Control method, control device, and power system

Abstract

A control method according to the present disclosure includes 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 side's power demand and a predicted value of an output of a solar power generation device. This control device is provided with: a memory that stores a predicted value for the power demand of a power demand side and a predicted value for the output of a solar power generation device; and a planner that executes a planning method that plans an output of a fuel cell device to compensate for a difference between a predicted value of a demand power of the power demander and a predicted value of an output of the solar power generation device.

Description

Technical Field

[0001] This disclosure relates to control methods, control devices, and power systems. Background Technology

[0002] Patent Document 1 describes a household distributed power supply device comprising a solar cell, a fuel cell, and an energy storage device. In the household distributed power supply device described in Patent Document 1, the control signal required for the output of the fuel cell is calculated as the difference between the load current and the output current of the solar cell. The energy storage device supplies the power shortage caused by the delay in the increased output of the fuel cell.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2004-208436 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] In existing technologies, the output of fuel cells is prone to fluctuations, and the degradation of fuel cells is easily accelerated.

[0008] Technical solutions for solving the problem

[0009] This disclosure provides a control method including the step of performing a first planning method, the first planning method being a method for planning the output of a fuel cell device to compensate for the difference between a predicted value of the electricity demand of an electricity demander (user) and a predicted value of the output of a solar power generation device.

[0010] Invention Effects

[0011] According to this disclosure, it is possible to suppress the deterioration of fuel cell devices. Attached Figure Description

[0012] Figure 1 This is a configuration diagram of the power system involved in the embodiments of this disclosure.

[0013] Figure 2 yes Figure 1 The diagram shows the functional block diagram of the control device and the database.

[0014] Figure 3 This is a flowchart illustrating the process of switching between the first planning method and the second planning method.

[0015] Figure 4A It is a graph showing the changes in various electrical powers and energy storage rates in the power system of this embodiment when the first planning method is implemented.

[0016] Figure 4B It is a graph showing the changes in various types of electricity and energy storage rate in the power system of this embodiment when the second planning method is implemented. Detailed Implementation

[0017] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. The present disclosure is not limited to the following embodiments.

[0018] (Implementation Method)

[0019] Figure 1 This is a configuration diagram of an electric power system according to an embodiment of the present disclosure. The electric power system 200 includes a control device 10, a database 20, a solar power generation device 30, a fuel cell device 40, and an energy storage device 50. The solar power generation device 30, the fuel cell device 40, and the energy storage device 50 are connected to a load 300 via a power line 102. Electricity 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 needed, electricity generated by the solar power generation device 30 and the fuel cell device 40 is stored in the energy storage device 50, and electricity is supplied from the energy storage device 50 to the load 300.

[0020] A commercial power source 100 is connected to power line 102. The power system 200 can also be interconnected with the commercial power source 100. It can either purchase electricity from the commercial power source 100 or sell surplus electricity back to the commercial power source 100 (counter-current flow). However, according to this embodiment, the power system 200 is unlikely to experience power shortages or surplus electricity.

[0021] The solar power generation device 30 includes a solar cell module 31, a first PCS (Power Conditioning Subsystem) 32, a first power meter (wattmeter, wattmeter) 33, and a first control device 34. The solar power generation device 30 may also include multiple solar cell modules 31. Each solar cell module 31 may consist of multiple solar panels. The first PCS 32 converts the power generated by the solar cell module 31 from direct current (DC) to alternating current (AC) and outputs it to the power line 102. The first power meter 33 detects the power output from the first PCS 32, which is the power generated by the solar power generation device 30. The first control device 34 is communicatively connected to the control device 10. The first control device 34 controls either the solar cell module 31 or the first PCS 32 according to instructions received from the control device 10. The first control device 34 may be a computer equipped with a storage device containing programs required for controlling the solar power generation device 30 and a processor that reads and executes the programs from the storage device.

[0022] 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 also have multiple fuel cell units 41 capable of operating independently. The power output of the fuel cell device 40 can be varied by adjusting the number of operating fuel cell units 41 and / or their output. The second PCS 42 converts the power output from the fuel cell unit 41 from direct current (DC) to alternating current (AC) and outputs it to the power line 102. The second power meter 43 detects the power output from the second PCS 42, which is the power output of the fuel cell device 40. The second control device 44 is communicatively connected to the control device 10. The second control device 44 controls either 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 equipped with a storage device containing programs required for controlling the fuel cell device 40 and a processor that reads and executes the programs from the storage device.

[0023] Examples of fuel cells used in fuel cell device 40 include solid polymer fuel cells, solid oxide fuel cells, phosphoric acid fuel cells, and molten carbonate fuel cells.

[0024] The energy storage device 50 includes a battery 51, a third power meter 52, a third power meter 53, and a third control device 54. The battery 51 may be a secondary battery. The energy storage device 50 may also have a capacitor instead of the battery 51. The third power meter 52 is a bidirectional power meter. When storing power in the battery 51, the third power meter 52 converts AC power to DC power. When discharging power from the battery 51, the third power meter 52 converts DC power to AC power. The third power meter 53 detects the power output from the third power meter 52 and the power input to the third power meter 52. That is, the third power meter 53 detects the charging power and discharging power of the energy storage device 50. The third control device 54 is communicatively connected to the control device 10. The third control device 54 controls either the battery 51 or the third power meter 52 according to instructions received from the control device 10. The third control device 54 may be a computer having a storage device that stores the program required for controlling the energy storage device 50 and a processor that reads from the storage device and executes the program.

