Information processing device, information processing method, and program

Abstract

To perform appropriate adjustment of supply and demand without actual demand data.SOLUTION: An information processing apparatus comprises a variation amount calculation unit which calculates an amount of variation of a first success rate from a demand response calculated on the basis of a feature quantity of a reference consumer by adding information of an evaluation object consumer to the feature quantity.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present proposal relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Demand response technology, which effectively supplies electricity by requesting and controlling electricity consumers such as businesses and homes to reduce their electricity demand, has been attracting attention. In this technology, an aggregator is responsible for aggregating multiple electricity consumers in accordance with instructions from an electricity supplier. When an aggregator receives a request from an electricity supplier to reduce electricity demand, it selects electricity consumers who will comply with this request from among electricity consumers who have a contractual relationship with the aggregator. When collecting electricity consumers to contract with, it is necessary to evaluate whether the electricity consumers are suitable for a demand response assessment in accordance with the agreement of the electricity supplier, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] WO2019 / 082426 Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, in one embodiment of the present proposal, an information processing device, an information processing method, and a program are provided that are capable of determining consumers that should perform appropriate supply and demand adjustment. [Means for solving the problem]

[0005] In order to solve the above problem, according to one embodiment of the present proposal, an information processing device is provided, which is provided with a fluctuation amount calculation unit that calculates a fluctuation amount of a first success rate for a demand response calculated based on the characteristics of a reference consumer by adding information of a consumer to be evaluated. [Brief description of the drawings]

[0006] [Figure 1] 1 is a block diagram showing a schematic configuration of an information processing apparatus according to a first embodiment. [Diagram 2] FIG. 4 is a diagram showing an example of feature amounts stored in a reference consumer information storage unit. [Diagram 3] 11 is a flowchart showing an example of processing operations of a first success rate calculation unit and a second success rate calculation unit. [Figure 4] 11 is a flowchart showing an example of a processing operation of a fluctuation amount calculation unit. [Diagram 5] FIG. 4 is a diagram showing the results of an effectiveness verification experiment of the first embodiment. [Figure 6] FIG. 4 is a diagram showing an example of a screen display of a display unit. [Figure 7] FIG. 11 is a block diagram showing a schematic configuration of an information processing apparatus according to a second embodiment. [Figure 8] 10 is a flowchart showing a processing operation of an information processing device according to a second embodiment. [Figure 9] 13A and 13B are diagrams showing an example of a result of a processing operation performed by an information processing device according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an embodiment of an information processing device, an information processing method, and a program will be described with reference to the drawings. The following description will focus on the main components of the information processing device, the information processing method, and the program, but the information processing device, the information processing method, and the program may include components and functions that are not shown or described. The following description does not exclude components and functions that are not shown or described.

[0008] (Overview of this technology) Demand response has been proposed as a technology to stabilize the power supply by encouraging the suppression of power demand and reducing power consumption during peak usage. Demand response makes it possible to change the actual demand of power consumers in accordance with the supply and demand situation of the power grid, eliminating the need to maintain inefficient thermal power plants and build new peak power sources, and is expected to streamline power generation capacity in the medium to long term. This technology is expected to efficiently implement demand response by having an aggregator (hereinafter referred to as an aggregator) perform integrated control of multiple consumers.

[0009] In this type of Negawatt aggregation business, aggregators collect negawatts from consumers and encourage them to reduce demand by setting fees and paying consumers who refrain from using electricity during peak times. Here, negawatts refer to the reduction in electricity demand (negative electricity) in contrast to normal electricity demand (positive electricity).

[0010] In the negawatt aggregation business, several mechanisms (negawatt trading) for utilizing the collected negawatts are available or being prepared. Representative mechanisms include public bidding for adjustment capacity, capacity markets, and supply and demand adjustment markets, but the negawatt trading assumed in this technology is not limited to these. A common feature of these negawatt trading is that when a demand response is activated, if the amount of collected negawatts matches the assessment corresponding to the target negawatt trading, a reward is given, and if not, a penalty is given. The assessment is performed for each time frame corresponding to the target negawatt trading, and the proportion of the total time frames that match the assessment is called the demand response success rate. A representative example of an assessment is one that evaluates whether the actual demand when a demand response is activated stays within the upper and lower constraints centered on the reduction amount requested when a demand response is activated against the amount of electricity (baseline) assumed as normal actual demand.

[0011] In the negawatt aggregation business, there is a risk that the actual demand fluctuates from the baseline due to noise contained in the actual demand of consumers, and the demand response is judged to be a failure. Therefore, it is important for the aggregator to improve the success rate of the demand response by collecting and bundling many consumers who have characteristics related to their actual demand that will improve the success rate of the demand response.

