KR102568486B1 - Server, method and computer program for providing guide information on demand response - Google Patents

Abstract

A server providing guide information on Demand Response (DR) includes an information input unit for receiving customer intention information about participation in a virtual demand response from a plurality of demand resources, a plurality of demand resources based on the customer intention information A value function calculation unit that calculates a value function including a first value function representing the probability of participation in each demand response and a second value function according to participation in the actual demand response, a plurality of demands based on participation in the actual demand response An expected value calculation unit that calculates expected values for the loss interval and gain interval for the actual demand response of the next order by using the probability weighting function and the calculated value function reflecting the propensity information of the resource, and based on the calculated expected value, the following and a guide information providing unit providing guide information for increasing compensation for the actual demand response of the order to at least one terminal among a plurality of demand resources.

Description

Server, method and computer program providing guide information for demand response (DR)

The present invention relates to a server, method, and computer program for providing guide information on demand response (DR).

As a method for managing power peaks, there is a method of recruiting demand resources willing to participate in demand response and having the corresponding demand resources fulfill power reduction capacity when demand response is issued. Here, demand-response is a system in which a pre-agreed amount of power consumption is reduced according to the “power-supply situation, peak load rate,” power-production and supply price, and the difference is compensated.

On the other hand, the DR power resource trading market is different from the general trading market. The first transaction behavior of the consumer based on the small amount of information provided by the DR trading market has a decisive effect on the maximum reward (profit) that can be obtained from the DR trading market, and the profit or loss from the first transaction has a decisive effect on the next transaction.

In the conventional DR power resource trading system, it is difficult to predict actions such as whether the demander wishes to further reduce after seeing the market conditions after the first transaction of the demander in the DR trading market.

In addition, in the case of the conventional DR power resource trading system, since the demander has to judge the market situation of the DR trading market himself, there is a case in which a desirable transaction cannot be made because a reasonable choice cannot be made due to psychological factors. It is difficult to confirm in advance the settlement amount to be settled when a transaction is made for a transaction price that changes according to the transaction time in the DR trading market.

Korean Registered Patent Publication No. 10-1908515 (registered on October 10, 2018)

The present invention is to solve the above-mentioned problems of the prior art, a value function calculated based on customer intention information of a plurality of demand resources for participation in a virtual demand response and a plurality of demand resources participating in an actual demand response. Based on the probability weighting function that reflects the propensity information, we want to calculate the expected value of the loss interval and gain interval for the next order's actual demand response.

In addition, the present invention intends to provide guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among a plurality of demand resources. However, the technical problem to be achieved by the present embodiment is not limited to the technical problems described above, and other technical problems may exist.

As a technical means for achieving the above-described technical problem, a server providing guide information for a demand response (DR) according to the first aspect of the present invention is involved in participation in a virtual demand response from a plurality of demand resources. an information input unit that receives customer intention information about the product; A value function calculation unit that calculates a value function including a first value function representing a probability of participation in the demand response for each of the plurality of demand resources and a second value function according to participation in the actual demand response based on the customer intention information ; Calculate expected values for loss intervals and gain intervals for the next actual demand response using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function and a guide information providing unit providing guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among the plurality of demand resources. there is.

According to a second aspect of the present invention, a method for providing guide information on a demand response (DR) in a server includes receiving customer intention information about participation in a virtual demand response from a plurality of demand resources; Calculating a value function including a first value function representing a probability of participation in a demand response for each of the plurality of demand resources and a second value function according to participation in an actual demand response based on the customer intention information; Calculate expected values for loss intervals and gain intervals for the next actual demand response using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function and providing guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among the plurality of demand resources.

According to the third aspect of the present invention, when a computer program stored in a medium including a sequence of instructions providing guide information on demand response (DR) is executed by a computing device, virtual demand from a plurality of demand resources A first value function representing a probability of participation in the demand response for each of the plurality of demand resources based on customer intention information received as input and a second value according to participation in the actual demand response Loss for the actual demand response of the next order by calculating a value function including a function, and using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function An instruction for calculating an expected value for an interval and a profit interval, and providing guide information for increasing compensation for the actual demand response of the next order to at least one terminal among the plurality of demand resources based on the calculated expected value. may include a sequence of

The above-described means for solving the problems is only illustrative and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, there may be additional embodiments described in the drawings and detailed description.

