JP7813148B2 - Load control device, load control method, and load control program - Google Patents

Description

The present invention relates to a load control device, a load control method, and a load control program.

Some consumers of electricity enter into contracts with electric power companies under a pricing structure based on the contracted power demand, which is determined by the maximum demand for electricity over the past year. Corporations, in particular, often use contracted power.

Contract power is determined based on the demand value. Demand value is data measured using a 30-minute maximum demand watt-hour meter installed by the electric power company, and is expressed as the average power consumption (kW) of electricity used over a 30-minute period. The maximum demand value is the largest demand value over the past year. For customers receiving less than 500 kW of high-voltage power, the maximum demand value is used to calculate the basic electricity charge, and if a large demand value is detected even once, that large demand value will apply for the entire year. In addition, for customers receiving 500 kW or more of high-voltage power, the contract power is determined through negotiation, and if the maximum demand value exceeds the contract power, a penalty fee will be paid that is higher than usual. In addition, negotiations to change the contract power are held based on that maximum demand value. Therefore, in order to keep electricity bills low, it is important not to exceed the maximum demand value.

The highest demand for the year occurs during the daytime in summer. Therefore, it is desirable for each consumer to control the load when demand is high, setting the maximum demand value as a target value and preventing demand from exceeding that target value. Conventionally, load control aimed at preventing demand from exceeding the target value has involved measuring the output of each load device with a sensor and issuing control commands based on the measurement results.

Other demand prediction technologies have been proposed to effectively prevent power demand from exceeding the limit. For example, one technology counts the number of power pulses input each time the power consumption reaches a reference power amount for a specified period, calculates a predicted demand value for each period based on the reference power amount and the number of pulses, and derives the end-point power consumption at the end of each period to correct the predicted demand value.

JP 2016-116381 A

However, the measurement information that can be used for control is often limited to pulse information from master meter data, and it is often difficult to obtain information on the actual operating output of each device. Therefore, conventional load control methods assume that the device is operating at rated output, and issue a control command value that is the rated output minus the target reduction amount. However, there are cases where the actual operating output of the device is less than the rated output, in which case demand cannot be reduced sufficiently and there is a risk that demand will exceed the target value.

Furthermore, technology that corrects predicted demand values based on end-point power consumption, which are calculated from power pulses input each time the amount of power consumed reaches a reference amount, does not take into account fluctuations in demand due to factors such as the season, day of the week, and time of day. Because power demand varies significantly depending on the season, day of the week, and time of day, even with this technology, there is a risk that demand may exceed the target value.

The present invention has been made in consideration of the above, and aims to provide a load control device, load control method, and load control program that appropriately suppresses excess demand relative to target values.

In the present invention, the demand prediction unit predicts the cumulative demand of a consumer load, including control-target equipment, for a predetermined period. The determination unit determines, based on the cumulative demand predicted by the demand prediction unit, whether the cumulative demand would exceed a target value if the output of the control-target equipment were not controlled. The first control unit, in accordance with the determination result by the determination unit, calculates a first reduction amount that makes the cumulative demand after first control equal to or less than the target value when the control-target equipment operates at rated output, and performs a first control of the output of the control-target equipment based on the first reduction amount. The second control unit, based on the result of the first control by the first control unit, calculates a second reduction amount that makes the cumulative demand after second control equal to or less than the target value, and performs a second control of the output of the control-target equipment based on the second reduction amount.

This invention makes it possible to appropriately prevent demand from exceeding the target value.

FIG. 1 is a schematic diagram of a load control system. FIG. 2 is a block diagram of the load control device according to the first embodiment. FIG. 3 is a diagram showing an example of numerical values when the provisional control is executed in the case where the load control is not executed before the provisional control. FIG. 4 is a diagram showing an example of numerical values when the main control is executed in the case where the load control is not executed before the provisional control. FIG. 5 is a diagram showing an example of numerical values when the temporary control is executed in the case where the load control is performed at 80% of the rated output before the temporary control. FIG. 6 is a diagram showing an example of numerical values when main control is executed in the case where load control is performed at 80% of the rated output before the provisional control. FIG. 7 is a flowchart of a load control process performed by the load control device according to the first embodiment. FIG. 8 is a diagram for explaining the effect of load control by the load control device according to the first embodiment. FIG. 9 is a block diagram of a load control device according to the second embodiment. FIG. 10 is a diagram illustrating an example of calculation of a correlation function between the total output of all load devices and an actual output reduction value by the load control device according to the second embodiment. FIG. 11 is a block diagram of a load control device according to the third embodiment. FIG. 12 is a diagram showing an example of the usage amount before and after the change when the control level is changed from LV0 to LV2. FIG. 13 is a diagram showing changes in usage amount for each change in control level. FIG. 14 is a diagram showing the change in usage amount when the control level is changed from LV0 to LV1. FIG. 15 is a hardware configuration diagram of the load control device.

The following describes in detail exemplary embodiments of the load control device, load control method, and load control program disclosed in the present invention, with reference to the accompanying drawings. Note that the load control device, load control method, and load control program disclosed in the present invention are not limited to the following exemplary embodiments.

Figure 1 is a schematic diagram of a load control system. The load control system 1 is arranged as part of the power system of each high-voltage consumer. As shown in Figure 1, the load control system 1 has a load control device 10, a master meter 11, and a pulse meter 12. The load control system 1 also has various load devices that consume electricity, such as air conditioning equipment 21 and lighting equipment 22. Here, load control refers to control that controls the output of the control-target devices provided in the load control system 1 to keep the demand, which is the cumulative demand for the output of all load devices of the high-voltage consumer, within the demand target value.

The master meter 11 is connected to the electric utility company 2. The master meter 11 receives power from the electric utility company. The master meter 11 then supplies power to various devices, including air conditioning equipment 21 and lighting equipment 22. The master meter 11 then detects changes in power consumption by high-voltage consumers. The master meter 11 is equipped with a pulse meter 12.

The pulse meter 12 extracts the total and instantaneous output values of all the load devices of the high-voltage consumer as pulses. The pulse meter 12 then outputs the extracted pulses to the load control device 10.

The load control device 10 controls the cumulative demand of all load equipment of high-voltage consumers by outputting control command values to controlled equipment such as air conditioning equipment 21, thereby reducing the output of the controlled equipment. Since demand is given as a 30-minute demand integration value, the load control device 10 calculates the control amount every 30 minutes, for example, and outputs a control command value. However, the output timing of the control command value is not limited to this and may be other timings.

The load control by the load control device 10 is described in detail below. Here, we will explain an example in which the load control device 10 controls the air conditioning equipment 21 as the controlled equipment, which accounts for a large portion of power consumption in the summer when maximum demand is likely to occur. However, the load control device 10 may also control other equipment or a combination of other equipment that acts as a load in the load control system 1 as the controlled equipment, thereby performing load control of all load equipment of high-voltage consumers.

Figure 2 is a block diagram of a load control device according to the first embodiment. As shown in Figure 2, the load control device 10 includes a demand prediction unit 101, a determination unit 102, a tentative control execution unit 103, an output estimation unit 104, and a main control execution unit 105.

