Information processing device, information processing method, and program

Abstract

To comprehensively maximize a profit in consideration of both the demand / supply and self-consumption of power.SOLUTION: A service provider server 1 includes: a selling price acquisition unit 101, a selling price prediction unit 102, a user consumption prediction unit 103, a power usage determination unit 104, and a control unit 105. The power usage determination unit 104 determines a power usage pattern of a user, based on a current selling price obtained by the selling price acquisition unit 101, a predicted amount of self-consumption predicted by the user consumption prediction unit 103, and a predicted selling price predicted by the selling price prediction unit 102, and also based on a predetermined rule for the purpose of maximizing a profit of the user. The control unit 105 controls power usage by the user based on the determined usage pattern.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] Conventionally, there is a first conventional technology that creates a generator operation plan and a trading plan in the supply and demand adjustment market to minimize profits and losses based on spot prices, adjustment power prices, adjustment power activation rates, fuel prices, etc. (see, for example, Patent Document 1). There is also a second prior art technique that calculates expected profits for both the wholesale electricity market and the supply and demand adjustment market, and determines the amount of bids to be made to each market (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-136832 [Patent Document 2] JP 2021-174344 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional technologies, including the above-mentioned first and second conventional technologies, aim to maximize profits on the premise that all generated electricity is sold, and there was no technology that maximizes profits comprehensively, including electricity supply and demand and self-consumption.

[0005] The present invention has been made in consideration of the above circumstances, and has an object to comprehensively maximize profits including power supply and demand and self-consumption. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: A buying and selling price acquisition means for acquiring, as a current buying and selling price, a current price at which a user buys and sells electricity in each of a plurality of electricity trading markets; a buying and selling price prediction means for predicting, for each of a plurality of electricity buying and selling markets, a price at which the user will buy and sell electricity during a predetermined time period in the future as a predicted buying and selling price; A user consumption prediction means for predicting an amount of power consumed by the user in a future predetermined time period as a predicted personal consumption amount; an electricity usage determination means for determining a usage pattern of the user's electricity based on the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and a predetermined rule for the purpose of maximizing the profit of the user; A control means for controlling the use of power by the user based on the determined use mode; Equipped with.

[0007] Further, an information processing method and a program according to one aspect of the present invention are a method and a program corresponding to the information processing device according to the aspect of the present invention described above. Effect of the Invention

[0008] According to the present invention, it is possible to comprehensively maximize profits including power supply and demand and self-consumption. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an overview of a service to which an embodiment of an information processing device of the present invention is applied. [Diagram 2] FIG. 2 is a block diagram showing an overall configuration of an information processing system for providing the present service of FIG. 1, the information processing system including an embodiment of an information processing device of the present invention. [Diagram 3] 3 is a block diagram showing a hardware configuration of a service provider server of an embodiment of an information processing device of the present invention in the information processing system of FIG. 2. FIG. [Figure 4] FIG. 4 is a functional block diagram showing a functional configuration for executing processing related to electricity trading, among the functional configurations of a service provider server having the hardware of FIG. 3. [Diagram 5] 3 is a diagram showing an example of a daily change in the wholesale price of electricity in the spot market of the information processing system of FIG. 2. [Figure 6] 6 is a flowchart for the service provider server of FIG. 4 to perform optimal operation through market analysis including the spot market of FIG. 5. [Figure 7] 7 is a diagram showing an example of an algorithm structure of a time series prediction model for the service provider server of FIG. 4 to perform the process of the flowchart of FIG. 6. FIG. [Figure 8] FIG. 5 is a diagram showing an example of a one-week operation mode of the service provider server of FIG. [Figure 9] FIG. 5 is a diagram showing another example of an operation form by the service provider server of FIG. 4, in which a user performs an operation giving priority to personal consumption. [Figure 10] FIG. 11 is a diagram showing an overview of a second service to which a second embodiment of an information processing system including an information processing device of the present invention is applied. [Figure 11] FIG. 11 is a diagram illustrating an example of a one-week operation mode of the service provider server of the second embodiment. [Figure 12] FIG. 13 is a diagram showing another example of a one-week operation form by the service provider server of the second embodiment, illustrating an example in which a user performs an operation giving priority to personal consumption. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. First, a first service provided by an information processing system including an embodiment of an information processing device of the present invention will be described with reference to FIG. FIG. 1 is a diagram showing an overview of a first service to which an embodiment of an information processing device of the present invention is applied.

