JP4152305B2 - Operation control system - Google Patents

Description

  The present invention relates to an operation control system that controls the operation of a cogeneration device that generates electric power and heat.

  In an apparatus that can supply both electric power and heat, such as a combined heat and power supply apparatus, the operation schedule of the apparatus is determined by two factors, namely, electric power demand and heat demand. Here, if the operation of the cogeneration device is stopped due to a sudden power failure, etc., if the cogeneration device does not have control information on what operation should be performed when the power is restored after the power failure, There arises a problem that proper operation according to the electric power demand and heat demand of the house cannot be performed. In order to solve such problems, information such as operation start time, operation end time, operation mode, current time, timer setting, etc. is stored in advance in a nonvolatile memory (EEPROM) to shut off power supply such as power failure There has been proposed a system that can reproduce the operation state before a power failure when returning from (see, for example, Patent Document 1).

  Also, in order to create the operation schedule of the combined heat and power unit faithfully with respect to the customer's past power demand and heat demand, a huge amount of past power demand data and heat demand data can be read. In order to save it, a large amount of memory is required.

JP 2002-61917 A

  Here, when a volatile memory is adopted as a memory for storing a huge amount of past power demand data and heat demand data in a readable manner, it is inexpensive, but power supply is stopped. Since the stored contents are erased, there is a problem that it is not possible to create an optimal operation schedule based on past power demand data and heat demand data at the time of restart. On the other hand, when a non-volatile memory is adopted as the memory, the stored contents are not erased even if the supply of power is stopped, but it is more expensive than a volatile memory, so it has a large capacity as described above. There is a problem in economy as a memory for storing data.

  Further, in the system described in Patent Document 1, various current operating conditions are stored in a non-volatile memory to prepare for the occurrence of a power failure. In this system, when the power is restored, The problem that the same driving | operation will be performed generate | occur | produces. In other words, since the same operation as at the time of a power outage is performed whenever the power is restored, there is a problem that appropriate operation cannot be performed for the power demand and heat demand of the customer at the time of power restoration. To do.

  The present invention has been made in view of the above-described problems, and its purpose is to control the operation of a combined heat and power supply apparatus that is appropriate for the power demand and heat demand of the customer at that time when the power is restored after a power failure. It is in the point of providing an inexpensive system that can perform the above.

  A first characteristic configuration of an operation control system according to the present invention for solving the above-described problem is an operation control system for controlling the operation of a combined heat and power generation device that generates electric power and heat, and includes electric power demand data of a consumer and Referring to the demand amount information collecting means for collecting the heat demand amount data at every predetermined timing, the power demand amount data and the heat demand amount data at the predetermined timing collected by the demand amount information collecting means , Referring to the operation control means for creating operation control information instructing the cogeneration device, the power demand data and the heat demand data for each predetermined timing collected by the demand information collection means Representative data stored in the nonvolatile memory by creating power demand representative data and heat demand representative data representing the power demand characteristics and heat demand characteristics of the consumer. The operation control means refers to the power demand representative data and the heat demand representative data stored in the nonvolatile memory at the time of power recovery after a power failure, and obtains the operation control information. The representative data creating means refers to the power demand amount data and the heat demand amount data at each first timing collected by the demand amount information collecting means, and is the same time existing for each unit period. A process of averaging the power demand data of the attributes with each other, and a second that has a longer interval than the first timing by averaging the power demand data collected at each first timing for each predetermined period The power demand representative data is created with the data for each second timing by performing conversion processing to data for each timing, and the heat demand of the same time attribute existing for each unit period A process for averaging the quantity data to each other, and the heat demand data collected at each of the first timings are averaged for each predetermined period, and each second timing having a longer interval than the first timing. The data is converted into data, and the heat demand representative data is created with the data for each second timing.

  According to the first feature configuration, even when a sudden power failure occurs in the combined heat and power supply device, the operation control means is a power demand representative that represents the power demand characteristics and heat demand characteristics of the consumer when the power is restored after the power failure. With reference to the data and the heat demand representative data, it is possible to create operation control information of the combined heat and power supply apparatus that is appropriate for the power demand and heat demand of the consumer at the time of power recovery.

