JP5668970B2 - Operation management device, operation management method, and operation management program - Google Patents

Description

  The present invention relates to a power supply device that supplies electric power for demand to a load device, an operation management device that manages an operation plan for the load device, 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.

In recent years, attention has been focused on a smart grid system that combines a power system using a private generator using solar power generation, wind power generation, and the like and a power system for purchasing power from an electric power company.
In this case, since the demand maximum value increases as the number of load devices simultaneously used increases, the demand maximum value can be reduced by shifting the usage time zone of the load device. Therefore, a demand control device aimed at reducing the maximum demand value is provided (for example, Patent Documents 1 and 2).

  The power consumption of this air-conditioning heat source machine, etc., is more volatile than work equipment such as lighting devices and computers, and the power consumption tends to fluctuate depending on the weather and indoor usage environment. For this reason, when there is a possibility that the average value of the purchased power in the demand time period may exceed the contracted power in accordance with fluctuations in the power consumption of the air conditioning heat source unit or the like, there is a need for a technique that reduces the power load.

JP-A-62-18926 JP 11-215700 A

  However, there is a problem that the temperature cannot be adjusted to the set temperature when the power supplied to the air-conditioning heat source unit is reduced during a period when the average value of the purchased power in the demand time period may exceed the contracted power.

  The present invention solves the above-described problem, and an operation management device, an operation management method, and an operation management program that reduce the influence on the indoor environment due to a deviation from a set temperature that occurs when the average power in the demand time period is reduced The purpose is to provide.

In view of the above-described problems, the operation management device according to the present invention associates the power predicted in the operation plan with the time as the power supplied to the heat source load device that adjusts the temperature to the set temperature that is predetermined on the prediction date. Based on the predicted power load value, the purchased power supplied from the purchased power source of the commercial power system exceeds the target value out of the power per predetermined time (demand time period) indicated by the predicted power load value. When it is determined by the demand excess determination unit that determines what is electric power, and the demand excess determination unit determines that the purchased power exceeds the target value, the target excess period is a time during which the purchased power exceeds the target value. The power load applied to the heat source load device is reduced so that the purchased power is less than or equal to the target value, and the purchased power does not exceed the target value. The heat source load defined in the operation plan so as to increase the amount of power load applied to the heat source load device during the peak first period, which is a period before the target excess period, that is reduced in the target excess period. A plan change unit for changing a set temperature of the apparatus , wherein the second target is a period before the first peak immediately before the first target excess period and a period different from the first target excess period among the plurality of target excess periods. When the excess periods overlap, the maximum value of the purchased power of the predicted power load value in the first target excess period and the maximum value of the purchased power of the predicted power load value in the second target excess period Comparing, the plan exceeding part with the higher maximum value and the plan change part which changes the electric power purchased in the period before the 1st peak are provided, It is characterized by the above-mentioned. In order to reduce the power load, it is possible to use means such as changing the set temperature or stopping the air conditioner.

  In the operation management apparatus described above, the target excess period includes a period in which the average of purchased power per predetermined time (demand time period) is maximum among the predicted power load value. To do.

In view of the above-described problems, the operation management method according to the present invention uses the power predicted by the operation management device as the power supplied to the heat source load device that adjusts the temperature to a preset temperature that is predetermined on the prediction date. Based on the predicted power load value associated with the time, the purchased power supplied from the purchased power source of the commercial power system out of the power per predetermined time (demand time period) indicated by the predicted power load value There determines what is power exceeding the target value, when the purchased electric power is Ru when determine constant exceeds the target value, the heat source load device to the target excess period the purchased electricity power is time to exceed the target value The power load is reduced so that the purchased power is less than or equal to the target value, and the time during which the purchased power does not exceed the target value is a period before the target excess period. To increase the amount that has been reduced in the target excess period for power load on the heat source load device over click previous period, when changing the set temperature of the heat source load device as defined in the operation plan, When the first pre-peak period immediately before the first target excess period among the plurality of target excess periods overlaps with the second target excess period, which is a period different from the first target excess period, in the first target excess period Comparing the maximum value of the purchased power of the predicted power load value and the maximum value of the purchased power of the predicted power load value in the second target excess period, the target excess of the higher maximum value is exceeded. The purchased power in the period and the period before the first peak is changed .