[0011] As shown in Fig. 1, the first service is provided by a service provider SA to each of m users U1 to Um (m is an integer value of 1 or more). The users U1 to Um are people (electricity demanders) who demand electric power and conduct business or the like. This electric power includes not only that supplied by an electric power supplier described later, but also that consumed by each of the users U1 to Um and renewable energy generated at their own home. That is, in the first service, there exist an electric power company DK1 having a balancing group as an electric power supplier, an electric power retailer DK2, n (n is an integer value of 1 or more independent of m) electric power markets DS1 to DSn, and users U1 to Um. The first service is a service in which the service provider SA acts as an intermediary between such electricity suppliers and electricity consumers, users U1 to Um, and combines the "consumption," "buying and selling," "storage," and "supply and demand adjustment" of electricity to maximize the profits of each of users U1 to Um in electricity production, consumption, and electricity trading (buying and selling).

[0012] The service provider SA mediates the power production and power trading of each of the users U1 to Um. Therefore, the service provider SA has a power storage device TD that stores power. The power storage device TD is intended to absorb the time lag that occurs in power trading. By utilizing the power storage device TD, it becomes possible to shift the time when power is generated from the time when it is self-consumed or sold, and as a result, profitability can be improved. The power storage device TD is, for example, a storage battery, a device for converting hydrogen, ammonia, or other chemical substances, a battery for an electric vehicle, or the like. In this example, only one power storage device TD is illustrated, but multiple power storage devices TD may be distributed and disposed, and the power storage devices TD may be managed by a third party as a management entity. In other words, the number and location of the power storage devices TD, and even the management entity, are not important.

[0013] As described above, a user Uk (k is an arbitrary integer value between 1 and m) is an electricity consumer, who demands electricity supplied from an electricity supplier and runs his / her own business or the like. However, at least some of the users U1 to Um have facilities capable of producing renewable energy, power storage devices TK, etc., and also act as power suppliers. Renewable energy sources include, for example, solar power generation, wind power generation, biomass power generation, hydroelectric power generation, geothermal power generation, wave power generation, pumped storage power generation, etc. Specifically, the user U1 has a user power generation system UGS for generating solar power as an example of renewable energy. The user power generation system UGS includes a solar power generation device SO, a power storage device TK, a control device CO, and the like. In the user power generation system UGS, the electric power generated by the solar power generation unit SO is stored in the power storage unit TK. Based on instructions from the service provider SA, the control device CO controls the use of electricity, such as whether to sell the electricity generated by the solar power generation device SO or the electricity stored in the temporary electricity storage device TK to others (e.g., electricity markets DS1 to DSn, an electric power company (supply BG) DK1 that does not go through these markets, an electric power company (retail electricity supplier) DK2, or other users Up (p is any integer value between 2 and m)) through the service provider SA, or to consume the electricity in a load FK within the user U1. Here, the load FK is a facility (e.g., a factory) for running the business of the user U1. In other words, consuming electricity in the load FK means that the user U1 consumes electricity in his / her own home.

[0014] The service provider SA mediates by using an electricity sales platform provided by the service provider server 1 shown in Fig. 2. The electricity sales platform uses AI (Artificial Intelligence) to calculate the predicted power generation amount every 30 minutes for each power generation method of each user U1 to Um, and provides it to the electric power company (supply BG) DK1 and the electric power company (retail electricity business operator) DK2. In addition, the electricity sales platform provides a platform that enables users Uk to optimally sell the electricity they generate to various locations, and automatically determines whether to sell or procure the electricity. The electricity sales platform performs transactions that maximize profits based on the difference between purchase price and sales price, based on the trends of electricity markets DSn, including the electricity market (forward market) DS1, the electricity market (spot market) DS2, the electricity market (time-ahead market) DS3, the electricity market (futures market) DS4, the electricity market (supply and demand adjustment market) DS5, etc. For example, the electricity sales platform performs processes such as purchasing cheap electricity, storing it in a power storage device TD, and selling it when the price rises.

