Information processing apparatus, information processing method, and information processing program

Abstract

To support determination of an appropriate bid price.SOLUTION: The information processing apparatus 100 presents a bid reference price of the power transaction market. The information processing apparatus 100 acquires information on the power transaction market and stores the information in the storage unit. The information processing apparatus 100 receives a contract success probability in the power transaction market. The information processing apparatus 100 determines the bidding reference price according to the accepted contract success probability based on the information related to the electricity transaction market stored in the storage unit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In the energy trading market, such as the spot market or the advance market, there are known technologies for supporting the determination of bid prices. For example, a conventional method is known in which the high or low bid price of a product in the advance market for energy is predicted, and a trading reference price (bid price) that serves as a basis for trading is determined based on the predicted value (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-005404 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned conventional technology may have difficulty in supporting the determination of an appropriate bid price. For example, bid prices in the electricity trading market have large statistical variations near high and low prices, and the prediction results obtained by the conventional technology may have errors. Therefore, it may be difficult for users to determine an appropriate bid price using the prediction results obtained based on the conventional technology. [Means for solving the problem]

[0005] Therefore, in order to solve the above-mentioned problems and achieve the objective, the information processing device of the present invention is an information processing device that presents a bid base price in an electricity trading market, and is characterized by having an information acquisition unit that acquires information about electricity trading and stores it in a memory unit, a reception unit that accepts the probability of successful execution in the electricity trading market, and a bid base price determination unit that determines a bid base price corresponding to the probability of successful execution accepted by the reception unit based on the information about the electricity trading stored in the memory unit. [Effects of the Invention]

[0006] The present invention has the effect of enabling support for determining an appropriate bid price. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an overall image of processing by an information processing device according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the configuration of the information processing device according to this embodiment. [Figure 3] FIG. 3 is a table showing an example of transaction information according to this embodiment. [Figure 4] FIG. 4 is a table showing an example of representative values ​​according to this embodiment. [Figure 5] FIG. 5 is a table showing an example of the correction coefficients according to this embodiment. [Figure 6] FIG. 6 is a table showing an example of the bidding base price according to this embodiment. [Figure 7] FIG. 7 is a diagram showing an example of determining the bid base price according to this embodiment. [Figure 8] FIG. 8 is a flowchart showing an example of the procedure of information processing according to this embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of the procedure of information processing according to this embodiment. [Figure 10] FIG. 10 is a diagram showing an example of a distribution model of the average contract price according to this embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of the time flow of the electricity trading market and the timing of determining the bid reference price. [Figure 12] FIG. 12 is a diagram showing an example of a distribution model of average contract prices according to the second modified example. [Figure 13] FIG. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, an embodiment (hereinafter, "embodiment") will be described with reference to the drawings. In the following description, common components are denoted by the same reference numerals, and repeated description will be omitted. Furthermore, the description of the embodiment described below does not limit the information processing device, information processing method, and information processing program according to the present invention.

[0009] <Introduction> First, an introduction to this embodiment will be provided. Fig. 1 is a diagram illustrating an overview of processing by an information processing device 100 according to this embodiment. The information processing device 100 shown in Fig. 1 is an example of a computer that provides a technology for determining a bid reference price for an energy transaction according to a contract success probability for the energy transaction input by a user, using information related to the energy transaction (hereinafter, sometimes referred to as "energy transaction information") including acquired past transaction information in the energy trading market (hereinafter, sometimes referred to as "transaction history information") and weather information.

[0010] (background) Since the revision of the Electricity Business Act in April 2016, electricity has been traded through the Japan Electric Power Exchange (JEPX). At JEPX, electricity is traded in electricity trading markets such as the spot market (day-ahead market) and the hourly market (internal market).

[0011] Here, we will further explain electricity trading in the electricity trading market. In electricity trading markets such as the spot market (day-ahead market) and the hourly market (intraday market), a day is divided into electricity measurement units (0 to 30 minutes and 30 to 60 minutes of each hour), and trading is carried out as 48 individual products. Hereinafter, in this embodiment, the aforementioned 30-minute divisions will be referred to as frames, and will be assigned numbers 1 to 48. For example, the period from midnight to 12:30 will be frame 1, the period from 12:30 to 1:00 will be frame 2, ..., and the period from 11:30 to 24:00 (12:00 the next day) will be frame 48.

[0012] The above-mentioned division method for the commodity is the same for the spot market (day-ahead market) and the hour-ahead market (intraday market). Hereinafter, in this embodiment, the above-mentioned 30-minute intervals will be referred to as "frames," and just like with commodities, the frames will also be assigned numbers from 1 to 48. Note that the unit of trading power is 0.1 MW (50 kWh of power for 30 minutes), and the bid price is specified as the price per kWh in cents (0.01 yen). In addition, in the following explanation of this embodiment, terms related to dates such as the previous day, the current day, and the next day will be based on the time of consumption (demand and supply) of the commodity, electricity (the current day).

[0013] For products in the spot market (day-ahead market), the bidding deadline is 10:00 a.m. the day before, and a single price per product is determined immediately after the deadline, using a single-price auction format.

[0014] On the other hand, products in the pre-hour market (intraday market) are traded (bought and sold) in an intraday format between 5 PM of the previous day and one hour before the start of product delivery. For example, products in time slot 1 can be traded between 5 PM of the previous day and 11 PM of the previous day, and products in time slot 2 can be traded between 5 PM of the previous day and 11:30 PM of the previous day. Similarly, products in time slot 48 can be traded between 5 PM of the previous day and 10:30 PM of the current day. As mentioned above, in the pre-hour market (intraday market), there is a separate trading period for each product, and trading for each product takes place within a predetermined period.

[0015] In order to trade electricity efficiently in the energy trading market, it is necessary to predict the trading price in advance and to bid at an appropriate price depending on the circumstances of the user placing the bid. For example, when placing a purchase bid, a user may specify a lower predicted trading price if they prioritize the most cost-efficient energy trading, or a higher predicted trading price if they prioritize securing electricity.

[0016] Therefore, in order to ensure appropriate electricity trading, a reference technique is known in which high or low bid prices in the electricity trading market are predicted and a trading reference price (bid price) is determined based on the predicted value.

