JP6725336B2 - Demand response planning device, method and program - Google Patents

Description

The present invention relates to a demand response planning device, method and program.

A demand response (power reduction) has been proposed to promote power consumption suppression and suppress power consumption during peak use to stabilize power consumption.
Specifically, an aggregate business operator (aggregator) collects Negative Watt from customers by methods such as setting charges and paying consideration to customers who have refrained from using during peak hours. By doing so, we are promoting power reduction. Here, the negative power means a decrease in the power demand (negative power) in the sense of being compared with normal power demand (positive power).

JP, 2013-161144, A

By the way, when an aggregate business makes a demand response request to each customer, it is common to select a customer for which a demand response is requested according to a predetermined customer priority that is associated with the customer in advance. Met.
However, for example, when there is a penalty condition at the time of failure (= when the demand response request cannot be met), even if the customer for which the demand response request is made is selected according to the customer priority, the aggregate business operator There is a risk that the penalty that will be incurred will increase and an inefficient demand response plan will result.

In order to achieve the above object, an embodiment of the present invention provides a demand response planning device, method and program capable of establishing a demand response plan satisfying a demand response request amount while complying with customer priority. It is an object.

The demand response planning apparatus of the embodiment plans a demand response for requesting a customer selected from a plurality of customers to reduce the demand power based on a request from a power supplier.

When requesting a demand response, the priority information storage unit stores in advance information about the customer priority, in which the customer priority indicating the priority of the customer who requests the demand response is stored in advance.

The penalty calculation unit determines the demand power reduction target and the demand power for each combination of consumers based on the contract information with the power supplier and the contract information with the consumer for each combination of the demanded customers for the demand reduction request. The expected penalty value is based on the sum of the achievement penalty expected value that is expected to be imposed based on the difference from the reduction prediction amount and the priority penalty expected value that is imposed when the customer priority is not met. calculate.

The plan creation unit creates a demand response plan for requesting the customer to reduce the power demand, which corresponds to the customer combination that minimizes the penalty expected value.

FIG. 1 is a schematic block diagram of a power supply and demand system according to an embodiment. FIG. 2 is a functional configuration block diagram of the demand response planning device that constitutes the aggregate provider system. FIG. 3 is an explanatory diagram of an example of the power reduction request data. FIG. 4 is an explanatory diagram of data examples of the customer history database. FIG. 5 is an explanatory diagram of an example of data in the contract information database. FIG. 6 is an explanatory diagram of an example of the demand response request data. FIG. 7 is an operation flowchart of the embodiment. FIG. 8 is an explanatory diagram of an example of setting the customer priority. FIG. 9 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty when the customers of the mand response request candidates are the customers A, B, and C. FIG. 10 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty (S) when the customers of the mand response request candidates are the customers A, C, and D. FIG. 11 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty (S) in the case where the customers of the mand response request candidates are the customer A, the customer C, and the customer E0. FIG. 12 is a processing flowchart of the achievement penalty calculation processing. FIG. 13 is an explanatory diagram of an example of the achievement penalty calculation in the case of requesting demand response to the customers A, C, and D to reduce the power. FIG. 14 is a diagram illustrating an example of the transition of the sum of the priority penalty Priority Penalty(s) and the achievement penalty Penalty(S) when the loop parameter n=1 is sequentially performed. FIG. 15 is a schematic configuration block diagram when the demand response planning device is configured as a computer.

[1] First Embodiment Next, an embodiment will be described in detail with reference to the drawings.
FIG. 1 is a schematic block diagram of a power supply and demand system according to an embodiment.
The power supply and demand system 100 is roughly classified into a power generation company system 111 that is operated by a power generation company that supplies generated power as a power supply company to generate power, and a grid company that supplies commercial power as a power supply company. Is operated and a commercial power system 112 supplies commercial power, a customer system 114 is operated by a plurality of consumers, and an agri that executes a demand response request from the power supplier to the consumers 114. The system includes an aggregate company system 115 operated by a gate company and an electric power exchange system 116 operated by an electric power exchange operating the electric power exchange market.

FIG. 2 is a functional configuration block diagram of the demand response planning device that constitutes the aggregate provider system.