[0015] Specifically, the service provider SA acquires, as a current buying and selling price, the current price at which the user Uk buys and sells electricity in each of the multiple electricity trading markets DS1 to DSn. In addition, the service provider SA predicts the amount of power that the user Uk will consume in a predetermined time period in the future as a predicted personal consumption amount. Furthermore, the service provider SA predicts, for each of the multiple power trading markets DSn, the price at which the user Uk will buy and sell power during a predetermined time period in the future, as a predicted buying and selling price. The service provider SA determines how user Uk will use electricity based on the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and predetermined rules aimed at maximizing user Uk's profits, and controls user Uk's use of electricity based on the determined usage pattern.

[0016] Here, the use of electricity by user Uk includes, for example, "consumption" of electricity generated by solar power generation equipment SO at a load FK of the user's own equipment, electricity trading ("buying and selling") including the sale of electricity generated by the solar power generation equipment SO and the purchase of electricity from others, and "storage" of electricity in a power storage device TK.

[0017] The predetermined rule is a rule that combines "consumption," "buying and selling," "storage," and "supply and demand adjustment" of electricity based on the current price and fluctuations in future prices and self-consumption to maximize the profits of the user Uk. For example, purchasing electricity when the price is low and storing it, and using it for self-consumption or sale, is an example of a predetermined rule. A rule that sells one's own power generation if it leads to maximizing profits is another example of a predetermined rule. For example, when selling, a rule that not only sells at the highest price, but also takes into account transaction costs (purchase price, land use fee, natural discharge, wheeling costs for transmitting electricity on the grid, etc.) and imbalance costs is another example of a predetermined rule. As a result, the user Uk can maximize the profits from the sale and purchase of electricity.

[0018] Next, an information processing system including a service provider server according to an embodiment of the information processing device of the present invention will be described with reference to FIG. FIG. 2 is a block diagram showing the overall configuration of an information processing system for providing the present service of FIG. 1, the information processing system including an embodiment of an information processing device of the present invention.

[0019] The information processing system shown in Figure 2 is configured to include a service provider server 1 managed by a service provider SA, an electric power company server (supply BG) 2-1 and an electric power company server (retail electricity supplier) 2-2, electricity market servers 3-1 to 3-n, a power generation equipment market server (capacity market) 4, and user power generation systems UGS-1 to UGS-m owned and managed by users U1 to Um, respectively.

[0020] The service provider server 1, the electric power company server (supply BG) 2-1, the electric power company server (retail electricity supplier) 2-2, the electricity market servers 3-1 to 3-n, the power generation equipment market server (capacity market) 4, and the user power generation systems UGS-1 to UGS-m are interconnected via a network NW such as the Internet.

[0021] Each of the user power generation systems UGS-1 to UGS-m is composed of, for example, a solar power generation device SO, a power storage device TK, a control device CO, etc., and is operated and managed by each of the users U1 to Um. In addition, each of the users U1 to Um concludes a contract to use the power sales platform with the service provider SA, and thus it becomes possible to control the supply and demand of power (whether to sell the generated power or to consume it in-house, etc.) and adjust the supply and demand by issuing instructions from the service provider server 1 to the control device CO.

[0022] The electric power company server (supply BG) 2-1 adjusts supply and demand in response to the predicted power generation amount from the service provider server 1, and is operated by a person in charge at the electric power company DK1. The electric power company server (electricity retailer) 2-2 adjusts supply and demand based on the results of the demand forecast from the service provider server 1, and is operated by a person in charge at the electric power company DK2. The electric power company server (supply BG) 2-1 and the electric power company server (retail electricity supplier) 2-2 trade electricity produced in the user power generation systems UGS-1 to UGS-m (without going through the electricity markets D1 to DSn) through interactions with the service provider server 1. The electricity market servers 3-1 to 3-n provide a market for trading electricity as energy, such as a forward market, a spot market, an advance market, a futures market, a supply and demand adjustment market, and the like. The power generation facility market server (capacity market) 4 provides a venue for trading the power supply capacity of power generation facilities. The electricity market is a trading market for the amount of electricity (kWh), DS1 to DSn (see FIG. 1). The capacity market is a trading market for supply capacity (kW). (See FIG. 1)

