JP6818491B2 - Demand response planning system, demand response planning method, and demand response planning program - Google Patents

Description

Embodiments of the present invention relate to a demand response planning system, a demand response planning method, and a demand response planning program.

Demand response systems for reducing power loads are becoming widespread. The demand response system issues a demand response (DR) to consumers when it is predicted that the power supply and demand will be tight in advance, and promotes reduction of power consumption. Consumers who have achieved their power reduction targets are given certain incentives. The predicted value of the electric energy reduced by the consumer is determined based on the baseline which is the predicted value of the electric energy consumed by the consumer if there is no demand response. Ideally, the baseline is calculated after all the power data consumed by the consumer is available.

For the current demand response system in which the amount of power reduction is notified after the demand response is implemented, there is a demand from consumers who want to know the target power reduction amount based on the baseline when the demand response is issued. However, when the demand response is issued, there may be a lack of consumer power data required for baseline calculation. Therefore, the predicted baseline value calculated when the demand response is issued may have an error as compared with the fixed baseline value calculated after the power data is fixed.

Japanese Unexamined Patent Publication No. 2015-186397

An object to be solved by the present invention is to provide a demand response planning system, a demand response planning method, and a demand response planning program capable of reducing an error in a baseline value calculated when a demand response is issued. Is.

The demand response planning system of the embodiment includes a power data loss determination unit, a missing power data complement unit, and a prediction baseline calculation unit. The power data loss determination unit acquires power data in which the customer's past power usage status is recorded at the time of issuing a demand response for promoting suppression of power supply and demand, and the power data is lost at the time of the issuance. Judge whether or not. The missing power data complementing unit complements the loss determined to be caused by the power data missing determining unit based on a predetermined complementing method. The prediction baseline calculation unit calculates a prediction baseline to be notified to the customer when the demand response is issued, based on the power data whose loss is complemented by the missing power data supplementing unit.

A block diagram showing the system configuration of the demand response planning system. The figure which shows the calculation example of the baseline by the 1st method. The figure which shows the calculation example of the baseline by the 2nd method. The figure explaining the complementation of power data. The figure explaining the complementation of power data. The figure explaining the complementation of power data. A flowchart showing the processing flow of the demand response planning system. The figure explaining the complementation of power data. The figure explaining the complementation of power data. The figure explaining the complementation of power data.

Hereinafter, the demand response planning system, the demand response planning method, and the demand response planning program of the embodiment will be described with reference to the drawings. Demand response is a mechanism that provides incentives to customers who have reduced power consumption when the supply and demand of power is tight, and suppresses power consumption to ensure a stable supply of power. The demand response planning system issues a demand response to the customer when it is predicted that the power supply and demand will be tight in advance, and also notifies the customer of the target power reduction amount of the customer.

(First Embodiment)
FIG. 1 is a block diagram showing a system configuration of the demand response planning system 1. The demand response planning system 1 includes, for example, a first power database 100, a second power database 110, a baseline calculation unit 200, a predicted baseline value database 150, a fixed baseline value database 151, and a consumer. It includes an incentive calculation unit 310, an incentive calculation unit 311 for an electric power company, a demand response receiving unit 400, and a demand response issuing unit 410.

The first electric power database 100 stores the past electric power usage data (electric power data) of each customer. The second electric power database 110 stores data (DR electric power data) as a result of implementing the electric power reduction performed by each customer after the demand response is issued. When the baseline calculation unit 200 receives the demand response issuance issued by the electric power company from the demand response reception unit 400, the baseline calculation unit 200 calculates the baseline based on the past power consumption data. The baseline is a predictive value of power consumption that serves as a reference when calculating power reduction.

Of these functional units, at least the baseline calculation unit 200 is realized by, for example, a processor such as a CPU (Central Processing Unit) executing a baseline calculation program. Further, some of the functional units of the baseline calculation unit 200 may be realized by hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), and FPGA (Field-Programmable Gate Array). , Software and hardware may work together. Further, the functional units other than the baseline calculation unit 200 may be similarly realized by a processor and a program, may be realized by hardware such as LSI, ASIC, and FPGA, and software and hardware collaborate. It may be something to do.

