JP2003329292A - Cogeneration device and control method thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To realize proper operation control for a cogeneration apparatus by estimating the electric power consumption and the quantity of hot water consumed. <P>SOLUTION: This cogeneration apparatus includes a means 2 for detecting the electric power demand, a means 3 for detecting the demand of hot water, a means 4 for detecting the information on a room temperature, and an estimating means 100 for estimating the electric power consumption and/or the demand of hot water. The estimating means 100 estimates the electric power consumption and/or demand of hot water after a designated time according to the information on the date and day of week, the information on the detected demand of electric power and demand of hot water and room temperature, and a detection value at that point of time and the numerical value before fixed time. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a cogeneration device (for example, a fuel cell device, a gas cogeneration device, etc.) that uses generated power of a fuel cell and its exhaust heat, and a control method thereof.

[0002]

2. Description of the Related Art A fuel cell device, for example, will be considered as a cogeneration device. In the fuel cell, the power demand and the amount of hot water stored in the hot water storage tank have a great influence on its operation. That is, if there is a demand for electric power, it is possible to operate the fuel cell device to generate electricity.
If the hot water storage tank is full of hot water, power generation operation cannot be performed. In order for the user to comfortably use the fuel cell, it is necessary to reliably supply electric power and hot water when the individual user needs. It is essential to accurately predict the life rhythm of each user, especially the demand for electricity and the demand for hot water supply.

However, in the prior art, it is difficult to accurately predict the electric power demand and the hot water supply demand for each user. Therefore, it is difficult to control the operation of the fuel cell based on such demand prediction. Little was done.
As a result, for example, when the power demand rapidly increases and continues for a long time, the power generation output of the fuel cell also increases, and if hot water is not consumed, the amount of hot water generated from the fuel cell increases and the hot water storage tank becomes full, The fuel cell will stop operating. If the operation of the fuel cell is frequently stopped, the life of the reformer is adversely affected and the energy consumption for starting the fuel cell increases.

[0004]

SUMMARY OF THE INVENTION The present invention has been proposed in view of the above-mentioned problems of the prior art, and predicts the power demand and hot water demand different for each user as accurately as possible, and An object of the present invention is to provide a fuel cell device and a control method thereof that can improve the operation efficiency of the battery device.

[0005]

As a result of various studies, the inventor has found that a cogeneration system (for example, a fuel cell system)
There is an extremely strong correlation between electricity demand and hot water demand when using so-called "case-based reasoning"
Is extremely effective for power demand and / or hot water supply demand. The present invention was created based on such findings.

The cogeneration system (for example, a fuel cell system) of the present invention has a means (2) for detecting power demand.
A means (3) for detecting hot water supply demand, a means (4A) for detecting information about room temperature (room temperature or temperature), and a prediction means (10) for predicting power consumption and / or hot water supply demand.
0), the predicting means (100) detects the information about the date and the day of the week, the detected value (measurement result) of the detected information about the electric power demand, the hot water supply demand, and the room temperature at that time and the time before a certain time or more. Numerical value (past case data:
For example, 24 hours before every 60 minutes, 60 minutes before,
Electric power demand before 10 minutes, hot water supply demand before 24 hours, 24
(Time before, 60 minutes before, 10 minutes before room temperature or temperature)
Based on (using case-based reasoning), the power consumption and / or hot water supply demand after a predetermined time is predicted (claim 1).

The cogeneration system (for example, a fuel cell system) of the present invention includes a control means (10) configured to suppress the operation stop of the fuel cell by referring to the predicted power consumption or the demand for hot water supply. (Claim 2)

Further, the control method of the cogeneration system (for example, a fuel cell system) of the present invention includes a detection step (ST2) for detecting information (room temperature or temperature) regarding power demand, hot water supply demand, and room temperature, and power consumption and And / or a prediction step (ST3 to ST6) of predicting hot water supply demand, and in the prediction step (ST3 to ST6), information regarding the date and the day of the week, and detected power demand, hot water supply demand, and room temperature information (room temperature or The detected value (measurement result) at that point in time and the numerical value at a time before a certain amount (past case data: for example, 24 hours before every 6 minutes, 6
Based on 0 minute before, 10 minute before power demand, 24 hour before hot water supply, 24 hour before, 60 minute before, 10 minute before room temperature or temperature) (using case-based reasoning),
The power consumption and / or hot water supply demand after a predetermined time is predicted (claim 3).