The demand response planning apparatus 10 is roughly divided into a demand response (DR) plan optimizing unit 11 for optimizing a demand response plan, a power generation operator system 111, a grid operator system 112, or each customer system and demand response. A communication interface (I/F) unit 12 for performing a communication interface operation for communicating with the plan optimization unit, an operation input unit 13 for an operator to input various operations, and a display unit 14 for displaying various information. An input/output interface (I/F) unit 15 that performs an input/output interface operation with the operation input unit 13 or the display unit 14; and a history information creation unit 16 that creates information regarding the demand response history of each consumer. Priority of calculating a customer priority, which is the priority of each customer, by referring to the customer history database (DB) 17 that stores the demand response history created by the history information creation unit 16 and the customer history database 17 Degree calculation unit 18, a customer priority information database (DB) 19 that stores the customer priority calculated by the priority calculation unit 18, and a reduction target information database (DB) that stores information about the power consumption reduction target. 20, a contract information database (DB) 21 that stores contract information related to demand response exchanged between the aggregate business operator and the customer, and a plan that stores various plan creation parameters for creating a demand response plan A creation parameter database (DB) 22 and a demand response request consumer union database (DB) 23 that stores combinations of demand response request target consumers are provided.

In the above configuration, the demand response plan optimization unit 11 refers to the reduction target information database 19 to calculate the target achievement penalty, and the target achievement degree penalty calculation unit 31 to refer to the customer priority information database 19 to give priority. A priority penalty calculation unit 32 that calculates a degree penalty and a customer combination creation unit 33 that creates a customer combination for which a demand response is requested are provided.

Next, power reduction request data from an energy supplier such as a power generator or a grid operator will be described.
FIG. 3 is an explanatory diagram of an example of the power reduction request data.
The power reduction request data 41 includes date data 42 in which the date of the power reduction request target is stored, one or more implementation time data 43 in which the implementation time zone of the power reduction request is stored, and each implementation time data 43. And one or more corresponding reduction target data 44.

More specifically, in the case of the example of FIG. 3, the date data 42=“August 10”, the first implementation time data 43-1=“14:00-14:30”, and the second Execution time data 43-2=“14:30-15:00”, and reduction target data 44-1=“2000.0 kWh” for the time zone corresponding to the first execution time data 43-1. The reduction target data 44-2 in the time zone corresponding to the second implementation time data 43-2=“2000.0 kWh”.

FIG. 4 is an explanatory diagram of data examples of the customer history database.
In FIG. 4, for ease of understanding, a case where there are four customers (customers A to D) as customers will be described as an example.

The customer history database 17 includes date data 51 in which a demand response execution date is stored, execution time data 52 in which a power reduction time zone in each customer is stored, and the reduced power consumption of all consumers for each power reduction time zone. Power reduction data 53 that stores (in the case of a negative value, additional power consumption), average reduction amount data 54 that stores the average reduction amount for each customer, and standard deviation data 55 of the power reduction amount are provided. ing.

More specifically, for example, the power reduction amount of the customer A at the date data=“August 3” implementation time data=“13:30-14:00”=“695.2 kWh”. The power reduction amount of B=“−30.4 kWh”, the power reduction amount of the customer C=“0.0 kWh”, and the power reduction amount of the customer D=“818.3 kWh”. Then, the average reduction amount of the customer A from 13:00 on August 3 to 15:00 on September 8 = "720.8 kWh", and the average reduction amount of the customer B = "498.6 kWh". The average reduction amount of the customer C=“927.9 kWh” and the average reduction amount of the customer D=“832.4 kWh”.

Further, the standard deviation of the customer A is “σA”, the standard deviation of the customer B is “σB”, the standard deviation of the customer C is “σC”, and the standard deviation of the customer D is “σD”. Is.

FIG. 5 is an explanatory diagram of an example of data in the contract information database.
Also in FIG. 5, for ease of understanding, a case where there are four customers (customers A to D) as customers will be described as an example.

The contract information data database 21 is an implementation time data 21A that stores the power reduction time zone of each consumer and a power reduction that stores the reducible power amount (maximum reducible power amount) of all the consumers for each power reduction time period. And the possible amount data 21B.

In FIG. 5, in the electric power reducible amount data 21B, a portion in which no numerical value is entered represents zero reducible electric power, that is, a contract (contract information) that the electric power reduction cannot be supported. On the other hand, regarding the part where the numerical value is entered, it is a contract (contract information) to reduce to the stated electric energy.

More specifically, for example, in the implementation time data 52=“13:00-13:00”, the reducible power amount of the consumer A is “700.0 kWh”, and the reducible power amount of the consumer B is “=”. 500.0 kWh”, the reducible power amount of the customer C=“0.0 kWh”, and the reducible power amount of the customer D=“800.0 kWh”.

