlation between the intensity of sunlight and the power generated by the solar power generation device 30. If there is sunlight, the power generation power (kW) is large, and if there is no sunlight, the power generation power is small. Therefore, the weather data indicating the intensity of sunlight can be used as learning data when creating a prediction model for predicting the power generation power of the solar power generation device 30. Instead of the intensity of sunlight, or together with the intensity of sunlight, the weather data may include the duration of sunlight for each time period. There is a correlation between the duration of sunlight and the power generation amount (kWh) of the solar power generation device 30.

[0024] The data indicating the weather may include data of a combination of time period and temperature. For example, when the temperature is high, the power consumption of the air conditioner increases, and the power consumption of the factory also increases. Even when the temperature is low, the power consumption of the air conditioner increases, and the power consumption of the factory also increases. Therefore, the weather data indicating the temperature can be used as learning data when creating a prediction model for predicting the power demand of the factory.

[0025] The data indicating the details of the weather is not limited to those described above. The data indicating the details of the weather may include other data that is correlated with the power generated by the solar power generation device 30, or may include other data that is correlated with the power demand.

[0026] In the database 20, the power data is data input from each of the power meters 33, 43, 53, and 302 via the control device 10. The power data can be time series data of the power generated by the photovoltaic power generation device 30, the power generated by the fuel cell device 40, the charging power of the power storage device 50, the discharging power of the power storage device 50, and the power consumption of the load 300. According to the power data, the power generated by the photovoltaic power generation device 30, the power generated by the fuel cell device 40, the charging power of the power storage device 50, the discharging power of the power storage device 50, and the power consumption of the load 300 at a specific time in the past can be specified. The power data indicating the power consumption of the load 300 can be used as learning data when creating a prediction model for predicting the power demand of the factory. The data of the power generated by the photovoltaic power generation device 30 can be used as learning data when creating a prediction model for predicting the power generated by the photovoltaic power generation device 30.

[0027] In the database 20, the power storage data is data input from the power storage device 50 via the control device 10. The power storage data is data indicating the power storage rate of the power storage device 50.

[0028] The control device 10 has a prediction unit 12 , a planning unit 14 and a control unit 16 .

[0029] The prediction unit 12 has a power demand prediction unit 12a and a photovoltaic power generation prediction unit 12b. In the power demand prediction unit 12a, the predicted value of the power demand is predicted based on the correlation between the actual weather record and the actual value of the power demand, and on the weather forecast. In the power demand prediction unit 12a, the predicted value of the power demand is predicted based on the operation record of the factory, the correlation between the actual weather record and the actual value of the power demand of the factory, the weather forecast, and the operation plan of the factory. In the photovoltaic power generation prediction unit 12b, the predicted value of the output of the photovoltaic power generation device 30 is predicted based on the correlation between the actual weather record and the actual value of the output of the photovoltaic power generation device 30, and on the weather forecast.

[0030] The power demand prediction unit 12a may be a program module including a prediction model for predicting the power demand of the load 300. The prediction model in the power demand prediction unit 12a is created using a power demand record, weather record, and factory operation record. Power data indicating the power consumption of the load 300 is used as the power demand record. Weather data is used as the weather record. The weather record is the weather record of an area where power consumers such as factories are located. Factory operation data is used as the factory operation record. The prediction model is, for example, a regression model. The power data, weather data, and factory operation data are supervised data in machine learning.

[0031] A predicted value of the power demand (power consumption) of the factory can be obtained by inputting the weather forecast and the operation plan of the factory into the prediction model in the power demand prediction unit 12a. In other words, the predicted value of the power demand of the factory, which is the objective variable, is obtained by using the weather forecast and the operation plan of the factory as explanatory variables. For example, data on the temperature at the time or time period when the predicted value of the power demand should be obtained is used as the weather forecast. The weather forecast may include data indicating the intensity of sunlight or the duration of sunlight. The weather forecast is a weather forecast for an area where power consumers such as factories exist. Data indicating whether the factory will be operating on the date and time when the predicted value of the power demand should be obtained is used as the operation plan of the factory.

[0032] The solar power generation prediction unit 12b may be a program module including a prediction model for predicting the power generation power of the solar power generation device 30. The prediction model in the solar power generation prediction unit 12b is created using the power generation record and weather record of the solar power generation device 30. Power data indicating the power generation power of the solar power generation device 30 is used as the power generation record of the solar power generation device 30. Weather data is used as the weather record. The prediction model is, for example, a regression model. The power data and weather data are supervised data in machine learning.