  Further, power demand representative data is created by averaging the same time attribute power demand data that exists for each unit period, and the heat demand representative data is the same time attribute heat demand data that exists for each unit period. Since they are created by averaging each other, representative data representing the past average power demand and heat demand of the consumer can be created. As a result, when the operation control means creates the operation control information with reference to the power demand representative data and the heat demand representative data, the actual power demand and heat demand of the consumer and the power supply output from the cogeneration device It is ensured that there is no significant gap between the quantity and the heat supply.

  Further, for example, the power demand using the power demand data and the heat demand data every two hours obtained by averaging the power demand data and the heat demand data every hour for every two hours. When representative data and heat demand representative data are created, the number of data required to be stored as power demand representative data and heat demand representative data is halved. Therefore, an expensive nonvolatile memory for storing them. The necessary cost can be reduced.

  A second characteristic configuration of an operation control system according to the present invention for solving the above-described problem is an operation control system for controlling the operation of a combined heat and power generation device that generates electric power and heat. Referring to the demand amount information collecting means for collecting the heat demand amount data at every predetermined timing, the power demand amount data and the heat demand amount data at the predetermined timing collected by the demand amount information collecting means , Referring to the operation control means for creating operation control information instructing the cogeneration device, the power demand data and the heat demand data for each predetermined timing collected by the demand information collection means Representative data stored in the nonvolatile memory by creating power demand representative data and heat demand representative data representing the power demand characteristics and heat demand characteristics of the consumer. The operation control means refers to the power demand representative data and the heat demand representative data stored in the nonvolatile memory at the time of power recovery after a power failure, and obtains the operation control information. The representative data creating means refers to the power demand amount data and the heat demand amount data for each first timing collected by the demand amount information collecting means, and refers to the first time attribute group and the first time attribute group. After multiplying the power demand data defined by the time attribute group of 2 by a weighting coefficient set for each attribute of the first time attribute group, the power demand data after multiplication is the second A process for averaging each attribute of the time attribute group, and the power demand data collected at each of the first timings are averaged for each predetermined period, and a time interval longer than the first timing is obtained. The power demand representative data is created with the data for each second timing, and is defined by the first time attribute group and the second time attribute group. After multiplying the heat demand data by the weighting coefficient set for each attribute of the first time attribute group, the heat demand data after multiplication is obtained for each attribute of the second time attribute group. A process of averaging each other, and the heat demand data collected at each first timing are averaged at a predetermined period to obtain data at a second timing having a longer interval than the first timing. Conversion processing is performed, and the heat demand representative data is created with data at each second timing.

  According to the second feature configuration, even when a sudden power failure occurs in the combined heat and power supply device, the operation control means is a power demand representative that represents the power demand characteristics and heat demand characteristics of the consumer when the power is restored after the power failure. With reference to the data and the heat demand representative data, it is possible to create operation control information of the combined heat and power supply apparatus that is appropriate for the power demand and heat demand of the consumer at the time of power recovery.

  In addition, when there is an attribute that you want to prioritize among multiple types of time attributes, weighting that is multiplied for each attribute so that the influence of the attribute you want to prioritize on the power demand characteristics and heat demand characteristics of the consumer will increase. The coefficient can be increased with respect to its preferred attributes. For example, power demand representative data and heat demand representative data are created by referring to past power demand data and heat demand data having a time attribute from Monday to Sunday and a time attribute from midnight to 24:00. When priority is given to the time attribute of Monday, the weighting coefficient for past power demand data and heat demand data collected from 0:00 to 24:00 on Monday is increased and collected from Tuesday to Sunday. Electricity demand from 0:00 to 24:00 collected from Monday to Sunday with a small weighting factor for the past electricity demand data and heat demand data from 0:00 to 24:00 It is possible to create power demand representative data that emphasizes the power demand characteristics of Monday by averaging the data at the same time of the quantity data and heat demand data. Kill.

  Further, for example, the power demand using the power demand data and the heat demand data every two hours obtained by averaging the power demand data and the heat demand data every hour for every two hours. When representative data and heat demand representative data are created, the number of data required to be stored as power demand representative data and heat demand representative data is halved. Therefore, an expensive nonvolatile memory for storing them. The necessary cost can be reduced.

  According to a third characteristic configuration of the operation control system according to the present invention, in addition to the first or second characteristic configuration, the operation control means is stored in the nonvolatile memory when the power is restored after a power failure. The first timing representing power demand characteristics and heat demand characteristics of the consumer, which is derived by performing data interpolation processing on the power demand representative data and heat demand representative data at each second timing. The operation control information is created with reference to each power demand representative data and heat demand representative data.