[0023] Specifically, the electricity market server (forward market) 3-1 provides an electricity market (forward market) DS1 on the Internet. In the electricity market (forward market) DS1, transactions are conducted up to three days before actual supply and demand. The electricity market server 3-2 (spot market) provides an electricity market (spot market) DS2 on the Internet. In the electricity market (spot market) DS2, transactions are carried out up to the day before actual supply and demand. The electricity market server 3-3 (hour-ahead market) provides the electricity market (hour-ahead market) DS3 on the Internet. In the electricity market (hour-ahead market) DS3, transactions are conducted up to one hour before actual supply and demand as a place for adjusting power generation and supply and demand on the day. The electricity market server 3-4 (futures market) provides an electricity market (futures market) DS4 on the Internet. In the electricity market (futures market) DS4, futures transactions are conducted up to 15 months in advance to hedge against future price fluctuation risks. The electricity market server 3-5 (supply and demand adjustment market) provides the electricity market (supply and demand adjustment market) DS5 on the Internet. In the electricity market (supply and demand adjustment market) DS5, the adjustment power required for frequency control and supply and demand balance adjustment in the power supply area is traded. The power generation equipment market server (capacity market) 4 provides the power generation equipment market (capacity market) HS on the Internet. In the power generation equipment market (capacity market) HS, transactions of future supply capacity required to ensure a stable supply of electricity are conducted.

[0024] In the following, when there is no need to distinguish between the user power generation systems UGS-1 to UGS-m, they will be collectively referred to as the "user power generation system UGS." Furthermore, when there is no need to distinguish between the power market servers 3-1 to 3-n, they will be collectively referred to as the "power market server 3."

[0025] The service provider server 1 manages the operation of the user power generation system UGS. The service provider server 1 executes various processes to provide the above-mentioned functions to the user power generation system UGS, the electric power company server (supply BG) 2-1, and the electric power company server 2-2 (electricity retailer). In addition, the service provider server 1 performs the buying and selling process of the electricity to be sold by the user power generation system UGS to the electric power company server (supply BG) 2-1, the electric power company server 2-2 (retail electricity supplier), and the electricity market server 3 on the electricity sales platform.

[0026] FIG. 3 is a block diagram showing a hardware configuration of a service provider server of an embodiment of the information processing device of the present invention in the information processing system of FIG.

[0027] The service provider server 1 comprises a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an output unit 16, an input unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0028] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 . The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0029] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0030] The output unit 16 is configured with a display such as a liquid crystal display, and displays various images. The input unit 17 is composed of various hardware buttons and the like, and inputs various information in response to instructions given by an operator.

[0031] The storage unit 18 is configured with a dynamic random access memory (DRAM) or the like, and stores various data. The communication unit 19 controls communications with other devices (such as the user power generation system UGS, the power company server 2, the power market server 3, and the power generation facility market server 4 shown in FIG. 2) via a network NW including the Internet.

[0032] The drive 20 is provided as necessary. Removable media 21, which may be a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is appropriately mounted in the drive 20. A program read from the removable media 21 by the drive 20 is installed in the storage unit 18 as necessary. The removable media 21 can also store various data stored in the storage unit 18 in the same manner as the storage unit 18.

[0033] 3, various processes described below can be executed by the service provider server 1. As a result, the service provider SA can provide various services described below to the users U1 to Um.

[0034] The functional configuration of the service provider server of the information processing system of FIG. 2 will be described with reference to FIG. FIG. 4 is a functional block diagram showing a functional configuration for executing processes related to electricity trading, among the functional configurations of the service provider server having the hardware of FIG.

[0035] As shown in FIG. 4, in one area of ​​the storage unit 18 of the service provider server 1, a power information DB 201 and a user information DB 202 are provided.

[0036] The power information DB 201 stores information on the supply, demand, and buying and selling of power and supply capacity obtained from the power company server 2, the power market server 3, the power generation facility market server 4, etc. For example, the buying and selling prices of power in a predetermined time period in the past in each power market DSn (information on past buying and selling results, etc.) are stored. In addition, the electricity information DB201 stores price scenario data as price data in the forward market, price scenario data as price data in the spot market, price scenario data as data in the ahead market, price scenario data as price data in the futures market, price scenario data as price data in the supply and demand adjustment market, price scenario data as price data in the capacity market, price scenario data as price data for imbalance costs, generator data, fuel price data, etc.