[0025] The third control device 54 sends the state of charge (SOC) of the energy storage device 50 to the control device 10 sequentially. The state of charge of the energy storage device 50 is stored in the database 20 in a time-series format.

[0026] Load 300 represents a power demander such as a factory. The fourth power meter 302, for example, detects the factory's power consumption (power consumption). The fourth power meter 302 can also be part of the power system 200.

[0027] Control device 10 issues control instructions to the first control device 34, the second control device 44, and the third control device 54 respectively, controlling the operation of the solar power generation device 30, the fuel cell device 40, and the energy storage device 50. Specifically, it sends information required for each control action (operation) to the first control device 34, the second control device 44, and the third control device 54 respectively. Control device 10 is a computer equipped with a storage device storing the programs required for controlling these devices and a processor that reads and executes the programs from the storage device. Control device 10 obtains 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 (e.g., 30-second period) and sends them to database 20. The power generated by the solar power generation device 30, the power generated by the fuel cell device 40, the charging power of the energy storage device 50, the discharging power of the energy storage device 50, and the power consumed by the load 300 can be determined from these detection values.

[0028] Database 20, comprised of a server computer, is communicatively connected to control device 10. Database 20 stores data in time-series format, including the power generated by solar power generation device 30, the power generated by fuel cell device 40, the charging power of energy storage device 50, the discharging power of energy storage device 50, the power consumed by load 300, and the energy storage rate of energy storage device 50. The energy (kWh) can be calculated based on the time-series data.

[0029] Control device 10 can be a cloud computer or an edge computer. Database 20 can be a database within a cloud server or a database within an edge server. The cloud computer and cloud server can be configured in a location away from where the solar power generation device 30, fuel cell device 40, and energy storage device 50 are installed. The cloud computer, acting as 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 edge server can be configured near where the solar power generation device 30, fuel cell device 40, and energy storage device 50 are installed.

[0030] Figure 2 yes Figure 1 The diagram shows the functional block diagram of the control unit 10 and the database 20. The database 20 stores plant operation data, weather data, electricity data, and energy storage data. The control unit 10 uses machine learning to create predictive models using the various data stored in the database 20, and uses these predictive models to create operating instructions sent to the fuel cell unit 40. The predictive models include models for predicting the plant's electricity demand and models for predicting the electricity generated by the solar power generation unit 30.

[0031] In database 20, the factory's operational data is input by the manager from terminal 21. This operational data is a combination of data indicating whether the factory is operating and date data. The data indicating whether the factory is operating indicates whether it is a rest day or a running day. Based on the factory's operational data, it is possible to determine whether a specific day in the past was a rest day or a running day. There is a correlation between the factory's electricity consumption and whether it is operating. The factory consumes more electricity on running days and less on rest days. Therefore, the factory's operational data can be used as training data when creating a predictive model to forecast the factory's electricity demand.

[0032] At a load of 300 ( Figure 1 Even for facilities outside of factories, there is a correlation between electricity consumption and operation. Examples of facilities outside of factories include commercial facilities, hospitals, schools, and office buildings. For instance, if a department store or other commercial facility has a load of 300, the combination of data representing any of the following—"rest days," "business days and weekdays," and "business days and holidays"—with date data can constitute operational data. Commercial facilities consume more electricity on business days and less on rest days. Even within business days, there are differences in electricity consumption between weekdays and holidays.

[0033] In database 20, weather data is input from weather information service 22 via communication networks such as the Internet. Weather data can also be input by administrators from terminal 21. Weather data is a combination of data representing weather conditions and date data. Data representing weather conditions is, for example, a combination of time period and sunshine intensity. Based on the weather data, the sunshine intensity for each past time period can be determined. Sunshine intensity is represented by multiple intensity levels, for example, from the intensity corresponding to sunny days to the intensity corresponding to rainy days. There is a correlation between sunshine intensity and the power generation of solar power generation device 30. If there is sunshine, the power generation (kW) is high, and if there is no sunshine, the power generation is low. Therefore, weather data representing sunshine intensity can be used as learning data when creating a predictive model for predicting the power generation of solar power generation device 30. Alternatively, the sunshine duration for each time period can be included in the weather data, either in place of sunshine intensity or together with sunshine intensity. There is a correlation between sunshine duration and the power generation (kWh) of solar power generation device 30.

[0034] Weather data can include combinations of time of day and temperature. For example, high temperatures increase air conditioning power consumption, thus increasing factory power consumption. Similarly, low temperatures also increase air conditioning power consumption, leading to increased factory power consumption. Therefore, weather data representing temperature can be used as training data when creating predictive models to forecast factory power demand.

[0035] The data representing weather information is not limited to the data mentioned above. The data representing weather information may also include other data related to the electricity generated by the solar power generation device 30, and may also include other data related to electricity demand.

[0036] In database 20, the power data is input from each power meter 33, 43, 53, 302 via control device 10. The power data can be time-series data of the power generated by solar power generation device 30, the power generated by fuel cell device 40, the charging power of energy storage device 50, the discharging power of energy storage device 50, and the power consumed by load 300. Based on the power data, the power generated by solar power generation device 30, the power generated by fuel cell device 40, the charging power of energy storage device 50, the discharging power of energy storage device 50, and the power consumed by load 300 at a specific time in the past can be determined. The power data representing the power consumed by load 300 can be used as training data when creating a predictive model for predicting the power demand of the plant. The power generation data of solar power generation device 30 can be used as training data when creating a predictive model for predicting the power generation of solar power generation device 30.