  By the third feature configuration, for example, 2 hours obtained by converting hourly power demand data and heat demand data into power demand data and heat demand data every two hours as described above When the operation control means referring to the power demand representative data and the heat demand representative data for each hour requires the hourly power demand representative data and the heat demand representative data to create the operation control information, every two hours By performing a data interpolation process for converting the data into hourly data, the operation control means can obtain hourly power demand representative data and heat demand representative data.

Hereinafter, an operation control system 10 according to the present invention will be described with reference to the drawings.
The operation control system 10 illustrated in FIG. 1 is a system that controls the operation of the cogeneration apparatus 21 that generates electric power and heat to be supplied to the consumer 20. Here, the operation control system 10 includes a demand amount information collecting unit 1 that collects power demand amount data and heat demand amount data of the customer 20 at predetermined timings, and a predetermined timing collected by the demand amount information collecting unit 1. With reference to the power demand data and the heat demand data for each, the operation control means 2 for creating the operation control information instructing the combined heat and power supply apparatus 21 and the predetermined amount collected by the demand information collecting means 1 A representative that generates power demand representative data and heat demand representative data representing the power demand characteristics and heat demand characteristics of the consumer 20 with reference to the power demand data and heat demand data, and stores them in the nonvolatile memory 5 The operation control means 2 is configured to include data creation means, and the power control representative data and heat demand stored in the nonvolatile memory 5 when the power is restored after a power failure. Referring to table data, it operates to create a running control information.

  The demand amount information collecting unit 1 collects the power demand amount data and the heat demand amount data of the consumer 20 at a predetermined timing and realizes a function of storing the data in the volatile memory 3. For example, power demand amount data and heat demand amount data per unit time are collected every hour, and the hourly data is stored in the volatile memory 3. The heat demand data can be collected more finely and stored in the volatile memory 3 such as hot water supply heat demand data and heating heat demand data. Here, the reason why the volatile memory 3 is used as the storage location of the power demand data and the heat demand data collected by the demand information collecting means 1 is that the power demand data and the heat demand as the data collection progresses. This is because an inexpensive storage device is required because of the huge amount of data.

  The operation control means 2 realizes a function of controlling the operation of the combined heat and power supply device 21 as will be described later. At that time, the operation control means 2 is the past power of the customer 20 itself collected by the demand amount information collecting means 1. Operation control information is created with reference to the demand data and the heat demand data.

  The representative data creating means realizes a function of creating power demand representative data and heat demand representative data representing the power demand characteristics and heat demand characteristics of the customer 20 as will be described later. The power demand representative data and the heat demand representative data are created by referring to the past power demand data and heat demand data of the customer 20 itself collected by the quantity information collecting means 1 and stored in the volatile memory 3. Do.

Next, control of the operation of the cogeneration apparatus 21 by the operation control means 2 will be described.
The operation control means 2 performs a data update process for updating and storing the past demand data for one day in a state in which it is associated with the day of the week based on the actual usage situation, and is stored every time the date changes. It is configured to perform predicted load calculation processing for obtaining predicted demand data for one day from the past demand data for the day.
Then, the operation control unit 2 determines whether or not to operate the combined heat and power supply device 21 from the predicted demand amount data every time one hour as a unit time elapses in a state where the predicted demand amount data for one day is obtained. The energy conservation level reference value calculation process for obtaining the standard value for energy conservation level is performed, and whether or not the current energy conservation level exceeds the energy conservation level reference value determined in the energy conservation level reference value calculation process. Thus, the operation availability determination process for determining whether the operation of the combined heat and power supply device 21 is possible is performed.

  In this way, when it is determined that the operation of the combined heat and power supply device 21 is possible in the operation availability determination process, the operation control means 2 is an operation that operates the combined heat and power supply device 21 for a unit time of one hour from that point. It is configured to set the operation time zone, operate the cogeneration device 21 during the operation time zone, and if the operation of the cogeneration device 21 is determined to be impossible, the operation of the cogeneration device 21 is stopped. Yes.

Then, the operation control means 2 is configured to stop the operation of the combined heat and power supply device 21 when the amount of hot water stored in the hot water storage tank 22 is full in the operation time zone.
Incidentally, although not shown, the amount of hot water stored in the hot water storage tank 22 is configured to be detected based on detection information of a plurality of thermistors provided in the hot water storage tank 22.