[0033] By inputting the weather forecast into the prediction model in the solar power generation prediction unit 12b, a predicted value of the output (power generation) of the solar power generation device 30 can be obtained. That is, the predicted value of the output of the solar power generation device 30, which is the objective variable, is obtained by using the weather forecast as an explanatory variable. As the weather forecast, for example, data indicating the intensity of sunlight or the duration of sunlight at the time or time period when the predicted value of the output of the solar power generation device 30 should be obtained is used.

[0034] In order to create each of the above-mentioned prediction models in the prediction unit 12, various data for a predetermined period (e.g., three months to one year) are used. The prediction models may be updated daily. This is expected to improve the prediction accuracy. The prediction models may be stored in the database 20.

[0035] The process of obtaining a predicted value by the prediction unit 12 is executed at a predetermined cycle. For example, a plurality of predicted values ​​from the present to 24 hours later are obtained at a cycle of once per hour. The plurality of predicted values ​​are, for example, predicted values ​​at 24 points at hourly intervals.

[0036] A process of correcting the predicted value may be performed. For example, the predicted value of the power demand is corrected by the following method. That is, the predicted value obtained from the prediction model may be corrected based on the actual value of the power demand in the most recent first period and the predicted value of the power demand in the second period corresponding to the first period. The most recent first period is a period from the current time back by a predetermined time (30 minutes to 1 hour). For example, when the current time is 10:00, the most recent first period is a period from 9:00 to 10:00. The actual value of the power demand in this period is stored in the database 20 in chronological order as the power consumption of the load 300. The actual value of the power demand in the first period may be an average value in the first period. Next, the second period corresponding to the first period is a period corresponding to the first period in the 24-hour period in which the predicted values ​​of the 24 points were obtained. For example, when a first period is from 9:00 to 10:00, the corresponding second period is from 9:00 to 10:00.

[0037] For example, assume that the actual value of the power demand in the first period from 9:00 to 10:00 is 100 kW. Meanwhile, assume that the predicted value of the power demand in the second period from 9:00 to 10:00 is 90 kW. In this case, the actual value exceeds the predicted value by 10 kW. Therefore, a correction is performed to add 10 kW to the predicted value of the power demand from 10:00 to 11:00. This can improve the prediction accuracy. Note that the difference between the actual value and the predicted value may be used as is for the correction, or a predetermined percentage (e.g., 80%) of the difference between the actual value and the predicted value may be used for the correction. The degree of correction is arbitrary. Also, of the predicted values ​​of 24 future points, only the predicted value to be used most recently may be corrected, or multiple predicted values ​​including the predicted value to be used most recently may be corrected.

[0038] The predicted value of the power generation power of the photovoltaic power generation device 30 may be corrected in the same manner. That is, the predicted value obtained from the prediction model may be corrected based on the actual value of the output of the photovoltaic power generation device 30 in the most recent third period and the predicted value of the output of the photovoltaic power generation device 30 in the fourth period corresponding to the third period. This can improve the prediction accuracy. The third period may be the same period as the first period or may be a different period. The fourth period may be the same period as the second period or may be a different period.

[0039] The predicted value obtained by the prediction unit 12 is stored in the database 20. With this configuration, it is possible to execute a prediction process for obtaining a predicted value and a planning process for obtaining a planned value asynchronously. The server that serves as the database 20 is an example of a storage device that stores a predicted value of power demand and a predicted value of the output of the photovoltaic power generation device 30.

[0040] The planner 14 includes a fuel cell output planner 14a. The fuel cell output planner 14a may be a program module for determining a planned value of the output (power generation) of the fuel cell device 40. The fuel cell output planner 14a acquires the predicted value Pd of the power demand and the predicted value Ppv of the output of the photovoltaic power generation device 30 from the predictor 12, and calculates the planned value of the output of the fuel cell device 40 using these predicted values. For example, when the current time is 8:50, the predicted value of the power demand and the predicted value of the output of the photovoltaic power generation device 30 for the time period from 9:00 to 10:00 are created in the predictor 12, and the planned value of the output of the fuel cell device 40 is calculated from these predicted values. The predicted value and the planned value may be expressed in instantaneous power (kW) or in power amount (kWh) per unit time.

[0041] In this embodiment, the planner 14 plans the output of the fuel cell device 40 so as to compensate for the difference between the predicted value Pd of the power demand and the predicted value Ppv of the output of the photovoltaic power generation device 30. By planning the output of the fuel cell device 40 using the predicted value, fluctuations in the output of the fuel cell device 40 can be suppressed compared to a case in which the output of the fuel cell device 40 is planned based only on the most recent actual values. Since there is a correlation between the lifespan of the stack of the fuel cell device 40 and the frequency of fluctuations in the output of the fuel cell device 40, suppressing fluctuations in the output of the fuel cell device 40 extends the lifespan of the fuel cell device 40.

