JP2017034797A - Baseline load estimation device and baseline load estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a baseline load estimation device capable of accurately performing DR evaluation.SOLUTION: The baseline load estimation device configured to estimate a baseline load of a user comprises: a baseline calculation part for estimating the baseline load while using result data including information about past power consumption of the user in accordance with an estimation system for the baseline load of the user selected by the user; and a baseline adjustment part for adjusting the baseline load that has been estimated by using the result data, in accordance with a baseline load adjustment system selected by the user for adjusting the estimated baseline load, the baseline load adjustment system being adapted to adjust the baseline load while defining result data on a day that is different from an estimation date of the baseline load, as a reference.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an energy management system used by an electric power company to implement demand response.

  Demand response (DR) is defined by the US Department of Energy (DOE) as follows: "When the unit price of electricity and the price of wholesale electricity that change with time are high, or when the power system is in danger in terms of reliability, In response to payment of incentives designed to encourage power savings, end users themselves change their power consumption from normal power consumption patterns. "

  A supply and demand control system called an EMS (energy management system) performs power generation prediction, supply and demand prediction, etc., makes an operation plan for power supply and demand, and controls a generator, a storage battery and the like to balance supply and demand. When creating an operation plan for power supply and demand, DR that promotes demand changes of consumers is performed in consideration of a balance between supply and demand.

  Among DRs, incentive type DRs that receive rewards as consideration for demand reduction by consumers are drawing attention. Refunds are made to customers according to the amount of demand reduction, so there is an advantage that there is no burden beyond the normal peak fee and is easy for consumers to accept.

  In the incentive type DR, if the DR is not performed, the reference line (baseline load: BL) indicating how much power was used is used, and the power actually consumed by the DR, that is, the power consumption is calculated. The load reduction amount is calculated in comparison with the actual load value shown. Based on this amount of load reduction, the incentive paid to the customer is determined. Therefore, accurately estimating BL is important for incentive DR. Non-patent document 1 below discloses a conventional technique for BL estimation that estimates BL.

For the estimation of BL, the past load actual value of the weekday customer on which the DR is not performed, that is, the weekday customer on which the customer did not suppress demand is used. The BL estimated value is calculated by any one of the following four BL estimating methods, for example.
・ Calculate the average value of the 5 days closest to the BL calculation target date.
・ Calculate the average value of the 10 days closest to the BL calculation target date.
・ Calculated by the temperature return on the 10th day closest to the BL calculation target date.
-Load on the same day of the week before the BL calculation target day (returns to the past if there is no applicable day).

  The BL estimated value obtained by the above estimation method is adjusted by the following equations (1) and (2) using the actual data including the past actual load values.

D _BLad (d, e, t): BL (baseline load) estimated value after adjustment c (d, e): Adjustment coefficient D _BL (d, t): BL (baseline load) estimated value before adjustment d : Date to estimate BL t: Time to estimate BL e: Date to be the basis for load adjustment s: Time to be the basis for load adjustment D _T (e, s): Actual load value of customer Here load on the day In an adjustment method called adjustment, the day (e) that serves as a reference for load adjustment is the day (d) for estimating BL. That is, e = d. Further, the time (s) serving as a reference for load adjustment is a time one to two hours before the time (t) for estimating the BL.

  In Non-Patent Document 1, it is reported that in the DR performed in the summer afternoon (from 13:00 to 17:00), the BL estimation accuracy is improved by performing the load adjustment on that day.

"Evaluation of baseline load estimation method in demand response demonstration test", 2014 Annual Conference of the Institute of Electrical Engineers of Japan, 6-015 (2014-3)

  However, Non-Patent Document 1 reports that in the DR performed in the morning in winter (from 8:00 to 10:00), the BL estimation accuracy deteriorates when the load adjustment is performed on the day. That is, in the DR performed in the morning in the winter season, the estimation accuracy of the BL is deteriorated due to the day load adjustment. This is thought to be due to the following factors.

  When DR is performed, the load in the DR time period decreases. It has been found that some of the reduced load is consumed in time zones other than the DR time zone (load shift). For example, when DR is performed in the morning in winter, the power consumed in the DR time zone is consumed in the time zone immediately before the DR. Therefore, the load in the time zone immediately before the DR increases compared to when the DR is not performed.