Similarly, for example, the implementation time data=“14:30-15:00” is the reducible power amount of the customer A=“700.0 kWh”, and the reducible power amount of the customer B is “500.0 kWh”. Then, the reducible power amount of the customer C=“1000.0 kWh” and the reducible power amount of the customer D=“800.0 kWh”.

FIG. 6 is an explanatory diagram of an example of the demand response request data.
The demand response request data 60 is roughly divided into request target data 61 indicating a demand response request target consumer, and demand response request data 62.

In the above configuration, the demand response request data 62 includes implementation time data 63 that represents a time zone in which a demand response is requested, and demand response request status data 64 that represents a demand response request status to each of the customers A to D. ing.

In the case of the example of FIG. 6, since the demand response requesting consumers are the four consumers A to D, the demand response request state data 64 is the demand indicating the demand response request state corresponding to the consumer A. Response request status data 64A, demand response request status data 64B representing a demand response request status corresponding to customer B, demand response request status data 64C representing a demand response request status corresponding to customer C, and customer D The demand response request state data 64D indicating the demand response request state is included.

As shown in FIG. 6, the combination of the customers who are the DR request targets created by the customer combination creation unit in a certain time zone on a certain date is the customer combination=“customer A, customer B, and customer D”. In the case of “,” the customer C is not requested to reduce power in the time zone of the date.

On the other hand, for the customers A, B, and D as well, the execution time data=“13:00-13:30”, “13:30-14:00”, “15:00-15:30”. ," 15:30-16:00", no power reduction request is made.

On the other hand, in the two time zones of the implementation time data=“14:00-14:30” and the implementation time data=“14:30-15:00”, the requested reduction power amount for the customer A=“700”. .0 kWh", the requested reduction power amount for the customer B = "500.0 kWh", and the requested reduction power amount for the customer D = "800.0 kWh".
As a result, if the required power reduction is achieved, a total power reduction of 2000.0 (=700.0+500.0+800.0) kwh can be achieved.

Next, the operation of the embodiment will be described.
FIG. 7 is an operation flowchart of the embodiment.
First, the optimum demand response plan selection unit 34 sets the set S* that stores the optimum combination of customers as an empty set (=φ), and stores the minimum penalty value (=the sum of the priority penalty and the achievement penalty) in the demand request. Initialization is performed to set the minimum penalty value LeastCost, which is a variable to be set, to an infinite value (+∞) (step S1).

Next, the optimum demand response plan selection unit creates the first customer combination S0 and sets it in the customer combination parameter S that stores the customer combination (step S2).
That is,
S←S0
And
The customer combination S may be created randomly, all combinations may be created in an exhaustive manner, or a combination close to the customer combination S created immediately before (a part of the customer may be replaced by other demands). The home may be replaced, the home may be deleted, a new customer may be added, or the like) may be selected. Alternatively, it may be created using a known meta-heuristic method such as simulated annealing, tabu search, genetic algorithm or the like.

Subsequently, the optimum demand response plan selection unit 34 sets the loop parameter n=1 (step S3) and calculates the priority penalty Priority Penalty (S) of the customer combination S (step S4). Here, the priority penalty Priority Penalty corresponds to the customer priority penalty expected value.

Here, the customer priority will be described.
The customer priority indicates the degree to which the demand response request is preferentially made when the demand response request is made. In the present embodiment, the smaller the customer priority value, the higher the priority. ..

FIG. 8 is an explanatory diagram of an example of setting the customer priority.
In the following description, it is assumed that there are five consumers A to E as consumers.

As shown in FIG. 8, the consumer priority of the consumer A=1, the consumer priority of the consumer B=2, the consumer priority of the consumer C=3, and the consumer priority of the consumer D. =4, and the customer priority of the customer E=5.
That is, it is assumed that the customer A has the highest priority and the priority decreases in the order of customer A→customer B→customer C→customer D→customer E.