[0042] The planned value Pfc of the output of the fuel cell device 40 is calculated, for example, as the difference (Pd-Ppv) between the predicted value Pd of the power demand and the predicted value Ppv of the output of the photovoltaic power generation device 30. Therefore, when the current time is 8:50, the planned value Pfc of the output of the fuel cell device 40 for the time slot from 9:00 to 10:00 can be (Pd-Ppv). When the difference is equal to or less than zero, that is, when (Pd-Ppv)≦0, the planned value Pfc of the output of the fuel cell device 40 is zero.

[0043] According to the method of this embodiment, a predicted value Pd of power demand and a predicted value Ppv of the output of the photovoltaic power generation device 30 are obtained. Therefore, even if a method for determining the output of the fuel cell device 40 based on the most recent data would cause the output of the fuel cell device 40 to be varied, in the method of this embodiment, it may be determined that it is better not to vary the output of the fuel cell device 40. This difference is due to the poor tracking ability of the output of the fuel cell device 40.

[0044] The process of calculating the planned values ​​by the planning unit 14 is executed at a predetermined cycle. For example, the planned values ​​required for operation in the upcoming time slot are calculated at a cycle of once an hour.

[0045] The planner 14 is an example of a planner that executes a planning method for planning the output of the fuel cell device 40 so as to compensate for the difference between the predicted value Pd of the power demand and the predicted value Ppv of the output of the photovoltaic power generation device 30.

[0046] In this specification, "compensating for the difference" means both compensating for the entire difference and compensating for only a part of the difference. In the above example, the difference between the predicted value Pd of the power demand and the predicted value Ppv of the output of the photovoltaic power generation device 30 is regarded as the planned value Pfc of the output of the fuel cell device 40, so that the entire difference is compensated for by the output of the fuel cell device 40.

[0047] The control unit 16 includes a fuel cell correction control unit 16a, an electricity storage control unit 16b, and a solar cell output suppression unit 16c.

[0048] In the control unit 16, the fuel cell correction control unit 16a may be a program module for determining whether or not the planned value Pfc of the output of the fuel cell device 40 needs to be corrected, and correcting the planned value Pfc as necessary. The fuel cell correction control unit 16a acquires the planned value Pfc of the output of the fuel cell device 40 from the planner 14, and acquires the actual value Rd of the demand power, the actual value Rpv of the output of the photovoltaic power generation device 30, and the state of charge (SOC) of the power storage device 50 from the database 20. The actual value Rd of the demand power is the power consumption of the load 300. In detail, the average value of the power consumption in a predetermined period can be used as the actual value Rd of the demand power. The average value of the power generated by the photovoltaic power generation device 30 in a predetermined period can be used as the actual value Rpv of the output of the photovoltaic power generation device 30. The average value is, for example, a moving average value. The predetermined period is, for example, a period from 30 minutes ago to the time when the most recent data was acquired (approximately the current time). The charge rate of the power storage device 50 is the charge rate of the power storage device 50 at the time when the most recent data was acquired (approximately the current time).

[0049] It is desirable for the power system 200 to avoid purchasing power from the commercial power source 100 and selling power to the commercial power source 100 (reverse power flow) as much as possible, and it is also desirable for the power storage device 50 to be able to charge and discharge at all times. If the power storage device 50 is in a fully charged state or a completely discharged state, it becomes difficult for the power storage device 50 to balance supply and demand in the power system 200. In this embodiment, taking into consideration the storage rate of the power storage device 50, the fuel cell correction control unit 16a corrects the planned value of the output of the fuel cell device 40 so as to prevent the storage rate of the power storage device 50 from becoming a fully charged state or a completely discharged state.

[0050] The fuel cell correction control unit 16a corrects the planned value Pfc of the output of the fuel cell device 40 based on the state of charge (SOC) of the power storage device 50 predicted from the difference between the actual value Rd of the power demand and the sum of the actual value Rpv of the output of the solar power generation device 30 and the planned value Pfc of the output of the fuel cell device 40. This makes it possible to create a command value for the output of the fuel cell device 40 while taking into account the state of charge of the power storage device 50, and ultimately to maintain the power storage device 50 in a state in which it can be charged and discharged.

[0051] Specifically, a value (Rd-Rpv-Pfc) is calculated by subtracting the sum of the actual value Rpv of the output of the photovoltaic power generation device 30 and the planned value Pfc of the output of the fuel cell device 40 from the actual value Rd of the power demand. This value is zero, a negative value, or a positive value.