  In the current day load adjustment of the conventional BL estimation method, the actual load value immediately before the DR time zone on the current day is used as a reference. In winter, the actual load value immediately before the DR time zone increases due to the load shift, so the reference value increases. That is, the load adjustment is performed excessively on the day, and the BL estimated value becomes large. As a result, the BL estimation accuracy deteriorates, and there is a problem that accurate DR evaluation cannot be performed.

  An object of this invention is to provide the apparatus (energy management system) etc. which can perform DR evaluation with high precision in view of the said subject.

  In order to achieve the above object, the baseline load estimation apparatus according to the present invention obtains information about the past power consumption of the consumer according to the baseline load estimation method of the consumer selected by the consumer. A baseline load calculation unit that estimates the baseline load using actual data including the baseline load adjustment method that adjusts the estimated baseline load selected by the consumer, the baseline A baseline load adjustment unit that adjusts the estimated baseline load using the actual data according to the baseline load adjustment method that adjusts the actual data on a date different from the estimated date of the load. The baseline load estimation device corresponds to EMS described later, and the power consumption corresponds to power demand described later.

  Further, in the baseline load estimation device of the present invention, an adjustment method presentation unit that, when selecting the baseline load adjustment method, presents data serving as a selection index together with the baseline load adjustment method on the display unit of the consumer. Further provision is a preferred embodiment. The adjustment method presentation unit corresponds to a BL adjustment method determination unit described later.

  Further, in the baseline load estimation device of the present invention, the baseline load adjustment method uses the actual data of the day closest to the baseline load estimation date among the calculation dates of the baseline load, It is a preferable aspect to adjust the estimated baseline load.

  Further, in the baseline load estimation device of the present invention, the baseline load adjustment method may include actual data similar to the weather on the estimated date of the baseline load among the calculation days of the baseline load. It is a preferred aspect to use and adjust the estimated baseline load.

  The baseline load estimation apparatus according to the present invention further includes an estimation method presenting unit that, when selecting the baseline load estimation method, presents data serving as a selection index together with the estimation method on the consumer display unit. Is a preferred embodiment. The estimation method presentation unit corresponds to a BL estimation method determination unit described later.

  Moreover, the baseline load estimation program of the present invention uses actual data including information on the past power consumption of the consumer according to the baseline load estimation method of the consumer selected by the consumer. Estimating the baseline load; and a baseline load adjustment method for adjusting the estimated baseline load selected by the consumer, the actual data on a date different from the estimated date of the baseline load Is a program for causing a computer to execute the step of adjusting the estimated baseline load using the record data in accordance with the baseline load adjustment method that adjusts based on the baseline.

  According to the present invention, DR evaluation can be performed with high accuracy.

It is a block diagram which shows an example of a structure of the system containing EMS which concerns on embodiment. It is a figure which shows the example of a display of DR implementation scheduled date displayed on the monitor for consumers in embodiment. It is a block diagram which shows an example of a structure of BL estimation part of EMS which concerns on embodiment. It is a block diagram which shows an example of the hardware constitutions which implement | achieve EMS which concerns on embodiment. It is a flowchart which shows an example of the estimation flow of BL in EMS which concerns on embodiment. It is a figure which shows an example of the performance data accumulate | stored in the performance database of EMS which concerns on embodiment. It is a figure which shows the example of a display of BL estimation system and BL estimation accuracy which are displayed on the monitor for consumers in embodiment. It is a figure which shows BL estimation precision by two BL adjustment systems in embodiment, and BL estimation precision by the conventional BL adjustment system.

  Hereinafter, embodiments will be described with reference to the drawings. The block diagram which shows an example of the structure of the system containing the energy management system (EMS) 10 which concerns on FIG. 1 is shown. The EMS 10, the consumer smart meter 17 on the consumer side, and the consumer monitor 18 are connected via a network 19. The EMS 10 is installed, for example, on the side of an electric power company (for example, a power generation company).

