JP2016001951A - Power demand prediction apparatus and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically calculate an incentive for implementing desired power demand in the case of performing demand response.SOLUTION: A price elasticity factor management section 17 previously stores a price elasticity factor matrix. The price elasticity factor is a factor showing the degree of change in power demand with respect to the degree of change in a power unit price (difference from a reference price etc.). A power price optimization section 18 calculates a power unit price variation by using the elasticity factor matrix and a desired power demand value on a demand response scheduled date arbitrarily set by a user. A (scheduled) power unit price on the demand response scheduled date is determined from the power unit price variation and the reference price.

Description

  The present invention relates to an electric power demand prediction system for predicting electric power consumption of a consumer.

  Demand response (DR) is defined by the US Department of Energy (DOE) as follows: “Electricity prices that change with time, wholesale power prices are high, or the power system is in danger of reliability. In response to payment of incentives designed to encourage savings, the end-users themselves change their power consumption from their normal power consumption pattern. "

  For example, a program called Dynamic Pricing reflects the price of the electricity market in the unit price of the customer's electricity rate, and the power company notifies the customer of the next day's electricity price (unit price of electricity for each time period) The house adjusts the amount of power used in accordance with the electricity price.

  For electric power companies, it is important to predict how much the demand will change when the unit price of electric power is changed. For this reason, a supply and demand operation management system that performs demand prediction has been devised (Patent Document 1). ).

  In the supply and demand operation management system of Patent Document 1, a power operation device is installed in a consumer, and the power management device installed in an electric power company is controlled by a power operation device installed in each consumer. Acquire information such as device information, electricity unit price, applicable charge plan, control details of electric devices, and weather conditions. Based on this information, an energy consumption model is used to calculate the power demand (power daily load curve) of each consumer.

  Moreover, in the supply and demand operation management system of Patent Document 1, a power operation apparatus is installed in a consumer, and the power management apparatus installed in the power company is controlled from the power operation apparatus installed in each consumer. Acquire information such as target device information, electricity usage unit price, applicable charge plan, control details of electrical devices, and weather conditions. Based on this information, an energy consumption model is used to calculate the power demand (power daily load curve) of each consumer. Furthermore, a charge plan is set at random to generate a plurality of patterns, and a power demand prediction value is calculated for each of the patterns. The best one is selected from these patterns, and the rate plan is applied.

JP 2010-166636 A

  However, in the demand-and-supply operation management system of Patent Document 1, since only the power demand of a consumer in which a power operation device (such as EMS) is installed is calculated, the power demand including the customer in which the power operation device is not installed There was a problem that the prediction could not be realized.

  In addition, since the supply and demand operation management system of Patent Document 1 adopts a method of selecting the best one from randomly generated rate plans when setting the power unit price, the power demand is within a desired range. There is no guarantee (optimum guarantee) that it fits. Also, in order to obtain a solution close to the desired range, it is necessary to generate and simulate a considerable number of patterns, and the calculation load is too large.

  An object of the present invention is to enable demand prediction including a customer who does not have a power operation device installed, to improve the accuracy of demand prediction particularly when a demand response is performed. An object is to provide a power demand prediction device or the like that calculates a power unit price so as to obtain a desired value.

A power demand prediction apparatus according to the present invention is an apparatus for predicting power demand related to an area composed of a plurality of consumers including one or more consumers that perform demand response according to an arbitrary incentive. Have
Actual data storage means for storing actual data related to past power demands of each consumer; and
Elastic coefficient management means for holding an elastic coefficient obtained by converting the degree of change in power demand according to the degree of incentive;
-Setting means for setting a desired power demand value on an arbitrary forecast target day;
The power demand forecast value or / and the demand plan value of each consumer obtained based on factors other than the incentive related to the actual data and the forecast target date are acquired, and the acquired data and the set desired power An incentive calculating means for calculating the incentive to make the power demand on the prediction target day substantially equal to the desired power demand value based on the demand value and the elasticity coefficient;

  According to the power demand prediction apparatus and the like of the present invention, it is possible to perform demand prediction including a consumer who does not have a power operation apparatus (EMS or the like) installed, and more particularly to improve the accuracy of demand prediction at the time of demand response execution. The unit price of power is calculated so that the power demand prediction value becomes a desired value.

It is a system configuration figure of the power demand prediction system of this example. It is a figure which shows roughly the process sequence in a demand prediction apparatus. It is a figure which shows the example of a data structure of a customer database. It is a figure which shows the data structural example of a performance database. (A) shows a setting example of the power unit price for each time zone, and (b) shows a power demand value corresponding to this setting example. It is a detailed structural example of a customer group management part. It is a process flowchart figure of a customer group preparation part. It is an example of detailed composition of a group representative demand data creation part. It is a data structural example of group representative demand data. It is a figure which shows the flow of the process which concerns on a group demand prediction model preparation part. It is a figure for demonstrating in detail a demand prediction part. It is a process flowchart figure of a price elasticity coefficient management part. It is a process flowchart figure of DR prediction part. It is a process flowchart figure of the electric power price optimization part in another Example. It is a figure which shows the specific example of the electric power demand used for description of another Example. It is a figure which shows roughly the process sequence in the demand prediction apparatus in another Example.

Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a system configuration diagram of the power demand prediction system of this example.
In this system, each consumer (office, factory, general household, etc.) has at least a smart meter 1 installed, and there are also customers with a customer management system (EMS) 2 installed. Shall. Note that demand (power demand) is synonymous with power consumption.

  Each smart meter 1 and each customer management system (EMS) 2 are connected to a network 3 such as the Internet or a dedicated line, and can transmit / receive predetermined data to / from a specific server device via the network 3. It has become. The specific server device is, for example, the demand prediction device 10 illustrated. The demand prediction device 10 transmits / receives predetermined data to / from the smart meter 1 and the customer management system (EMS) 2 of each consumer, for example. Details will be described later.

  The predetermined data transmitted by the smart meter 1 is, for example, “customer No.” (a customer identification ID assigned in advance to each consumer), “date and time and power demand measurement value”, and the like. Further, in the case of a consumer who performs demand response (DR), the smart meter 1 receives and displays data such as a demand response execution scheduled date and time and a power price unit price (plan) at that time from the demand prediction device 10. To do.

  The demand response is basically intended to control the demand of each customer by some kind of incentive. Here, as an example of the incentive, the setting / change of the electricity price unit price by time zone is taken as an example. However, the present invention is not limited to this example. As an incentive, for example, reward may be given to a consumer in some form such as provision of points or provision of coupons. Normally, a consumer who performs a demand response is contracted with an electric power company or the like in advance.

  Alternatively, in the case of a consumer in which the consumer management system (EMS) 2 is installed, the demand response execution scheduled date and time and the power price unit price (plan) may be configured to be received and displayed by the EMS 2.

  The demand prediction device 10 is installed on, for example, an electric power company (such as a power generation company) or a community energy management system (CEMS) company side. Basically, for a consumer who has a demand response contract, the unit price of the power price can be arbitrarily determined on the demand prediction device 10 side (electric utility, etc.) when the demand response is performed.

  The demand prediction device 10 includes various databases of a customer database 11 and a performance database 12, a customer group management unit 15, a demand prediction model creation unit 13, a demand prediction unit 14, a price elasticity coefficient management unit 17, a demand response (DR). It comprises various processing function units of the prediction unit 16.

  Note that the demand prediction device 10 is realized by a general-purpose computer such as a server device, for example, and is not particularly shown, but is a general general-purpose computer such as a CPU, a storage device (hard disk), a memory, a communication function unit, and an input / output interface. It has the composition of. For example, a predetermined application program is stored in the storage device in advance, and the processing functions of the various processing function units 13 to 17 described above are realized by the CPU reading and executing the application program. For example, the customer database 11 and the performance database 12 are constructed on the storage device.

  As described above, the demand prediction device 10 is connected to each customer via a communication line (network 3), collects the customer's power demand data measured by the smart meter 1, and stores it in the results database 12. To do. Moreover, it notifies with respect to the consumer who implements a demand response by displaying the electric power price unit price (plan), the demand response implementation scheduled date and time, etc. on the smart meter 1 or the like.

  On the other hand, a consumer can be classified into a consumer provided with a consumer management system (EMS) 2 and a consumer without EMS 2. In a customer with EMS2, EMS2 optimizes the power usage status in the customer according to the power price or the like. Moreover, although EMS2 automatically creates a customer's planned power usage amount (demand plan data), this is an existing technology and will not be particularly described here. The EMS 2 notifies the demand prediction apparatus 10 of the automatically created demand plan data. The demand prediction device 10 additionally stores this demand plan data in the customer database 11 or the like, for example.

  In addition, from the above, since the customer with EMS2 is basically always “with demand plan data”, it is always classified into group 3 in this example. However, the present invention is not limited to the grouping example, and there may be a customer who “has demand plan data” and “no demand response” (however, in this example, such a case is not considered).

  The demand prediction apparatus 10 predicts the power demand of, for example, the entire region on an arbitrary prediction target date (for example, the next day) based on the data collected from each consumer and data set in advance.

  The power demand prediction process is performed for each group by grouping each consumer according to the presence or absence of DR execution or the presence or absence of a demand plan. Further, depending on the group, a correction process corresponding to the incentive is performed.

  For example, power demand prediction is performed for a customer group without EMS 2 (no demand plan data). This power demand prediction process itself may be the use of an existing method. Further, for the customer group “with demand response”, correction according to the incentive (in this example, power price unit price (for example, scheduled price for each time zone on the next day)) for this power demand prediction result I do. For a customer group with EMS2 (with demand plan data), the demand plan is corrected according to the incentive.