  When the description of the data update processing is added, the past demand data for one day indicating how much power load, hot water supply heat load as heat load and heating heat load were present in which time zone of the day and day of week. It is configured to be updated and stored in the associated state.

First, the past demand data will be described. The past demand data consists of three types of demand data, namely, power demand data, hot water heat demand data, and heating heat demand data. As shown in FIG. The past demand data for the day is stored in a state of being divided for each day of the week from Sunday to Saturday.
The past demand data for one day includes 24 pieces of power demand data per unit time, 24 pieces of hot water supply heat demand data per unit time, with 1 hour of 24 hours as a unit time, and It consists of 24 pieces of heating heat demand data per unit time.

When the configuration for updating the past demand amount data as described above is added, the demand amount information collecting unit 1 represents the power load per unit time, the hot water supply heat load, and the heating heat load from the actual usage state. The actual demand data for one day is stored in association with the day of the week in a state where the collected demand data is stored.
When the actual demand data for one day is stored for one week, new past demand data is obtained by adding the past demand data and the actual demand data at a predetermined ratio for each day of the week. Thus, the obtained past demand data is stored, and the past demand data is updated.

Specifically, taking Sunday as an example, as shown in FIG. 2, the past demand data D1m corresponding to Sunday among the past demand data and the actual demand data corresponding to Sunday among the actual demand data. From A1, new past demand data D1 (m + 1) corresponding to Sunday is obtained by the following [Equation 1], and the obtained past demand data D1 (m + 1) is stored.
In the following [Equation 1], D1m is past demand data corresponding to Sunday, A1 is actual demand data corresponding to Sunday, K is a constant of 0.75, and D1 (m + 1) ) As new past demand data.

[Equation 1]
D1 (m + 1) = (D1m × K) + {A1 × (1-K)}

When the predicted load calculation process is described, it is executed every time the date changes, and the predicted demand for one day of how much power load, hot water heat load, and heating heat load is predicted in which time zone of the day. It is configured to determine quantity data.
That is, out of the seven past demand data for each day of the week, by adding the past demand data corresponding to the day of the day and the actual demand data of the previous day at a predetermined ratio, how much power is consumed in which time zone Whether the load, hot water supply heat load, or heating heat load is predicted, the predicted demand data for one day is calculated.

More specifically, taking as an example the case of obtaining the forecast demand data for one day on Monday, as shown in FIG. 2, seven past demand data D1m to D7m for each day of the week and seven actual demands for each day of the week are shown. Since the quantity data A1 to A7 are stored, from the past demand data D2m corresponding to Monday and the actual demand data A1 corresponding to Sunday of the previous day, one day of Monday is calculated by the following [Equation 2]. The demand demand data B for the minute is obtained.
As shown in FIG. 3, the predicted daily demand data B for one day is predicted power demand data for one day, predicted hot water demand data for one day, predicted heating heat demand for one day. 3, (b) in FIG. 3 shows the predicted power load for one day, (b) in FIG. 3 shows the predicted hot water supply heat load for one day, and (c) in FIG. ) Shows the predicted heating heat load for one day.
In the following [Equation 2], D2m is past demand data corresponding to Monday, A1 is actual demand data corresponding to Sunday, Q is a constant of 0.25, and B is Predicted demand data.

[Equation 2]
B = (D2m × Q) + {A1 × (1-Q)}

  When the energy saving standard value calculation process is described, it is executed every time 1 hour as a unit time, and is necessary between the current time and the reference value time using the predicted hot water supply heat demand data. When the combined heat and power supply device 21 is operated so as to cover the required amount of stored hot water, an energy saving standard value that can realize energy saving by operating the combined heat and power supply device 21 is obtained.

  For example, assuming that the unit time is 1 hour and the reference value time is 12 hours, first, from the predicted power load, predicted hot water supply heat load, and predicted heating heat load based on the predicted demand data, 3], as shown in FIG. 4, when the predicted energy-saving degree when operating the combined heat and power supply device 21 is obtained every 12 hours for 12 hours up to 12 hours ahead, and when the combined heat and power supply device 21 is operated The estimated hot water storage amount that can be stored in the hot water storage tank 22 is obtained for 12 hours every 12 hours.