[0012] Thus, aggregators need to collect many consumers with characteristics that will improve the success rate of demand response. Until now, there has been no method to determine whether a consumer has a high success rate of demand response other than acquiring and analyzing time-series data on the consumer's actual demand.

[0013] If actual demand data could be obtained from each power consumer, the power consumer could be appropriately evaluated. However, since actual demand data is generally confidential information, it is not easy to obtain actual demand data from each power consumer in practice. For this reason, a problem has arisen in which aggregators cannot collect consumers with characteristics that improve the success rate of demand response.

[0014] As an evaluation method for consumers who collect negawatts, the above-mentioned Patent Document 1 describes a diagnostic device that returns a response including the results of diagnosing electrical equipment associated with the consumer that is unable to respond to demand response.

[0015] On the other hand, Patent Document 1 focuses on the evaluation of electrical equipment attached to consumers who have already participated in negawatt trading, and does not provide a technology for evaluating and selecting consumers as to whether they are appropriate as negawatt consumers without actual demand data at a stage prior to participating in negawatt trading.

[0016] In contrast, the present proposal is characterized by making it possible to select consumers who will improve the success rate of demand response in negawatt aggregation businesses without obtaining actual demand data from the consumers.

[0017] (First embodiment) Fig. 1 is a block diagram showing a schematic configuration of an information processing device 1 according to a first embodiment. Each block in the information processing device 1 in Fig. 1 is a software module that functions by executing a program on, for example, a general-purpose PC (personal computer). Alternatively, at least some of the blocks in the information processing device 1 in Fig. 1 can be configured with hardware components such as an IC (Integrated Circuit).

[0018] 1 includes, as an essential component, a fluctuation amount calculation unit 3. The fluctuation amount calculation unit 3 calculates a fluctuation amount of a first success rate for a demand response calculated based on the feature amount by adding information of an evaluation target consumer to the feature amount of a reference consumer.

[0019] The information processing device 1 of FIG. 1 may also include a first success rate calculation unit 2. The first success rate calculation unit 2 calculates a success rate for demand response based on the feature amount of the reference consumer. Demand response means, for example, suppressing power demand in response to a request to reduce the demand for power. The reference consumer refers to a consumer whose feature amount is known. Since the reference consumer is often composed of multiple consumers, hereinafter, they are collectively referred to as a reference consumer group. In addition, when the reference consumers included in the reference consumer group can be divided into several groups, each group is referred to as a reference consumer group. In the following, an example in which there are multiple reference consumer groups will be mainly described.

[0020] The feature quantities of the reference consumer group include at least one of the total contract reduction amount, which is the sum of the demand suppression amounts of each reference consumer when demand response is activated, the variability of the power demand of the reference consumer group, and the maximum deviation amount of the reference consumer group. The variability is the total variance of the time series data of the difference between the actual demand and the baseline of each reference consumer. The maximum deviation amount is the largest value among the time series data of the difference between the actual demand and the baseline for each reference consumer of the reference consumer group.

[0021] The baseline is the expected power demand of consumers in the absence of demand suppression requests, and may be calculated according to the guidelines for energy resource aggregation business, or may be a value calculated based on an original demand forecast. The time granularity required for calculating various features based on time series data represented by the difference between actual demand and the baseline may be determined by a method in line with the assessment of each electricity market, or the user may independently set the required time granularity.

[0022] The feature amount of the reference consumer is acquired, for example, from the reference consumer information storage unit 4 in FIG. 1. FIG. 2 is a diagram showing an example of the feature amount stored in the reference consumer information storage unit 4. The reference consumer information storage unit 4 in FIG. 2 stores, for each of a plurality of reference consumer groups, a reference consumer ID for identifying each reference consumer group, a total contracted reduction amount [kW], and a degree of variation "kW 2 ] and the maximum deviation amount [kW] are stored in correspondence with each other.

[0023] The first success rate calculation unit 2 in Fig. 1 acquires the feature amount of the reference consumer group from the reference consumer information storage unit 4 and calculates the success rate of the demand response. The first success rate calculation unit 2 calculates the success rate of the demand response (first success rate) for each of the multiple reference consumer groups. The method of calculating the success rate of the demand response will be described later.

[0024] The fluctuation amount calculation unit 3 in Fig. 1 calculates the amount of success rate fluctuation between the first success rate and the second success rate, using the success rate (first success rate) for demand response of the reference consumer group and the success rate (second success rate) for demand response when the evaluation target consumer is added to the reference consumer. The evaluation target consumer is a consumer other than the reference consumer and is capable of reducing power demand in response to a demand reduction request. The fluctuation amount calculation unit 3 calculates how much the success rate for demand response changes when the evaluation target consumer is added to the reference consumer.

