JP2014155390A - Energy optimum control apparatus, control method, and control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy optimum control technology capable of optimizing the overall energy efficiency by highly efficiently controlling a plurality of dispersed heat sources.SOLUTION: The energy optimum control apparatus includes an operation plan development section 170 develops a combination of a CGS 3 to be operated so that heat and electric power suited to demand forecasting information are obtained on the basis of performance information of the CGS 3 including a power supply for generating heat using primary energy and generating electric power and demand predicting information predicting heat demand and power demand.

Description

  Embodiments of the present invention relate to an energy optimal control technique for optimally controlling energy generated by a distributed electric heat source such as a cogeneration system.

  A cogeneration system (hereinafter referred to as CGS) is a system that supplies primary energy such as gas by converting it into heat and electricity. For this reason, CGS is also called a combined heat and power system. The CGS is basically used to increase energy efficiency by consuming heat and electricity in a single facility such as a plant, a factory, and a building.

  Thus, a system that optimally controls the power and heat generated by the CGS in a single facility has been proposed. This conventional technique predicts power demand and heat demand in a facility, and also measures in real time. Based on the predicted and measured power demand and heat demand, the system controls the generator and the heat recovery device so that the energy saving rate is maximized.

JP-A-2-245453

  A system that performs control in a single facility as described above attempts to adjust supply energy to demand by changing the output of CGS power and heat. However, when the output of CGS is reduced according to demand, the efficiency of conversion from primary energy to electricity and heat decreases.

  That is, the CGS basically has the highest energy efficiency when it is continuously operated at its full rating, rather than operating while changing the output. For this reason, it is difficult to achieve 100% of the CGS performance in a conventional system that presupposes changing the output.

  Further, when a plurality of CGSs are installed in a facility, these CGSs can be said to be small-scale energy supply sources configured separately from large-scale facilities such as power plants. For this reason, CGS can be regarded as a plurality of distributed power sources and distributed heat sources.

  Here, for a plurality of distributed power sources, there is a mechanism for acquiring and analyzing power supply and demand information and maintaining the supply and demand balance of the system. Therefore, it is also conceivable that a plurality of CGSs are also regarded as distributed power sources and the supply and demand balance is controlled based on the power supply and demand information. However, CGS outputs heat together with electric power. For this reason, depending on the startup status of the CGS, even if the power supply-demand balance is optimally controlled, there is a possibility that heat will be generated wastefully in relation to the heat demand.

  The present invention has been proposed to solve the above-described problems of the prior art, and its purpose is to optimize the overall energy efficiency by efficiently controlling a plurality of distributed heat sources. To provide a control device, a method, and a program.

  In order to achieve the above object, the energy optimal control device of the present embodiment is based on performance information of a plurality of heat sources that generate heat using primary energy and heat demand prediction information that is a prediction of heat demand. And an operation plan creation unit that creates a combination of heat sources to be operated as an operation plan so that heat according to the heat demand prediction information is obtained.

  In addition, another aspect can also be grasped as a method for realizing the functions of the above-described units by a computer or an electronic circuit and a program executed by the computer.

Explanatory drawing which shows the example of the smart community to which the energy optimal control apparatus of embodiment is applied Explanatory drawing which shows the difference in the performance of several CGS Explanatory drawing which shows the example of the energy consumption apparatus which receives the supply of energy from CGS and operates Explanatory diagram showing the flow of information and energy in smart communities The block diagram which shows the structure of the energy optimal control apparatus of embodiment Explanatory diagram showing factors that determine driving patterns Block diagram showing the operation plan creation unit Flow chart showing the flow of processing of the energy optimum control device Explanatory drawing which shows the starting pattern of CGS according to heat demand Explanatory drawing which shows the starting pattern of CGS according to electric power demand Explanatory drawing which shows the combination of CGS corresponding to heat demand prediction information Explanatory drawing which shows the combination of CGS corresponding to electric power demand prediction information Explanatory drawing which shows the combination of CGS corresponding to heat demand prediction information Explanatory drawing which shows the operation plan of CGS corresponding to heat demand prediction information and electric power demand prediction information Explanatory drawing which shows the combination of CGS corresponding to the heat demand prediction information in the operation plan of FIG. Explanatory drawing which shows the starting pattern of CGS according to heat demand and electric power demand

The energy optimal control apparatus of this embodiment is demonstrated with reference to drawings.
[A. Smart community]
First, a smart community S, which is an environment to which the present embodiment is applied, will be described with reference to FIGS. The smart community S includes a power plant 1, a boiler 2, a cogeneration system (CGS) 3, a customer 4, a storage battery 5, a heat storage tank 6, and the like. However, the present embodiment is not limited to this, and can be widely applied as an EMS (Energy Management System) for controlling a control target device installed in a predetermined area.

[1. Power plant]
The power plant 1 includes power generation facilities such as thermal power, hydropower, and nuclear power, and is connected to a power system 7 via a grid interconnection device 11. Thereby, the power plant 1 can supply the power generated by the power generation facility to the power system 7. In addition, description is abbreviate | omitted about the details, such as transmission and substation equipment for supplying electric power.

  The power plant 1 is provided with a control system (control computer) for controlling each facility such as a power generation facility. The control system is connected to the energy optimum control device 100 via the network 9. Thereby, the power plant 1 can transmit and receive information to and from the energy optimum control device 100. The power plant 1 does not necessarily belong to only the smart community S, and may supply power to other demand areas.

[2. boiler]
The boiler 2 is a heat source device that converts primary energy such as gas into heat. The boiler 2 is connected to the heat system 8 via the grid interconnection device 21. Thereby, the boiler 2 can supply the generated heat to the heat system 8.