  By performing the above-described processing, it is possible to predict power demand including a customer without EMS2, and to obtain an accurate power demand prediction result corresponding to the content of demand response (amount of change in power unit price, etc.). it can.

Here, FIG. 2 schematically shows a processing procedure in the demand prediction apparatus 10.
Hereinafter, the processing function units and the database structure of the demand prediction apparatus 10 will be described with reference to FIGS.

  The consumer database 11 stores consumer basic information (contract power, building, scale, business type, location, etc.), presence / absence of DR execution, presence / absence of EMS2, presence / absence of demand plan creation, and the like. These data (referred to as “customer data”) are set in advance.

  In addition, when a consumer with DR (demand response) is given some incentive to the consumer (for example, changing the unit price of electricity or giving points), a demand response (change in power consumption) can occur. . Such a consumer, for example, makes a contract with a power provider in advance with DR implementation.

FIG. 3 shows a data configuration example of the customer database 11.
In the illustrated example, customer data (table) 30 stored in the customer database 11 is associated with each customer identification ID (customer No. 31), and includes an address 32, a business type 33, and a contract. The data items include power 34, demand response execution 35, demand plan creation 36, and group 37. The address 32, the business type 33, and the contract power 34 are not particularly described.

  The demand response execution 35 stores the presence / absence of DR execution. The demand plan creation 36 stores whether or not a demand plan has been created (in the case of a demand plan, a pointer or the like indicating a storage area for demand plan data may be further stored). The group 37 stores the group identification number of the group to which the customer belongs.

  Here, in this example, each consumer is classified into three groups. This grouping is executed by the customer group management unit 15 and will be described later. The grouping result is stored in the group 37.

  The performance database 12 includes, for each customer, past power consumption data (power demand performance data), actual power unit price values, factors other than power unit prices (weather conditions (temperature, humidity, weather, season, etc.), Day of the week, holidays / weekdays, time of day, etc .; in other words, “factors other than incentives”, which are collectively referred to as “environmental factors”), actual data such as demand plan data, etc. Are stored (collectively referred to as “demand performance data”).

In FIG. 4, the example of a data structure of the results database 12 is shown.
The record database 12 stores demand record data 40 for each customer. That is, as shown in the figure, the demand record data 40 is composed of a header part and a data part. Are stored, and the data section stores various data shown in the figure related to the consumer.

  In the example shown in the figure, the data items in the data section are date 41, day 42, holiday / weekday 43, temperature (° C.) 44, humidity (%) 45, power demand (kW) 46, power price (unit price) (¥ / kWh) 47 or the like, but is not limited to this example. As described above, these data are all past performance data. Note that the power price (unit price) may be referred to as a power unit price. Alternatively, the power price may basically be regarded as a unit price of power.

  The customer group management unit 15 groups customers based on customer data (table) 30 stored in the customer database 11. The result is reflected in the group 37 (that is, the group 37 is blank before the grouping process).

Here, in this example, as described above, consumers are divided into three groups, and are classified into any one of the following group 1, group 2, and group 3.
・ Group 1: DR not implemented ・ Group 2: DR implemented and no demand plan ・ Group 3: DR implemented and demand plan exist In other words, each customer is not granted a group (DR implementation) that gives incentives Divide into groups (DR not implemented; group 1). Further, the DR implementation customers are divided into a customer group (group 3) that creates a demand plan and a customer group (group 2) that does not create a demand plan.

  Incentives include changing the unit price of electricity and giving points to consumers (points can be used as local currency or receiving resident services, for example). Not exclusively.

  The customer group management unit 15 executes the grouping process of customers as described above, but also executes a process of acquiring arbitrary data from various databases and passing it to other functional units at any time. To do. For example, as an example, learning is performed or demand prediction by the demand prediction unit 14 is performed by assigning corresponding data to a later-described demand prediction model created in advance corresponding to each group. . For example, for the demand prediction model (for example, before learning) related to group 1, demand actual data (however, representative data to be described later in this example) is input to group 1 consumers. To learn.

  The customer group management unit 15 refers to the customer data (table) 30 and determines, for each customer, a group to which the customer falls based on the demand response execution 35 and the demand plan creation 36. Then, the determination result is stored in the group 37. For example, if it is determined that the group 2, “2” is stored in the group 37.

  As a group determination method, for example, if the demand response execution 35 is “not executed”, it is determined as a group 1. If the demand response execution 35 is “execute”, the demand plan creation 36 is further referred to. If “no plan”, the group 2 is determined, and if “plan”, the group 3 is determined. To do.

  Since the group 1 and the group 2 do not create a demand plan on the customer side, the demand prediction unit 14 performs power demand prediction using a demand prediction model (for example, after learning) described later. The demand prediction model has a processing function for calculating a power demand prediction value based on “factors other than incentives” (the above environmental factors) and the like (details will be described later).

  Group 3 does not perform power demand prediction based on a demand prediction model because a customer creates a demand plan. In addition, since the group 1 does not perform DR, a DR prediction model to calculate a change in demand due to incentives is not created, and naturally, DR prediction and correction are not performed.

  For group 2, the DR prediction unit 16 calculates the amount of change in power demand according to an incentive (for example, a change in power unit price) for group 2 (performs DR prediction), and the demand prediction unit based on this amount of change. 14, the power demand prediction value obtained by 14 is corrected. When the incentive is a power price as in this example, DR prediction is performed using a price elasticity coefficient described later.

  For group 3, demand plan data is used instead of the power demand forecast value. That is, the DR prediction unit 16 calculates the amount of change in power demand due to incentives (such as a change in power unit price) for the group 3 (performs DR prediction), and corrects the demand plan data based on the amount of change.

For example, when the incentive is a power price as in this example, DR prediction is performed using a price elasticity coefficient described later.
Finally, the power demand prediction values of each group (DR prediction results are reflected for groups 2 and 3) are added together by the integrating unit 21, and the entire power demand prediction value (for example, the entire region) is output.

  The demand prediction model creation unit 13 includes the demand result data 40 (“factors other than incentives”) stored in the result database 12 for the groups 1 and 2 among the groups divided by the customer group management unit 15 ( A demand prediction model (in this example, a learned neural network, etc.) is created with the input of the above-mentioned environmental factors and the like) and the output of the “predicted value of power demand”.

  “Factors other than incentives” (environmental factors) include, for example, weather conditions (weather, temperature, humidity, season, etc.), days of the week, holidays / weekdays, time zones, and residential areas. An existing method (for example, multiple regression analysis or neural network) can be used as a method for creating a demand prediction model.

  For example, a power demand prediction system using a neural network is well known, and for example, existing methods described in the following references may be used. That is, a learned neural network is generated by causing a neural network to learn by using past power demand record values and the environmental factors at that time. This learned neural network becomes the demand prediction model.

・ Reference 1; JP 07-046761 A ・ Reference 2; JP 2000-276460 A ・ Reference 3; “Proceedings of the IASTED International Conference 248-039 'Peak Load Forecasting using Neural Networks and Fuzzy Inference'"
However, in the above example, the demand prediction model corresponding to each group is created for group 1 and group 2, but not for group 3. This is because the group 3 is a “customer group that creates a demand plan” as described above, and therefore demand plan data is used instead of the demand forecast result. Details will be described later.

  The demand forecasting unit 14 creates a demand forecast model created for each of the groups 1 and 2 and “a factor other than an incentive (environmental factor, etc.)” on the forecast date (for example, the next day). For example, the power demand prediction result of the prediction target day is obtained based on, for example, obtaining forecast information such as the next day's weather and temperature from the weather forecast site. This is because, for example, by inputting “factors other than incentives (environmental factors)” to the “learned neural network”, the output is obtained as a power demand prediction result on the prediction target day. It is.

  By using the demand prediction model, the demand prediction unit 14 performs processing for obtaining a power demand prediction result for the group 1 and a power demand prediction result for the group 2, for example, as shown in FIG. And the electric power demand prediction result regarding the group 1 is made to input into the integrating | accumulating part 21 as it is. On the other hand, the power demand prediction result relating to group 2 is input to the DR prediction unit 16 and corrected by a process using an elastic coefficient, which will be described later, to obtain a more accurate power demand prediction result according to incentive provision. Can do. Then, the corrected power demand prediction result is input to the integrating unit 21.

  For group 3, the above-described demand plan data is acquired and input to the DR prediction unit 16, and correction is performed by processing using an elastic coefficient, which will be described later, so that more accurate power corresponding to incentive provision can be obtained. Demand forecast results can be obtained. Then, the corrected power demand prediction result is input to the integrating unit 21. In the case of Group 3, the demand plan data is corrected. For convenience, here, the result of correcting the demand plan data is assumed to be the “corrected power demand forecast result” as described above. .

The integration process by the integration unit 21 is not an essential process. Therefore, the process of causing the integration unit 21 to input the power demand prediction result is not an essential process.
In the example of FIG. 2, the customer group management unit 15 acquires the demand plan data and inputs it to the DR prediction unit 16, but is not limited to this example.

  The price elasticity coefficient management unit 17 manages an elasticity coefficient that is a value obtained by coefficientizing the degree (degree) of change in power demand (according to the degree (degree) of the incentive) when the incentive is given. As the elasticity coefficient, a value arbitrarily set in advance is stored, or calculated and stored by the price elasticity coefficient management unit 17 (a specific example is shown in FIG. 12 and will be described later). For example, when considering a change in the power unit price as an incentive, the elasticity coefficient Eij is expressed by the following equation (1).