[0017] However, the reference technology may have difficulty in supporting the determination of an appropriate bid price. Specifically, the high and low bid prices have a large statistical variance, and the error in the prediction results of the high and low prices predicted by the reference technology is large. Therefore, it may be difficult for a user to determine an appropriate bid price based on the prediction results.

[0018] (Overall Overview of Processing by Information Processing Device 100) In order to solve the above-mentioned problems, the information processing device 100, which presents the bid base price in the energy trading market, determines the bid base price to be used as reference information when determining the bid price, in accordance with the contract success probability of the desired energy transaction input by the user, and outputs the determined bid base price to the user. Now, returning to Figure 1, a series of processing flows of the information processing device 100 will be described.

[0019] The information processing device 100 acquires energy trading information and stores it in the storage unit 120 ((1) in FIG. 1). For example, the information processing device 100 acquires trading history information, weather information, and the like as the energy trading information and stores them in the storage unit 120.

[0020] The information processing device 100 receives the contract success probability in the energy trading market ((2) in FIG. 1). Next, the information processing device 100 determines ((3-3) in FIG. 1) a bid base price according to the received contract success probability ((3-2) in FIG. 1) based on the energy trading information stored in the memory unit 120 ((3-1) in FIG. 1). The information processing device 100 can output the determined bid base price to a user or the like ((4) in FIG. 1).

[0021] Through the above-described process, the information processing device 100 according to the present embodiment presents a bid base price according to the user's desired contract success probability, thereby achieving the effect of supporting the determination of an appropriate bid price for realizing the user's desired energy transaction.

[0022] <Description of Information Processing Device 100> Next, a detailed description will be given of the functions of the information processing device 100 according to this embodiment. Fig. 2 is a diagram showing an example of the configuration of the information processing device 100 according to this embodiment.

[0023] (Information processing device 100) 2, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing device 100 also includes an input unit (not shown) such as a keyboard or a touch panel for receiving input from a user or the like, and a display unit (not shown) such as a display or a printer for displaying the results of information processing by the information processing device 100 to the user or the like.

[0024] (Communication unit 110) The communication unit 110 outputs the correction coefficient and bidding base price for each product, and performs communications related to obtaining energy trading information and inputting the contract success probability. The communication unit 110 is realized by a NIC (Network Interface Card) or the like. The communication unit 110 is connected to a network via wired or wireless connection as necessary, and can transmit and receive information bidirectionally.

[0025] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 stores data and programs used for various processes by the control unit 130. As shown in FIG. 2 , the storage unit 120 has an energy trading information DB 121, a representative value DB 122, a correction coefficient DB 123, and a bid reference price DB 124.

[0026] (Electricity trading information DB121) The energy trading information DB 121 is a database that stores energy trading information, which is information related to transactions in the energy trading market. An example of the energy trading information stored in the energy trading information DB 121 will now be described with reference to Fig. 3. Fig. 3 is a table diagram showing an example of the energy trading information according to this embodiment.

[0027] As shown in FIG. 3, the energy trading information DB 121 stores representative value calculation data 121a that is used by a representative value prediction unit 133 (described later) to calculate a representative value, and correction coefficient calculation data 121b that is used by a correction coefficient calculation unit 134 (described later) to calculate a correction coefficient.

[0028] First, the representative value calculation data 121a will be described. As shown in the upper part of Fig. 3, the energy trading information DB 121 stores the spot market price (yen), the frame number, the day of the week, and the temperature (°C) as the representative value calculation data 121a in a table format or the like, in association with "No."

[0029] The spot market price (yen) mentioned above is information indicating the trading price of a spot market product for which electricity is transferred on the same day and in the same time slot as the product for which the bid base price is to be calculated. The time slot number is information for identifying a product in the electricity trading market. The day of the week is information indicating the day on which the target product, electricity, will be transferred. The temperature (°C) is information indicating the predicted temperature on the day and time slot on which the target product, electricity, will be transferred.

[0030] For example, the electricity trading information DB121 stores the spot market price "8.43 yen", the time slot number "1", the day of the week "Saturday", and the temperature "12.10 (℃)" in a table format or the like as individual data of the representative value calculation data 121a identified by No. "1".

[0031] Next, the correction coefficient calculation data 121b will be described. As shown in the lower part of Fig. 3, the energy trading information DB 121 stores the date of delivery (delivery date), the frame number, the time-ahead market contract price (yen / kWh), and the contract amount (MWh / h) as the correction coefficient calculation data 121b in a table format or the like, in association with "No.", which is information for identifying individual data of the correction coefficient calculation data 121b.

[0032] (Typical value DB122) The representative value DB 122 is a database that stores representative values ​​for each product in the energy trading market predicted by the representative value prediction unit 133, which will be described later. An example of the representative value stored in the representative value DB 122 will now be described with reference to Fig. 4. Fig. 4 is a table diagram showing an example of the representative value according to this embodiment.

[0033] 4, the representative value DB 122 stores the frame numbers and the representative values ​​in association with each other. For example, the representative value DB 122 stores the frame numbers in a table format in which the frame number "25" is associated with the representative value "12.08," etc.

[0034] The frame number is information for identifying a product in the energy trading market. The representative value (yen) is a representative value of the trading price in the energy trading for each frame number, predicted by the representative value prediction unit 133 described below.

[0035] Here, the "representative value" will be explained in more detail. In this embodiment, the representative value is a value that has a correlation with the trading reference price that is the prediction target and has little statistical variation (for example, a value whose standard deviation is below a predetermined threshold). For example, representative values ​​in the energy trading market include the average successful contract price, median successful contract price, and n% point successful contract price in the market before the current day.

[0036] The average successful execution price mentioned above is the average price of all successful execution transactions of a certain product. The median successful execution price is the median price of all successful execution transactions of a certain product. The n% successful execution price is the price at which, for example, n is a real number between 0 and 100, and the number of transactions with an execution price lower than n% of the total price of all successful execution transactions of a certain product is the price at which n% of all successful execution transactions of a certain product are lower than n.