  The smart meter 17 and the consumer monitor 18 are provided on each consumer side (office, factory, general household, etc.). The number of smart meters 17 and consumer monitors 18 provided on each consumer side is not limited to one, and a plurality of smart meters 17 and consumer monitors 18 may be provided. In the example shown in FIG. 1, only one customer is shown, but generally there are a plurality of customers, each of which is connected to the EMS 10 via the network 19. In addition, as described above, the consumer may be based on a certain range of communities including offices, factories, general households, etc., instead of individual units such as offices, factories, general households.

  The network 19 is a communication line such as the Internet or a dedicated line. Data is transmitted and received between the EMS 10, the smart meter 17, and the customer monitor 18 via the network 19.

  The smart meter 17 is a measuring instrument such as electric power having a communication function. The smart meter 17 transmits various data to the EMS 10 via the consumer monitor 18 and the network 19. The transmitted data is, for example, a power demand measurement value (a power demand value shown in FIG. 5 to be described later). The power demand is synonymous with power consumption and actual load value.

  The consumer monitor 18 is a device that displays power usage status and information from the EMS 10. The customer monitor 18 for a customer who performs DR receives data such as the scheduled DR implementation date and time from the EMS 10 and displays, for example, as shown in FIG. 2 according to the received data. In FIG. 2, the scheduled execution date and time of DR (August 2, 13:00 to 17:00 on August 2, 2012) is displayed on the liquid crystal screen of the consumer monitor 18. Can be confirmed. As shown in FIG. 2, the data transmission of the scheduled DR implementation date is performed on the day before the scheduled DR implementation date (August 1, 2012).

  The consumer monitor 18 displays a BL estimation method, a BL adjustment method, and an estimated or adjusted BL, which will be described later, on a liquid crystal screen or the like. The displayed information is received from the EMS 10 via the network 19. The consumer monitor 18 has a baseline load estimation method display unit, a BL adjustment method display unit, and a BL display unit (not shown), and the BL estimation method, the BL adjustment method, and estimation or adjustment are performed on each display unit. Each BL may be displayed.

  The EMS 10 includes a data input unit 11, a power demand prediction unit 12, a demand planning unit 13, a baseline load (BL) estimation unit 14, a demand response (DR) evaluation unit 15, and a performance database 16. The performance database 16 is stored in an auxiliary storage device (not shown), and the power demand prediction unit 12, the demand planning unit 13, the BL estimation unit 14, and the DR evaluation unit 15 are read on the RAM and executed by the CPU. It can also function as a program to be executed.

  The data input unit 11 inputs various data. The data input unit 11 inputs data from input means such as a keyboard and a mouse, and data transmitted from the smart meter 17 via the network 19, for example. Further, for example, a weather forecast site (not shown) is connected to the network 19, and forecast data of weather conditions such as temperature and humidity from the next day can be input from the data input unit 11 from the weather forecast site. .

  The performance database 16 stores various data including at least a consumer's power demand as performance data in a table format. The EMS 10 collects performance data from the smart meter 17 of the customer via the network 19. The collected performance data is accumulated in the performance database 16.

  The power demand prediction unit 12 uses the data of the prediction target day (prediction target date data), the weather forecast data, and the actual data of the actual result database 16 to predict the power demand on the prediction target day (for example, the next day). Do. The prediction target date data is data such as the year, month, day, weekday / holiday, etc. of the prediction target date. The prediction target date data can be input from the data input unit 11. The weather forecast data is data relating to weather conditions such as temperature, humidity, wind speed, weather, etc. on the prediction target day (next day or later), and can be obtained from, for example, a weather forecast site as described above. The performance data can be acquired from the performance database 16.

  Methods for predicting power demand using forecast target date data, weather forecast data, and actual data are known. For example, References (Proceedings of the IASTED International Conference 248-039, “Peak Load Forecasting using Neutral Networks and Fuzzy Interference ”). That is, the demand prediction is calculated by a demand prediction model in which the output is a power demand prediction value with the past power demand, weather, temperature, season, day of the week, power price unit price, etc. of the actual data as input data. As the demand prediction model, a neural network disclosed in the above-mentioned reference can be used.

  The supply and demand planning unit 13 compares the power demand predicted value of the next day calculated by the power demand prediction unit 12 with the demand target value, and when there is a difference (for example, when the power demand predicted value exceeds the demand target value). Performs DR and determines that DR is not performed if there is no difference. Here, the demand target value means a supply plan value calculated by an operation plan for power supply and demand. When a change in demand is necessary to calculate the difference between the predicted power demand value and the demand target value and balance the supply and demand, DR is performed to cause the demand change.