[0025] When there are multiple reference consumer groups, the fluctuation amount calculation unit 3 calculates the fluctuation amount for each reference consumer group. When there are multiple consumers to be evaluated, the fluctuation amount calculation unit 3 calculates the fluctuation amount for each of the multiple reference consumer groups for each consumer to be evaluated. In this specification, the consumer to be evaluated is referred to as the consumer to be evaluated whether it is a single consumer or is composed of multiple consumers.

[0026] 1 may include a second success rate calculation unit 5. The second success rate calculation unit 5 calculates a success rate (second success rate) for demand response in a state where the evaluation target evaluation is added to the reference consumer, based on the characteristic amount of the evaluation target consumer and the characteristic amount of the reference consumer.

[0027] The information processing device 1 in Fig. 1 may include a DR success rate storage unit 6. DR is an abbreviation for demand response. The DR success rate storage unit 6 stores the first success rate calculated by the first success rate calculation unit 2 and the second success rate calculated by the second success rate calculation unit 5 in association with each of a plurality of reference consumer groups.

[0028] The fluctuation amount calculation unit 3 calculates the amount of fluctuation in the demand response success rate by using the first success rate calculated by the first success rate calculation unit 2 and the second success rate calculated by the second success rate calculation unit 5. The fluctuation amount calculation unit 3 calculates the above-mentioned amount of fluctuation by referring to the success rates (the first success rate and the second success rate) stored in the DR success rate storage unit 6. The calculation method will be described later.

[0029] The information processing device 1 in Fig. 1 may include a success rate fluctuation amount storage unit 7. The success rate fluctuation amount storage unit 7 stores the fluctuation amount calculated by the fluctuation amount calculation unit 3. Since the fluctuation amount differs for each of the multiple reference consumer groups, the success rate fluctuation amount storage unit 7 stores the fluctuation amount in association with each reference consumer group.

[0030] The information processing device 1 in FIG. 1 may include an operation input unit 8, a display unit 9, and an input / output interface (I / F) unit 10.

[0031] The operation input unit 8 inputs the features of the reference consumer group and the features of the consumer to be evaluated. The user may input the features at the operation input unit 8, or the features may be input from another device via the operation input unit 8. The features input from the operation input unit 8 are input to the first success rate calculation unit 2, the second success rate calculation unit 5, the reference consumer information storage unit 4, etc. via the input / output I / F unit 10. The input / output I / F unit 10 receives the fluctuation amount stored in the above-mentioned success rate fluctuation amount storage unit 7 and sends it to the display unit 9.

[0032] 3 is a flowchart showing an example of the processing operation of the first success rate calculation unit 2 and the second success rate calculation unit 5. First, the characteristic quantities of the consumer to be evaluated are acquired (step S1). The first success rate calculation unit 2 acquires the characteristic quantities of the consumer to be evaluated inputted in the operation input unit 8 via the input / output I / F unit 10. The characteristic quantities acquired by the first success rate calculation unit 2 in step S1 include, for example, the total contracted reduction amount and the degree of variability of the consumer to be evaluated.

[0033] Before or after the processing of step S1, the first success rate calculation unit 2 acquires the features of the reference consumer from the reference consumer information storage unit 4 (step S2). When performing the processing of step S2, the first success rate calculation unit 2 specifies a reference consumer ID to access the reference consumer information storage unit 4, and acquires the corresponding total contract reduction amount, the degree of variation, and the maximum deviation amount. In step S2, if there are multiple reference consumer groups, the first success rate calculation unit 2 acquires the features of all reference consumer groups included in the specified reference consumer group set I.

[0034] Next, the first success rate calculation unit 2 calculates the success rate (first success rate) of the demand response of each reference consumer group based on the feature amount of the reference consumer group (step S4). In this specification, the first success rate of the reference consumer group i is referred to as SRref,i.

[0035] Next, the second success rate calculation unit 5 calculates the success rate (second success rate) for demand response when the evaluation target consumer is added to each reference consumer group based on the characteristics of the evaluation target consumer and the characteristics of each reference consumer group (step S5). In this specification, the second success rate when the evaluation target consumer is added to the reference consumer group i is referred to as SRadd,i.

[0036] The processes of steps S4 and S5 are performed for all reference consumer groups (steps S3 to S6). The success rates SRref,i and SRadd,i calculated in steps S4 and S5 are output in association with the reference consumer group i (step S7). The output success rates SRref,i and SRadd,i are stored in the DR success rate storage unit 6 in association with the reference consumer group i.

[0037] In the model for calculating the success rate of the demand response in steps S4 and S5, for example, a lumped inequality is used. There are several variations of the lumped inequality, but an example using Bernstein's inequality will be described below. Note that the success rates SRref,i and SRadd,i of the demand response may be calculated using a model using a lumped inequality other than Bernstein's inequality.