The demand prediction unit 101 receives pulses output from the pulse meter 12 and acquires pulse data. Next, the demand prediction unit 101 uses the acquired pulse data to calculate the cumulative demand from 0 to 15 minutes during the 30-minute control period.

Next, the demand prediction unit 101 predicts the cumulative demand for 15 to 30 minutes using the cumulative demand for 0 to 15 minutes and the demand at the 15 minute mark. Any prediction method may be used. For example, before provisional control, the demand prediction unit 101 may use the cumulative demand for 0 to 15 minutes as the predicted value for the cumulative demand for 15 to 30 minutes. Here, the demand prediction unit 101 can determine whether provisional control has been performed by, for example, receiving a notification from the provisional control execution unit 103 that provisional control has been performed.

Furthermore, when provisional control ends and full control is performed, the demand prediction unit 101 receives input of the actual output reduction value due to provisional control from the output estimation unit 104. The demand prediction unit 101 then predicts the accumulated demand for 15 to 30 minutes after provisional control, taking into account the actual output reduction value due to provisional control. The predicted value of accumulated demand for 15 to 30 minutes after provisional control is the predicted value of accumulated demand for 15 to 30 minutes if provisional control is performed at the 15th minute. Any prediction method may be used as long as it takes into account the amount of output reduction when provisional control is performed. For example, the demand prediction unit 101 may use the value obtained by subtracting the demand actually reduced by provisional control from the accumulated demand for 0 to 15 minutes as the predicted value of accumulated demand for 15 to 30 minutes after provisional control.

In this way, the demand prediction unit 101 predicts the cumulative demand of the consumer load, including the controlled equipment, for a predetermined period. In this embodiment, the predetermined period corresponds to 30 minutes, which corresponds to the demand.

Next, the demand prediction unit 101 calculates a predicted value for the 30-minute integrated demand by adding the predicted value of the integrated demand from 0 to 15 minutes and the predicted value of the integrated demand from 15 to 30 minutes, and then multiplies this calculated value by 2 to calculate the predicted hourly demand value. In other words, by multiplying by 2, the demand prediction unit 101 converts the 30-minute integrated demand value in kWh into a demand value in kW. Here, after provisional control but before execution of main control, the demand prediction unit 101 calculates the predicted demand value after provisional control, i.e., the predicted demand value when provisional control is executed.

Then, if the current state is before the provisional control, the demand prediction unit 101 outputs the calculated demand prediction value to the determination unit 102. On the other hand, if the current state is after the provisional control but before the actual control, the demand prediction unit 101 outputs the calculated demand prediction value to the actual control execution unit 105 along with an instruction to execute the actual control.

The determination unit 102 has a pre-defined demand target value determined from the contracted power. Before the provisional control, the determination unit 102 receives an input of a demand prediction value, which is a predicted value of the integrated demand before the provisional control, from the demand prediction unit 101. Next, the determination unit 102 determines whether the demand prediction value exceeds the demand target value.

If the demand prediction value does not exceed the demand target value, the determination unit 102 determines that load control is unnecessary. In this case, the load control device 10 terminates the load control process. On the other hand, if the demand prediction value exceeds the demand target value, the determination unit 102 outputs the demand prediction value after tentative control to the tentative control execution unit 103 along with an instruction to execute tentative control.

The tentative control execution unit 103 also has a demand target value in advance. The tentative control execution unit 103 receives input of a demand prediction value along with an instruction to execute tentative control from the determination unit 102. The tentative control execution unit 103 then subtracts the demand target value from the demand prediction value to calculate a reduction target value for the cumulative demand from 15 to 30 minutes.

The tentative control execution unit 103 also stores in advance information on the rated output of the air conditioning equipment 21, which is the equipment to be controlled. The tentative control execution unit 103 then multiplies the target reduction value of the accumulated demand from 15 to 30 minutes by 60/15 to calculate the target reduction value of the output for one hour. In other words, by multiplying by 60/15, the tentative control execution unit 103 converts the 15-minute accumulated demand value, in units of kWh, into an output value, in units of kW.

Next, the tentative control execution unit 103 calculates the ratio of the output reduction target value to the rated output of the air conditioning equipment 21, and sets the calculated value as a command value. The tentative control execution unit 103 then outputs the command value to the air conditioning equipment 21, and controls the air conditioning equipment 21 to reduce the output to the ratio indicated by the command value. As a result, pulse data after tentative control is input to the demand prediction unit 101.

The tentative control execution unit 103 is an example of a "first control unit." Then, based on the determination result by the determination unit 102, the tentative control execution unit 103 calculates a first reduction amount that will keep the accumulated demand after the first control below a target value, assuming that the controlled equipment is operating at rated output, and performs a first control of the output of the controlled equipment based on the first reduction amount. In other words, the tentative control execution unit 103 calculates the first reduction amount assuming that the controlled equipment is operating at rated output. Here, the target value for output reduction due to tentative control is an example of a "first reduction amount." More specifically, the tentative control execution unit 103 calculates the first reduction amount based on the difference between the predicted value of accumulated demand when no control is performed and the target value, and the rated output.

The output estimation unit 104 receives pulses output from the pulse meter 12 and acquires pulse data. The output estimation unit 104 then uses the pulse data to calculate the demand 15 minutes before the execution of the provisional control. The output estimation unit 104 also uses the pulse data to calculate the demand 15 minutes before the execution of the full control. The output estimation unit 104 then subtracts the demand 15 minutes after the execution of the full control from the demand 15 minutes before the execution of the provisional control to calculate the actual output reduction value of the air conditioning equipment 21. The output estimation unit 104 then outputs the calculated actual output reduction value to the demand prediction unit 101.

This control execution unit 105 also has a demand target value in advance. It receives input of the actual output reduction value of the air conditioning equipment 21 from the output estimation unit 104. It also receives input of the demand prediction value after tentative control from the demand prediction unit 101, along with an instruction to execute this control. Then, this control execution unit 105 subtracts the demand target value from the demand prediction value after tentative control to calculate the reduction target value for the accumulated demand from 15 to 30 minutes.

The control execution unit 105 also stores in advance information on the rated output of the air conditioning equipment 21, which is the equipment to be controlled. The control execution unit 105 then multiplies the target reduction value of the accumulated demand from 15 to 30 minutes by 60/15 to calculate the target reduction value of the output for one hour. In other words, by multiplying by 60/15, the control execution unit 105 converts the accumulated demand value for 15 minutes, in units of kWh, into an output value in units of kW.

Next, the control execution unit 105 calculates the ratio of the output reduction target value to the rated output of the air conditioning equipment 21. In this case, because load control is being performed using provisional control, the control execution unit 105 calculates the ratio of the output reduction target value including the control amount used in provisional control. That is, the control execution unit 105 multiplies the rated output by the command value used in provisional control to calculate the output after provisional control, and then subtracts the output reduction target value from the calculated output after provisional control to calculate the ratio to the rated output, thereby calculating the ratio of the output reduction target value to the rated output. Next, the control execution unit 105 sets the calculated value as the command value. The control execution unit 105 then outputs the command value to the air conditioning equipment 21 and controls the air conditioning equipment 21 to reduce the output to the ratio indicated by the command value. In this way, the control execution unit 105 can control the output of the air conditioning equipment 21 so that the integrated demand value of all load equipment of high-voltage consumers does not exceed the demand target value.