[Equation 3]
Energy saving level P = {(EK1 + EK2 + EK3) / required energy of cogeneration apparatus 21} × 100

However, EK1 is a function with the effective power generation output E1 as a variable, EK2 is a function with E2 as a variable, EK3 is a function with E3 as a variable,
EK1 = Effective power generation output E1 power plant primary energy conversion value = f1 (Effective power generation output E1, required energy at the power plant)
EK2 = energy conversion value in the conventional water heater of heating heat output E2 = f2 (heating heat output E2, burner efficiency (during heating))
EK3 = Effective hot water storage heat output E3 energy conversion value in conventional water heater = f3 (Effective hot water storage heat output E3, burner efficiency (during hot water supply))
Necessary energy of the combined heat and power unit 21: 5.5 kW
(The amount of city gas required when the cogeneration unit 21 is operated for one hour is 0.433 m ³ )
Unit power generation required energy: 2.8kW
Burner efficiency (heating): 0.8
Burner efficiency (with hot water supply): 0.9

  Moreover, each of the effective power generation output E1, the heating heat output E2, and the effective hot water storage heat output E3 is obtained by the following [Equation 4] to [Equation 6].

[Equation 4]
E1 = Power consumption at the power load of the customer 20 = Power generation power of the combined heat and power supply device 21− (Power consumption of the electric heater + Power consumption of various auxiliary machines)

  Here, the electric heater is a device used to heat the water in the hot water storage tank 22 by the surplus power of the combined heat and power supply device 21, and various auxiliary devices include a cooling water circulation pump used as an auxiliary in the combined heat and power supply device 21, Equipment and machines such as hot water circulation pumps.

[Equation 5]
E2 = Amount of heat consumed by the hot water heater of customer 20

[Equation 6]
E3 = (amount of heat generated in the combined heat and power supply device 21 + amount of heat recovered by the electric heater−heating heat output E2) −a heat loss

  However, the amount of heat recovered by the electric heater = power consumption of the electric heater × heat efficiency of the heater.

And in the state which calculated | required the prediction energy saving degree and prediction hot water storage amount for every 12 hours as shown in FIG. 4, first, the prediction necessity required from the predicted hot water supply heat demand data to 12 hours ahead is required. The hot water storage amount is obtained, and the hot water storage amount in the hot water storage tank 22 at the present time is subtracted from the predicted required hot water storage amount, and the necessary hot water storage amount required up to 12 hours ahead is obtained.
For example, when a hot water supply heat load of 9.8 kW is predicted 12 hours later from the predicted hot water supply heat demand data, and the hot water storage amount in the hot water storage tank 22 is 2.5 kW at the present time, The necessary hot water storage required in the meantime is 7.3 kW.

  And in the state where the predicted hot water storage amount of the unit time is added, until the total predicted hot water storage amount reaches the required hot water storage amount, select from among the unit time for 12 units that has the highest predicted energy saving level I am going to go.

For example, as described above, when the required hot water storage amount is 7.3 kW, as shown in FIG. 4, first, the unit from 7 hours to 8 hours ahead, which has the highest predicted energy saving degree, is used. Select the time, and add the predicted hot water storage amount per unit time.
Next, a unit time from 6 hours ahead to 7 hours ahead with a high predicted energy saving degree is selected, and the predicted hot water storage amount in the unit time is added, and the predicted hot water storage amount at that time is 1.1 kW.
Moreover, the unit time from 5 hours ahead to 6 hours ahead with the highest predicted energy saving degree is selected, and the predicted hot water storage amount in the unit time is added, and the predicted hot water storage amount at that time is 4.0 kW. .

In this way, when the selection of the unit time from the one with the high predicted energy saving value and the addition of the predicted hot water storage amount are repeated, the unit time from 8 hours ahead to 9 hours ahead as shown in FIG. When is selected, the combined predicted hot water storage amount reaches 7.3 kW.
If it does so, the energy-saving degree of the unit time from 8 hours ahead to 9 hours ahead will be set as an energy-saving degree reference value, and in the thing shown in FIG.

When the operation determination process is further described, it is executed every time 1 hour as a unit time elapses, and the above-mentioned [several numbers are calculated from the current power load, the predicted hot water supply heat load, and the current heating heat load. 3] to obtain the current energy saving level.
If the current energy saving level exceeds the energy saving level reference value, it is determined that the operation of the combined heat and power supply device 21 is possible. If the current energy saving level is less than the energy saving level reference value, the operation of the combined heat and power supply device 21 is impossible. I try to distinguish.