[0038] Bernstein's inequality states that the sum of the independent random variables Xi is a constant value. It is an inequality that expresses the restriction on the probability P of an event that is greater than TIFF0007675575000001.tif12140 using the features of random variables. Bernstein's inequality is expressed by the following equation (1).

number

[0039] In equation (1), c is the largest possible value for each independent random variable, and ΣVar[Xj] is the sum of the variances of the random variables.

[0040] By treating the random variable Xi as time series data of the difference between the actual demand of each consumer in a consumer group and the baseline, it is possible to construct a constraint equation for the probability of the values ​​that can be taken by the sum of the random variables Xi, i.e., the difference between the actual demand of the consumer group and the baseline.

[0041] Below, we will explain modeling of the success rate of demand response when a supply and demand adjustment market is assumed. The method of this embodiment can be applied to other negawatt transactions by changing the conditional equation of the event of the random variable included in the left side of Bernstein's inequality into a format suitable for each negawatt transaction.

[0042] In the evaluation of the supply and demand adjustment market, the region in which a demand response is judged to be successful is determined by the demand reduction command value when a demand response is invoked. Under the current system, the actual demand of the entire group of selected consumers who will reduce demand must be kept within an error range of ±10% of the command value, which indicates the amount of reduction requested when a demand response is invoked, based on the baseline. The error range may be subject to change due to future system changes, so values ​​other than the above error range may be used. The command value is determined with the maximum value being the amount of contribution that the aggregator has applied for. Consumers submit their contracted reduction amounts to the aggregator. TIFF0007675575000003.tif9140 is presented. Therefore, the demand response success judgment threshold TIFF0007675575000004.tif8140 is expressed by the following equation (2) for a reference consumer group having n consumers.

number

[0043] Using the above ideas, the limit on the success rate of demand response in a supply and demand balancing market, where actual demand needs to stay in an area with upper and lower constraints, is expressed by the following equation (3).

number

[0044] ΣVar[Xj] is the variability of the reference consumer group, TIFF0007675575000007.tif8140 means 10% of the total contract reduction amount, and c means the maximum deviation amount. Therefore, the demand response success rate of the reference consumer group i is modeled by the following equation (4) using the corresponding feature amount.

number

[0045] In step S3 described above, by calculating equation (4), the demand response success rate (first success rate) SRref,i can be obtained only from the feature amount of the actual demand data without directly analyzing the actual demand data.

[0046] In the above-mentioned step S4, the demand response success rate (second success rate) SRadd,i of the new consumer group i obtained by adding the evaluation target consumer to the reference consumer group i is calculated. The formula used for mathematical modeling in step S4 is the same as that in step S3, but the variation amount ΔV and the contract reduction amount are added as the feature amounts of the evaluation target consumer. By inputting TIFF0007675575000009.tif13140, SRadd,i is calculated using the following formula (5).

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[0047] By calculating equation (5), the demand response success rate (second success rate) SRadd,i can be obtained using only the features of the consumer group i, without directly analyzing the actual demand data of the consumer group i.

[0048] 4 is a flowchart showing an example of the processing operation of the fluctuation amount calculation unit 3. First, the fluctuation amount calculation unit 3 acquires all success rates SRref,i, SRadd,i stored in the DR success rate storage unit 6 (step S11).

[0049] Next, for all i, the demand response success rate fluctuation amount ΔSRi is calculated (steps S12 to S14). The success rate fluctuation amount ΔSRi is most simply expressed as the difference between SRref,i and SRadd,i, as shown in the following formula (6). ΔSRi = SRadd,i - SRref,i … (6)

[0050] The demand response success rate fluctuation amount may be calculated by any method other than equation (6) as long as it is a calculation using SRref,i and SRadd,i. For example, it may be calculated by adding an appropriate correction term or coefficient to equation (6), or it may be calculated by any function with SRref,i and SRadd,i as parameters.

[0051] Next, the acquired demand response success rate fluctuation amount ΔSRi is outputted and stored in the success rate fluctuation amount storage unit 7, for example.

[0052] By the above operations, the demand response success rate fluctuation amount ΔSRi can be calculated from the demand response success rate obtained by modeling.

[0053] (Verification of validity) Next, the contents of an experiment to verify the validity of the demand response success rate fluctuation amount ΔSRi calculated by the information processing device 1 according to the first embodiment will be described. In the verification experiment, with negawatt trading in mind, which requires that the actual demand stay in an area with upper and lower constraints, a comparison is considered between the demand response success rate fluctuation amount ΔSRbernstein output using only the feature amount of the consumer using Bernstein's inequality and the demand response success rate fluctuation amount ΔSRdata output based on a detailed analysis of the actual data, using actual data of the consumer for a certain period.