[0037] The representative value may be something other than price as long as it has a correlation with the prediction target. For example, the power usage rate may be used as the representative value.

[0038] (correction coefficient DB123) The correction coefficient DB 123 is a database that stores correction coefficients calculated by a correction coefficient calculation unit 134, which will be described later. An example of the correction coefficients stored in the correction coefficient DB 123 will now be described with reference to Fig. 5. Fig. 5 is a table diagram showing an example of correction coefficients according to this embodiment.

[0039] 5, the correction coefficient DB 123 stores correction coefficient identification information and correction coefficients in a table format or the like, in association with "No.", which is information for identifying individual correction coefficients. For example, the correction coefficient DB 123 stores correction coefficient identification information "A" identified by No. "1" and a correction coefficient "0.98" in a table format or the like.

[0040] The correction coefficient identification information is information for identifying the calculated correction coefficient, and may be, for example, the frame number of the target representative value, or information for identifying the contract success rate used in the calculation when the correction coefficient is calculated for each contract success rate input by the user. The correction coefficient is a value that is multiplied by the representative value to calculate a price according to an arbitrary contract success probability.

[0041] (Bid base price DB124) The bid base price DB 124 is a database that stores bid base prices determined by the bid base price determination unit 135, which will be described later. An example of the representative value stored in the bid base price DB 124 will now be described with reference to Fig. 6. Fig. 6 is a table diagram showing an example of a bid base price according to this embodiment.

[0042] 6, the bid base price DB 124 stores frame numbers and bid base prices (yen) in association with each other. For example, the bid base price DB 124 stores frame numbers such as "25" and bid base prices such as "13.02" in a table format.

[0043] The frame number is information for identifying a product in the electricity trading market. The bid base price is a bid base price that is determined by the bid base price determination unit 135 (described later) and serves as a basis for the transaction price in the electricity trading for each frame number.

[0044] (control unit 130) Here, we return to Fig. 2 to continue the explanation. The control unit 130 is realized by a processor, a micro processing unit (MPU), a central processing unit (CPU), or the like executing various programs stored in the storage unit 120 using RAM as a work area. The control unit 130 is also realized by an integrated circuit (IC) such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). As shown in Fig. 2, the control unit 130 includes an information acquisition unit 131, a reception unit 132, a representative value prediction unit 133, a correction coefficient calculation unit 134, a bid base price determination unit 135, and an output unit 136.

[0045] (Information acquisition unit 131) The information acquisition unit 131 acquires, from the server 300 or the like, energy trading information such as representative value calculation data 121a and correction coefficient calculation data 121b as information used to predict the representative value, and stores the information in the energy trading information DB 121.

[0046] For example, the information acquisition unit 131 acquires, as the representative value calculation data 121a, information such as price history in the spot market and the hourly market, weather forecasts such as temperature, seasons, months, distinctions between weekdays and holidays, days of the week, slot numbers, whether or not there are any special events, etc. The information acquisition unit 131 can acquire any information that can be used to predict the representative value.

[0047] Furthermore, the information acquiring unit 131 acquires, as the correction coefficient calculation data 121b, information relating to the history of transaction prices in the time-ahead market from the day before yesterday up to a certain period of time in the past (hereinafter simply referred to as "price history information"). For example, the information acquiring unit 131 acquires information such as past transaction prices, contract times, and contracted amounts of electricity going back a predetermined number of days from the current day (base date) as price history information including price information reflecting various considerations such as weather, events, and the state of electricity supply and demand.

[0048] In addition, if the representative value can be directly acquired and no prediction is performed, the information acquisition unit 131 may directly acquire the representative value from an external source. For example, the information acquisition unit 131 can acquire, as the representative value, the price of the product for which the bid base price is to be derived and the price in the spot market for the same time slot.

[0049] (Reception Department 132) The reception unit 132 receives the contract success probability and the desired amount of energy to be traded in the energy trading market desired by the user. For example, the reception unit 132 receives one or more contract success probabilities and the desired amount of energy to be traded via an input unit or the like provided in a terminal device operated by the user or the information processing device 100.

[0050] (Representative value prediction unit 133) The representative value prediction unit 133 predicts the representative value based on a predetermined prediction model using the representative value calculation data 121a stored in the energy trading information DB 121. Specifically, the representative value prediction unit 133 predicts the representative value by inputting the spot market price, the slot number, the day of the week, and the temperature stored in the representative value calculation data 121a into a prediction model that has been constructed in advance using the spot market price, the slot number, the day of the week, and the temperature as explanatory variables.

[0051] For example, when predicting representative values ​​for products with frame numbers 25 to 36 shown in Figure 4, the representative value prediction unit 133 inputs representative value calculation data 121a for a certain period into the prediction model and predicts representative values ​​for each of frame numbers 25 to 36.

[0052] (Correction coefficient calculation unit 134) The correction coefficient calculation unit 134 calculates a correction coefficient according to the contract success probability accepted by the acceptance unit 132 based on the transaction history information included in the energy transaction information acquired by the information acquisition unit 131.

[0053] Specifically, the correction coefficient calculation unit 134 calculates a correction coefficient using price history information included in the transaction history information and the desired amount of energy to be traded. First, the correction coefficient calculation unit 134 extracts contract data for a certain number of products from the price history information. Next, the correction coefficient calculation unit 134 multiplies the number of extracted products by a set contract success probability, and calculates the convergence target number of products as a numerical value obtained by rounding off to the nearest integer.

[0054] The correction coefficient calculation unit 134 counts the number of products (the number of successfully traded products) for which the contract amount at which the contract is successful is greater than the desired energy amount for trading at the price calculated by multiplying the calculated representative value for each product by the correction coefficient candidate value.The correction coefficient calculation unit 134 then searches for a correction coefficient candidate value that makes the counted number of successfully traded products equal to the convergence target number of products, and sets this as the correction coefficient.

[0055] When selling electricity, if the total amount of electricity traded at a certain price or higher in the contract data for a specific product is greater than the desired amount of electricity to be traded, the contract for that product is deemed successful at that price. On the other hand, when buying electricity, if the total amount of electricity traded at a certain price or lower in the contract data for a specific product is greater than the desired amount of electricity to be traded, the contract for that product is deemed successful at that price.