In view of the above-described problems, an operation management program according to the present invention is predicted in an operation plan as electric power to be supplied to a control computer of an operation management apparatus and a heat source load apparatus that adjusts the temperature to a preset temperature that is predetermined on the prediction date. Based on the predicted power load value associated with the time, the power is supplied from the purchased power source of the commercial power system out of the power per predetermined time (demand time period) indicated by the predicted power load value. purchased electric power allowed is determined what a power exceeding the target value, when the purchased electric power is Ru when determine constant exceeds the target value, the target excess period is the time the purchased electric power exceeds the target value While reducing the power load applied to the heat source load device so that the purchased power is less than or equal to the target value, it is the time during which the purchased power does not exceed the target value and exceeds the target The setting of the heat source load device stipulated in the operation plan so as to increase the amount reduced in the target excess period for the power load applied to the heat source load device during the peak first period which is a past period than When the temperature is changed, when the first pre-peak period immediately before the first target excess period among the plurality of target excess periods overlaps with the second target excess period that is different from the first target excess period, Comparing the maximum value of the purchased power of the predicted power load value in the first target excess period with the maximum value of the purchased power of the predicted power load value in the second target excess period; The power purchase power in the higher target excess period and the period before the first peak is changed .

  ADVANTAGE OF THE INVENTION According to this invention, the fluctuation | variation of the electric energy of demand electric power can be made small, and the electric energy of demand electric power can be stabilized.

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 thermal load prediction result data which concern 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 the real-time DR process 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 figure for demonstrating the outline 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 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 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 block diagram which shows an example of a structure of the smart grid system which concerns on 2nd Embodiment of this invention.

<First Embodiment>
Hereinafter, an example of a smart grid system including an operation schedule management device 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 power consumption fluctuates more rapidly than the work equipment 4 and the power consumption tends to fluctuate depending on the weather and the 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. The air conditioning heat source equipment 3 adjusts the temperature of a medium (for example, water) that circulates through the heat circulation mechanism 34, and adjusts the temperature of the room through the air conditioner 33 installed in each room, for example. It is. 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 62 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 apparatus 1 collects various information in advance for optimal operation control of the air conditioning heat source equipment 3, work equipment 4, and power supply 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 electrical storage device 63, the power purchase power supply 64), the electric power which this electric power source outputs, and its timing are controlled. More specifically, the power supply output control device 7 controls the power output from the first generator 61, the second generator 62, and the power storage device 63 in the power source. In addition, the electric power which the electric power purchase power supply 64 outputs from the 1st generator 61, the 2nd generator 62, and the electrical storage device 63 with respect to the demand of a load apparatus (the air-conditioning heat-source equipment 3 and the work equipment 4). Electricity is insufficient for electric 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.
As shown in FIG. 2, the operation management apparatus 1 includes a first storage unit 101, an air conditioning thermal load prediction unit 102, a second storage unit 103, a power generation output prediction processing unit 104, an operation plan creation unit 105, and 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 Part 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 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 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 unit 102 obtains measured data from the power supply device 6, the air conditioning heat source facility device 3 and the work facility device 4 from the power output control unit 7 and the load power control unit 8 at regular time intervals. Captured 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 unit 102 is connected to a server on the Internet that stores weather forecast information announced by the Japan Meteorological Agency, for example, and the weather forecast information is set for each preset time. Download. The air conditioning heat load prediction 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 unit 102 creates prediction data based on actual measurement data and weather forecast data, and starts, for example, an ANN (Artificial Neural Network) load prediction program using a neural network. Predict load data.
That is, the air conditioning heat load prediction 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. Therefore, the amount of heat (air conditioning heat load) that is predicted to be necessary is calculated.
The air conditioning heat load prediction unit 102 writes the generated air conditioning heat load prediction 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 heat load prediction unit 102 is not limited to the above function, and can predict load data using a technique other than the neural network.