[0037] The user information DB 202 stores information on each of the users U1 to Um, and also stores information on the transaction costs relating to electricity for the users U1 to Um as transaction cost information. Specifically, transaction cost information for each of the users U1 to Um is stored as information for each of the users U1 to Um. For example, for the user U1, the transaction cost information includes the transaction cost incurred by the user U1 in buying and selling electricity, as well as the purchase price of the storage device TK, land use fees, and wheeling costs for transmitting electricity through the grid.

[0038] Also, as shown in FIG. 4, when processing related to electricity trading is executed in the CPU 11 of the service provider server 1, a trading price acquisition unit 101, a trading price prediction unit 102, a user consumption prediction unit 103, an electricity usage determination unit 104, a control unit 105, a power generation amount acquisition unit 106, etc. function. The buying and selling price acquisition unit 101 acquires, as a current buying and selling price, the current price at which users U1 to Um buy and sell electricity in each of a plurality of electricity trading markets DSn. Specifically, the buying and selling price acquisition unit 101 acquires, from each of a plurality of power trading markets DSn, the current price at which the users U1 to Um buy and sell power as the current buying and selling price for each power trading market DSn.

[0039] The buying and selling price prediction unit 102 predicts, as a predicted buying and selling price, the price at which users U1 to Um will buy and sell electricity during a predetermined time period in the future, for each of a plurality of electricity buying and selling markets DSn. Specifically, the buying and selling price prediction unit 102 refers to the electricity information DB201 and predicts, based on the past buying and selling prices of electricity for a specified time period for each electricity trading market DSn stored in the electricity information DB201, the prices at which users U1 to Um will buy and sell electricity during a specified time period in the future for each electricity trading market DSn, as predicted buying and selling prices.

[0040] The user consumption prediction unit 103 predicts the amount of power consumed by the users U1 to Um in a predetermined time period in the future as predicted personal consumption. Specifically, the user consumption prediction unit 103 refers to the user information DB 202 and predicts the amount of electricity that users U1 to Um will consume during a specified time period in the future as a predicted home consumption amount based on the users' past electricity consumption records stored in the user information DB 202. In addition, the user consumption prediction unit 103 predicts the predicted personal consumption amount in consideration of the operation plans of the users U1 to Um and the prediction results of future weather. Furthermore, the user consumption prediction unit 103 predicts the home consumption prediction amount based on the accumulation request from the control unit 105, the user's operation plan, and the prediction result of future weather.

[0041] The power usage determination unit 104 determines the power usage patterns of users U1 to Um based on the current buying and selling prices, the predicted self-consumption amount, the predicted buying and selling prices, and predetermined rules aimed at maximizing the profits of users U1 to Um. Furthermore, the power usage determination unit 104 accesses a user information DB 202 in which information on the transaction costs of power for users U1 to Um is stored as transaction cost information, and determines the usage mode after taking the transaction cost information into account. Specifically, the electricity usage decision unit 104 determines the usage pattern based on the current buying and selling price acquired by the buying and selling price acquisition unit 101, the predicted home consumption amount predicted by the user consumption prediction unit 103, the predicted buying and selling price predicted by the buying and selling price prediction unit 102, predetermined rules, and the transaction cost information in the user information DB 202. Here, the predetermined rule includes, for example, a rule that gives top priority to obtaining revenues equal to the purchase price of the power generation equipment and the power storage equipment by the users U1 to Um, based on the purchase price and the useful life of the equipment.

[0042] The usage forms include a form of using a storage facility in which electricity provided from electricity sources including users U1 to Um and multiple electricity markets DS1 to DSn is stored in a specified electricity storage means such as an electricity storage device TD based on the available capacity that can be charged by estimating the charging rate of the electricity storage device TD, and the electricity is discharged and provided to electricity destinations including users U1 to Um and multiple electricity markets DS1 to DSn. In addition, the usage patterns include a pattern in which electricity is purchased and stored in a power storage device TD when the current buying and selling price is a predetermined first price in a predetermined electricity trading market DS1 to DSn, and the electricity discharged from the power storage device TD when the predicted buying and selling price is a second price higher than the first price is used for self-consumption by users U1 to Um or for sale to at least some of the multiple electricity trading markets DS1 to DSn.