[0056] If the amount of energy desired to be traded is sufficiently small, the correction coefficient calculation unit 134 may use as a criterion for successful execution whether the value obtained by multiplying the calculated representative value for each product by the correction coefficient candidate value is not higher than the highest price or not lower than the lowest price for the same product. Furthermore, if multiple contract success probabilities are input, the correction coefficient calculation unit 134 repeatedly executes the above-mentioned process a number of times corresponding to the number of input contract success probabilities.

[0057] (Bidding Reference Price Determination Unit 135) The bid base price determination unit 135 determines the bid base price based on at least one of the representative value determined based on the energy trading information and the predicted value of the representative value, and the correction coefficient calculated by the correction coefficient calculation unit 134. Specifically, the bid base price determination unit 135 determines the bid base price by multiplying at least one of the representative value and the predicted value of the representative value by the correction coefficient.

[0058] Here, an example of the process for determining the bid base price according to this embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the process for determining the bid base price according to this embodiment.

[0059] First, the correction coefficient calculation unit 134 calculates the convergence target number of products. For example, when the price history information for the past three months is used as the correction coefficient calculation source data, the correction coefficient calculation unit 134 multiplies the total number of extracted products (48 (total number of products per day) × 91 (number of days in three months) = 4,368) by 0.9 (probability of successful execution: 90%) and rounds off the obtained empirical result to calculate the convergence target number of products (3,931).

[0060] The correction coefficient calculation unit 134 calculates a representative value for each product from the contract data of all the extracted products. Next, the correction coefficient calculation unit 134 multiplies the calculated representative value for each product by a correction coefficient candidate value, the initial value of which is "1," to calculate the amount of energy that will be traded successfully if a bid is made for the target product at that price.

[0061] The correction coefficient calculation unit 134 sums up the calculated amounts of energy for the successfully traded transactions. Then, the correction coefficient calculation unit 134 counts the number of products for which the total amount of energy for the successfully traded transactions is greater than the desired amount of energy to be traded (the number of successfully traded products).

[0062] Here, if the number of successfully executed products does not match the convergence target number of products, the correction coefficient calculation unit 134 updates the correction coefficient candidate value so that the number of successfully executed products approaches the convergence target number of products. For example, when placing a buy bid, if the number of successfully executed products is less than the convergence target number of products, the correction coefficient calculation unit 134 adds a correction coefficient and recalculates. On the other hand, if the number of successfully executed products exceeds the convergence target number of products, the correction coefficient calculation unit 134 subtracts the correction coefficient and recalculates.

[0063] By repeating the above-mentioned process, the correction coefficient calculation unit 134 determines the correction coefficient candidate value "1.08" when the number of successfully executed products matches the convergence target number of products as the correction coefficient for the input successful execution probability of "90%" ((1) in Figure 7).

[0064] Next, the bid base price determination unit 135 calculates the bid base price for each frame ((3) in FIG. 7) by multiplying the correction coefficient "1.08" ((1) in FIG. 7) corresponding to the contract success probability of "90%" by the representative value for each frame ((2) in FIG. 7) predicted by the representative value prediction unit 133. For example, the bid base price determination unit 135 multiplies the representative value "12.08" for frame number "25" by the correction coefficient "1.08" to calculate the bid base price "13.02" for frame number "25".

[0065] The bid base price determination unit 135 repeats the above process for each frame. As a result, for example, the bid base price determination unit 135 determines the bid base price for "25 frames" with the contract success probability of "90%" input by the user as "13.02" ((4) in FIG. 7).

[0066] (output unit 136) The output unit 136 outputs the bidding base price determined by the bidding base price determination unit 135 to the user together with the contract success probability input by the user. Furthermore, if multiple contract success probabilities are input, the output unit 136 outputs the bidding base price determined for each contract success probability to the user. Furthermore, the output unit 136 can output the contract success probability together with the bidding base price.

[0067] (Processing Procedure) From here, the processing procedure by the information processing device 100 according to this embodiment will be described with reference to Fig. 8 and Fig. 9. Fig. 8 and Fig. 9 are flowcharts showing an example of the information processing procedure according to this embodiment. Fig. 8 is a flowchart of processing related to determining the bid base price. Fig. 9 is a flowchart of processing related to calculating a correction coefficient.

[0068] First, the procedure of the process for determining the bid base price will be described with reference to Fig. 8. The information acquisition unit 131 acquires energy trading information (S101). Furthermore, the reception unit 132 receives the contract success probability (S102).

[0069] If the predicted representative value is to be used (Yes in S103), the representative value prediction unit 133 predicts the representative value (S104). On the other hand, if the predicted representative value is not to be used (No in S103), the information acquisition unit 131 acquires information to be used as the representative value from an external server or the like (S105).

[0070] The correction coefficient calculation unit 134 calculates the correction coefficient (S106). Next, the bidding base price determination unit 135 multiplies the representative value by the correction coefficient to determine a bidding base price according to the contract success probability (S107). Next, the output unit 136 outputs the determined bidding base price (S108). Then, the information processing device 100 ends the processing.

[0071] Next, a process for calculating a correction coefficient will be described with reference to Fig. 9. The correction coefficient calculation unit 134 extracts contract data for each product from the price history information included in the transaction history information (S201).

[0072] The receiving unit 132 receives the contract success probability and the desired amount of energy to be traded from the user (S202). The representative value prediction unit 133 calculates a representative value of the contract data extracted for each product (S203).

[0073] The correction coefficient calculation unit 134 counts the number of products for which the amount of energy desired to be traded is successfully agreed at a price obtained by multiplying the representative value by the correction coefficient candidate value (S204). If a predetermined termination condition is not met (No in S205), the correction coefficient calculation unit 134 updates the correction coefficient candidate value and returns to the previous step (S206). The predetermined termination condition may be, for example, whether the counted number of products matches the convergence target number of products, or whether the number of loops of the counting process exceeds a predetermined number.

[0074] On the other hand, if the predetermined termination condition is met (Yes in S205), the correction coefficient calculation unit 134 determines the correction coefficient candidate value at the time of counting termination as the correction coefficient (S207). Then, the information processing device 100 terminates the process.