[0054] In the demonstration experiment, for example, actual demand time series data in 5-minute intervals from 39 actual consumers is used. From this, 10 consumers are selected and bundled to form a reference consumer group. The other consumers are individually bundled with the reference consumer group, and ΔSRbernstein and ΔSRdata are calculated and compared.

[0055] The target dates for data analysis are weekdays from 2019 / 10 / 1 to 2019 / 12 / 25. ΔSRbernstein is calculated using data from weekdays in the period two months prior to the evaluation date and time. In other words, the evaluation from 2019 / 10 / 1 to 2019 / 10 / 31 uses data from 2019 / 8 / 1 to 2019 / 9 / 30, the evaluation from 2019 / 11 / 1 to 2019 / 11 / 30 uses data from 2019 / 9 / 1 to 2019 / 10 / 31, and the evaluation from 2019 / 12 / 1 to 2019 / 12 / 25 uses data from 2019 / 10 / 1 to 2019 / 11 / 30.

[0056] When evaluating ΔSRbernstein, outliers included in the actual demand or baseline are excluded. The evaluation time period is from 15:00 to 18:00. The amount of power contribution is set to 0 kW, and the area for determining the success of demand response is set to 10% of the maximum contribution possible for the consumer group. As the baseline, the equivalent day adoption method described in the Guidelines for Energy Resource Aggregation Business is used.

[0057] FIG. 5 is a diagram showing the results of the effectiveness verification experiment of the first embodiment. Graphs g1 and g2 in FIG. 5A are graphs in which the values ​​of the success rate fluctuations ΔSRbernstein and ΔSRdata when the evaluation target consumer is added to the reference consumer are connected by a broken line. From the graphs g1 and g2, it can be confirmed that the demand response success rate fluctuations using only the feature quantities of the actual demand according to Bernstein's inequality and the actual demand response success rate fluctuations match to the extent of several percent for almost all consumers. Graph g3 in FIG. 5B shows the difference between graphs g1 and g2. As can be seen from the graph g3, the standard deviation is 2.5 in the difference between the demand response success rate fluctuations of the graphs g1 and g2.

[0058] The above demonstration experiment confirms that the demand response success rate fluctuation amount calculated using only the features of actual demand according to the processing procedures in Figures 3 and 4 matches with high accuracy the actual demand response success rate fluctuation amount calculated using the actual demand data itself.

[0059] Fig. 6 is a diagram showing an example of a screen display of the display unit 9. As shown in Fig. 6, within the screen of the display unit 9, for example, a first display area D1, a second display area D2, and a third display area D3 are provided.

[0060] In the first display area D1, an input field for a user to input is displayed. The input field is a field for inputting a feature amount of a consumer to be evaluated. As the feature amount, for example, a field for inputting a contract reduction amount and a degree of variation is provided.

[0061] In the second display area D2, the fluctuation amount of the demand response success rate of the consumer group, which is the reference consumer group selected by the user plus the consumer to be evaluated, is displayed in association with the identification information of the reference consumer group. In the example of Fig. 6, the fluctuation amount of the success rate when the reference consumer group A is selected is 7.2%, and the fluctuation amount of the success rate when the reference consumer group C is selected is -4.3%.

[0062] Selection of the reference consumer group to be displayed in the second display area D2 can be performed, for example, in the third display area D3. In the third display area D3, characteristic quantities are displayed for each of the multiple reference consumer groups. As characteristic quantities, for example, the total contract reduction amount, the degree of variation, and the maximum deviation amount are displayed. Also, check buttons are provided for selecting one of the multiple reference consumer groups. The user can arbitrarily select one of the reference consumer groups by using the check buttons.

[0063] In the second display area D2, for each reference consumer group checked by the user in the third display area D3, the amount of fluctuation in the demand response success rate obtained by adding the evaluation target consumer to the selected reference consumer group is displayed.

[0064] In this way, in the first embodiment, by using the feature values ​​of the consumer to be evaluated and the feature values ​​of the reference consumer group without using the actual demand data of the consumer to be evaluated, it is possible to calculate the fluctuation amount of the demand response success rate when the consumer to be evaluated is added to the reference consumer group. Therefore, even if the actual demand data of the consumer to be evaluated cannot be obtained, it is possible to determine whether the consumer to be evaluated contributes to improving the demand response success rate and quickly determine which reference consumer group is the best to combine the consumer to be evaluated with.

[0065] Second Embodiment In the above-mentioned first embodiment, an example of acquiring features from a consumer to be evaluated has been described, but there may be cases where features cannot be acquired from the consumer to be evaluated. In the second embodiment described below, even if features cannot be acquired from the consumer to be evaluated, various conditions of the consumer to be evaluated that satisfy the user's needs for a specific reference consumer group are output and presented to the consumer to be evaluated, thereby making it possible to collect the consumer's information.