(14) Prediction correction process The air conditioning thermal load prediction unit 102 corrects the load data from the difference between the actual load data subjected to operation control by the ANN load prediction process and the prediction data, and reflects it in the creation of the next optimum operation plan. Let
That is, the air conditioning thermal load prediction unit 102 takes in measured data from the power output control unit 7 and the load power control unit 8 at regular time intervals, and sets the first predicted air conditioning thermal load value that coincides with the time when this operation control was performed. Read from the storage unit 101. The air conditioning thermal load prediction unit 102 calculates, for example, a deviation between the actual measurement data and the air conditioning thermal load prediction value, and creates a correction value corresponding 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 has time on the horizontal axis and output power of the 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 unit 102 described above, and the first storage unit 101 Are stored in the 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 111 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 air conditioning heat source operation plan The operation plan creation unit 105 assigns the 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 unit 102. 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 amount of heat that can be radiated or stored by the air conditioning heat source device 31 or 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 value. 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.

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.
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. Therefore, 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 62. 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 that is the amount of heat discharged from 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 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.

  In addition, 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 machine become optimal by mathematical programming. In this case, when the demand is low, 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 and CO2 emission minimization. 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 prediction unit 106 inputs the operation plan data from the operation plan creation unit 105 and detects the maximum value (hereinafter, demand peak value) of the 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 demand 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 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 13 changes greatly 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. 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 unit 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 facility device 3 and the work facility device 4. Output to the output unit 115.
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 instructing unit 110 sets the actual measured value to the demand target value as shown in FIG. May exceed. 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 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 unit 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 operation plan management method according to the present embodiment will be described with reference to FIG. FIG. 9 is a flowchart for explaining an outline of the operation plan management method according to the present embodiment.
As shown in FIG. 9, first, the air conditioning thermal load prediction unit 102 performs an actual measurement data collection process and a weather forecast data collection process. 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, and generates power output prediction that indicates the power predicted to be generated by the generator 61 according to the weather. 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.
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. 10 is a block diagram illustrating an example of the configuration of the plan DR creation unit 109.
As shown in FIG. 10, 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, an operation plan change unit 194, and an output unit 195. Prepare.
The storage unit 190 stores contract power data 196 indicating contract power that is a demand target value. The storage unit 190 stores air conditioner plan DR set temperature data 197 and air conditioner plan DR duration data 198. The air conditioner plan DR set temperature data 197 includes information indicating a set temperature to be changed in the plan DR creation. The air conditioner plan DR duration data 198 includes information indicating a period during which the set temperature is changed in the plan DR creation. The storage unit 190 is based on the contract power information, the set temperature information, and the duration information input from the external operation unit 200, and the contract power data 196, the air conditioner plan DR set temperature data 197, and the air conditioner plan DR. The duration data 198 can be rewritten.

The input unit 191 inputs the predicted power load value indicated by the operation plan data (the air conditioning heat source operation plan and the power supply facility operation plan) 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 operation plan change unit 194. Further, the demand peak value determination unit 193 calculates a continuous target excess period Tb in which the demand power amount continuously exceeds the demand target value C, and outputs it to the operation plan change unit 194.

