JP2014027784A - Operation management device, operation management method, and operation management program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an operation management device capable of reducing a difference between an estimated operation plan and an actual operation state in an air-conditioning facility, when the difference occurs.SOLUTION: The operation management device comprises: an air-conditioning heat load estimation processing unit for calculating an air-conditioning heat load estimation value that is an estimated value of a heat amount required to be supplied from each of a plurality of air-conditioning heat source facility apparatuses on an estimation target date; and an operation plan creation unit for creating an air-conditioning heat source operation plan which assigns use power to at least one of the plurality of air-conditioning heat source apparatuses so as to perform control depending on a temperature at an exit for outputting heat of the air-conditioning heat source facility and assigns use power depending on a heat output value to another air-conditioning heat source apparatus, in assigning use power for each time to each air-conditioning heat source facility apparatus generating a heat amount shown by the air-conditioning heat load estimation value.

Description

  The present invention relates to an operation management apparatus, an operation management method, and an operation management program.

  Purchased power supplied by a power supplier (hereinafter referred to as a power company) using a commercial power system, which is supplied to a customer such as a facility equipped with a load storage device such as a thermal storage air conditioning system or a lighting device. This is called demand. This electric power company needs to prepare a supply facility according to the amount of electric power used most frequently in the facility of the customer to be supplied. Therefore, in order to make the burden of expenses for the facilities of a plurality of consumers fair, measure the demand power used in facilities per measurement period (hereinafter referred to as demand time limit) that is a set unit time, Contract power may be determined according to the largest maximum demand power among a plurality of measured demand powers. For example, the actual value of the average demand power at the demand time limit in 30 minutes unit at the customer's facility, etc. is measured, and the maximum value among the actual values of the average demand power for each demand time period for one month is the maximum demand power of the month (Demand maximum value), and a value that does not exceed the larger of the demand maximum value of the month and the demand maximum value of the previous 11 months is determined as the contract power. As an apparatus for performing this demand control, for example, there is one described in Patent Document 1

  Moreover, in such demand control devices, load forecasting is performed using weather forecasts, peak power can be controlled to an arbitrary target power, and air conditioning heat sources and distributed power supply operation schedules to save energy and costs. A load control method for smart grids that automatically plan and operate is being devised.

  In this method, as shown in FIG. 28, an air conditioning load every hour or 30 minutes is predicted, and the heat output of the air conditioning heat source and the operation time zone are automatically determined so as to match the load. In FIG. 28, the horizontal axis represents time and the vertical axis represents the air conditioning load. For example, consider a case where cooling is performed in the heat source configuration shown in FIG. In this heat source configuration, a heat source A, a heat source B, and a heat source C are provided for a building. Each heat source is provided with a pump. In this heat source configuration, when the predicted air conditioning load at 8 o'clock is 300 kW, for example, as shown in FIG. 30, the heat source A with respect to the predicted load of 300 kW is optimal for targets such as minimum demand, energy saving, and cost saving. Is 70 kW, heat source B is 150 kW, heat source C is 80 kW, and the heat radiation amount is set for each heat source.

JP 2008-11618 A

  However, in the system having the heat source configuration described above, when the operation is actually performed, for example, when the weather forecast is different from the actual weather, the amount of heat supplied from the heat source that is operated using the predicted heat release amount as a command value, There may be a deviation from the actual heat load. When the deviation occurs, if the load result is higher than the predicted load, the supply heat amount (operation result) is insufficient (FIG. 31), the circulating water temperature becomes high, and the indoor temperature cannot be maintained at the set temperature. If the actual load is smaller than the predicted load, the amount of heat supplied (operating record) is too large (FIG. 32), the circulating water temperature becomes low, and in some cases, the protective function is activated in the refrigerator or the like, and it stops.

  The heat source is operated according to the predicted command value in order to keep the daily power profile within the target power, and if the power consumption of the heat source becomes larger than predicted by operating differently from the prediction, the target power may be exceeded. Because.

  Also, if the heat source is a heat storage tank and the weather forecast differs from the actual weather, the actual remaining storage amount will be less than the planned remaining storage amount when performing air conditioning using heat storage from the heat storage tank Will occur. If it does so, in the time slot | zone after that, heat storage will be consumed earlier than planned, and heat storage will be lost before the end of an air-conditioning time slot | zone. In order to continue the necessary heat supply even after the heat storage is lost, it is necessary to increase the operation output of the heat source, but the power consumption increases accordingly, and the demand target may be exceeded.

  The present invention has been made in view of such circumstances, and an object of the present invention is to reduce the difference between the predicted operation plan and the actual operation status in the air conditioning facility. An operation management device, an operation management method, and an operation management program are provided.

  In order to solve the above-described problem, the present invention provides an air-conditioning heat load prediction unit that calculates an air-conditioning heat load prediction value that is a value obtained by predicting the amount of heat that needs to be supplied from each of a plurality of air-conditioning heat source equipment on a prediction target date. When the electric power used for each time of each air-conditioning heat source equipment that generates the amount of heat indicated by the predicted air-conditioning heat load is assigned, the heat of the air-conditioning heat source equipment for at least one of the plurality of air-conditioning heat source equipment An operation plan creation unit that creates an air conditioning heat source operation plan that assigns power to be controlled according to the temperature of the outlet that performs output and assigns power to be used according to the heat output value for other air conditioning heat source equipment It is characterized by that.

  Moreover, this invention detected the detection part which detects the thermal storage amount of a thermal storage tank, the 1st memory | storage part which memorize | stores the planned value of the thermal storage amount of the said thermal storage tank for every time slot | zone in an air-conditioning operation period, and the said detection part detected Comparing the heat storage amount with the planned value of the heat storage amount corresponding to the time zone in which the detection was performed, and the planned value of the power consumption of the air conditioning facility for each time zone in the operation plan for operating the air conditioning facility Based on the comparison result of the second storage unit that stores a certain power plan value and demand target value, and the comparison unit, when the detected heat storage amount is less than or equal to the plan value of the heat storage amount, the detection is If there is a time zone in which the power plan value is less than or equal to the demand target value after the performed time zone, and there is a time zone in which the power plan value is less than or equal to the demand target value, Difference between demand target value and power plan value In response, characterized in that it comprises, and operation plan creating unit for changing the operation plan to assign so as to operate the heat source.

  Further, in the operation management device described above, the operation plan creation unit, after changing the operation plan, when the heat storage amount detected by the detection unit is greater than or equal to the plan value of the heat storage amount, The operation plan after the time zone when the heat storage amount detected by the detection unit is equal to or more than the planned value of the heat storage amount is returned to the operation plan before the change.

  Further, the present invention is an operation management method in an operation management apparatus that is a computer, wherein the air conditioning heat load prediction processing unit predicts the amount of heat that needs to be supplied from each of a plurality of air conditioning heat source equipment devices on the prediction target date. The air conditioning heat load predicted value is calculated, and the operation plan creation unit assigns the electric power used at each time of each air conditioning heat source facility device that generates the amount of heat indicated by the air conditioning heat load predicted value. Assign power usage to control according to the temperature of the outlet that performs heat output of the air conditioning heat source equipment equipment for at least one of the equipment equipment, and assign power usage according to the heat output value to other air conditioning heat source equipment equipment An air conditioning heat source operation plan is created.

  Moreover, this invention is the operation management method in the operation management apparatus which is a computer, Comprising: A detection part detects the heat storage amount of a thermal storage tank, and the comparison part is the time in the air-conditioning operation period memorize | stored in the 1st memory | storage part. For each band, the planned value of the heat storage amount of the heat storage tank is compared with the heat storage amount detected by the detection unit, and the operation plan creation unit determines whether the detected heat storage amount is based on the comparison result of the comparison unit. A second storage unit that stores a power plan value and a demand target value, which are plan values of power consumption of the air conditioning equipment for each time zone in an operation plan for operating the air conditioning equipment when the heat storage amount is equal to or less than the planned value , It is searched whether there is a time zone in which the power plan value is less than or equal to the demand target value after the time zone in which the detection is performed, and the time in which the power plan value is less than or equal to the demand target value If there is a belt, the hoax According to the difference between the de target value and the power planned value, and changes the operation plan to assign so as to operate the heat source.

  In addition, the present invention provides an air conditioning heat load prediction processing unit that calculates an air conditioning heat load prediction value that is a value obtained by predicting the amount of heat that needs to be supplied from each of a plurality of air conditioning heat source equipment on a prediction target date, the air conditioning An outlet for performing heat output of the air conditioning heat source facility equipment for at least one of the plurality of air conditioning heat source equipment devices when allocating electric power used for each time of each air conditioning heat source equipment device that generates the amount of heat indicated by the heat load predicted value Operation management to function as an operation plan creation unit that creates an air conditioning heat source operation plan that allocates power to be controlled according to the temperature of the air and assigns power to other air conditioning heat source equipment according to the heat output value It is a program.

  In addition, the present invention provides a computer with a detection unit that detects a heat storage amount of the heat storage tank, a planned value of the heat storage amount of the heat storage tank for each time zone in the air conditioning operation period stored in the first storage unit, and a detection unit. Comparing the detected heat storage amount with the comparison unit, based on the comparison result of the comparison unit, when the detected heat storage amount is less than or equal to the planned value of the heat storage amount, the time in the operation plan for operating the air conditioning equipment A second storage unit that stores a power plan value that is a plan value of power consumption of the air-conditioning equipment for each band and a demand target value is referred to, and the power plan value is the demand demand after the time zone in which the detection is performed. It is searched whether there is a time zone where the target value is less than or equal to the target value, and when there is a time zone where the power plan value is less than or equal to the demand target value, depending on the difference between the demand target value and the power plan value To operate the heat source Operation plan creating unit for changing the operation plan to allocate a operation management program to function as a.

  As described above, according to the present invention, even if there is a difference between the predicted operation plan and the actual operation state in the air conditioning equipment, the difference can be reduced.

It is a block diagram showing an example of composition of a smart grid system concerning a 1st embodiment of the present invention. It is a block diagram which shows an example of a structure of the operation management apparatus which concerns on 1st Embodiment of this invention. It is a figure which shows an example of the air-conditioning heat load estimated value which concerns on 1st Embodiment of this invention. It is a figure which shows an example of the electric power generation output prediction result data which concern on 1st Embodiment of this invention. It is a figure for demonstrating an example of the preparation method of the operation plan which concerns on 1st Embodiment of this invention. It is a figure for demonstrating an example of the electric power load predicted value calculated | required by the driving | operation plan process which concerns on 1st Embodiment of this invention. It is a figure for demonstrating an example of the electric power load estimated value calculated | required by the driving | operation plan process and plan DR process which concern on 1st Embodiment of this invention. It is a figure for demonstrating an example of real-time DR (Demand Response) processing concerning a 1st embodiment of the present invention. It is a figure which shows the example of the plant | facility which is the control object of the demand power which concerns on 1st Embodiment of this invention. It is a flowchart for demonstrating an example of the driving | running plan management method which concerns on 1st Embodiment of this invention. It is a block diagram which shows an example of a structure of the plan DR preparation part which concerns on 1st Embodiment of this invention. It is a flowchart for demonstrating an example of the production method of plan DR which concerns on 1st Embodiment of this invention. It is a figure which shows an example of the electric power load estimated value which concerns on 1st Embodiment of this invention. It is a figure which shows the other example of the electric power load estimated value which concerns on 1st Embodiment of this invention. It is a block diagram which shows an example of a structure of the load power control apparatus which concerns on 1st Embodiment of this invention. It is a figure which shows the example of the timing of the real-time DR process which concerns on 1st Embodiment of this invention. It is a figure which shows an example of the priority table which concerns on 1st Embodiment of this invention. It is a flowchart which shows an example of the real-time DR process of the prediction mode which concerns on 1st Embodiment of this invention. It is a flowchart which shows an example of the real-time DR process of the performance mode which concerns on 1st Embodiment of this invention. It is a figure showing an example of the facility which is the control object of demand power. It is a figure showing the relationship between the estimated load and the operation plan of a heat source. It is a figure showing the relationship between a load performance and a driving performance. It is a figure showing the relationship between a load performance and a driving performance. It is a figure showing the relationship between the estimated load and the operation plan of a heat source. It is a figure showing the relationship between a thermal load and time. It is a figure showing the relationship between electric power and time. It is a figure showing the relationship between a thermal load and time. It is a figure showing the relationship between an air-conditioning load and time. It is a figure showing an example of a heat source structure. It is a figure showing the relationship between the estimated load and the operation plan of a heat source. It is a figure showing the relationship between a load performance and a driving performance. It is a figure showing the relationship between a load performance and a driving performance.