According to any one of the above-described problem solving means of the present invention, the present invention is a value function calculated based on customer intention information of a plurality of demand resources for participation in virtual demand response and a plurality of demands participating in actual demand response Based on the probability weighting function in which the propensity information of the resource is reflected, the expected values of the loss interval and gain interval for the next order actual demand response may be calculated.

In addition, the present invention may provide guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among a plurality of demand resources.

Through this, the present invention reduces the number of times demand resources suffer continuous losses when participating in demand response, maximizes compensation when participating in demand response, induces participation in demand response transactions, and helps to reasonably participate in demand response. there is.

1 is a block diagram of a guide information providing server for providing guide information on DR (Demand Response) according to an embodiment of the present invention.
2A to 2D are diagrams for explaining a method of providing guide information on demand response according to an embodiment of the present invention.
3 is a flowchart illustrating a method of providing guide information for demand response according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail so that those skilled in the art can easily practice the present invention with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected" but also the case where it is "electrically connected" with another element interposed therebetween. . In addition, when a certain component is said to "include", this means that it may further include other components without excluding other components unless otherwise stated.

In this specification, a "unit" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Further, one unit may be realized using two or more hardware, and two or more units may be realized by one hardware.

In this specification, some of the operations or functions described as being performed by a terminal or device may be performed instead by a server connected to the terminal or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal or device connected to the corresponding server.

Hereinafter, specific details for the implementation of the present invention will be described with reference to the accompanying configuration diagram or process flow chart.

1 is a block diagram of a guide information providing server 10 providing guide information on DR (Demand Response) according to an embodiment of the present invention.

Referring to FIG. 1, the guide information providing server 10 includes an information input unit 100, a value function calculator 110, an expected value calculator 120, a guide information provider 130, and a power reduction estimation unit ( 140) may be included. Here, the expected value calculator 120 may include a probability weighting function calculator 122 . However, the guide information providing server 10 shown in FIG. 1 is only one implementation example of the present invention, and various modifications are possible based on the components shown in FIG. 1 .

The information input unit 100 may provide a questionnaire asking for information on industry type and power usage time to a plurality of demand resources.

In addition, the information input unit 100 may receive basic information including industry information of the demand resource and time information (eg, season and time zone) of the demand resource as a response to the corresponding questionnaire from a plurality of demand resources. .

The information input unit 100 may provide a plurality of demand resources with a questionnaire asking about customer intentions regarding participation in the virtual demand response.

Also, the information input unit 100 may receive customer intention information about participation in the virtual demand response from a plurality of demand resources as a response to the corresponding questionnaire. Here, the customer intention information may include an intention to participate in additional reduction according to a request of the server and an intention to participate in power transaction between a plurality of demand resources.

The information input unit 100 provides a plurality of demand resources with a questionnaire on whether to participate in additional reduction capacity or electricity trading based on a virtual demand response situation (eg, season, time, day of the week, etc. for a virtual demand response issuance) can do.

For example, the questionnaire is based on the industry of the demand resource (customer), DR issuance status information, DR issuance timing (eg, seasonal, monthly), and DR issuance time zone. It may be about how much profit (the desired amount of additional reduction) you want to receive in a hypothetical situation where you pay $50 and make a transaction (sale) by reducing more than the contracted capacity.

The information input unit 100 may receive customer intention information about participation in the virtual demand response from a plurality of demand resources as a response to the corresponding questionnaire.

For example, the average response (customer opinion information) according to the questionnaire is 125 ( = 2.5), it can be predicted that demand resources will demand 2.5 times the purchase price to participate in this demand response transaction.

In other words, since the DR market is unfamiliar and the transaction price is not represented as a fixed value in the actual transaction, it is important for customers to estimate the amount experienced in the DR market and reflect it in the transaction market. Therefore, it is necessary to induce demand resources to participate in DR market transactions by estimating the psychological value of demand resources participating in actual demand response.