  Here, Eij is a coefficient relating to the amount of movement of demand from time zone i to time zone j. Alternatively, Eij can be defined as a coefficient indicating the power demand change rate in time zone i according to the power price change rate in time zone j. There is a case where i = j. ΔDi is the change in demand in time zone i, Di is the original demand in time zone i (demand in Pj), ΔPj is the change in power price in time zone j, and Pj is the original change in time zone j. Power price (base price, etc.).

  The elasticity coefficient Eij is calculated by, for example, the above formula (1) by a developer or the like in advance based on, for example, the past unit price of power and the actual value of power demand. The elasticity coefficient Eij is obtained for each of the cases i = j and i ≠ j.

  In the case of the above example, the elasticity coefficient Eij can be said to be the price elasticity coefficient Eij. The price elasticity coefficient Eij can be said to be a coefficient of the degree (degree) of change in power demand according to the degree (degree) of change in power unit price (difference from the reference price, etc.).

  Here, the meaning of the elasticity coefficient Eij will be described in more detail using an example shown in FIG. In the example shown in FIG. 5, for the sake of simplicity, it is assumed that one day is divided into three time zones A, B, and C shown in FIG. As an example, the time zone A is in the morning, the time zone B is in the daytime, and the time zone C is in the evening, but is not limited to this example. In addition, the power unit price is set in units of these time zones.

  FIG. 5A shows an example of setting the power unit price for each time zone A, B, C. Here, the original power unit price (for example, the reference price) for each of the time zones A, B, and C is indicated by a solid line on the diagram, and the power unit price after any change is indicated by a dotted line on the diagram.

  Here, by using an example in which the power unit price is changed only in any one of the three time zones, the elasticity coefficient Eij is obtained using the power demand record data before and after the change. As illustrated, in this example, the power unit price is changed only in the time zone B. That is, for the time zone B, for example, the reference price is the power unit price P2, but is changed (increased) to the power unit price P3 on a certain day. On the other hand, regarding the time zones A and C, the power unit price P1 (base price) remains the same and is not changed.

For example, when the time zone B is daytime, especially in the summer, the amount of power demand is very large, so that the customer himself / herself takes action to suppress the power demand by raising the power unit price.
FIG. 5B shows power demand values according to the setting example of the power unit price in FIG. The power demand curve in the case where the power unit price is the solid line is also shown by the solid line in FIG. Similarly, the power demand curve when the power unit price is the dotted line is also indicated by a dotted line in FIG. These are obtained from past performance data.

  Here, the examples shown in FIGS. 5A and 5B are examples, but in reality, a change in the power price in a certain time zone naturally affects the power demand in that time zone. It also affects the power demand in other time zones. That is, when the unit price of power in time zone B is increased as in the above example, the power demand in time zone B naturally decreases as shown by the dotted line in FIG. However, for example, in a factory or the like, if the daily production volume is determined, the daily power demand amount will hardly change even if the power unit price changes. In other words, if the power demand in the time zone B is reduced by lowering the machine operation rate in the time zone B, the machine operation rate in the other time zones A and C is increased accordingly, and the time zones A and C are increased. The power demand will increase (as shown by the dotted line in FIG. 5B).

In the above example, j in the above equation (1) is B, and i is A, B, and C, respectively. That is, the elasticity coefficients E _AB , E _BB , and E _CB are obtained as the elasticity coefficient Eij. That is, a coefficient indicating how much the power demand amount in the time zones A, B, and C changes according to the change (change amount) of the power unit price in the time zone B is obtained. In the example shown in the drawing, the change amount ΔP _B of the power unit price in time zone B is 10. However, the present invention is not limited to this example.

In the example shown in FIG. 5, the denominator in the above equation (1) is “(P3−P2) / P2”.
Further, for example, with respect to the time zone A, as shown in FIG. 5B, the original power demand is D _A (shown by hatching), and ΔD _A (shown by shading) is shown by changing the power unit price. If it is increased by (part), the numerator in the above formula (1) becomes “ΔD _A / D _A ”. Therefore, the elasticity coefficient E _AB is obtained as described below.

E _AB = (ΔD _A / D _A ) / {(P3-P2) / P2}
That is, the elasticity coefficient E _AB indicating the degree (degree) of change in the power demand amount in the time zone A according to the change in the power unit price in the time zone B (and further according to the change amount ΔP _B = 10) is obtained. It will be.

Other elasticity coefficients E _BB and E _CB can be obtained in substantially the same manner.
Further, in the same manner as described above, elasticity coefficients E _AA , E _BA , E indicating the degree (degree) of change in power demand in each time zone A, B, C when the power unit price in time zone A is changed. _CA can also be determined. Similarly, the elasticity coefficients E _AC , E _BC , E _CC indicating the degree (degree) of change in the power demand in each time zone A, B, C when the power unit price in the time zone C is changed may be obtained. it can.

  Then, when a matrix E (matrix) having nine elasticity coefficients obtained as described above as elements is defined (stored in the price elasticity coefficient management unit 17), the following equation (2) is used using this matrix E: Thus, it is possible to calculate the amount of change in demand in each time zone according to the price change in each time zone.

Here, A, B, and C are symbols representing time zone divisions (in this example, 1 day is divided into 3 divisions), ΔD is the amount of change in demand in each time zone, and ΔP is the power unit price in each time zone. The amount of change. That is, the power demand amount of change [Delta] D _A time zone A, [Delta] D _B power demand change of time zone B, [Delta] D _C is the power demand variation time period C. ΔP _A is the power unit price change amount in time zone A. ΔP _B is the power unit price change amount in time zone B. [Delta] P _C is the electricity unit price variation time periods C.

Each electricity unit price variation ΔP _A, ΔP _B, ΔP _C, for example those set by the user arbitrarily determined. This can be set again and again. The amount of change in power demand is obtained each time the setting is made, and the user refers to the “corrected power demand prediction result (predicted value)” of the groups 2 and 3 and the output of the integrating unit 21 obtained thereby. When the user becomes close to the power demand desired by the user, for example, the set value of the power unit price change amount at that time is regarded as a correct answer (for example, used for determining the power unit price on the next day).

  Further, the matrix E may be created and held for each of the groups 2 and 3, or only one may be held regardless of the group. Further, the group 2DR prediction model 24 and the group 3DR prediction model 25 shown in FIG. 2 are considered to correspond to, for example, those obtained by applying the matrix E for each of the groups 2 and 3 to the equation (2), respectively. You can do it. Further, in the group 2DR prediction model 24 and the group 3DR prediction model 25, the power demand predicted value / demand plan data is corrected by using the power demand change amount obtained by the above equation (2). It may be considered that a function for obtaining the “demand result of power demand” is included.

  The correction target data to be input to the group 3DR prediction model 25 may be demand plan data of each group 3 customer or group 3 representative demand plan data 60 described later.

  The DR prediction unit 16 uses the price elasticity coefficient matrix E held by the price elasticity coefficient management unit 17 and uses the price elasticity coefficient matrix E according to the degree of incentive provision (for example, the power unit price change amount ΔP) (2) above. The power demand change amount ΔD for each time zone is calculated according to the equation.

Further, for the group 2, the DR prediction unit 16 uses the calculated power demand change amount ΔD () for the power demand prediction value obtained by the demand prediction unit 14 (for example, the total value for each time zone A, B, C). The time periods A, B, and C) are added together and output to the integrating unit 21 as the “corrected power demand predicted value” for group 2. For example, in the example of FIG. 5, by summing the [Delta] D _A against _{D A,} power demand prediction value after correction for the time zone _{A (= D A + ΔD A} ) is, it will be obtained.

  Further, for the group 3, the DR prediction unit 16 adds the calculated power demand change amount ΔD (each time zone A, B, C) to the demand plan data (for example, the total value for each time zone A, B, C). Are added together and output to the integrating unit 21 as the “corrected power demand predicted value” for the group 3.

  The integrating unit 21 integrates the above-described three inputs (the power demand predicted values of the groups 1, 2 and 3 (groups 2 and 3 are corrected)) and outputs the total power demand predicted value. In addition, this may be an integration result for each time zone A, B, C of the prediction target day, or may be an integration result of the entire prediction target day.

Hereinafter, the various processing function units shown in FIGS. 1 and 2 will be described in more detail with reference to FIGS.
First, with reference to FIGS. 6-10, the said customer group management part 15 is demonstrated in detail.

  First, as shown in FIG. 6, the customer group management unit 15 includes various processing function units such as a customer group creation unit 51, a group representative demand data creation unit 52, and a group demand prediction model creation unit 53. The group demand prediction model creation unit 53 may be regarded as an example of the demand prediction model creation unit 13. That is, here (especially in FIG. 2), the demand prediction model creation unit 13 may be regarded as a part of the customer group management unit 15.

The consumer group creation unit 51 divides the customers into groups (in this example, the consumer group creation unit 51 classifies them into any one of the groups 1, 2, and 3). Details of the processing example are shown in FIG.
The group representative demand data creation unit 52 generates group representative demand data 61 for each group based on the results database 12 and outputs the group representative demand data creation unit 53 for the groups 1 and 2. For group 3, group 3 representative demand plan data 60 is generated and output to the DR prediction unit 16 based on demand plan data and the like. Details are shown in FIG.