[0075] (effect) Next, the effects achieved by the information processing device 100 according to this embodiment will be described. In order to efficiently trade electricity in the energy trading market, it is necessary for users to submit bids at appropriate bidding prices according to their circumstances. However, conventional techniques for predicting high or low prices have large errors in the prediction results, which can make it difficult for users to determine appropriate bidding prices.

[0076] Therefore, the information acquisition unit 131 of the information processing device 100 according to this embodiment acquires energy trading information and stores it in the storage unit 120 (energy trading information DB 121). The reception unit 132 of the information processing device 100 receives the contract success probability in the energy trading market. The bid base price determination unit 135 of the information processing device 100 determines a bid base price according to the contract success probability received by the reception unit 132, based on the energy trading information stored in the storage unit 120 (energy trading information DB 121).

[0077] Specifically, the correction coefficient calculation unit 134 of the information processing device 100 calculates a correction coefficient corresponding to the contract success probability accepted by the acceptance unit 132, based on the transaction history information included in the energy transaction information acquired by the information acquisition unit 131. Then, the bid base price determination unit 135 determines the bid base price based on at least one of the representative value determined based on the energy transaction information and the predicted value of the representative value, and the correction coefficient calculated by the correction coefficient calculation unit 134. For example, the bid base price determination unit 135 determines the bid base price by multiplying at least one of the representative value and the predicted value of the representative value by the correction coefficient.

[0078] Therefore, information processing device 100 has the effect of enabling support for determining an appropriate bid price. The effects achieved by information processing device 100 will now be described in more detail.

[0079] (First effect regarding determination of the base bid price according to the probability of successful execution) In bidding in the energy trading market, if the highest bid price is lower than the bid price in a sell bid, or if the lowest bid price is higher than the bid price in a buy bid, the transaction will fail. Furthermore, the price of electricity, which is the subject of trading in the energy trading market, fluctuates due to various factors, but statistical variance tends to be particularly large near the highest and lowest bid prices. Therefore, even if a bid is made based on a predicted highest or lowest price, the transaction may not be executed as expected, and the user may not achieve the desired trading result.

[0080] Therefore, users who place bids take actions to increase the probability of successful execution by setting a price with a predetermined margin for the obtained prediction result as the bid price. However, with the high and low price prediction results predicted by conventional methods, it is unclear how much the success rate of execution will improve depending on the margin set.

[0081] Furthermore, the above-mentioned margins may vary depending on the user's bidding stance. For example, the margins set may differ between a user who prioritizes a high probability of successful execution and a user who prioritizes profits in the event of a successful execution, even if the probability of successful execution is low. Therefore, it is difficult for a user to determine an appropriate bid price according to their bidding stance based solely on the predicted high and low prices predicted by conventional methods.

[0082] Therefore, the information processing device 100 presents the user with a bid base price according to the contract success probability, thereby enabling the user to confirm a bid base price according to an arbitrary contract success probability that matches the user's bidding stance. As a result, the information processing device 100 has the effect of facilitating the user's decision on the bid price.

[0083] In addition, by presenting the contract success probability together with the bid base price, the information processing device 100 has the effect of improving the explainability of the bid base price. For example, in a case where the bidder and the electricity provider are different, such as when an aggregator that collects electricity from consumers and trades it in the electricity trading market makes a bid and explains to the consumer the reason for bidding at that price, the information processing device 100 can improve the explainability of the bid base price, thereby increasing convenience for both the consumer and the bidder.

[0084] (Second effect on determining the base bid price according to the probability of successful execution) In the electricity trading market, importance is placed not only on whether the transaction price is "high" or "low," but also on aspects such as "what is the probability of securing the desired amount of electricity." For example, in the stock trading market, even if a transaction is not concluded, no loss greater than the opportunity loss occurs. However, since electricity is an infrastructure and supply shortages must be avoided, in addition to price, the perspective of "what is the probability of a transaction being concluded" is also important. Therefore, presenting a bid reference price according to the probability of successful conclusion has the advantage that it is easier for users placing bids to use than information such as high and low prices.

[0085] Therefore, the information processing device 100 presents the user with a contract success probability as an index of "what is the probability of securing the desired amount of power." Therefore, the information processing device 100 presents a bidding base price according to the contract success probability, making it easier for the user to determine a bid price.

[0086] For example, the information processing device 100 allows the user to select whether to increase the probability of successful execution if the user wants to place a bid with certainty, or to lower the probability of successful execution if the user wants to bid at a higher price even if it means taking a risk. Therefore, the information processing device 100 can support the user in determining a bid price more effectively than before by repeatedly determining the bid base price according to the probability of successful execution at a predetermined timing.

[0087] (Third effect regarding determination of the base bid price according to the probability of successful execution) The information processing device 100 can change the contract success probability each time and determine the bidding base price according to the contract success probability. The appropriate value of the above-mentioned "contract success probability" varies depending on the industry to which the user belongs, the state of electricity demand, etc., so it can be changed arbitrarily by the user depending on the "industry," "day of the week," "weather," "facility usage amount," etc.

[0088] For example, in the semiconductor industry, where power supply is important, the probability of successful execution may be set high because power shortages are unacceptable. On the other hand, in industries where flexible responses are possible, such as postponing the operation of equipment when electricity costs are high, the probability of successful execution may be set low.

[0089] Furthermore, the probability of successful execution may also change during trading hours in the energy trading market. For example, a low probability of successful execution may be set at the start of intraday trading in order to execute transactions as cheaply as possible, and a high probability of successful execution may be set at the end of intraday trading when the required amount of electricity must be secured. Also, in situations where the electricity supply cannot be prevented from being short, such as when there is a shortage of renewable energy supply due to bad weather or when power plants are shut down due to accidents or disasters, a higher probability of successful execution may be set than before.

[0090] Even in cases where electricity demand fluctuates due to various factors as described above, the information processing device 100 can support users in determining appropriate bid prices by determining a bid base price according to the entered probability of successful contract.