  The control system for controlling the boiler 2 is connected to the energy optimum control device 100 via the network 9. Thereby, the boiler 2 can transmit / receive information with the energy optimal control apparatus 100. In addition, the boiler 2 does not necessarily belong to only the smart community S, and may supply heat to other demand areas.

[3. CGS]
The CGS 3 is a combined heat and power system that generates and generates heat using primary energy, and can be regarded as both a power source and a heat source. Compared with a large-scale energy supply source such as the power plant 1, the CGS 3 that is relatively small and installed in a plurality of locations is equivalent to a distributed power source and a distributed heat source. Moreover, CGS3 is the energy supply apparatus used as the control object by this embodiment.

  A gas turbine system, a gas engine system, a fuel cell system, and the like that generate and generate heat using gas as primary energy are included in the CGS 3. A system that generates and generates heat using other fuels such as oil as primary energy is also included in CGS3.

  In the present embodiment, a plurality of CGSs 3 are controlled. The plurality of CGSs 3 may include those having different energy supply capacities, or may include the same. Each CGS 3 outputs electric power and heat at a certain ratio. That is, the ratio of power and heat that each CGS 3 outputs during operation does not change.

  For example, FIG. 2 shows an example in which power and heat outputs during operation of a plurality of CGS (1) to (5) are converted into calories (for example, kcal) and compared. As shown in FIG. 2, the output of each CGS (1) to (5) is different in both power and heat. In addition, each CGS (1) to (5) has a case where the power exceeds the heat output ((1) (2) (4) (5)) and a case where the power and heat output are equivalent ((3) ) Note that CGS in which heat exceeds the output of power may be included. The conversion ratio between heat calorie and electricity kilowatt is fixed.

  The output side of power from the CGS 3 is connected to the power system 7. The heat output side from the CGS 3 is connected to the heat system 8. Thereby, CGS3 can supply electric power and heat to the consumer 4, as shown in FIG.

  The control system that controls the CGS 3 is connected to the energy optimum control device 100 via the network 9. Thereby, the control system of CGS3 can control by information, such as an operation plan transmitted from the energy optimal control apparatus 100. FIG.

[4. Consumer]
The customer 4 is a group of equipment that receives supply of electric power and heat from the power plant 1, the boiler 2, and the CGS 3 via the electric power system 7 and the thermal system 8. As shown in FIG. 4, the facilities constituting the consumer 4 are a plurality of energy consuming devices 41 managed as a predetermined unit and facilities associated therewith. The predetermined group may be, for example, a plant, a factory, a building, or the like, or a contractor with an electric power company or the like.

  The energy consuming device 41 includes a device that consumes power and a device that consumes heat. In the present embodiment, both a device that consumes electricity and a device that consumes heat are referred to as a load for convenience. Examples of devices that consume electricity include lighting, a television, an air conditioner, and a refrigerator as shown in FIG. Examples of devices that consume heat include air conditioners, refrigerators, and water heaters. There are also energy consuming devices 41 that operate by receiving both electricity and heat, such as air conditioners and refrigerators.

  Each consumer 4 has a smart meter 42 as shown in FIGS. 3 and 4. The smart meter 42 is a measuring device configured by a computer connected to the network 9. The smart meter 42 has a communication function and can transmit and receive various information such as power demand information and heat demand information of the energy consuming equipment 41 in each consumer 4.

[5. Storage battery / heat storage tank]
The storage battery 5 is an energy storage device using a chargeable / dischargeable secondary battery. The storage battery 5 is connected to the power system 7. Thereby, the storage battery 5 can charge the power received from the power system 7 and discharge the charged power to the power system 7. The storage battery 5 functions as a power source when discharged.

  The heat storage tank 6 is an energy storage device that stores heat using a stored heat medium. The heat storage tank 6 is connected to the heat system 8. Thereby, the heat storage tank 6 can store the heat from the heat system 8 and dissipate the stored heat to the heat system 8. The heat storage tank 6 functions as a heat source when radiating heat.

  Although the storage battery 5 and the heat storage tank 6 are also equipped with the apparatus for grid connection, description is abbreviate | omitted. A control system that controls the storage battery 5 and the heat storage tank 6 is connected to the energy optimum control device 100 via the network 9. Thereby, the energy optimal control apparatus 100 can control charging / discharging of the storage battery 5, and the thermal storage heat dissipation of the thermal storage tank 6. FIG.

[B. Energy optimal control device]
As shown in FIG. 5, the energy optimal control device 100 includes a transmission / reception unit 110, a storage unit 120, a demand acquisition unit 130, a demand analysis unit 140, a demand prediction unit 150, a performance information acquisition unit 160, an operation plan creation unit 170, information An output unit 180 is included.

[1. Transmitter / Receiver / Storage Unit]
The transmission / reception unit 110 is a processing unit that is connected to the network 9 and transmits / receives information to / from the outside. The storage unit 120 is a processing unit that stores various types of information necessary for processing of the energy optimum control apparatus 100.

[2. Demand acquisition department]
The demand acquisition unit 130 is a processing unit that acquires load-side demand information transmitted from the smart meter 42 of each consumer 4 and received via the transmission / reception unit 10. The demand acquisition unit 130 includes a power demand acquisition unit 131 and a heat demand acquisition unit 132.

  The power demand acquisition unit 131 is a processing unit that acquires power demand information on the load side. The power demand information on the load side can be obtained from the power consumption amount of the energy consuming device 41 measured by the smart meter 42.

  The heat demand acquisition unit 132 is a processing unit that acquires heat demand information on the load side. The heat demand information on the load side can be obtained from the amount of heat consumed by the energy consuming device 41 measured by the smart meter 42. The demand information acquired by the demand acquisition unit 130 is stored in the storage unit 120. In addition, demand information is information which shows the quantity corresponding to power consumption and heat consumption, and what was converted on the calorie base shall be used in this embodiment.