[0037] In database 20, the energy storage data is data input from the energy storage device 50 via control device 10. The energy storage data represents the energy storage rate of the energy storage device 50.

[0038] The control device 10 includes a prediction unit 12, a planning unit 14, and a control unit 16.

[0039] The forecasting unit 12 includes a power demand forecasting unit 12a and a solar power generation forecasting unit 12b. In the power demand forecasting unit 12a, the predicted power demand is based on the correlation between actual weather conditions (historical weather conditions) and actual power demand values ​​(historical actual values), as well as weather forecasts. Additionally, in the power demand forecasting unit 12a, the predicted power demand is based on the factory's operating performance (actual production operation), the correlation between actual weather conditions and the factory's actual power demand values, weather forecasts, and the factory's operating plan. In the solar power generation forecasting unit 12b, the predicted output of the solar power generation device 30 is based on the correlation between actual weather conditions and the actual output of the solar power generation device 30, as well as weather forecasts.

[0040] The demand power forecasting unit 12a may be a program module that includes a forecasting model for predicting the power demand of load 300. The forecasting model in the demand power forecasting unit 12a is created using actual power demand data, weather data, and plant operating data. As actual power demand data, power data representing the power consumption of load 300 is used. As weather data, weather data is used. The weather data is the weather data of the region where the power demander, such as the plant, is located. As plant operating data, plant operating data is used. The forecasting model is, for example, a regression model. The power data, weather data, and plant operating data are supervised (labeled) data in machine learning.

[0041] By inputting weather forecasts and factory operating plans into the forecasting model in the electricity demand forecasting unit 12a, the predicted value of the factory's electricity demand (electricity consumption) can be obtained. In other words, by using weather forecasts and factory operating plans as explanatory variables, the predicted value of the factory's electricity demand, which is the target variable, can be obtained. For example, the weather forecast can use temperature data for the time or period when the predicted electricity demand should be received. The weather forecast may also include data indicating the intensity or duration of sunshine. The weather forecast is for the region where the electricity-consuming party, such as the factory, is located. The factory operating plan can use data indicating whether the factory is operating on the date and time when the predicted electricity demand should be received.

[0042] The solar power generation prediction unit 12b may be a program module that includes a prediction model for predicting the electricity generated by the solar power generation device 30. The prediction model in the solar power generation prediction unit 12b is created using actual power generation data of the solar power generation device 30 and actual weather data. As actual power generation data of the solar power generation device 30, electricity data representing the electricity generated by the solar power generation device 30 can be used. As actual weather data, weather data can be used. The prediction model is, for example, a regression model. The electricity data and weather data are supervised data in machine learning.

[0043] By inputting a weather forecast into the prediction model in the solar power generation prediction unit 12b, a predicted value for the output (electric power generated) of the solar power generation device 30 can be obtained. In other words, the weather forecast is used as an explanatory variable to obtain a predicted value for the output of the solar power generation device 30, which is the target variable. For example, the weather forecast can use data representing the intensity or duration of sunshine at the time or period when the predicted value of the output of the solar power generation device 30 should be obtained.

[0044] To create the various forecasting models described above in forecasting unit 12, various data over a predetermined period (e.g., 3 months to 1 year) can be used. The forecasting models can also be updated daily. Therefore, improved forecasting accuracy can be expected. The forecasting models can also be stored in database 20.

[0045] The process of obtaining predicted values ​​by the prediction unit 12 is performed at a predetermined cycle. For example, multiple predicted values ​​from the present to 24 hours later are obtained at a cycle of once per hour. The multiple predicted values ​​are, for example, predicted values ​​at 24 points every hour.

[0046] The predicted values ​​can also be revised. For example, the predicted power demand can be revised using the following method: The predicted values ​​obtained from the prediction model can also be revised based on the actual power demand value of the most recent period 1 and the predicted power demand value of the corresponding period 2. The most recent period 1 is the period from the current time to a predetermined time (30 minutes to 1 hour) prior to the current time. For example, if the current time is 10:00, the most recent period 1 is the period from 9:00 to 10:00. The actual power demand value during this period is stored in database 20 as the power consumption of load 300 in a time series manner. The actual power demand value of period 1 can be the average value within period 1. Next, the period corresponding to period 1 refers to the period within 24 hours from which the predicted values ​​of 24 points are obtained, corresponding to period 1. For example, if period 1 is from 9:00 to 10:00, the corresponding period 2 is also from 9:00 to 10:00.

[0047] For example, suppose the actual electricity demand for the period from 9:00 to 10:00 in period 1 is 100 kW. On the other hand, suppose the predicted electricity demand for the period from 9:00 to 10:00 in period 2 is 90 kW. In this case, the actual value exceeds the predicted value by 10 kW. Therefore, a correction of 10 kW is made to the predicted electricity demand for the period from 10:00 to 11:00. This improves the accuracy of the prediction. Furthermore, the difference between the actual and predicted values ​​can be used for correction as is, or a predetermined percentage (e.g., 80%) of the difference can be used. The degree of correction is arbitrary. Additionally, correction can be made only for the most recently used prediction among the predictions for the next 24 points, or correction can be made for multiple predictions including the most recently used prediction.