[0052] When the above value (Rd-Rpv-Pfc) is a negative value, it can be said that there is a high possibility that there will be a surplus of power in the future, based on the most recent actual value. Therefore, the storage rate after a predetermined time (for example, one hour) when the power storage device 50 is charged with power equivalent to the absolute value of the value (Rd-Rpv-Pfc) is predicted from the most recent storage rate. The storage rate after the predetermined time is calculated from the most recent storage rate of the power storage device 50, the capacity of the power storage device 50, and the charging power of the power storage device 50. When the storage rate after the predetermined time exceeds a predetermined upper limit value that is smaller than the fully charged state (100%), the planned value Pfc of the output of the fuel cell device 40 is corrected so that the storage rate after the predetermined time is equal to or smaller than the upper limit value. For example, the planned value Pfc of the output of the fuel cell device 40 is corrected by subtracting a correction value from the planned value Pfc of the output of the fuel cell device 40 or multiplying it by a correction value less than 1. As a result, the fuel cell device 40 is operated at an output smaller than the planned value Pfc. The upper limit of the charge rate is set to, for example, 80% of the capacity of the power storage device 50.

[0053] When the above value (Rd-Rpv-Pfc) is a positive value, it can be said that there is a high possibility that power will be insufficient in the future, based on the most recent actual value. Therefore, the storage rate after a predetermined time (for example, after one hour) when power equivalent to the value (Rd-Rpv-Pfc) is discharged from the power storage device 50 is predicted from the most recent storage rate. If the storage rate after the predetermined time falls below a predetermined lower limit value that is higher than the fully discharged state (0%), the planned value Pfc of the output of the fuel cell device 40 is corrected so that the storage rate after the predetermined time is equal to or higher than the lower limit value. For example, the planned value Pfc of the output of the fuel cell device 40 is corrected by adding a correction value to the planned value Pfc of the output of the fuel cell device 40 or multiplying it by a correction value that exceeds 1. As a result, the fuel cell device 40 is operated with an output higher than the planned value Pfc. The lower limit value of the storage rate is set to, for example, 20% of the capacity of the power storage device 50.

[0054] When the above value (Rd-Rpv-Pfc) is zero, the planned value Pfc is not corrected. Also, when the charge rate after a predetermined time is equal to or higher than the lower limit and equal to or lower than the upper limit, the planned value Pfc is not corrected.

[0055] The fuel cell correction control unit 16a uses the planned value Pfc or the corrected planned value Pfc' to create a command value for the output of the fuel cell device 40. The command value is sent to the second control device 44 of the fuel cell device 40. The second control device 44 controls the operation of the fuel cell unit 41 so that power in accordance with the command value is generated.

[0056] The process of correcting the planned value Pfc of the output of the fuel cell device 40 may be executed in synchronization with the process of creating the planned value Pfc, or may be executed in a control cycle shorter than that of the process of creating the planned value Pfc. The planned value Pfc may be corrected when it is determined that the charge rate of the power storage device 50 exceeds an upper limit value or falls below a lower limit value.

[0057] In the control unit 16, the power storage control unit 16b may be a program module for controlling the power storage device 50 to adjust the charging and discharging power of the power storage device 50. The power storage control unit 16b acquires the actual value Rd of the power demand, the actual value Rpv of the output of the photovoltaic power generation device 30, and the actual value Rfc of the output of the fuel cell device 40 from the database 20. Since the output of the fuel cell device 40 follows the command value, the command value of the output of the fuel cell device 40 may be regarded as the actual value Rfc of the output of the fuel cell device 40.

[0058] The power storage control unit 16b controls the power storage device 50 so as to compensate for the difference between the actual value Rd of the power demand and the sum of the actual value Rpv of the output of the photovoltaic power generation device 30 and the actual value Rfc of the output of the fuel cell device 40 by charging or discharging the power storage device 50. With this configuration, it is possible to achieve a balance between supply and demand in the power system 200, so that it is possible to avoid purchasing power from the commercial power source 100 and selling power to the commercial power source 100 as much as possible. This is advantageous from the viewpoint of avoiding the use of power from the commercial power source 100 and increasing the renewable energy utilization rate.

[0059] Specifically, a value (Rd-Rpv-Rfc) is calculated by subtracting the actual value Rpv of the output of the photovoltaic power generation device 30 and the actual value Rfc of the output of the fuel cell device 40 from the actual value Rd of the power demand. This value is zero, a negative value, or a positive value. When the value (Rd-Rpv-Rfc) is a negative value, there is a surplus of power, so a command value for the charging power of the power storage device 50 is created so that the power storage device 50 is charged with the surplus power. When the value (Rd-Rpv-Rfc) is a positive value, there is a shortage of power, so a command value for the discharging power of the power storage device 50 is created so that the power shortage is compensated for by discharging the power storage device 50. When the value (Rd-Rpv-Rfc) is zero, a command value for the charging and discharging power of the power storage device 50 is created so that the charging and discharging power is zero. The actual value Rd of the power demand is the power consumption of the load 300. The most recent power consumption can be used as the actual value Rd of the power demand. The most recent output can be used as the actual value Rpv of the output of the photovoltaic power generation device 30 and the actual value Rfc of the output of the fuel cell device 40. The most recent power consumption and the most recent output are values ​​included in the power data stored in the database 20.