  The group demand prediction model creation unit 53 generates the group demand prediction models 22 and 23 of the groups 1 and 2 by the process substantially similar to the demand prediction model creation unit 13. The group demand prediction models 22 and 23 are generated. Details will be described later with reference to FIG.

First, the customer group creation unit 51 will be described with reference to FIG.
FIG. 7 is a processing flowchart of the customer group creation unit 51.
In the figure, the customer group creation unit 51 executes the processing shown in the figure for each processing target record, with each record in the table 30 of the customer database 11 being sequentially processed. That is, with reference to the demand response execution 35 of a process target record, it is determined whether it is a consumer who performs a demand response (DR) (step S11). If the demand response execution 35 is “execute”, the determination in step S11 is YES, and the process proceeds to step S12. On the other hand, if the demand response execution 35 is “not executed” (step S11, NO), it is determined that the customer is the group 1 and the “1” is stored in the group 37 of the processing target record. (Step S15).

  Moreover, in step S12, it is determined whether it is a consumer who produces a demand plan. If the “target plan creation” 36 of the processing target record is “planned”, it is determined that the customer is a customer who creates a demand plan (step S12, YES). It is determined that it exists, and the above “3” is stored in the group 37 of processing target records (step S13).

  On the other hand, if “Create a demand plan” 36 of the record to be processed is “No plan”, it is determined that the customer is not a consumer who creates a demand plan (step S12, NO), and this consumer is the group 2 described above. It is determined that the value is “2” and stored in the group 37 of records to be processed (step S14).

Next, the group representative demand data creation unit 52 will be described with reference to FIG.
In the example illustrated in FIG. 8, the group representative demand data creation unit 52 includes a performance data extraction unit 71 and a group representative demand data calculation unit 72.

  For the groups 1 and 2, the performance data extraction unit 71 refers to the customer database 11 and the performance database 12, and extracts and acquires the performance data of all customers belonging to the group from the performance database 12 for each group. Memorize temporarily.

  This is because, for example, for group 1, the customer numbers 11 of all records in which the group 37 is “1” in the customer database 11. 31 is acquired. And from the results database 12, the acquired customer No. Acquire performance data of all 31 consumers. That is, the customer No. However, the acquired customer No. Data that is the same as any of 31 (demand actual data; for example, data items of date 41 to power price 47) is acquired. In this manner, the demand record data of all customers belonging to the group 1 is acquired. Similarly, the demand result data of all the consumers belonging to the group 2 are acquired.

  The group representative demand data calculation unit 72 uses the representative value (integrated value / average value) for each data item of the demand actual data based on the demand actual data obtained by the actual data extracting unit 71 for each of the groups 1 and 2. And the like are output as the group representative demand data 61 of the group.

  For example, if group 1 is taken as an example, a representative value (integrated value / average value, etc.) for each data item is calculated based on the acquired actual demand data of all customers belonging to group 1. This is, for example, for each data item (temperature) for each day of the week (or for holidays / weekdays) and for each time zone (for example, in units of one hour or 30 minutes; depending on the data storage format of demand performance data). 44, humidity 45, electric power demand 46, electric power price 47) representative values (integrated value / average value, etc.) are calculated. In the following description, the case of day of the week is taken as an example, but it may be holiday / weekday.

  Basically, regarding the power demand, an integrated value (total value) of the power demand 46 of all the consumers belonging to the group 1 is used as a representative value. For example, an integrated value (total value) of the power demand 46 of all consumers belonging to the group 1 is obtained for each day of the week and for each time zone (for example, in units of one hour), and this is used as a representative value. However, the value is not limited to the integrated value, and an average value (= integrated value / number of customers), a median value, a mode value, or the like may be used as a representative value.

  Regarding the temperature, humidity, and power price, the average values of the temperature 44, humidity 45, and power price 47 of all consumers belonging to group 1 are basically used as representative values. Also, for each day of the week and for each time zone (for example, in units of one hour), the average values of the temperature 44, humidity 45, and power price 47 of all consumers belonging to the group 1 are obtained and used as representative values . However, not only the average value but also the median value or the mode value may be used as the representative value.

Group representative demand data 61 is created from the calculation result of the representative value.
FIG. 9 shows a data configuration example of the group representative demand data 61. The data content in FIG. 9 relates to group 1.

  In the example shown in FIG. 9, the group representative demand data 61 includes a header part and a data part. In the header part, group No. (Group identification ID) is stored, and “1” is stored in this example as described above.

  In the data portion, for example, representative values (temperature 64, humidity 65, power demand 66, power price 67) for each data item for each date and time 68 (each time zone) are stored for each day of the week 62. In addition, from the above, in this description, the illustrated holiday / weekday 63 is excluded.

  In the example shown in the figure, for example, the top record is the representative value (integrated value / average value, etc.) of the temperature, humidity, power demand, and power price at 00:00 (0: 0 to 0:59) on Sunday. 64, humidity 65, power demand 66, and power price 67. As described above, these representative values are calculated based on the demand record data of all customers belonging to the group 1.

  Although not shown in FIG. 8, the actual data extraction unit 71 and the group representative demand data calculation unit 72 also generate the group 3 representative demand plan data 60. First, the result data extraction unit 71 extracts all stored demand plan data. The demand plan data stores data generated and transmitted by the EMS 2 of each customer in the group 3. The demand plan data of each consumer is composed of scheduled power demand values for each time zone of the next day (not limited to the above time zones A, B, C, but may be, for example, one hour unit). is there. The group representative demand data calculation unit 72 calculates, for example, the total value (integrated value) of the scheduled power demand values of all the customers in the group 3 for each time period, so that the group 3 representative demand plan data is calculated. 60 is generated.

  However, instead of the total value (integrated value), for example, an average value, a median value, or a mode value may be obtained (these are called representative values). In any case, the group 3 representative demand plan data 60 is generated based on the calculated representative value.

  The group representative demand data 61 generated as described above is passed to the group demand prediction model creation unit 53, and group demand prediction models 22 and 23 are generated as shown in FIG.

  That is, the group demand prediction model creation unit 53 generates the group 1 demand prediction model 22 based on the group representative demand data 61 related to the group 1, and the group 2 demand prediction model based on the group representative demand data 61 related to the group 2. 23 is generated.

  Here, if a neural network is taken as an example and group 1 is taken as an example, the group demand prediction model creation unit 53 first determines that the power price 67 is a reference price (described later) from the group representative demand data 61 related to group 1. All records that are PAs to be extracted) are extracted. Then, for all the extracted records, data of data items other than the power demand 66 is used as input data of the neural network, and the power demand 66 is used as a teacher signal for learning.

  That is, the power demand according to each environmental factor when the power price 67 is the reference price (in other words, when there is no incentive) is learned. As a result, the demand prediction unit 14 that uses the learned neural network later obtains a predicted value of the power demand according to the environmental factor of the forecast target day (and when the power price 67 is the reference price). .

  As already described, the output of the neural network is a predicted power demand value. The learning is repeatedly performed until, for example, the output of the neural network corresponding to the input data becomes substantially the same as the power demand 66. Thereby, the learned neural network described above is generated as the group 1 demand prediction model 22.

  As described above, basically, the input data of the neural network at the time of learning is desirably data whose power price is, for example, a reference price PA described later. As a result, based on past performance data, the relationship between the electricity demand and the weather, date and time (or season and time zone, etc.), day of the week (or holiday / weekday, etc.) when the electricity price is the reference price PA, A neural network can be trained. However, the present invention is not limited to this example.

  Later, the demand prediction unit 14 inputs the weather, temperature, day of the week, time, and the like of the prediction target day (for example, the next day) to the learned neural network, so that the demand prediction unit 14 responds to the conditions such as the weather (further power A predicted power demand value (when the price is the reference price PA) is output. Further, the amount of change in power demand corresponding to “the amount of change in price from the reference price PA (ΔPj)” is calculated using the above equation (2), thereby correcting the power demand predicted value.

The group 2 demand prediction model 23 is generated for the group 2 in substantially the same manner as in the case of the group 1 described above.
FIG. 11 is a diagram for explaining the demand prediction unit 14 in detail.

  As already described in FIG. 2, the demand forecasting unit 14 uses the group 1 demand forecast model 22 for the group 1 and uses the group 2 demand forecast model 23 for the group 2. Data is generated.

  In FIG. 11, when the demand prediction model is the above-described neural network (learned) as an example, and further, taking the group 1 as an example, the demand prediction unit 14 adds the group 1 demand prediction model 22 to the above. On the other hand, by inputting predetermined data, the power demand prediction value (group 1 representative demand prediction data 81 in the figure) of the group 1 on the prediction target date (the next day or the like) is output.

  The predetermined data is the predicted date basic data shown in the figure. However, the present invention is not limited to this example. For example, the latest power demand record data may be further input to the neural network (learned). For example, for the group 1 demand prediction model 22, the group representative demand data 61 (group 1 representative demand data 61-1) related to the group 1 may be further input.

  The group 1 representative demand data 61-1 includes, for example, various data shown in FIG. On the other hand, the forecast date basic data includes weather forecast information (estimated temperature, predicted humidity, etc.) of a forecast target date (next day, etc.), and may further include information such as days of the week and holidays / weekdays. The forecast date basic data is basically the same kind of data as the data used as input data when learning the neural network.