[0048] The same method can also be used to correct the predicted power output of the solar power generation device 30. That is, the predicted value obtained from the prediction model can be corrected based on the actual output value of the solar power generation device 30 in the most recent third period and the predicted output value of the solar power generation device 30 in the fourth period corresponding to the third period. This improves prediction accuracy. The third period can be the same as the first period or a different period. The fourth period can be the same as the second period or a different period.

[0049] The predicted values ​​obtained by the prediction unit 12 are stored in the database 20. With this configuration, prediction processing to obtain the predicted values ​​and planning processing to obtain the planned values ​​can be performed asynchronously. The server that functions as the database 20 is an example of a memory that stores predicted values ​​for electricity demand and predicted values ​​for the output of the solar power generation device 30.

[0050] The planning unit 14 includes a fuel cell output planning unit 14a. The fuel cell output planning unit 14a can be a program module for calculating the planned value of the output (generated electricity) of the fuel cell unit 40. The fuel cell output planning unit 14a obtains the predicted power demand value Pd and the predicted output value Ppv of the solar power generation device 30 from the prediction unit 12, and uses these predicted values ​​to calculate the planned value of the output of the fuel cell unit 40. For example, at the current time of 8:50, the prediction unit 12 creates the predicted power demand value for the period from 9:00 to 10:00 and the predicted output value of the solar power generation device 30, and calculates the planned value of the output of the fuel cell unit 40 based on these predicted values. The predicted and planned values ​​can be expressed in instantaneous power (kW) or in electrical energy per unit time (kWh).

[0051] In this embodiment, the planning unit 14 plans the output of the fuel cell device 40 to compensate for the difference between the predicted power demand value Pd and the predicted output value Ppv of the solar power generation device 30. By using the predicted value to plan the output of the fuel cell device 40, fluctuations in the output of the fuel cell device 40 can be suppressed compared to planning the output of the fuel cell device 40 based solely on the most recent performance value. Since there is a correlation between the lifespan of the fuel cell device 40 stack and the frequency of fluctuations in the output of the fuel cell device 40, the lifespan of the fuel cell device 40 can be extended by suppressing fluctuations in the output of the fuel cell device 40.

[0052] The planned output value Pfc of the fuel cell device 40 is calculated, for example, using the difference (Pd - Ppv) between the predicted electricity demand value Pd and the predicted output value Ppv of the solar power generation device 30. Therefore, at the current time of 8:50, the planned output value Pfc of the fuel cell device 40 during the period from 9:00 to 10:00 can be (Pd - Ppv). When the difference is less than or equal to zero, i.e., when (Pd - Ppv) ≤ 0, the planned output value Pfc of the fuel cell device 40 is zero.

[0053] According to the method of this embodiment, the predicted value of the electricity demand Pd and the predicted value of the output of the solar power generation device 30 Ppv are calculated. Therefore, even if a method that determines the output of the fuel cell device 40 based on recent data would cause the output of the fuel cell device 40 to change, in the method of this embodiment, it is sometimes determined that it is better to deliberately prevent the output of the fuel cell device 40 from changing. This difference is due to the poor followability of the output of the fuel cell device 40.

[0054] The processing of planned values ​​obtained by the planning department 14 is carried out on a predetermined cycle. For example, the planned values ​​required for operation in the most recent time period are obtained on an hourly basis.

[0055] Planning Unit 14 is an example of a planner that executes a planning method to compensate for the difference between the predicted value Pd of the power demand and the predicted value Ppv of the output of the planned fuel cell device 40.

[0056] In this specification, "compensation difference" means both the total compensation difference and the partial compensation difference. In the example above, the difference between the predicted power demand Pd and the predicted output Ppv of the solar power generation device 30 is considered as the planned output Pfc of the fuel cell device 40, and therefore the entire difference is compensated using the output of the fuel cell device 40.

[0057] The control unit 16 includes a fuel cell correction control unit 16a, an energy storage control unit 16b, and a solar cell output suppression unit 16c.

[0058] In the control unit 16, the fuel cell correction control unit 16a can be a program module for determining whether the planned value Pfc of the output of the fuel cell device 40 needs to be corrected and correcting the planned value Pfc as needed. The fuel cell correction control unit 16a obtains the planned value Pfc of the output of the fuel cell device 40 from the planning unit 14, and obtains the actual value Rd of the demanded power, the actual value Rpv of the output of the solar power generation device 30, and the storage rate (SOC) of the energy storage device 50 from the database 20. The actual value Rd of the demanded power is the power consumed by the load 300. In detail, the average value of the power consumed over a predetermined period can be used as the actual value Rd of the demanded power. As the actual value Rpv of the output of the solar power generation device 30, the average value of the generated power of the solar power generation device 30 over a predetermined period can be used. The average value is, for example, a moving average. The predetermined period is, for example, the period from 30 minutes ago to the time when the most recent data was obtained (approximately the current time). The storage rate of the energy storage device 50 is the storage rate of the energy storage device 50 at the time when the most recent data was obtained (approximately the current time).

[0059] For the power system 200, it is desirable to avoid purchasing electricity from and selling electricity to the commercial power source 100 (counter-current flow) as much as possible, and it is desirable that the energy storage device 50 can be charged and discharged normally (at any time). If the energy storage device 50 is in a fully charged or fully discharged state, it is difficult to achieve a supply and demand balance in the power system 200 through the energy storage device 50. In this embodiment, the energy storage rate of the energy storage device 50 is taken into account, and the planned value of the output of the fuel cell device 40 is corrected in the fuel cell correction control unit 16a to prevent the energy storage rate of the energy storage device 50 from becoming fully charged or fully discharged.