In carrying out the control method of the cogeneration system (for example, a fuel cell system) of the present invention, the operation mode is controlled so as to suppress the operation stop of the fuel cell by referring to the predicted power consumption or hot water supply demand. Determine (ST
7) is preferred (Claim 4).

According to the present invention having such a configuration, not only the electric power demand but also the hot water supply demand having a strong correlation therewith is referred to. Further, according to the present invention, not only the detected value (measurement result) at that time of the information (room temperature or temperature) related to the detected power demand, hot water supply demand, and room temperature, but also for each date and day of the week (every 1 minute) ) Record the measured data together with the current value every 24 hours, 60 minutes, and 10 minutes, and make a database. At the time of prediction after 24 hours, the similar example in the state closest to the current time (at the time of prediction) is selected from the database before 24 hours. In other words, not only the detected value (measurement result) at that time,
Numerical values before a certain time (past case data: power demand before 24 hours, 60 minutes before, 10 minutes before, hot water supply demand before 24 hours, room temperature before 24 hours, 60 minutes before, 10 minutes before or (Temperature) is stored in a database and is referred to. That is, the present invention uses the case-based reasoning that is extremely effective for predicting the electric power demand and / or the hot water supply demand by converting past numerical values into a database for each time series and referring to similar cases at the time of prediction.

Since the forecast is based on the parameters having an extremely strong correlation with the electric power demand and the hot water supply demand, and the technique (case-based reasoning) which is extremely effective for predicting the electric power demand and / or the hot water demand is used, According to the present invention, it is possible to accurately predict the demand for electric power and / or the demand for hot water supply, which has been conventionally difficult.

In implementing the present invention, the following devices and methods can be combined. For example, a means (2, 3) for detecting a power load or a heat load, a means (4) for inputting information about climate, and a power load based on the detected power load, heat load, and information about climate (8). Alternatively, the operation stop of the fuel cell (B) is suppressed (S11 to S17) by referring to the means (9) for predicting the hot water consumption and the predicted power load (2) or the hot water consumption (3). A control device (A) for controlling (FIG. 1,
(See FIG. 4).

The control means (A) performs control (S23 to S26) to stop the operation during the longest period in which the power load (2) is continuously (separately set) or less than the threshold value. Device (see FIGS. 1 and 5).

The control means (A) consumes less than the minimum power that can be generated by the fuel cell (B) (S31).
In this case, a device for performing control so as to discard (S33) the amount of hot water (S32) that would be surplus if operated at the minimum power that can be generated (see FIGS. 1 and 6).

The control means (A) predicts the amount of excess hot water before the time zone when the amount of hot water consumption is maximum (S41).
~ S43), a device for controlling the discarding of the excess hot water (S44) (see FIGS. 1 and 7).

Means (2, 3) for detecting electric power load or heat load, and means (4) for inputting information about climate
And a means (9) for predicting the power load or hot water consumption based on the detected power load and heat load and information on climate (8), and a control means (A). Is a device that switches the response time in control (S55) such that the difference between the generated power (B) and the consumed power is minimized (S51 to S54) (see FIGS. 1 and 8).

A power load or heat load is detected (S3),
Information on the climate is input (S3), and the power load or hot water consumption is predicted (S5 to S8) based on the detected power load and heat load and the information on the climate (8), and the predicted power load or hot water is predicted. A method of controlling the operation stop of the fuel cell by referring to the consumption amount (S11 to S17) (FIGS. 3 and 4).

A method for obtaining a period during which the predicted power load is continuously below the threshold value (S22), and performing control (S23 to S26) so as to stop the operation during the longest period (FIG. 5). .

When the power consumption is below the minimum power that can be generated by the fuel cell (B) (S31), the amount of hot water (S3) that will be surplus if operating at the minimum power that can be generated.
A method of controlling (2) to be discarded (S33) (FIG. 6).

A method of predicting the amount of excess hot water before the time when the amount of hot water consumption is maximum (S41 to S43) and performing control so as to discard the excess hot water (S44) (FIG. 7).