[0066] Fig. 7 is a block diagram showing a schematic configuration of an information processing device 1a according to the second embodiment. The information processing device 1a in Fig. 7 includes, as essential components, a feature amount range determination unit 11, a fluctuation amount classification unit 12, and an extraction unit 13. The information processing device 1a in Fig. 7 may also include a feature amount range storage unit 14 and a consumer condition storage unit 15.

[0067] The feature amount range determination unit 11 determines the range of feature amounts that the consumer to be evaluated can have. In the second embodiment, it is assumed that feature amounts cannot be obtained from the consumer to be evaluated. Therefore, the feature amount range determination unit 11 preliminarily assumes the range of feature amounts that the consumer to be evaluated can have. The feature amount range determination unit 11 may determine the range of feature amounts according to the size of the reference consumer group, or may determine the range of feature amounts based on information input by the user. The range of feature amounts determined by the feature amount range determination unit 11 is stored, for example, in the feature amount range storage unit 14.

[0068] The fluctuation amount classification unit 12 calculates the fluctuation amount by adding the evaluation target consumer having the feature amount within the feature amount range determined by the feature amount range determination unit 11 to the reference consumer stored in the reference consumer information storage unit 4. When the feature amount within the feature amount range includes multiple discrete feature amounts, the operation of the fluctuation amount calculation unit 3 is used to calculate the demand response success rate fluctuation amount for all combinations of the feature amounts, and classifies the amounts according to their magnitude. When the feature amount within the feature amount range is a continuous value, the success rate fluctuation amount can be calculated by solving a function for calculating the demand response success rate fluctuation amount. The fluctuation amount classification unit 12 may acquire the range of the feature amount of the evaluation target consumer from the feature amount range storage unit 14.

[0069] The extraction unit 13 extracts conditions of feature quantities of the consumer to be evaluated that are included in a classification that satisfies a pre-specified demand response success rate fluctuation quantity from the classification of the fluctuation quantities calculated by the fluctuation quantity classification unit 12. The extracted conditions of feature quantities of the consumer to be evaluated are stored in, for example, the consumer condition storage unit 15.

[0070] The information processing device 1a in Fig. 7 may include an operation input unit 8, a display unit 9, and an input / output I / F unit 10, similar to Fig. 1. The operation input unit 8 is used to input, for example, a range of features and a designation of a reference consumer to be used for evaluation. The input / output I / F unit 10 transmits information inputted by the operation input unit 8 to the feature range determination unit 11 and the fluctuation amount classification unit 12, and transmits the conditions of the features stored in the consumer condition storage unit 15 to the display unit 9.

[0071] 8 is a flowchart showing the processing operation of the information processing device 1a according to the second embodiment. First, the range of the feature quantity of the consumer to be evaluated is determined (step S21). Here, the feature quantity range determination unit 11 determines the range of the feature quantity based on the range of the feature quantity inputted via the operation input unit 8, for example.

[0072] Next, within the range of the determined feature amount, the fluctuation amount classifying unit 12 calculates and classifies the fluctuation amount of the demand response success rate for the reference consumer group (step S22).

[0073] When the feature value within the feature value range is a continuous value, classification is performed using a function that represents the demand response success rate fluctuation amount. The function that represents the demand response success rate fluctuation amount can be changed in various ways, but for example, by using equation (6) that represents the demand response success rate fluctuation amount, and expanding it for the feature value of the evaluation target consumer and performing a first-order approximation, it can be expressed as the following equation (7). α in equation (7) is a condition for the demand response success rate fluctuation amount specified by the user, and classification is performed based on this amount.

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[0074] Next, based on the calculated demand response success rate fluctuation amount, the extraction unit 13 extracts conditions of the feature quantities of the evaluation target consumers in the classification that satisfies a pre-specified success rate (step S23). Here, the user may specify only one success rate or multiple success rates.

[0075] Through the above processing operations, it is possible to acquire conditions for the feature quantities of the consumer to be evaluated that satisfy the fluctuation amount of the demand response success rate designated by the user. The conditions for the feature quantities extracted in step S23 are displayed on the display unit 9, for example.

[0076] 9A and 9B are diagrams showing an example of the results of processing performed by the information processing device 1a according to the second embodiment. Fig. 9A is a diagram showing the conditions of the feature quantities of the evaluation target consumers to be added to the reference consumer group P, and Fig. 9B is a diagram showing the conditions of the feature quantities of the evaluation target consumers to be added to the reference consumer group Q.

[0077] 9A and 9B show a line gr4 where the demand response success rate fluctuation is 0, a line gr5 where the fluctuation increases by 1%, and a line gr6 where the fluctuation decreases by 1%. The horizontal axis of FIG. 9A and FIG. 9B shows the degree of variation, which is one of the feature quantities of the evaluation target consumer, and the vertical axis shows the contract reduction amount, which is another feature quantity. In FIG. 9A and FIG. 9B, multiple evaluation target consumers that satisfy the feature quantity conditions determined by feature quantity range determination unit 11 are plotted.