This actual control execution unit 105 is an example of a "second control unit." Based on the results of the provisional control by the provisional control execution unit 103, which is the first control unit, the actual control execution unit 105 calculates a second reduction amount that will make the accumulated demand after the second control equal to or less than the target value, and performs a second control of the output of the controlled-target equipment based on the second reduction amount. More specifically, the actual control execution unit 105 calculates the second reduction amount based on the difference between the first accumulated demand and the target value when the first control is performed, which is predicted by the demand prediction unit 101 based on the actual reduction amount due to the provisional control of the output of the controlled-target equipment, and the rated output. Here, the target value for output reduction due to this control is an example of a "second reduction amount."

Figure 3 is a diagram showing example values when temporary control is executed when load control is not performed before temporary control. Figure 4 is a diagram showing example values when full control is executed when load control is not performed before temporary control. Next, with reference to Figures 3 and 4, we will explain the calculation of command values for preventing the demand target value from exceeding the target value when load control is not performed before temporary control. Four patterns of power demand, Cases #1 to #4, will be explained here. In each of Cases #1 to #4, the cumulative demand from 0 to 15 minutes is 26.5 kWh, and the demand at 15 minutes is 106 kW. The target demand value is 100 kW, and the rated output of the air conditioning equipment 21 is 30 kW.

Let us consider case #1. Using pulse data acquired from the pulse meter 12, the demand prediction unit 101 calculates the cumulative demand from 0 to 15 minutes as 26.5 (kWh). Here, the cumulative demand from 0 to 15 minutes is represented as (A). Next, the demand prediction unit 101 uses the cumulative demand from 0 to 15 minutes as the predicted value of the cumulative demand from 15 to 30 minutes, and calculates the predicted value of the cumulative demand from 15 to 30 minutes as 26.5 (kWh). However, this method of calculating the predicted value is just an example, and other calculation methods can also be used. Here, the predicted value of the cumulative demand from 15 to 30 minutes is represented as (C). Next, if the predicted demand value is represented as (D), the formula for calculating the demand prediction value by the tentative control execution unit 103 in this case is represented as D = (A + C) × 2. Therefore, the demand prediction unit 101 calculates the demand prediction value as (26.5 + 26.5) x 2 = 106 (kWh). In this case, since the demand prediction value exceeds the demand target value, the determination unit 102 decides to execute load control.

Next, if the target reduction value for the integrated demand from 15 to 30 minutes is (F), the formula for calculating the target reduction value for the integrated demand from 15 to 30 minutes by the tentative control execution unit 103 in this case is expressed as F = (D - E) / 2. Therefore, the tentative control execution unit 103 calculates the target reduction value for the integrated demand from 15 to 30 minutes as (106 - 100) / 2 = 3 (kWh). Next, if the target reduction value for output is (I), the formula for calculating the target reduction value for output by the tentative control execution unit 103 in this case is expressed as I = F x 60/15. Therefore, the tentative control execution unit 103 calculates the target reduction value for output as 3 x 60/15 = 12. Next, if the rated output is expressed as (G) and the command value is expressed as (J), the formula for calculating the command value by the tentative control execution unit 103 in this case is expressed as J = (G - I) / G x 100. Therefore, the tentative control execution unit 103 calculates the command value as (30 - 12) / 30 x 100 = 60(%). The tentative control execution unit 103 then outputs the calculated command value to the air conditioning equipment 21, controlling the air conditioning equipment 21 to operate at 60(%) of the rated output.

In case #1, the output estimation unit 104 uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 106 (kW), as shown in FIG. 3. The output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the actual control as 94 (kW), as shown in FIG. 4. If the demand 15 minutes before the provisional control is represented as (B), the demand 15 minutes before the actual control is represented as (B'), and the actual output reduction value is represented as (K), the calculation formula for the actual output reduction value by the output estimation unit 104 is expressed as K = B - B'. Therefore, the output estimation unit 104 calculates the actual output reduction value due to the provisional control as 106 - 94 = 12 (kW). In this case, if the excess of demand after the provisional control over the demand target value is represented as L, then L = (I - K) x 15/30. That is, the excess over the demand target value after provisional control is (12 - 12) x 15/30 = 0, as shown in Figure 3. In this case, the load control device 10 is able to keep the accumulated demand of all load devices of high-voltage customers within the demand target value through provisional control.

Therefore, in practice, the provisional control command value is maintained even during this control. Here, we will explain the process of this control in this case step by step. In this control, the demand prediction unit 101 uses pulse data acquired from the pulse meter 12 to calculate the cumulative demand from 0 to 15 minutes as 26.5 (kWh), as shown in Figure 4. Next, the demand prediction unit 101 subtracts the output reduction value per 15 minutes due to provisional control from the cumulative demand from 0 to 15 minutes before this control to calculate the predicted cumulative demand from 15 to 30 minutes when provisional control is performed. Here, if the predicted cumulative demand from 15 to 30 minutes after provisional control is represented as (C'), the predicted cumulative demand from 15 to 30 minutes after provisional control by the demand prediction unit 101 can be expressed as C' = A - (K x 15/60). Therefore, the demand prediction unit 101 calculates the predicted value of the integrated demand from 15 to 30 minutes after the provisional control as 26.5 - 12 x 15/60 = 23.5 (kWh). Next, if the demand prediction value is (D'), the calculation formula for the demand prediction value by the control execution unit 105 is expressed as D' = (A + C') x 2. Therefore, the control execution unit 105 calculates the demand prediction value as (26.5 + 23.5) x 2 = 100 (kWh). Next, if the reduction target value of the integrated demand from 15 to 30 minutes is (F'), the calculation formula for the reduction target value of the integrated demand from 15 to 30 minutes by the control execution unit 105 in this case is expressed as F' = (D' - E)/2. Therefore, the control execution unit 105 calculates the target reduction value for the integrated demand from 15 to 30 minutes as (100-100)/2 = 0 (kWh). Next, if the target reduction value for output is (I'), the calculation formula for the target reduction value for output by the control execution unit 105 is expressed as I' = F' x 60/15. Therefore, the control execution unit 105 calculates the target reduction value for output as 0 x 60/15 = 0. Next, if the command value is expressed as (J'), the calculation formula for the command value by the control execution unit 105 is expressed as J' = (G x J/100-I')/G x 100. Therefore, the control execution unit 105 calculates the command value as (30 x 60/100-0)/30 x 100 = 60 (%). The control execution unit 105 then outputs the calculated command value to the air conditioning equipment 21, controlling the air conditioning equipment 21 to operate at 60% of the rated output. As described above, the provisional control is able to keep the accumulated demand of all load equipment of high-voltage customers within the demand target value, so the control execution unit 105 may actually use the provisional control command value as is, without calculating the command value described here.

Next, we will explain Case #2. As shown in Figure 3, the numerical values before the provisional control are the same in Case #2 as in Case #1. In this case, the provisional control execution unit 103 outputs the calculated command value to the air conditioning equipment 21, as in Case #1, and controls the air conditioning equipment 21 to operate at 60% of the rated output.