The power load (2) or the heat load (3) is detected, the climate information (4) is input, and the power load or hot water is detected based on the detected power load, heat load and climate information (8). The consumption amount is predicted (9) so that the difference between the generated power and the consumed power is minimized (S51 to S5).
The response time in control is switched to 4) (S55).
Method (Figure 8).

[0022]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to FIGS. In addition, in the illustrated embodiment, a fuel cell is illustrated as an example of the cogeneration device.

FIG. 1 shows the overall configuration of the fuel cell device including a control unit. In FIG. 1, for the entire apparatus, for example, various command input means 1 for inputting various commands from start / stop to mode selection and the like, power demand measuring means 2 for measuring power demand, that is, power consumption, hot water supply Hot water supply demand measuring means 3 for measuring demand, that is, hot water (hot water) consumption, room temperature measuring means 4 for measuring room temperature or temperature (room temperature, etc .: information about room temperature), and means having calendar function for season / date data, etc. (Calendar function) 5, timer function 6 as a time measuring means, current hot water storage amount measuring means 7, control device (control means) A, and fuel cell device B
And an external load 13.

The control unit A stores a database 8 for storing the electric power demand data, hot water supply demand data, room temperature data, seasonal and date data, and past numerical values, and various electric power demands and / or hot water demands. Predicting means 100 for predicting and control mode (control method) selecting means 1
It is composed of 0 and. The fuel cell device B is
It is composed of a hydrogen producing means 11 such as a reformer and a cell stack 12.

Here, in FIG. 1, only room temperature data is displayed, and temperature data is not displayed. It is not only the air temperature that is directly related to the power demand or hot water supply demand, but also the indoor or indoor temperature where the user lives, which is sufficiently related. While the room temperature is large, the difference between measurement points is small except for the air-conditioned room, and the room temperature measuring means has less restrictions on installation compared to the air temperature measuring means by, for example, incorporating a remote controller switch. It is based on the reason such as what seems to be. However, the above reasons are not dominant. Therefore, it is possible to use the air temperature as a control parameter instead of the room temperature.

In the control means 10, the user selects a control mode or performs automatic selection. Here, in automatic selection, the amount of gas used and the amount of electric power are calculated by the method described below from the forecast data 24 hours ahead once a day, and the gas and electricity charges are calculated from the charge table with the minimum number of shutdowns. Calculate and choose the cheapest method. Alternatively, select the most environmentally friendly one such as the amount of CO2 emitted. The control mode selection by the selection means 10 will be described later with reference to FIG.

The predicting means 100 includes the electric power demand data from the electric power demand measuring means 2, the hot water supply demand data from the hot water supply demand measuring means 3, and the room temperature data from the room temperature measuring means 4.
Seasonal and date data from the calendar function 5 and past power demand data stored in the database 8,
From hot water supply demand data and room temperature data (past case data), a so-called “case-based reasoning” method is used to calculate a predicted value for electric power demand and / or hot water supply demand.

When case-based reasoning is used, a large number of cases are made into a database and stored in a memory (database 8), and when there is a similar case, the stored case is retrieved. If there is no similar case, the closest case is selected from the stored cases. At this time, it is possible to perform more accurate prediction by adding data of a portion having no similar data or performing learning by sequentially rewriting data in the latest case. Specifically, for example, it is first classified by the day of the week, and the forecast 24 hours after midnight on Saturday (for example, the correlation between the power demand and the day of the week, the time zone, the weather, and the temperature) is calculated every one minute. If you do it, the state at the time of prediction (day of week,
The data most similar to time, temperature, hot water supply demand, etc.) is selected and used as the predicted value. In this way, for the prediction after 24 hours, if the data of the current value before 24 hours and at that time are stored every minute for one week for each week, the fuel cell operation of the next day will be performed. The amount of data needed to make an estimate of the electricity demand and / or hot water supply demand needed to make
By further accumulating and learning the data, it is possible to utilize a highly accurate similar example 24 hours ahead.

Next, the operation control of the fuel cell system shown in FIG. 1 will be described with reference to FIG.