<First Embodiment>
Hereinafter, an example of a smart grid system including an operation management apparatus 1 according to an embodiment of the present invention will be described with reference to FIG.
FIG. 1 is a schematic block diagram illustrating an example of a smart grid system according to the present embodiment. As shown in FIG. 1, the smart grid system includes an operation management device 1, an air conditioning heat source facility device 3, a work facility device 4, a power supply device 6, a power supply output control device 7, and a load power control device 8. .
This smart grid system includes, for example, an air conditioning heat source equipment 3 and a work equipment 4 as load devices. In addition, since the air-conditioning heat-source equipment 3 is provided with the heat source, the fluctuation of power consumption is large compared with the work-equipment equipment 4, and the power consumption tends to fluctuate according to the weather and indoor usage environment.

The air conditioning heat source equipment 3 includes an air conditioning heat source unit 31, an external air conditioner 32, an air conditioner 33, a heat circulation mechanism 34, and a package air conditioner 35. Among the air conditioning heat source equipment 3, the air conditioning heat source unit 31, the external air conditioner 32, and the air conditioner 33 adjust the temperature of the medium (for example, water) that circulates through the thermal circulation mechanism 34, for example, each room. It is the equipment which adjusts the indoor temperature via the air conditioner 33 installed in the. The air conditioning heat source equipment 3 uses the heat circulation mechanism 34 to provide the external air conditioner 32 and the air conditioner 33 with the amount of heat required by the load device.
This heat circulation mechanism 34 is, for example, a pipe 341 that is stretched around each room and filled with a medium (liquid or gas) that retains the amount of heat given by the air conditioning heat source unit 31, a pump 342 that circulates this medium, It includes a heat storage tank 343 that stores a medium that holds the amount of heat.
The air conditioning heat source unit 31 includes, for example, a heat pump, a gene link, and the like, and performs a heating process for increasing the temperature of the medium filled in the pipe 341 and a cooling process for decreasing the temperature of the medium.
The outside air conditioner 32 takes in outside air and adjusts the outside air temperature to some extent according to the room temperature.
The air conditioner 33 uses the temperature of the medium heat-treated or cooled by the air-conditioning heat source device 31 to adjust the outside air taken in by the external air conditioner 32 according to the room temperature.
The package air conditioner 35 adjusts the outside air taken in by the external air conditioner 32 according to the room temperature without using the temperature of the medium heated and cooled by the air conditioning heat source device 31.

The work equipment 4 includes a PC (Personal Computer) 41, a lighting device 42, and an OA (Office Automation) device 43.
The PC 41, the lighting device 42, and the OA device 43 are work facility devices that are often installed in buildings such as offices, and are examples of the work facility device 4 according to the present invention.

In addition, the smart grid system includes a first generator 61, a second generator 62, a storage battery 63, and a power purchase power supply 64 as power supply devices 6 that supply power to these load devices.
The first generator 61 generates electric power by private power generation using wind energy, solar energy, or the like. Since the power generation energy of the first generator 61 depends on the weather, the output power is not constant.
The second generator 62 is a generator such as a gas engine generator or a gas turbine generator. Since the second generator 62 does not use the power generation energy that depends on the weather, the output power can be adjusted.
The storage battery 63 stores the generated power generated by the first generator 61 and the second generator 62 and the purchased power output from the purchased power source 64.
The power purchase power supply 64 outputs, for example, the power purchased by the user from the power company (power purchase power).
Regarding power purchase from this power company, the contract power C [kw] is determined according to the user, and the average power used for a predetermined time (demand time limit) exceeds the contract power C. In such a case, an additional fee such as a penalty is imposed on the payment fee determined in advance according to the contract power C. Here, the contract power may be hereinafter referred to as a demand target value C. Hereinafter, the predetermined fixed time corresponds to a demand time period, and is a fixed time arbitrarily determined, for example, 30 minutes.

The operation management apparatus 1 manages the air conditioning heat source operation plan and the power supply facility operation plan.
This air conditioning heat source operation plan is a plan in which the thermal load required by the load device corresponding to the air conditioning heat source is allocated for each demand time period and this allocation is shown for each time. The air-conditioning heat source operation plan is, for example, a load device corresponding to an air-conditioning heat source predicted in each time zone of the day in order to supply an amount of heat expected to adjust the temperature to a preset temperature predetermined on the prediction date. Indicates the amount of heat dissipated and the amount of heat stored in the heat storage tank.
The power supply facility operation plan is a plan showing allocation of power sources (generated power and purchased power) to be supplied to all load devices at each time. This power supply facility operation plan is based on the predicted power load expected to be fed from the power supply device 6 for each power output (first generator 61, second generator 62, and storage battery included in the power supply device 6). 63 and the schedule of operation for each purchased power source 64). For example, the power supply facility operation plan indicates the power required for the power supply device 6 to operate every predetermined time (demand time limit) in order to supply the supply power predicted in each time zone of the day.
The details of the air conditioning heat source operation plan and the power supply facility operation plan will be described later.

The operation management device 1 inputs in advance various information for optimal operation control of the air conditioning heat source equipment 3, work equipment 4, and power supply equipment 6, and based on the information, air conditioning heat load prediction processing and power generation output prediction. Process. In this air conditioning thermal load prediction process, a process of calculating the amount of heat (air conditioning thermal load) predicted to be necessary to adjust the temperature to a predetermined set temperature is performed. In the power generation output prediction process, a process of calculating power predicted to be generated by the first generator 61 is performed.
The operation management device 1 performs an operation plan creation process based on the results of the air conditioning thermal load prediction process and the power generation output prediction process. The operation plan creation unit 105 creates an air-conditioning heat source operation plan according to the predicted air-conditioning heat load based on these prediction results, and from the purchased power source 64 according to the predicted air-conditioning heat load. Indicates the power load of each power source (the first generator 61, the second generator 62, the storage battery 63, and the purchased power source 64) of the power source device 6 at which the purchased power becomes an arbitrary target value (for example, the minimum value). Create a power plant operation plan.
The basic process of the operation management apparatus 1 is to collect a variety of information and create an air conditioning heat load prediction process and a power generation output prediction process, and a process step 1 to create an air conditioning heat source operation plan and a power supply facility operation plan based on the results. This is two processing steps, that is, a process step 2 for performing an operation plan creation process.
In addition to these two processing steps, the operation management apparatus 1 according to the present embodiment performs a process for executing the plan DR and a process for executing the real-time DR according to circumstances.
Details will be described later.

The power output control device 7 supplies power from the power supply device 6 to the air conditioning heat source equipment device 3 and the work equipment device 4 that are load devices based on the operation plan, plan DR, and real-time DR processing by the operation management device 1. While specifying the source (the 1st generator 61, the 2nd generator 62, the storage battery 63, the power purchase power supply 64), the electric power which this electric power source outputs, and its timing are controlled. Specifically, the power output control device 7 controls the power output from the first generator 61, the second generator 62, and the storage battery 63 in the power source. Note that the power output from the power purchase power supply 64 is the power output from the first generator 61, the second generator 62, and the storage battery 63 in response to the demand of the load device (the air conditioning heat source equipment 3 and the work equipment 4). Then there is insufficient power.
The load power control device 8 controls the operation of the air conditioning heat source equipment 3 and the work equipment 4 based on the operation plan, plan DR, and real-time DR processing by the operation management device 1.

  Next, an example of the configuration of the operation management device 1 will be described with reference to FIG. FIG. 2 is a block diagram illustrating an example of the configuration of the operation management apparatus 1 according to the present embodiment. In this figure, the operation management apparatus 1 includes a first storage unit 101, an air conditioning thermal load prediction processing unit 102, a second storage unit 103, a power generation output prediction processing unit 104, an operation plan creation unit 105, and an electric power load. Prediction unit 106, third storage unit 107, demand excess determination unit 108, plan DR creation unit 109, real-time DR execution instruction unit 110, output unit 115, data management processing unit 116, program management processing unit 117.

The first storage unit 101 stores weather forecast data 111, power load pattern data 112, actual measurement data 113, and evaluation axis setting data 114.
The weather forecast data 111 is data in which weather forecast information indicating the expected weather at a predetermined time of the day is associated with a time zone (or time) at regular intervals. This weather forecast information is information indicating the weather, temperature, humidity, precipitation probability, sunshine duration, and the like that are expected in the associated time zone.

  The power load pattern data 112 includes, for example, information indicating a pattern of power consumption consumed by the work facility device 4 according to the time zone of the day. More specifically, in the time zone from 9:00 to 20:00 when many people are active during the day, the power consumption is larger than the time zone from 24:00 to 5:00 when many people are sleeping. Will increase. Moreover, the power consumption on holidays is less than on weekdays. Thus, the power consumption in the time zone of the day differs according to the human behavior pattern. The power load pattern data 112 is obtained by patterning this difference in accordance with the number of work equipment and the user.

  Moreover, the power load pattern data 112 includes information indicating a calculation pattern of power consumption consumed by the air conditioning heat source equipment 3 according to, for example, a time zone of a day. More specifically, even in an air conditioner installed in a room of the same size, the temperature change in the room in a day varies depending on the number of people entering the room, the number of air conditioners, and the sun. For example, when a meeting is being held all day, there are many people going in and out, so the temperature in the room changes throughout the day and the power load on the air conditioning heat source equipment 3 increases. On the other hand, when there is no plan for a meeting all day, there is little need to maintain the room temperature at a comfortable temperature, and it may be higher than the room temperature (comfort temperature) that can be comfortably in summer, even in winter. The temperature may be lower than the comfortable temperature or may be stopped. Since the power load pattern data 112 is used to calculate the power load on the air conditioning heat source equipment 3 by calculation, the operation ratio and temperature adjustment of the day are patterned according to the usage status of the room where the air conditioner 33 is installed. It has become.

The actual measurement data 113 indicates actual measurement values of power consumption actually consumed by the load devices of the air conditioning heat source equipment 3 and the work equipment 4 and actual measurement values of supply power actually supplied from the power supply device 6. This actual measurement value is measured in each device at regular time intervals, for example, and is associated with a time zone per day.
The evaluation axis setting data 114 includes a reference value for dealing with evaluations such as energy saving.

The air conditioning heat load prediction processing unit 102 predicts the amount of heat required for indoor temperature adjustment based on the weather forecast data indicating the expected weather and the power load pattern data 112. In other words, the air-conditioning heat load prediction processing unit 102 calculates an air-conditioning heat load prediction value indicating the amount of heat (air-conditioning heat load) predicted to be necessary to adjust the temperature to a predetermined set temperature. This air conditioning heat load predicted value is the amount of heat, and can be rephrased as load power applied to the air conditioning heat source unit 31 by converting into electric power based on characteristics such as the outside air temperature and partial load factor of the air conditioning heat source unit 31.
An example of the predicted air conditioning heat load is shown in FIG. As shown in the graph of FIG. 3, the predicted air-conditioning heat load can be represented by a graph in which the horizontal axis represents time and the vertical axis represents the air-conditioning heat load. As illustrated, the air conditioning heat load during the day is greater than the air conditioning heat load during the night.
Returning to FIG. 2, the processing of the air conditioning thermal load prediction processing unit 102 will be specifically described. The air conditioning heat load prediction processing unit 102 performs the following processes (11) to (15).

(11) Measured data collection processing The air conditioning thermal load prediction processing unit 102 obtains measured data from the power supply device 6, the air conditioning heat source equipment 3 and the work equipment 4 from the power output control device 7 and the load power control device 8 at regular time intervals. And stored in the actual measurement data 113 of the first storage unit 101.

(12) Weather forecast data collection processing The air conditioning thermal load prediction processing unit 102 is connected to a server on the Internet that stores weather forecast information and the like announced by the Japan Meteorological Agency, for example, for each time set in advance. Download information. The air conditioning heat load prediction processing unit 102 stores the downloaded weather forecast information in the first storage unit 101 as the weather forecast data 111 in association with the time zone of the day.

(13) ANN Load Prediction Processing The air conditioning thermal load prediction processing unit 102 creates prediction data based on actual measurement data and weather forecast data, for example, starts an ANN (Artificial Neural Network) load prediction program using a neural network. To predict the load data.
That is, the air conditioning thermal load prediction processing unit 102 reads the weather forecast data 111, the power load pattern data 112, and the actual measurement data 113 from the first storage unit 101, and adjusts the temperature to a predetermined set temperature based on the read data. Calculate the amount of heat (air conditioning heat load) predicted to be necessary. The air conditioning thermal load prediction processing unit 102 writes the generated air conditioning thermal load predicted value in the first storage unit 101. For this ANN load prediction process, an existing technology (see, for example, JP-A-2006-78009) can be used. The air conditioning thermal load prediction processing unit 102 is not limited to the above-described function, and can predict load data using a technique other than a neural network.
In addition, the air conditioning heat load prediction processing unit 102 calculates an air conditioning heat load prediction value that is a value obtained by predicting the total value of the heat supply amounts of the plurality of air conditioning heat source facility devices on the prediction target date.

