JP6519385B2 - Baseline load estimation device and baseline load estimation program - Google Patents

Description

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

  Demand response (DR), according to the definition of the US Department of Energy (DOE), “when the unit price of electricity and the wholesale electricity price change with time are high, and when the power system is in crisis in terms of reliability, In response to the payment of an incentive designed to promote the saving of power, the end-user himself changes the power consumption from the normal power consumption pattern.

  The supply and demand control system called EMS (energy management system) performs power generation forecasting, supply and demand forecasting, etc., makes an operation plan of power supply and demand, and controls generators and storage batteries to balance supply and demand. When formulating an operation plan for power supply and demand, we will carry out a DR that promotes demand changes for consumers, taking into consideration the balance between supply and demand.

  Among DRs, incentive-type DR, in which a customer receives a reward as compensation for reduced demand, is attracting attention. Since the customer is reimbursed according to the demand reduction amount, there is no burden beyond the usual peak charge, and there is an advantage that the customer can easily accept it.

  In the incentive type DR, if DR was not performed, using the reference line (baseline load: BL) indicating how much power was used, DR was actually performed using DR, that is, power consumption. The load reduction amount is calculated in comparison with the actual load value shown. Based on this load reduction amount, the incentive paid to the consumer is determined. Therefore, accurate estimation of BL is important for incentive DR. The prior art of the BL estimation system which estimates BL is disclosed by the following nonpatent literature 1.

For the estimation of BL, the past load actual value of the demander on a weekday (BL calculation target day) on which the DR was not performed, that is, a weekday on which the demander did not suppress demand is used. The calculation of the BL estimated value is performed, for example, by any of the following four BL estimation methods.
・ Calculate the average value of the nearest 5 days of the BL calculation day.
• Determine the average value of the nearest 10 days of the BL calculation day.
・ Determined by temperature regression of 10 days close to the BL calculation day.
· Load on the same day of the previous week of the BL calculation day (If there is no relevant day, go back to the past).

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

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 : Day of estimating BL t: Time of estimating BL e: Day of load adjustment s: Time of load adjustment D _T (e, s): Load value of customer Here, load of the day In the adjustment method called adjustment, the day (e) serving as a reference for load adjustment is the day (d) for estimating BL. That is, e = d. Further, time (s) serving as a reference for load adjustment is a time two hours before the time (t) at which BL is estimated.

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

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

  However, in Non-Patent Document 1, it is reported that, in the DR performed on the morning of winter (from 8 o'clock to 10 o'clock), if load adjustment is performed on that day, the BL estimation accuracy is deteriorated. That is, in DR performed on the morning of winter, load adjustment on the day degrades the estimation accuracy of BL. This is considered to be due to the following factors.

  When DR is performed, the load in the DR time zone decreases. It is known that a part of the reduced load is consumed in a time zone other than the DR time zone (load shift). For example, when DR is performed in the winter morning, power consumed in the DR time zone is consumed in the time zone immediately before DR. Therefore, the load in the time zone immediately before DR will increase compared to when DR is not performed.

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

  An object of the present invention is to provide an apparatus (energy management system) or the like capable of performing DR evaluation with high accuracy in view of the above-mentioned problems.

  In order to achieve the above object, the baseline load estimation device of the present invention, according to the estimation method of the baseline load of the customer selected by the customer, information of the past power consumption of the customer And a baseline load adjustment method for adjusting the estimated baseline load selected by the customer, wherein the baseline load calculation unit estimates the baseline load using actual data including the baseline load adjustment method. And a baseline load adjustment unit configured to adjust the estimated baseline load using the actual data according to the baseline load adjustment method of adjusting the actual data on a day different from the estimated day of the load. The baseline load estimation device corresponds to the EMS described later, and the power consumption corresponds to the power demand described later.