The power reduction amount estimator 140 may estimate a potential power reduction amount in response to demand based on customer intention information. For example, the power reduction amount estimator 140 determines whether the first demand resource contracted based on customer intention information of the first demand resource who answered a questionnaire including a question on participation in the initial virtual demand response. When an actual demand response is issued rather than a reduction capacity, a potential power reduction amount that can be further reduced by the first demand resource may be estimated.

The power reduction amount estimator 140 may calculate an appropriate transaction price range for an additional power reduction amount additionally implemented by each demand resource when a demand response is issued based on the estimated potential power reduction amount for each of a plurality of demand resources.

The information input unit 100 receives customer intention information on whether or not to accept the appropriate transaction price range from a plurality of demand resources when the calculated appropriate transaction price range is presented to a plurality of demand resources when an actual demand response is issued. can do.

The value function calculation unit 110 calculates a value function including a first value function representing a probability of participation in a demand response for each of a plurality of demand resources based on customer intention information and a second value function according to participation in an actual demand response. can be calculated

Here, the value function is a function representing the psychological value of the gain or loss obtained by the demand resource in the demand response. This value function has the characteristics of reference point dependence, diminishing sensitivity, and loss aversion.

Here, 'reference point dependence' is a characteristic that indicates that the value measured based on the reference point (neutral interval of psychological gains and losses) is more important than absolute utility. For example, a person whose wealth has increased from 20 million won to 25 million won may be happier than a person whose wealth has decreased from 50 million won to 45 million won.

'Reduced sensitivity' refers to the characteristic that it is sensitive to changes when the value of profit or loss is small, but the sensitivity decreases as the value increases. For example, even with the same temperature difference of 3 degrees, a rise of 1 degree to 4 degrees is equivalent to feeling warmer than a rise from 21 to 24 degrees.

'Loss aversion' is a characteristic in which a loss of the same amount feels much greater than a gain of the same amount, and dissatisfaction due to the same amount of loss is greater than satisfaction due to the same amount of gain. For example, dissatisfaction from a loss of 1,000 won is 2 to 2.5 times greater than satisfaction from a gain of 1,000 won.

The value function may be expressed as in [Equation 1] and may have a graph form as shown in FIG. 2a.

[Equation 1]

,

,

Here, x is the definition of gain or loss, v(x) is a value function representing the psychological value obtained from x, is a coefficient representing the degree of loss aversion. Here, if 0 < α and β < 1, the graph of the value function is concave for gain and the graph of value function is convex for loss.

The value function based on customer intention information can be used to estimate the expected value for each of a plurality of demand resources for the compensation of a transaction in the demand response market, and can also be used to estimate the depreciation for the compensation of a transaction for each next order. there is.

The value function calculation unit 110 may calculate a value function for each situation by setting a predetermined condition (eg, a demand response policy) in customer intention information.

Meanwhile, since positions (eg, buy, sell, neutral) acting in actual demand response transactions are different for each of a plurality of demand resources, it is important to reflect them in the value function.

The value function calculation unit 110 may calculate a second value function by reinforcement learning of customer intention information (participation response information on whether to participate in a virtual demand response) and behavioral positions for an actual demand response of each of a plurality of demand resources. there is. Here, the behavioral position for the actual demand response may be expressed as probabilities for each of buying, selling, and neutral.

The value function calculation unit 110 may estimate a distribution through simulation of customer intention information and calculate a representative value through the estimated distribution. Here, the simulation may include, for example, methods such as Monte Carlo, bootstrap, and jackknife, which are parameter and non-parameter estimation methods. Depending on the shape of the distribution, the representative value may be used as, for example, the mean or median value.

The value function calculation unit 110 calculates a loss avoidance coefficient representing the degree of loss avoidance of a plurality of demand resources based on a probability distribution estimated through simulation of customer intention information, and calculates a value function using the calculated loss avoidance coefficient can be calculated.