(14) Prediction correction processing The air conditioning thermal load prediction processing unit 102 corrects the load data from the difference between the actual load data and the prediction data subjected to operation control by the ANN load prediction processing, and prepares the next optimum operation plan. To reflect.
In other words, the air conditioning thermal load prediction processing unit 102 fetches measured data from the power output control device 7 and the load power control device 8 at regular time intervals, and sets the predicted air conditioning thermal load value that coincides with the time when this operation control was performed. 1 Read from the storage unit 101. For example, the air conditioning thermal load prediction processing unit 102 calculates a deviation between the actually measured data and the predicted air conditioning thermal load, and creates a correction value according to the deviation.

The second storage unit 103 stores power generation output pattern data 131, stored power data 132, and weather forecast data 133.
The power generation output pattern data 131 stores information indicating the minimum output value and the maximum output value of each power source (the first generator 61, the second generator 62, and the purchased power source 64).
The stored power data 132 stores information indicating the minimum storage amount and the maximum storage amount of the storage battery 63.
The weather forecast data 133 is data in which the weather forecast information indicating the forecasted weather at a predetermined time of day is associated with time zones (or times) at regular intervals, as described above. This weather forecast information is information indicating the weather, temperature, humidity, precipitation probability, sunshine duration, and the like that are expected in the associated time zone.

The power generation output prediction processing unit 104 calculates the generated power supplied from the power system by the power supply device 6 based on the weather forecast data indicating the expected weather, and outputs the power generation output prediction result data.
An example of this power generation output prediction result data is shown in FIG. As shown in the graph of FIG. 4, the power generation output prediction result data takes time on the horizontal axis and the power generation output (output power) of the first generator 61 on the vertical axis. For example, in the illustrated example, the output power can be acquired only during the day and not at night.
Returning to FIG. 2, the processing of the power generation output prediction processing unit 104 will be specifically described. The power generation output prediction processing unit 104 performs the following processes (21) and (22).

(21) Weather forecast data collection process The power generation output prediction processing unit 104 collects weather forecast information in the same manner as the (12) weather forecast data collection process by the air conditioning thermal load prediction processing unit 102 described above, and the second storage unit 103 as weather forecast data 133.

(22) Power Generation Output Prediction Processing The power generation output prediction processing unit 104 creates prediction data based on the weather forecast data, and starts, for example, a photovoltaic power generation prediction program using the power load pattern data 112 and the weather correction coefficient. The output data is predicted. That is, the power generation output prediction processing unit 104 refers to the weather forecast data 133 and calculates the power predicted that the first generator 61 can generate power according to the weather.

<Description of the configuration for creating the operation plan>
Based on the air conditioning thermal load prediction value, the power generation output prediction result data, and the information in the second storage unit, the operation plan creation unit 105 operates the operation plan for one day at least before the first day (in the specification) The operation plan includes operation plan data indicating an air conditioning heat source operation plan and a power supply facility operation plan. The operation plan creation unit 105 can execute a dedicated program for optimization as the operation plan, and can create an optimal air conditioning heat source operation plan and power supply facility operation plan.
An example of an operation plan creation method by the operation plan creation unit 105 will be described with reference to FIG. FIG. 5 is a diagram for conceptually explaining the air conditioning heat source operation plan and the power supply facility operation plan created by the operation plan creation unit 105. The operation plan creation unit 105 according to the present embodiment includes, for example, The following processing is performed by performing optimization so that the demand is minimized by mathematical programming described later. Here, an example in which the air conditioning heat source unit 31 includes a plurality of air conditioning heat source units 31A and an air conditioning heat source unit 31B will be described.

1) About creation of an air conditioning heat source operation plan The operation plan creation unit 105 assigns an amount of heat that can be generated by the air conditioning heat source unit 31 and the heat storage tank 343 based on the predicted air conditioning heat load value created by the air conditioning heat load prediction processing unit 102. And create an air conditioning heat source operation plan. That is, the operation plan creation unit 105 calculates the amount of heat generated by the air conditioning heat source unit 31 and the amount of heat that can be stored in the heat storage tank 343 for each demand period, according to the predicted air conditioning heat load. The operation plan creation unit 105 displays the calculated amount of heat and the amount of heat to be stored (that is, the amount of heat indicated by the air-conditioning heat load prediction result value) for each time of operation (for example, in a demand time limit). Create an air conditioning heat source operation plan that shows the allocation of This is shown in the graph of FIG.
In the graph of FIG. 5A, the horizontal axis represents time, and the vertical axis represents the heat source production heat amount of the air conditioning heat source unit 31 or the heat storage tank discharge heat storage amount of the heat storage tank 343. In this graph, a positive value on the vertical axis means heat release, and a negative value means heat storage. As shown in the figure, the operation plan creation unit 105 calculates the sum of the production heat amount produced by the air conditioning heat source unit 31 and the heat radiation amount (that is, the heat amount radiated by the air conditioning heat source unit 31 and the heat storage tank 343) for each demand time period in step ST100. The air-conditioning heat source operation plan is created so as to be equal to the air-conditioning heat load predicted value obtained in step.
The graph shown in FIG. 5A indicates that the air-conditioning heat source unit 31A generates heat and stores the amount of heat in the heat storage tank 343 between 0:00 and 6:00. Moreover, it shows that 31A of air-conditioning heat-source equipment produces | generates calorie | heat amount between 6: 00-8: 00, and thermally radiates the calorie | heat amount of the thermal storage tank 343. Moreover, between 8:00 to 16:00, while the air-conditioning heat-source equipment 31A and the air-conditioning heat-source equipment 31B produce | generate an amount of heat, it shows that a thermal storage tank radiates. Moreover, it shows that the air-conditioning heat source machine 31A and the air-conditioning heat source machine 31B generate heat during 16:00 to 22:00. Moreover, between 22:00 to 24:00, while the air-conditioning heat-source equipment 31A produces | generates calorie | heat amount, it shows storing this calorie | heat amount in the thermal storage tank 343. FIG.

2) Calculation of predicted power load value corresponding to predicted air conditioning heat load result This operation plan creation unit 105 produces the amount of heat indicated by the air conditioning heat source operation plan by the air conditioning heat source unit 31 for each demand time period calculated as described above. The power consumption required for this is calculated with reference to the power load pattern data 112. The operation plan creation unit 105 refers to the power load pattern data 112 for the power (power load) necessary for all load devices of the air conditioning heat source equipment 3 and the work equipment 4 in addition to the power consumption of the air conditioning heat source machine 31. To calculate. That is, the operation plan creation unit 105 calculates the power consumption necessary to cover the thermal load indicated by the air conditioning thermal load predicted value and the power consumption necessary for another predicted load device. As described above, the power consumption of the load device calculated by the operation plan creation unit 105 based on the predicted air-conditioning heat load is the power indicated by the demand (power purchased to be supplied to the consumer) and the generated power supplied to the consumer. Therefore, hereinafter, it is referred to as a predicted power load. An example of the predicted power load value is shown in the graph of FIG. In the graph of FIG. 5B, the horizontal axis indicates time, and the vertical axis indicates power load.
In the present embodiment, the operation plan creation unit 105 creates the power supply facility operation plan so that the demand is an arbitrary target value. For convenience of explanation, the power load predicted value is described below. In the explanation, the prediction value corresponding to only the demand excluding the generated power supplied to the consumer among the demand power will be explained.

Moreover, when the operation plan preparation part 105 allocates the electric power used for every time of each air-conditioning heat-source equipment apparatus which produces | generates the calorie | heat amount which an air-conditioning heat load predicted value shows, it air-conditions about at least 1 unit among these air conditioning heat source apparatuses. A command is assigned so as to perform control according to the temperature of the outlet that performs heat output of the heat source equipment, and an air conditioning heat source operation plan that assigns electric power used according to the predicted heat output value to other air conditioning heat source equipment is created.
The operation plan creation unit 105 assigns the external air conditioner 32 or the air conditioner 33 using the heat circulation mechanism 34 as the air conditioning heat source equipment to be controlled according to the temperature of the outlet. In this embodiment, the case where it is the air-conditioning heat source equipment apparatus by the heat storage of the air conditioner 33, ie, a heat storage tank (for example, heat storage tank 343 of FIG. 9) is demonstrated. About the air conditioner 33, it cools using the heat storage of a heat storage tank (for example, heat storage tank 343 of FIG. 9). Therefore, in the range in which cold water is generated in advance and stored in the heat storage tank, the operation of the refrigerator (refrigerator 310) does not occur even if cooling is performed. What is necessary is to predict the power consumption of the air conditioner on the aircraft side. Thereby, the difference between the predicted power consumption of the air conditioner 33 and the actual power consumption hardly occurs. Therefore, even if the actual heat load fluctuates, the difference between the predicted heat load and the actual heat load can be absorbed by controlling the air conditioner 33 based on the outlet temperature of the cold water. And can be controlled so as not to exceed the target power. In addition, here, the operation plan creation unit 105 assigns the air conditioner 33 and the heat storage tank 343 so as to issue an operation command for performing an on / off operation.
Moreover, the operation plan preparation part 105 allocates air-conditioning heat-source equipment other than the air-conditioning heat-source equipment which is controlled according to the temperature of an exit so that control which designates a thermal radiation amount with a command value may be performed.
Here, the temperature of the cold water conveyed from the heat storage tank to the air conditioner in each room is used as the temperature of the outlet that performs heat output of the air conditioning heat source equipment. For example, a detection result of a sensor 350 described later is used.

3) Creation of power supply facility operation plan The operation plan creation unit 105 is based on the predicted power load values of the air conditioning heat source facility device 3 and the work facility device 4 calculated as described above, and the demand is an arbitrary target value (for example, the minimum value). The power supply is allocated so that A power supply facility operation plan shows the allocation of the power supply for each demand period.
For example, when the predicted power load value is lower than the demand target value that is the contract power C, the operation plan creation unit 105 increases the purchased power to the predicted power load value or more and stores the purchased power in the storage battery 63. The power supply equipment operation plan to be used. When the predicted power load value exceeds the demand target value C, the operation plan creation unit 105 keeps the maximum value of the purchased power below the demand target value C and reduces the shortage to the first generator 61 and the first generator 61. The power supply facility operation plan is created so as to be supplemented with the electric power from the two generators 62 and the electric power from the storage battery 63.
FIG. 5C is a graph showing the predicted power load value with the horizontal axis representing time and the vertical axis representing power load. As shown in FIG. 5 (c), the predicted power load value is lower than the demand target value C between 0:00 and 6:00. For this reason, the operation plan creation unit 105 creates a power supply facility operation plan for increasing the purchased power to a power load predicted value or more and storing the purchased power in the storage battery 63. On the other hand, the predicted power load value exceeds the demand target value C between 6:00 and 18:00. For this reason, the operation plan creation unit 105 sets the maximum value of the purchased power to the demand target value C or less, sets the power load equal to or higher than the demand target value C to the power from the first generator 61 and the second generator 62 and the storage battery. In order to make up for the power from 63, power is allocated for each demand period. The operation plan creation unit 105 sets the shortage between 0:00 and 6:00 based on the output prediction of the first generator 61 indicated by the power generation output prediction result data predicted by the power generation output prediction processing unit 104. The power supply facility operation plan is created by combining the purchased power stored in the storage battery 63 and the generated power from the second generator 62.

  Specifically, the operation plan creation unit 105 creates a power supply facility operation plan using a mathematical programming method based on the following evaluation function, variable, and constraint conditions.

The abbreviations shown in the mathematical expressions are as follows.
nt {nt = 1, 2,...} is information indicating a demand time limit in a time zone in a daily schedule. When the demand time period is 30 minutes, the maximum value of nt (nt_max) = 24 [hour] / demand time period (0.5) [hour] = 48.
ng {ng = 1, 2,...} indicates the number of first generators 61 and second generators 62.
nb {nb = 1, 2,...} indicates the number of storage batteries 63.
nh {nh = 1, 2,...} indicates the number of air conditioning heat source units 31.
nhs {nhs = 1, 2,...} indicates the number of heat storage tanks 343.
Pg indicates output power from the first generator 61 and the second generator 62.
Pb indicates the output power of the storage battery 63. Discharge is indicated by a positive numerical value.
Qh indicates the amount of heat produced by the air conditioning heat source, which is the amount of heat produced by the air conditioning heat source unit 31.
Qhs indicates the amount of heat stored in the heat storage tank, which is the amount of heat radiated by the heat storage tank 343.