  Further, in the baseline load estimation device of the present invention, an adjustment method presentation unit for presenting data serving as a selection indicator together with the baseline load adjustment method on the display unit of the customer when the baseline load adjustment method is selected 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 closest day of the baseline load estimation day among the calculation target days of the baseline load, It is a preferred aspect that it is a scheme to adjust the estimated baseline load.

  Further, in the baseline load estimation device of the present invention, the baseline load adjustment method includes, among the calculation days of the baseline load, actual data of weather similar days similar to the weather of the estimated date of the baseline load. It is a preferred embodiment to use the scheme to adjust the estimated baseline load.

  Further, the baseline load estimation device according to the present invention further includes an estimation method presentation unit for presenting data serving as a selection indicator together with the estimation method on the display unit of the customer when the estimation method of the baseline load is selected. Is a preferred embodiment. The estimation method presentation unit corresponds to a BL estimation method determination unit described later.

  Further, the baseline load estimation program of the present invention uses actual data including information on the past power consumption of the customer according to the estimation method of the baseline load of the customer selected by the customer. Estimating the baseline load, and a baseline load adjustment method for adjusting the estimated baseline load selected by the customer, wherein actual data of a date different from the estimated date of the baseline load And adjusting the estimated baseline load using the actual result data according to the baseline load adjustment method of adjusting with reference to.

  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 | achieves EMS which concerns on embodiment. It is a flowchart which shows an example of the presumed 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 displayed on the monitor for consumers in embodiment, and BL estimation precision. 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.

  Embodiments will be described below with reference to the drawings. FIG. 1 is a block diagram showing an example of the configuration of a system including an energy management system (EMS) 10 according to the embodiment. The EMS 10 and the consumer smart meter 17 and the consumer monitor 18 on the consumer side are connected via the network 19. The EMS 10 is installed, for example, on the side of an electric power company (for example, a power generation company or the like).

  The smart meter 17 and the consumer monitor 18 are provided on each consumer side (office, factory, general household, etc.). The smart meter 17 and the consumer monitor 18 provided on each consumer side are not limited to one each, and may be provided in plurality. Further, although only one consumer is shown in the example shown in FIG. 1, generally, there are a plurality of consumers, each of which is connected to the EMS 10 via the network 19. Further, the consumer may be a unit of a certain range of community including the office, the factory, the general household, etc. instead of the individual unit such as the office, the factory, the general household as described above.

  The network 19 is a communication line such as the Internet or a dedicated line. Data transmission / reception is performed between the EMS 10, the smart meter 17, and the consumer monitor 18 via the network 19.

  The smart meter 17 is a measuring instrument such as 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 data to be transmitted is, for example, a power demand measurement value (value of power demand shown in FIG. 5 described later) or the like. Power demand is synonymous with power consumption and load actual value.

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

  Further, the consumer monitor 18 displays a BL estimation method, a BL adjustment method, or an estimated or adjusted BL 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, BL adjustment method, estimation or adjustment is performed on each display unit. Each of the BLs 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 results 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 Can also function as a

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

  The performance database 16 stores various data including at least the power demand of the consumer as a performance data in the form of a table. The EMS 10 collects actual 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 forecast target day (the forecast target day data), the weather forecast data, and the actual data of the actual result database 16 to calculate the power demand forecast of the target day (for example, the next day). Do. The forecast target day data is data such as the date of the forecast target day, the day of the week, and weekdays / holidays. Forecast target day 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. of a forecast target day (the next day or later), and can be acquired from, for example, a weather forecast site as described above. Achievement data can be acquired from the achievement database 16.

  Techniques for forecasting power demand using forecasted day data, weather forecast data and actual data are known, for example, Proceedings of the IASTED International Conference 248-039, “Peak Load Forecasting using Neutral Networks The existing methods described in “and Fuzzy Interference” can be used. That is, the demand forecast is calculated by a demand forecasting model whose output is the power demand forecast value, using the past power demand of actual data, weather, temperature, season, day of the week, power price unit price, etc. as input data. As a demand forecasting model, the neural network etc. which are disclosed by the said reference can be used.