[3. Demand Analysis Department]
The demand analysis unit 140 is a processing unit that analyzes energy demand information acquired by the demand acquisition unit 130. The analysis of the demand information is performed, for example, by obtaining the demand information of the entire load side in the smart community S in time series by summing the demand information of each customer 4 at each time of each day. The demand analysis unit 140 includes a power demand analysis unit 141 and a heat demand analysis unit 142.

  The power demand analysis unit 141 is a processing unit that obtains the power demand information of the entire load side based on the power demand information of each customer 4 by the method as described above. The heat demand analysis unit 142 is a processing unit that obtains the heat demand information of the entire load side based on the heat demand information of each customer 4 by the method as described above. The load side demand information analyzed by the demand information analysis unit 140 is stored in the storage unit 120. This demand information becomes demand performance data.

[4. Demand forecasting department]
The demand prediction unit 150 is a processing unit that performs demand prediction based on the actual demand data stored in the storage unit 120. The demand prediction is performed as follows, for example. First, based on past demand record data and meteorological data and meteorological forecast data on the forecast date to be forecasted, a similar date that is closest to the forecast date is obtained from the viewpoint of day of the week, weather, and the like. And the demand information of the whole load of the said similar day is made into the demand forecast information of a forecast day.

  However, the method of demand prediction is not limited to this, and any method used at present or in the future can be used. The unit period, which is the minimum unit of prediction, can be set freely, such as 1 second or several seconds, 1 minute or several minutes, 1 hour or several hours, 1 day or multiple days, 1 week or multiple weeks, 1 month or multiple months, etc. it can. The period to be predicted can also be set freely such as one day or multiple days, one week or multiple weeks, one month or multiple months, one year or multiple years. The same applies to an operation plan based on demand forecast information described later.

  Note that it is not preferable that the CGS 3 is frequently turned on and off in a short time. For this reason, it is suitable to make several months or one year into the object of prediction and an operation plan. However, the demand forecast can be made for one day, one week, one month, several months, or one year, and an operation plan corresponding to this can be made.

  The demand prediction unit 150 includes a power demand prediction unit 151 and a heat demand prediction unit 152. The power demand prediction unit 151 is a processing unit that creates power demand prediction information based on past power demand record data by the above-described method. The heat demand prediction unit 152 is a processing unit that creates heat demand prediction information based on past heat demand record data by the above-described method. The demand prediction information is also information indicating the amount of energy, and in this embodiment, information converted on a calorie basis is used.

[5. Performance information acquisition unit]
The performance information acquisition unit 160 is a processing unit that acquires performance information related to the performance of the CGS 3 stored in the storage unit 120 in advance. The performance information includes the power and heat supply capability during operation of the CGS 3, and the primary energy amount necessary for outputting the power and heat corresponding to the supply capability. Various parameters for obtaining the supply capacity and the primary energy amount may be used as performance information.

  For example, the rated power generation efficiency, the rated exhaust heat utilization efficiency, the rated power generation output, the rated exhaust heat output, the amount of primary energy used per unit time, and the like of the CGS 3 can be used as performance information. The above graph of FIG. 2 can be understood as an illustration of the power and heat supply capability during rated operation of each CGS (1) to (5). Note that the power and heat performance information output by the CGS 3 is also information indicating the amount of energy, and is converted on a calorie basis.

[6. Operation planning section]
Based on the demand prediction information predicted by the demand prediction unit 150 and the performance information acquired by the performance information acquisition unit 160, the operation plan creation unit 170 operates so as to obtain an energy supply amount according to the demand prediction information. A processing unit for creating a plan. The operation plan is information in which a combination of energy supply devices and energy adjustment devices to be operated is determined according to the energy amount per unit period in the demand prediction information.

  The energy supply device includes CGS3. The energy adjusting device includes an existing power source and heat source (not shown) in addition to the storage battery 5 and the heat storage tank 6. The boiler 2 is also an energy adjustment device.

  Note that “according to the demand prediction information” desirably obtains an energy supply amount that matches the energy amount predicted for the demand, but is not necessarily limited to the case where they match. Depending on the combination of energy supply devices, even if there is an excess or deficiency with respect to the demand forecast amount, it is included in “in response”.

  That is, “in response” may be a level in which the combination of energy supply devices is changed so that the energy supply amount increases or decreases according to the increase or decrease in the energy amount indicated by the demand prediction information. The excess or deficiency of the energy supply amount with respect to the demand prediction information can be adjusted by an energy adjustment device, as will be described later. In addition, the excess energy supply can be released.

  More specifically, the operation plan creation unit 170 creates an operation pattern of the energy supply device and the energy adjustment device. The operation pattern is information in which energy supply devices to be activated and energy adjustment devices selected for each unit period are arranged in time series in accordance with the change of the demand prediction information with time.

  Factors that determine the operation pattern of the energy supply device include, for example, one or both of heat demand and power demand, and the time during which the demand continues. In FIG. 6, the points determined by both the heat demand and the power demand and the time during which the demand continues are indicated by ◯. Depending on heat demand and power demand, one or more energy supply devices to be activated can be determined. The start-up time of the energy supply device can be determined according to the time when the equivalent demand continues.

  When one operation pattern is created, this is the operation plan for the energy supply device. When a plurality of operation patterns can be created, the operation plan creation unit 170 selects an operation pattern that minimizes the primary energy used by the energy supply device. This selected operation pattern becomes the operation plan of the energy supply device.

  In addition, the operation plan creation unit 170 accumulates the energy supply amount that exceeds the energy amount of the demand prediction information in the determined operation plan, and adjusts the energy adjustment so as to supplement the energy supply amount that is insufficient for the energy amount of the demand prediction information. Adjust the operation plan according to the equipment.