Here, a specific description will be given with reference to FIG. FIG. 11 shows a graph of the predicted electric power load after the operation plan shown in FIG. 6 and a table showing the relationship between the installation temperature of the air conditioner changed by the plan DR creation unit 109 and the duration.
As shown in FIG. 11, there are two demand peak values in the predicted power load value, demand peak values P1 and P2, and both exceed the demand target value C. The predicted power load value shows demand peak values at 11:00 and 15:00, and exceeds the demand target value C between 10:30 and 11:30 and between 14:30 and 15:30. . The demand peak value P1 at 11:00 is smaller than the demand peak value P2 at 15:00.
The demand peak value determination unit 193 inputs the demand peak value P1 and the demand peak value P2 extracted by the demand peak value extraction unit 192, and continuously sets the demand time period corresponding to the demand peak value P1 and the demand peak value P2. Obtained as the excess period Tb. The length of the continuous target excess period Tb is defined as Tq in the air conditioner plan DR duration data 198 of the storage unit 190, and the demand peak value determination unit 193 refers to the air conditioner plan DR duration data 198. Then, the time Tq is obtained as the length of the continuous target excess period Tb. Here, since the time Tq is the same length as the demand time period, the demand peak value determination unit 193 obtains the demand time period corresponding to the demand peak value P1 and the demand peak value P2 as the continuous target excess period Tb.

  Based on the information input from the demand peak value determination unit 193, the operation plan change unit 194 includes a demand time limit power load predicted power amount included in the continuous target excess period Tb that is a time zone in which the demand target value C is exceeded. N demand time periods are extracted in descending order of predicted power load. The operation plan change unit 194 obtains a pre-peak period Ta that is Tp hours before the start time of the earliest time period among the extracted time periods. The length of the pre-peak period Ta is defined as the time Tp in the air conditioner plan DR duration data 198 of the storage unit 190, and the operation plan change unit 194 refers to the air conditioner plan DR duration data 198. Thus, the time Tp is obtained as the length of the pre-peak period Ta.

The operation plan changing unit 194 refers to the storage unit 190 and changes the set temperature of the air conditioner 33 in the pre-peak period Ta to the temperature P on the operation plan (air conditioning heat source operation plan and power supply facility operation plan). That is, the set temperature in the pre-peak period Ta is defined as the set temperature P in the air conditioner plan DR set temperature data 197 in the storage unit 190. The operation plan changing unit 194 refers to the air conditioner plan DR set temperature data 197 to obtain the set temperature P as the set temperature of the air conditioner 33 in the pre-peak period Ta.
Further, the operation plan changing unit 194 changes the set temperature of the air conditioner 33 in the continuous target excess period Tb to the temperature Q on the operation plan (the air conditioning heat source operation plan and the power supply facility operation plan) with reference to the storage unit 190. To do. That is, the set temperature in the continuous target excess period Tb is defined as the set temperature Q in the air conditioner plan DR set temperature data 197 in the storage unit 190. The operation plan change unit 194 refers to the air conditioner plan DR set temperature data 197 to obtain the set temperature Q as the set temperature of the air conditioner 33 in the continuous target excess period Tb.
In this way, the operation plan changing unit 194 outputs the output unit 195 with the air conditioning heat source operation plan and the power supply facility operation plan in which the set temperature has been changed as the plan DR.
The output unit 195 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 input unit 191 of the plan DR creation unit 109 receives 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 ST21). .

  Next, the demand peak value extraction unit 192 extracts the demand peak value of the power value from the predicted power load value (step ST22). For example, the demand peak value extraction unit 192 extracts the demand peak value P1 from the predicted power load value shown in FIG. The demand peak value P1 is a predicted power load value corresponding to the demand time period (n = 22) in AM11: 0 to 11:30. The demand peak value extraction unit 192 also extracts the demand peak value P2 from the predicted power load value. The demand peak value P2 is a predicted power load value corresponding to the demand time period D (n = 30) in AM15: 0 to 15:30. Note that the power amount of the predicted power load value of the demand peak value P2 is larger than the power amount of the predicted power load value of the demand peak value P1.