  As described above, the operation control means 2 refers to the past power demand data and heat demand data stored in the volatile memory 3 and creates a future operation control information of the combined heat and power supply device 21. explained. Here, when a sudden power failure or the like occurs on the customer 20 side, the storage content of the volatile memory 3 disappears with the stop of the power supply from the power source, so the operation control means 2 returns to the power source after the power failure. In some cases, the operation control information to be given to the combined heat and power supply device 21 may not be created. However, in the operation control system 10 according to the present invention, the representative data creating means represents the power demand characteristic and the heat demand characteristic of the customer 20. Power demand representative data and heat demand representative data are created in advance and stored in the non-volatile memory 5 that retains the stored contents even when the power supply from the power supply is stopped. When the power is restored after a power failure, the power demand representative data and the heat demand representative data are referred to in the same manner as the above-described past demand data, and the operation control information of the combined heat and power supply device 21 is created. Door can be.

  The power demand representative data and heat demand representative data created by the representative data creation means will be described below. However, since the method for creating the power demand representative data and the heat demand representative data is the same, the description regarding the creation of the heat demand representative data may be omitted.

  FIG. 5 illustrates power demand representative data for every hour (an example of a time attribute) created for each day of the week (an example of a time attribute). The representative data creating means creates the power demand representative data and the heat demand representative data with reference to the past power demand data and heat demand data stored in the volatile memory 3. When creating the power demand representative data and the heat demand representative data, information on time attributes of past power demand data and heat demand data is used.

  For example, past power demand amount data and heat demand amount data stored in the volatile memory 3 are combined with information such as the month, day, day of the week, and time when the data collection is performed by the demand amount information collecting unit 1. Included. Therefore, when generating the hourly power demand representative data for each day of the week illustrated in FIG. 5, the representative data creating means uses the past power demand data stored in the volatile memory 3 for each day of the week and 1 For example, all the hourly power demand data on Monday are collected, and the power demand data collected at the same time are averaged with each other. As a result, the power demand representative data representing the power demand characteristics on Monday for the customer 20 is created in the form of an hourly power demand value. Then, by performing such averaging processing for each day of the week, power demand representative data representing the power demand characteristics of each day for the consumer 20 as shown in FIG. 5 is created. .

  The power demand representative data and the heat demand representative data created as described above by the representative data creation means are stored in the nonvolatile memory 5. The representative data can be created when representative data for each week as illustrated in FIG. 5 is generated once a week and representative data for 7 days is stored in the nonvolatile memory 5 or on the day. If the representative data that matches the day of the week attribute is created once a day and stored in the non-volatile memory 5 (for example, created when the combined heat and power unit 21 is started), or matches the day of the week attribute Various settings such as when representative data is created a plurality of times a day and stored in the nonvolatile memory 5 each time (for example, when the combined heat and power supply device 21 is started and every three hours) are set. Is possible. Also, new representative data can be created by referring to the representative data created in the past stored in the nonvolatile memory 5 and the demand data stored in the volatile memory 3.

  And this electric power demand representative data can also be used as the past demand amount data illustrated in FIG. 2, so that the operation control means 2 is the non-volatile memory 5 at the time of power recovery after a sudden power failure occurs in the combined heat and power supply device 21. , And the operation control information of the combined heat and power supply apparatus 21 can be created by referring to the desired power demand representative data and the heat demand representative data as the predicted demand amount data.

  As described above, based on two attributes of the time attribute, the day of the week attribute group including each day of the week attribute from Monday to Sunday and the time attribute group including each time attribute from 0:00 to 24:00. Although the example which produces electric power demand representative data and heat demand representative data was demonstrated, it can be arbitrarily set about which electric power demand representative data and heat demand representative data are produced using which time attribute.

  For example, power demand representative data and heat demand representative data including hourly demand amount values from midnight to 24:00 are created using only the time-related attributes without using the day-related attributes. You can also. In this case, the power demand data and the heat demand data collected at the same time (having the same time attribute) are collected for all the power demand data and the heat demand data stored in the volatile memory 3. By performing the averaging process, it is possible to create power demand representative data and heat demand representative data including a demand amount value every hour from 0 o'clock to 24 o'clock that is unrelated to the day of week attribute. Therefore, no matter what day of the week the power demand representative data and the heat demand representative data stored in the nonvolatile memory 5 are used for the operation control means 2 to create the operation control information of the combined heat and power supply device 21 It is ensured that there is no significant difference between the actual power demand and heat demand of the house 20 and the power supply amount and heat supply amount output from the combined heat and power supply device 21.