Δg indicates a generator start / stop state in which the first generator 61 and the second generator 62 are in a start state or in a stop state.
Δb indicates a storage battery start / stop state indicating whether the storage battery 63 is in a start state or a stop state.
Δh indicates an air conditioning heat source start / stop state indicating whether the air conditioning heat source unit 31 is in the start state or in the stop state.
Pp represents the purchased power output from the purchased power supply 64.
Pl is the predicted load power, which is a predicted power load value.
Ql is the predicted heat load of the air conditioning heat load.
Pg_min indicates a generator minimum output that is a minimum value of output power of the first generator 61 and the second generator 62.
Pg_max indicates a generator maximum output that is the maximum value of the output power of the first generator 61 and the second generator 62.

Pb_min indicates the storage battery minimum output that is the minimum value of the stored power of the storage battery 63.
Pb_max indicates the storage battery maximum output that is the maximum value of the stored power of the storage battery 63.
Qh_min is the air conditioning heat source minimum production heat quantity indicating the minimum value of the heat quantity produced by the air conditioning heat source unit 31.
Qh_max is an air conditioning heat source maximum production heat quantity indicating the maximum value of the heat quantity produced by the air conditioning heat source unit 31.
Zb is a storage battery remaining storage amount indicating the remaining amount of stored power stored in the storage battery 63.
Zhs is the heat storage tank residual heat storage amount indicating the remaining amount of heat storage amount stored in the heat storage tank 343.
Zb_min is a storage battery minimum storage amount indicating a minimum value of stored power stored in the storage battery 63.
Zb_max is a storage battery maximum storage amount indicating the maximum value of the storage power stored in the storage battery 63.
Zhs_min is a heat storage tank minimum heat storage amount indicating the minimum value of the heat storage amount stored in the heat storage tank 343.
Zhs_max is the heat storage tank maximum heat storage amount indicating the maximum value of the heat storage amount stored in the heat storage tank 343.
PE is a penalty.
α is a penalty coefficient.

  Moreover, the operation plan preparation part 105 determines the power supply equipment operation plan of the day when a generator, a storage battery, and an air-conditioning heat-source equipment become optimal by mathematical programming. In this case, the operation plan creation unit 105 can perform operations such as energy saving, cost saving, and C2 emission minimization by setting the optimum purpose to energy saving, cost saving, CO2 emission minimization, etc. during a period when demand is small. It becomes. In addition, when the maximum demand predicted value predicted by energy saving and cost saving exceeds the demand target value, the operation plan creation unit 105 minimizes demand by applying mathematical programming with the optimal purpose as demand minimization. Determine the daily power facility operation plan for the generator, storage battery, and air conditioning heat source.

<Description of the configuration for performing the plan DR>
The power load predicting unit 106 inputs the operation plan data from the operation plan creating unit 105 and predicts by detecting a maximum value (hereinafter, demand peak value) of an average value of demand per demand time period (demand power). More specifically, when the air conditioning heat source operation plan is created by the operation plan creation unit 105, the power supply allocation is performed as shown in FIG. 5C based on the predicted power load shown in FIG. The power supply operation plan shown is created. Here, the power supply facility operation plan created by the operation plan creation unit 105 optimizes the demand to the minimum value, and the allocation of purchased power is not necessarily equal to or less than the demand target value C.
Here, with reference to FIG. 6, the ratio of the demand power which shows the demand of the power purchase power supply 64 in the power supply equipment operation plan which the operation plan preparation part 105 produced in the electric energy is demonstrated. FIG. 6 shows an example of a power supply facility operation plan for demand when there are two demand peak values, which are the maximum values of demand power, and both exceed the demand target value C.
FIG. 6 is a graph showing demand power with time on the horizontal axis and power on the vertical axis.
FIG. 6 shows data 1 indicating demand power before the operation plan is executed and data 2 indicating demand power after the operation plan is executed. Note that the data 1 is a comparative example when not according to the present invention, and the operation plan creation unit 105 according to the present invention calculates demand power as shown in the data 2.
As shown in the figure, the demand power of data 2 shows demand peak values at 11:00 and 15:00, and the demand target value C between 10:30 and 11:30 and between 14:30 and 15:30. Over. The demand peak value P1 at 11:00 is smaller than the demand peak value P2 at 15:00.
The power load prediction unit 106 detects the demand peak value of the demand power, and detects the maximum demand peak value among the detected demand peak values as the demand maximum value. In the example illustrated in FIG. 6, the power load prediction unit 106 detects the demand peak value P2 as the demand maximum value. The demand maximum value, as described above, measures the actual value of demand power at the customer's facility, etc., and the maximum value among the actual demand power values for one month is called the demand maximum value for the month. .

In addition, the demand power shown in the data 2 usually increases during working hours. The reason for this is that while the power consumption by the work equipment 4 changes relatively flat (smooth), the power consumption by the power equipment such as the air conditioning heat source 31 greatly changes according to changes in weather conditions and the like. It is. This data 2 has a lower demand peak value than data 1.
Based on the operation plan data, the power load prediction unit 106 detects the demand power that peaks at the time 11:00 and the time 15:00 shown in the data 2 after the operation plan is implemented, and outputs the demand power to the demand excess determination unit 108. When a plurality of demand powers corresponding to the demand peak value exceeding the detected demand target value are detected, the power load prediction unit 106 outputs the demand load value to the demand excess determination unit 108 in descending order of the demand power value. May be.

The third storage unit 107 stores setting value data 171.
The set value data 171 is information indicating a demand target value C, for example, contract power C.

The demand excess determination unit 108 receives the demand power of the demand peak value and the operation plan data from the power load prediction unit 106, and the peak value of the average value of the demand corresponding to the demand time limit indicated in the power supply facility operation plan (maximum) Value) exceeds the demand target value that is the contract power amount C.
When the demand excess determination unit 108 determines that at least one demand peak value exceeds the demand target value C, the control signal for controlling the plan DR creation unit 109 to create the plan DR. Is output to the plan DR creation unit 109.
On the other hand, when it is determined that all the demand peak values do not exceed the demand target value, the demand excess determination unit 108 together with the determination result indicating that the demand peak value does not exceed the demand target value, the operation plan creation unit 105 The operation plan data created by the above is output to the output unit 115.

Further, when the plan DR created by the plan DR creation unit 109 is input, the demand excess determination unit 108 purchases power that is a commercial power system among the predicted DR power load per demand time limit in the plan DR. It is determined whether or not the peak value of the average demand corresponding to the demand time period supplied from the power supply 64 exceeds the demand target value amount C.
When the demand excess determination unit 108 determines that at least one demand peak value exceeds the demand target value, the demand excess determination unit 108 instructs the real-time DR execution instruction unit 110 to execute the real-time DR. The signal is output to the real-time DR execution instruction unit 110.
On the other hand, when it is determined that the peak value of the average demand corresponding to all demand time periods does not exceed the demand target value, the demand excess determination unit 108 indicates that the demand peak value does not exceed the demand target value. Together with the determination result, the operation plan data created by the operation plan creation unit 105 and the plan DR created by the plan DR creation unit 109 are output to the output unit 115.

When the demand excess determination unit 108 determines that at least one demand peak value exceeds the demand target value C, the plan DR creation unit 109 has the demand peak value exceeding the demand target value in the power supply facility operation plan A plan DR indicating an operation plan for changing the predicted power load per demand time period is created.
For example, the plan DR creation unit 109 calculates a DR power load predicted value obtained by subtracting the power load predicted value for the demand time period in which the demand power exceeds the demand target value by the amount by which the demand power exceeds the demand target value. . The plan DR creation unit 109 assigns power loads of the air conditioning heat source equipment 3 and the work equipment 4 based on the calculated DR power load predicted value, and creates a plan DR. The demand changed by the plan DR creation unit 109 is referred to as a demand predicted value.
For example, as shown in FIG. 6, the demand time period (n) in which the demand power exceeds the demand target value in the data 2 after the operation plan is implemented is 30 minutes from 10:30 (n = 21) and 11:00. 30 minutes from 00 minutes (n = 22), 30 minutes from 14:30 (n = 29), and 30 minutes from 15:00 (n = 30).
The plan DR creation unit 109 selects, for example, one of the air conditioning heat source equipment 3 and the work equipment 4 for the demand time period (for example, n = 21, 22, 29, 30) when the demand power exceeds the demand target value. The plan DR is created so that the operation of the external air conditioner 32 and the lighting device 42 in the partial area is stopped or the output is reduced. For example, the plan DR creation unit 109 changes the operation plan so that the demand peak value is equal to or less than the demand target value C as shown in FIG. FIG. 7 is a graph illustrating demand power, with the horizontal axis representing time and the vertical axis representing power. The main purpose of the external air conditioner 32 is to ventilate the room. After sufficient ventilation, no major problems will occur even if it is stopped for a while.

<Description of configuration for performing real-time DR>
The real-time DR execution instruction unit 110 determines that the demand peak value does not exceed the demand target value when the demand excess determination unit 108 determines that at least one demand peak value exceeds the demand target value. Except for the time zone, the power output control device 7 and the load power control device 8 are controlled based on the operation plan data and the plan DR data.
The real-time DR execution instructing unit 110 executes real-time DR processing in a time zone where the demand peak value is expected to exceed the demand target value. The real-time DR execution instructing unit 110 provides an instruction signal indicating that the load power control device 8 is instructed to control the demand power according to the actual value of the amount of power supplied to the air conditioning heat source equipment 3 and the work equipment 4. Output to the output unit 115.
FIG. 8 is a graph showing demand power, with the horizontal axis representing time and the vertical axis representing power. Referring to FIG. 8, if the demand power cannot be suppressed below the demand target value even by the plan DR creation unit 109, the real-time DR execution instruction unit 110 uses the actual measured value as the demand target value as shown in FIG. May be exceeded. The real-time DR execution instructing unit 110 predicts a demand corresponding to the demand time limit, and executes a real-time DR process when it is determined that the demand target value is exceeded. The real-time DR process will be described later in detail.

The output unit 115 outputs the input operation plan creation data (information indicating the air conditioning heat source operation plan and the power supply facility operation plan) to the power output control device 7 and the load power control device 8.
The output unit 115 outputs the input planned DR data and the real-time DR execution instruction signal to the load power control device 8.

The data management processing unit 116 manages various collected data. It has reference functions such as actual measurement data, weather forecast data, predicted load data, and operation plan data, a download function, a correction function such as pattern data, a correction function for schedule data, and a parameter setting function such as contract power and optimization evaluation axis.
The program management processing unit 117 performs schedule management for optimal operation control such as at what timing each process for creating an optimal operation plan is started. In the schedule management, the program is controlled at the start time based on the daily processing schedule data.

Next, an example of the air conditioning heat source equipment 3 will be described.
FIG. 9 is a diagram illustrating an example of the air conditioning heat source equipment 3. The load power control device 8 controls the power demand of the plurality of air conditioning heat source equipment 3 provided in the air conditioning equipment that cools the room with nighttime power, for example.
In FIG. 9, among the air conditioning heat source equipment 3, the refrigerator 310 that is the air conditioning heat source unit 31, the heat storage tank 343 of the heat circulation mechanism 34, the air conditioners 33-1 to 33-4, and the pump 342 (primary pump 342-1 and secondary pump 342-2) will be described as an example. In the facility to be controlled, there are a plurality of control target devices that operate such a plurality of power units. The refrigerator 310 includes, for example, a compressor 36 and a primary pump 342-1. The compressor is supplied with 50 kW power, and the primary pump 342-1 is supplied with 5 kW power. The secondary pump 342-2 supplies the water stored in the heat storage tank 343 to the plurality of air conditioners 33-1 to 33-4. The secondary pump 342-2 is supplied with 5 kW of power. A plurality of chilled water valves 37 (a chilled water valve 37-1, a chilled water valve 37-2, a chilled water valve 37-3, a chilled water valve 37-4,...) Respectively air-condition the chilled water supplied from the secondary pump 342-2. Supply to machine 33-1 to 33-4. Each of the air conditioners 33-1 to 33-4 includes a fan (fan 38-1, fan 38-2, fan 38-3, fan 38-4,...), And the fan has a power of 2 kW. Is supplied. Here, the air conditioners 33-1 to 33-4 perform floor blowing air conditioning in which cold air based on supplied cold water is supplied into the double floor and then blown into the room.

  The temperature sensor 350 is attached to a pipe to which the heat storage tank 343 and the secondary pump 342-2 are connected, and detects the temperature of cold water conveyed from the heat storage tank 343 to each of the air conditioners 33-1 to 33-4. Then, the detection result is output to the operation plan creation unit 105.