In case #2, the output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 106 (kW), as shown in Figure 3. The output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the actual control as 99 (kW), as shown in Figure 4. The output estimation unit 104 then calculates the actual output reduction value due to the provisional control as 106 - 99 = 7 (kW), as shown in Figure 3. Here, even though the same control was performed on the air conditioning equipment 21 during the provisional control, the actual output reduction value differs from that in case #1 because the output of the air conditioning equipment 21 was not at rated output. In this case, the excess over the demand target value after the provisional control is (12 - 7) x 15/30 = 2.5, as shown in Figure 3. In this case, the load control device 10 is unable to suppress the accumulated demand of all load equipment of high-voltage customers within the demand target value through provisional control.

Next, in this control, the demand prediction unit 101 uses pulse data acquired from the pulse meter 12 to calculate the cumulative demand from 0 to 15 minutes as 26.5 (kWh), as shown in FIG. 4. Next, the demand prediction unit 101 calculates the predicted value of cumulative demand from 15 to 30 minutes after provisional control as 26.5 - 7 x 15/60 = 24.75 (kWh). Next, the control execution unit 105 calculates the predicted demand value as (26.5 + 24.75) x 2 = 102.5 (kWh). Next, the control execution unit 105 calculates the target reduction value of cumulative demand from 15 to 30 minutes as (102.5 - 100)/2 = 1.25 (kWh). Next, the control execution unit 105 calculates the target reduction value of output as 1.25 x 60/15 = 5. Next, the control execution unit 105 calculates the command value as (30 x 60/100 - 5)/30 x 100 = 43.33 (%). The control execution unit 105 then outputs the calculated command value to the air conditioning equipment 21, controlling the air conditioning equipment 21 to operate at 43.33 (%) of the rated output. This allows the load control device 10 to keep the accumulated demand of all load equipment of high-voltage customers within the demand target value.

In case #3, the calculation process for each value is the same as in case #2, except for the differences in values, so we will omit the explanation.

Next, we will explain case #4. As shown in Figure 3, the numerical values before the provisional control are the same in case #4 as in case #1. In this case, the provisional control execution unit 103 outputs the calculated command value to the air conditioning equipment 21, as in case #1, and controls the air conditioning equipment 21 to operate at 60% of the rated output.

In case #4, the output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 106 (kW), as shown in FIG. 3. The output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the actual control as 106 (kW), as shown in FIG. 4. The output estimation unit 104 then calculates the actual output reduction value due to the provisional control as 106 - 106 = 0 (kW), as shown in FIG. 3. In this case, the output of the air conditioning equipment 21 remains unchanged during the first provisional control. In other words, it is assumed that the air conditioning equipment 21 was operating at 60% or less of its rated output before the provisional control. Therefore, in this case, the command value is reduced by a predetermined amount to perform the second provisional control. Here, the predetermined amount may be, for example, 5%. The load control device 10 then calculates the actual output reduction value after the second provisional control based on the command value, and performs the same actual control as in case #2 using the calculated reduction value. In Figure 4, the values obtained in actual control are not displayed because the first provisional control shown in Figure 3 cannot be used to move on to actual control.

Figure 5 shows example values when temporary control is performed when load control is performed at 80% of rated output before temporary control. Figure 6 shows example values when full control is performed when load control is performed at 80% of rated output before temporary control. Next, with reference to Figures 5 and 6, calculation of command values to prevent exceedance of the demand target value when load control is performed at 80% of rated output before temporary control will be summarized. Here, four patterns of power demand, Cases #5 to #8, will be explained. In each of Cases #5 to #8, the cumulative demand from 0 to 15 minutes is 26 kWh, and the demand at 15 minutes is 104 kW. The demand target value is 100 kW, and the rated output of the air conditioning equipment 21 is 30 kW. When load control has already been performed before temporary control, the command value is calculated taking into account the load control ratio, which differs from when load control was not performed before temporary control.

Let's consider case #5. As shown in FIG. 5, the demand prediction unit 101 uses pulse data acquired from the pulse meter 12 to calculate the cumulative demand from 0 to 15 minutes as 26 (kWh). Next, the demand prediction unit 101 uses the cumulative demand from 0 to 15 minutes as the predicted value of the cumulative demand from 15 to 30 minutes, and calculates the predicted value of the cumulative demand from 15 to 30 minutes as 26 (kWh). Next, the tentative control execution unit 103 calculates the predicted demand value as (26 + 26) x 2 = 104 (kWh). In this case, the predicted demand value exceeds the target demand value, so the determination unit 102 decides to execute load control.

The tentative control execution unit 103 calculates the target reduction value for the cumulative demand from 15 to 30 minutes as (104 - 100) / 2 = 2 (kWh). Next, the tentative control execution unit 103 calculates the target reduction value for output as 2 x 60 / 15 = 8. Next, the formula for calculating the command value by the tentative control execution unit 103 when load control is being performed at 80% of the rated output is expressed as J = (G x 0.8 - I) / G x 100. Therefore, the tentative control execution unit 103 calculates the command value as (30 x 0.8 - 8) / 30 x 100 = 53.33 (%). The tentative control execution unit 103 then outputs the calculated command value to the air conditioning equipment 21, controlling it to operate at 53.33 (%) of the rated output.

Next, the output estimation unit 104 uses the pulse data obtained from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 104 (kW), as shown in FIG. 5. The output estimation unit 104 also uses the pulse data obtained from the pulse meter 12 to calculate the demand 15 minutes before the full control as 96 (kW), as shown in FIG. 6. The output estimation unit 104 then calculates the actual output reduction value due to the provisional control as 104 - 96 = 8 (kW), as shown in FIG. 5. In this case, the excess over the demand target value after the provisional control is (8 - 8) x 15/30 = 0, as shown in FIG. 5. In this case, the load control device 10 is able to keep the accumulated demand of all load equipment of high-voltage customers within the demand target value through provisional control.

Therefore, in practice, the command value of the provisional control is maintained even in this control. Here, we will explain the process of this control in this case step by step. In this control, the demand prediction unit 101 uses pulse data acquired from the pulse meter 12 to calculate the cumulative demand from 0 to 15 minutes as 26 (kWh), as shown in FIG. 6. Next, the demand prediction unit 101 calculates the predicted value of the cumulative demand from 15 to 30 minutes after the provisional control as 26 - 8 x 15/60 = 24 (kWh). Next, the control execution unit 105 calculates the predicted demand value as (24 + 24) x 2 = 100 (kWh). Next, the control execution unit 105 calculates the target reduction value of the cumulative demand from 15 to 30 minutes as (100 - 100)/2 = 0 (kWh). Next, the control execution unit 105 calculates the target reduction value of the cumulative demand from 15 to 30 minutes as 0 x 60/15 = 0. Next, the control execution unit 105 calculates the command value as (30 x 53.33 / 100 - 0) / 30 x 100 = 53.33 (%). The control execution unit 105 then outputs the calculated command value to the air conditioning equipment 21, controlling the air conditioning equipment 21 to operate at 53.33 (%) of the rated output.

Next, we will explain case #6. As shown in Figure 5, the numerical values before the provisional control are the same in case #6 as in case #5. In this case, the provisional control execution unit 103 outputs the calculated command value to the air conditioning equipment 21, as in case #5, and controls the air conditioning equipment 21 to operate at 53.33 (%) of the rated output.