[0060] A command value for the charging / discharging power of the power storage device 50 is sent from the power storage control unit 16b to the third control device 54 of the power storage device 50. The third control device 54 controls the operation of the power storage device 50 so that the power storage device 50 is charged with power according to the command value or discharged with power according to the command value. The charging / discharging power of the power storage device 50 is adjusted to a desired value by controlling the third PCS 52.

[0061] In the control unit 16, the solar cell output suppression unit 16c may be a program module for suppressing the output of the solar power generation device 30. When the power storage rate of the power storage device 50 exceeds a first threshold, the solar cell output suppression unit 16c creates a command value for the solar power generation device 30 to suppress the output of the solar power generation device 30. When the power storage rate of the power storage device 50 falls below a second threshold, the suppression of the output of the solar power generation device 30 is released. The first threshold of the power storage rate is, for example, 90% of the capacity of the power storage device 50. The second threshold of the power storage rate is, for example, 85% of the capacity of the power storage device 50. With this configuration, it is possible to prevent the power storage device 50 from reaching a fully charged state, and therefore it is possible to avoid the sale of power to the commercial power source 100 (reverse power flow) as much as possible.

[0062] The process of suppressing the output of the solar power generation device 30 is as follows. The solar cell output suppression unit 16c sets a value equal to or less than the difference between the sum of the actual value Rd of the power demand and the chargeable power Sc of the power storage device 50 and the actual value Rfc of the output of the fuel cell device 40 as the upper limit value of the output of the solar power generation device 30. With this configuration, it becomes possible to use the generated power of the solar power generation device 30 without waste.

[0063] Specifically, a value (Rd+Sc-Rfc) is calculated by subtracting the actual value Rfc (kW) of the output of the fuel cell device 40 from the sum of the actual value Rd (kW) of the power demand and the chargeable power Sc (kW) of the power storage device 50. The value (Rd+Sc-Rfc) or a value less than this is set as the upper limit value of the output of the photovoltaic power generation device 30. The actual value Rd of the power demand is, for example, the power consumption at an arbitrary time point in the past, and may be the average value of the power demand in the most recent time period (for example, the most recent one hour), or may be the minimum value of the power demand in the most recent time period. The chargeable power Sc of the power storage device 50 is the maximum charging power of the power storage device 50 or any power less than this. The maximum charging power of the power storage device 50 is a design value of the power storage device 50 that is determined depending on the type of the storage battery 51, etc. In other words, the power storage device 50 is not charged with power exceeding the maximum charging power. The actual value Rfc of the output of the fuel cell device 40 is, for example, the most recent output value of the fuel cell device 40.

[0064] When the output of the solar power generation device 30 is not suppressed, the first PCS 32 performs MPPT (Maximum Power Point Tracking) control so that the maximum output is supplied from the solar power generation device 30 to the power line 102. On the other hand, when an upper limit value of the output of the solar power generation device 30 is set, the first PCS 32 operates the solar power generation device 30 at an operating point deviating from the optimal operating point on the IV curve so that the output of the solar power generation device 30 does not exceed the upper limit value. In other words, the first PCS 32 does not perform MPPT control. In this embodiment, the first PCS 32 has such a function.

[0065] As described above, in this embodiment, a prediction model for predicting the power demand of the factory and a prediction model for predicting the power generated by the photovoltaic power generation device 30 are used. In order to create a prediction model, various data for a sufficient number of days are necessary. In the period until a prediction model with sufficient accuracy is created, a planned value of the output of the fuel cell device 40 can be calculated using the most recent data on the power demand and the most recent data on the output of the photovoltaic power generation device 30. Specifically, the difference between the most recent power demand and the most recent output of the photovoltaic power generation device 30 is calculated as the planned value of the output of the fuel cell device 40. The most recent power demand is, for example, the average value of the power demand in the most recent one hour. The most recent output of the photovoltaic power generation device 30 is, for example, the average value of the output of the photovoltaic power generation device 30 in the most recent one hour. The average value is, for example, a moving average value. The most recent power demand may be the minimum value of the power demand in the most recent time period (for example, the most recent one hour).

[0066] In this specification, a method of calculating a planned value of the output of the fuel cell device 40 using a prediction model is defined as a first planning method, and a method of calculating a planned value of the output of the fuel cell device 40 using only the most recent data is defined as a second planning method.