Next, an example of the operation plan management method according to the present embodiment will be described with reference to FIG.
FIG. 10 is a flowchart for explaining an outline of the operation plan management method according to the present embodiment.
As shown in FIG. 10, first, the air conditioning thermal load prediction processing unit 102 performs actual measurement data collection processing and weather forecast data collection processing. Further, the power generation output prediction processing unit 104 performs weather forecast data collection processing (step ST1).
Next, the power generation output prediction processing unit 104 performs power generation output prediction processing. That is, the power generation output prediction processing unit 104 refers to the power generation output pattern data 131, the stored power data 132, and the weather forecast data 133 to generate power that indicates power predicted to be generated by the first generator 61 according to the weather. Output prediction result data is calculated (step ST2).
The air conditioning thermal load prediction processing unit 102 performs an air conditioning thermal load prediction process. That is, the air-conditioning heat load prediction processing unit 102 calculates an air-conditioning heat load prediction value indicating the amount of heat (air-conditioning heat load) predicted to be necessary to adjust the temperature to a predetermined set temperature (step ST3). Since this air conditioning load prediction uses weather forecast data, it is executed several times a day at the timing when the weather forecast is updated.

Next, the operation plan creation unit 105 performs an operation plan creation process (step ST4). That is, the operation plan creation unit 105 calculates the amount of heat produced by the air-conditioning heat source unit 31 and the amount of heat stored in the heat storage tank 343 corresponding to the amount of heat indicated by the air-conditioning heat load prediction value for each demand time period, thereby predicting the air-conditioning heat load. Create an air conditioning heat source operation plan corresponding to the result value.
Moreover, the operation plan preparation part 105 allocates the external air conditioner 32 and the air conditioner 33 as an air-conditioning heat-source equipment apparatus, when producing an air-conditioning heat-source operation plan. And the operation plan preparation part 105 allocates instruction | command so that it may control according to the temperature of the exit which performs heat output about the air conditioning machine 33, and it controls by the command value according to the estimated thermal load about the thermal storage tank 343. Assign as follows.
Further, the operation plan creation unit 105 calculates a predicted power load value required when the air conditioning heat source unit 31 manufactures the amount of heat indicated by the predicted air conditioning heat load result value based on the calculated air conditioning heat source operation plan.
And the operation plan preparation part 105 allocates power supply power so that a demand may become arbitrary target values (for example, minimum value) based on this electric power load prediction value. In other words, the operation plan creation unit 105 calculates an optimized operation schedule for the power supply facility operation plan. The operation plan creation unit 105 calculates the power load of the load device other than the thermal load when calculating the predicted power load. Since the power consumption of lighting / OA equipment other than this air conditioning heat source changes almost the same every day, it is given as a pattern.
Thereby, the operation plan preparation part 105 can optimize the electric power load of the air-conditioning heat-source equipment apparatus 3 and the work equipment apparatus 4 based on the air-conditioning heat load which an air-conditioning heat load prediction value shows.

Then, based on the operation plan data indicating the operation plan created by the operation plan creation unit 105, the power load prediction unit 106 detects the demand peak value per demand time period and outputs it to the demand excess determination unit 108.
The demand excess determination unit 108 determines whether the demand peak value input from the power load prediction unit 106 is larger than the demand target value C, which is contract power (step ST5).
When the demand peak value is larger than the demand target value C (demand peak value> demand target value C), the plan DR creation unit 109 creates a plan DR (step ST6). On the other hand, when the demand peak value is equal to or less than the demand target value C (demand peak value ≦ demand target value C), the plan DR creation unit 109 does not create the plan DR (step ST7). That is, the demand excess determination unit 108 uses the operation plan data (the air-conditioning heat source operation plan and the power supply facility operation plan) created by the operation plan creation unit 105 via the output unit 115 and the power output control device 7 and the load power control device 8. To output.

Next, the demand excess determination unit 108 compares the demand predicted value changed in the plan DR with the demand target value C based on the plan DR created by the plan DR creation unit 109. The demand excess determination part 108 determines whether this demand predicted value is larger than the demand target value C (step ST8).
When the demand predicted value is larger than the demand target value C (demand predicted value> demand target value C), the real time DR execution instructing unit 110 executes the real time DR (step ST9). On the other hand, when the demand predicted value is less than or equal to the demand target value C (demand predicted value ≦ demand target value C), the real time DR execution instructing unit 110 does not execute the real time DR (step ST10). That is, the demand excess determination unit 108 uses the output unit 115 to output the operation plan data created by the operation plan creation unit 105 and the plan DR created by the plan DR creation unit 109 via the output unit 115. To output.

  As described above, the operation plan creation unit 105 creates an air-conditioning heat source operation plan based on the predicted air-conditioning heat load, converts the air-conditioning heat load into an electric power load, and calculates an electric power load prediction value. Create a power facility operation plan that shows the allocation of power corresponding to the value. As a result, the air conditioning heat source operation plan and the power supply facility operation plan can be coordinated, and power devices such as generators and storage batteries and air conditioning heat source equipment devices such as heat source devices and air conditioners are controlled in cooperation, Demand control for controlling demand to an arbitrary target value can be realized.

  Further, as described above, when the demand exceeds the demand target value by the operation plan process by the operation plan creation unit 105, the operation management apparatus 1 creates the plan DR by the plan DR creation unit 109 and performs the demand prediction. Calculate the value. As a result, it is possible to prevent the demand peak value from exceeding the demand target value and the demand from exceeding the contract power C. Thereby, when the average power per demand time limit used exceeds the contract power C, it is possible to avoid a situation in which an additional fee such as a penalty is imposed on the payment fee determined in advance according to the contract power C. . This provides economic benefits.

The following can also be performed using the present invention.
For example, in the operation plan process of the operation plan creation unit 105, the cost of the energy unit price is changed according to the time and given by the hourly charges, peak time adjustment contracts made by Japanese electric power companies, or performed by US electric power companies Cost optimization operation can be performed in consideration of the hourly charge.
In addition, by using real-time DR processing, it becomes possible to respond in real time when there is a demand for load adjustment from the power company to the customer side due to natural energy fluctuations assumed in the future smart grid.

<Example of Plan DR Creation Processing by Plan DR Creation Unit 109>
Next, an example of the plan DR creation process by the plan DR creation unit 109 will be described.
FIG. 11 is a block diagram illustrating an example of the configuration of the plan DR creation unit 109.
As shown in FIG. 11, the plan DR creation unit 109 includes a storage unit 190, an input unit 191, a demand peak value extraction unit 192, a demand peak value determination unit 193, a time limit determination unit 194, and an operation plan change unit. 195 and an output unit 196.
The storage unit 190 stores information indicating contract power that is a demand target value.
The input unit 191 inputs the predicted power load value indicated by the operation plan data from the operation plan creation unit 105 or the demand excess determination unit 108.
The demand peak value extraction unit 192 extracts a demand from the predicted power load value, and extracts the maximum value (demand peak value) of the average value of the demand corresponding to the demand time limit.
The demand peak value determination unit 193 compares the input demand peak value with the demand target value C. When the demand peak value is larger than the demand target value C (demand peak value> demand target value C), the demand peak value determination unit 193 indicates that the demand peak value exceeds the demand target value C. The information is output to the time limit determination unit 194.

  The time limit determination unit 194 is a period including a generation period of the demand peak value exceeding the demand target value C and exceeding the demand target value C based on the information input from the demand peak value determination unit 193 It is determined whether (the continuous target excess period Tb) continues for the time limit B or longer. This time limit B is a time that is experimentally obtained in advance as a time that does not cause any trouble even if the external air compressor 32 is stopped.

Based on the information input from the time limit determination unit 194, the operation plan change unit 195 includes the average power amount of the power load prediction of the demand time limit included in the continuous target excess period Tb that is the time zone in which the demand target value C is exceeded. Then, N demand time periods are extracted in descending order of the average power amount in the power load prediction. The operation plan change unit 195 changes the operation plan of the external air conditioner 32 corresponding to the pre-peak period Ta before A time before the start time of the earliest time period among the extracted N time periods. For example, the operation plan change unit 195 changes the operation plan to a fully open operation that maximizes the load output of the external air conditioner 32 in the pre-peak period Ta. The operation plan changing unit 195 changes the operation plan of the external air conditioner 32 so as to stop the operation of the external air conditioner 32 in the continuous target excess period Tb. The operation plan change unit 195 outputs the output unit 196 as a plan DR that is obtained by changing the operation plan in this way.
The output unit 196 outputs the input plan DR to the demand excess determination unit 108.

Next, an example of a plan DR creation method according to the present embodiment will be described with reference to FIG.
As shown in FIG. 12, the plan DR creation unit 109 is initialized so that all time zones are automatically operated (step ST21).
Then, the input unit 191 of the plan DR creation unit 109 inputs the power load predicted value from the operation plan creation unit 105 or the demand excess determination unit 108, and outputs it to the demand peak value extraction unit 192 (step ST22). Here, a predicted power load value (hereinafter, power load predicted value P-100) as shown in FIG. 13 or a predicted power load value (hereinafter, power load predicted value P-200) as shown in FIG. 14 is input. This will be described below using an example input by 191. In FIGS. 13 and 14, the horizontal axis represents time, and the vertical axis represents power, which represents a predicted power load value.

  Next, the demand peak value extraction unit 192 extracts a demand peak value from the predicted power load value. Here, for example, the demand peak value extraction unit 192 extracts the demand peak value 151 from the predicted power load value P-100. The demand peak value 151 is a predicted power load value corresponding to a demand time period (n = 22) at 11:00 to 11:30. Further, the demand peak value extraction unit 192 extracts demand peak values 201 and 202 from the predicted power load value P-200. The demand peak value 201 is a predicted power load value corresponding to the demand time period D (n = 22) at 11: 00 to 11:30. The demand peak value 202 is a predicted power load value corresponding to the demand time period (n = 31) from 15:30 to 16:00. Note that the average power amount of the power load prediction of the demand peak value 201 is larger than the average power amount of the power load prediction of the demand peak value 202.

  Then, when there are a plurality of extracted demand peak values, the demand peak value extraction unit 192 assigns identification codes K (K = 1, 2,...) In descending order of the predicted power load value. When there is one extracted demand peak value, the demand peak value extraction unit 192 assigns an identification code K = 1 to this demand peak value.

Next, the demand peak value extraction unit 192 selects the demand peak values in ascending order of the identification code K, and outputs them to the demand peak value determination unit 193 (step ST23).
For example, when the predicted power load value P-100 is input, the demand peak value extraction unit 192 selects the demand peak value 151 with the identification code K = 1, and outputs it to the demand peak value determination unit 193. On the other hand, when the predicted power load value P-200 is input, the demand peak value extraction unit 192 selects the demand peak value 201 with the identification code K = 1 and outputs it to the demand peak value determination unit 193.

Then, the demand peak value determination unit 193 compares the input demand peak value with the demand target value C (step ST24). When the demand peak value is larger than the demand target value C (demand peak value> demand target value C), the demand peak value determination unit 193 indicates that the demand peak value exceeds the demand target value C. The information is output to the time limit determination unit 194.
For example, when the predicted power load value P-100 is input to the demand peak value extraction unit 192, the demand peak value determination unit 193 compares the demand peak value 151 with the demand target value C. Here, since the demand peak value 151 exceeds the demand target value C, the demand peak value determination unit 193 displays information indicating that the demand peak value 151 exceeds the demand target value C as a time limit determination unit 194. Output to.
When the predicted power load value P-200 is input to the demand peak value extraction unit 192, the demand peak value determination unit 193 compares the demand peak value 201 with the demand target value C. Here, since the demand peak value 201 exceeds the demand target value C, the demand peak value determination unit 193 displays information indicating that the demand peak value 201 exceeds the demand target value C as a time limit determination unit 194. Output to.

Next, the time limit determination unit 194 is a period including the generation period of the demand peak value exceeding the contract power C and exceeds the demand target value C based on the information input from the demand peak value determination unit 193. It is determined whether or not the continuous target excess period Tb continues for the time limit B or longer (step ST25). Here, the time limit B is 1 hour.
For example, when the time limit determination unit 194 inputs information indicating that the demand peak value 151 exceeds the demand target value C from the demand peak value determination unit 193, the demand time value that is the generation period of the demand peak value 151 is displayed. Refer to the power load prediction value stored in the storage unit 190 for the average power amount of the power load prediction corresponding to the demand time period (n = 21, 20,...) Immediately before the time period (n = 22). obtain. Here, since the average power amount of the power load prediction corresponding to the demand time period (n = 21) is larger than the demand target value C, the time limit determination unit 194 determines that the continuous target excess period Tb1 is the demand time period × 2 (that is, , 30 minutes × 2 = 1 hour). Subsequently, the time limit determination unit 194 calculates the average power amount of the power load prediction corresponding to the demand time period (n = 20) immediately before the demand time period (n = 21) determined to be included in the continuous target excess period Tb1. It is obtained by referring to the predicted power load value stored in the storage unit 190. Here, since the average power amount of the power load prediction corresponding to the demand time period (n = 20) is equal to or less than the demand target value C, the time limit determination unit 194 determines that the demand time period (n = 20) exceeds the continuous target. It is not included in the period Tb1, and it is determined that the continuous target excess period Tb1 has started after the demand time period (n = 20).