Next, an example of calculating the actual priority penalty Priority Penalty (S) will be described.
First, when the customer priority is set as shown in FIG. 8, the priority penalty Priority Penalty is calculated when the demand response request candidate customers are the customer A, the customer B, and the customer C. An example will be described.
FIG. 9 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty when the customers of the mand response request candidates are the customers A, B, and C.
In the present embodiment, the priority penalty of each customer is defined as the difference between the value of the preset customer priority and the value of the posterior priority in the order in which each customer is set as a demand response request candidate. However, the priority penalty Priority Penalty is defined as the sum of squared values of the differences between the customer priority and the posterior priority of each customer.
Therefore, as shown in FIG. 9, the customer priority of the customer A=1, the customer priority of the customer B=2, the customer priority of the customer C=3, and the demand of the customer D. House priority=4, consumer E's consumer priority=5, key house A's posterior priority=1, consumer B's posterior priority=2, consumer C's posterior priority= When 3, the posterior priority of the consumer D is 4, and the posterior priority of the consumer E is 5, the demand response request candidate consumers are defined as the consumer A, the consumer B, and the consumer C. In this case, the priority penalty Priority Penalty(S) is
Priority Penalty (S)=0
Becomes

FIG. 10 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty (S) when the customers of the mand response request candidates are the customers A, C, and D.
As shown in FIG. 10, the posterior priority of the consumer A is 1, the posterior priority of the consumer B is 4, the posterior priority of the consumer C is 2, and the posterior priority of the consumer D is 3. When the posterior priority of the customer E is 5, the priority penalty Priority Penalty (S) when the demand response request candidate customers are the customer A, the customer C, and the customer D is
Priority Penalty(S)
=(1-1)^2+(2-4)^2+(3-2)^2
+(4-3)^2 +(5-5)^2
=0+4+1+1+0
= 6
Becomes

FIG. 11 is an explanatory diagram of a calculation example of the priority penalty Priority Penalty (S) in the case where the customers of the mand response request candidates are the customer A, the customer C, and the customer E0.
Therefore, as shown in FIG. 11, the posterior priority of the consumer A=1, the posterior priority of the consumer C=2, the posterior priority of the consumer C=2, and the posterior priority of the consumer D=3. When the posterior priority of the consumer E is 3, the priority penalty Priority Penalty when the demand response request candidate consumers are the consumer A, the consumer C, and the consumer E is
Priority Penalty(S)
=(1-1)^2+(2-4)^2+(3-2)^2
+(4-5)^2 +(5-3)^2
=0+4+1+1+0
=10.

Next, the optimum demand response plan selection unit calculates the achievement penalty Target Penalty (S) of the customer combination S (step S5).

FIG. 12 is a processing flowchart of the achievement penalty calculation processing.
First, the optimum demand response plan selecting unit 34 creates a penalty function corresponding to the power reduction state of the consumer (for example, when power reduction fails) (step S12).
Here, as the penalty function, when using the mean square error function of the reduction results for the reduction target, when using the well-type function with the reduction target as the central value, or the function that imposes a penalty only when the reduction target is not reached, Can be mentioned.

For example, in the case of the mean square error function, the achievement penalty TargetPenality(S) is set to increase in proportion to the square of the difference between the reduction result and the reduction target. In the case of the well function, a certain achievement penalty Target Penalty (S) is imposed when the absolute value or the ratio of the difference between the actual reduction and the reduction target is equal to or larger than a certain value. In the case of a function that imposes a certain penalty only when the reduction target is not reached, the reduction amount does not reach the reduction target, the reduction target minus a constant value, or the value obtained by multiplying the reduction target by a certain ratio. A performance penalty (TargetPenality(S)) is imposed on.

These functions are examples, and the value of the achievement penalty TargetPenalty(S) can be appropriately set based on the relationship between the actual reduction and the reduction target.

Further, when considering the priority order of each customer regarding the cost, certainty, response speed, etc. in executing the demand response to the customer, the deviation from the predetermined priority order is taken as the achievement penalty Target Penalty( It is also possible to add to S).

Next, the optimum demand response plan selection unit 34 creates a predicted distribution of the power reduction amount based on the past demand response results of each consumer (step S12).
In this case, a method of estimating the distribution function independently for each customer may be used, or a method of estimating the distribution function for each combination of customers may be used. Further, a method of forming a histogram and using the reduction amount as it is for sampling may be used, or a method of estimating parameters after assuming a functional form such as normal distribution and then sampling may be used.

In addition, if there is a common tendency in the amount of reduction among multiple customers due to the same type of industry, a Bayesian method is used in which sampling is performed using a common prior distribution. Good. Furthermore, in the initial stage where there is no information on consumers, a distribution with large variance is used, and methods such as reinforcement learning and banded algorithms, which are methods that increase the accuracy as information gathers, may be used.