  In order to perform the above processing, the operation plan creation unit 170 includes an operation pattern creation unit 171 and an adjustment unit 172.

[6-1. Operation pattern creation section]
The operation pattern creation unit 171 is a processing unit that determines an operation pattern to be an operation plan for the energy supply device as described above. As illustrated in FIG. 7, the operation pattern creation unit 171 includes a demand determination unit 171a, a device selection unit 171b, an operation pattern generation unit 171c, an energy determination unit 171d, and a determination unit 171e.

(Demand judgment part)
The demand determination unit 171a is a processing unit that determines the level of demand prediction information in a unit period based on demand prediction information.

(Equipment selection part)
The device selection unit 171b is a processing unit that selects the CGS 3 to be activated in accordance with the level of the demand prediction information determined by the demand determination unit 171a. That is, the device selection unit 171b selects a combination of one or more CGSs 3 that can obtain heat and power corresponding to the demand prediction information.

  That is, it is selected whether to operate each CGS 3 with respect to the electric power demand and heat demand of the customer 4 of the smart community S as a whole. The CGS 3 to be operated is assumed to be the most efficient operating state inherent to the CGS 3. This is normally 100% of the rating and is a so-called full operation state. When operating in the most efficient state of 100%, as described above, the power and heat output of each CGS 3 and their ratios are also different. In this way, when there are a plurality of different CGSs 3 in the system, the optimum combination of ON and OFF is determined for each CGS 3 in accordance with a change in demand.

  Selection of the combination of one or more CGS3 to operate can be performed based on the supply capability of the heat and electric power obtained from the performance information of each CGS3. For example, when the energy supply capability of any one CGS 3 satisfies the energy amount of the demand prediction information in the unit period, the CGS 3 activated in the unit period is one of them.

  Further, for example, when only one energy supply capacity of the CGS 3 does not satisfy the energy amount of the demand prediction information in the unit period, the plurality of CGSs 3 are activated. In general, the CGS 3 is more energy efficient, for example, less primary energy is required when a small number of units are operated with the maximum capacity.

  Such selection of the CGS 3 can also be performed, for example, according to an activation pattern stored in the storage unit 120 in advance. As will be described later, the activation pattern is information that predetermines the CGS 3 to be activated in accordance with the energy amount level of the demand prediction information. That is, in the present embodiment, the priority order is not set in advance for each CGS 3, but is set so as to respond by changing the combination of CGS 3 to be activated in accordance with the demand prediction information for each unit period. .

(Driving pattern generator)
The operation pattern generation unit 171c is a processing unit that generates an operation pattern of the energy supply device. The operation pattern is information on the energy supply device selected in accordance with the demand prediction information for each unit period. The selection of the energy supply device by the operation pattern generation unit 171c can be performed based on the activation pattern as described above. Further, one or a plurality of operation patterns can be generated.

(Energy judgment part)
The energy determination unit 171d is a processing unit that obtains the amount of primary energy to be used for a plurality of operation patterns generated by the operation pattern generation unit 171c. The amount of primary energy used in each operation pattern can be obtained from the performance information of the activated CGS3. Note that the amount of primary energy to be calculated may be a value indicating the amount of resources such as necessary gas and oil, or may be a value obtained by converting the amount of resources into an energy amount.

(Decision part)
The determination unit 171e is a processing unit that determines, as an operation plan, an operation pattern that minimizes the amount of primary energy obtained by the energy determination unit 171d. For example, an operation pattern having the minimum primary energy amount converted to calories is determined. Thus, for example, instead of minimizing the amount of gas when viewed with one CGS 3, the amount of gas when viewed as a whole is minimized.

  Note that it is not always necessary that the number of CGS 3 to be activated is small. Each conversion efficiency of CGS3 is different. For this reason, from the viewpoint of not outputting excess energy in relation to the demand prediction information, there is also a CGS 3 that is activated preferentially even if the conversion efficiency is low.

[6-2. Adjustment unit]
The adjustment unit 172 is a processing unit that determines the operation of the energy adjustment device in order to adjust the operation plan. The adjustment unit 172 includes a power supplement information generation unit 172a, a charge information generation unit 172b, a heat supplement information generation unit 172c, and a heat storage information generation unit 172d.

(Power supplement information generator)
The power supplement information generation unit 172a generates power supplement information for controlling power of an existing power source such as discharge of the storage battery 5 so as to secure a supply amount of power that is insufficient for the demand prediction information in the operation plan. It is.

(Charge information generator)
The charging information generation unit 172b is a processing unit that generates charging information for controlling charging of the storage battery 5 so as to accumulate a supply amount of electric power exceeding the demand prediction information in the operation plan.

(Thermal Complementary Information Generator)
In the operation plan, the heat supplement information generation unit 172c provides heat supplement information for controlling the heat of the existing heat source such as heat dissipation from the heat storage tank 6 and the boiler 2 so as to secure a supply amount of heat that is insufficient for the demand prediction information. A processing unit to be generated.

(Heat storage information generator)
The heat storage information generation unit 172d is a processing unit that generates heat storage information for controlling heat storage in the heat storage tank 6 so as to accumulate a supply amount of heat that exceeds the demand prediction information in the operation plan. As described above, the operation plan determined by the operation pattern creation unit 171 and adjusted by the adjustment unit 172 is stored in the storage unit 120.

[7. Information output section]
The information output unit 180 is a processing unit that outputs the operation plan created by the operation plan creation unit 170 to the transmission / reception unit 110. The transmission / reception unit 110 transmits the operation plan to each CGS 3.

  The energy optimum control device 100 can be realized by controlling a computer including a CPU or the like with a predetermined program. The program in this case realizes the processing of each unit as described above by physically utilizing computer hardware.