Prior to the operation control, the prediction of the electric power demand and / or the hot water supply demand is started (FIG. 2: Step ST1 is Y).
ES). Here, in the illustrated embodiment, it is assumed that the power demand and / or the hot water supply demand for the day and day are predicted every day at 00:00 am. However, this is merely an example, and, of course, the prediction may be performed at other times.

In the prediction, first, the electric power demand measuring means 2 sends the electric power demand data to the predicting means 100, the hot water supply demand measuring means 3 sends the hot water supply demand data to the predicting means, and the room temperature data measuring means 4 predicts the room temperature data. The calendar function 5 sends the season / date data to the prediction means (step ST2). Along with that, step ST2
Then, the power demand data, hot water supply demand data, and room temperature data stored in the database 8 are, for example, data before 24 hours, data before 60 minutes, data before 10 minutes, and data at the time of measurement at that database. 8 to the prediction means 100.

Next, as described above, the prediction is performed by calculating the power demand and / or the hot water supply demand using the case-based reasoning (step ST3). Here, in the prediction of the electric power demand and / or the hot water supply demand, for example, a numerical value after 24 hours (electric power demand and / or hot water supply demand) is obtained (steps ST4 and ST4A). However, the power demand is forecasted not only after 24 hours but also after 60 minutes and after 10 minutes. When starting the fuel cell, if it takes 60 minutes from the start to the start of power generation, depending on the machine,
This is because the power demand 60 minutes after the start-up becomes very important. On the other hand, after 10 minutes, it is important to correct the responsiveness of the reformer in the fuel cell device and prevent wasteful power generation.

The prediction of the power demand and / or the hot water supply demand (steps ST3, ST4, ST4A) is performed until 24 hours later.
It is performed every 1 minute (step ST5). This allows
The pattern of power demand and / or hot water supply demand up to 24 hours later will be predicted. Then, the operation plan of the fuel cell shown in FIG. 1 for the day is completed (step S
T6).

When the operation plan of the fuel cell is completed in step ST6, the operation mode of the fuel cell is determined (step ST7). The operation mode is determined so that the operation of the fuel cell is not stopped as much as possible. More details will be described later with reference to FIG. After the operation mode of the fuel cell is determined (after step ST7 ends),
Wait for the fuel cell to start up (step ST8 is NO loop).

If the fuel cell is activated (step ST
8 is YES), which corresponds to the case of starting after a long stop. In that case, the power demand and / or hot water supply demand immediately after the start is set using the predicted value of the power demand and / or the hot water supply demand 60 minutes after the start, and the operation is performed (step ST9).

If the operation immediately after starting the fuel cell is changed to the normal operation, the fuel demand is constantly referred to using the power demand and / or hot water supply demand pattern obtained in step ST6 while referring to the predicted value after 10 minutes. The battery output is set (step ST10). This step ST10 is performed until the operation of the fuel cell ends (YES in step ST11) (NO in step ST11).

Next, referring to FIG. 3 and subsequent figures of the accompanying drawings,
A technique for controlling the operation of the fuel cell device (of FIG. 1) suitably combined with the embodiment of the present invention (described in FIGS. 1 and 2) will be described. FIG. 3 is a part for setting and inputting various conditions and a part for predicting various physical quantities, and FIG. 4 is a part for showing fuel cell stop suppression control, showing a flow of control after being transmitted from the control unit to the hydrogen production means. It includes.

The control flow will be described with reference to FIGS. 3 and 4. Focusing on the fact that the fuel cell operating rate changes (has a characteristic) depending on the day of the week and the time of the day, step S1
Then, set the calendar including the days of the week.

In the next step S2, a data acquisition period for creating a prediction model is set (for example, the data acquisition period is one week from now) before performing the control.
The prediction time, that is, X hours later (for example, 24 hours later), which is a value that determines how many hours later the physical quantity is predicted, is set. During the 24 hours, the number of corrections is set every Y minutes (for example, 30 minutes), that is, the number of corrections is 48 times. This is because if the control command is calculated without learning (rewriting) the database while the prediction is made 24 hours ahead in the means 10, the influence of a sudden change in temperature or a sudden outing is large. Furthermore, the threshold (W%) of partial load efficiency and the duration (V minutes)
To set. (As an example of the control, for example, the operation is stopped when the state where the partial load efficiency is equal to or less than the threshold value W (70)% continues for V (120) minutes or more.)