The value function calculation unit 110 may modify the machined loss aversion coefficient based on a result of participation in the actual demand response of each of the plurality of demand resources.

The probability weighting function calculation unit 122 may calculate a probability weighting function based on a profit/loss result for each of a plurality of demand resources when participating in an actual demand response. Here, the probability weighting function is used to convert objective probability into subjective probability, and a function for a profit interval or loss interval in the DR market can be estimated using this function.

Since the profit section and the loss section in the DR market's profit/loss section show an asymmetric shape, the probability weight function calculation unit 122 uses a logit model for each of the profit section and the loss section. Can estimate the cumulative probability density function there is.

The graph form of the probability weighting function using the cumulative probability density function is shown in FIG. 2B and can be expressed as in [Equation 2].

[Equation 2]

(gain interval, x>0)

(interval of loss, x<0)

Demand resources in a profit range with a high probability may show a tendency to pursue risk aversion, and resources in demand in a loss range with a high probability may show a tendency to pursue risk. Resources in demand in a profit range with low probability may show a risk-seeking tendency, and resources in demand in a loss range with a low probability may show a tendency to pursue risk aversion.

Referring to [Equation 2] and FIG. 2B together, when the probability p is small, it is overestimated, and when the probability p is somewhat large, it is underestimated. In other words, with the establishment of sensitivity sensitivity, for example, a change in probability p from 0.1 to 0.9 to 1 has a much greater psychological effect (when accepting a price and choosing a transaction in the DR trading market) than a change from 0.4 to 0.5. . If you check the graph of the probability weighted graph, you can see that the slope becomes steeper as it approaches 0.

For example, after the transaction of the first demand response is completed, different profit and loss results for a plurality of demand resources ( ) ranking information based on Assuming that, a value less than 0 means a loss, and a value greater than 0 means a profit. [Equation 3] may be used to calculate the loss interval and the gain interval.

[Equation 3]

,

,

,

,

The probability weighting function calculation unit 122 may reflect propensity information of a plurality of demand resources based on participation in an actual demand response in the calculated probability weighting function.

Returning to FIG. 1 , the expected value calculation unit 120 determines the actual demand response of the next order by using a probability weighting function reflecting propensity information of a plurality of demand resources based on participation in the actual demand response and a calculated value function. It is possible to calculate the expected value for the loss interval and the gain interval. Here, participation in actual demand response may include one of participation in demand response contracted by a plurality of demand resources, participation in electricity transaction between a plurality of demand resources, and participation in additional reduction according to a request of the server. .

The expected value calculation unit 120 may calculate the expected value of the actual demand response of the next order based on the sum of the expected values of the loss interval and the gain interval for the actual demand response of the previous order using [Equation 4]. there is. When [Equation 4] is used, the expected value of the next actual demand response may reflect the characteristics and tendencies of the demand resource (customer) and the actual demand response transaction situation.

[Equation 4]

For example, referring to FIG. 2C , the number of cases in which each demand resource participates in the actual demand response is 6, the probability that each demand resource participates in the actual demand response is the same in each case, and each demand resource participates in the actual demand response. When participating in demand response, it is assumed that the profit and loss result is the same as in FIG. 2c.

The probability that the profit or loss result for the actual demand response for each demand resource is 0 or the gain obtained by the demand resource in the demand response can be expressed as in [Equation 5]. That is, the probability that the P&L result is 0 is 1/2, and the probability that the P&L result is +2, +4, and +6 is 1/6, respectively.

[Equation 5]

The probability that the profit or loss result for the actual demand response for each demand resource is 0 or the loss of the demand resource in the demand response can be expressed as [Equation 6]. That is, the probability that the P&L result is 0 is 1/2, and the probability that the P&L result is -1, -3, and -5 is 1/6, respectively.

[Equation 6]

The expected value of a demand response market is , so the value function (v) and the probability weighting function ( The calculation of is as follows.

The guide information providing unit 130 may provide guide information for increasing compensation for the next actual demand response based on the calculated expected value to the terminal of at least one demand resource among a plurality of demand resources.