[0043] In addition, the power usage decision unit 104 determines the usage pattern by adopting as a predetermined rule either a first rule aimed at maximizing the profits of each of the multiple bases (individual factories) of users U1 to Um, or a second rule aimed at maximizing the profits of a group consisting of the multiple bases. The power usage decision unit 104 determines the usage pattern based on the purchase price and useful life of the power generation equipment (in this embodiment, a solar power generation device SO, etc.) and power storage equipment (for example, a power storage device TK, etc.) by users U1 to Um, adopting as a predetermined rule the rule that the highest priority is given to obtaining revenue equal to the purchase price. Furthermore, the power usage determination unit 104 determines the usage pattern based on the charging rate of the power storage facility (e.g., the power storage device TK, etc.) in response to a storage request from the control unit 105, and the available battery capacity taking into consideration the capacity decrease status from the initial capacity (deterioration status of the power storage performance).

[0044] The control unit 105 controls the power usage of the users U1 to Um based on the usage mode determined by the power usage determination unit 104. Specifically, the control unit 105 executes control to output a storage request to one or more of the registered power storage facilities (e.g., power storage device TD) capable of storing power, as at least a part of the control of power usage by users U1 to Um.

[0045] The power generation amount acquisition unit 106 acquires the predicted results of the amount of power generated during a specified time period in the future by the power generation equipment (e.g., solar power generation equipment SO) owned by the users U1 to Um from a functional block (not shown) of the service provider server 1 or another information processing device (not shown), and calculates the predicted power generation amount as an index to be used by the buying and selling price prediction unit 102 to predict the predicted buying and selling price and the power usage determination unit 104 to determine the usage pattern based on the prediction results. That is, the buying and selling price prediction unit 102 can predict the predicted buying and selling price with higher accuracy by using the predicted power generation amount in addition to the past buying and selling price of electricity in a specified time period. Also, the power usage determination unit 104 can determine the power usage pattern with higher accuracy by using the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and the predicted power generation amount in addition to the specified rule aimed at maximizing the profits of users U1 to Um. In this way, the "predicted power generation amount" is not the "prediction result" itself, but the name of an index obtained by processing the prediction result (calculating based on the prediction result). Specifically, for example, the predicted power generation amount is calculated as follows. In other words, the power generation amount acquisition unit 106 can calculate the "predicted power generation amount" by multiplying the prediction result by a safety factor based on the "width of the confidence interval of the predicted power generation amount" and the "possibility of imbalance costs occurring." Here, the width of the confidence interval of the predicted power generation amount may be, for example, a confidence interval of ±10% for the predicted value of the predicted power generation amount in a time period where the reliability (accuracy) of the prediction is high, and a confidence interval of ±30% for the predicted value of the predicted power generation amount in a time period where the reliability (accuracy) of the prediction is low. The values ​​shown here are merely examples, and other values ​​may be used. Also, the imbalance cost may include, for example, fines and penalties.

[0046] As described above, according to the information processing system of this embodiment, the mode of electricity usage is determined so as to maximize profits based on the current buying and selling prices of electricity in multiple electricity markets DS1 to DSn, the predicted amount of electricity self-consumption, and the predicted buying and selling prices of electricity, so that users U1 to Um can maximize their overall profits through electricity supply and demand (buying and selling and supply and demand adjustment), including consumption of self-generated electricity by solar power generation equipment SO and purchases and sales from others.

[0047] Next, the operation of the information processing system of this embodiment will be described with reference to FIGS. As described above, the information processing system of this embodiment can maximize the overall profits by controlling the power of each of the users U1 to Um through supply and demand (buying and selling and adjusting supply and demand) of the power of each of the users U1 to Um. However, for the sake of convenience, a specific example will be described below using the control of the power of the user U1 as an example. FIG. 5 is a diagram showing an example of a daily change in the wholesale price of electricity in the spot market of the information processing system of FIG. It is assumed that the user U1 shown in FIG. 1 is, for example, a company that operates a warehouse and has installed, for example, the power generation equipment shown in FIG. 1 (such as a solar power generation device SO with a production capacity of 1 MW or the like) in the warehouse. In the case of 1MW, the power generation is expected to be around 1,314,000kWh. (1000KW×24 hours×365 days×15%=1314000kWh( / year)) User U1 has a past record of using only 500,000 kWh of power per year, so the user U1 is unable to use up all of the generated power through self-consumption alone, and must find other ways to use the remaining 814,000 kWh. Therefore, we decided to use electricity by purchasing it when electricity rates are low, storing it, and selling it when rates are high, thereby managing a total electricity consumption of 1,314,000 kWh (per year).