  A method for executing the processing of each unit described above, a program, and a recording medium on which the program is recorded are also an aspect of the present embodiment. Moreover, how to set the range processed by hardware and the range processed by software including a program is not limited to a specific mode. For example, any of the above-described units can be configured as a circuit that realizes each process.

  The energy optimum control device 100 includes a normal input / output device or the like (keyboard, mouse, display, printer, input / output terminal, etc.), but the description thereof is omitted. In this input / output device, it is possible to input information necessary for the processing of each part described above, and to display, print, etc. the processing result of each part.

  Furthermore, the energy optimal control apparatus 100 is not limited to that realized by a single computer. The storage unit 120 may be configured as a computer for a database or the like as a computer separate from other units and connected by a network. The processing of each unit can also be configured as a device that performs processing in a linked manner with a plurality of computers connected via a network.

[C. Operation of the embodiment]
[1. Overview of processing]
This embodiment enables optimal control of the CGS 3 that can supply power and heat simultaneously. That is, when there are a plurality of distributed power sources and distributed heat sources, control is performed as to whether each distributed power source and distributed heat source is fully operated or stopped depending on the demand situation.

[2. Processing details]
The procedure of the operation plan creation process of this embodiment will be described with reference to the flowchart of FIG.
[2-1. Acquisition of demand information]
First, the demand acquisition unit 130 acquires the demand information on the load side transmitted from the smart meter 42 installed in each consumer 4 and received by the transmission / reception unit 110 (step 101). That is, the power demand acquisition unit 131 acquires load-side power demand information. Further, the heat demand acquisition unit 132 acquires heat demand information on the load side.

[2-2. Analysis of demand information]
The demand analysis unit 140 analyzes the load side demand information (step 102). That is, the power demand analysis unit 141 obtains time-series power demand information for the entire load side. Moreover, the heat demand analysis part 142 calculates | requires the time-sequential heat demand information of the whole load side.

[2-3. Demand forecast]
The demand prediction unit 150 performs demand prediction based on the analyzed demand information on the load side (step 103). That is, the power demand prediction unit 151 creates power demand prediction information for a period to be predicted that is stored in the storage unit 120 in advance. Further, the heat demand prediction unit 151 creates heat demand prediction information for the period corresponding to the prediction.

[2-4. Creation of operation plan]
On the other hand, the performance information acquisition unit 160 acquires CGS3 performance information from the storage unit 120 (step 104). Then, the operation plan creation unit 170 creates an operation plan based on the demand prediction information and the performance information. The operation plan is created by the operation pattern creation unit 171 and the adjustment unit 172 as described above. An example of such an operation plan creation process will be described with reference to FIGS.

(Demand judgment)
First, the demand determination unit 171a of the operation pattern creation unit 171 determines the demand level indicated by the demand prediction information for each unit period.

(Equipment selection)
And the apparatus selection part 171b selects the energy supply apparatus to start according to the demand level for every unit period.

  As an example of selecting an energy supply device by the device selection unit 171b, a case where the CGS 3 is selected according to a startup pattern will be described. FIG. 9 is an example of a table showing a startup pattern according to heat demand. In FIG. 9, patterns P1 to Pn of CGS (1) to (5) to be started are determined according to heat demand levels A to E (see FIG. 11).

  FIG. 10 is an example of a table showing activation patterns according to power demand. In FIG. 10, patterns p1 to pn of CGS (1) to (5) to be activated are determined according to the power demand levels a to f (see FIG. 12).

  Note that the tables in FIGS. 9 and 10 are not exhaustive. In the portion of “...”, A blank in which no value is particularly set, or any one or a combination of CGS (1) to (5) and other CGS 3 is set.

  Based on such an activation pattern, the device selection unit 171b selects CGS3 as follows, and the operation pattern generation unit 171c generates an operation pattern (step 105). Such operation pattern generation processing will be described separately for a case based on only the heat demand prediction information and a case based on the heat demand prediction information and the power demand prediction information.

<When selecting based on heat demand forecast only>
When the heat demand prediction information in the unit period is the heat demand level A, the device selection unit 171b performs CGS (2) single operation, CGS (1) single operation, or CGS (3) (4) multiple operation. select. It should be noted that the comparison determination between the demand level and the demand prediction information may have a certain range such as whether the demand level is within a threshold range set in the storage unit 120.

  In addition, as the heat demand prediction information becomes the heat demand levels B to D, the device selection unit 171b performs CGS (2) (3) corresponding to level B and CGS (2) (3) (3) corresponding to level C. 4) Select multiple operations of CGS (1) (2) corresponding to level D.

  Furthermore, when the heat demand prediction information reaches the heat demand level E, the device selection unit 171b selects a single operation of CGS (5) or a plurality of operations of CGS (1) (2) (3) corresponding to the level E. .

  The operation pattern generation unit 171c arranges the CGSs 3 selected according to the heat demand level in time series as described above. At this time, the CGS 3 generates an operation pattern that is not frequently turned on and off. For example, as shown in FIG. 11, when the level of heat demand changes from A → B → C → D → E, the following four operation patterns [1] to [4] can be generated. FIG. 11 shows an example of the operation pattern [1], where the vertical axis indicates the amount of energy, the horizontal axis indicates time, and the change in demand forecast information is indicated by a curve, and each CGS (1) to (5) is shown. ) In the form of a square (the same applies to FIG. 15). The amount of energy is converted into calories.