[0060] The fuel cell correction control unit 16a corrects the planned output value Pfc of the fuel cell device 40 based on the difference between the sum of the actual power demand value Rd, the actual output value Rpv of the solar power generation device 30, and the planned output value Pfc of the fuel cell device 40. This allows the creation of a command value for the output of the fuel cell device 40 while taking into account the energy storage rate of the fuel cell device 50, thereby maintaining the fuel cell device 50 in a state capable of charging and discharging.

[0061] Specifically, the value (Rd-Rpv-Pfc) is calculated by subtracting the actual output value Rpv of the solar power generation device 30 and the planned output value Pfc of the fuel cell device 40 from the actual power demand value Rd. This value can be zero, negative, or positive.

[0062] When the aforementioned value (Rd-Rpv-Pfc) is negative, it can be said that the probability of future power surplus is high when making predictions based on the most recent performance value. Therefore, the energy storage rate is predicted based on the most recent energy storage rate, after a predetermined time (e.g., 1 hour) when the energy storage device 50 is charged with power equivalent to the absolute value of (Rd-Rpv-Pfc). The energy storage rate after the predetermined time is calculated based on the most recent energy storage rate of the energy storage device 50, the capacity of the energy storage device 50, and the charging power of the energy storage device 50. If the energy storage rate after the predetermined time exceeds a predetermined upper limit value that is less than the full charge state (100%), the planned output value Pfc of the fuel cell device 40 is corrected in a way that the energy storage rate after the predetermined time is below the upper limit value. For example, the planned output value Pfc of the fuel cell device 40 is corrected by subtracting a correction value from the planned output value Pfc of the fuel cell device 40 or by multiplying the planned output value Pfc of the fuel cell device 40 by a correction value less than 1. Therefore, the fuel cell unit 40 will operate at a lower output than the planned value Pfc. The upper limit of the energy storage rate is set, for example, to 80% of the capacity of the energy storage unit 50.

[0063] When the aforementioned value (Rd-Rpv-Pfc) is positive, it can be said that the probability of future power shortage is high when making predictions based on recent performance values. Therefore, the energy storage rate is predicted based on the recent energy storage rate, after a predetermined time (e.g., 1 hour) when the energy storage device 50 discharges power equivalent to the value (Rd-Rpv-Pfc). If the energy storage rate after the predetermined time is lower than a predetermined lower limit greater than the fully discharged state (0%), the planned output value Pfc of the fuel cell device 40 is corrected in a way that the energy storage rate after the predetermined time is above the lower limit. For example, the planned output value Pfc of the fuel cell device 40 is corrected by adding a correction value or multiplying it by a correction value greater than 1. As a result, the fuel cell device 40 will operate with an output greater than the planned value Pfc. The lower limit of the energy storage rate is, for example, set to 20% of the capacity of the energy storage device 50.

[0064] When the aforementioned value (Rd-Rpv-Pfc) is zero, the planned value Pfc is not adjusted. Furthermore, when the energy storage rate after the predetermined time is above the lower limit and below the upper limit, the planned value Pfc is also not adjusted.

[0065] The fuel cell correction control unit 16a uses the planned value Pfc or the corrected planned value Pfc' to create an instruction value for the output of the fuel cell device 40. The instruction value is sent to the second control unit 44 of the fuel cell device 40. The second control unit 44 controls the operation of the fuel cell unit 41 to generate electricity according to the instruction value.

[0066] The process of correcting the planned value Pfc of the fuel cell device 40 output can be performed synchronously with the process of creating the planned value Pfc, or it can be performed with a shorter control cycle than the process of creating the planned value Pfc. The planned value Pfc can be corrected at the time when it is determined that the energy storage rate of the energy storage device 50 exceeds the upper limit value and at the time when it is determined that the energy storage rate of the energy storage device 50 is below the lower limit value.

[0067] In the control unit 16, the energy storage control unit 16b can be a program module for controlling the energy storage device 50 to regulate the charging and discharging power of the energy storage device 50. The energy storage control unit 16b obtains the actual value Rd of the required power, the actual value Rpv of the output of the solar power generation device 30, and the actual value Rfc of the output of the fuel cell device 40 from the database 20. The output of the fuel cell device 40 follows the command value, so the command value of the output of the fuel cell device 40 can also be regarded as the actual value Rfc of the output of the fuel cell device 40.

[0068] The energy storage control unit 16b controls the energy storage device 50 to compensate for the difference between the actual power demand value Rd and the sum of the actual output value Rpv of the solar power generation device 30 and the actual output value Rfc of the fuel cell device 40 through the charging or discharging of the energy storage device 50. With this configuration, supply and demand balance can be achieved in the power system 200, thus minimizing the purchase and sale of electricity from and to the commercial power source 100. This is advantageous from the perspective of increasing the utilization rate of renewable energy by avoiding the use of electricity from the commercial power source 100.

[0069] Specifically, the value (Rd - Rpv - Rfc) is calculated by subtracting the output values ​​Rpv of the solar power generation device 30 and Rfc of the fuel cell device 40 from the actual power demand value Rd. This value can be zero, negative, or positive. When (Rd - Rpv - Rfc) is negative, there is surplus power; therefore, an instruction value for charging power of the energy storage device 50 is created to charge the energy storage device 50 with the surplus power. When (Rd - Rpv - Rfc) is positive, there is insufficient power; therefore, an instruction value for discharging power of the energy storage device 50 is created to compensate for the insufficient power through discharging of the energy storage device 50. When (Rd - Rpv - Rfc) is zero, an instruction value for charging and discharging power of the energy storage device 50 is created to make the charging and discharging power zero. The actual power demand value Rd is the power consumed by the load 300. The most recent power consumption can be used as the actual power demand value Rd. The actual output values ​​Rpv of the solar power generation device 30 and Rfc of the fuel cell device 40 can be obtained from the most recent output. The most recent power consumption and the most recent output are values ​​contained in the power data stored in the database 20.