[0078] For the reference consumer group P in Figure 9A, only the consumer A being evaluated is able to achieve an increase in the fluctuation in the demand response success rate of within 1%, while the other consumers B, C, and D being evaluated experience a decrease in the fluctuation in the demand response success rate of 1% or more.

[0079] For the reference consumer group Q in Figure 9B, the consumers A and B to be evaluated can achieve an increase in the fluctuation rate of the demand response success rate of within 1%, but if a decrease in the fluctuation rate of the demand response success rate of within 1% is acceptable, the consumers C and D to be evaluated can also be included as candidates.

[0080] In this way, in the second embodiment, even if the characteristics of the consumer to be evaluated cannot be obtained, by determining the range of the characteristics of the consumer to be evaluated, it is possible to select a consumer to be evaluated that meets the conditions of the demand response success rate fluctuation amount.

[0081] (Third embodiment) In the first and second embodiments, it is assumed that the actual demand data of the evaluation target consumer cannot be used completely, but the actual demand data of the reference consumer group can be used, but there are cases where the actual demand data of the evaluation target consumer can be used. In this case, by utilizing the fact that the actual demand data of each evaluation target consumer can be converted into a feature quantity and the demand response success rate fluctuation amount can be calculated, it is possible to solve a complex combinatorial optimization problem with the reference consumer group in a short time, even for a large evaluation target consumer group including many consumers.

[0082] (Fourth embodiment) In contrast to the embodiments described so far, when actual demand data of the reference consumer group is not available, the optimal combination of the consumer to be evaluated and the reference consumer group can be selected by acquiring characteristics of the reference consumer group that improve the fluctuation amount of the demand response success rate based on the characteristics of the consumer to be evaluated.

[0083] At least a part of the information processing device 1, 1a described in each of the above-mentioned embodiments may be configured with hardware or software. When configured with software, a program that realizes at least a part of the functions of the information processing device 1, 1a may be stored in a recording medium such as a flexible disk or a CD-ROM, and may be read and executed by a computer. The recording medium is not limited to a removable one such as a magnetic disk or an optical disk, but may be a fixed recording medium such as a hard disk device or a memory.

[0084] In addition, a program that realizes at least a part of the functions of the information processing device 1, 1a may be distributed via a communication line (including wireless communication) such as the Internet. Furthermore, the program may be encrypted, modulated, or compressed and distributed via a wired line or wireless line such as the Internet, or stored in a recording medium.

[0085] The aspects of the present disclosure are not limited to the above-mentioned individual embodiments, but include various modifications that may be conceived by a person skilled in the art, and the effects of the present disclosure are not limited to the above-mentioned contents. In other words, various additions, modifications, and partial deletions are possible within the scope of the conceptual idea and intent of the present disclosure derived from the contents defined in the claims and their equivalents. [Explanation of symbols]

[0086] 1 Information processing device, 1a Information processing device, 2 First success rate calculation unit, 3 Fluctuation amount calculation unit, 4 Reference consumer information storage unit, 5 Second success rate calculation unit, 6 DR success rate storage unit, 7 Success rate fluctuation amount storage unit, 8 Operation input unit, 9 Display unit, 10 Input / output I / F unit, 10 Input / output interface (I / F) unit, 11 Feature amount range determination unit, 12 Fluctuation amount classification unit, 13 Extraction unit, 14 Feature amount range storage unit, 15 Consumer condition storage unit

Claims

1. a first success rate calculation unit that calculates a first success rate for the demand response by using the feature amount of the reference consumer; a second success rate calculation unit that calculates a second success rate for the demand response by using the characteristic amount of the evaluation target consumer and the characteristic amount of the reference consumer; a variation amount calculation unit that calculates a difference between the first success rate and the second success rate as a variation amount of the first success rate, The information processing device, wherein the feature amount includes at least one of an amount of power reduction in response to demand response, a degree of variation over time of a difference between an actual demand and a baseline, and a maximum deviation amount between the actual demand and the baseline.

2. The information processing apparatus according to claim 1 , wherein the first success rate calculation unit calculates the first success rate when responding to a request to reduce demand for power.

3. The information processing apparatus according to claim 1 , wherein the reference consumer is a consumer whose characteristic amount is known, or a consumer group consisting of a plurality of consumers whose characteristic amount is known.

4. a reference consumer information storage unit that stores each of the plurality of reference consumers in association with a feature amount of the corresponding reference consumer; The information processing device according to claim 1 , wherein the first success rate calculation unit calculates the first success rate using features of at least a portion of the reference consumers acquired from the reference consumer information storage unit.