[1] (2) → (2) (3) → (2) (3) (4) → (1) (2) → (5)
[2] (3) (4) → (2) (3) → (2) (3) (4) → (1) (2) → (1) (2) (3)
[3] (1) → (2) (3) → (2) (3) (4) → (1) (2) → (5)
[4] (1) → (2) (3) → (2) (3) (4) → (1) (2) → (1) (2) (3)

  Note that most of the energy amount in the actual demand prediction information is covered by the power from the power plant 1 and the heat from the boiler 2. That is, the energy amount in the demand forecast information and the energy supply amount satisfying this amount have a huge base amount. In the present embodiment, the fluctuation part excluding the base amount can be absorbed by the distributed power source and the distributed heat source.

<When selecting power demand forecasts>
As shown in FIG. 11 and FIG. 12, during the time of the heat demand level A, the power demand level changes from d → a. At this time, if (2) (3) (4) → (2) based on the startup pattern of FIG. 10, the heat demand information is greatly exceeded during the time of the power demand level d. The same applies if the power demand level is reduced to c. Therefore, the power demand level is lowered to b. In this case, the device selection unit 171b sets the CGS 3 to be activated as (1) → (2) or (1) → (3) (4) based on the activation pattern of FIG. These (1), (2), (3), and (4) are modes that can be converted to each other at the heat demand level A from the startup pattern of FIG. There is no problem even if it changes.

  Also, as shown in FIGS. 11 and 12, during the time of the heat demand level E, the power demand level changes from f → e. In this case, the device selection unit 171b sets the CGS 3 to be activated as (1) (2) → (5). In addition, from the power demand levels e and f, setting the CGS 3 to be activated during the time of the heat demand level E to (1) (2) (3) greatly exceeds the power demand information. ) (2) (3) cannot be considered.

From the above, the operation pattern generation unit 171c generates the following operation pattern when the power demand prediction information is further added to the above operation patterns [1] to [4].
[5] (1) → (2) → (2) (3) → (2) (3) (4) → (1) (2) → (5)
[6] (1) → (3) (4) → (2) (3) → (2) (3) (4) → (1) (2) → (5)

  FIG. 12 is an example illustrating the operation pattern of [5], in which the vertical axis indicates the amount of energy, the horizontal axis indicates time, and the change in power demand prediction information is indicated by a curve, and each CGS (1) to ( The power output of 5) is shown in a square. The amount of energy is converted into calories.

(Energy judgment)
Furthermore, the energy determination part 171d calculates | requires the quantity of the primary energy which each uses about the driving | operation pattern produced | generated as mentioned above (step 106). That is, in the case of the operation pattern based only on the heat demand prediction information, when the CGS 3 is started according to the operation patterns [1] to [4], the total amount of primary energy required for each is calculated.

  In the case of an operation pattern that also includes power demand prediction information, when the CGS 3 is activated according to the operation pattern [5] [6], the total amount of primary energy required for each is calculated.

(Determination of driving pattern)
Then, the determination unit 171e determines an operation pattern in which the primary energy amount determined by the energy determination unit 171d is the smallest. This is performed by the following procedure, for example.

  First, the determination unit 171e substitutes the number of operation patterns, which is the final price, for the variable k (step 107), and substitutes the initial value 1 for the variable L (step 108). In the first operation pattern, since it is not possible to determine whether or not the primary energy is minimum (NO in Step 109), L is incremented (Step 110).

  Next, the determination unit 171e compares the first operation pattern with the second operation pattern, and adopts the operation pattern with the smaller primary energy amount (YES in Step 109) as the minimum operation pattern ( Step 111). If L does not exceed k (NO in step 112), L is incremented on the assumption that the operation pattern of the last rank has not been verified (step 110).

  The determining unit 171e compares the operation pattern with the smallest primary energy amount with the operation pattern with the third rank order. If the primary energy amount with the third operation pattern is large (YES in Step 109), the determination unit 171e Is incremented (step 110). When the primary energy amount of the third operation pattern is less than the first (NO in Step 109), the determination unit 171e employs the third as the minimum operation pattern (Step 111).

  When the processes of Step 109 to Step 112 are repeated and L exceeds k (YES in Step 112), the operation pattern of the last rank has been verified. The determination unit 171e determines the operation pattern that is adopted as the minimum primary energy amount at that time as the operation plan (Step 113).

(Driving pattern adjustment)
The adjustment unit 172 creates information for adjusting the excess or deficiency of the energy supply amount corresponding to the demand prediction information for the determined operation pattern (step 114). For example, the power supplement information generation unit 171a generates power supplement information so that the amount of power that is insufficient for the energy amount of the power demand prediction information is covered by power from the storage battery 5 and the existing power source. In addition, the charging information generation unit 172b generates charging information so that the storage battery 5 is charged for the amount of power that exceeds the energy amount of the power demand prediction information.

  Moreover, the heat complement information generation part 172c produces | generates heat complement information so that it may be covered with the electric power from existing heat sources, such as the thermal storage tank 6 and the boiler 2, about the amount of heat | fever which is insufficient for heat demand prediction information. Furthermore, the heat storage information generation unit 172d generates heat storage information so as to store heat in the heat storage tank 6 for the amount of heat that exceeds the energy amount of the heat demand prediction information.

  The operation pattern adjusted as described above is stored in the storage unit 120 as the determined operation plan (step 115). The determined operation plan is output to each CGS 3 by the information output unit 180 via the transmission / reception unit 110 (step 116).

  According to the output operation plan, each CGS3, the storage battery 5, the heat storage tank 6, etc. start, and the energy supply suitable for demand prediction information is performed. That is, the CGS 3, the storage battery 5, and the heat storage tank 6 are operated so that an energy supply amount that satisfies the demand prediction information can be secured.

[3. Example of operation plan]
Examples of operation plans determined by the above procedure are shown in FIGS. FIGS. 13 and 14 show changes in power demand prediction information and heat demand prediction information as curves, with the energy amount on the vertical axis and time on the horizontal axis, and the heat and power of each CGS (1) to (5). The output is shown in square. The amount of energy is converted into calories.