When the time limit determination unit 194 receives information indicating that the demand peak value 151 exceeds the demand target value C from the demand peak value determination unit 193, the time limit determination unit 194 is a demand period that is the generation period of the demand peak value 151. Refer to the power load prediction value stored in the storage unit 190 for the average power amount of power load prediction corresponding to the demand time period (n = 23, 24...) Immediately after the time period (n = 22). obtain. Here, since the average power amount of the power load prediction corresponding to the demand time period (n = 23) is equal to or less than the demand target value C, the demand time period (n = 23) is not included in the continuous target excess period Tb1 and is limited. The time determination unit 194 determines that the continuous target excess period Tb1 has ended before the demand time limit (n = 23).
Therefore, the time limit determination unit 194 determines that the continuous target excess period Tb1 starts from the demand time period (n = 22) and ends at the demand time period (n = 23). That is, the time limit determination unit 194 determines that the continuous target excess period Tb1 is 1 hour and continues for the time limit B or longer.

  When it is determined that the continuous target excess period Tb1 continues for the time limit B or longer (step ST25-YES), the time limit determination unit 194 displays information indicating the demand time limit included in the continuous target excess period Tb1. The data is output to the change unit 195.

Based on the information input from the time limit determination unit 194, the operation plan change unit 195 includes the average power amount of the power load prediction of the demand time period included in the continuous target excess period Tb that is the time zone in which the contract power C is exceeded. N demand periods are extracted in descending order of the average power amount in the power load prediction (step ST26).
That is, when the time limit determination unit 194 determines that the time (for example, 1 hour 45 minutes) from the time tα to tβ is the continuous target excess period Tb1, the operation plan change unit 195 sets the continuous target excess period Tb1. Let N be an integer obtained by dividing by the demand time period. Here, it can be expressed as N = four (continuous target excess period Tb / demand time limit). In the above example, N = fore (3.5) = 3.

For example, in the power load predicted value P-100, based on the demand peak value 151 with the identification code K = 1, information indicating the demand time limit (n = 22, 23) included in the continuous target excess period Tb1 is displayed. A case of outputting to 195 will be described.
In this case, the operation plan changing unit 195 extracts N = 2 demand time periods (n = 22, 23).
Next, the operation plan change unit 195 determines whether or not the identification code K = 1 (step ST27). Here, since the identification code K = 1 (step ST27-YES), the operation plan changing unit 195 has a peak that is A hours before the start time of the earliest time zone among the extracted N time zones. The air conditioning heat source operation plan and the power supply facility operation plan of the external air conditioner 32 corresponding to the previous period Ta1 are changed. For example, the operation plan change unit 195 changes the air conditioning heat source operation plan and the power supply facility operation plan to the fully open operation that maximizes the load output of the external air conditioner 32 in the pre-peak period Ta1 (step ST28).

Then, the operation plan changing unit 195 changes the air conditioning heat source operation plan and the power supply facility operation plan of the external air conditioner 32 so as to stop the operation of the external air conditioner 32 in the continuous target excess period Tb1 (step ST29).
Next, the operation plan change unit 195 determines whether or not all N demand time periods corresponding to the continuous target excess period Tb1 are continuous (step ST30).
When the N demand time periods corresponding to the continuous target excess period Tb1 are not all continuous (step ST30-NO), the operation plan change unit 195 performs external adjustment for the time zones between the non-continuous time zones. The air-conditioning heat source operation plan and the power supply facility operation plan are changed to the fully open operation that maximizes the load output of 32 (step ST31).

On the other hand, the case where the identification code K is not 1 in step ST27 (step ST27-NO), that is, when there are a plurality of demand peak values, the case where the demand peak value of K ≧ 2 is included in the continuous target excess period Tb will be described. For example, in the power load prediction value P-200, based on the demand peak value 202 with the identification code K = 2, information indicating the demand time limit (n = 30, 31) included in the continuous target excess period Tb3 is displayed. A case of outputting to 195 will be described.
The operation plan change unit 195 determines that the time zone from the time A before the latest start time of the N time zones to the latest end time is the time of the already changed air conditioning heat source operation plan and power supply facility operation plan. It is determined whether or not the band overlaps (step ST32).

For example, the operation plan change unit 195 sets the A time before 15:00 which is the start time of the demand time period (n = 30) which is the earliest time period of the continuous target excess period Tb3 as the pre-peak period Ta3. To do. Then, the operation plan change unit 195 determines 12:00, which is the earliest start time of the pre-peak period Ta3, as the start position of the operation change period. The operation plan change unit 195 determines that 16:00, which is the latest end time of the continuous target excess period Tb3, is the start position of the operation change period.
Here, as shown in FIG. 14, in the power load prediction value P-200, the continuous target excess period Tb2 is indicated by the demand time period (n = 21, 22) based on the demand peak value 201 with the identification code K = 1. The period from 10:30 to 11:30, the period before peak Ta2 is determined in advance by the period from 7:30 to 10:30 indicated by the demand time period (n = 15 to 20), and the operation plan change unit 195 .

Then, the operation plan change unit 195 has the operation change period calculated based on the demand peak value 202 with the identification code K = 2 based on the demand peak value 201 with the identification code K = 1 whose operation plan has already been changed. It is determined whether or not it overlaps the pre-peak period Ta2 or the continuous target excess period Tb2.
Here, the operation change period is 12:00 to 16:00, and the operation plan change unit 195 determines that neither the pre-peak period Ta2 nor the continuous target excess period Tb2 overlaps (step ST32—YES).
Next, the operation plan changing unit 195 moves to step ST28.

On the other hand, when the time limit determination unit 194 determines in step ST25 that the continuous target excess period Tb does not continue for the time limit B or longer (step ST25-NO), the demand time limit included in the continuous target excess period Tb is indicated. The information is output to the operation plan change unit 195.
Then, the operation plan change unit 195 performs the air-conditioning heat source operation plan of the external air conditioner 32 corresponding to the pre-peak period Ta that is A hours before the start time of the earliest time period in which the demand target value C is exceeded. And change the power plant operation plan. For example, the operation plan change unit 195 changes the air-conditioning heat source operation plan and the power supply facility operation plan to the fully open operation that maximizes the load output of the external air conditioner 32 in the pre-peak period Ta (step ST33).
Next, the operation plan change unit 195 changes the air conditioning heat source operation plan and the power supply facility operation plan of the external air conditioner 32 to stop the operation of the external air conditioner 32 in the continuous target excess period Tb immediately after the pre-peak period Ta. (Step ST34).

  As described above, the plan DR creation unit 109 determines the daily start / stop schedule of the external air conditioner based on the power load predicted value of the demand indicated by the operation plan created by the operation plan creation unit 105. A plan DR with a reduced peak value can be created. Thereby, it is possible to prevent the demand from exceeding the demand target value and the maximum value of the demand from exceeding the contract power C. As a result, when the average maximum power per demand time limit used exceeds the contract power C, it is possible to avoid a situation in which an additional fee such as a penalty is imposed on the payment fee determined according to the contract power C in advance. it can. This provides economic benefits.

  As described above, the external air compressor mainly aims to perform indoor ventilation, and no major problem will occur even if it is stopped for a while after sufficient ventilation. Therefore, the plan DR creation unit 109 according to the present invention performs the following processing when the demand peak value is expected to exceed a preset threshold value (demand target value) in the operation plan. Reduce demand peak values. That is, the plan DR creation unit 109 creates a plan DR in which the external air conditioner 32 is fully opened before the time when the demand peak value is reached and the external air conditioner 32 is stopped during the demand peak value time zone. To do.

  Further, as described above, when the demand peak value exceeding the demand target value C is two times, if the second demand peak value is after A time after the end of the first external air conditioner stop time, the external adjustment is performed again. Stop the machine. At that time, the external air conditioner is fully opened from time A before the external air conditioner is stopped. That is, when the air conditioning heat source operation plan and the power supply facility operation plan are already changed so that the operation of the external air conditioner 32 is fully opened or stopped, the plan DR creation unit 109 determines the changed period and the period to be changed this time. A plan DR is created so as not to overlap.

The planned DR creation unit 109 may control the output so that the CO ₂ concentration is in a steady state within A time and the demand peak value is also reduced, instead of fully opening the external air conditioner 32. Is possible. Specifically, the plan DR creation unit 109 controls the output of the external air conditioner 32 based on the measurement results obtained by measuring the power demand and the CO ₂ concentration of the air conditioning heat source equipment 3 and the work equipment 4 in real time. The output of the external air conditioner 32 can be optimally controlled.

<Real-time DR processing by the real-time DR execution instruction unit 110>
Next, an example of real-time DR processing by the real-time DR execution instruction unit 110 will be described.
FIG. 15 is a block diagram illustrating a configuration of the load power control device 8 according to the present embodiment.
As illustrated in FIG. 15, the load power control device 8 includes a target value storage unit 81, a measurement unit 82, a predicted value calculation unit 83, a prediction difference calculation unit 84, a performance difference calculation unit 85, and a priority order storage unit. 86, a power control unit 87, and an input / output unit 88.
In the target value storage unit 81, a target value of demand power (maximum demand power) that is the maximum among the actual values of demand power measured for a plurality of predetermined measurement periods (demand time periods) is determined in advance. Remembered. That is, the demand target value C is stored.

The measuring unit 82 measures the actual value of the demand power in the demand time period that is the measurement target period. Here, the measurement part 82 measures the electric power used by the equipment (refrigerator, pump, fan, etc.) that uses electric power on the floor to be controlled, and calculates an estimated value of the average demand electric power at the end of the demand period. To do. For example, as shown in FIG. 16, the time points of T1, T2, T3, T4,. Here, if the demand time limit is X minutes (for example, 30 minutes), T2 is T1 + X minutes, T3 is T2 + X minutes, and T4 is T3 + X minutes.
The measuring unit 82 measures the actual value of the demand power every predetermined time (for example, 3 minutes) within the demand time period, and calculates the estimated value of the average demand power.
Moreover, the measurement part 82 memorize | stores the driving data which is the track record value of the measured electric power used in the priority order memory | storage part 86 mentioned later.

  The predicted value calculation unit 83 is a predicted value of demand power in the measurement target period at a time before the start of measurement of the actual value in the measurement target period that is the measurement target of the actual value among a plurality of consecutive measurement periods. Is calculated. For example, in FIG. 17, the predicted value calculation unit 83 has a time point (for example, T1-T0-Y minutes) before a time (for example, T1−T0−Y) determined from the time point T1 when measurement in the demand time period between T1 and T2 is started. In T0 + Y), a predicted value of average demand power in the demand time period between T1 and T2 is calculated. Here, as a prediction value calculation method, for example, a method using a neural network model as disclosed in a patent document (Japanese Patent Laid-Open No. 2006-78009) can be applied. For example, the predicted value is calculated by modeling demand power according to the outside air temperature, humidity, wind speed, air volume, air conditioning operation time, day of the week, season, and the like. The calculation process of the prediction difference by the predicted value calculation unit 83 is executed, for example, before each predetermined time period (for example, 3 to 5 minutes). For this reason, for example, if the demand time limit is 30 minutes, the predicted value calculation processing by the predicted value calculation unit 83 is executed 48 times a day.

  The prediction difference calculation unit 84 compares the prediction value calculated by the prediction value calculation unit 83 with the target value stored in the target value storage unit 81. When the prediction value exceeds the target value, the prediction value and the target The difference from the value is calculated. The difference calculated by the prediction difference calculation unit 84 is a DR required amount indicating the amount of demand power that needs to be reduced by the power control process (DR (demand response) process).

  The actual difference calculation unit 85 compares the estimated value of average demand power based on the actual value measured by the measuring unit 82 with the target value stored in the target value storage unit 81, and the estimated value exceeds the target value. In this case, the difference between the estimated value and the target value is calculated. The difference calculated by the result difference calculation unit 85 is a DR required amount indicating the amount of demand power that needs to be reduced by the DR process. The performance difference calculation unit 85 performs a performance difference calculation process at regular time intervals (for example, 3 minutes) within the demand time period.