The baseline calculation unit 200 includes, for example, a prediction baseline calculation processing unit 210 and a definite baseline calculation processing unit 220. The prediction baseline calculation processing unit 210 calculates the prediction baseline calculated after the demand response is issued. The prediction baseline calculation processing unit 210 includes, for example, a first power data loss determination unit 211, a first power loss data complementation unit 212, and a prediction baseline calculation unit 213. In the prediction baseline calculation processing unit 210, the prediction baseline is calculated by the cooperation of the above-mentioned functional blocks.

The first power data loss determination unit 211 acquires the first power data used for the calculation of the prediction baseline among the power data stored in the first power database 100. The electric power data is, for example, data collected by a wired or wireless line from each customer's electric power meter (smart meter or the like). Therefore, depending on the line condition, all the power data may not be available and the power data may be missing. The first power data loss determination unit 211 determines whether or not the first power data has a first loss.

When it is determined that the first loss has occurred, the first missing power data complementing unit 212 complements the first missing based on a predetermined complementing method. The predetermined complement method will be described in detail later.

The prediction baseline calculation unit 213 calculates the prediction baseline value notified to the customer based on the supplemented first power data. The prediction baseline calculation unit 213 calculates the prediction baseline based on the calculated prediction baseline value. The forecast baseline is the forecast baseline value assigned to each time zone within the demand response execution time zone. Forecast baselines are used to calculate power reduction targets and customer incentives. The customer incentive value is an index showing the incentive given to the customer according to the amount of power reduced by the customer.

The prediction baseline calculation unit 213 stores the prediction baseline in the prediction baseline value database 150. The demand response issuing unit 410 issues a demand response to the customer and notifies the customer of the target power reduction amount of the customer based on the prediction baseline.

The definite baseline calculation processing unit 220 includes, for example, a second power data loss determination unit 221, a second deficit power data complement unit 222, and a definite baseline calculation unit 223. The fixed baseline calculation processing unit 220 calculates the fixed baseline value after executing the demand response. The fixed baseline value is calculated in a state where the first power data is supplemented after a certain period of time has elapsed after the demand response is executed.

The deterministic baseline calculation processing unit 220 calculates the deterministic baseline based on the deterministic baseline value. The fixed baseline is the fixed baseline value assigned to each time zone within the demand response execution time zone. The fixed baseline calculation processing unit 220 includes a second power data loss determination unit 221, a second missing power data supplement unit, and a 222 fixed baseline calculation unit 223. In the fixed baseline calculation processing unit 220, the fixed baseline is calculated by the cooperation of the above functional blocks.

The second power data loss determination unit 221 acquires the second power data in which the first loss of the first power data is complemented from the first power database 100 after a predetermined period of time has elapsed after the demand response is executed. The second power data is complemented by collecting the first missing data occurring in the first power data from each customer's watt-hour meter. However, the second power data is not perfect, and there may be some defects.

The second power data loss determination unit 221 determines whether or not the second power data has a second loss. The second loss of the second power data is smaller than the first loss of the first power data. Therefore, the amount of data of the second power data is larger than the amount of data of the first power data. Here, it is necessary for the second missing power data complementing unit 222 to newly complement the second missing and calculate the definite baseline.

The second missing power data complementing unit 222 complements the determined second missing power based on a predetermined complementing method. Various methods can be used as the complement method here. For example, a method according to the complementing method described later may be used. Which method to use depends on the contractual terms with the customer. The fixed baseline calculation unit 223 calculates the fixed baseline value notified to the customer based on the supplemented second power data. The fixed baseline calculation unit 223 calculates the fixed baseline based on the fixed baseline value.

The fixed baseline value is used to calculate the fixed power reduction amount and the operator incentive value. The business operator incentive value is an index showing the incentive given by the business operator according to the amount of power reduction. The fixed baseline calculation unit 223 stores the calculated fixed baseline in the fixed baseline value database 151.

Next, the incentive given to the customer for the amount of power reduction after the demand response is implemented will be described. The amount of power reduction is determined in advance from the forecast baseline value at the time of contract with the customer. After a predetermined period of time has elapsed after the demand response is executed, the incentive calculation unit 310 with the customer calculates the customer incentive value. The incentive calculation unit 310 with the customer calculates the customer incentive value based on the prediction baseline and the DR power data regarding the power reduction amount stored in the second power database 110. The customer incentive value is notified to the customer by a notification means (not shown), for example, after the demand response is executed.