In case #6, the output estimation unit 104 also uses pulse data obtained from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 104 (kW), as shown in Figure 5. The output estimation unit 104 also uses pulse data obtained from the pulse meter 12 to calculate the demand 15 minutes before the actual control as 99 (kW), as shown in Figure 6. The output estimation unit 104 then calculates the actual output reduction value due to the provisional control as 104 - 99 = 5 (kW), as shown in Figure 5. Here, even though the same control was performed on the air conditioning equipment 21 in the provisional control, the actual output reduction value differs from that in case #5, presumably because the output of the air conditioning equipment 21 was not 80% of its rated output. In this case, the excess over the demand target value after the provisional control is (8 - 5) x 15/30 = 1.5, as shown in Figure 5. In this case, the load control device 10 is unable to suppress the cumulative demand of all load devices of high-voltage customers within the demand target value through provisional control.

Next, in this control, the demand prediction unit 101 uses pulse data acquired from the pulse meter 12 to calculate the cumulative demand from 0 to 15 minutes as 26 (kWh), as shown in FIG. 6. Next, the demand prediction unit 101 calculates the predicted value of cumulative demand from 15 to 30 minutes after provisional control as 26 - 5 x 15/60 = 24.75 (kWh). Next, the control execution unit 105 calculates the predicted demand value as (26 + 24.75) x 2 = 101.5 (kWh). Next, the control execution unit 105 calculates the target reduction value of cumulative demand from 15 to 30 minutes as (101.5 - 100)/2 = 0.75 (kWh). Next, the control execution unit 105 calculates the target reduction value of output as 0.75 x 60/15 = 3. Next, the control execution unit 105 calculates the command value as (30 x 53.33 / 100 - 3) / 30 x 100 = 43.33 (%). The control execution unit 105 then outputs the calculated command value to the air conditioning equipment 21, controlling the air conditioning equipment 21 to operate at 43.33 (%) of the rated output. This allows the load control device 10 to keep the accumulated demand of all load equipment of high-voltage customers within the demand target value.

In case #7, the calculation process for each value is the same as in case #6, except for the differences in values, so explanation will be omitted.

Next, we will explain case #8. As shown in Figure 5, the numerical values before the provisional control are the same in case #8 as in case #5. In this case, the provisional control execution unit 103 outputs the calculated command value to the air conditioning equipment 21, as in case #5, and controls the air conditioning equipment 21 to operate at 53.33 (%) of the rated output.

In case #8, the output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes before the provisional control as 104 (kW), as shown in FIG. 5. The output estimation unit 104 also uses pulse data acquired from the pulse meter 12 to calculate the demand 15 minutes after the provisional control as 104 (kW), as shown in FIG. 6. The output estimation unit 104 then calculates the actual output reduction value due to the provisional control as 104 - 104 = 0 (kW), as shown in FIG. 5. In this case, the output of the air conditioning equipment 21 does not change during the first provisional control. In other words, it is considered that the air conditioning equipment 21 was operating at 53.33% or less of its rated output before the provisional control. Therefore, in this case, the command value is reduced by a predetermined amount to perform the second provisional control. The load control device 10 calculates the actual output reduction value after the second provisional control based on the command value, and performs the same main control as in case #6 using the calculated reduction value.

Figure 7 is a flowchart of the load control process performed by the load control device according to the first embodiment. Next, the flow of the load control process performed by the load control device 10 according to this embodiment will be described with reference to Figure 7 .

The demand prediction unit 101 acquires pulse data representing the total output value of all load devices of the high-voltage consumer from the pulse meter 12. Then, the demand prediction unit 101 uses the acquired pulse data to predict demand, which is an integrated value over 30 minutes (step S1).

The determination unit 102 compares the demand prediction value calculated by the demand prediction unit 101 with the demand at 15 minutes, and determines whether the demand prediction value exceeds the demand target value (step S2). If the demand prediction value does not exceed the demand target value (step S2: No), the load control device 10 terminates the load control process.

On the other hand, if the demand prediction value exceeds the target value (Step S2: Yes), the tentative control execution unit 103 calculates a reduction target value for the output of the controlled device from the demand prediction value and the demand target value. Then, the tentative control execution unit 103 calculates a command value for tentative control using the rated output and reduction target value of the controlled device (Step S3).

Next, the tentative control execution unit 103 executes tentative control of the controlled device using the calculated command value (step S4).

The output estimation unit 104 calculates the output before and after the provisional control using pulse data representing the total output of all load devices of the high-voltage consumer transmitted from the pulse meter 12. The output estimation unit 104 then estimates the output of the target device using the output before and after the provisional control (step S5).

Next, the main control execution unit 105 determines whether the output of the target device has decreased due to the tentative control (step S6). If the output of the target device has not decreased due to the tentative control (step S6: No), the main control execution unit 105 instructs the tentative control execution unit 103 to re-execute the tentative control. Upon receiving the instruction to re-execute the tentative control, the tentative control execution unit 103 calculates a new tentative control command value by subtracting a predetermined amount from the calculated tentative control command value (step S7). The tentative control execution unit 103 then returns to step S4.

On the other hand, if the output of the target device has been reduced by the provisional control (Step S6: Yes), the control execution unit 105 acquires a demand prediction value that takes into account the output reduction value due to the provisional control from the demand prediction unit 101. Then, the control execution unit 105 calculates a command value for the provisional control using the output estimation result, the demand prediction value, and the demand target value (Step S8).

Then, the main control execution unit 105 executes the main control of the controlled device using the calculated command value (step S9).

Figure 8 is a diagram illustrating the effect of load control by the load control device according to Example 1. Graph 201 is a graph comparing the amount of output reduction when no load control is performed, when load control is performed assuming the controlled equipment is operating at rated output, and when load control according to this example is performed. Graph 201 has output on the vertical axis, and the horizontal axis shows the cases when no load control is performed, when load control is performed assuming the controlled equipment is operating at rated output, and when load control according to this example is performed, arranged from left to right on the page. Rated output 211 represents the rated output of the controlled equipment. Operating output 212, which corresponds to the output when no load control is performed, represents the actual operating output of the controlled equipment before control was performed.

When load control is performed assuming that the controlled equipment is operating at rated output, the load control device 10 calculates an estimated reduction amount 213 to prevent the demand target value from being exceeded, and outputs the value obtained by subtracting the estimated reduction amount 213 from the rated output 211 as the control command value. With this control, if the operating output 212 is lower than the rated output 211, the actual reduction amount 214 will actually be reduced from the output of the controlled equipment, making it difficult to sufficiently reduce the output.

In contrast, when load control according to this embodiment is performed, the load control device 10 uses past demand data and the like as learning data to calculate the estimated reduction amount 215 so as not to exceed the demand target value. In this case, the estimated reduction amount 215 matches the actual reduction amount 216. Therefore, the load control device 10 can issue appropriate control commands to the controlled equipment so as not to exceed the demand target value.