The priority order storage unit 86 includes a priority order that is determined in advance according to the DR requirement calculated by the prediction difference calculation unit 84 or the result difference calculation unit 85, and a load device that reduces supply power among a plurality of load devices. A priority order table that is associated with supply reduction target devices is stored in advance.
FIG. 17 is a diagram illustrating a data example of the priority order table stored in the priority order storage unit 86. The operation record data is information in which record values such as demand power of the operating load device are measured and stored by the measuring unit 82. For example, the compressor 36 and the primary pump 342-1 use (50 kW + 5 kW =) 55 kW of power. The secondary pump 342-2 uses 5 kW of power. The plurality of fans 38-1, 38-2, ... provided in the air conditioners 33-1, 33-2, ... use (2 kW x 20 units =) 40 kW of power.
Note that an example in which there are 20 air conditioners 33 is described here.
The total power is the total value of power used by the supply reduction target devices. The single DR effect is the amount of power demand that can be reduced by control in the corresponding priority order. The cumulative DR effect is the cumulative amount of power demand that can be reduced by control from priority 1 to that priority.

  The priority (1-5) indicates that the larger the number, the larger the difference between the target value and the predicted value or the actual value, and the amount of reduction in demand power in the associated supply reduction target device increases. Here, a symbol “▼” is attached to an item whose supply power changes for each priority. For example, in the priority order 1, the power demand of the compressor and the primary pump 342-1 is reduced from 100% to 70%. Thereby, the demand power of (55 (kW) × 30 (%) =) 16.5 kW decreases. In the priority order 2, the power demand to the compressor and the primary pump 342-1 is cut off and reduced from 70% to 0%. Thereby, the demand power of (55 (kW) × 70 (%) =) 38.5 kW is reduced. Thus, even if the capacity of the compressor 36 is reduced, it is considered that while the cold energy remains in the heat storage tank 343, the indoor thermal environment is not affected.

  In priority 3, half of the plurality of cold water valves are closed. Thereby, since it is thought that the load electric power of the secondary pump 342-2 becomes half, the demand electric power of (5 kW × 2 =) 2.5 kW decreases. Even if the cold water valve is closed in this manner, the slab by the floor blowing air conditioning is cooled, so that it is considered that the deterioration of the indoor environment can be kept to a minimum if air is blown by the fan. Here, the cold water valve is opened and closed so as to rotate a plurality of air conditioners in a staggered arrangement. In priority 4, all chilled water valves are closed. Thereby, since the load electric power of the secondary pump 342-2 becomes 0, the demand electric power of 22.5 kW decreases. If all the fans are stopped at priority 5, the demand power of 20.0 kW is reduced.

  In this way, by setting in advance a priority order for reducing demand power for each load device to which power is supplied, the demand power can be reduced so as not to affect the indoor environment of the facility. Can do. For example, when reducing power supply to an air conditioning facility that has little influence on other load devices as a target device for DR processing, a compressor, a pump, or an air conditioner in a refrigerator is used so as not to reduce the cooling capacity for the room as much as possible. It is possible to systematically reduce the power demand in an air conditioning facility operating in conjunction with each other.

When the information indicating the execution instruction of the real time DR is input from the operation management device 1 via the input / output unit 88, the power control unit 87 executes the real time DR process as follows.
The power control unit 87 is stored in the priority order storage unit 86 in association with the priority order corresponding to the DR required amount calculated by the prediction difference calculation unit 84 or the DR required amount calculated by the performance difference calculation unit 85. The supply reduction target device is read, and a warning for reducing the power demand in the demand period is output. Here, for example, the load power control device 8 is provided with a buzzer for outputting a warning sound and a display (display unit) for displaying information so that the buzzer is output for the warning sound and the supply is reduced according to the required amount of DR. Information on the target device may be displayed on the display. Or you may make it transmit the control signal which reduces or stops demand power with respect to the load apparatus which is a supply reduction object apparatus according to DR required amount.

  Thus, unlike the conventional demand control process, the power control unit 87 does not determine that the target value is exceeded only during the demand time period and executes the DR process during the remaining time, but is calculated before the demand time period. Both the prediction mode DR process using the predicted value and the performance mode DR process using the estimated value calculated during the demand time period are performed. That is, the DR process in the prediction mode allows the DR process to be started simultaneously with the start of the demand time period, and can be corrected by the DR process in the result mode when the predicted value and the actual value deviate. .

  Next, an operation example of the load power control device 8 will be described with reference to the drawings. FIG. 18 is a flowchart illustrating an operation example of real-time DR processing by the load power control device 8. The predicted value calculation unit 83 determines that T2 and T3 at a time (for example, T2-Y) before a time (for example, T2) when a demand time period (for example, between T2 and T3 in FIG. 16) is started. A predicted value of average demand power for (T2 + X) minutes is calculated (step ST41). The prediction difference calculation unit 84 reads the demand target value of the maximum demand power stored in the target value storage unit 81 (step ST42). Then, the prediction difference calculation unit 84 determines whether or not the predicted value exceeds the target value (step ST43). Here, if the prediction difference calculation unit 84 determines that the predicted value does not exceed the target value (step ST43—NO), the power control unit 87 does not execute the real-time DR process.

  In step S43, if the prediction difference calculation unit 84 determines that the predicted value exceeds the target value (step ST43-YES), the difference between the predicted value and the target value is calculated as the DR required amount (step ST44). And the measurement part 82 acquires the driving performance data of each load apparatus at the time, and memorize | stores it in the priority order memory | storage part 86 (step ST45). The power control unit 87 reads the supply reduction target device associated with the priority order corresponding to the DR required amount from the priority order table stored in the priority order storage unit 86 (step ST46). And the electric power control part 87 performs the real-time DR process with respect to the supply reduction object apparatus determined in step ST46 between T2 and T3 (T2 + X minutes) (step ST47).

FIG. 19 is a flowchart illustrating an operation example of the real-time DR process in the performance mode by the load power control device 8. The load power control device 8 executes the real-time DR process based on the actual value after the demand start time, but the real-time DR process during the dead time period (m0 minutes) that is a fixed time until the actual power value can be acquired. Is not executed (step ST61).
The performance difference calculation unit 85 calculates the performance difference at intervals of m minutes (step ST62), and when the time acquired from its own time measuring function is T + Y (Y = Y + m) minutes (step ST63). Performs real-time DR processing of the mode. First, the measurement part 82 calculates the estimated value of the average demand power based on the actual value of the demand power of the load device (step ST64). The performance difference calculation unit 85 reads the target value of the maximum demand power stored in the target value storage unit 81 (step ST65). And the performance difference calculation part 85 determines whether an estimated value exceeds target value (step ST66). Here, if the performance difference calculation unit 85 determines that the estimated value does not exceed the target value (step ST66—NO), the power control unit 87 does not execute the real-time DR process.

  In step ST66, when the result difference calculation unit 85 determines that the estimated value exceeds the target value (step ST66-YES), the power control unit 87 calculates the difference between the estimated value and the target value as the DR required amount. (Step ST67). And the measurement part 82 acquires the driving performance data of each load apparatus in the time, and memorize | stores it in the priority memory | storage part 86 (step ST68). And the electric power control part 87 reads the supply reduction object apparatus matched with the priority according to DR required amount from the priority table memorize | stored in the priority memory | storage part 86 (step ST69). And the electric power control part 87 performs DR process with respect to the supply reduction object apparatus determined in step ST69 between T + Y and T + X minutes (step ST70).

  As described above, according to the present embodiment, the real-time DR process is performed based on the predicted value of average demand power calculated by a neural network model or the like before the actual value of demand power within the demand time period is measured. It becomes possible to do. That is, after measuring the actual value of the demand power within the demand time limit as in the past, the DR processing time can be increased compared with the case where the estimated value of the average demand power is calculated and the DR processing is performed. The effect is increased. For example, in the past, the DR process could not be executed until a certain time (T1 + m0) after the actual value of demand power was acquired from the start point (T1) of the demand time limit. For example, the DR process can be accurately started from the start time point (T1) of the demand time limit.

In the present embodiment, the prediction value calculation unit 83 performs the calculation process of the prediction value by the neural network model every predetermined time before the demand time limit. However, for example, once a day, the demand time interval A predicted value for 24 hours for each (X minutes) may be calculated and stored.
In the present embodiment, the load power control device 8 has been described as including the measurement unit 82, but the measurement unit 82 may be included in a computer device outside the load power control device 8. Alternatively, for example, when an existing control system for controlling demand power is provided in a facility targeted for demand power control, the power control unit 87 of the load power control device 8 supplies the existing control system to the supply reduction target device. Information may be transmitted to reduce the power demand by the control system.

According to the first embodiment, the heat storage tank 343 is controlled by the on / off operation, that is, the pump 342 of the heat storage tank 343 is controlled by the on / off operation, and the air-cooled heat pump is controlled by the command value corresponding to the heat load. As a result, even if an error occurs that causes the actual load to be larger than the predicted load, the error can be absorbed and operation can be performed without excess or deficiency. Further, even in this case, since the refrigerator is not operated, the difference between the actual power consumption and the predicted power consumption can be eliminated.
Here, a facility whose demand power is controlled as shown in FIG. 20 will be further described as an example. Here, the air conditioner 33 is connected to a heat storage tank (water tank) 343, the refrigerator 40a, and the refrigerator 40b that constitute the air conditioning heat source apparatus 31. The heat storage tank 343 is connected to the refrigerator 310 having the compressor 36 via the primary pump 342-1. The heat storage tank 343 is, for example, a water tank. Further, the secondary pump 342-2 circulates a medium that holds the amount of heat given by the heat storage tank 343 to the air conditioner 33. The pump 40 a-2 circulates the medium that holds the amount of heat given by the refrigerator 40 a to the air conditioner 33. The pump 40b-2 circulates the medium that holds the amount of heat given by the refrigerator 40b to the air conditioner 33.
FIG. 21 shows the relationship between the load prediction that is the predicted heat load and the operation plan of the heat source in such a facility. As shown in this figure, when the predicted heat load is 300 kW, the load power control device 8 operates in accordance with the operation plan created by the operation plan creation unit 105, and the load power control device 8 uses the heat source A (for example, the refrigerator 40a that is an air-cooled heat pump). ) Is operated with a command value corresponding to 70 kW, the heat source B (for example, the refrigerator 40b that is an air-cooled heat pump) is operated with a command value corresponding to 150 kW, and the heat source C (for example, the heat storage tank 343) is turned on / off. Control to drive by.

  At this time, when the actual load is 280 kW with respect to the predicted load of 300 kW, as shown in FIG. 22, the heat source A operates with a command value corresponding to 70 kW and the heat source B according to 150 kW. The heat source C is operated by an on / off operation command. In this case, the heat source C performs an on / off operation based on the temperature of the cold water detected in the piping on the output side of the heat storage tank 343. For example, the heat storage tank 343 inputs an on / off operation command output from the load power control device 8 in accordance with the operation plan created by the operation plan creation unit 105. The heat storage tank 343 stops the operation of the heat storage tank 343 when A ≧ B when the set temperature of the cold water on the output side of the heat storage tank 343 is A ° C. and the detected temperature of the cold water is B ° C. (OFF), and when A <B, the heat storage tank 343 is operated (ON). Here, since the temperature of cold water fluctuates according to the actual heat load, the heat source C can be operated with a heat output of about 60 kW (a heat radiation amount smaller than the load prediction) according to the actual load. As a result, even if an error occurs that causes the actual load to be smaller than the load prediction, the error can be absorbed and operation can be performed without excess or deficiency. In addition, since the operation of the refrigerator does not occur here, the difference between the actual power consumption and the predicted power consumption can be eliminated.

  On the other hand, when the actual load is 350 kW with respect to the predicted load of 300 kW, as shown in FIG. 23, the heat source A operates with a command value corresponding to 70 kW and the heat source B according to 150 kW. The heat source C is operated by an on / off operation command. Even in this case, the heat storage tank 343 can be operated with a heat output of about 130 kW (a greater heat dissipation amount than the load prediction) according to the actual load. As a result, even if an error occurs that causes the actual load to be larger than the load prediction, the error can be absorbed and operation can be performed without excess or deficiency. Further, even in this case, since the refrigerator is not operated, the difference between the actual power consumption and the predicted power consumption can be eliminated.