In the next step S3, collection of output (electric power demand and / or hot water supply demand) data and input (climate, temperature, etc.) data is started. Then, the process proceeds to the next step S4.

In step S4, the control unit A determines whether or not the data collection period has been exceeded. If the data collection period has been exceeded (YES in step S4), the process proceeds to the next step S5, and if not exceeded (step S4)
NO in step S3), the process from step S3 is repeated.

In step S5, load prediction (power demand and / or hot water supply demand forecast) is started. Then, in the next step S6, model creation for prediction of future electric power and hot water supply is performed by a prediction method such as case-based reasoning or an autoregressive model. As an autoregressive model, if the various collected data are applied to a model formula, the order of the model formula, constants, etc. can be determined from the conventional case to complete the formula showing the power consumption. Then, based on the conventional case, it is possible to create a model formula for a certain period and change the factor of "time" in the formula to predict the power consumption.

The case-based reasoning is the same as that described with reference to FIGS. There are other prediction methods such as a method using a neural network and a method using a spectrum model.

In the next steps S71 and S72, the total electric power and the consumed hot water supply amount after X hours from the present are predicted based on the above-mentioned prediction. That is, the power demand is predicted from the power demand (power consumption), hot water supply demand (hot water consumption), room temperature measurement values, past data (past data described with reference to FIG. 2), date and day of the week. (Step S71),
A hot water supply demand is predicted from the predicted power demand (step S72). Alternatively, from power demand (power consumption), hot water supply demand (hot water consumption), room temperature measurement values, past data (past data described with reference to FIG. 2), date and day of the week,
The hot water supply demand may be predicted (parenthesized in step S71), and the electric power demand may be predicted from the predicted hot water supply demand (parenthesized in step S72). If the power demand and the hot water supply demand are predicted (steps S71 and S72 end), the process proceeds to step S8.

Here, the prediction of the electric power demand and the hot water supply demand is the same as that described with reference to FIGS. In FIG. 3, after predicting either the electric power demand or the hot water supply demand, the other is predicted using the prediction result, but it is also possible to obtain the electric power demand and the hot water supply demand at the same time.

In step S8, the hot water storage amount after X hours is calculated from the following calculation formula. That is, the hot water consumption amount is subtracted from the current hot water storage amount after adding the value obtained by dividing the power generation amount based on the load prediction power curve by the power generation efficiency of the fuel cell and further multiplying it by the exhaust heat efficiency after X hours. The amount of hot water stored. here,
In order to accurately reflect the change in efficiency with the passage of time, it is preferable to install a sensor required for calculating efficiency. It is also preferable to consider the heat radiation amount of the hot water storage tank. Then, as described in the means 10, the means 1 determines whether or not the control is automatically selected in step S9. If YES (selection), the process proceeds to step S10.

In step S10, M1 to M in FIGS.
5, the calculation is performed based on the prediction result of X time ahead.
Next, in step S10 ', the one with the smallest number of operation stop times X hours ahead, of which the utility cost (or CO2
Select the lowest emission amount, NOx emission amount)
Proceed to (any one of M1 to M5). On the other hand, if NO in step S9, the process proceeds to M selected by the means 1 (one of M1 to M5).

Then, the process proceeds to step S11 after block M in FIG. Note that FIG. 4 shows the control relating to the stop suppression of the fuel cell device B.

At step S11, the controller A
The temperature sensor installed at the bottom of the hot water tank determines whether or not the hot water tank is filled with hot water.

If it is determined that the hot water storage tank is full (YES in step S11), the process proceeds to the next step S12, and if it is determined that it is not full (NO in step S11), the process proceeds to step S15.

In step S12, the control unit A calculates the time Z from the present time to the scheduled end time of the hot water tank full filling, and then proceeds to the next step S13.

In step S13, the fuel cell power generation pattern in which the power generation output is reduced is calculated so that the hot water storage tank becomes full at the scheduled full end time. Then, the next step S14
Proceed to.

In step S14, the control unit A determines whether or not it will be full after Z hours. If it is full after Z hours (YES in step S14), the process returns to step S13, and if it is not full (NO in step S13), the process proceeds to step S15.