  When performing DR, as described above, data required for display is transmitted from the EMS 10 to the consumer monitor 18 via the network 19 in order to display the scheduled DR implementation date and the like on the consumer monitor 18. .

  The BL estimator 14 estimates the BL when performing DR. As shown in FIG. 3, the baseline load (BL) estimation method determining unit 31, the baseline load (BL) calculating unit 32, the baseline A load (BL) adjustment method determination unit 33 and a baseline load (BL) adjustment unit 34 are included. Details of each component of the BL estimation unit 14 will be described later.

  The DR evaluation unit 15 calculates the difference between the estimated load actual value changed by the estimated BL and DR, and evaluates the effect of DR. In the peak rebate (PTR), which is one of the DR methods, incentives are given to consumers according to the amount of change in power demand (kwh) during peak hours. The incentive is obtained by the following equation (3).

  Here, the amount of change in power demand is the difference between the baseline load (BL) and the actual actual load value. Incentives are not limited to monetary rewards. For example, rewards may be given to consumers in some way, such as points or coupons.

  Next, an example of a hardware configuration for realizing the EMS 10 will be described with reference to FIG. The hardware configuration shown in FIG. 4 includes, for example, a central processor unit (CPU) 41, a random access memory (RAM) 42, a hard disk drive (HDD) 43, a communication interface 44, and a bus 45. The CPU 41, RAM 42, HDD 43, and communication interface 44 are connected to each other via a bus 45, for example.

  The CPU 41 reads a program for performing various processes of the EMS 10 stored in the HDD 43 or the like via the bus 45, temporarily stores the read program in the RAM 42, and performs various processes according to the program. For example, it mainly functions as the power demand prediction unit 12, the demand planning unit 13, the BL estimation unit 14, and the DR evaluation unit 15 described above.

  The RAM 42 temporarily stores a part of an OS (Operating System) program and application programs to be executed by the CPU 41. The RAM 42 stores various data necessary for processing by the CPU 41. The HDD 43 stores application programs for performing various processes of the EMS 10, data necessary for the processes of the EMS 10, and the like, for example, mainly function as the above-described performance database 16.

  The communication interface 44 transmits / receives data to / from the outside (the smart meter 17 or the customer monitor 18), and functions mainly as the data input unit 11 described above, for example. The bus 45 is a communication path that mediates transmission / reception of control signals, data signals, and the like between the devices.

  Next, an example of an operation flow of a system including the EMS 10 according to the embodiment will be described with reference to FIG. As shown in FIG. 5, the consumer smart meter 17 transmits the actual data including the power demand to the EMS 10 at predetermined intervals (for example, every 30 minutes or every hour) via the network 19. The EMS 10 acquires performance data (step S501). Then, the EMS 10 accumulates (stores) the acquired performance data in the performance beta base 16 (step S502).

  Here, FIG. 6 shows a data configuration example of the results database 16. The performance data stored in the performance database 16 is past data. In FIG. 6, the record data accumulated in the record database 16 is in a table format, but may be stored in a format other than the table format.

  As shown in FIG. 6, the record data of the record database 16 is composed of a header part and a data part. The header part has a customer number (customer No. in FIG. 6) or a group number (group No. in FIG. 6). As described above, the customer number is a customer identification ID assigned to each customer in advance. Consumers may be divided into a predetermined number of groups (for example, groups such as office buildings and commercial facilities). In this case, each group is identified by a group number (group number). In the actual data shown in FIG. 6, the customer number or group number is “1”.

  The data part has data items of date, day of the week, holiday / weekday, temperature, humidity, power demand, power price unit price, and DR flag. The power price unit price corresponds to, for example, the incentive coefficient described above, and is used when calculating monetary incentives. The DR flag is an index indicating whether or not DR has been performed, and “1” is stored when the DR is performed, and “0” is stored when the DR is not performed. Note that each piece of information in the data section shown in FIG. 6 is an example, and other information may be included, or all information may not be included.