[0067] FIG. 3 is a flowchart showing a process of switching between the first planning method and the second planning method. Each process shown in FIG. 3 is executed by the control device 10. In step S1, the second planning method is executed. In step S2, it is determined whether a predetermined period has elapsed. After the predetermined period has elapsed, in step S3, the second planning method is switched to the first planning method. In step S4, the first planning method is executed. That is, after the second planning method is executed for a predetermined period, the second planning method is switched to the first planning method. The predetermined period is, for example, three months to one year.

[0068] Prior to the first planning method, the second planning method is executed to plan the output of the fuel cell device 40 so as to compensate for the difference between the actual value Rd of the power demand of the factory and the actual value Rpv of the output of the photovoltaic power generation device 30, thereby making it possible to collect learning data required for creating a prediction model. This makes it possible to improve the accuracy of prediction when the first planning method is executed. After the second planning method is executed for a predetermined period of time, the method for planning the output of the fuel cell device 40 is switched from the second planning method to the first planning method. In the first planning method, the predicted value Pd of the power demand of the factory is predicted based on the actual value Rd of the power demand of the factory. In the first planning method, the predicted value Ppv of the output of the photovoltaic power generation device 30 is predicted based on the actual value Rpv of the output of the photovoltaic power generation device 30.

[0069] According to the flowchart in Fig. 3, the switch from the second planning method to the first planning method is automatically performed after a predetermined period of time has elapsed. However, the switch from the second planning method to the first planning method may also be performed in response to an input from an administrator.

[0070] FIG. 4A is a graph showing the fluctuations of various powers and the storage rate in the power system of this embodiment when the first planning method is executed. FIG. 4B is a graph showing the fluctuations of various powers and the storage rate in the power system of this embodiment when the second planning method is executed. The horizontal axis of the graph represents the passage of time. The vertical axis on the left side of the upper graph represents power (kW). The vertical axis on the right side of the upper graph represents the storage rate (%). The vertical axis of the lower graph represents the purchased power from the commercial power source or the reverse flow power (kW) to the commercial power source. The power consumption W1 of the load 300 and the output of the photovoltaic power generation device 30 are data obtained from an actual facility. The output W3 of the fuel cell device 40, the charge / discharge power W4 of the storage device 50, the storage rate W5, and the reverse flow power W6 are data obtained by computer simulation.

[0071] The power consumption W1 of the load 300 is consistent between Figures 4A and 4B. In the examples of Figures 4A and 4B, no upper limit is set for the output W2 of the solar power generation device 30, so the output W2 of the solar power generation device 30 also is consistent between Figures 4A and 4B.

[0072] As shown in Fig. 4B, when the output of the fuel cell device 40 was planned based on the most recent data without prediction using a prediction model, the output W3 of the fuel cell device 40 fluctuated greatly over time. The storage rate W5 of the power storage device 50 was between 80% and 100% in most time periods. Therefore, during the period when the output W2 of the solar power generation device 30 exceeded the power consumption W1 (particularly from June 13th to June 14th), the charging power W4 of the power storage device 50 could not be increased. In the example of Fig. 4B, reverse flow power W6 occurred.

[0073] In contrast, as shown in FIG. 4A, when prediction was performed using a prediction model, the output W3 of the fuel cell device 40 fluctuated little over time. For example, the output W3 was maintained at approximately 100 kW from June 11 to June 12. Since the output fluctuation of the fuel cell device 40 was small, damage to the stack was suppressed. This leads to a longer life of the fuel cell device 40. The storage rate W5 of the power storage device 50 was maintained between 20% and 80% throughout the entire range. Therefore, during the period when the output W2 of the photovoltaic power generation device 30 exceeded the power consumption W1 (particularly from June 13 to June 14), the charging power W4 of the power storage device 50 was increased and the power storage device 50 absorbed the surplus power. In the example of FIG. 4A, no reverse power flow occurred.

[0074] (Other embodiments) (Additional Note) The above description of the embodiments discloses the following techniques.

[0075] (Technology 1) A control method comprising a step of executing a first planning method for planning an output of a fuel cell device so as to compensate for a difference between a predicted value of a power demand of an electric power consumer and a predicted value of an output of a photovoltaic power generation device. With this configuration, it is possible to suppress deterioration of the fuel cell device.

[0076] (Technology 2) The control method according to the first aspect corrects a planned value of the output of the fuel cell device based on a storage rate of the power storage device predicted from a difference between an actual value of the power demand of the power consumer and a sum of an actual value of the output of the photovoltaic power generation device and a planned value of the output of the fuel cell device. With this configuration, it is possible to create a command value for the output of the fuel cell device while taking into account the storage rate of the power storage device, and thus to maintain the power storage device in a state in which it can be charged and discharged.