In step S15, the flow rate of the reformed gas is determined prior to the power generation pattern calculated from the power load prediction pattern, and the process proceeds to step S16, in which the reformed gas is injected into the fuel cell B.

At the next step S17, the controller A
Determines through the timer 6 whether or not Y minutes have passed since the reformed gas was introduced. If Y minutes have elapsed since the injection of the reformed gas (YES in step S17), the process returns to step S7 to repeat the prediction and control. If not, step S15 and subsequent steps are repeated.

According to the "control for suppressing the number of operation stoppages", which is the first embodiment having the above configuration and control method,
The time required until the amount of hot water storage is full can be predicted based on various types of prediction data, and the hot water storage speed can be monitored in real time. For example, even if the amount of hot water consumed is small, the operation of the fuel cell B will not stop. . As a specific example,
According to FIG. 14, which is a characteristic diagram in the case of “normal control in which the hot water storage tank is stopped when the storage tank is full”, the fuel cell B four times on the line d
Shutdown is shown. On the other hand, FIGS.
According to the "control for suppressing the number of operation stoppages" described in connection with the above, the operation stoppage of the fuel cell B is d on the line d in FIG.
Only once as indicated by the 0m point. Note that FIG. 9 and FIG. 10 described later are graphs in the same format as that of FIG.

Next, the "control not to operate in a part where the partial load efficiency is lowered", that is, the so-called "partial load mode" will be described with reference to the flowchart of FIG. In the control flow of FIG. 5, before the block M of FIG.
The configuration of the device is the same as that shown in FIG.

In step S21 following the last block M in FIG. 3, the control unit A controls the partial load efficiency within a range from the start of prediction to 24 hours later (the predicted power load is divided by the rated power output for 100 minutes). Calculated as a rate).

In the next step S22, the time of the partial load efficiency threshold value W% or less is calculated in each section, and the process proceeds to the next step S23. Control device A in step S23
Calculates the longest section of W% or less. It should be noted that the stop of the fuel cell B for an instantaneous time α minutes (for example, 1 minute) or less is regarded as the continuous operation.

Here, the threshold value W% is the ratio of the partial load to the rated load at which the efficiency sharply decreases, as shown in the characteristic curve of FIG. 15 showing the efficiency against the load ratio.

In the fuel cell, the efficiency is highest at the rated output, and the arrangement and specifications of each device are defined for the rated output.

In step S23, the section in which the partial load efficiency is W% or less is longest is calculated.

In the next step S24, an operation pattern for stopping the operation in the longest section, that is, an operation pattern for reducing the number of stops is calculated, and the next step S25.
Proceed to.

At step S25, the controller A
In other sections, the hot water tank is full of hot water and it is determined whether or not to stop the operation. If the operation is stopped (YES in step S25), the process proceeds to step S26. If the operation is not stopped (NO in step S25), the process indicated by * in FIG. 4, that is, step S26.
Proceed to 15.

In step S26, control is performed so as to reduce the operating time before and after the longest section, and the process returns to step S24. The above is the control in the “partial load mode”.

According to the above-mentioned "control not to operate in a part where the partial load efficiency is lowered", that is, the so-called "partial load mode",
As shown by the line d in FIG. 10, the operation is not performed in the part where the partial load efficiency is lowered (about 400 minutes after the start of measurement), but the operation is stopped only once in other sections. Absent. That is, since the electricity consumption and the hot water supply consumption are predicted, the operation stop of the fuel cell can be suppressed.

Next, so-called "warm water disposal control (1)" will be described with reference to the flowchart of FIG. still,
In the control flow of FIG. 6, the process before the step indicated by the symbol “M” is the same as that in FIG. 3, and the device configuration is also the same as in FIG.

In step S31 continuing from the last block M in FIG. 3, the control unit A determines whether or not the power consumption is less than the minimum power generation value of the fuel cell, for example, 0.3 kW. If less than 0.3 kW (step S31
In YES), the process proceeds to step S32. 0.3 kW
If it is above (NO in step S31), step S31 is repeated.