  The first row of the performance data shown in FIG. 6 is “10:00 on April 1, 2012”, “Monday”, “Weekday”, “Temperature 20 ° C.”, “Humidity 45%”, “Power demand 75 kW”. , “Electric power unit price 20 yen / kwh”, “DR flag 0”. In the example shown in FIG. 6, various data are acquired every 30 minutes and accumulated in the performance database 16. Therefore, it becomes the past performance data accumulated every 30 minutes.

  Returning to the operation flow of FIG. 5, the BL estimation method determination unit (also referred to as an estimation method presentation unit) 31 determines to use a BL estimation method selected in advance by the consumer (step S503). For example, the BL estimation method determination unit 31 stores storage information such as a storage unit (not illustrated) that stores association information in which a customer number (or group number) is associated with information on a BL estimation method selected in advance by a consumer. The BL estimation method corresponding to the consumer is recognized, and it is decided to use the estimation method. Similarly, in a BL adjustment method determination unit (also referred to as an adjustment method presentation unit) 33 to be described later, association information in which a customer number (or group number) and information on a BL adjustment method selected in advance by a customer are associated with each other. With reference to storage information such as a storage unit (not shown) to be stored, it is determined to use the BL adjustment method corresponding to the customer.

  Here, a method for causing a consumer to select a BL estimation method in advance will be described. In order to allow the consumer to select the BL estimation method, the BL estimation method determination unit 31 presents the BL estimation method and the BL estimation accuracy based on the BL estimation method to the consumer, as will be described later. Specifically, the BL estimation method determination unit 31 prepares one or a plurality of BL estimation methods, presents them to the consumer, and calculates the BL estimation accuracy of each BL estimation method. The calculated BL estimation accuracy is presented to the consumer.

The following four examples of the BL estimation method to be presented are conceivable, but are not limited to these, and other BL estimation methods may be used. These BL estimation methods are used when calculating the BL estimation value. Specific calculation of the BL estimated value will be described later. In addition, a BL calculation object day says the weekday (Weekday when the consumer did not suppress demand) where DR was not implemented as mentioned above.
・ Calculate the average value of the 5 days closest to the BL calculation target date.
・ Calculate the average value of the 10 days closest to the BL calculation target date.
・ Calculated by the temperature return on the 10th day closest to the BL calculation target date.
-Load on the same day of the week before the BL calculation target day (returns to the past if there is no applicable day).

  Here, an example of the BL estimation method, the BL estimation method “determining the average value of the nearest five days of the BL calculation target date” will be described. “Determining the average value of the five days closest to the BL calculation target date” means that the average value of the load actual value of the customer for five days (which may be arbitrary) on weekdays on which DR is not performed is a predetermined value. The calculation is performed every time (for example, every 30 minutes), and the calculated values are used as BL estimation values in this BL estimation method. More specifically, weekdays (August 1 (Friday), July 31 (Thursday), which did not perform DR on the nearest 5 days including a certain BL calculation target day (August 1 (Friday)), It means that the average value of the load actual value of the customer on July 30 (Wednesday), July 28 (Monday), and July 25 (Friday) is calculated for each predetermined time.

  Further, the BL estimation method determination unit 31 calculates the BL estimation value for the past BL calculation target date (for example, 30 days), and calculates the BL estimation accuracy by obtaining an absolute error from the actual load value. The smaller the absolute error, the higher the estimation accuracy. The absolute error is obtained by the following equation (4). In this case, m is 30, and n is 48 if 24 hours are divided by time every 30 minutes. Based on these values, an absolute error is obtained.

d: BL calculation target day (m)
t: BL calculation target time (n)
D _BL (d, t): BL estimated value D _C (d, t): Load actual value As a method of presenting the BL estimation method and BL estimation accuracy to the consumer, EMS 10 (for example, BL estimation method determination unit 31) is used. There is a method of transmitting presentation data (information of BL estimation method and BL estimation accuracy) via the network 19 and presenting the data to the consumer monitor 18. An example of the BL estimation method and the BL estimation accuracy on the liquid crystal screen of the consumer monitor 18 is shown in FIG. In FIG. 7, four BL estimation methods and respective BL estimation accuracy (absolute error) are presented. In this example, the absolute error of the BL estimation method “average value of 10 days closest to the BL calculation target date” is shown. Smallest and best estimation accuracy.