[0070] The command value for the charging and discharging power of the energy storage device 50 is sent from the energy storage control unit 16b to the third control device 54 of the energy storage device 50. The third control device 54 controls the operation of the energy storage device 50 so that it charges or discharges power according to the command value. By controlling the third PCS 52, the charging and discharging power of the energy storage device 50 is adjusted to the desired value.

[0071] In the control unit 16, the solar cell output suppression unit 16c can be a program module for suppressing the output of the solar power generation device 30. When the energy storage rate of the energy storage device 50 exceeds a first threshold, the solar cell output suppression unit 16c creates an instruction value for the solar power generation device 30 to suppress its output. When the energy storage rate of the energy storage device 50 is lower than a second threshold, the suppression of the solar power generation device 30's output is released. The first threshold for the energy storage rate is, for example, 90% of the capacity of the energy storage device 50. The second threshold for the energy storage rate is, for example, 85% of the capacity of the energy storage device 50. With this configuration, it is possible to prevent the energy storage device 50 from reaching a fully charged state, thus minimizing the possibility of selling electricity to the commercial power supply 100 (counter-current flow).

[0072] The process for suppressing the output of the solar power generation device 30 is described below. The solar cell output suppression unit 16c sets a value below the difference between the sum of the actual power demand value Rd and the rechargeable power Sc of the energy storage device 50 and the actual output value Rfc of the fuel cell device 40 as the upper limit value of the output of the solar power generation device 30. With this configuration, the generated power of the solar power generation device 30 can be used without any surplus.

[0073] Specifically, the value (Rd + Sc - Rfc) is calculated by subtracting the output value Rfc (kW) of the fuel cell device 40 from the sum of the actual power demand Rd (kW) and the rechargeable power Sc (kW) of the energy storage device 50. A value (Rd + Sc - Rfc) or less than (Rd + Sc - Rfc) is set as the upper limit of the output of the solar power generation device 30. The actual power demand Rd is, for example, the power consumed at any point in the past, which can be the average power demand over the most recent period (e.g., the most recent hour) or the minimum power demand over the most recent period. The rechargeable power Sc of the energy storage device 50 is the maximum charging power of the energy storage device 50 or any power below it. The maximum charging power of the energy storage device 50 is the design value of the energy storage device 50, determined according to the type of battery 51, etc. That is, the energy storage device 50 is not charged with power exceeding the maximum charging power. The output value Rfc of the fuel cell device 40 is, for example, the most recent output value of the fuel cell device 40.

[0074] When the output of the solar power generation device 30 is not suppressed, the first PCS 32 performs MPPT (Maximum Power Point Tracking) control to ensure that the maximum output is supplied from the solar power generation device 30 to the power line 102. Conversely, when an upper limit is set for the output of the solar power generation device 30, the first PCS 32 operates the solar power generation device 30 at an operating point on the IV curve that deviates from the optimal operating point, ensuring that the output of the solar power generation device 30 does not exceed the upper limit. In other words, MPPT control is not performed. In this embodiment, the first PCS 32 has this function.

[0075] As explained above, in this embodiment, a predictive model for forecasting the power demand of the plant and a predictive model for forecasting the power generation of the solar power generation unit 30 are used. To create the predictive models, a sufficient number of days of various data are required. During the period until a predictive model of sufficient accuracy is created, the planned value of the output of the fuel cell unit 40 can be calculated using the most recent power demand data and the most recent output data of the solar power generation unit 30. Specifically, the difference between the most recent power demand and the most recent output of the solar power generation unit 30 is calculated as the planned value of the output of the fuel cell unit 40. The most recent power demand is, for example, the average power demand over the most recent past hour. The most recent output of the solar power generation unit 30 is, for example, the average output of the solar power generation unit 30 over the most recent past hour. The average value is, for example, a moving average. The most recent power demand can also be the minimum power demand within the most recent time period (e.g., the most recent hour).

[0076] In this specification, the method of calculating the planned value of the output of the fuel cell device 40 using a predictive model is defined as the first planning method. The method of calculating the planned value of the output of the fuel cell device 40 using only the most recent data is defined as the second planning method.

[0077] Figure 3 This is a flowchart illustrating the process of switching between the first planning method and the second planning method. Figure 3 The processes shown are those 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, a switch is made from the second planning method to the first planning method. In step S4, the first planning method is executed. That is, after the second planning method has been executed for a predetermined period, a switch is made from the second planning method to the first planning method. The predetermined period is, for example, 3 months to 1 year.

[0078] By executing a second planning method prior to the first planning method, which plans the output of the fuel cell unit 40 to compensate for the difference between the actual power demand value Rd of the plant and the actual output value Rpv of the solar power generation unit 30, learning data required to create a prediction model can be collected. This improves the accuracy of predictions when executing the first planning method. After a predetermined period of execution of the second planning method, the method for planning the output of the fuel cell unit 40 switches from the second planning method to the first planning method. In the first planning method, the predicted power demand value Pd of the plant is predicted based on the actual power demand value Rd of the plant. In the first planning method, the predicted output value Ppv of the solar power generation unit 30 is predicted based on the actual output value Rpv of the solar power generation unit 30.