In step S32, when the power consumption becomes less than the minimum power generation possible value (for example, 0.3 kW), or when the operation is performed at 0.3 kW, the control device A causes the fuel cell B to temporarily generate the power. Calculate the amount of excess hot water until the hot water tank is full when operating below the minimum possible power value (0.3 kW), that is, the difference between the amount of hot water during operation at the minimum power that can be generated and the amount of hot water when operating below the minimum power To do. Then, the process proceeds to step S33.

In step S33, the calculated amount of warm water to be discarded is discharged to the outside, and the control proceeds to the step indicated by * in FIG. 4 (step S15). The above is the control of the “warm water discard mode (1)”.

According to the "warm water discarding control (1)", the so-called "warm water discarding mode (1)", the hot water that will be an excess when the operation is continued below the minimum power generation value is discarded. Therefore, the operation stop of the fuel cell B is suppressed.

Next, so-called "warm water disposal control (2)" will be described with reference to FIG. In the control flow shown in FIG. 7, before the step shown by reference sign M, it is the same as that described in FIG. 3, and the configuration of the apparatus is also the same as that described in FIGS.

In step S41 continuing from the final block M in FIG. 3, the controller A calculates the power generation curve when the fuel cell B always generates power regardless of whether the hot water tank is full or not.

At the next step S42, the controller A
Determine if the hot water tank is full. If the hot water storage tank is full (YES in step S42), the process proceeds to the next step S43, and if it is not full (NO in step S42), the control is step S15 which is a step indicated by * in FIG. Move on to.

In step S43, the maximum amount of hot water consumption is expected in the range of 24 hours ahead, for example, the hot water discard amount is calculated so as not to fill up before 8 o'clock to 10 o'clock at night.

In the next step S44, the calculated amount of warm water to be discarded is discharged to the outside, and the control shifts to the step marked with * in FIG. 4 (step S15).

According to the "warm water disposal control (part 2)", the so-called "warm water disposal mode (part 2)", power generation is constantly monitored, and hot water is supplied so as not to fill the hot water storage tank in a specific time zone. Can be discarded.

Next, the load response step control, so-called "load response step mode" will be described with reference to the flowchart of FIG. Before M5 in FIG. 8 is the same as in FIG. 3, and the configuration of the device is also the same as in FIG.

Continuing from the last block M in FIG. 3, the control unit A sets the number of steps Δw in step S51. Here, the step Δw is a size for increasing / decreasing the output (electric power) of the fuel cell per unit time. Specific examples are 70 W / min, 17 W / min, and so on.

In step S52, the control device A calculates the operation pattern with each step number Δw based on the predicted value, and then proceeds to the next step S53.

In step S53, the area of the difference between the predicted value of the power consumption and the predicted value of the power generation amount of the fuel cell, that is, the integral value F in the elapsed time of the number of steps described above, is obtained in each calculation operation pattern.

In step S54, the controller A selects a pattern that minimizes the area F of the difference between the predicted value of power consumption and the predicted value of fuel cell power generation amount for each set time division.

Then, in the next step S55, the control device A sends a control signal to the fuel cell B so that the area F becomes the minimum throughout, and the fuel cell operates based on the control signal. The process moves to the step indicated by * in FIG. 4 (step S15).

According to the "load response step control", so-called "load response step mode", as shown in FIG. 13, by the selection or control, the number of steps Δw from the start of measurement without significant change in power consumption until 29 minutes have elapsed. Is set to 17 W / min, and Δw is switched to 70 W / min after 29 minutes when the change in power consumption is large. Here, the vertical axis of FIG. 13 represents the power value, the horizontal axis represents time, the c line represents the power load (power consumption), and the d line represents the fuel cell generated power (fuel cell power generation amount). In this way, by changing the number of steps according to the magnitude of the change in power consumption, the power generation amount of the fuel cell B can be made to follow this, and the fuel cell can be operated efficiently.

Note that in FIG. 11 (the format of the graph is the same as in FIG. 13), the step number Δw is 70 in all measurement time zones.
The characteristics when fixed to W / min are shown. Compared to FIG. 13 in which the load response step control is performed, the difference between the power consumption and the fuel cell power generation amount is large particularly in the first half of the measurement time zone (inefficient. ) Represents that.