Specifically, the guide information providing unit 130 divides the plurality of demand resources participating in the actual demand response into a first group including at least one demand resource generating revenue through the actual demand response and generating no revenue through the actual demand response. It can be classified into a second group including at least one demand resource that is not available. That is, the second group may be a group that has suffered damage through demand response.

The guide information providing unit 130 may provide guide information (for example, information as shown in FIG. 2D ) on the next actual demand response to the terminal of at least one demand resource belonging to the second group in the actual demand response. there is.

The guide information may include a first option for the probability and profit of profit due to participation in the next actual demand response, and a second option for the amount of damage due to non-participation in the next actual demand response.

For example, referring to FIG. 2D , after the first actual demand response transaction, if the demand resource belonging to the second group is in a position to sell electricity in the next cycle, the transaction capacity of the remaining power and the transaction capacity belonging to the second group Probability of profit in the next electricity transaction may be calculated based on the number of demand resources, and profit may be calculated based on an expected value according to a probability weighting function.

The guide information may include a probability of generating a profit and a profit when sales are made in the next demand response. If the demand resource does not participate in the transaction, a probability of loss is calculated based on the expected transaction volume, and the guide information may include information on an estimated amount of loss when the demand resource does not participate in the next demand response.

The probability weighting function calculation unit 122 may correct the probability weighting function calculated based on the behavioral position of each of the plurality of demand resources when the actual demand response is completed.

The expected value calculation unit 120 may update the expected values for the loss section and gain section of the next order actual demand response based on the corrected probability weighting function.

On the other hand, those skilled in the art, the information input unit 100, the value function calculation unit 110, the expected value calculation unit 120, the probability weighting function calculation unit 122, the guide information providing unit 130, and the power reduction estimation unit ( 140), it will be fully understood that each can be implemented separately, or one or more of them can be integrated and implemented.

3 is a flowchart illustrating a method of providing guide information for demand response according to an embodiment of the present invention.

Referring to FIG. 3 , in step S301, the guide information providing server 10 may receive customer intention information about participation in virtual demand response from a plurality of demand resources.

In step S303, the guide information providing server 10 calculates a first value function representing the probability of participation in the demand response for each of a plurality of demand resources and a second value function according to participation in the actual demand response based on the input customer intention information. A value function can be calculated that includes

In step S305, the guide information providing server 10 uses a probability weighting function and a calculated value function in which propensity information of a plurality of demand resources based on participation in the actual demand response is reflected, and a loss interval for the actual demand response of the next order and You can calculate the expected value for the profit interval.

In step S307, the guide information providing server 10 may provide the terminal of at least one demand resource among a plurality of demand resources with guide information for increasing compensation for the next actual demand response based on the calculated expected value. .

In the above description, steps S301 to S307 may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present invention. Also, some steps may be omitted if necessary, and the order of steps may be changed.

An embodiment of the present invention may be implemented in the form of a recording medium including instructions executable by a computer, such as program modules executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts thereof should be construed as being included in the scope of the present invention. .

10: Guide information providing server
100: information input unit
110: value function calculator
120: expected value calculator
122: probability weighting function calculator
130: guide information provision unit
140: power reduction estimation unit

Claims (18)