[0079] according to Figure 3 The flowchart automatically switches from the second planning method to the first planning method after a predetermined period. However, the switch can also be initiated based on manager input.

[0080] Figure 4A It is a graph showing the changes in various types of electricity and energy storage rate in the power system of this embodiment when the first planning method is implemented. Figure 4B This is a graph showing the changes in various types of electricity and energy storage rate in the power system of this embodiment when the second planning method was implemented. The horizontal axis of the graph represents the passage of time. The vertical axis on the left side of the upper graph represents electricity (power) (kW). The vertical axis on the right side of the upper graph represents energy storage rate (%). The vertical axis of the lower graph represents electricity purchased from commercial power sources or reverse flow electricity to commercial power sources (kW). The power consumption W1 of load 300 and the output of solar power generation device 30 are data obtained from actual existing facilities. The output W3 of fuel cell device 40, the charging and discharging power W4 of energy storage device 50, the energy storage rate W5, and the reverse flow electricity W6 are data obtained through computer simulation.

[0081] The power consumption W1 of a load of 300 is... Figure 4A and Figure 4B Consistent with the middle. In Figure 4A as well as Figure 4B In the example, no upper limit is set for the output W2 of the solar power generation device 30; therefore, the output W2 of the solar power generation device 30 is also... Figure 4A and Figure 4B Consistent with the middle.

[0082] like Figure 4BAs shown, without making predictions based on predictive models but planning the output of the fuel cell unit 40 based on recent data, the output W3 of the fuel cell unit 40 fluctuated significantly over time. The energy storage rate W5 of the energy storage device 50 remained between 80% and 100% for most of the period. Therefore, during periods when the output W2 of the solar power generation device 30 exceeded the power consumption W1 (especially June 13th-14th), it was impossible to increase the charging power W4 of the energy storage device 50. Figure 4B In the example, reverse current power W6 was generated.

[0083] In contrast, such as Figure 4A As shown, with predictions based on the predictive model, the output W3 of the fuel cell unit 40 varies little over time. For example, from June 11th to June 12th, the output W3 remained approximately 100kW. Because the output of the fuel cell unit 40 varies little, damage to the fuel cell stack can be suppressed. This contributes to the longevity of the fuel cell unit 40. The energy storage rate W5 of the energy storage device 50 remains between 20% and 80% throughout the region. Therefore, during periods when the output W2 of the solar power generation device 30 exceeds the power consumption W1 (especially from June 13th to June 14th), the charging power W4 of the energy storage device 50 is increased, and the energy storage device 50 absorbs the excess power. Figure 4A In the example, no reverse current power was generated.

[0084] (Other implementation methods)

[0085] (Postscript)

[0086] Based on the above description of the embodiments, the following technologies are disclosed.

[0087] (Technology 1)

[0088] A control method includes the step of executing a first planning method, said first planning method being a method of planning the output of a fuel cell device to compensate for the difference between a predicted value of the electricity demand from the electricity demander and a predicted value of the output of a solar power generation device. According to this configuration, the degradation of the fuel cell device can be suppressed.

[0089] (Technology 2)

[0090] According to the control method described in Technology 1, the planned output value of the fuel cell device is corrected based on the energy storage rate of the energy storage device, which is predicted by the difference between the actual power demand value of the power demander and the sum of the actual output value of the solar power generation device and the planned output value of the fuel cell device. With this configuration, the command value for the output of the fuel cell device can be created while taking into account the energy storage rate of the energy storage device, thereby maintaining the energy storage device in a state capable of charging and discharging.

[0091] (Technology 3)

[0092] According to the control method described in technique 1 or 2, the upper limit of the output of the solar power generation device is set as the difference between the actual value of the electricity demanded by the electricity demander and the sum of the rechargeable power of the energy storage device (below the maximum charging power of the energy storage device) and the actual value of the output of the fuel cell device. With this configuration, the generated electricity of the solar power generation device can be used without any surplus.

[0093] (Technology 4)

[0094] According to the control method described in Technique 1, the energy storage device is controlled to compensate for the difference between the actual power demand of the power demander and the sum of the actual output of the solar power generation device and the output of the fuel cell device through charging or discharging of the energy storage device. With this configuration, supply and demand balance can be achieved in the power system, thus minimizing the purchase and sale of electricity from and to commercial power sources.

[0095] (Technology 5)

[0096] According to the control method described in Technique 1, the predicted value of the electricity demand of the electricity demander is obtained by correcting the value based on the actual value of the electricity demand of the electricity demander in the most recent first period and the predicted value of the electricity demand of the electricity demander in the second period corresponding to the first period, based on the correlation between the actual weather conditions of the electricity demander's region and the actual value of the electricity demand of the electricity demander, as well as the value predicted by the weather forecast of the electricity demander's region.

[0097] (Technology 6)

[0098] According to the control method described in Technology 5, the electricity demander includes a factory, and the predicted value of the electricity demand of the electricity demander is a value obtained by correcting the value predicted based on the actual value of the electricity demand of the electricity demander in the first period and the predicted value of the electricity demand of the electricity demander in the second period, based on the correlation between the actual operating performance of the factory and the actual value of the electricity demand of the electricity demander, as well as the weather forecast and the operating plan of the factory.