Graph 202 shows the changes in cumulative power consumption due to these controls. Line 223 represents the changes when no control is performed. Line 224 represents the changes when load control is performed assuming that the controlled equipment is operating at rated output. Line 225 represents the changes when load control according to this embodiment is performed. Value 222 represents the target demand value. Time point 221 is the timing when control is initiated.

As shown by line 223, if no control is performed, the cumulative power amount will continue to increase after time 221, just as it did before, and as shown by line 223, the predicted demand value will exceed the target demand value. Therefore, control is performed at time 221, but if load control is performed assuming that the controlled equipment is operating at rated output, the reduction amount will be insufficient, and the cumulative power amount will ultimately exceed the target demand value, as shown by line 224. In contrast, when load control according to this embodiment is performed, the reduction amount can be appropriately estimated, and the cumulative power amount can be kept within the target demand value.

In the above example, prediction and control are performed when 15 minutes have passed since the demand count time limit, but the timing is not limited to this and can be any timing, such as 5 minutes, 10 minutes, 20 minutes, etc., or every minute.

As explained above, the load control device 10 according to this embodiment performs provisional control based on the demand prediction results, estimates the output of the controlled equipment when provisional control is performed, and calculates a command value based on the estimated output and taking into account the actual operating output to keep the cumulative amount of power within the target value. By using the command value calculated in this way to control the controlled equipment and reduce output, it is possible to appropriately prevent demand from exceeding the target value.

Figure 9 is a block diagram of a load control device according to Example 2. The load control device 10 according to this example performs provisional control for a certain period of time as a learning period, and after the learning period has elapsed, estimates the output of the controlled device from pulse data using the control results of the provisional control and performs control. Details of the load control device 10 according to this example are described below. The load control device 10 according to this example has a correlation function generation unit 106 in addition to the respective units of Example 1. Here, explanations of the operation of the respective units similar to those of Example 1 will be omitted.

If the determination unit 102 determines that the demand prediction value has exceeded the demand target value for a predetermined period of time, such as one month, the tentative control execution unit 103 assumes that the controlled equipment is operating at rated output and performs load control so that the demand falls within the demand target value.

The output estimation unit 104 calculates the actual output reduction value of the controlled equipment each time load control is executed by the tentative control execution unit 103 during the learning period. The output estimation unit 104 then outputs the calculated actual output reduction value together with the total output of all loads of high-voltage consumers at the current 15-minute point, which is obtained from pulse data acquired from the pulse meter 12, to the demand prediction unit 101.

During the learning period, each time load control is executed by the tentative control execution unit 103, the correlation function generation unit 106 acquires and collects the total output of all loads of high-voltage consumers and the actual output reduction value at that time from the output estimation unit 104. After the learning period has elapsed, the correlation function generation unit 106 generates a correlation function that represents the relationship between the total output of all loads of high-voltage consumers collected during the learning period and the estimated result of the actual output reduction value. The correlation function generation unit 106 then outputs the generated correlation function to the actual control execution unit 105. In other words, the correlation function generation unit 106 generates a correlation function that represents the relationship between the output of the demand overload over a certain period and the actual reduction amount calculated by the output estimation unit 104.

Figure 10 is a diagram showing an example of calculation of a correlation function between the total output of all load devices and the actual output reduction value by the load control device of Example 2. In Figure 10, the horizontal axis represents the total output of all load devices, which is the total output of all load devices of a high-voltage consumer, and the vertical axis represents an estimate of the actual output reduction value of the air conditioning device 21, which is the controlled device. For example, as shown in Figure 10, the correlation function generation unit 106 plots each of the collected data on a two-dimensional plane having axes representing the total output of all load devices of a high-voltage consumer and the actual output reduction value. In Figure 10, the points included in frame 231 represent the actual output reduction value for the total output of all load devices when 70% of the rated output is set as the command value. Furthermore, the points included in frame 232 represent the actual output reduction value for the total output of all load devices when 50% of the rated output is set as the command value. Next, the correlation function generation unit 106 generates a straight line 233 that approximates the plotted points. The correlation function generation unit 106 then outputs the generated function representing the straight line 233 to the demand prediction unit 101 as a correlation function.

After the learning period has elapsed, the demand prediction unit 101 obtains from the correlation function generation unit 106 a correlation function that represents the relationship between the total output of all loads of high-voltage consumers and the estimated output of the controlled equipment. The demand prediction unit 101 also calculates the total output of all loads of high-voltage consumers from pulse data obtained from the pulse meter 12. The demand prediction unit 101 then uses the correlation function to calculate the output of the controlled equipment corresponding to the calculated total output of all load equipment. For example, if the correlation function is a function representing the straight line 233 in Figure 10, and the demand prediction unit 101 obtains value 234 as the total output of all load equipment, it obtains value 235 as an estimate of the actual output reduction value. The demand prediction unit 101 then predicts the demand after the provisional control using the estimate of the actual output reduction value. The demand prediction unit 101 then outputs the demand prediction value to the actual control execution unit 105.

Here, the output estimation unit 104 may estimate the output of the controlled equipment each time load control is executed by the tentative control execution unit 103 during the learning period from the actual output reduction value. In this case, the correlation function generation unit 106 generates a correlation function that represents the relationship between the total output of all loads of high-voltage consumers collected during the learning period and the estimated output of the controlled equipment. Then, the correlation function generation unit 106 outputs the generated correlation function to the demand prediction unit 101.

The control execution unit 105 receives an input of a demand prediction value determined using a correlation function from the demand prediction unit 101. The control execution unit 105 then determines a command value using the acquired demand prediction value, demand target value, and rated output. The method for calculating the command value here is the same as in Example 1. The control execution unit 105 then transmits the command value to the controlled equipment to perform load control of the controlled equipment.

As described above, the load control device according to this embodiment repeats tentative control during a learning period, and uses the data collected thereby to generate a correlation function between the total output of all load devices and the estimated value of the output of the controlled devices. The load control device then estimates the output of the controlled devices corresponding to the measured total output of all load devices using the generated correlation function, and controls the controlled devices using the estimated result. In this way, by estimating the output of the controlled devices corresponding to the measured total output of all load devices based on the results of multiple tentative controls, the impact of results in unusual cases can be reduced, and the output of the controlled devices can be accurately estimated. This makes it possible to more appropriately suppress excess demand relative to the target value.

Figure 11 is a block diagram of a load control device according to Example 3. In Examples 1 and 2, fluctuations in demand when the control amount is changed are estimated based on the rated output of the controlled equipment. However, fluctuations in demand for equipment not subject to control due to controlling the output of the controlled equipment, as well as fluctuations in demand due to other external factors, are also possible. Therefore, the load control device 10 according to this example analyzes and learns the extent to which demand changes when a predetermined control level is changed in actual performance, and performs load control based on the learning results. The load control device 10 according to this example has an actual average reduction amount calculation unit 107 in addition to the components of Example 1. In the following explanation, explanations of the operation of the components similar to those of Example 1 will be omitted.

The load control device 10 according to this embodiment has preset control levels, in which the control amount is divided into stages. In this embodiment, the load control device 10 has preset control levels LV0 to LV4. Control level LV0 is when no load control is performed, and the control amount is 0. The control amounts increase in the order of control levels LV0 to LV4. For example, the load control device 10 outputs a command value for 80% of the rated output at control level LV1, a command value for 70% of the rated output at control level LV2, a command value for 60% of the rated output at control level LV3, and a command value for 50% of the rated output at control level LV4.