  Regarding the group 2, in the same manner as in the example of the group 1, the group 2 demand forecasting model 23 is made to input the forecast date basic data shown in the figure, whereby the group 2 representative demand forecast data 82 of the forecast target date is input. Is output. Further, the group representative demand data 61 (group 2 representative demand data 61-2) related to the group 2 is further input to the group 2 demand prediction model 23 in substantially the same manner as the group 1 example. It doesn't matter.

As described with reference to FIG. 2, the group 1 representative demand prediction data 81 is output to the integrating unit 21, and the group 2 representative demand prediction data 82 is output to the DR prediction unit 16.
FIG. 12 is a processing flowchart of the price elasticity coefficient management unit 17.

  This is a process for calculating each elasticity coefficient Eij in advance and storing it as the price elasticity coefficient matrix E. During operation, the price elasticity coefficient management unit 17 basically performs no processing. (Other functional units only refer to the stored price elasticity coefficient matrix E).

  In FIG. 12, the price elasticity coefficient management unit 17 searches the performance database 12, and acquires the power demand 46 (referred to as demand data DA) when the power price 47 is the reference price PA (step S21). The reference price PA corresponds to, for example, Pj (original power price in the time zone j) in the above formula (1), and is determined in advance by the user (power generation company, etc.) of the system, for example. The basic power unit price for each time zone A, B, C.

  Further, by searching the performance database 12, the power price 47 is an arbitrary price PB (however, PB ≠ PA; that is, any price other than PA may be used, but a developer or the like may specify it arbitrarily) In this case, the power demand 46 (referred to as demand data DB) is acquired (step S22).

  The data acquired in step S21 and the data acquired in step S22 have substantially the same weather conditions (temperature, humidity, etc.) and day of the week (in other words, conditions other than the power unit price are substantially the same). It is desirable. That is, for example, predetermined weather conditions (temperature and humidity) are determined in advance, the performance database 12 is searched, and data whose temperature 44 and humidity 45 substantially match the predetermined temperature and humidity is extracted. The demand data DA and DB may be obtained from the extracted data.

In steps S21 and S22, a time zone (for example, the time zones A, B, and C) may be further added as a search condition. This time zone may be specified arbitrarily by the user, for example. For example, when obtaining the above-described elasticity coefficients E _AB , E _BB , E _CB as the elasticity coefficient Eij (that is, according to the change in the power unit price of the time zone B, the power demand in the time zones A, B, C) When obtaining a coefficient indicating how the quantity changes, the data is extracted from the data with the date 41 in the time zone B.

  Next, a rate of change in price (a denominator in the above equation (1)) is calculated from the reference price PA and the price PB (step S23). That is, since Pj = PA and ΔPj = PB−PA in the above equation (1), the rate of change in price is (PB−PA) / PA.

  Further, the demand change rate (numerator in the above equation (1)) is calculated from the demand data DA and DB (step S24). That is, since Dj = DA and ΔDj = DB−DA in the above equation (1), the rate of change in demand is (DB−DA) / DA.

Since the denominator and numerator of the above equation (1) are determined by the processing of steps S23 and S24, the price elasticity coefficient is calculated from the equation (1) (step S25).
More specifically, for example, a case where the above-described elasticity coefficient E _AB is obtained (i = A, j = B) is taken as an example. In this case, first, in step S21, for example, actual data 40 of an arbitrary customer (arbitrary customer No.) is processed, and the electric power price 47 in the time zone B in the actual data 40 is the reference price PA. The power demand 46 (integrated value etc.) in the time zone A on the same day is acquired as the demand data DA.

  Further, in step S22, the customer's record data 40 is the same as in step S21, and the day obtained in step S21 is the same as that of the weather conditions (weather, temperature, humidity, season), etc. Among them, the day in which the power price in the time zone B is a price other than the reference price PA (the price PB) is searched for and obtained, and the power demand 46 (integrated value, etc.) in the time zone A on the same day is obtained from the demand data DB. Get as.

In addition, as a condition of the “day” to be obtained, it may be added that all the power prices in the time zone other than the time zone B are the reference price in the time zone.
Using the PA, DA, PB, and DB acquired by the above processing, the price elasticity coefficient is calculated by the processing of steps S23 to S25.

  If the price elasticity coefficient is calculated by executing the above processing for all the consumers, the sum or average value thereof (= sum of price elasticity coefficient ÷ number of customers) is used as the final price elasticity coefficient. You may make it register into the price elasticity coefficient management part 17. FIG.

  Alternatively, the “all customers” may mean “all customers” for each of the groups 2 and 3. In addition, for example, whether the sum or the average value is used may be determined according to the content of the correction target. That is, if the correction target is the power demand forecast value of group 2, and if this is the group 2 representative demand forecast data 82, the price elasticity coefficient calculation method according to the contents of the representative data at the time of learning. You should decide. That is, when the representative data at the time of learning is the integrated value, the price elasticity coefficient may be obtained by the above sum. When the representative data at the time of learning is the average value, the price elasticity coefficient may be obtained from the average value. In any case, the developer or the like may determine as appropriate.

The process for obtaining the elasticity coefficient may be performed manually by a developer or the like. Further, the elasticity coefficient may be arbitrarily determined by a developer or the like based on experience or intuition.
FIG. 13 is a processing flowchart of the DR prediction unit 16.

  In FIG. 13, the group 2 demand forecast data (such as the group 2 representative demand forecast data 82) is acquired from the demand forecast unit 14 (step S31), and the group 3 representative demand plan data 60 is obtained from the customer group management unit 15. Is acquired (step S32).

Then, the user, each time zone A, B, [Delta] P for each C (price change from the reference price; the ΔP _A, ΔP _B, ΔP _C, etc.) arbitrarily set (step S33). Note that it is not necessary to set all time zones ('0' is automatically set to ΔP related to the time zones not set).

When the setting in step S33 is completed and the user gives an instruction to execute processing, the DR prediction unit 16 acquires the price elasticity coefficient matrix E from the price elasticity coefficient management unit 17 (step S34). Using the price change ΔP set in step S33, the demand change prediction amount ΔD (ΔD _A , ΔD _B , ΔD _C, etc.) for each time zone A, B, C is calculated by the above equation (2). To do. Then, using the calculated ΔD (the above-mentioned ΔD _A , ΔD _B , ΔD _C, etc.), the group 2 representative demand forecast data 82 and the group 3 representative demand plan data 60 acquired in steps S31 and S32 are respectively corrected. Thus, the corrected power demand prediction data for groups 2 and 3 is obtained (step S35).

This, for example, the power demand prediction value of the time period A in group 2 represent forecast data 82, by adding the [Delta] D _A, obtains the power demand prediction data corrected according to the time zone A for group 2. The same applies to the other time zones B and C. The same applies to group 2. Note that ΔD may have a negative value.

  As described above, according to the present method, power demand prediction is performed as the group 1 and group 2 for each of the consumers that are not provided with the customer management system (EMS2). Is corrected according to the incentive. Moreover, a demand plan is correct | amended according to an incentive also about the group 3 of the consumer provided with the consumer management system (EMS2). As a result, it is possible to make a demand prediction including a customer who does not have the power management device (EMS2) installed, and to improve the accuracy of the demand prediction when the demand response is performed.

Here, in one embodiment described above, the (2) for each time slot to be assigned to expressions of electricity unit price variation ΔP _A, ΔP _B, ΔP _C, for example but the user has shall be set arbitrarily determined However, the present invention is not limited to this example. In particular, the purpose of demand response implementation is basically to make the overall power demand for each time zone substantially the same as any power demand desired by the user. For example, of course, there is an upper limit (maximum output) in the power generation capacity of the power generation facility of the electric power company. For example, during the daytime in summer (the above time zone B etc.), the power demand supplies (power generation amount). There will be a greater risk. For this reason, for example, as described with reference to FIG. 5, the power demand in the time zone B is reduced by increasing (increasing the price) the power unit price in the time zone B.

  However, it is not known how much the power unit price in time zone B will be increased to obtain the desired power demand, or how much the power unit price in time zones A and C will be further reduced to achieve the desired power demand. For this reason, in the above-described embodiment, the power unit price change amount for each time zone is set again and again based on the idea of trial and error.

  On the other hand, in another embodiment described below, the amount of change in the power unit price so that the total power demand for each time slot is substantially the same as the arbitrary power demand desired by the user is semi-automatically. It will be obtained. That is, an appropriate amount of change in power unit price can be obtained without user effort.

  In the other embodiments described below, the power price (change from the base price or the amount of change) will be described as an example of incentive provision, and in accordance with this, the degree of incentive The elasticity coefficient matrix obtained by converting the degree of change in power demand as an example is the price elasticity coefficient matrix E used in the above equation (2), for example, but of course it is not limited to these examples. As the incentive provision, for example, the point provision described above may be used. In that case, the performance data corresponding to the point provision is stored and the elasticity coefficient corresponding to the point provision is used.

In addition, the power price optimization unit 18 described below can be considered as an example of an incentive calculation function unit (not shown) having the following functions.
The incentive calculation function unit (not shown), for example, obtains the predicted power demand value of group 2 or / and the demand plan value of group 3 obtained based on factors other than the incentive related to the actual data and the prediction target date (such as weather forecast). get. Then, based on the acquired data and the set desired power demand value and the elasticity coefficient, an incentive is calculated so that the power demand value on the prediction target day is substantially equal to the desired power demand value.