[0077] (Technology 3) The control method according to the first or second aspect of the present invention sets an upper limit of the output of the solar power generation device to a value equal to or less than the difference between the sum of the actual value of the demand power of the power consumer and the chargeable power of the power storage device, which is equal to or less than the maximum charging power of the power storage device, and the actual value of the output of the fuel cell device. With this configuration, it becomes possible to use the generated power of the solar power generation device without waste.

[0078] (Technology 4) The control method according to the first aspect controls the power storage device so as to compensate for a difference between an actual value of the power demand of the power consumer and a sum of an actual value of the output of the photovoltaic power generation device and an actual value of the output of the fuel cell device by charging or discharging the power storage device. With this configuration, it is possible to balance supply and demand in the power system, and therefore it is possible to avoid purchasing power from and selling power to commercial power sources as much as possible.

[0079] (Technology 5) The control method described in Technology 1, wherein the predicted value of the power demand of the power consumer is a value predicted based on a correlation between actual weather conditions in the area where the power consumer is located and the actual value of the power demand of the power consumer, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the power demand of the power consumer in a most recent first period and the predicted value of the power demand of the power consumer in a second period corresponding to the first period.

[0080] (Technology 6) The power consumer includes a factory, and the predicted value of the power demand of the power consumer is a value predicted based on a correlation between the operation record of the factory, the weather record, and the actual value of the power demand of the power consumer, the weather forecast, and the operation plan of the factory, and corrected based on the actual value of the power demand of the power consumer in the first period and the predicted value of the power demand of the power consumer in the second period.

[0081] (Technology 7) The control method described in Technology 1, wherein the predicted value of the output of the solar power generation device is a value predicted based on a correlation between actual weather conditions in the area where the power consumer is located and the actual value of the output of the solar power generation device, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the output of the solar power generation device in a most recent third period and the predicted value of the output of the solar power generation device in a fourth period corresponding to the third period.

[0082] (Technology 8) The control method according to the first aspect of the present invention further includes a step of executing a second planning method before the first planning method, which plans the output of the fuel cell device so as to compensate for the difference between the actual value of the power demand of the power consumer and the actual value of the output of the photovoltaic power generation device, and a step of switching to the first planning method after executing the second planning method for a predetermined period of time, in which the predicted value of the power demand of the power consumer is predicted based on the actual value of the power demand of the power consumer, and the predicted value of the output of the photovoltaic power generation device is predicted based on the actual value of the output of the photovoltaic power generation device. With this configuration, it is possible to collect learning data necessary for creating a prediction model. This makes it possible to improve the accuracy of prediction when the first planning method is executed.

[0083] (Technology 9) A control device comprising: a memory that stores a predicted value of power demand of an electric power consumer and a predicted value of output of a solar power generation device; and a planner that executes a planning method for planning the output of a fuel cell device so as to compensate for the difference between the predicted value of power demand of the electric power consumer and the predicted value of output of the solar power generation device.

[0084] (Technology 10) A power system comprising a solar power generation device, a fuel cell device, and a control device according to a 9th aspect of the present invention. [Industrial Applicability]

[0085] The technology disclosed herein is useful for power systems including photovoltaic power generation devices and fuel cell devices, and is particularly useful for power systems that are distributed power sources. [Explanation of symbols]

[0086] 10 Control device 12 Prediction Department 14 Planning Department 16 Control section 20 Database 21 Terminals 22 Weather Information Services 30. Solar power generation equipment 31 Solar Cell Module 32 1st PCS 33 1st wattmeter 34 First control device 40 Fuel cell equipment 41 Fuel Cell Unit 42 2nd PCS 43 Second wattmeter 44 Second control device 50 Energy storage device 51 Storage battery 52 3rd PCS 53 Third wattmeter 54 Third control device 100 Commercial power 102 Power Lines 200 Power Systems 300 Load 302 4th wattmeter

Claims

1. A method for planning the output of a fuel cell device so as to compensate for the difference between a predicted value of the power demand of an electric power consumer and a predicted value of the output of a photovoltaic power generation device, comprising: A control method that corrects the planned value of the output of the fuel cell device based on the storage rate of the storage device predicted from the difference between the actual value of the power demand of the power consumer and the sum of the actual value of the output of the solar power generation device and the planned value of the output of the fuel cell device.

2. A method for planning the output of a fuel cell device so as to compensate for the difference between a predicted value of the power demand of an electricity consumer and a predicted value of the output of a solar power generation device, comprising: A control method in which an upper limit value for the output of the solar power generation device is set to a value less than the difference between the actual value of the power demand of the power consumer and the sum of the chargeable power of the storage device that is less than the maximum charging power of the storage device, and the actual value of the output of the fuel cell device.