The incentive calculation unit 311 with the electric power company determines the amount of power reduction and the electric power incentive based on the fixed baseline value and the DR power data stored in the second power database 110 after a certain period of time has passed after the demand response is executed. Calculate with value. The business operator incentive value is notified after a predetermined period of time has elapsed after the demand response is executed. The difference between the business incentive value and the customer incentive value is the profit in the demand response business. In the present embodiment, the prediction baseline is used to calculate the customer incentive value, but a definite baseline that is determined after the demand response is executed can also be used.

However, since the customer is notified of the amount of power reduction based on the forecast baseline when the demand response is issued, it may be disadvantageous for the customer to be notified of a different confirmed baseline at a later date. Therefore, in the present embodiment, the incentive value based on the prediction baseline is notified. What kind of notification is given depends on the contract with the customer. In the present embodiment, since the incentive value based on the forecast baseline is notified, it is important to bring the forecast baseline closer to the definite baseline.

Hereinafter, a method in which the first missing power data complementing unit 212 complements the missing first power data will be described in detail. First, the basic calculation method of the forecast baseline will be described. FIG. 2 is a diagram showing a calculation example of the prediction baseline by the first method. The first method is to compare the power data of the demand response implementation date with the past power data before the implementation date, and consider the difference between the power data of the implementation date and the past power data to obtain the demand response. It is a method to calculate the prediction baseline after the announcement. The first method will be described below.

For example, in order to calculate the forecast baseline for the demand response implementation time zone (13: 00-15: 00) on the demand response implementation date (5/18), first, the most recent 5 days (5 / 11-) of the demand response implementation date. Of 5/15), demand data for 4 days with a large average amount of power during the demand response implementation time is selected (1).

Here, the past data of the top 4 days excluding the data of 5/13, which has the lowest average value of power consumption, is selected. From the selected 4-day past data, the average value data for each time zone (9: 00-12: 00, 13: 00-15: 00) is calculated (2). The time zone from 9:00 to 12:00 is set as the correction adjustment time zone (A) on the day, and the time zone from 13:00 to 15:00 is set as the demand response execution time zone (B). The current day correction adjustment time zone (A) is a time zone in which data is referred to for calculating the prediction baseline in the current day demand response execution time zone (B). On the demand response implementation date, the value of the power data is higher than that of the past data because the power demand is increasing.

Therefore, in the calculation of the forecast baseline of the demand response implementation date, the forecast baseline is calculated according to the tendency of high power demand on the day, instead of simply using the average of the past data. Therefore, in calculating the prediction baseline of the demand response execution time zone, the difference between the average value of the power data measured on the day of the demand response implementation day and the average value of the past data for 4 days is calculated.

On the demand response implementation date, the data for 6 frames (1 frame is, for example, 30 minutes) from 4 hours to 1 hour (9: 00-12: 00) before the demand response execution start time (13:00) is displayed. It has been recorded. Then, each data of the difference of 6 frames from each average value of 6 frames of the current day data and each average value of 6 frames of the past data of 4 days in the same time zone is calculated (3). Then, the average value (150 kWh / frame) of the difference data for 6 frames is calculated, and this value is used as the correction adjustment amount on the day (4). Then, the predicted baseline value (kWh) is calculated by adding the correction adjustment amount on the day to each average value data for each time of the demand response execution time zone. Then, the predicted baseline value is allocated to each time of the demand response execution time zone to obtain a predicted baseline (5).

Next, an example of calculating the prediction baseline by the second method is shown. FIG. 3 is a diagram showing a calculation example of the prediction baseline by the second method. The second method is to calculate the expected baseline using the power data of the day pre-measured in the time zone before the time when the demand response is issued. For example, the case where the demand response issuance notice is given 10 minutes (9:30) before the time (9:40) when the demand response is executed is shown. The prediction baseline b is determined by using the average value of the power data a in a certain time zone before the demand response issuance as the prediction baseline value and allocating the prediction baseline value to each time zone in which the demand response is performed. ..

Specifically, when the demand response issuance notice is given at 9:30, the average value of the power data from 9:00 to 9:30 is used as the prediction baseline value. For example, the average value of the power data in the baseline calculation target time zone (6 frames from 9:00 to 9:30: 1 frame is, for example, in units of 5 minutes) is the predicted baseline value. For example, the predicted baseline value is calculated as (508 + 513 + 525 + 535 + 531 + 526) / 6 = 523 (kWh). Then, the prediction baseline value b is determined by allocating the prediction baseline value (523kWh) to each time zone in the demand response execution time (9: 40-10: 40).