Further, in FIG. 12 (the format of the graph is the same as in FIG. 13), the step number Δw is 17 in all measurement time zones.
The characteristics when fixed at W / min are shown. Compared with FIG. 12 in which the load response step control is performed, particularly after the start of measurement, 29
It shows that the difference between the power consumption and the power generation amount of the fuel cell is large (inefficient) between the minutes and the 36 minutes.

The illustrated embodiment is merely an example,
It is additionally noted that the description is not intended to limit the technical scope of the present invention. For example, in the illustrated embodiment, a fuel cell is illustrated as a cogeneration device, but the present invention can be applied to a gas cogeneration device and other cogeneration systems.

[0088]

The effects of the present invention are listed below. (1) The hot water supply demand, which has a close correlation with the power demand,
The prediction accuracy is improved by adding it to the parameters to be referred to in the demand prediction. (2) According to the simulation, the prediction accuracy by the conventional method that does not use the hot water supply demand for prediction was 65.6%, whereas the prediction accuracy of the present invention was improved to 85.9%.

[Brief description of drawings]

FIG. 1 is a block diagram showing the overall configuration of the present invention.

FIG. 2 is a flowchart showing the control of the present invention.

FIG. 3 is the first half of a control flow chart preferably combined with the present invention.

FIG. 4 is a second half of a flowchart showing “control for suppressing the number of operation stops”.

FIG. 5 is a flowchart showing “control not operating in a portion where partial load efficiency is reduced”.

FIG. 6 is a flowchart showing “hot water disposal mode (No. 1)”.

FIG. 7 is a flowchart showing “hot water disposal mode (No. 2)”.

FIG. 8 is a flowchart showing a “load response step mode”.

FIG. 9 is a characteristic diagram showing an effect of “control for suppressing the number of operation stops”.

FIG. 10 is a characteristic diagram showing an effect of “control not operating in a portion where partial load efficiency is reduced”.

FIG. 11 is a characteristic diagram when the load response step amount is uniformly 70 W / min in the “load response step mode”.

FIG. 12 is a characteristic diagram when the load response step amount is uniformly set to 17 W / min in the “load response step mode”.

FIG. 13 is a characteristic diagram when the load response step amount is changed from 17 W / min to 70 W / min in the middle in the “load response step mode”.

FIG. 14 is a diagram showing characteristics such as electric power in the related art.

FIG. 15 is a characteristic diagram illustrating a threshold W of partial efficiency.

[Explanation of symbols]

1 ... Various command input means 2 ... Power load measurement 3 ... Heat load (hot water load, etc.) measurement 4 ... Climate data 5 ... Calendar function 6 ... Timekeeping means (timer) 7 ... Current hot water storage amount measuring means 8 ... Database 9 ... Various load / data prediction means 10 ... Control mode selection means 11 ... Hydrogen production means 12 ... Cell stack 13 ... External load 100 ... Prediction means A: Control device B: Fuel cell

─────────────────────────────────────────────────── ─── Continuation of front page (51) Int.Cl. ⁷ Identification code FI theme code (reference) H02J 3/38 H02J 3/38 G

Claims (4)

[Claims] 1. A means for detecting a demand for electric power, a means for detecting a demand for hot water supply, a means for detecting information about room temperature,
Predicting means for predicting power consumption and / or hot water supply demand, the predicting means comprising information regarding the date and day of the week, detected values of the detected electric power demand, hot water supply demand, and room temperature at that time and a certain value or more. A cogeneration device configured to predict power consumption and / or hot water supply demand after a predetermined time based on a numerical value at a previous time. 2. The cogeneration system according to claim 1, further comprising a control unit configured to suppress the operation stop of the fuel cell with reference to the predicted power consumption or hot water supply demand. 3. A detection step of detecting information on electric power demand, hot water supply demand, room temperature, and a prediction step of predicting power consumption and / or hot water supply demand, wherein the prediction step includes information on date and day of the week, The present invention is characterized by predicting the power consumption and / or hot water supply demand after a predetermined time based on the detected power demand, hot water supply demand, detected value of information about room temperature at that time, and the numerical value at a time before a certain time or more. Control method for cogeneration system. 4. The control method of the cogeneration system according to claim 3, wherein the operation mode is determined so as to suppress the operation stop of the fuel cell with reference to the predicted power consumption or hot water supply demand.