  In addition, the example of creating the power demand representative data and the heat demand representative data having the demand amount values from 0:00 to 24:00 for one day ignoring the attributes related to the day of the week has been explained, It is also possible to create power demand representative data and heat demand representative data having demand amount values from 0 o'clock to 24 o'clock for one day in which attributes of specific days are emphasized.

  For example, power demand representative data and heat demand representative data are created by referring to past power demand data and heat demand data having a time attribute from Monday to Sunday and a time attribute from midnight to 24:00. When giving priority to the time attribute of Monday, set the weighting coefficient to multiply the past power demand data and heat demand data collected from 0:00 to 24:00 on Monday to 4 0:00 collected from Monday to Sunday after setting the weighting coefficient to multiply the past power demand data and heat demand data collected from 0:00 to 24:00 on Sunday. The data at the same time of the power demand data and the heat demand data from 14:00 to 24:00 is averaged (after the multiplication by setting the above-mentioned weighting coefficients to 4/7 and 0.5 / 7, 1 day power demand representative data in which the power demand characteristics of Monday are emphasized by adding the data at the same time of the power demand data and the heat demand data from 24:00 to 24:00. Can be created.

  That is, the power defined by the first time attribute group (corresponding to the above-mentioned day-of-week attributes from Monday to Sunday) and the second time attribute group (corresponding to the above-mentioned respective time attributes from midnight to 24:00). After multiplying the demand data by the weighting coefficient set for each attribute of the first time attribute group (every day of the week), the multiplied power demand data is obtained for each attribute of the second time attribute group (time Power demand representative data is averaged with each other), and the first time attribute group corresponds to the above-mentioned Monday-Sunday attribute) and the second time attribute group (from 0:00 to 24:00) After multiplying the heat demand data defined by the time attribute of each time) by the weighting coefficient set for each attribute (each day of the week) of the first time attribute group, the heat demand data after multiplication Is averaged for each attribute (time) of the second time attribute group Representative data by creating a can create an attribute of impact is large include electricity demand representative data and heat demand representative data were to be prioritized.

<Another embodiment>
<1>
The power demand representative data shown in FIG. 5 is data having a power demand value for every hour, but measures such as reducing the amount of information stored in the nonvolatile memory 5 can be taken. For example, the power demand amount data is data collected every hour by the demand amount information collecting means 1, and after converting this hourly data into data every two hours, the power demand every two hours Electric power demand representative data having a value can be created and stored in the nonvolatile memory 5.

  Specifically, as shown in FIG. 6, the power demand representative data at every timing of 1 hour shown in FIG. 5A is averaged at every timing of 2 hours to represent power demand representative data every 2 hours. Can be created and stored in the nonvolatile memory 5. In this case, since the number of data that needs to be stored as the power demand representative data is halved, the cost required for the expensive nonvolatile memory 5 for storing them can be reduced.

  As described in the above-described embodiment, the operation control unit 2 obtains the predicted demand data for one day of the day, and from the predicted demand data every time one hour as a unit time elapses. In addition to performing an energy saving level reference value calculation process for obtaining an energy saving level reference value that is a criterion for determining whether or not to operate the combined heat and power supply device 21, the current energy level reference value obtained by the energy saving level reference value calculation process is greater than the current level. It is configured to perform an operation availability determination process for determining whether the operation of the combined heat and power supply device 21 is possible or not depending on whether or not the actual energy saving level is higher.

  Therefore, after the power demand representative data and heat demand data stored every two hours stored in the non-volatile memory 5 are converted into hourly power demand representative data and heat demand data by data interpolation processing, the data is obtained. Must be used as the above-mentioned forecast demand data. As the data interpolation processing, various commonly used methods can be used. For example, there are methods illustrated in FIGS. 7A and 7B.

  The data interpolation processing illustrated in FIG. 7A is data interpolation that uses one adjacent data as it is, and specifically interpolates 2 o'clock data using 1 o'clock data. In addition, the data interpolation processing illustrated in FIG. 7B is data interpolation using an average value of two adjacent data. For example, the data at 2 o'clock is obtained by using the average value of the data at 1 o'clock and 3 o'clock. Is interpolated.