  In addition, the method of having a consumer select BL estimation system is not limited to the specific example shown on the monitor 18 for consumers. The frequency of selecting the BL estimation method is about once a year because it is often implemented when a new contract is made or a contract is continued. Therefore, as another specific example of the method for allowing the consumer to select the BL estimation method, it is conceivable to exchange it in writing or to select it at a site on the Internet.

  The consumer confirms the BL estimation method and the BL estimation accuracy presented on the consumer monitor 18 and determines the BL estimation method. A signal at the time of selection is transmitted to the EMS 10 via the network 19. As described above, the BL estimation method determination unit 31 can determine to use the BL estimation method selected by the consumer.

  Here, although the BL estimation accuracy is presented as an index that supports the determination (selection) of the BL estimation method by the consumer, one or more scenarios (which can be determined by the consumer) Data) may be presented to the consumer. For example, when a certain BL estimation method is selected, information indicating that a merit can be received in summer but not so much in winter may be presented for each BL estimation method. The same applies to presentation of BL estimation accuracy by the BL adjustment method determination unit 33 described later.

  The BL calculating unit 32 estimates the BL (calculates the BL estimated value) by applying the customer performance data to the determined BL estimating method (step S504). For example, when the BL estimation method “determining the average value of the 5 days closest to the BL calculation target date” is selected as the BL estimation method, the average value of the load actual values for 5 days on weekdays when DR is not performed Are calculated every predetermined time (for example, every 30 minutes) and set as BL estimation values. Here, the estimated BL value for every 30 minutes is calculated for 24 hours, but only a desired time zone (for example, a time zone for performing DR) may be calculated.

Next, the BL adjustment method determination unit 33 determines to use the BL adjustment method selected by the consumer (step S505).
Here, a method for causing a consumer to select a BL adjustment method in advance will be described. In order to make the consumer select the BL adjustment method, the BL adjustment method determination unit 33 presents the BL adjustment method and the BL estimation accuracy based on the BL adjustment method to the customer as described later. Specifically, the BL adjustment method determination unit 33 prepares one or more BL adjustment methods, presents them to the consumer, and calculates the BL estimation accuracy of each BL adjustment method. The calculated BL estimation accuracy is presented to the consumer.

  The BL adjustment method presented in the embodiment is a BL adjustment method that adjusts based on the actual data of a different date that is not the same as the BL estimation date among the BL calculation target dates, and the following two examples can be considered. However, it is not limited to these. Here, the BL estimation date refers to a date on which BL is to be estimated, that is, a date on which BL is to be estimated. The BL adjustment method here is, for example, when the BL estimation date is August 1 (Friday), actual data for July 31 (Thursday) on the BL calculation target date that is different from August 1 (Friday) This is a BL adjustment method that adjusts with reference to.

  One BL adjustment method is an immediate load adjustment method. This method is a method of adjusting the BL with reference to the actual data of the day closest to the BL estimation date (closest day) among the BL calculation target dates. In the above formulas (1) and (2), This is a method in which the day (e), which is the reference for load adjustment, is the day closest to the BL estimated date (d) among the BL calculation target days.

  Another BL adjustment method is a weather-like daily load adjustment method. This method is a method of adjusting the BL with reference to the actual data on the weather similar day of the BL estimation date among the BL calculation target days. In the above formulas (1) and (2), the load adjustment standard is used. The day (e) to be met is a weather similarity day of the BL estimation date (d) among the BL calculation target days. The weather-similar day refers to a day on which one or a plurality of weather-related indicators such as maximum temperature, minimum temperature, maximum humidity, minimum humidity, and weather are similar. A case where it is determined that the two are similar is, for example, a case where the difference in the index to be compared is within a predetermined threshold. In contrast to the maximum temperature, for example, if the temperature difference is within ± 3 ° C., it is determined that they are similar. If there is no record data judged to be similar, the temperature difference may be widened within, for example, ± 4 ° C. to make a similar judgment.

  In addition, the BL adjustment method determination unit 33 adjusts the BL for the past BL calculation target date (for example, for 30 days), and calculates the BL estimation accuracy by obtaining an absolute error from the actual load value. The absolute error is obtained using the above equation (4).