Then, the optimum demand response plan selection unit 34 sets an infinite value (+∞) to the penalty minimum value penalty_min, which is a variable for storing the minimum value of the achievement penalty TargetPenality(S), and sets the optimum combination of consumers. The set S* to be stored is set as an empty set (=φ) (step S13).

Then, the optimal demand response plan selection part 34 sets the loop parameter k=1 (step S4), and creates the kth consumer combination S(k) (step S15).
The customer combination S(k) may be created randomly, all combinations may be created exhaustively, or a combination close to the customer combination S(k-1) created immediately before (demand A part of the house may be replaced with another customer, deleted, or a new customer may be added) may be selected. Alternatively, it may be created using a known meta-heuristic method such as simulated annealing, tabu search, genetic algorithm or the like.

Next, the optimum demand response plan selection unit 34 creates the k-th customer combination S(created in step S based on the penalty function created in step S114 and the predicted distribution of power reduction for each customer created in step S12. The achievement degree expected value Penalty when the power is reduced in step k) is calculated (step S16).

More specifically, for example, the customer combination S(k) is sampled T times using the reduction amount prediction distribution of each customer created in step S12, and a combination of the reduction amounts of each customer is generated. ..
Here, the reduction amount of the customer i at the t-th time is x _ti .
At this time, the total amount of reduction by customers is expressed by the following equation.

On the other hand, when the penalty function is calculated by the squared error with the reduction target A, the penalty at the t-th time is expressed by the following equation.

Therefore, the achievement penalty expected value Penalty at the time of T trials is calculated by the following equation.

Next, the optimum demand response plan selection unit 34 determines whether or not the achievement penalty expected value Penalty calculated in step S16 is less than the current minimum penalty value penalty_min (step S17). That is, the optimum demand response plan selection unit determines whether or not the following expression is satisfied.
Penalty <penalty_min

In the determination of step S17,
Penalty <penalty_min
If it is determined that the above condition is satisfied (step S17; Yes), the expected penalty value Penalty calculated in step S16 is substituted for the minimum penalty value penalty_min, and the set S* is set as the customer combination S(k). (Step S18).
penalty_min ← Penalty
S*←S(k)
Then, the process returns to step S15 again, k=k+1 is set, and the same process is repeated until k=kmax.

In the determination of step S17,
Penalty≧penalty_min
, That is,
Penalty <penalty_min
When it is determined that the above condition is not satisfied (step S17; No), the process proceeds to step S15 again, k=k+1 is set, and the same process is repeated until k=kmax.

Here, a specific example of calculating the penalty expected value will be described.
For simplification of the description, a case will be described as an example in which a demand response request is made to the customers A, C, and D among the five customers A to E to reduce the power.

FIG. 13 is an explanatory diagram of an example of the achievement penalty calculation in the case of requesting demand response to the customers A, C, and D to reduce the power.

In this case, the reduction target value contract amount in each slot of the consumer A (electric energy reduction unit period; for example, 30 minutes) is 80 kWh and the reduction target value contract amount of the consumer C is 25 kWh as shown in FIG. The reduction target value contract amount of the customer D is assumed to be 25 kWh.
Further, the reduction target value in each slot is assumed to be 100 kWh in order to simplify the explanation.

In FIG. 13, "1" is stored in the column corresponding to each slot in the column of the reduction target value contract amount of each consumer when a demand response request is made in the slot, and the demand response request is made in the slot. If there is not, it is assumed that "0" is stored.

For example, in the first slot (denoted as “slot 1” in the figure), “0” is stored in the column corresponding to each slot in the column of the reduction target value contract amount of the customer A, and “0” is stored in the column corresponding to each slot of the reduction target value contract amount column, and “1” is stored in the column corresponding to each slot of the reduction target value contract amount column of the customer D. Therefore, only the customer D is requested to request a reduction target value contract amount of 25 kWh.

Therefore, the total reduction request amount in this case is 25 kWh. As a result, the power amount obtained by subtracting the reduction target amount from the total reduction request amount is 25-100=-75 kWh, and the squared error thereof is 5625 (=-75^2).

Further, for example, in the fifth slot (indicated as “slot 5” in the figure), “1” is stored in the column corresponding to each slot in the column of the reduction target value contract amount of the customer A, “1” is stored in the column corresponding to each slot of the column of the reduction target value contract amount of C, and “1” is stored in the column corresponding to each slot of the column of the reduction target value contract amount of the customer D. Therefore, the demand response request of the reduction target value contract amount of 80 kWh to the customer A, the reduction target value contract amount of 25 kWh to the customer C, and the reduction target value contract amount of 25 kWh to the customer D is requested.