  FIG. 13 is an operation plan created based only on the heat demand prediction information shown in FIG. In this operation plan, the operation pattern of [1] (2) → (2) (3) → (2) (3) (4) → (1) (2) → (5) It is adopted as there is.

  First, CGS (2) is activated according to the heat demand level A, and when it reaches the heat demand level B, (3) is activated in addition to CGS (2). When the heat demand level C is reached, the CGS (4) is further activated. Next, when the heat demand level D is reached, CGS (3) (4) is stopped and CGS (1) is started. And when it becomes the heat demand level E, CGS (1) (2) is stopped and it switches so that all may be covered with CGS (5).

  In this case, as shown in FIG. 11, the amount of heat that exceeds the heat demand prediction information is stored in the heat storage tank 6, and the amount of heat that is insufficient for the heat demand prediction information is supplemented from the existing heat source. Moreover, as shown in FIG. 13, the electric energy which exceeds electric power demand prediction information is charged to the storage battery 5, and the electric power which is insufficient in electric power demand prediction information supplements from the existing power supply.

  FIG. 14 is an operation plan created by adding the power demand prediction information of FIG. 12 to the heat demand prediction information of FIG. In this operation plan, the operation pattern of [5] (1) → (2) → (2) (3) → (2) (3) (4) → (1) (2) → (5) It was adopted as the smallest amount.

  First, the CGS (1) is operated according to the power demand level a, and when it reaches the power demand level b, the operation is switched to the CGS (2) operation. When power demand level c (heat demand level B) is reached, CGS (3) is started in addition to CGS (2), and when power demand level d (heat demand level C) is reached, CGS (2) (3) In addition, CGS (4) is started. When the heat demand level D is reached and the power demand f is further reached, CGS (1) (2) is stopped and CGS (5) is started.

  In this case, as shown in FIG. 15, the amount of heat that exceeds the heat demand prediction information is stored in the heat storage tank 6, and the amount of heat that is insufficient for the heat demand prediction information is supplemented from the existing heat source. Moreover, as shown in FIG. 14, the electric energy which exceeds electric power demand prediction information is charged to the storage battery 5, and the electric energy which is insufficient in electric power demand prediction information supplements from the existing power supply.

[D. Effects of the embodiment]
In the present embodiment as described above, the following effects are obtained.
(1) Since the amount of energy indicated by the heat demand prediction information is obtained by combining one or a plurality of CGSs 3 that are distributed heat sources capable of obtaining high conversion efficiency by full operation, the overall energy conversion efficiency can be optimized. That is, instead of adjusting the output of each CGS 3, the demand is covered by combining several CGS 3 that are in full operation, so that primary energy such as gas can be converted into heat or the like most efficiently.

(2) By generating an operation plan based on the heat demand prediction information, it is possible to suppress the generation of excess heat exceeding the demand. Furthermore, since the control based on the power demand prediction information can be added to the control based on the heat demand prediction information, not only the heat but also the extra generated power can be suppressed.

(3) By creating an operation plan with heat demand prediction information as a main component, it is possible to reduce as much as possible the excess and deficiency of heat, which is difficult to control the storage and release compared to electric power. Therefore, the calorie | heat amount which the adjustment part 172 adjusts using the thermal storage tank 6 and another heat source can be reduced. Moreover, even if excess or deficiency with respect to the power demand prediction information occurs, charging / discharging by the storage battery 5 can easily store and release energy, so that the response is good and adjustment is easy.

(4) By combining one or a plurality of CGSs 3 having various performances, it is possible to flexibly respond to various changing demands. In particular, by preparing a CGS3 activation pattern in advance according to demand information, it is possible to select a CGS3 to be activated easily and at high speed. Even when the CGS 3 must be selected according to both the heat demand prediction information and the power demand prediction information, a simple process can be performed by preparing an activation pattern corresponding to the heat demand and the power demand.

(5) Since the operation plan that minimizes the primary energy to be used is created, the demand can be satisfied while suppressing the overall energy consumption. For example, it is not preferable that the CGS 3 is frequently turned on and off in a short time. However, depending on the performance and combination of each CGS 3, even if the demand fluctuates somewhat, the primary energy may be less if the operation is continued. In such a case, efficient operation is possible without the CGS 3 being frequently turned on and off. Furthermore, by calculating the amount of energy such as primary energy, power demand, heat demand, power, and heat output by calculating the calorie that is a common unit, processing in consideration of heat and power is facilitated.

[E. Other Embodiments]
The present embodiment is not limited to the above aspect, and the aspects exemplified below can also be configured. For example, the device selected to satisfy the energy amount of the demand forecast information is not limited to CGS. It can also comprise so that the heat according to heat demand prediction information may be obtained combining the apparatus used as a heat source.

  The selection of the energy supply device according to the demand prediction information is not limited to the above method. A plurality of levels combining the heat demand and the power demand may be set in advance, and the activation pattern of the energy supply device to be activated may be set corresponding to each level. For example, as shown in FIG. 16, the table that defines the patterns PX1 to PXn of CGS (1) to (5) to be activated in accordance with the levels Aa to Ab. It is an example. Thereby, determination of the energy supply apparatus to start becomes easy.

  Moreover, you may determine the combination of the energy supply apparatus to start using the computing equation which minimizes primary energy from the performance information of an energy supply apparatus. In general, the smaller the number of devices that are activated simultaneously, the smaller the amount of primary energy in many cases. For this reason, you may select energy supply equipment so that the number of units to start may decrease. Furthermore, the unit of information used for the above-mentioned various processings only needs to be converted and unified.