  The method of presenting the BL adjustment method and the BL estimation accuracy to the consumer is different from the above-described BL estimation method and the method of presenting the BL estimation accuracy only in the presented content. The description is omitted because it is similar.

  The consumer confirms the BL adjustment method and the estimation accuracy presented on the consumer monitor 18 and determines the BL adjustment method. A signal at the time of selection is transmitted to the EMS 10 via the network 19. As described above, the BL adjustment method determination unit 33 can determine to use the BL adjustment method selected by the consumer.

  The BL adjustment unit 34 applies the customer's performance data according to the determined BL adjustment method, and adjusts BL (adjusts the BL estimated value) using the above-described equations (1) and (2) ( Step S506). For example, when “closest load adjustment method” is selected as the BL adjustment method, BL calculation is performed using the actual data of the day closest to the BL estimation date (closest day) among the weekdays on which DR is not performed. The BL estimated value estimated by the unit 32 is adjusted by Expression (1) and Expression (2).

  In FIG. 8, the effect by EMS10 of this invention is shown. FIG. 8 shows the result of the accuracy evaluation of the BL calculated by the conventional BL adjustment method and the BL calculated by the BL adjustment method of the present invention. It can be seen that the BL calculated by the BL adjustment method of the present invention has a smaller absolute error and higher estimation accuracy than the BL calculated by the conventional BL adjustment method. If the BL estimation accuracy is high, the DR can be evaluated with high accuracy, and a highly reliable energy management system can be provided.

  In the above-described embodiment, the main processing of EMS has been described as being executed by software processing by the CPU. However, all or part of this processing may be realized by hardware.

  Further, the above-described embodiment is not limited to the above-described embodiment, and various changes can be made without departing from the gist of the embodiment.

10 EMS
DESCRIPTION OF SYMBOLS 11 Data input part 12 Electric power demand prediction part 13 Demand plan part 14 Baseline load (BL) estimation part 15 Demand response (DR) evaluation part 16 Performance database 17 Consumer smart meter 18 Consumer monitor 19 Network 31 Baseline load (BL ) Estimation method determination unit 32 Baseline load (BL) calculation unit 33 Baseline load (BL) adjustment method determination unit 34 Baseline load (BL) adjustment unit 41 CPU
42 RAM
43 HDD
44 Communication interface 45 Bus

Claims (6)

A baseline load estimation device for estimating a customer's baseline load,
A baseline load calculation unit that estimates the baseline load using actual data including information on past power consumption of the consumer in accordance with an estimation method of the baseline load of the consumer selected by the consumer. When,
A baseline load adjustment method for adjusting the estimated baseline load selected by the consumer, wherein the baseline load adjustment is adjusted based on actual data on a date different from the estimated date of the baseline load. In accordance with a method, a baseline load adjustment unit that adjusts the estimated baseline load using the actual data,
A baseline load estimation device comprising:   The apparatus according to claim 1, further comprising: an adjustment method presenting unit that, when selecting the baseline load adjustment method, presents data serving as a selection index together with the baseline load adjustment method on the display unit of the customer. Baseline load estimation device.   The baseline load adjustment method is a method of adjusting the estimated baseline load using actual data on the date closest to the estimated date of the baseline load among the calculation days of the baseline load. The baseline load estimation apparatus according to claim 1 or 2, wherein   The baseline load adjustment method adjusts the estimated baseline load using actual data similar to the weather on the estimated date of the baseline load among the calculation days of the baseline load. The baseline load estimation apparatus according to claim 1, wherein the baseline load estimation apparatus is a system that performs the above-described process.   5. The method according to claim 1, further comprising: an estimation method presentation unit that presents data serving as an index of selection on the display unit of the customer together with the estimation method when the estimation method of the baseline load is selected. The baseline load estimation apparatus according to claim 1. A baseline load estimation program for estimating a customer's baseline load,
Estimating the baseline load using actual data including information on past power consumption of the consumer according to an estimation method of the baseline load of the consumer selected by the consumer;
A baseline load adjustment method for adjusting the estimated baseline load selected by the consumer, wherein the baseline load adjustment is adjusted based on actual data on a date different from the estimated date of the baseline load. Adjusting the estimated baseline load using the performance data according to a method,
Baseline load estimation program to be executed by a computer.