Next, the calculation of the prediction baseline when the demand response is performed on 11/17 and the data a of the prediction baseline calculation target time zone used in the first method is missing will be described. FIG. 4 is a diagram illustrating complementation of power data. As shown in the upper part of FIG. 4, there is a loss in the past power data in the time zone (9: 00-11: 00) when the demand response is executed.

For example, in the data of 11/10, the data of the time zone from 10:00 to 10:30 is missing. To make up for this deficiency, two data (110 and 130) of the time zone (9: 30-10: 00, 10: 30-11: 00) before and after the time zone data of 10: 00-10: 30 Is used. As shown in FIG. 4, the average value (110 + 130) / 2 = 120 of the two data is used as the complementary data. Each loss in the power data of the other day is complemented in a similar way.

Next, a case where there is a defect in the past data of the correction adjustment time zone on the day of 11/17 will be described. FIG. 5 is a diagram illustrating complementation of power data. As shown in the upper part of FIG. 5, there is a loss of power data in a part of the time zone (6: 00-6: 30) of the day correction adjustment time zone (5: 00-8: 00) on 11/17. It is happening. To make up for this deficiency, two data (80, 90) in the time zone (5: 30-6: 00, 6: 30-7: 00) before and after the data in the time zone of 6: 00-6: 30 ) Is used. As shown in the lower part of FIG. 5, the average value (80 + 90) / 2 = 85 of these two data is used as complementary data.

Next, a case where the demand response is executed on 11/17 and the data of the prediction baseline calculation target time zone used in the second method is missing will be described. FIG. 6 is a diagram illustrating complementation of power data. As shown in the upper part of FIG. 6, a part of the time zone (C) (8: 30-8: 35) of the forecast baseline calculation target time zone (A) (8: 20-8: 50) on 11/17. ), There is a loss of power data. In order to complement this defect, two data (520, 560) in the time zone (8: 25-8: 30, 8: 35-8: 40) before and after the data in the time zone (C) are complemented. Used in the calculation of.

As shown in the lower part of FIG. 6, the average value (520 + 560) / 2 = 540 of the two data adjacent to the missing data is used as the complementary data. The prediction baseline value is the average value of 6 frames (1 frame is in units of 5 minutes) from 30 minutes before the announcement of the demand response issuance to the time of the announcement of the demand response issuance. The predicted baseline value is calculated as (500 + 520 + 540 + 560 + 540 + 520) ÷ 6 = 530 (kWh). Then, the prediction baseline value 530 (kWh) is arranged for each time zone of the demand response execution time (B) (9: 40-10: 40) to determine the prediction baseline b.

As described above, the first missing power data complementing unit 212 complements the first missing by calculating the average value of the data stored in the time zones before and after the time zone in which the first missing occurs. As for the complement method, in addition to the method of calculating the data loss by the average value, the data may be complemented by an interpolation method using a function such as a least squares method or a spline interpolation method.

Hereinafter, the flow of the processing method in the demand response planning system 1 will be described. FIG. 7 is a flowchart showing a processing flow of the demand response planning system 1. The power supply and demand is tight, and the demand response receiving unit 400 receives the demand response issuance from the electric power company. At that time, the prediction baseline calculation processing unit 210 starts the calculation of the prediction baseline value at the time when the demand response is issued (step S100). The first power data loss determination unit 211 determines whether or not the power data (first power data acquired from the first power database) required for the baseline calculation is missing (step S101).

When it is determined that the first power data has a defect (step S101: Yes), the first missing power data complement unit 212 complements or predicts the first defect of the first power data based on a predetermined complement method. (Step S102). The prediction baseline calculation unit 213 calculates the prediction baseline at the time when the demand response is issued based on the supplemented first power data (step S103). If it is determined in step S101 that there is no loss in the first power data, step S103 is executed.

The demand response issuing unit 410 issues a demand response to the customer (step S104), and simultaneously notifies the customer of the amount of power reduction based on the prediction baseline. The customer executes the demand response according to the issuance of the demand response (step S105). After executing the demand response, the fixed baseline total processing unit 220 starts calculating the fixed baseline. The second power data loss determination unit 221 acquires the second power data from the first power database 100 after a certain period of time has elapsed after the demand response is executed. The second power data loss determination unit 221 determines whether or not the power data (second power data) required for the baseline calculation is missing (step S106).