Therefore, the total reduction demand amount in this case is 130 kWh (=80+25+25 kWh). As a result, the power amount obtained by subtracting the reduction target amount from the total reduction request amount is 130-100=30 kWh, and the squared error thereof is 900 (=30^2).
The achievement penalty Penalty(S) is obtained by obtaining the sum of squared errors in the first slot to the sixth slot and further taking the square root of the sum of squares.

Specifically, in the case of the example of FIG.
Achievement penalty Target Penalty(S)
=√(5625+5625+25+900+900+900)
= 118.2
Has become.

Next, the optimum demand response plan selection unit 34 determines whether the sum of the priority penalty Priority Penalty(s) calculated in step S4 and the achievement penalty TargetPenalty(S) calculated in step S5 is equal to or less than the minimum penalty value LeastCost. It is determined (step S6).

That is,
LeastCost≧Priority Penalty(s)
+Target Penalty(S)
It is determined whether or not is satisfied.

In the determination of step S6,
LeastCost≧Priority Penalty(s)
+Target Penalty(S)
If the condition is satisfied (step S6; Yes), the minimum penalty value LeastCost is updated (step S7).

In particular,
LeastCost=Priority Penalty(s)
+Target Penalty(S)
Then, the process proceeds to step S8 with the set S ← the customer combination parameter S storing the optimum combination of the customers.

Further, in the determination of step S6,
LeastCost≧Priority Penalty(s)
+Target Penalty(S)
Is not satisfied, that is,
LeastCost <Priority Penalty(s)
+Target Penalty(S)
If it is (step S6; No), the next candidate N(s) for the customer combination is created and set as the customer combination parameter S (step S8).
Then, the process shifts to step S3 again, and the same process is repeated until the loop parameter n reaches the maximum value Nmax of n.

FIG. 14 is a diagram illustrating an example of the transition of the sum of the priority penalty Priority Penalty(s) and the achievement penalty Penalty(S) when the loop parameter n=1 is sequentially performed.
As shown in FIG. 14, in the range of n=1 to 5, that is, in the range of the customer combination parameter S=S0 to S4, the sum of the priority penalty Priority Penalty(s) and the achievement penalty Target Penalty(S) ( Total penalty) is
91.5→107.4→131.1→91.0→77.4
The minimum penalty value LeastCost in this range is
91.5→91.5→91.5→91.0→77.4
Therefore, the minimum penalty value LeastCost is gradually updated.

As described above, according to the present embodiment, it is possible to provide a demand response planning device capable of making a demand response plan satisfying the demand priority while complying with the customer priority.
Further, it becomes possible to further increase the profit while supplying the power demand of the consumer.
Becomes

[3] Modification of Embodiment In the above description, the method of minimizing the sum of the priority penalty and the achievement penalty (for example, the penalty amount) imposed on the aggregate business operator has been described. Maximize the “amount gained by the aggregate operator-the fine amount paid by the aggregate operator” in consideration of the amount obtained, the incentive amount paid to the customer or the incentive amount received as the aggregate operator, or the fine amount paid. Such optimization may be performed. In this case, in the processing of step S4 and step S5 in the embodiment, it can be realized simply by exchanging the expected value calculation part.

FIG. 15 is a schematic configuration block diagram when the demand response planning device is configured as a computer.
The demand response planning device 10 includes a control device 71 such as a CPU, an input device 72 such as a keyboard and a mouse, a display device 73 such as a display device, and a communication device 74 that communicates with an external device via a communication network or the like. , A storage device 75 including an external storage device such as a HDD or a CD drive device, or a semiconductor storage device such as a ROM or a RAM, and has a hardware configuration using a normal computer.

The program executed by the demand response planning apparatus of the present embodiment is a file of an installable format or an executable format, and is a computer such as a CD-ROM, a flexible disk (FD), a CD-R, a DVD (Digital Versatile Disk). It may be provided by being recorded on a recording medium readable by the.

Further, the program executed by the demand response planning apparatus of this embodiment may be stored in a computer connected to a network such as the Internet and provided by being downloaded via the network. Further, the program executed by the demand response planning apparatus of the present embodiment may be provided or distributed via a network such as the Internet.