  Then, when there are a plurality of extracted demand peak values, the demand peak value extraction unit 192 assigns a code 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 a 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 code K and outputs them to the demand peak value determination unit 193.
For example, the demand peak value extraction unit 192 selects the demand peak value P2 with the code K = 1 and outputs it to the demand peak value determination unit 193. Next, the demand peak value extraction unit 192 selects the demand peak value P1 with the code K = 2, 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. 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 operation plan change unit 194.
For example, the demand peak value determination unit 193 compares the demand peak value P2 with the demand target value C. Here, since the demand peak value P2 exceeds the demand target value C, the demand peak value determination unit 193 displays information indicating that the demand peak value P2 exceeds the demand target value C as an operation plan change unit 194. Output to.
Further, the demand peak value determination unit 193 refers to the air conditioner plan DR duration data 198 and obtains, for example, that the length of the continuous target excess period Tb is the time Tp, and outputs it to the operation plan change unit 194. To do. That is, the demand peak value determination unit 193 outputs information indicating that the time Tq including the demand peak value P2 is the continuous target excess period Tb1 to the operation plan change unit 194. Similarly, the demand peak value determination unit 193 outputs information indicating that the time Tq including the demand peak value P1 is the continuous target excess period Tb2 to the operation plan change unit 194. Here, the time Tq is 30 minutes. Therefore, the continuous target excess period Tb1 is a period from 15:00 to 15:30, and the continuous target excess period Tb2 is a period from 11:00 to 11:30.

  Based on the information input from the demand peak value determination unit 193, the operation plan change unit 194 corresponds to the power corresponding to the demand peak value of the demand time limit included in the continuous target excess period Tb that is the time zone in which the contract power C is exceeded. N consecutive target excess periods Tb are extracted in descending order of the electric energy of the predicted load value.

Here, the operation plan changing unit 194 extracts N = 2 continuous target excess periods Tb1 and Tb2. First, the operation plan change unit 194 first performs an operation plan change process for the demand peak value P2 with the sign K = 1.
That is, the operation plan change unit 194 refers to the air conditioner plan DR duration data 198 to obtain the pre-peak period Ta of the time Tp. Then, the operation plan changing unit 194 obtains a pre-peak period Ta1 that is Tp time before the start time of the continuous target excess period Tb corresponding to the demand peak value P2. Here, Tp is 3 hours. Therefore, the pre-peak period Ta1 is a period from 12:00 to 15:00.
Next, the operation plan changing unit 194 refers to the air conditioner plan DR set temperature data 197 to obtain the set temperature P as the set temperature of the air conditioner 33 in the pre-peak period Ta. The operation plan change unit 194 changes the set temperature of the air conditioner 33 in the pre-peak period Ta1 to the temperature P on the operation plan (step ST23).

  Then, the operation plan changing unit 194 refers to the air conditioner plan DR set temperature data 197 to obtain the set temperature Q as the set temperature of the air conditioner 33 in the continuous target excess period Tb. The operation plan changing unit 194 changes the set temperature of the air conditioner 33 in the continuous target excess period Tb1 to the temperature Q on the operation plan (step ST24).

Next, the operation plan change unit 194 performs an operation plan change process for the demand peak value P1 with the sign K = 2. At this time, the operation plan change unit 194 determines whether or not the continuous target excess period Tb2 of the demand peak value P1 with the symbol K = 2 overlaps with the already changed operation plan time zone. Processing may be performed. In this continuous target excess period Tb2, since the time zone does not overlap with any of the pre-peak period Ta1 and the continuous target excess period Tb1 whose operation plan has already been changed, the following processing is performed.
That is, the operation plan change unit 194 changes the set temperature of the air conditioner 33 in the pre-peak period Ta2 to the temperature P on the operation plan, and sets the set temperature of the air conditioner 33 in the continuous target excess period Tb2 to the temperature Q. Change above.

  As described above, the plan DR creation unit 109 determines the demand peak by determining the daily start / stop schedule of the air conditioner based on the predicted power load value of the demand indicated by the operation plan created by the operation plan creation unit 105. A plan DR with a reduced 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. Thereby, when the 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 in advance according to the contract power C. . This provides economic benefits. Note that the plan DR creation unit 109 creates a plan DR that is changed to, for example, a change in set temperature or a stop of an air conditioner in order to reduce the power load.