When it is determined that there is a second loss of power data (step S106: Yes), the second loss power data complement unit 222 complements the second loss based on a predetermined complement method (step). S107). The fixed baseline calculation unit 223 calculates the fixed baseline based on the supplemented second power data (step S108). After a certain period of time has elapsed after the demand response is executed, the incentive calculation unit 311 calculates the amount of power reduction due to the power saving performed by the customer based on the fixed baseline and the DR power data (step S109).

The incentive calculation unit 310 derives the power saving result of each demand response based on the DR power data and the prediction baseline stored for each past demand response execution. Then, the incentive calculation unit 311 derives the result of the demand response for each demand response actually performed by the customer based on the DR power data stored for each past demand response execution and the fixed baseline value. (Step S110).

The incentive calculation unit 311 calculates the customer incentive value from the power saving result for each demand response implementation. Then, the incentive calculation unit 311 calculates the reward amount (business operator incentive value) for each demand response implementation from the power saving result (step S111).

As described above, according to the demand response planning system 1, it is possible to calculate a prediction baseline that serves as a reference for the amount of power that the customer should reduce when the demand response is issued. Then, the demand response planning system 1 can calculate the prediction baseline by supplementing even if there is a defect in the power data used for calculating the prediction baseline. As a result, the demand response planning system 1 can notify the customer of the target power reduction amount based on the prediction baseline when the demand response is issued.

(Second Embodiment)
Hereinafter, the second embodiment will be described. In the first embodiment, in the demand response planning system 1, when there is a deficiency in the power data used for the calculation of the prediction baseline, the deficient data is complemented by calculating the average value of the data before and after the deficiency. On the other hand, the demand response planning system 1 in the second embodiment complements the lack of power data by calculating the average value of the data for the past several days in the same time zone.

The case where there is a deficiency in the data of the forecast baseline calculation target time zone on 11/17 will be described. FIG. 8 is a diagram illustrating complementation of power data. As shown in FIG. 8, there is a defect in the historical data that is the basis for calculating the forecast baseline for the demand response on November 17. For example, out of the past data for several days in the baseline calculation target time zone (9: 00-11: 00), a defect occurs in a part of the time zone (10: 30-11: 00) on November 14th. ing. In order to supplement this deficiency, the average value of the past data for n days (n is an arbitrary number) from the date of the deficiency is used.

Here, assuming that n = 4, the past data for 4 days from 11/10 to 11/13 in the time zone of 10:30-11: 00 is selected. Then, the average value (140 + 130 + 140 + 130) / 4 = 135 of the data for four days in the time zone of 10:30-11: 00 is used as the complementary data. Further, assuming that n = 3 in FIG. 8, the average value (130 + 140 + 130) / 3 = 133 of the data for 3 days of 11 / 11-11 / 13 in the time zone of 10:30-11: 00 is used as the complementary data. Be done.

As described above, the first missing power data complementing unit 212 is the average value of the data in the same time zone in which the first loss occurs for n days (n is a natural number) before the date when the first loss occurs. Is calculated, and this average value is used to supplement the first defect. As a result, the demand response planning system 1 can calculate the prediction baseline by the complement method with the date as the time axis when there is a deficiency in the power data used for calculating the prediction baseline value.

(Third Embodiment)
Hereinafter, the third embodiment will be described. In the second embodiment, the demand response planning system 1 complements the date as a time axis when there is a deficiency in the power data used for calculating the forecast baseline value. In the demand response planning system 1 according to the third embodiment, the predicted value of the missing data is calculated and complemented from the tendency of the data on the day when the missing data occurs.

A case where there is a deficiency in the data of the calculation target time zone when calculating the prediction baseline by the method of the second method will be described on 11/17. FIG. 9 is a diagram illustrating complementation of power data. As shown in FIG. 9, a part of the time zone (C) (8: 45-8: 50) of the forecast baseline calculation target time zone (A) (8: 20-8: 50) on November 17th. There is a loss of power data.

Since this missing data is located in the last time zone (C) of the prediction baseline calculation target time zone (A), the method of supplementing with the data of two adjacent time zones is not suitable. Focusing on the trend of electricity demand on this day, we can see that the electricity demand in the time zone (8: 20-8: 45) for the 5 frames (1 frame is in units of 5 minutes) immediately before the time zone (C) is on an upward trend. Be done. Then, the missing data in the time zone (C) is expected to follow this upward trend.