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

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

  The BL estimation unit 14 estimates BL when performing DR, and as illustrated in FIG. 3, a baseline load (BL) estimation method determination unit 31, a baseline load (BL) calculation unit 32, and a baseline A load (BL) adjustment method determination unit 33 and a baseline load (BL) adjustment unit 34 are configured. Details of each configuration of the BL estimation unit 14 will be described later.

  The DR evaluation unit 15 calculates the difference between the estimated load value changed by the estimated BL and DR, and evaluates the effect of the DR. Peak Rebate (PTR), which is one of DR methods, provides consumers with an incentive 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 load actual value. The incentive is not limited to a monetary reward, but may be a reward to the consumer in some form, such as the grant of points or the coupon.

  Next, an example of a hardware configuration for realizing the EMS 10 will be described with reference to FIG. The hardware configuration illustrated 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, the RAM 42, the HDD 43, and the communication interface 44 are connected to one another via, for example, a bus 45.

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

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

  The communication interface 44 exchanges data with the outside (the smart meter 17 and the consumer monitor 18), and functions as, for example, the data input unit 11 described above. The bus 45 is a communication path that mediates exchange 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 smart meter 17 of the customer transmits the performance 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 actual 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 an example of the data configuration of the results database 16. The actual data accumulated in the actual result database 16 is past data. In FIG. 6, although the performance data accumulated in the performance database 16 is in a table format, it may be stored in a format other than the table format.

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

  The data unit has data items of date and time, day of the week, holiday / weekday, temperature, humidity, power demand, power unit price, and DR flag. The electricity price unit price corresponds to, for example, the above-described incentive coefficient, and is used when calculating a monetary incentive. The DR flag is an index indicating whether or not the DR has been performed, and stores “1” when the DR is performed and “0” when the DR is not performed. Each piece of information in the data section shown in FIG. 6 is an example, and may have information other than these, and may not have all the information.

  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%", "Electricity Demand 75kw" , “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 results database 16. Therefore, it becomes past performance data accumulated every 30 minutes.

  Returning to the operation flow of FIG. 5, the BL estimation scheme determination unit (also referred to as estimation scheme presentation unit) 31 determines to use the BL estimation scheme previously selected by the customer (step S503). For example, stored information such as a storage unit (not illustrated) that stores association information in which the BL estimation method determination unit 31 associates the customer number (or the group number) with the information of the BL estimation method selected by the customer in advance. And recognize the BL estimation method corresponding to the consumer and decide to use that estimation method. Similarly, in the BL adjustment method determination unit (also referred to as adjustment method presentation unit) 33 described later, association information in which the customer number (or group number) and information of the BL adjustment method selected by the customer in advance are associated It refers to stored information such as a storage unit (not shown) to be stored, and decides to use the BL adjustment method corresponding to the customer.

  Here, a method of causing the consumer to select the BL estimation method in advance will be described. In order to cause 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 according to the method to the consumer, as described later. Specifically, the BL estimation method determination unit 31 prepares one or more BL estimation methods, presents these to the customer, and calculates the BL estimation accuracy of each BL estimation method. , Present the calculated BL estimation accuracy to the customer.

Although the following four can be considered as an example of the BL estimation system to show, it is not restricted to these and another BL estimation system may be used. These BL estimation schemes are used when calculating BL estimation values. The specific calculation of the BL estimated value will be described later. Note that, as described above, the BL calculation target day refers to a weekday on which the DR was not performed (a weekday on which the customer did not suppress the demand).
・ Calculate the average value of the nearest 5 days of the BL calculation day.
• Determine the average value of the nearest 10 days of the BL calculation day.
・ Determined by temperature regression of 10 days close to the BL calculation day.
· Load on the same day of the previous week of the BL calculation day (If there is no relevant day, go back to the past).