In addition, in this 1st Embodiment, although the operation plan preparation part 105 demonstrated the case where the thermal storage tank 343 was allocated as an air-conditioning heat-source equipment apparatus of the object controlled according to the temperature of an exit, a refrigerator (air cooling heat pump) is used. You may make it allocate.
Even when the refrigerator is allocated, the operation plan creation unit 105 allocates a part of the refrigerator (for example, the heat source C) to be turned on / off, and the other refrigerator 40a (heat source A) and the refrigerator 40b (heat source B) are allocated. ) Is assigned to be controlled by a command value corresponding to the thermal load (FIG. 24).
In this case, when the actual load is 280 kW with respect to the load prediction of 300 kW, the actual heat load is reduced while reducing the difference between the predicted power consumption and the actual power consumption by operating the heat source C by 20 kW. Air conditioning can be performed in accordance with (FIG. 22). On the other hand, when the actual heat load is 350 kW with respect to the load prediction of 300 kW, the heat source C is operated 50 kW larger, thereby reducing the difference between the predicted power consumption and the actual power consumption. The air conditioning according to the heat load can be performed (FIG. 23).
At this time, only the power consumption of the heat source C is different between the prediction and the actual result, but the difference is the minimum necessary. However, when the power consumption of the entire building exceeds the target power, the operation management device 1 performs capacity control of the air conditioning and lighting equipment for the excess, so that the output and power consumption are reduced, and below the target power. Can be maintained.
As described above, in this example, the heat output value is determined for the heat source to be controlled, and the command value corresponding to the heat output value is not output to the air conditioner for control. A part of the heat source machine is controlled to be turned on and off. As a result, even if an error occurs between the heat load prediction and the actual heat load, the error can be eliminated (or minimized), and the influence on the indoor environment can be prevented while not exceeding the target power. Can be eliminated.

Next, a second embodiment will be described.
In this embodiment, the configuration is the same as that of the first embodiment, but the function of the operation management device 1 is partially different. Hereinafter, the difference in function will be described.
In the present embodiment, the following points are assumed regarding a system (BEMS: Building and Energy Management System) that controls air conditioning and power in a building. That is, it is an air conditioning system having a heat storage tank, and covers the cooling load during the summer daytime by radiating heat from the heat storage and chasing the heat source. In addition, it is assumed that facility operation is performed so that the power consumption of the entire building is kept below a certain value (demand target value).
Considering the power cost and energy efficiency for air conditioning, the heat storage in the heat storage tank is used up at the end of the air conditioning time zone (for example, 8:00 to 19:00), and the chasing operation of the heat source in the daytime air conditioning time zone is minimized. It is desirable to make it.

  In response to this, load forecasts are made using weather forecasts, etc., and power is kept below the target demand value, and air conditioning heat sources and distributed power supply operation schedules are automatically planned to save energy and costs. A load control method is also proposed. The output of the heat source (production heat quantity) is variable, and the relationship between the output and the power consumption at that time is known.

In this system, a heat load is predicted before the air conditioning operation start time, and a plan for chasing the heat source within a range not exceeding the demand target value while satisfying the heat demand is made. As control after the start of air conditioning operation, it is possible to absorb the error between the prediction and the actual heat load by allowing the heat radiation amount from the heat storage tank to be controlled by the heat load, so plan while supplying the necessary heat Street power consumption.
In such control, when the actual heat load becomes higher than predicted, the heat storage is consumed earlier than planned, so the heat storage is lost before the end of the air conditioning time period. In order to continue the necessary heat supply even after the heat storage is lost, it is necessary to increase the chasing operation output of the heat source, but the power consumption increases accordingly, and the demand target may be exceeded.

  Therefore, in the second embodiment, the amount of heat released from power, heat source operation, and heat storage is monitored, and by correcting the sequential operation plan based on the result, it is necessary even if the heat load prediction is deviated. While supplying heat, power is also kept below the demand target.

  The operation plan creation unit 105 measures the amount of heat released from the heat storage tank at regular intervals from the start of the air conditioning operation, and sequentially stores the remaining amount in a memory area provided in itself. In this measurement, for example, the operation plan creation unit 105 measures the outlet temperature of the heat storage tank with the sensor 350 before the start of the air conditioning operation, and measures the temperature difference with the sensor 350 at regular intervals after the start of the air conditioning operation. The amount of heat used, that is, the amount of heat released is obtained from the obtained temperature difference and the capacity of the heat storage tank 343. Here, the amount of heat stored in the heat storage tank 343 before the start of the air conditioning operation can be calculated from the temperature and capacity in the heat storage tank 343. Therefore, the operation plan creation unit 105 calculates the remaining amount of heat storage from the difference between the heat storage amount before starting the air conditioning operation and the heat radiation amount.

  The first storage unit 101 stores the planned heat storage amount for each time. For example, as shown in FIG. 25, the planned amount of stored heat stores a value that gradually decreases with the passage of time. In FIG. 25, the horizontal axis represents time, the vertical axis represents the heat load or the remaining amount, and the graph represents the heat load and the remaining amount.

Then, when the air-conditioning operation is started and a predetermined time arrives, the operation plan creation unit 105 determines whether or not the actual remaining amount at that time is equal to or less than the remaining amount of the plan at that time. To do. Here, as shown in FIG. 25, for example, when the operation plan creation unit 105 detects that the actual remaining amount is equal to or less than the plan remaining amount at 10:00, the power plan value It is searched whether or not there is a time zone in which is less than the demand target value. Here, as shown in FIG. 26, at a time later than the measurement time, a search result is obtained from 12:00 to 13:00 as a time zone in which the power plan value is less than 400 kW which is the power demand target value. FIG. 26 is a graph showing a power plan, with the horizontal axis representing time and the vertical axis representing power.
Then, when a time zone (12:00 to 13:00) in which the power plan value is less than or equal to the demand target value is detected, the operation plan creation unit 105 determines the difference between the demand target value and the power plan value in that time zone. A new operation plan is generated by increasing the power source output by allocating as much as the power consumption increase, and the original operation plan is updated. The operation plan in which the output of the heat source is increased is, for example, an operation plan in which air conditioning using the heat storage tank 343 is stopped and operation of other air conditioners is increased. And the operation plan preparation part 105 returns the output of a heat source to a plan value, when the remaining amount of a plan becomes equal to an actual remaining amount.

  According to this embodiment, as shown in FIG. 25, when the operation plan creation unit 105 does not perform the above-described control, if the heat storage in the heat storage tank is used up earlier than planned, the heat storage is used up. The heat source must be operated, exceeding the planned power consumption. However, when the operation plan creation unit 105 performs the above-described control, as shown in FIG. 27, in the time zone from 12:00 to 13:00, cooling is performed by another air conditioner without using the heat storage from the heat storage tank 343. Do. Alternatively, the refrigerator 310 is operated in the time zone from 12:00 to 13:00 to store heat. In FIG. 27, the horizontal axis represents time, and the vertical axis represents the heat load or the remaining storage amount, which is a graph representing a power plan. As a result, even if the actual heat load is higher than expected and the heat storage is used earlier than the planned remaining amount of heat storage, the power consumption is kept below the demand target while supplying the necessary heat. be able to. Further, even when the remaining amount of heat storage is used earlier than planned, it can approach the planned remaining amount of heat storage, so that the stored heat in the heat storage tank 343 is used up and the refrigerator is operated. It is possible to prevent the predicted power consumption from being exceeded.

  2 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into the computer system and executed to manage the operation. May be performed. Here, the “computer system” includes an OS and hardware such as peripheral devices.

Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.
The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included. The program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes designs and the like that do not depart from the gist of the present invention.

DESCRIPTION OF SYMBOLS 1 Operation management apparatus 3 Air-conditioning heat source equipment 4 Work equipment 6 Power supply equipment 7 Power supply output control device 8 Load power control device 31 Air-conditioning heat source machine 32 External air conditioner 33 Air-conditioning machine 61 1st generator 62 2nd generator 63 Storage battery 64 Purchased power supply 102 Air-conditioning thermal load prediction processing unit 104 Power generation output prediction processing unit 105 Operation plan creation unit 106 Power load prediction unit 108 Demand excess determination unit 109 Plan DR creation unit (plan change unit)
110 Real-time DR execution instruction unit (real-time demand power control unit)
350 sensor

Claims (7)

An air-conditioning heat load prediction processing unit that calculates an air-conditioning heat load prediction value that is a value obtained by predicting the amount of heat that needs to be supplied from each of the plurality of air-conditioning heat source equipment on the prediction target day;
When allocating the electric power used for each time of each air conditioning heat source equipment that generates the amount of heat indicated by the predicted air conditioning heat load value, the heat output of the air conditioning heat source equipment is obtained for at least one of the plurality of air conditioning heat source equipment. An operation plan creation unit that creates an air conditioning heat source operation plan that allocates power to be controlled according to the temperature of the outlet to be performed and allocates power to be used according to the predicted heat output value for other air conditioning heat source equipment,
An operation management device comprising: A detection unit for detecting the amount of heat stored in the heat storage tank;
A first storage unit that stores a planned value of the heat storage amount of the heat storage tank for each time zone in the air conditioning operation period;
A comparison unit that compares the heat storage amount detected by the detection unit with the planned value of the heat storage amount corresponding to the time zone in which the detection was performed;
A second storage unit that stores a power plan value and a demand target value, which are plan values of power consumption of the air conditioning equipment for each time period in an operation plan for operating the air conditioning equipment;
Based on the comparison result of the comparison unit, when the detected heat storage amount is equal to or less than the planned value of the heat storage amount, the power planned value is equal to or less than the demand target value after the time period when the detection is performed. If there is a time zone in which the power plan value is less than or equal to the demand target value, the heat source is operated according to the difference between the demand target value and the power plan value. An operation plan creation unit that changes the operation plan so as to be assigned,
An operation management device comprising: The operation plan creation unit
After the operation plan is changed, when the heat storage amount detected by the detection unit becomes equal to or greater than the planned value of the heat storage amount, a time period in which the heat storage amount detected by the detection unit is equal to or greater than the planned value of the heat storage amount The operation management apparatus according to claim 2, wherein the subsequent operation plan is returned to the operation plan before the change. An operation management method in an operation management device that is a computer,
The air conditioning heat load prediction processing unit calculates an air conditioning heat load prediction value that is a value obtained by predicting the amount of heat that needs to be supplied from each of the plurality of air conditioning heat source equipment on the prediction target day,
When the operation plan creation unit allocates the electric power used for each time of each air conditioning heat source equipment that generates the amount of heat indicated by the predicted air conditioning heat load value, the air conditioning heat source for at least one of the plurality of air conditioning heat source equipment It is characterized by assigning power to be controlled according to the temperature of the outlet that performs heat output of equipment and creating an air conditioning heat source operation plan that assigns power to other air conditioning heat source equipment according to the heat output value. Operation management method to do. An operation management method in an operation management device that is a computer,
The detection unit detects the amount of heat stored in the heat storage tank,
The comparison unit compares the plan value of the heat storage amount of the heat storage tank and the heat storage amount detected by the detection unit for each time period in the air conditioning operation period stored in the first storage unit,
Based on the comparison result of the comparison unit, when the detected heat storage amount is less than or equal to the plan value of the heat storage amount, the operation plan creation unit concerned the air conditioning facility for each time zone in the operation plan for operating the air conditioning facility Referring to a second storage unit that stores a power plan value that is a plan value of power consumption and a demand target value, a time during which the power plan value is equal to or less than the demand target value after the time zone in which the detection is performed It is searched whether there is a belt, and when there is a time zone in which the power plan value is equal to or less than the demand target value, the heat source is operated according to the difference between the demand target value and the power plan value. An operation management method characterized by changing an operation plan to be assigned to On the computer,
An air conditioning heat load prediction processing unit that calculates an air conditioning heat load prediction value that is a value obtained by predicting the amount of heat that needs to be supplied from each of the plurality of air conditioning heat source equipment on the prediction target day,
When allocating the electric power used for each time of each air conditioning heat source equipment that generates the amount of heat indicated by the predicted air conditioning heat load value, the heat output of the air conditioning heat source equipment is obtained for at least one of the plurality of air conditioning heat source equipment. An operation plan creation unit that creates an air conditioning heat source operation plan that allocates power to be controlled according to the temperature of the outlet to be performed and allocates power to be used according to the heat output value for other air conditioning heat source equipment.
Operation management program to function as. On the computer,
A detection unit for detecting the amount of heat stored in the heat storage tank,
A comparison unit that compares the planned value of the heat storage amount of the heat storage tank with the heat storage amount detected by the detection unit for each time period in the air conditioning operation period stored in the first storage unit,
Based on the comparison result of the comparison unit, when the detected heat storage amount is less than or equal to the planned value of the heat storage amount, the planned value of the power consumption of the air conditioning facility for each time zone in the operation plan for operating the air conditioning facility Whether or not there is a time zone in which the power plan value is equal to or less than the demand target value after the time zone in which the detection is performed, with reference to the second storage unit that stores the power plan value and the demand target value. When there is a time zone in which the power plan value is equal to or less than the demand target value, the operation plan is assigned so that the heat source is operated according to the difference between the demand target value and the power plan value. Operation plan creation department to change,
Operation management program to function as.

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