Further, the program of the demand response planning apparatus of the present embodiment may be incorporated in a ROM or the like in advance and provided.

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

10 Demand Response Planning Device 11 Demand Response Plan Optimization Section 12 Communication I/F Section 13 Operation Input Section 14 Display Section 15 Input/Output I/F Section 16 History Information Creation Section 17 Customer History Database 18 Priority Calculation Section 19 Reduction Target Information database 21 Contract information data database 21A Implementation time data 21B Power reduction potential data 31 Target achievement penalty calculation unit 32 Priority penalty calculation unit 33 Combination creation unit 41 Power reduction request data 42 Date data 43 Implementation time data 44 Reduction target data 51 Date Data 52 Implementation Time Data 53 Power Reduction Data 54 Average Reduction Data 55 Standard Deviation Data 60 Demand Response Request Data 61 Request Target Data 62 Demand Response Request Data 71 Control Device 72 Input Device 73 Display Device 74 Communication Device 75 Storage Device 100 Electricity supply and demand system 111 Power generation company system 112 System operator system 114 Customer system (consumer)
115 Aggregate Business System 116 Electricity Exchange System Priority Penalty(s) Priority Penalty Target Penalty(S) Achievement Penalty

Claims (6)

A demand response planning device that plans a demand response for requesting a demand selected from a plurality of consumers based on a request from an electric power supplier to reduce demand power,
When requesting the demand response, a priority information storage unit that stores in advance information about the customer priority that stores in advance the customer priority that represents the priority of the customer who requests the demand response,
Based on contract information with the power supplier and contract information with the customer for each combination of the customers for which demand reduction of demand power is requested, a reduction target of the power demand and demand power for each combination of the customers Expected value to be imposed based on the difference from the reduction prediction amount of the expected value and expected penalty value based on the sum of the priority penalty expected value to be imposed when the customer priority is not met. A penalty calculation unit that calculates
A plan creating unit that creates a demand response plan for requesting a customer to reduce the demand power corresponding to the combination of the customers who minimizes the penalty expected value;
Demand response planning device. The penalty calculating unit calculates the achievement penalty expected value based on a history of past demand power reduction of the consumer.
Demand response planning device according to claim 1 Symbol placement. The penalty calculation unit, based on the history of the demand power reduction of the consumer in the past, creates a prediction distribution of the power reduction amount, and calculates the achievement penalty expected value based on the created prediction distribution,
The demand response planning device according to claim 1 . When minimizing the penalty amount imposed on the aggregate operator, the penalty expected value is minimized, or the aggregate amount is obtained from the amount obtained by the aggregate operator with the execution of the created demand response plan. The case where the amount obtained by subtracting the amount paid by the gate operator is maximized is the minimization of the penalty expected value,
The demand response planning device according to any one of claims 1 to 3 . A method executed by a demand response planning device for planning a demand response for requesting a demand selected from a plurality of consumers to reduce demand power based on a request from an electric power supplier. ,
In the case of requesting the demand response, a step of pre-storing information regarding the customer priority in which the customer priority indicating the priority of the customer who requests the demand response is stored in advance,
Based on the contract information with the power supplier and the contract information with the customer for each combination of the customers for which demand reduction of the power demand is requested, the reduction target of the power demand and the power demand for each combination of the customers Expected value to be imposed based on the difference between the expected reduction amount and the expected value of the penalty based on the sum of the priority penalty expected value to be imposed when the customer priority is not met. And the process of calculating
A step of creating a demand response plan for requesting a customer to reduce the demand power corresponding to the combination of the customers who minimizes the penalty expected value;
A method with. A program for controlling a demand response planning device by a computer, which plans a demand response for requesting a demander to reduce demand power from a plurality of demanders based on a request from a power supplier And
The computer,
When requesting the demand response, a means for pre-storing information regarding a customer priority in which a customer priority indicating a priority of a customer who requests the demand response is stored in advance,
Based on contract information with the power supplier and contract information with the customer for each combination of the customers for which demand reduction of demand power is requested, a reduction target of the power demand and demand power for each combination of the customers Expected value to be imposed based on the difference from the reduction prediction amount of the expected value and expected penalty value based on the sum of the priority penalty expected value to be imposed when the customer priority is not met. Means for calculating
A means for creating a demand response plan for requesting a customer to reduce the demand power corresponding to the combination of the customers that minimizes the penalty expected value;
A program that works by doing.

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