3. A method for planning the output of a fuel cell device so as to compensate for the difference between a predicted value of the power demand of an electricity consumer and a predicted value of the output of a solar power generation device, comprising: A control method in which the predicted value of the power demand of the power consumer is a value predicted based on a correlation between actual weather conditions in the area where the power consumer is located and the actual value of the power demand of the power consumer, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the power demand of the power consumer for the most recent first period and the predicted value of the power demand of the power consumer for a second period corresponding to the first period.

4. The electricity consumer includes a factory.

4. The control method according to claim 3, wherein the predicted value of the power demand of the power consumer is a value predicted based on a correlation between the operation record of the factory, the weather record, and the actual value of the power demand of the power consumer, the weather forecast, and the operation plan of the factory, and is corrected based on the actual value of the power demand of the power consumer for the first period and the predicted value of the power demand of the power consumer for the second period.

5. A method for planning the output of a fuel cell device so as to compensate for the difference between a predicted value of the power demand of an electricity consumer and a predicted value of the output of a solar power generation device, comprising: A control method in which the predicted value of the output of the solar power generation device is a value predicted based on a correlation between actual weather conditions in the area where the power consumer is located and the actual value of the output of the solar power generation device, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the output of the solar power generation device for the most recent third period and the predicted value of the output of the solar power generation device for a fourth period corresponding to the third period.

6. A step of executing a first planning method for planning the output of a fuel cell device so as to compensate for the difference between a predicted value of the power demand of an electricity consumer and a predicted value of the output of a solar power generation device; a step of executing, before the first planning method, a second planning method for planning the output of the fuel cell device so as to compensate for a difference between an actual value of the power demand of the power consumer and an actual value of the output of the solar power generation device; executing the second planning method for a predetermined period of time and then switching to the first planning method; Equipped with The predicted value of the power demand of the power consumer is predicted based on an actual value of the power demand of the power consumer, A control method, wherein the predicted value of the output of the photovoltaic power generation device is predicted based on an actual value of the output of the photovoltaic power generation device.

7. A storage device that stores a predicted value of the power demand of the power consumer and a predicted value of the output of the solar power generation device; a planner that executes a planning method for planning the output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of the power consumer and a predicted value of output of the solar power generation device; Equipped with A control device that corrects the planned value of the output of the fuel cell device based on the storage rate of the storage device predicted from the difference between the actual value of the power demand of the power consumer and the sum of the actual value of the output of the solar power generation device and the planned value of the output of the fuel cell device.

8. A storage device that stores a predicted value of power demand of a power consumer and a predicted value of output of a solar power generation device; a planner that executes a planning method for planning the output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of the power consumer and a predicted value of output of the solar power generation device; Equipped with A control device that sets an upper limit value for the output of the solar power generation device to a value that is less than or equal to the difference between the actual value of the power demand of the power consumer and the sum of the chargeable power of the storage device that is less than the maximum charging power of the storage device, and the actual value of the output of the fuel cell device.

9. A storage device that stores a predicted value of the power demand of the power consumer and a predicted value of the output of the solar power generation device; a planner that executes a planning method for planning the output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of the power consumer and a predicted value of output of the solar power generation device; Equipped with A control device wherein the predicted value of the power demand of the power consumer is a value predicted based on the correlation between actual weather conditions in the area where the power consumer is located and the actual value of the power demand of the power consumer, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the power demand of the power consumer for the most recent first period and the predicted value of the power demand of the power consumer for a second period corresponding to the first period.

10. A storage device that stores a predicted value of power demand of a power consumer and a predicted value of output of a solar power generation device; a planner that executes a planning method for planning the output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of the power consumer and a predicted value of output of the solar power generation device; Equipped with A control device in which the predicted value of the output of the solar power generation device is a value predicted based on the correlation between actual weather conditions in the area where the power consumer is located and the actual value of the output of the solar power generation device, and a weather forecast for the area where the power consumer is located, and is corrected based on the actual value of the output of the solar power generation device for the most recent third period and the predicted value of the output of the solar power generation device for a fourth period corresponding to the third period.

11. A storage device that stores a predicted value of power demand of a power consumer and a predicted value of output of a photovoltaic power generation device; a planner that executes a first planning method for planning an output of a fuel cell device so as to compensate for a difference between a predicted value of power demand of the power consumer and a predicted value of output of the solar power generation device; Equipped with before the first planning method, a second planning method is executed to plan the output of the fuel cell device so as to compensate for a difference between an actual value of the power demand of the power consumer and an actual value of the output of the solar power generation device; executing the second planning method for a predetermined period of time, and then switching to the first planning method; The predicted value of the power demand of the power consumer is predicted based on an actual value of the power demand of the power consumer, A control device wherein the predicted value of the output of the solar power generation device is predicted based on an actual value of the output of the solar power generation device.

12. A solar power generation device; a fuel cell device; A control device according to any one of claims 7 to 11; A power system comprising:

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