  The power source or heat source to be controlled is not limited as long as output control is possible, and any facility that can be used now or in the future can be applied. When it takes time until the output of the desired energy amount is obtained after the power source or the heat source is activated, it is possible to control the activation to be accelerated in response to this. Thereby, a desired energy supply amount can be obtained reliably.

  The power source and the heat source may be different in performance or the same. For example, by targeting a plurality of CGS3 having the same performance, the amount of energy to be output can be determined by the number of CGS3, and the calculation is simplified. Moreover, in order to prevent a specific thing about a power supply or a heat source being used intensively, the order | rank which may be used by a uniform frequency | count may be set.

  An energy supply apparatus is not limited to what was illustrated by said embodiment. For example, an absorption chiller / heater, a solar water heater, or the like can be used as the heat source. As the power source, a solar power generation device, a wind power generation device, a power generation device using an engine, or the like can be used.

  Each storage area of each information stored in the storage unit can be configured as a storage unit for each information. Typically, these storage units can be configured by various built-in or externally connected memories, hard disks, and the like.

  However, any storage medium that can be used now or in the future can be used as the storage unit. A register or the like used for calculation can also be regarded as a storage unit. The mode of storage includes not only a mode in which memory is stored for a long time but also a mode in which data is temporarily stored for processing and deleted or updated in a short time.

  The specific contents and values of the information used in the embodiment are free and are not limited to specific contents and numerical values. In the embodiment, in the excess or deficiency with respect to the value indicated by the information, the magnitude determination, the judgment of coincidence mismatch, etc., it is determined that the value is included as above, below, larger, smaller, exceeding, not exceeding, exceeding, below It's also free to decide not to include the value as less than or less. Therefore, even if “above” is read as “above” and “not enough” is read as “below”, it is substantially the same.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

1 ... Power plant 2 ... Boiler 3 ... CGS
DESCRIPTION OF SYMBOLS 4 ... Consumer 5 ... Storage battery 6 ... Thermal storage tank 7 ... Electric power system 8 ... Thermal system 9 ... Network 11, 21 ... Grid connection apparatus 41 ... Energy consumption apparatus 42 ... Smart meter 100 ... Energy optimal control apparatus 110 ... Transmission / reception part 120 ... storage unit 130 ... demand acquisition unit 131 ... power demand acquisition unit 132 ... heat demand acquisition unit 140 ... demand analysis unit 141 ... power demand analysis unit 142 ... heat demand analysis unit 150 ... demand prediction unit 151 ... power demand prediction unit 152 ... Heat demand prediction unit 160 ... performance information acquisition unit 170 ... operation plan creation unit 171 ... operation pattern creation unit 171a ... demand determination unit 171b ... device selection unit 171c ... operation pattern generation unit 171d ... energy determination unit 171e ... determination unit 172 ... adjustment Unit 172a ... power supplement information generation unit 172b ... charge information generation unit 172c ... heat supplement information generation unit 172d ... heat storage Multi-address generating unit 180 ... information output section

Claims (13)

  A heat source that is operated so that heat according to the heat demand prediction information is obtained based on performance information of a plurality of heat sources that generate heat using primary energy and heat demand prediction information that predicts heat demand The energy optimal control apparatus characterized by having the operation plan preparation part which produces the combination of these as an operation plan.   The energy optimal control device according to claim 1, wherein the heat source is a heat source configured as a cogeneration system including a power source that generates power using primary energy and generates electric power. The operation plan creation unit
Based on the heat demand prediction and the heat source performance information, and the power demand prediction information and the power demand prediction information predicted power demand, the heat and power corresponding to the heat demand prediction information and the power demand prediction information are The optimal energy control apparatus according to claim 2, wherein a combination of cogeneration systems to be operated is created as an operation plan so as to be obtained.   The energy optimal control apparatus according to any one of claims 1 to 3, wherein the operation plan creation unit sets an operation plan as a combination that minimizes primary energy to be used.   The said operation plan preparation part produces an operation plan based on the starting pattern which preset the heat source to start according to the level of a heat demand, The any one of Claims 1-4 characterized by the above-mentioned. Energy optimal control device.   The said operation plan preparation part produces an operation plan based on the starting pattern which preset the combination of the cogeneration system to start according to the level of a heat demand and an electric power demand. Item 4. The energy optimum control device according to Item 3.   The energy optimal control apparatus according to claim 3, further comprising: a power supplement information generation unit configured to generate information for controlling a power source other than the power source so as to compensate for power that is insufficient in the power demand prediction information.   The energy optimal control apparatus according to claim 3, further comprising a charge information generation unit that generates information for controlling charging of the storage battery so as to store electric power exceeding the power demand prediction information.   9. The heat supplement information generation unit configured to generate information for controlling a heat source other than the heat source so as to compensate for heat that is insufficient for the heat demand prediction information. Energy optimal control device.   The energy optimum according to any one of claims 1 to 9, further comprising a heat storage information generation unit that generates information for controlling heat storage of the heat storage device so as to store heat exceeding the heat demand prediction information. Control device.   The energy optimization control device according to claim 2, wherein the operation plan creation unit creates an operation plan based on information obtained by converting heat and electric power into a common unit.   The computer or electronic circuit can obtain heat according to the heat demand prediction information based on performance information of a plurality of heat sources that generate heat using primary energy and heat demand prediction information that predicts the heat demand. Thus, the energy optimal control method characterized by performing the operation plan creation process which produces the combination of the heat source to drive | operate as an operation plan.   Based on the performance information of a plurality of heat sources that generate heat using primary energy and the heat demand prediction information that predicts the heat demand, the computer can obtain heat according to the heat demand prediction information. An energy optimum control program for executing an operation plan creation process for creating a combination of heat sources to be operated as an operation plan.

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