[0091] (Effects of using representative values ​​or predicted representative values) The information processing device 100 uses a representative value or its predicted value with small statistical variations to determine the bidding reference price according to the probability of successful execution. Therefore, the information processing device 100 can suppress statistical variations compared to predicted values ​​of high and low prices based on conventional methods.

[0092] Here, a supplementary explanation will be given of the representative values ​​and predicted values ​​of the representative values ​​used by the information processing device 100. Fig. 10 is a diagram showing an example of a distribution model of the average contract price according to this embodiment.

[0093] As shown in Figure 10, because the high and low bid prices have large statistical variations, the probability distribution of the probability density function when price is treated as a random variable tends to be broad. However, this distribution changes depending on representative values ​​such as the average successful execution price and median successful execution price in the market before the day, and the distribution of the probability density function when the representative value is narrowed to a certain range of values ​​tends to be narrower than the distribution of the probability density function for all data.

[0094] Furthermore, the distribution of representative values ​​such as the average successful execution price and the median successful execution price in the pre-hour market on the day is more stable than the high and low prices. Therefore, by using the representative values ​​and predicted values ​​of the representative values, the information processing device 100 can present a bid reference price according to the successful execution probability with higher accuracy.

[0095] The above-mentioned distribution model assumes a relatively simple model in which the distribution of the probability density function of the contract success probability when the price is a random variable slides in proportion to the representative value, etc. In other words, it assumes a model in which the probability density function when the correction coefficient is a random variable does not change even if the representative value changes.

[0096] Furthermore, even when the bidding base price according to the probability of successful execution changes according to the representative value, the information processing device 100 calculates a bidding base price linked to the representative value, and can therefore present the user with a bidding base price that has less statistical variation than conventional methods.

[0097] If the amount of energy desired to be traded is sufficiently small, the information processing device 100 can eliminate the need to acquire the amount of energy desired to be traded by using as the criterion for successful contract whether the value obtained by multiplying the calculated representative value for each product by the correction coefficient candidate value is not higher than the highest price or not lower than the lowest price of the same product. Therefore, the information processing device 100 can reduce the amount of data to be held by using only the representative value and the highest or lowest price from the price history of the hourly market.

[0098] (Effect of determining the base bid price at any time) Although the information processing device 100 targets the pre-hour market, it may also target intraday trading other than the pre-hour market. For example, even in intraday trading where prices change frequently during trading hours, the information processing device 100 can maximize trading profits by repeatedly determining the bid base price according to the contract success probability.

[0099] As described above, the information processing device 100 determines the bid base price according to the contract success probability at any timing designated by the user. Here, an example of determining the bid base price related to the information processing device 100 will be described using the time flow of the energy trading market with reference to FIG.

[0100] Fig. 11 is a diagram illustrating an example of the time flow in the electricity trading market and the timing of determining the bid base price. Fig. 11 shows an example of the time flow in the electricity trading market and the timing of determining the bid base price.

[0101] For example, the information processing device 100 can determine the bidding base price from 10:00 on the previous day to one hour before the start of delivery ((1) in FIG. 11). Also, for example, the information processing device 100 can determine the bidding base price for any item in any frame on the day of the transaction ((2) in FIG. 11).

[0102] <Modification> The following describes modified examples realized by the information processing device 100 according to this embodiment.

[0103] (First Modification) The first modification uses data that has been narrowed down based on a predetermined condition. The information processing device 100 (correction coefficient calculation unit 134) according to the first modification calculates a correction coefficient using data that satisfies the predetermined condition from among the transaction history information acquired by the information acquisition unit 131.

[0104] For example, the information processing device 100 calculates the correction coefficient using data on past products that have feature quantities similar to those of the product to be predicted. Specifically, the information processing device 100 narrows down the feature quantities based on conditions such as the contracted energy, the price range of the representative value, weather forecast (temperature, etc.), season, month, distinction between weekday and holiday, day of the week, time slot number, and whether or not there is a special event.

[0105] The above-described predetermined conditions are not particularly limited as long as they are applicable to the calculation process of the correction coefficient. For example, the information processing device 100 may use something other than products as a comparison unit when narrowing down the search results. For example, the information processing device 100 may use only data from days when the feature values ​​are close to the delivery date of the product to be predicted, in calculating the correction coefficient.

[0106] Furthermore, the information processing device 100 may exclude specific data from the product data used to calculate the correction coefficient. For example, the information processing device 100 may calculate the correction coefficient using data excluding contract data of specific bidders who have problems such as violation of regulations.

[0107] The basic embodiment described above is based on a relatively simple model in which the distribution of the probability density function of the probability of successful execution when the price is a random variable slides in proportion to the representative value, but the distribution of the probability density function may change depending on the above conditions. For example, when the representative value becomes higher than a certain value, a change such as an increase in the probability of execution at a higher price may be observed.

[0108] Therefore, the information processing device 100 according to the first variant achieves the effect of being able to present a bid reference price according to the probability of successful execution with even higher accuracy by narrowing down the data to past product data having features similar to those of the product to be predicted.

[0109] (Second Modification) The second modified example is a modified example in which a correspondence relationship between the contract success probability and the price or the representative value is obtained. The information processing device 100 (correction coefficient calculation unit 134) according to the second modified example calculates the correction coefficient as a continuous function of the contract success probability. Specifically, the information processing device 100 calculates several pairs of contract success probabilities and their correction coefficients in a correction coefficient calculation flow, thereby obtaining a correspondence relationship between the contract success probability corresponding to the correction coefficient and the price / representative value.

[0110] The second modified example will now be described with reference to Fig. 12. Fig. 12 is a diagram showing an example of a distribution model of average contract prices according to the second modified example. Fig. 12 shows an example of deriving a continuous function using data used to calculate a correction coefficient. The example shown in Fig. 12 is an example of "selling" in electricity trading.

[0111] First, the information processing device 100 calculates a value by dividing the maximum price at which the amount of energy desired to be traded can be contracted by the representative value of the data for that product for the data of the past 100 products used to calculate the correction coefficient ((1) in FIG. 12). Next, the information processing device 100 sorts the divided values ​​in descending order and obtains divided values ​​corresponding to the contract success probability in increments of 1% ((2) in FIG. 12).