5. a feature acquisition unit that acquires a feature of the evaluation target consumer, An information processing device according to any one of claims 1 to 4, wherein the second success rate calculation unit calculates the second success rate based on the characteristics of the evaluation target consumer and the characteristics of the reference consumer acquired by the characteristic acquisition unit.

6. The information processing device described in claim 5, wherein the fluctuation amount calculation unit calculates the fluctuation amount based on the characteristics of the evaluation target consumer and the characteristics of the reference consumer acquired by the characteristic amount acquisition unit without acquiring actual demand data of the evaluation target consumer.

7. The first success rate calculation unit calculates a plurality of the first success rates by using feature amounts of a plurality of the reference consumers, An information processing device as described in claim 5 or 6, wherein the fluctuation amount calculation unit calculates the difference between the multiple first success rates calculated by the first success rate calculation unit and the multiple second success rates calculated by the second success rate calculation unit based on the characteristics of the evaluation target consumer and each of the characteristics of the multiple reference consumers.

8. The first success rate calculation unit calculates the first success rate based on a concentrated inequality including a term related to the feature amount of the reference consumer; The information processing device according to claim 1 , wherein the second success rate calculation unit calculates the second success rate based on a concentrated inequality including terms related to features of the reference consumer and the evaluation target consumer.

9. The information processing apparatus according to claim 1 , further comprising a difference output unit that outputs the fluctuation amount calculated by the fluctuation amount calculation unit in association with the reference consumer.

10. a fluctuation amount storage unit that stores the fluctuation amount calculated by the fluctuation amount calculation unit in association with the reference consumer, The information processing device according to claim 9 , wherein the difference output unit outputs the amount of fluctuation stored in the amount of fluctuation storage unit in association with the reference consumer.

11. a feature input unit for inputting the feature of the evaluation target consumer; a difference output unit that uses the feature input by the feature input unit to output the fluctuation amount calculated by the fluctuation amount calculation unit for each of the plurality of reference consumers together with information on the corresponding reference consumer, The information processing apparatus according to claim 7 , wherein the feature amount acquisition unit acquires the feature amount inputted by the feature amount input unit.

12. a display unit that displays the first display area and the second display area on the same screen; The feature input unit inputs a feature of the evaluation target consumer in an input field provided in the first display area, The information processing device according to claim 11 , wherein the difference output unit displays the difference calculated by the fluctuation amount calculation unit for each of the plurality of reference consumers in the second display area together with information on the corresponding reference consumer.

13. the display unit displays a third display area, which displays the feature quantities of the plurality of reference consumers, together with the first display area and the second display area on the same screen; the third display area includes a selection button for arbitrarily selecting a reference consumer for which the fluctuation amount calculation unit is to calculate the fluctuation amount from among the plurality of reference consumers, The information processing device according to claim 12 , wherein the difference output unit displays the difference calculated by the fluctuation amount calculation unit for a reference consumer selected by the selection button in the second display area together with information on the corresponding reference consumer.

14. A feature range determination unit that determines a range of features of a target consumer using at least one of the size of the reference consumer or information input by a user; an extraction unit that extracts, from the fluctuation amount calculated by the fluctuation amount calculation unit, a condition for a feature amount of the evaluation target consumer for obtaining a pre-specified success rate for a demand response, The information processing device according to claim 1 , wherein the fluctuation amount calculation unit calculates the fluctuation amount by adding the evaluation target consumer having a feature amount within the range of the feature amount determined by the feature amount range determination unit to the reference consumer.

15. The information processing device according to claim 14 , further comprising a consumer condition output unit that outputs conditions of the characteristic quantities of the evaluation target consumer extracted by the extraction unit.

16. A computer comprising: Calculating a first success rate for the demand response by using the feature amount of the reference consumer; calculating a second success rate for the demand response by using the characteristic amount of the evaluation target consumer and the characteristic amount of the reference consumer; calculating a difference between the first success rate and the second success rate, that is, a fluctuation amount of the first success rate; The information processing method, wherein the feature amount includes at least one of an amount of power reduction in response to demand response, a degree of variation over time of a difference between actual demand and a baseline, and a maximum deviation amount between actual demand and the baseline.

17. On the computer, Calculating a first success rate for the demand response by using the feature amount of the reference consumer; calculating a second success rate for the demand response by using the characteristic amount of the evaluation target consumer and the characteristic amount of the reference consumer; calculating a difference between the first success rate and the second success rate, that is, a fluctuation amount of the first success rate, The program, wherein the feature amount includes at least one of an amount of power reduction in response to demand response, a degree of variation over time of the difference between the actual demand and a baseline, and a maximum deviation amount between the actual demand and the baseline.

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