Further, when the air conditioner 33 is used for cooling, by setting the set temperature P in the pre-peak period Ta lower than the set temperature Q in the continuous target excess period Tb, the time period when the peak value is reached. It is possible to operate with a higher power load first. Thereby, while suppressing the raise of the electric power load at the time of a peak, the temperature rise in the continuous target excess period Tb can be prevented by lowering the indoor installation temperature from the pre-peak period Ta to increase the cooling.
On the other hand, when the air conditioner 33 is used for heating, by setting the set temperature P in the pre-peak period Ta higher than the set temperature Q in the continuous target excess period Tb, the peak time period is exceeded. It is possible to operate with a higher power load first. Thereby, while suppressing the raise of the electric power load at the time of a peak, the temperature fall in the continuous target excess period Tb can be prevented by raising indoor installation temperature from the period before peak Ta, and making heating stronger.
Therefore, it contributes to the reduction of human discomfort and can mitigate the deterioration of the indoor environment.

  In addition, this invention demonstrated using the example which changes the preset temperature of the air conditioner 33 on an operation plan (an air-conditioning heat-source operation plan and a power supply equipment operation plan). However, the present invention is not limited to this, and the operation plan of the apparatus capable of reducing the operation amount of the heat source is changed. For example, an air conditioner fan or a pump may be used.

<Example of air conditioning heat source equipment 3>
Next, an example of the air conditioning heat source equipment 3 will be described.
FIG. 13 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. 13, among the air-conditioning heat source equipment 3, the refrigerating machine 310 that is the air-conditioning heat source machine 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.

<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. 14 is a block diagram illustrating a configuration of the load power control device 8 according to the present embodiment.
As shown in FIG. 14, 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. 15, the time points 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. 16, 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 process by the predicted value calculation unit 13 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 11, and if 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 14 is a DR requirement that indicates 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. 16 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 30-1, 30-2,... Use (2 kW × 20 units =) 40 kW of power. In addition, the example which is 20 air conditioners 30 is demonstrated 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 heat remains in the water 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 15. 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. 17 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. 15) 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 284 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. 18 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.

Second Embodiment
The present invention is not limited to the configuration described above, and may be configured as shown in FIG. 19, for example. FIG. 19 is a schematic block diagram illustrating an example of a smart grid system according to the second embodiment. In addition, about the structure provided with the function similar to the structure shown in FIG. 1, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.
As illustrated in FIG. 19, the operation management device 1 ′ further includes a communication unit 93 as compared with the operation management device 1 described above. The communication unit 93 is connected to the central management device 92 via a network.
The central management device 92 includes an information collection unit 92, a management storage unit 922, a management control unit 923, a communication unit 924, an output unit 925, a power output control device 7, and a load power control device 8. .
The information collection unit 92 inputs the actual measurement value detected by the indoor environment information detection unit 91 and stores it in the management storage unit 922 in association with the time when the actual measurement value was measured.
The management control unit 923 is a computer that centrally manages the central management device 92. The management control unit 923 transmits the actual measurement value acquired from the indoor environment information detection unit 91 acquired by the information collection unit 921 to the operation management apparatus 1 ′ via the communication unit 924. In addition, the management control unit 923 outputs the actual measurement value stored in the management storage unit 922 to the output unit 925.
The output unit 925 is, for example, a display device or a printing device, and displays or prints information input from the management control device 923.
The indoor environment information detection unit 91 includes detection units such as a temperature information detection unit 911 and a humidity information detection unit 912, and outputs the detected actual measurement values to the central management device.

With this configuration, even in the case of supplying power to the load device by combining the amount of generated power and the purchased power in a building facility equipped with solar power generation facilities or the like, this power supply is controlled by the operation management device 1 ′. Can manage the power supply to the facility.
Moreover, even if the heat load varies depending on the location of each building, the usage of the facility, etc., the peak value of purchased power can be suppressed on a building-by-building basis.