In addition, the incentive calculation function unit (not shown), for example, the demand forecast value obtained based on the forecasted power demand value or / and the demand plan value on the forecast target day (for example, the entire demand forecast including group 1). Value) and the set desired power demand value is obtained as the power demand change amount. Then, based on the power demand change amount and the inverse matrix E ⁻¹ of the elasticity coefficient matrix E, an incentive change amount that is a change amount from an incentive standard (for example, a reference price) is obtained, so that An incentive is calculated so that the power demand is substantially equal to the desired power demand value.

Further, for example, the set desired power demand value is a desired value related to the overall power demand including the second group and the third group (and also including the first group).
In the case of this example, the incentive calculation function unit is, for example, an overall demand forecast value including the power demand forecast value of the second group, the demand plan value of the third group (and also the power demand forecast value of the first group). And the difference with the set desired electric power demand value is calculated | required as a whole electric power demand change amount. Then, an incentive change amount that is a change amount from the incentive reference is obtained based on the power demand change amount and the inverse matrix E- ¹ of the matrix of elasticity coefficients. This makes it possible to calculate an incentive that causes the overall power demand on the prediction target date to be approximately equal to the desired power demand value.

  Alternatively, for example, the set desired power demand value is a desired value related to the power demand for each group of the second group and the third group. In this example, it is assumed that the matrix of the elasticity coefficient is set for each of the second group and the third group.

In the case of this example, the incentive calculation function unit obtains, for example, the difference between the predicted power demand value of the second group and the desired power demand value related to the second group as the power demand change amount related to the second group. . Then, based on the power demand change amount and the inverse matrix E- ¹ of the elasticity coefficient matrix relating to the second group, an incentive change amount that is a change amount from the incentive reference is obtained. Accordingly, it is possible to calculate an incentive that causes the power demand related to the second group on the prediction target date to be substantially equal to the desired power demand value related to the second group.

  Although explanation is omitted, the power related to the third group for the third target group is also substantially the same as the second group (however, the demand plan value is used instead of the power demand forecast value). It is possible to calculate an incentive so that the demand is approximately equal to the desired power demand value related to the third group.

In another embodiment, the demand prediction apparatus 10 further includes a power price optimization unit 18 shown in FIG.
The power price optimizing unit 18 also includes a setting function unit (not shown) that allows the user to set a desired power demand value on an arbitrary prediction target date, for example. The setting function unit includes a function of allowing the user to input an arbitrary power demand value on the screen by, for example, displaying a power demand value input screen (not shown) on the display. The power price optimization unit 18 calculates and outputs a power price corresponding to the desired power demand value, but this does not necessarily guarantee that the power price is “optimum”.

  The power price optimizing unit 18 uses the setting function unit (not shown) to obtain a desired power demand value desired by the user regarding the prediction target date (for example, a power demand curve indicated by a solid line or a dotted line in FIG. 5B). Is entered. This may be an overall power demand value or a power demand value for each of the groups 2 and 3. Or not only the power demand curve shown by a solid line or a dotted line in FIG. 5B, but also a desired power demand value for each time zone A, B, C may be input, for example.

  However, the power demand curve shown by a solid line or a dotted line in FIG. 5B is, for example, a power demand value in units of one hour or 30 minutes (a power demand value for each time zone divided into 24 or 48 divisions per day). You may consider that. Therefore, it can be said that the power demand curve is synonymous with the power demand value for each of the time zones A, B, and C obtained by dividing the day into three, only the number of sections. Conversely, the power demand value for each of the time zones A, B, and C can also be regarded as a kind of the power demand curve.

  The power price optimizing unit 18 further receives a power demand forecast value and the like on the prediction target day. This is for inputting the power demand prediction value calculated by the demand prediction unit 14 related to the group 2, the demand plan value of the group 3, and the like.

  And the electric power price optimization part 18 calculates | requires the difference (electric power demand variation | change_quantity) between the said user desired value regarding the electric power demand of prediction object day, the said estimated value, etc., and this demand differential value (electric power demand variation | change_quantity) and the said Using the elasticity coefficient matrix E, the power unit price change amount for each time zone A, B, C according to the user desired value is obtained. This is obtained by the formula (3), the formula (3) ', the formula (4), etc. described later.

That is, first, the above equation (2) can be simplified and expressed as the following equation (2) ′.
ΔD = E · ΔP (2) ′ Formula This is a calculation formula for obtaining the power demand change amount ΔD according to the elasticity coefficient matrix E set in advance and the price change amount ΔP. Then, the power price optimizing unit 18 obtains the price change amount ΔP according to the elasticity coefficient matrix E stored in advance and the power demand change amount ΔD that is arbitrarily set by using this calculation formula. The formula can be obtained.

ΔP = E ⁻¹ · ΔD (3) In other words, the equation for obtaining the price change amount ΔP by multiplying the matrix of the power demand change amount ΔD by the inverse matrix of the elasticity coefficient matrix E is the above (3 ). The power unit price is an example of an incentive, and the power price change amount ΔP (change amount from the reference price of the power unit price) is an example of “change amount from the incentive reference”.

Relative to the (3), for example, when the power demand change amount corresponding to the user desired setpoint was [Delta] D _0, so that the price variation [Delta] P ₀ corresponding to the [Delta] D ₀ is determined (following (3) 'formula).

ΔP ₀ = E ⁻¹ · ΔD ₀ (3) ′ Equation This price change amount ΔP ₀ is, for example, for each time zone (every time zone A, B, C, or in units of one hour or 30 minutes). The price change amount is not limited to this example.

In the description of the above example, the user sets a desired power demand value, and the power price optimizing unit 18 calculates the difference between the set value and the predicted value as described above. The power demand change amount ΔD ₀ is calculated. As described above, the form of data input and set by the user and the form of the processing result may be various, and in this description, an example is shown and is not limited to this example. .

For example, the desired power demand value set by the user may be a desired value related to the power demand value of the entire group 1 to group 3, for example. In this example, for example, the power demand change amount ΔD ₀ is the total power demand. The amount of change.

Alternatively, the desired power demand value set by the user may be, for example, a desired value regarding the power demand value for each group of group 2 and group 3. In this example, for example, the power demand change amount ΔD ₀ is It becomes the difference between the predicted value of the power demand for each group of group 2 and group 3 and the desired value. Further, in this example, the elasticity coefficient matrix E is, for example, the elasticity coefficient matrix E for each of the groups 2 and 3. In the case of this example, the price change amount ΔP ₀ corresponding to the ΔD ₀ is also calculated for each of the groups 2 and 3. In the case of this example, an average value of two types of calculated price change amounts ΔP ₀ may be output as the final price change amount ΔP ₀ .

Then, the power price optimization unit 18 calculates an appropriate power unit price (= PA + ΔP ₀ ) by adding (adding) the price change amount ΔP ₀ to the original power unit price (reference price PA). can do. The power unit price is also, for example, the power price for each time zone (every time zone A, B, C, or 1 hour unit or 30 minute unit), but is not limited to this example.

  The electric power unit price calculated by the electric power price optimizing unit 18 is notified to each consumer who “has a demand response”. By notifying such a power unit price, it can be expected that the power demand on the prediction target date (demand response execution date) is close to the power demand desired by the user. Further, the power unit price calculated by the power price optimization unit 18 may be passed to the DR prediction unit 16. The DR prediction unit 16 performs the prediction process using the power unit price calculated by the power price optimization unit 18 instead of the power unit price arbitrarily set by the user in the embodiment. It may be.

  If the above expression (3) is expressed in the same manner as the above expression (2) without simplification, the following expression (4) is obtained.

The power price optimizing unit 18 calculates the power demand change amount for each time zone A, B, and C according to the user desired setting value related to the overall power demand (ΔD _A , ΔD _B , ΔD _C ). and when, for example using the above equation (4), the desired setting value each time period a corresponding to, B, electricity unit price variation [Delta] P a for each _C, [Delta] P _B, to calculate the [Delta] P _C Become.

Alternatively, the power price optimizing unit 18 calculates the amount of change in power demand for each time zone A, B, and C of the prediction target day for each of the groups 2 and 3 according to the user desired setting value (ΔD _A , ΔD _B , ΔD _C ), for example, the above equation (4) is used for each of the groups 2 and 3, and the power unit price change amount ΔP _{A for} each time zone A, B, C according to the desired set value. will be calculated [Delta] P _B, the [Delta] P _C.

Electricity price optimization section 18 further obtains the electricity unit price variation [Delta] P _A, [Delta] P _B, the [Delta] P _C, each time zone A, B, by summing (adding) the base price for each C, prediction target day It is possible to calculate an appropriate power unit price for each of the time zones A, B, and C. Then, the calculated power unit price is notified to each consumer who “has demand response”. By notifying such a power unit price, it can be expected that the power demand on the prediction target date (demand response execution date) is close to the power demand desired by the user.

In the above example, the user inputs a desired power demand value. However, the present invention is not limited to this example. For example, the user inputs a power demand value change amount (ΔD _A , ΔD _B , ΔD _C ). (Of course, in this case, it is not necessary to calculate the amount of change).

As described above, as an example, the user inputs a desired power demand value for each of groups 2 and 3, but not limited to this example, the desired overall power demand value (all the values including group 1 are included). The user may input a desired value for the total power demand value of the group. In the case of this example, the total value (total demand forecast value) of the demand forecast values of the three groups related to the forecast target date (the demand forecast values of groups 1 and 2 and the demand plan value of group 3 by the demand forecasting unit 14). For example, the difference between the demand forecast overall value and the “desired overall power demand value” input by the user is calculated as the power demand change amount for each time zone A, B, C on the forecast target day ( ΔD _A , ΔD _B , ΔD _C ).