In a server providing guide information on demand response (DR),
an information input unit that receives customer intention information about participation in a virtual demand response from a plurality of demand resources;
A value function calculation unit that calculates a value function including a first value function representing a probability of participation in the demand response for each of the plurality of demand resources and a second value function according to participation in the actual demand response based on the customer intention information ;
Calculate expected values for loss intervals and gain intervals for the next actual demand response using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function an expected value calculation unit; and
A guide information provider providing guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among the plurality of demand resources;
The value function represents a psychological value for a gain or loss obtained by a demand resource in a demand response.
According to claim 1,
Further comprising a power reduction amount estimator for estimating a potential power reduction amount in demand response based on the customer intention information, the server.
According to claim 1,
The value function reflects the value reference point dependence on the importance of the value measured based on the interval in which the psychological gain and loss are neutral, or the sensitivity of the sensitivity according to the size of the value of the profit and loss.
According to claim 1,
The value function calculation unit calculates a loss avoidance coefficient representing a degree of loss avoidance of the plurality of demand resources based on a probability distribution estimated through simulation of the customer intention information,
The server calculating the value function using the calculated loss aversion coefficient.
According to claim 4,
Wherein the value function calculation unit corrects the calculated loss aversion coefficient based on a result of participation in the actual demand response of each of the plurality of demand resources.
According to claim 1,
The server, wherein the value function calculation unit calculates the second value function by reinforcement learning of the customer intention information of each of the plurality of demand resources and a behavioral position for the actual demand response.
According to claim 1,
The expected value calculator
A server comprising a probability weighting function calculation unit for calculating the probability weighting function using a cumulative probability density function based on the profit and loss results for each of the plurality of demand resources when participating in the actual demand response.
According to claim 7,
The probability weighting function calculation unit corrects the probability weighting function based on the behavioral position of each of the plurality of demand resources at the end of the actual demand response;
The server, wherein the expected value calculation unit updates expected values for a loss period and a profit period of the next order actual demand response based on the corrected probability weighting function.
According to claim 1,
The guide information providing unit sets the plurality of demand resources participating in the actual demand response to a first group including at least one demand resource generating revenue through the actual demand response and at least one resource not generating revenue through the actual demand response. To classify into a second group containing one demand resource, the server.
According to claim 9,
wherein the guide information providing unit provides guide information on the next actual demand response to a terminal of at least one demand resource belonging to the second group in the actual demand response.
According to claim 1,
The participation in the actual demand response includes one of participation in the demand response contracted by the plurality of demand resources, participation in electricity transaction between the plurality of demand resources, and participation in additional reduction according to the request of the server. which is the server.
According to claim 11,
The server, wherein the customer intention information includes an intention to participate in the additional reduction and an intention to participate in a power transaction between the plurality of demand resources.
In a method for providing guide information on a demand response (DR) in a server,
receiving customer intention information about participation in a virtual demand response from a plurality of demand resources;
Calculating a value function including a first value function representing a probability of participation in a demand response for each of the plurality of demand resources and a second value function according to participation in an actual demand response based on the customer intention information;
Calculate expected values for loss intervals and gain intervals for the next actual demand response using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function doing; and
Providing guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among the plurality of demand resources;
Wherein the value function represents a psychological value for a gain or loss obtained by a demand resource in a demand response.
According to claim 13,
The participation in the actual demand response includes one of participation in the demand response contracted by the plurality of demand resources, participation in electricity transaction between the plurality of demand resources, and participation in additional reduction according to the request of the server. which way.
15. The method of claim 14,
Wherein the customer intention information includes an intention to participate in the additional reduction and an intention to participate in a power transaction between the plurality of demand resources.
According to claim 13,
Providing the guide information to at least one terminal among the plurality of demand resources
The plurality of demand resources participating in the actual demand response are divided into a first group including at least one demand resource generating revenue through the actual demand response and at least one demand resource not generating revenue through the actual demand response. And classifying into a second group comprising.
17. The method of claim 16,
Providing the guide information to at least one terminal among the plurality of demand resources
and providing guide information on the next-order actual demand response to a terminal of at least one demand resource belonging to the second group in the actual demand response.
A computer program stored in a medium containing a sequence of instructions providing guide information for a demand response (DR),
When the computer program is executed by a computing device,
Receive customer intention information about participation in virtual demand response from a plurality of demand resources;
Calculate a value function including a first value function representing a probability of participation in the demand response for each of the plurality of demand resources and a second value function according to participation in the actual demand response based on the customer intention information;
Calculate expected values for loss intervals and gain intervals for the next actual demand response using a probability weighting function reflecting propensity information of the plurality of demand resources based on participation in the actual demand response and the calculated value function do,
And a sequence of instructions for providing guide information for increasing compensation for the next actual demand response based on the calculated expected value to at least one terminal among the plurality of demand resources,
The value function represents a psychological value for a gain or loss obtained by a demand resource in a demand response, a computer program stored in a medium.

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