<2>
In the above-described embodiment and another embodiment, it has been described that the nonvolatile memory in which the representative data is stored is installed in the operation control system 10, but the installation location is not particularly limited. For example, if it is connected to the operation control system 10 via a communication line and data communication can be performed between the representative data creation means 4 and the operation control means 2, the nonvolatile memory 5 is installed in a remote place. It may be installed inside a predetermined system.

It is a functional block diagram of an operation control system. It is explanatory drawing in a data update process. It is a graph which shows the predicted demand amount for 1 day. It is explanatory drawing in an energy-saving standard value calculation process. (A)-(g) is an example of the electric power demand representative data for every day of the week. It is an example of electric power demand representative data. It is an example of the electric power demand representative data after data interpolation.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Demand amount information collection means 2 Operation control means 3 Volatile memory 4 Representative data creation means 5 Non-volatile memory 10 Operation control system 20 Consumer 21 Cogeneration apparatus 22 Hot water storage tank

Claims (3)

An operation control system for controlling the operation of a cogeneration device that generates electric power and heat,
Demand information collecting means for collecting power demand data and heat demand data of consumers at a predetermined timing;
Operation control means for creating operation control information for instructing the combined heat and power supply device with reference to the power demand amount data and the heat demand amount data collected at the predetermined timing collected by the demand amount information collecting means,
Electric power demand representative data representing the electric power demand characteristic and the thermal demand characteristic of the consumer with reference to the electric power demand quantity data and the thermal demand quantity data for each predetermined timing collected by the demand quantity information collecting means. And representative data creation means for creating heat demand representative data and storing it in a nonvolatile memory,
The operation control means creates the operation control information by referring to the power demand representative data and the heat demand representative data stored in the nonvolatile memory when the power is restored after a power failure.
The representative data creating means refers to the power demand data and the heat demand data for each first timing collected by the demand information collecting means,
Processing for averaging power demand amount data having the same time attribute existing for each unit period, and the first timing by averaging the power demand amount data collected at each first timing for each predetermined period Conversion processing to data at each second timing with a longer interval, and creating the power demand representative data with data at each second timing,
The process of averaging the heat demand data having the same time attribute existing for each unit period, and the heat demand data collected at each of the first timings are averaged for each predetermined period and the first The operation control system which performs the conversion process to the data for every said 2nd timing whose interval is longer than this timing, and produces the said heat demand representative data with the data for every said 2nd timing. An operation control system for controlling the operation of a cogeneration device that generates electric power and heat,
Demand information collecting means for collecting power demand data and heat demand data of consumers at a predetermined timing;
Operation control means for creating operation control information for instructing the combined heat and power supply device with reference to the power demand amount data and the heat demand amount data collected at the predetermined timing collected by the demand amount information collecting means,
Electric power demand representative data representing the electric power demand characteristic and the thermal demand characteristic of the consumer with reference to the electric power demand quantity data and the thermal demand quantity data for each predetermined timing collected by the demand quantity information collecting means. And representative data creation means for creating heat demand representative data and storing it in a nonvolatile memory,
The operation control means creates the operation control information by referring to the power demand representative data and the heat demand representative data stored in the nonvolatile memory when the power is restored after a power failure.
The representative data creating means refers to the power demand data and the heat demand data for each first timing collected by the demand information collecting means,
After multiplying the power demand data defined by the first time attribute group and the second time attribute group by a weighting coefficient set for each attribute of the first time attribute group, A process of averaging power demand data for each attribute of the second time attribute group, and averaging the power demand data collected at each first timing for each predetermined period The data is converted into data for each second timing having a longer interval than the timing, and the power demand representative data is created with the data for each second timing,
Multiplication after multiplying the heat demand data defined by the first time attribute group and the second time attribute group by a weighting coefficient set for each attribute of the first time attribute group A process of averaging the subsequent heat demand data for each attribute of the second time attribute group, and averaging the heat demand data collected at each first timing for each predetermined period The operation control system which performs the conversion process to the data for every 2nd timing longer than a 1st timing, and produces the said heat demand representative data by the data for every said 2nd timing.   The operation control means performs a data interpolation process on the power demand representative data and the heat demand representative data at each second timing stored in the nonvolatile memory when the power is restored after a power failure. The operation control information is created by referring to the power demand representative data and the heat demand representative data for each of the first timings, which are derived, representing the power demand characteristics and heat demand characteristics of the consumer. 2. The operation control system according to 2.

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