  Here, the BL estimation method of “determining the average value of the nearest 5 days of the BL calculation target date” of an example of the BL estimation method will be described. “Determine the average value of the nearest 5 days of the BL calculation day” means the average value of the actual load values of the customers of the 5 days (which may be arbitrary) of the weekday when DR was not implemented. It means to calculate every time (for example, every 30 minutes), and let those calculated be BL estimated values in this BL estimation method. More specifically, on a weekday (August 1 (Fri), July 31 (Thu)) on which no DR on the nearest 5 days including a certain BL calculation day (August 1 (Fri)) was conducted. It means that the average value of the actual load value of the customer on July 30 (Wed), July 28 (Mon), July 25 (Fri)) is calculated at each predetermined time.

  In addition, the BL estimation method determination unit 31 calculates a BL estimation accuracy by calculating a BL estimated value for a past BL calculation target date (for example, for 30 days) and calculating an absolute error from a load actual 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. An absolute error is determined based on these values.

d: Date for BL calculation (m)
t: BL calculation target time (n times)
D _BL (d, t): BL estimated value D _C (d, t): load actual value As a method of presenting the BL estimation method and the BL estimation accuracy to the customer, the EMS 10 (for example, the BL estimation method determination unit 31) There is a method of transmitting presentation data (BL estimation method and information of BL estimation accuracy) via the network 19 and presenting the data to the consumer monitor 18. An example of presentation 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 accuracies (absolute errors) are presented, and in this example, the absolute error of the BL estimation method of "the average value of the nearest 10 days of the BL calculation target date" is It is the smallest and the estimation accuracy is the best.

  Note that the method of having the consumer select the BL estimation method is not limited to the specific example presented on the consumer monitor 18. The frequency of selecting the BL estimation method is about once a year because it is often performed when a new contract is made or a contract is continued. Therefore, as another specific example of the method of asking the customer to select the BL estimation method, it can be considered to exchange in writing or to have the user select on 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. Then, as described above, the BL estimation method determination unit 31 can determine to use the BL estimation method selected by the customer.

  Here, the BL estimation accuracy is presented as an index to support the determination (selection) of the BL estimation method by the customer, but one or more scenarios (a) where it is possible to make a determination that the customer can accept Data) may be presented to the consumer. For example, when a certain BL estimation method is selected, information may be presented for each BL estimation method, such as information that benefits in summer but not much in winter. The same applies to the presentation of the BL estimation accuracy by the BL adjustment method determination unit 33 described later.

  The BL calculation unit 32 applies the actual data of the customer to the determined BL estimation method to estimate the BL (calculate a BL estimated value) (step S504). For example, when the BL estimation method of “determine the average value of the nearest 5 days of the BL calculation target date” is selected as the BL estimation method, the average value of the load actual values over 5 days of weekdays without performing DR Are calculated every predetermined time (for example, every 30 minutes), and these are used as BL estimated values. Here, although the BL estimated value for every 30 minutes is calculated for 24 hours, you may make it calculate only for a desired time slot | zone (for example, the time slot | zone which implements DR).

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

  The BL adjustment method presented in the embodiment is a BL adjustment method that adjusts based on actual data of different days other than the BL estimation day among BL calculation target days, and the following two can be considered as an example However, it is not limited to these. Here, the BL estimation day refers to a day on which BL is to be estimated, that is, a day on which BL is to be estimated. The BL adjustment method here is, for example, when the BL estimation day is on August 1 (Fri), actual data of July 31 (Thu) on the day of the BL calculation different from August 1 (Fri). It is a BL adjustment method which adjusts on the basis of.

  One BL adjustment method is a close-to-day load adjustment method. This method is a method of adjusting the BL based on the actual data of the closest day (closest day) among the BL calculation target days, and in the above-described formulas (1) and (2), This is a method in which the day (e) serving as the reference for load adjustment is the day closest to the BL estimated day (d) among the BL calculation target days.