In addition, the program for realizing the function of the processing unit in the present invention is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read by the computer system and executed to control operation management. In addition, the demand power may be controlled. Here, the “computer system” includes an OS and hardware such as peripheral devices.
The “computer system” includes a WWW system having a homepage providing environment (or display environment). 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 transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

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

Claims (4)

Based on the predicted power load value indicating the power predicted in the operation plan as the power supplied to the heat source load device that adjusts the temperature to the preset temperature that is determined in advance on the predicted date, the power load predicted value indicates of power per fixed time predetermined, and determines the demand exceeds the determination unit what a power purchased electric power supplied from the power purchase power of the commercial power system exceeds the target value,
When the demand excess determination unit determines that the purchased power exceeds a target value, the power load applied to the heat source load device during the target excess period, which is a time during which the purchased power exceeds the target value, is purchased. The electric power is reduced so as to be equal to or less than the target value, and the power source load device is applied to the heat source load device during a peak first period which is a time period during which the purchased power does not exceed the target value and is in a period before the target excess period. A plan change unit for changing a set temperature of the heat source load device defined in the operation plan so as to increase a reduced amount in the target overperiod for the electric power load , and among the plurality of overtarget periods When the first pre-peak period immediately before the first target excess period overlaps with the second target excess period, which is a period different from the first target excess period, the first target excess period The maximum value of the purchased power in the predicted power load value and the maximum value of the purchased power in the predicted power load value in the second target excess period are compared, and the target having the higher maximum value is compared. A plan change unit for changing the purchased power in the excess period and the period before the first peak;
An operation management device comprising: The target overage period is
The operation management apparatus according to claim 1, further comprising a period in which an average of the purchased power per predetermined time among the predicted power load values is a maximum. The operation management device
Based on the predicted power load value indicating the power predicted in the operation plan as the power supplied to the heat source load device that adjusts the temperature to the preset temperature that is determined in advance on the predicted date, the power load predicted value indicates of power per constant predetermined time, to determine what power purchase power supplied from the power purchase power of the commercial power system is a power greater than the target value,
If the purchased electric power is Ru When determine constant exceeds the target value, the power purchase target excess period to the heat source load device power load on the power purchase power said target value power is the time exceeding the target value The power load applied to the heat source load device during the first peak period that is a time during which the purchased power does not exceed the target value and is a period before the target excess period. When changing the set temperature of the heat source load device defined in the operation plan so as to increase the amount reduced in the excess period, the first immediately before the first target excess period among the plurality of target excess periods. When a period before one peak overlaps with a second target excess period that is a period different from the first target excess period, the maximum of the purchased power of the predicted power load value in the first target excess period. The power purchase predicted value in the second target excess period is compared with the maximum value of the purchased power, and the power purchase in the target excess period and the period before the first peak with the higher maximum value is compared. Change power,
An operation management method characterized by that. In the control computer of the operation management device,
Based on the predicted power load value indicating the power predicted in the operation plan as the power supplied to the heat source load device that adjusts the temperature to the preset temperature that is determined in advance on the predicted date, the power load predicted value indicates of power per fixed time predetermined, to determine what power purchase power supplied from the power purchase power of the commercial power system is a power greater than the target value,
If the purchased electric power is Ru When determine constant exceeds the target value, the power purchase target excess period to the heat source load device power load on the power purchase power said target value power is the time exceeding the target value The power load applied to the heat source load device during the first peak period that is a time during which the purchased power does not exceed the target value and is a period before the target excess period. When changing the set temperature of the heat source load device defined in the operation plan so as to increase the amount reduced in the excess period, the first immediately before the first target excess period among the plurality of target excess periods. When a period before one peak overlaps with a second target excess period that is a period different from the first target excess period, the maximum of the purchased power of the predicted power load value in the first target excess period. The power purchase predicted value in the second target excess period is compared with the maximum value of the purchased power, and the power purchase in the target excess period and the period before the first peak with the higher maximum value is compared. To change the power,
An operation management program characterized by that.

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