[0048] Prices in the wholesale electricity market fluctuate greatly throughout the day. Assume that the electricity market price changes as shown in FIG. 5 in the spot market on a certain day (e.g., January 2nd) of a certain year (e.g., 2022). In this spot market, volatility (range of price fluctuation) ranges from 7 yen / kWh to 20 yen / kWh. In this case, user U1 has the following two options. One option, if electricity has not been stored in advance, is to store electricity rather than use it yourself from 8:00 to 15:30, and during that time the company uses electricity at a fixed price from the power company. Another option is to consume electricity for yourself from 8:00 to 15:30 and sell it at the price range from 16:00 onwards.

[0049] The operation of the information processing system will be described below with reference to the flowchart in FIG. 6 and FIG. FIG. 6 is a flow chart for the service provider server of FIG. 4 to carry out optimal operation through market analysis including the spot market of FIG. FIG. 7 is a diagram showing an example of an algorithm structure of a time series prediction model for the service provider server of FIG. 4 to execute the process of the flowchart of FIG. As shown in FIG. 6, in step S101, the power usage decision unit 104 inputs the self-consumption feed-in purchase price, the feed-in purchase price by the supply BG, and the feed-in purchase price by the retail electricity business operator, and in step S102, it utilizes a time series prediction model (deep learning) to analyze the trends in electricity market prices such as the self-consumption feed-in purchase price, the feed-in purchase price by the supply BG, the feed-in purchase price by the retail electricity business operator, and the forward market price, spot market price, time-ahead market price, futures market price, and supply and demand adjustment market price, thereby determining the form of electricity usage in accordance with future market price trends. The pre-hour market prioritizes price and time, and is a continuous market format in which individual bids are matched and transactions are completed at any time. It also allows electricity to be bought and sold one hour later, making it easier to trade flexibly compared to the spot market. On the other hand, in the spot market, bidding must take place between three days before actual supply and demand and 10:00 a.m. on the day before actual supply and demand, so it is necessary to determine what the bid price should be, taking into account time series forecasts for all markets to date and variables due to external conditions (LNG prices, etc.). In addition, because the spot market is a single-price auction format where prices are determined in 30-minute intervals, if too much electricity is sold compared to the amount of electricity required, the supply and demand balance cannot be maintained, and there is a risk that this will cause prices to fall. It is necessary to accurately analyze this point and predict future market price trends.

[0050] Next, in step S103, the power usage decision unit 104 utilizes a time series prediction model (deep learning) to analyze the predicted amount of power generation by the solar power generation device SO owned by user U1 and the power generation facilities owned by users U2 to Um other than user U1, and also predicts and analyzes the predicted amount of self-consumption by the load FK owned by each user, thereby grasping the total amount of power whose usage pattern should be determined in accordance with future trends in market prices.

[0051] Then, in step S104, the power usage determination unit 104 utilizes a time-series prediction model (deep learning) to analyze the impact on the market price when the total amount of power handled by the service provider SA is sold.

[0052] In step S105, the power usage decision unit 104 takes into consideration the predicted power generation amount from the power generation facilities owned by the users U1 to Um to whom the service provider SA provides services and the predicted self-consumption demand, and decides on the appropriate form of power usage for each of the users U1 to Um at each time from among self-consumption, electricity sales, electricity storage, and supply and demand adjustment.

[0053] The time series prediction model used in the process of Fig. 6 above is composed of an input layer to which reference information is input, two intermediate layers, and an output layer that outputs the learning results of the intermediate layers, as shown in Fig. 7. By using multiple intermediate layers, deep learning is possible.