  Another BL adjustment method is a weather similar day load adjustment method. This method is a method of adjusting BL based on actual data of weather similar day of BL estimation day among BL calculation object days, and in the above-mentioned formula (1) and formula (2), a standard of load adjustment In this method, the day (e) which becomes the weather forecasting day of the BL calculation day is set as the weather similar day of the BL estimation day (d). The weather similar day refers to, for example, a day on which one or more of the weather-related indicators such as maximum temperature, minimum temperature, maximum humidity, minimum humidity, weather are similar. As a case where it judges that they are similar, for example, it is a case where the difference of the index to contrast is less than a predetermined threshold. In the case of the comparison of the maximum temperature, for example, if the temperature difference is within ± 3 ° C., it is judged to be similar. If there is no actual data determined to be similar, the temperature difference may be expanded within, for example, ± 4 ° C. to make a similar determination.

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

  The BL adjustment method and the method of presenting the BL estimation accuracy to the consumer are different from the BL estimation method and the method of presenting the BL estimation accuracy only in the contents to be presented, and the transmission method and presentation format of the data for presentation are The description is omitted because it is similar.

  The customer confirms the BL adjustment method and the estimation accuracy presented on the customer 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. Then, as described above, the BL adjustment method determination unit 33 can determine to use the BL adjustment method selected by the customer.

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

  FIG. 8 shows the effect of the EMS 10 of the present invention. FIG. 8 shows the results of the evaluation of the accuracy between 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 BL calculated by the BL adjustment method of the present invention has a smaller absolute error and higher estimation accuracy than BL calculated by the conventional BL adjustment method. If the estimation accuracy of BL is high, DR can be evaluated with high accuracy, and a highly reliable energy management system can be provided.

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

  In addition, the above-described embodiment is not limited to the above-described embodiment, and various modifications can be made without departing from the scope of the embodiment.

10 EMS
11 Data Input Unit 12 Power Demand Forecasting Unit 13 Demand Planning Unit 14 Baseline Load (BL) Estimation Unit 15 Demand Response (DR) Evaluation Unit 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 estimator for estimating a baseline load of a customer, comprising:
A baseline load calculating unit configured to estimate the baseline load using performance data including information on past power consumption of the customer according to an estimation method of the baseline load of the customer selected by the customer; When,
A baseline load adjustment method for adjusting the estimated baseline load selected by the customer, wherein the baseline load adjustment is performed based on actual data of a day different from the estimated date of the baseline load. And a baseline load adjuster for adjusting the estimated baseline load using the actual data according to a scheme.
A baseline load estimation device comprising:   The system according to claim 1, further comprising: an adjustment method presentation unit that presents data serving as a selection indicator together with the baseline load adjustment method on the display unit of the customer when the baseline load adjustment method is selected. Baseline load estimator.   The baseline load adjustment method is a method of adjusting the estimated baseline load using actual data of the closest day of the baseline load estimation day among calculation days of the baseline load. The baseline load estimation device according to claim 1 or 2, characterized in that:   The baseline load adjustment method adjusts the estimated baseline load using performance data of a weather similar day similar to the weather of the estimated baseline load day among the calculation days of the baseline load. The baseline load estimation device according to claim 1 or 2, characterized in that:   The method according to any one of claims 1 to 4, further comprising: an estimation method presentation unit for presenting data serving as a selection indicator together with the estimation method on the display unit of the customer when the estimation method of the baseline load is selected. The baseline load estimator according to any one of the preceding claims. A baseline load estimation program for estimating a baseline load of a customer, comprising:
Estimating the baseline load using performance data including information of past power consumption of the customer according to an estimation method of the baseline load of the customer selected by the customer;
A baseline load adjustment method for adjusting the estimated baseline load selected by the customer, wherein the baseline load adjustment is performed based on actual data of a day different from the estimated date of the baseline load. Adjusting the estimated baseline load using the historical data according to a scheme;
Baseline load estimation program to run on a computer.