The tentative control execution unit 103 performs load control to suppress demand to the demand target value, assuming that the controlled equipment operates at rated output over a fixed learning period, such as one month. However, in this embodiment, the tentative control execution unit 103 outputs a command value corresponding to a control level that provides the closest control amount equal to or greater than the control amount that suppresses demand to the demand target value. The tentative control execution unit 103 outputs information about the control level of the load control performed on the air conditioning equipment 21 to the actual average reduction amount calculation unit 107.

The actual average reduction amount calculation unit 107 receives control level input from the tentative control execution unit 103 during the learning period. Furthermore, the actual average reduction amount calculation unit 107 acquires pulses representing the output value of all load devices of the high-voltage consumer at each timing output from the pulse meter 12. The actual average reduction amount calculation unit 107 then acquires pulse data based on the pulses and uses the acquired pulse data to calculate the usage amount, which is the one-minute value in kWh of the output of all load devices of the high-voltage consumer.

Next, the actual average reduction amount calculation unit 107 obtains the usage amounts for the 5 to 10 minutes immediately before and after the control level change. The actual average reduction amount calculation unit 107 then calculates the pre-change usage amount, which is the average of the usage amounts for the 5 to 10 minutes immediately before the control level change, and the post-change usage amount, which is the average of the usage amounts for the 5 to 10 minutes immediately after the control level change.

Figure 12 shows an example of the usage amount before and after the change when changing from control level LV0 to control level LV2. In Figure 12, the control level changes at 14:01 minute and 20 seconds, and the value for the minute before 14:02 is a mixture of control level LV0 and control level LV2. Therefore, the actual average reduction amount calculation unit 107 does not use the value for the minute before 14:02 to calculate the usage amount before and after the change. In the case of Figure 12, the actual average reduction amount calculation unit 107 calculates 0.8726 kWh, which is the average usage amount for the 10 minutes from 13:52 to 14:01, which is included in range 301, as the usage amount before the change. In this way, to reduce the impact of demand fluctuations, the actual average reduction amount calculation unit 107 does not include the usage amount at 13:51, which is 10 minutes before the change time, in its calculation. Additionally, the actual average reduction amount calculation unit 107 calculates the post-change usage amount as 0.9984 kWh, which is the average usage amount for the eight minutes from 14:03 to 14:10, which is included in range 302 and is the amount before the next control level change occurs. In this case, since 0.9984 - 0.8726 x 60 ≒ 7.55, it can be assumed that changing from control level LV2 to control level LV0 increased demand by approximately 7.55 kW.

Figure 13 is a diagram showing the change in usage amount for each change in control level. Figure 14 is a diagram showing the change in usage amount when changing from control level LV0 to control level LV1. In each of the graphs shown in Figures 13 and 14, the horizontal axis represents the usage amount before the change, and the vertical axis represents the usage amount after the change. Here, we will explain the change in usage amount due to changes in control level with reference to Figures 13 and 14.

Figure 13 is a matrix of graphs on a coordinate plane, each plotting points representing the correspondence between pre-change and post-change usage amounts for each change in control level collected during the learning period. The scales for each graph in Figure 13 are omitted, but they are the same as those for the graphs in Figure 14. Furthermore, for each graph in Figure 13, the number written either above or below the graph indicates the pre-change usage control level, and the number written either to the left or right indicates the post-change usage control level.

For example, the top graph in the leftmost column of Figure 13 is a graph that shows the change in usage when changing from control level LV0 to control level LV1, the same as the graph in Figure 14. Here, because there were no actual changes from control level LV1 to control level LV4 or from control level LV4 to control level LV1 during the learning period, no points are plotted on the corresponding graphs.

Each point 311 in Figure 13 is a point plotted on a coordinate plane that represents the correspondence between the usage amount before the change and the usage amount after the change when the control level collected during the learning period changes. Point 311 in Figure 14 is a point plotted on a coordinate plane that represents the correspondence between the usage amount before the change and the usage amount after the change when the control level collected during the learning period changes from LV0 to LV1. Also, line 312 in Figures 13 and 14 is a line on which the usage amount before the change and the usage amount after the change coincide. For example, in Figure 14, many of the points 311 are distributed below line 312, which indicates that applying control at control level LV1 tends to reduce demand compared to when the control level is uncontrolled (LV0).

Referring to Figure 13, overall, graphs located to the upper right of the diagonal line on which the control levels are plotted tend to have points 311 distributed above line 312, as these represent changes in which the control levels decrease. Graphs located to the lower left of the diagonal line on which the control levels are plotted tend to have points 311 distributed below line 312, as these represent changes in which the control levels increase. Comparing the graphs in the leftmost column of Figure 13, which show control level LV0 before the change, shows that the higher the control level after the change, the greater the usage before the change. This reflects the fact that the greater the demand between 0 and 15 minutes, the greater the reduction in output between 15 and 30 minutes.

During the learning period, the actual average reduction amount calculation unit 107 accumulates information on the pre-change usage amount and post-change usage amount for each change in control level made by the tentative control execution unit 103. After the learning period has elapsed, the actual average reduction amount calculation unit 107 uses the information on the pre-change usage amount and post-change usage amount for each change in control level to calculate the actual average reduction amount, which is the amount of output reduction based on actual performance.

Here, if the actual average reduction amount is represented as d _i (i = 0 to 4), d _i is the value expected as a decrease in usage when changing from control level LV0 to control level LVi, and this value is called the expected decrease amount. However, d ₀ = 0. The expected decrease in usage when changing from control level LVi to control level LV0 is also d _i . The expected decrease when changing from control level d _i to control level d _j (j = 0 to 4, k ≠ i) is d _i - d _k . The actual average reduction amount calculation unit 107 finds d _i that satisfies the above conditions as a solution to the minimization problem of the following equations (1) to (3).

Here, K(i, j) is the number of pre-change and post-change usage amounts obtained when changing from control level LVi to control level LVj during the learning period. Also, b(i, j, k) is the kth value among the differences between the pre-change and post-change usage amounts when changing from control level LVi to control level LVj. For example, when the kth change in usage amount when changing from control level LV2 to control level LV0 is shown in Figure 12, b(2, 0, k) = 0.9984 - 0.87266.

This allows the actual average reduction amount calculation unit 107 to calculate d _i (i = 0 to 4), which is the actual average reduction amount when control is performed at each of the control levels LV1 to LV4 from a no-control state. Thereafter, the actual average reduction amount calculation unit 107 outputs the actual average reduction amount corresponding to each of the control levels LV1 to LV4 to the main control execution unit 105.

In this way, the actual average reduction amount calculation unit 107 calculates the output of the demand overload, and calculates the actual average reduction amount, which is the actual reduction amount of the consumer load output according to the first reduction amount, based on the output of the demand overload over a certain period of time and the first reduction amount due to temporary control.