[0112] Then, the information processing device 100 approximates the obtained correspondence relationship with a continuous piecewise linear function or a curve, and derives a correction coefficient for the contract success probability as a continuous function ((3) in FIG. 12). The information processing device 100 calculates a correction coefficient for the input contract success probability using the derived function.

[0113] In the correction coefficient calculation process shown in the above-described embodiment, when calculating the convergence target number of products, rounding is performed to convert to an integer, so the correction coefficient for the contract success probability becomes a stepped function (the dashed line in the graph shown in Figure 12 (3)). Therefore, for example, it may happen that the value of the correction coefficient does not change whether the contract success probability is "80%" or "81%."

[0114] On the other hand, in the correction coefficient calculation process according to the second modification, the correction coefficient for the contract success probability becomes a continuous function (solid line in the graph shown in (3) of FIG. 12). Therefore, the information processing device 100 according to the second modification can more accurately calculate the correction coefficient according to the change in the contract success probability.

[0115] In addition, the information processing device 100 according to the second modified example may calculate the function of the correction coefficient by fitting the probability of successful execution obtained from the extracted data to the cumulative distribution function of a certain probability distribution, where the probability density function of the probability of successful execution when the correction coefficient is a random variable.

[0116] The information processing device 100 according to the first modification narrows down the data used when calculating the correction coefficient, so the total number of products available for calculating the correction coefficient is reduced, and the correction coefficient is susceptible to becoming a step-like function. In contrast, the information processing device 100 according to the second modification can calculate the correction coefficient more accurately than the first modification, even when the number of data items fluctuates significantly due to the narrowing down of the data, as described above.

[0117] (Third Modification) The third modification is a modification in which the representative value is nonlinearly transformed. The information processing device 100 (the bid base price determination unit 135) according to the third modification multiplies at least one of the nonlinearly transformed representative value and the predicted value of the representative value by a correction coefficient to determine the bid base price.

[0118] As described above, when determining the bid base price, the information processing device 100 according to the third modification does not simply multiply the representative value by the correction coefficient, but instead performs nonlinear transformation on the representative value based on a formula such as "bid base price = f (representative value) × correction coefficient", where "f" is a function representing the nonlinear transformation.

[0119] For example, the case where the representative value is the average successful execution price will be described. The information processing device 100 according to the basic embodiment described above calculates the bidding base price by multiplying the representative value by a correction coefficient. This is based on the fact that the distribution of the probability density function of the successful execution probability shifts in proportion to the average successful execution price.

[0120] However, if the distribution of the probability density function of the probability of successful execution shifts to higher prices as the average successful execution price increases, the error may become larger as the average successful execution price increases.

[0121] Even in the above-described case, the information processing device 100 according to the third modification can suppress errors by performing nonlinear conversion on the representative value and then multiplying it by the correction coefficient based on a formula such as "bid base price = (representative value)^α × correction coefficient", where "^" represents exponentiation and "α" is a constant greater than 1.

[0122] Therefore, the information processing device 100 according to the third modification has the effect of enabling a more appropriate determination of the bidding base price even if the relationship between the representative value and the probability distribution of the contract success probability is nonlinear.

[0123] (Fourth Modification) The fourth modification is a modification that uses a predetermined learning model (hereinafter, may be simply referred to as a "correction coefficient calculation model") that has been trained to calculate a correction coefficient. The information processing device 100 (correction coefficient calculation unit 134) according to the fourth modification calculates a correction coefficient based on the correction coefficient calculation model that has been trained to input energy trading information and output a correction coefficient.

[0124] The information processing device 100 according to the fourth modification is capable of calculating correction coefficients at high speed based on a correction coefficient calculation model, without sequentially calculating correction coefficients from past price history information.

[0125] The information processing device 100 can use any data used for learning, without any particular limitation, as long as it can be used to calculate correction coefficients. The information processing device 100 can also use a model constructed using machine learning such as a neural network, a gradient boosting tree, or an SVM (Support Vector Machine), and is not particularly limited as long as it can calculate correction coefficients.

[0126] (Fifth Modification) The fifth modified example is a modified example in which a plurality of contract success probabilities are used to determine a bidding reference price according to each contract success probability.

[0127] Specifically, the information processing device 100 (correction coefficient calculation unit 134) according to the fifth modification calculates a correction coefficient for each of the multiple contract success probabilities received by the reception unit 132. Then, the information processing device 100 (bid base price determination unit 135) according to the fifth modification determines a bid base price for each of the multiple contract success probabilities based on at least one of the representative value and the predicted value of the representative value determined based on the energy trading information and the correction coefficient for each of the multiple contract success probabilities calculated by the correction coefficient calculation unit 134.

[0128] As a result, the information processing device 100 according to the fifth modification enables a user to make a more appropriate determination of a bid price by comparing the bidding reference prices for a plurality of contract success probabilities.

[0129] (Sixth Modification) The sixth variant simplifies the information acquisition process and the correction coefficient calculation process based on the idea that the influence of the desired amount of energy to be traded on the correction coefficient can be ignored when the amount of energy traded in the market is sufficiently small.

[0130] Specifically, the information processing device 100 (information acquisition unit 131) according to the sixth modified example acquires information on the highest price or lowest price from the price history information. Next, the information processing device 100 (correction coefficient calculation unit 134) according to the sixth modified example counts the number of contracts of the product based on whether the price obtained by multiplying the representative value by the correction coefficient candidate value is lower than the highest price of the target product in the case of selling, or higher than the lowest price in the case of buying.

[0131] As a result, the information processing device 100 according to the sixth modification can simplify information acquisition and correction coefficient calculation, thereby reducing the amount of information to be acquired and the number of processing steps.

[0132] (Data, etc.) The electricity trading information, the probability of successful contract, the bidding reference price, the transaction history information, the price history information, the correction coefficient candidates, the correction coefficients, the names of the functional parts of the information processing device 100, the steps, the processes, the names of the steps or processes, etc. used in the description of the above embodiment are merely examples and can be changed as desired.