Alternatively, in the example in which the “desired overall power demand value” is input by the user, the power price optimizing unit 18 determines that the “desired overall power demand value” It may be configured to calculate “power demand value”. In this case, the power price optimizing unit 18 obtains the amount of change in power demand (ΔD _A , ΔD _B , ΔD _C ) for each time zone A, B, C on the prediction target day for each of the groups 2, 3. , it will be calculated the electricity unit price variation [Delta] P _a, [Delta] P _B, the [Delta] P _C on the basis of this.

FIG. 14 is a processing flowchart of the power price optimization unit 18.
In FIG. 14, the power price optimizing unit 18 acquires the power demand prediction result of the prediction target date related to the group 2 obtained by the demand prediction unit 14 (step S41). Furthermore, the demand plan data of the said group 3 are acquired from the consumer group management part 15 (step S42). For example, the group 3 representative demand plan data 60 is acquired, but the present invention is not limited to this example. The demand plan data for each consumer belonging to group 3 may be acquired.

  In the above description, the data (for example, representative data) used for the processing may be various such as an integrated value, an average value, etc., but here, an integrated value (total value) is used as an example. To do. Thus, the power demand prediction result of the prediction target date acquired in step S41 means “the power demand prediction total value of all consumers belonging to group 2” (for example, one hour unit) on the prediction target date. . Similarly, the group 3 representative demand plan data 60 acquired in step S42 means “the total power demand plan value of all consumers belonging to group 3” (for example, one hour unit) on the prediction target date.

  Further, although not shown in the figure, the power demand prediction result on the prediction target date related to the group 1 obtained by the demand prediction unit 14 may be acquired. That is, in this example, “the total power demand prediction value of all customers belonging to group 1” (for example, one hour unit) on the prediction target date may be acquired. Further, by adding the obtained power demand prediction results (such as one hour unit) for each of the groups 1, 2, 3 for each time zone (such as one hour unit), It is also possible to calculate the power demand prediction result for each time zone (such as the whole area) in (for example, the whole area) and display it in a graph or the like.

  Here, as an example, it is assumed that a curve indicated by a solid line in FIG. 15 is displayed as the overall power demand prediction result (for example, the entire area) (for example, one hour unit). Further, an upper limit value as shown in the figure is displayed on this display screen. This upper limit value is, for example, the maximum power generation amount (for example, the maximum per hour) of this electric power company (meaning supply limitation).

  A user (a person in charge of an electric power company or the like) determines whether or not the prediction target date is set as a demand response execution date by referring to such a display, for example. For example, in the above tentative example, the curve (power demand prediction curve) indicated by the solid line in FIG. 15 exceeds the upper limit value in the time zone B, so it is determined that it should be the demand response implementation date. And when it is set as a demand response implementation date, the power demand forecast value (it shall be called "desired demand value Dd according to desirable time zone") which he considers desirable is input (step S43).

  An example of the input “desired demand value Dd by time zone” is a curve indicated by a dotted line in FIG. 15 (referred to as a desired demand value curve). This is drawn directly by the user, for example, by mouse operation or finger operation input on the touch panel. Note that the desired demand value curve may be regarded as an aggregate of desired power demand values for each time zone of a fine division such as 1 hour unit or 30 minute unit. If the above equation (4) is used, the power price optimizing unit 18 calculates, for example, the total for each time zone A, B, C based on the desired demand value curve indicated by the dotted line in FIG. A power demand value may be calculated.

Note that the present invention is not limited to the above example. For example, the user inputs the total power demand value for each of the time zones A, B, and C (for example, “ΔD _A + D _A ” in the case of the time zone A). It may be.

Note that the power demand prediction result for each group or the entire group is not limited to a data example such as one hour unit as shown by a solid line in FIG. 15, but is a total power demand prediction value for each time zone A, B, C ( For example, in the case of time zone A, it may be D _A ) shown in the figure.

In the following, for the sake of simplicity, the total desired demand value for each of the time zones A, B, and C (for example, in the case of the time zone A) is used as the “desired demand value by time zone Dd” and the power demand prediction result. In the illustrated example, “ΔD _A + D _A ”) and a demand forecast value (for example, in the case of time zone A, D _{A in} the figure) are input / calculated.

The power price optimizing unit 18, after completing the processing of the above steps S <b> 41 to S <b> 43, based on the “desired demand value Dd by time zone” and the data acquired in steps S <b> 41 and S <b> 42, The difference data ΔDd between the user desired value and the predicted value for each of the time zones A, B, and C is obtained for each of the groups 2 and 3. That is, the power demand change amount (here, ΔD _A2 , ΔD _B2 , ΔD _C2 ) related to the group ₂ is obtained, and the power demand change amount related to the group 3 (here, ΔD _A3 , ΔD _B3 , ΔD _C3 ) is obtained. Obtained (step S44).

If the example shown in FIG. 5B is the predicted value (solid line) and the desired value (dotted line) for the entire groups 1 to 3, the total change in power demand is, for example, for time zone A. the ΔD _a. This ΔD _A is composed of the power demand change amount of the group 2 and the power demand change amount of the group 3 regarding the time zone A (the group 1 does not change the demand due to the incentive). From this, as a simple example, it may be a [Delta] D _A 2 equal parts. That is, regarding time zone A, the power demand change amount of group 2 may be “ΔD _A / 2”, and the power demand change amount of group 3 may be “ΔD _A / 2”. Of course, it is not limited to this example.

For example, as the “desired demand value by time zone” Dd, “ΔD _A + D _A ” shown in the figure is input for the time zone _A , and “D _B −ΔD _B ” (not shown) is input for the time zone B. For the time zone C, “ΔD _C + D _C ” (not shown) is input. Based on this Dd, “desired demand value by time zone” for each of group 2 and group 3 is obtained. For example, this is obtained according to the ratio of the number of customers of each of the group 2 and the group 3 to the total number of customers including the group 1, but the present invention is not limited to this example.

In this example, if the total number of customers is 100, the number of customers in group 2 is 20, and the number of customers in group 3 is 40, the “desired demand value by time zone” of group 2 When (Dd _A2 , Dd _B2 , Dd _C2 ) and “desired demand value by time zone” of group 3 are (Dd _A3 , Dd _B3 , Dd _C3 ), they are calculated as follows.

Dd _A2 = ((ΔD _A + D _A ) −Group 1 demand forecast value) × (20) / (20 + 40)
Dd _B2 = ((D _B −ΔD _B ) −Group 1 demand forecast value) × (20) / (20 + 40)
Dd _C2 = ((ΔD _C + D _C ) −demand demand value of group 1) × (20) / (20 + 40)
Dd _A3 = ((ΔD _A + D _A −Group 1 demand forecast value)) × (40) / (20 + 40)
_{Dd B3 = ((D B -ΔD} B - Group 1 forecast value)) × (40) / ( 20 + 40)
Dd _C3 = ((ΔD _C + D _C ) −demand demand value of group 1) × (40) / (20 + 40)
Since the group 1 does not change demand due to incentives, the desired values of the group 2 and the group 3 are calculated from the predicted values of the entire groups 1 to 3.

The “desired demand values by time zone” (Dd _A2 , Dd _B2 , Dd _C2 ) of the group 2 and the “desired demand values by time zone” (Dd _A3 , Dd _B3 , Dd _C3 ) of the group 3 are, for example, steps. The user may arbitrarily determine and input at S43. Of course, in this example, the calculation process is not necessary.

And the demand forecast value / demand plan value for each time zone for each of the groups 2 and 3 obtained in the above steps S41 and S42 (or obtained on the basis of the obtained data) is expressed as (D _A2 , If D _B2 , D _C2 ) and Group 3 are (D _A3 , D _B3 , D _C3 ), the power demand change amount can be calculated as follows.

(ΔD _A2 , ΔD _B2 , ΔD _C2 ) = (Dd _A2 −D _A2 , Dd _B2 −D _B2 , Dd _C2 −D _C2 )
(ΔD _A3 , ΔD _B3 , ΔD _C3 ) = (Dd _A3 −D _A3 , Dd _B3 −D _B3 , Dd _C3 −D _C3 )
Next, the elasticity coefficient matrix E is acquired from the price elasticity coefficient management unit 17 (step S45). In the case where the elasticity coefficient matrix E is set for each of the groups 2 and 3, the elasticity coefficient matrix E corresponding to each of the groups 2 and 3 is acquired.

Finally, for each of the groups 2 and 3, using the amount of change in power demand obtained in step S44 and the elasticity coefficient matrix E obtained in step S45, the time zones A, B, C another power unit price variation ΔP _a, ΔP _B, obtains the [Delta] P _C (step S46).

As described above, in some cases, the elasticity coefficient matrix E corresponding to each group 2 or 3 is used.
Further still, (dividing by 2 by summing each time period A, B, for each C) electricity unit price variation [Delta] P _A of each group 2 obtained in step S46, [Delta] P _B, the average value of [Delta] P _C And this may be the final processing result of step S46.

  In addition, as described above, the power unit price for each time zone A, B, and C on the prediction target date (demand response execution date) may be obtained from the power unit price variation obtained in step S46. Furthermore, you may make it notify the calculated | required electric power unit price to each consumer of the groups 2 and 3. FIG. Thereby, it can be expected that the total power demand on the prediction target date is substantially the same as the “desired demand value by time zone Dd” set in step S43. It can be expected that the power demand does not exceed the upper limit value at least particularly with respect to the time zone B.