Therefore, in order to supplement this defect, the data of the defective portion is based on the tendency of the power data for n frames (n is an arbitrary natural number) immediately before the data in the time zone (C) (8: 45-8: 50). The predicted value of is calculated. For example, the data for the time zone of n = 5 frames in the prediction baseline calculation target time zone (A) tends to increase according to y = 5x + 500 ... (Equation 1), so that the time zone (C) is missing. The predicted value of the data is calculated as 525 based on Equation 1. As shown in FIG. 9, the predicted value 525 is used as complementary data.

Then, the average value of 6 frames (1 frame is in units of 5 minutes) in the prediction baseline calculation target time zone (A) is expressed as the prediction baseline value. The predicted baseline value is calculated as (500 + 505 + 510 + 515 + 520 + 525) ÷ 6 = 512.5 (kWh). Then, the prediction baseline value 512.5 (kWh) is arranged for each time zone of the demand response execution time (B) (9: 00-10: 00) to determine the prediction baseline b.

This complementation method may be applied when there is a defect in the data of the time zone (8: 20-8: 25) of the first frame of the prediction baseline calculation target time zone (A). In addition, the days when the trends match are not limited to one day, and the average value of the power data in the same time zone as the time zone in which there is a defect is calculated based on the data for n days (n is a natural number) to supplement the defect. You may go.

As described above, the first missing power data complementing unit 212 calculates the expected value based on the tendency of the data stored in the same time zone in the past when the first missing power data has the first loss. Then, this predicted value is used to supplement the first defect. Then, according to the demand response planning system 1, even if the data used for calculating the prediction baseline is lost at the beginning or the end of the time-based array of the calculation time zone, the predicted value of the missing data is calculated and the prediction baseline is set. Can be calculated.

(Fourth Embodiment)
In the third embodiment, the demand response planning system 1 complements the power data used for the calculation of the prediction baseline according to the tendency of the power data on the demand response implementation date when there is a defect. On the other hand, the demand response planning system 1 in the fourth embodiment selects the data of the day when the tendency of the power data loss most matches from the past data, and calculates the predicted value of the missing data from the selected data. To complement.

A case where there is a deficiency in the data of the time zone for which the prediction baseline is calculated will be described when calculating the prediction baseline by the method of the second method on 11/17. FIG. 10 is a diagram illustrating complementation of power data. As shown in the upper part of FIG. 11, a part of the time zone (C) (8: 45-8: 50) of the forecast baseline calculation target time zone (A) (8: 20-8: 50) on 11/17. ), There is a loss of power data. As shown in the middle part of FIG. 10, focusing on the tendency of each power demand of each data for the past n days (n is a natural number), the forecast baseline calculation target time zone (A) power data of 11/10 And the tendency with the power data of the forecast baseline calculation target time zone (A) on November 17 are the most consistent.

Therefore, as shown in the lower part of FIG. 10, the predicted value of the missing data in the time zone (C) is calculated to be 525 based on the power data of 11/10. Therefore, the predicted baseline value is calculated as (500 + 505 + 510 + 515 + 520 + 525) ÷ 6 = 512.5 (kWh). Then, the prediction baseline value 512.5 (kWh) is arranged for each time zone of the demand response execution time (B) (9: 00-10: 00) to determine the prediction baseline b. In the above method, the data of the day when the tendency of the power data matches as a numerical value is selected, but the data whose tendency matches in a similar form may be selected.

As described above, the first missing power data complementing unit 212 tends to collect data on the date on which the first loss occurs from each data for n days (n is a natural number) before the date on which the first loss occurs. A predicted value based on the data of the date having the closest tendency is calculated, and this predicted value is used to supplement the first defect. Then, according to the demand response planning system 1, the prediction baseline can be determined using the calculated predicted value.

The data complementation methods in the demand response planning system 1 according to the first to fourth embodiments have been described individually, but even if each of these methods is arbitrarily combined to obtain the optimum solution. Good. When changing by combining complementary methods, a clause specified in advance by a contract with the customer may be provided.