[0099] (Technology 7)

[0100] According to the control method described in Technique 1, the predicted value of the output of the solar power generation device is obtained by correcting the value based on the actual output value of the solar power generation device in the most recent third period and the predicted value of the output of the solar power generation device in the fourth period corresponding to the third period, based on the correlation between the actual weather conditions in the region where the electricity demander is located and the actual output value of the solar power generation device, as well as the value predicted by the weather forecast in the region where the electricity demander is located.

[0101] (Technology 8)

[0102] The control method according to technology 1 further includes: a step of performing a second planning method before the first planning method, the second planning method being a method of planning the output of the fuel cell device to compensate for the difference between the actual power demand of the power demander and the actual output of the solar power generation device; and a step of switching back to the first planning method after the second planning method has been performed for a predetermined period, wherein the predicted power demand of the power demander is predicted based on the actual power demand of the power demander, and the predicted output of the solar power generation device is predicted based on the actual output of the solar power generation device. With this configuration, learning data required to create a prediction model can be collected. Therefore, the accuracy of predictions when performing the first planning method can be improved.

[0103] (Technology 9)

[0104] A control device includes: a memory storing predicted values ​​of electricity demand from an electricity demander and predicted values ​​of the output of a solar power generation device; and a planner executing a planning method for planning the output of a fuel cell device to compensate for the difference between the predicted values ​​of electricity demand from the electricity demander and the predicted values ​​of the output of the solar power generation device.

[0105] (Technology 10)

[0106] An electric power system comprising a solar power generation device, a fuel cell device, and the control device described in Technique 9.

[0107] Industrial availability

[0108] The technology disclosed herein is useful for power systems equipped with solar power generation devices and fuel cell devices. The technology disclosed herein is particularly useful for power systems serving as distributed power sources.

[0109] Explanation of reference numerals in the attached figures

[0110] 10 Control unit; 12 Forecasting unit; 14 Planning unit; 16 Control unit; 20 Database; 21 Terminal; 22 Weather information service; 30 Solar power generation device; 31 Solar cell module; 32 First PCS; 33 First power meter; 34 First control unit; 40 Fuel cell device; 41 Fuel cell unit; 42 Second PCS; 43 Second power meter; 44 Second control unit; 50 Energy storage device; 51 Battery; 52 Third PCS; 53 Third power meter; 54 Third control unit; 100 Commercial power supply; 102 Power line; 200 Power system; 300 Load; 302 Fourth power meter.

Claims

1. A control method, The method includes the step of implementing a first planning method, which is a method for planning the output of a fuel cell device to compensate for the difference between the predicted power demand of the electricity demander and the predicted output of a solar power generation device.

2. The control method according to claim 1, The planned output value of the fuel cell device is adjusted based on the energy storage rate of the energy storage device predicted by the sum of the actual power demand value of the electricity demander, the actual output value of the solar power generation device, and the planned output value of the fuel cell device.

3. The control method according to claim 1 or 2, The upper limit of the output of the solar power generation device is set as the difference between the actual value of the electricity demanded by the electricity demander and the sum of the rechargeable power of the energy storage device below the maximum charging power of the energy storage device and the actual value of the output of the fuel cell device.

4. The control method according to claim 1, The energy storage device is controlled to compensate for the difference between the actual power demand of the power demander and the sum of the actual output of the solar power generation device and the actual output of the fuel cell device by charging or discharging the energy storage device.

5. The control method according to claim 1, The predicted electricity demand value of the electricity demander is obtained by adjusting the actual electricity demand value of the electricity demander in the most recent first period and the predicted electricity demand value of the electricity demander in the second period corresponding to the first period, based on the correlation between the actual weather conditions in the region where the electricity demander is located and the actual electricity demand value of the electricity demander, as well as the predicted value of the weather forecast in the region where the electricity demander is located.

6. The control method according to claim 5, The electricity demanders include factories. The predicted electricity demand value of the electricity demander is obtained by correcting the value predicted based on the actual electricity demand value of the electricity demander in the first period and the predicted electricity demand value of the electricity demander in the second period, based on the correlation between the actual operating performance of the factory and the actual weather performance and the actual electricity demand value of the electricity demander, as well as the value predicted by the weather forecast and the factory's operating plan.

7. The control method according to claim 1, The predicted output value of the solar power generation device is obtained by adjusting the actual output value of the solar power generation device in the most recent third period and the predicted output value of the solar power generation device in the fourth period corresponding to the third period, based on the correlation between the actual weather performance of the region where the electricity demander is located and the actual output value of the solar power generation device, as well as the value predicted by the weather forecast of the region where the electricity demander is located.

8. The control method according to claim 1, further comprising: The step of the second planning method is performed before the first planning method. The second planning method is a method of planning the output of the fuel cell device to compensate for the difference between the actual value of the electricity demand of the electricity demander and the actual value of the output of the solar power generation device. and The step of switching to the first planning method after the second planning method has been executed for a predetermined period. The predicted electricity demand of the electricity demander is based on the actual electricity demand of the electricity demander. The predicted output of the solar power generation device is based on the actual output of the solar power generation device.

9. A control device comprising: A memory that stores forecasts of electricity demand from demanders and forecasts of the output of solar power generation devices; and A planner that executes a planning method to plan the output of a fuel cell device to compensate for the difference between the predicted power demand of the electricity demander and the predicted output of the solar power generation device.

10. An electric power system, comprising: Solar power generation devices; Fuel cell devices; and The control device according to claim 9.

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