The control execution unit 105 receives input of the actual average reduction amount corresponding to each control level LV1 to LV4 from the actual average reduction amount calculation unit 107. When actually performing load control, the control execution unit 105 then calculates the target reduction value for the accumulated demand from 15 to 30 minutes from the demand prediction value and target demand value obtained from the demand prediction unit 101. The control execution unit 105 then identifies, from the information on the actual average reduction amount corresponding to each control level LV1 to LV4, the control level that results in the smallest actual average reduction amount that is equal to or greater than the calculated target reduction value for the accumulated demand from 15 to 30 minutes. The control execution unit 105 then outputs a command value for the identified control level to the controlled equipment, such as the air conditioning equipment 21.

In this way, the control execution unit 105 according to this embodiment calculates the difference between the predicted value of the accumulated demand when the control predicted by the demand prediction unit 101 is not performed and the target value. Then, the control execution unit 105 determines the actual average reduction amount according to the calculated difference, and sets the first reduction amount in the provisional control corresponding to the determined actual average reduction amount as the second reduction amount in the actual control.

As explained above, the load control device according to this embodiment acquires usage amounts, which are one-minute values in kWh of the output of all load devices of high-voltage consumers before and after the point in time when the control level changes during the provisional control performed during the learning period. The load control device then uses the usage amounts to determine the usage amounts before and after the change, and calculates an actual average reduction amount, which represents the reduction in demand from an uncontrolled state when controlled at each control level, using the calculated actual average reduction amount. The load control device then determines the control level and controls the controlled devices so as to achieve the demand target value using the calculated actual average reduction amount.

This allows control to be performed that takes into account fluctuations in demand for equipment other than the controlled equipment and fluctuations in demand due to other external factors, allowing for more appropriate control of the overall demand for high-voltage consumer load equipment. This makes it possible to more appropriately prevent demand from exceeding the target value.

(Hardware configuration)
15 is a hardware configuration diagram of the load control device. An example of a hardware configuration for realizing each function of the load control device 10 according to each embodiment will now be described with reference to FIG.

As shown in FIG. 15, the load control device 10 according to each embodiment includes a CPU (Central Processing Unit) 91, memory 92, a hard disk 93, and a network interface 94. The CPU 91 is connected to the memory 92, hard disk 93, and network interface 94 via a bus.

The network interface 94 is an interface for communication between the load control device 10 and external devices. The network interface 94 relays communication between the CPU 91 and controlled devices such as the pulse meter 12 and air conditioning equipment 21, for example.

The hard disk 93 is an auxiliary storage device. The hard disk 93 stores various programs including programs for realizing the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, output estimation unit 104, and main control execution unit 15, as illustrated in FIG. 2. The hard disk 93 also stores various programs including programs for realizing the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, output estimation unit 104, main control execution unit 15, and correlation function calculation unit 16, as illustrated in FIG. 9. The hard disk 93 also stores various programs including programs for realizing the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, main control execution unit 15, and actual average reduction amount calculation unit 107, as illustrated in FIG. 11.

Memory 92 is a main storage device. Memory 92 can be, for example, a dynamic random access memory (DRAM).

The CPU 91 reads various programs from the hard disk 93, expands them into the memory 92, and executes them. As a result, the CPU 91 realizes the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, output estimation unit 104, and main control execution unit 105 illustrated in FIG. 2. The CPU 91 also realizes the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, output estimation unit 104, main control execution unit 105, and correlation function calculation unit 106 illustrated in FIG. 9. The CPU 91 also realizes the functions of the demand prediction unit 101, judgment unit 102, tentative control execution unit 103, main control execution unit 105, and actual average reduction amount calculation unit 107 illustrated in FIG. 11.

REFERENCE SIGNS LIST 1 Load control system 2 Electric utility 10 Load control device 11 Master meter 12 Pulse meter 21 Air conditioning equipment 22 Lighting equipment 101 Demand prediction unit 102 Determination unit 103 Provisional control execution unit 104 Output estimation unit 105 Main control execution unit 106 Correlation function generation unit 107 Actual average reduction amount calculation unit

Claims (7)

a demand prediction unit that predicts an integrated demand of a consumer load including the controlled device for a predetermined period;
a determination unit that determines whether or not the integrated demand in a state where the output of the control-target device is not controlled exceeds a target value based on a predicted value of the integrated demand by the demand prediction unit;
a first control unit that calculates a first reduction amount that makes the integrated demand after a first control equal to or less than the target value, assuming that the control-target device operates at a rated output, according to a determination result by the determination unit, and performs a first control of the output of the control-target device based on the first reduction amount;
a second control unit that calculates a second reduction amount based on a result of the first control by the first control unit, such that the integrated demand after the second control is equal to or less than the target value, and performs a second control of the output of the controlled device based on the second reduction amount. The load control device described in claim 1, characterized in that the first control unit calculates the first reduction amount based on the difference between the predicted value of the integrated demand in a state where control is not performed and the target value, and the rated output. an output estimation unit that calculates an actual reduction amount due to the first control based on information from a meter that measures the output of the consumer load;
the demand prediction unit predicts a first integrated demand in a case where the first control is performed, based on the actual reduction amount calculated by the output estimation unit;
The load control device according to claim 1 or 2, wherein the second control unit calculates the second reduction amount based on the difference between the first integrated demand and the target value and the rated output. a correlation function generating unit that generates a correlation function representing a relationship between an output of the consumer load during a certain period and the actual reduction amount calculated by the output estimating unit,
The load control device according to claim 3, characterized in that the demand prediction unit predicts the first integrated demand by acquiring the actual reduction amount corresponding to the output of the consumer load based on the correlation function generated by the correlation function generation unit. an actual average reduction amount calculation unit that calculates the output of the consumer load based on information from a meter that measures the output of the consumer load, and calculates an actual average reduction amount, which is an actual reduction amount of the output of the consumer load according to the first reduction amount, based on the output of the consumer load over a certain period of time and the first reduction amount;
The load control device according to claim 1, characterized in that the second control unit determines the actual average reduction amount according to the difference between the predicted value of the integrated demand when the control predicted by the demand prediction unit is not performed and the target value, and sets the first reduction amount corresponding to the determined actual average reduction amount as the second reduction amount. Forecasting the cumulative demand for a consumer load, including the controlled equipment, for a predetermined period;
determining whether the integrated demand would exceed a target value if the output of the control-target device were not controlled based on the predicted value of the integrated demand;
According to the determination result, assuming that the control-target device operates at a rated output, a first reduction amount is calculated to make the integrated demand after a first control equal to or less than the target value, and a first control is performed on the output of the control-target device based on the first reduction amount;
a second reduction amount for making the integrated demand after the second control equal to or less than the target value based on a result of the first control, and performing a second control on the output of the controlled device based on the second reduction amount. Forecasting the cumulative demand for a consumer load, including the controlled equipment, for a predetermined period;
determining whether the integrated demand would exceed a target value if the output of the control-target device were not controlled based on the predicted value of the integrated demand;
According to the determination result, assuming that the control-target device operates at a rated output, a first reduction amount is calculated to make the integrated demand after a first control equal to or less than the target value, and a first control is performed on the output of the control-target device based on the first reduction amount;
A load control program that causes a computer to execute a process of: calculating a second reduction amount that makes the integrated demand after the second control equal to or less than the target value based on a result of the first control; and performing a second control on the output of the controlled device based on the second reduction amount.