According to at least one embodiment described above, at the time of issuance of the demand response for promoting the suppression of power supply and demand, the power data in which the customer's past power usage status is recorded is acquired, and the power data is lost at the time of issuance. A power data loss determination unit 211 that determines whether or not a defect has occurred, and a missing power data complement unit 212 that complements a defect determined by the power data loss determination unit 211 based on a predetermined complement method. , Demand by having a prediction baseline calculation unit 213 that calculates a prediction baseline notified to the customer when a demand response is issued based on the power data whose loss is complemented by the missing power data supplementing unit. It is possible to reduce the error of the baseline value calculated when the response is issued.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention as well as the invention described in the claims and the equivalent scope thereof.

For example, in each of the above embodiments, the demand response planning system 1 for reducing the amount of electric power has been described as an example, but the present invention is not limited to this case, and the demand response planning system 1 is an energy source such as gas or heat other than electric power. It may be applied to an energy resource supply system that supplies resources. Further, the demand response planning system 1 may be applied to a system that targets CO2 credits related to CO2 emissions trading and emissions of other energy resources such as greenhouse gases. That is, the demand response planning system 1 suppresses the supply and demand of gas, heat, energy resources, exhaust gas, water, and other supply and demand objects that are subject to market transactions through supply equipment such as plants, in addition to the purpose of suppressing the supply and demand of electricity. May be used for.

1 ... Demand response planning system, 100 ... 1st power database, 110 ... 2nd power database, 150 ... Predicted baseline value database, 151 ... Confirmed baseline value database, 200 ... Baseline calculation unit, 210 ... Predicted baseline calculation Processing unit, 211 ... 1st power data loss determination unit, 212 ... 1st power data loss determination unit, 213 ... Prediction baseline calculation unit, 220 ... Confirmed baseline total processing calculation unit, 221 ... Second power data loss determination unit , 222 ... 2nd Missing Power Data Complementary Unit, 223 ... Fixed Baseline Calculation Unit, 310 ... Incentive Calculation Unit with Consumers, 311 ... Incentive Calculation Unit with Electricity Provider, 400 ... Demand Response Receiver, 410 ... Demand Response issuing department

Claims (3)

In issuance time of demand response for prompting power supply repression issued by the power company, the acquired historical power data power use state is recorded in the power company customer from the power database, and the acquired the A power data loss determination unit that determines whether or not the power data is missing,
Based on a predetermined complement method, a missing power data complementing unit that complements the loss determined to be caused by the power data missing determining unit, and
By performing statistical processing on the power data whose loss has been supplemented by the missing power data supplementing unit for a period corresponding to the demand response execution time zone , the customer is notified when the demand response is issued. Prediction baseline calculation unit that calculates the prediction baseline, and
Equipped with a,
The missing power data complementing unit obtains a tendency closest to the tendency of the power data on the date on which the loss occurs from the power data for n days (n is a natural number) before the date on which the loss occurs. The power data of the date of possession is selected, an expected value based on the selected electric power data is calculated, and the calculated expected value is used to supplement the defect.
Demand response planning system. The computer
At the time of the issuance of the demand response to promote the suppression of the power supply and demand issued by the electric power company, the electric power data in which the customer's past electric power usage status is recorded is acquired from the electric power database .
It is determined whether or not the acquired power data is missing, and the result is determined.
Complement the defect determined to be occurring based on a predetermined complementation method,
By performing statistical processing on the power data to which the deficiency is complemented for a period corresponding to the demand response execution time zone, a prediction baseline to be notified to the customer when the demand response is issued is calculated .
When compensating for the deficiency, the tendency closest to the tendency of the power data on the date on which the deficiency occurs is obtained from the power data for n days (n is a natural number) before the date on which the deficiency occurs. The power data of the date to have is selected, an expected value based on the selected electric power data is calculated, and the calculated expected value is used to supplement the defect.
Demand response planning method. On the computer
At the time of the issuance of the demand response to promote the suppression of the power supply and demand issued by the electric power company, the electric power data in which the customer's past electric power usage status is recorded is acquired from the electric power database .
It is made to judge whether or not the acquired power data has a defect, and it is determined.
Based on a predetermined complementation method, the defect determined to be occurring is complemented and
By performing statistical processing on the power data to which the deficiency is complemented for a period corresponding to the demand response execution time zone, the prediction baseline notified to the customer when the demand response is issued is calculated .
When compensating for the defect, the tendency closest to the tendency of the power data on the date on which the defect occurs is obtained from the power data for n days (n is a natural number) before the date on which the defect occurs. The electric power data of the date of possession is selected, an expected value based on the selected electric power data is calculated, and the calculated expected value is used to supplement the defect.
Demand response planning program.

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