Further still, for example, electricity unit price variation [Delta] P _A obtained in step S46, [Delta] P _B, the [Delta] P _C, passing the DR prediction unit 16, by executing the processing of the DR prediction unit 16, or further integrated By displaying the integration result by the unit 21, it is checked whether the overall demand prediction (after correction) is close to the user's desire (or whether the power demand exceeds the above upper limit even for a moment) You may be able to do it.

FIG. 16 is a diagram schematically illustrating a processing procedure in the demand prediction apparatus according to another embodiment. In FIG. 16, the description of the same parts as in FIG. 2 is omitted.
As shown in the figure, a desired demand curve, demand prediction results of groups 1 and 2, group 3 demand plan data, elasticity coefficient matrix E, and the like are input to the power price optimization unit 18 to generate and output an optimal power price value. Is done. The desired demand curve is input by the user in step S43, and the optimum power price value is calculated in step S46.

  As described above, according to another embodiment, at the time of demand response execution, it is predicted how much the demand changes when the degree of incentive provision is changed, and whether the predicted power demand is within the upper limit of the supply capacity. How to give an incentive that meets the desired power demand is automatically calculated. It is useful because it is possible to give an optimal incentive to an electric power company so as to achieve a desired electric power demand.

1 Smart Meter 2 Customer Management System (EMS)
3 Network 10 Demand Prediction Device 11 Customer Database 12 Results Database 13 Demand Prediction Model Creation Unit 14 Demand Prediction Unit 15 Customer Group Management Unit 16 Demand Response (DR) Prediction Unit 17 Price Elasticity Coefficient Management Unit 18 Electricity Price Optimization Unit 21 Integration unit 22 Group 1 demand forecast model 23 Group 2 demand forecast model 30 Table 31
32 Address 33 Business type 34 Contract power 35 Demand response implementation 36 Demand plan preparation 37 Group 41 Date 42 Day 43 Holiday / Weekday 44 Air temperature (° C)
45 Humidity (%)
46 Electricity demand (kW)
47 Electricity price (unit price) (¥ / kWh)
51 Customer Group Creation Unit 52 Group Representative Demand Data Creation Unit 53 Group Demand Forecast Model Creation Unit 60 Group 3 Representative Demand Plan Data 61 Group Representative Demand Data 62 Day of Week 63 Holiday / Weekday 64 Temperature 65 Humidity 66 Electricity Demand 67 Electricity Price 68 Date 71 Actual data extractor 72 Group representative demand data calculator 81 Group 1 representative demand forecast data 82 Group 2 representative demand forecast data

Claims (14)

An apparatus for predicting power demand related to an area composed of a plurality of consumers including one or more consumers who perform demand response according to an arbitrary incentive,
Actual data storage means for storing actual data related to the past power demand of each consumer;
An elastic coefficient management means for holding an elastic coefficient obtained by converting the degree of change in power demand according to the degree of incentive;
Setting means for setting a desired power demand value on an arbitrary forecast target day;
Obtaining the demand demand value or / and the demand plan value of each consumer obtained based on factors other than the incentive related to the actual data and the forecast target date, and obtaining the obtained data and the set desired power demand An incentive calculating means for calculating the incentive to make the power demand on the prediction target day substantially equal to the desired power demand value based on the value and the elasticity coefficient;
A power demand prediction apparatus comprising: The incentive calculating means includes
A difference between the demand forecast value obtained based on the forecast power demand value or / and the demand plan value on the forecast target date and the set desired power demand value is obtained as a power demand change amount, and the power Based on the demand change amount and the inverse matrix of the coefficient of elasticity matrix, an incentive change amount that is a change amount from the reference of the incentive is obtained, so that the power demand on the prediction target day becomes the desired power demand value. The power demand prediction apparatus according to claim 1, wherein the incentive is set so as to be substantially equal. The elasticity coefficient matrix is a matrix E of each elasticity coefficient corresponding to a combination of a plurality of time zones,
The incentive calculating means includes
Using the power demand change amount ΔD for each time zone on the prediction target date and the inverse matrix E ⁻¹ of the elasticity coefficient matrix E, the incentive change for each time zone according to the following equation (3): Calculating the amount ΔP,
The power demand prediction apparatus according to claim 2, wherein ΔP = E ⁻¹ · ΔD (3). In the case where the plurality of time zones are time zones A, B, and C obtained by dividing a day into three,
The incentive calculating means includes
The power demand change amount ΔD for each of the time zones A, B, C is (ΔD _A , ΔD _B , ΔD _C ),
When the incentive change amount ΔP for each of the time zones A, B, and C is (ΔP _A , ΔP _B , ΔP _C ), the incentive change amount ΔP for each time zone is calculated by the following equation (4). calculate,
The power demand prediction apparatus according to claim 3. Customer group management means for grouping the plurality of consumers according to the presence or absence of demand response and the presence or absence of demand plan;
A demand prediction model created in advance based on the actual data of the consumers belonging to the second group for the second group that is the group without the demand plan with the demand response among the groups, and the prediction target Demand forecasting means for obtaining the power demand forecast value of the second group on the forecast date based on factors other than the incentive on the day,
With respect to the third group, which is the group with the demand response and the demand plan among the groups, a demand plan acquisition means for acquiring the demand plan value of the forecast target date of a consumer of the third group;
The power demand prediction apparatus according to claim 1, further comprising: The incentive calculating means includes
Based on the set desired power demand value, the second group power demand forecast value or / and the third group demand plan value, and the elasticity coefficient, The power demand prediction apparatus according to claim 5, wherein the incentive is calculated so as to be substantially equal to a desired power demand value. The desired power demand value set by the setting means is a desired value related to the overall power demand including the second group and the third group,
The incentive calculating means includes
The difference between the total demand forecast value including the second group power demand forecast value and the third group demand plan value and the set desired power demand value is obtained as the total power demand change amount. , Based on the power demand change amount and the inverse matrix of the elasticity coefficient matrix, by obtaining an incentive change amount that is a change amount from the incentive reference, the total power demand on the prediction target day is The power demand prediction apparatus according to claim 5 or 6, wherein the incentive is calculated so as to be substantially equal to a desired power demand value. The desired power demand value set by the setting means is a desired value related to the power demand for each group of the second group and the third group,
The elasticity coefficient matrix is set for each of the second group and the third group,
The incentive calculating means includes
A difference between the predicted power demand value of the second group and a desired power demand value related to the second group is obtained as a power demand change amount related to the second group, and the power demand change amount and the second group are determined. Based on the inverse matrix of the elasticity coefficient matrix, an incentive change amount that is a change amount from the reference of the incentive is obtained, so that the power demand related to the second group on the prediction target date is related to the second group The power demand prediction apparatus according to claim 5 or 6, wherein the incentive is calculated so as to be substantially equal to a desired power demand value. The desired power demand value set by the setting means is a desired value related to the power demand for each group of the second group and the third group,
The elasticity coefficient matrix is set for each of the second group and the third group,
The incentive calculating means includes
The difference between the demand plan value of the third group and the desired power demand value related to the third group is obtained as a power demand change amount related to the third group, and the power demand change amount and the third group related Based on the inverse matrix of the elasticity coefficient matrix, by obtaining an incentive change amount that is a change amount from the standard of the incentive, the power demand related to the third group on the prediction target date is the desired value related to the third group. The power demand prediction apparatus according to claim 5, wherein the incentive is calculated so that the power demand value is substantially equal to the power demand value.   For each group without the demand plan, it further comprises a demand prediction model creating means for generating the demand prediction model corresponding to the group based on the actual data of the consumers belonging to the group in advance. The power demand prediction apparatus according to claim 5.   With respect to each of the second group and the third group, the power demand prediction result or the demand plan is corrected according to the incentive on the prediction date calculated by the incentive calculating means, respectively. 8. The power demand prediction apparatus according to claim 7, further comprising demand prediction correction means for calculating a power demand prediction value. It is further grouped into a first group which is a group without the demand response by the customer group management means,
Accumulating means for accumulating the power demand prediction results related to the first group and the power demand prediction values at the time of granting the incentives of the second group and the third group, respectively, and calculating an overall demand prediction value;
The power demand prediction apparatus according to claim 11, further comprising: The incentive is a power unit price,
The elasticity coefficient is a price elasticity coefficient obtained by converting the degree of change in power demand according to the degree of change from the reference price of the power unit price,
The price elasticity coefficient is a price elasticity coefficient Eij related to arbitrary time zones i and j when the day is divided into a plurality of time zones, and the price elasticity coefficient Eij is expressed by the following equation (1). Is,
{ΔDi: Demand change in time zone i
Di: Original demand in time zone i ΔPj: Change in electricity price in time zone j
Pj: Original power price (base price) in time zone j}
The power demand prediction apparatus according to claim 4. A computer of an apparatus for predicting power demand for an area consisting of a plurality of consumers, including one or more consumers performing a demand response in response to any incentive;
Actual data storage means for storing actual data related to the past power demand of each consumer;
An elastic coefficient management means for holding an elastic coefficient obtained by converting the degree of change in power demand according to the degree of incentive;
Setting means for setting a desired power demand value on an arbitrary forecast target day;
Obtaining the demand demand value or / and the demand plan value of each consumer obtained based on factors other than the incentive related to the actual data and the forecast target date, and obtaining the obtained data and the set desired power demand An incentive calculating means for calculating the incentive to make the power demand on the prediction target day substantially equal to the desired power demand value based on the value and the elasticity coefficient;
Program to function as.