[0133] For example, the electricity trading information DB 121 stores the spot market price, the time slot number, the day of the week, and the temperature as the representative value calculation data 121a in a table format or the like, in association with "No." This is information for identifying individual data of the representative value calculation data 121a, but the items to be stored and the information within the items are not limited.

[0134] For example, the electricity trading information DB 121 stores the date of delivery (delivery date), the frame number, the time-ahead market contract price (yen / kWh), and the contract amount (MWh / h) as the correction coefficient calculation data 121b in a table format or the like, in association with "No." This is information for identifying individual data of the correction coefficient calculation data 121b, but the items to be stored and the information within the items are not limited.

[0135] For example, the representative value DB 122 stores frame numbers and representative values ​​in association with each other, but the items to be stored and the information within the items are not limited.

[0136] For example, the correction coefficient DB 123 stores correction coefficient identification information and correction coefficients in a table format or the like, in association with "No.", which is information for identifying individual correction coefficients, but the items to be stored and the information within the items are not limited.

[0137] For example, the bid base price DB 124 stores a frame number and a bid base price in association with each other, but the items stored and the information within the items are not limited.

[0138] (Flowcharts, etc.) The steps in the flowcharts may be interchanged as long as there is no contradiction, and some steps may not be performed. In addition, conjunctions such as "next," "continue," "further," "at this time," and "on this occasion" used in the explanation of the flowcharts do not limit the order or timing of the execution of the processes in the flowcharts.

[0139] (others) Of the processes described in the above embodiments and variations, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information, including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings, can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown.

[0140] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution or integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc.

[0141] The above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that fall within the so-called equivalent range. Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0142] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, a control section can be read as control means or a control circuit.

[0143] Although some of the embodiments have been described in detail above with reference to the drawings, these are merely examples, and it is possible to implement the present embodiments in other forms that have undergone various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the Disclosure of the Invention section.

[0144] <Hardware configuration> The information processing device 100 according to this embodiment is realized, for example, by a computer 1000 configured as shown in Fig. 13. Fig. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100 according to this embodiment. The computer 1000 has a configuration in which a CPU 1100, a RAM 1200, a ROM 1300, an auxiliary storage device 1400, a communication I / F (interface) 1500, and an input / output I / F (interface) 1600 are connected by a bus 1800.

[0145] The CPU 1100 operates and controls each unit based on a program stored in the ROM 1300 or the auxiliary storage device 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0146] The auxiliary storage device 1400 stores programs executed by the CPU 1100, data used by the programs, etc. The communication I / F 1500 receives data from other devices via a predetermined communication network NW (including closed-area wireless communication in this embodiment) and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network NW. The CPU 1100 controls output devices such as displays and printers, and input / output devices 1700 such as keyboards and mice, via the input / output I / F 1600. The CPU 1100 acquires data from the input / output devices 1700 via the input / output I / F 1600. The CPU 1100 also outputs generated data to the input / output devices 1700 via the input / output I / F 1600.

[0147] For example, when the computer 1000 functions as various devices according to the present embodiment, the CPU 1100 of the computer 1000 executes a program loaded onto the RAM 1200 to realize the functions of the control unit. [Explanation of symbols]

[0148] 100 Information processing device 110 Communications Department 120 Storage section 121 Electricity trading information DB 122 Representative Value DB 123 Correction coefficient DB 124 Bidding reference price DB 130 Control Unit 131 Information Acquisition Department 132 Reception Department 133 Representative Value Prediction Unit 134 Correction coefficient calculation unit 135 Bidding Standard Price Determination Department 136 Output section

Claims

1. An information processing device that presents a bid reference price in an electricity trading market, an information acquisition unit that acquires information about energy trading and stores the information in a storage unit; a reception unit that receives a contract success probability in the energy trading market; a bid base price determination unit that determines a bid base price according to the contract success probability received by the reception unit, based on information about the energy transaction stored in the storage unit; An information processing device comprising:

2. The bid reference price determination unit determining the bid reference price based on at least one of a representative value determined based on information about the energy trade acquired by the information acquisition unit and a predicted value of the representative value; 2. The information processing apparatus according to claim 1, wherein:

3. a correction coefficient calculation unit that calculates a correction coefficient according to the contract success probability received by the reception unit based on past transaction information of the energy trading market included in the information on the energy trading acquired by the information acquisition unit, The bid reference price determination unit determining the bid base price based on at least one of a representative value determined based on the information on the energy trade and a predicted value of the representative value, and the correction coefficient calculated by the correction coefficient calculation unit; 3. The information processing apparatus according to claim 2, wherein:

4. The bid reference price determination unit multiplying at least one of the representative value and the predicted value of the representative value by the correction coefficient to determine the bid base price; 4. The information processing apparatus according to claim 3,

5. The bid reference price determination unit multiplying at least one of the nonlinearly transformed representative value and the predicted value of the representative value by the correction coefficient to determine the bid reference price; 4. The information processing apparatus according to claim 3,

6. The correction coefficient calculation unit Calculating the correction coefficient as a continuous function of the contract success probability; 4. The information processing apparatus according to claim 3,

7. The correction coefficient calculation unit calculating the correction coefficient using data that satisfies a predetermined condition from among past transaction information of the energy trading market acquired by the information acquisition unit; 4. The information processing apparatus according to claim 3,

8. The correction coefficient calculation unit calculating the correction coefficient based on a predetermined learning model that has been trained to input information about the energy trading and output the correction coefficient; 4. The information processing apparatus according to claim 3,

9. An information processing method executed by an information processing device that presents a bid reference price in an electricity trading market, comprising: an information acquisition step of acquiring information about energy trading and storing the information in a storage unit; a receiving step of receiving a contract success probability in the energy trading market; a bid base price determination step of determining a bid base price according to the contract success probability received in the receiving step, based on information about the energy transaction stored in the storage unit; An information processing method comprising:

10. An information processing program executed by an information processing device that presents a bid reference price in an electricity trading market, an information acquisition step of acquiring information about energy trading and storing the information in a storage unit; a receiving step for receiving a contract success probability in the energy trading market; a bid base price determination step of determining a bid base price according to the contract success probability received in the receiving step, based on information about the energy transaction stored in the storage unit; An information processing program comprising:

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