[0054] In some cases, trading is optimized on a daily basis, but there are also characteristics such as prices tending to rise on Wednesdays and fall on Sundays (because factories are not operating). Therefore, instead of operating on a daily basis, it is possible to adopt a method that aims for optimal operation within each range, such as 3 days, 1 week, 2 weeks, 1 month, or 3 months, and it is expected that the operating policy will differ significantly depending on the period (especially the way the power storage device is used, etc.). In this regard, this information processing system can select the optimal operation according to the period setting.

[0055] Here, it is assumed that user U1 uses this service by combining self-consumption and market sales as an optimal operation on a weekly basis out of 1,314,000 kWh ( / year).

[0056] As a result, the power usage of the user U1 is controlled based on the operation mode for one week as shown in FIG. 8 and FIG. FIG. 8 is a diagram showing an example of a one-week operation mode by the service provider server of FIG. FIG. 9 is a diagram showing another example of an operation form by the service provider server of FIG. 4, in which a user performs an operation giving priority to personal consumption.

[0057] For example, in the example of FIG. 8, on Monday, the weather is fine and the amount of power generated by user U1 is 3600 kWh. When the weather is fine, the electricity market price is low, so the electricity usage determination unit 104 determines the usage pattern (operation pattern) to sell only 600 kWh at a relative electricity selling amount of 15 yen / kWh, and to self-consume the remaining 3000 kWh. As a result, the amount of electricity generated by user U1 is 3600 kWh, the self-consumption is 3000 kWh, the relative electricity selling amount is 600 kWh, and the profit is 9000 yen. The operation format from Tuesday to Sunday will be as shown in Figure 8. As a result, user U1's weekly earnings from Monday to Sunday will be 315,000 yen.

[0058] In addition, if user U1 were to implement an operation that prioritizes self-consumption, the operation pattern for one week would be as shown in Fig. 9. However, this is assuming that user U1 is a company-scale user that uses 3000 kWh / day. In this case, according to the example of FIG. 9, for example, on Tuesday, the weather was fine and the amount of power generated by user U1 was 3600 kWh. In this case, the power usage determination unit 104 determined a usage pattern (operation pattern) in which 3000 kWh of the 3600 kWh of the user U1's power generation amount is self-consumed and the remaining 600 kWh is sold at the spot market price of 20 yen / kWh. As a result, the user U1's power generation amount is 3600 kWh, with 3000 kWh of self-consumption and 600 kWh of market sales, resulting in a revenue of 12000 yen for that day. In addition, the operation pattern from Monday to Sunday will be as shown in FIG. 9. As a result, in the case of operation prioritizing self-consumption, the weekly revenue of user U1 from Monday to Sunday will be 129,000 yen.

[0059] Next, a second service provided by an information processing system including an embodiment of the information processing device of the present invention will be described with reference to FIGS. 10 is a diagram showing an outline of a second service to which an embodiment of the information processing device of the present invention is applied. Note that the system configuration for realizing the second service, the hardware configuration of the service provider server 1, and the functional configuration of the service provider server 1 (such as the relationship between each functional block) are assumed to be the same as those in the first embodiment, and therefore a detailed description thereof will be omitted.

[0060] As shown in Fig. 10, the second service is provided by a service provider SA to each of m users U1 to Um (m is an integer value of 1 or more). In this second service, the users U1 to Um are entities (electricity suppliers) engaged in the business of supplying electric power. This electric power includes not only that supplied by electric power suppliers described later, but also renewable energy generated by each of the users U1 to Um. That is, in the second service, there are an electric power company DK1 having a balancing group as an electric power supplier, an electric power retailer DK2, n (n is an integer value of 1 or more independent of m) electric power markets DS1 to DSn, and users U1 to Um.

[0061] The second service is a service in which the service provider SA acts as an intermediary between users U1 to Um, who are both electricity suppliers and electricity consumer, and combines the "consumption," "buying and selling," "storage," and "supply and demand adjustment" of electricity to maximize the profits of each of users U1 to Um in electricity production, consumption, and electricity trading (buying and selling).

[0062] The service provider SA mediates the power production and power trading of each of the users U1 to Um. Therefore, the service provider SA has a power storage device TD that stores power. The power storage device TD is intended to absorb the time lag that occurs in power trading. By utilizing the power storage device TD, it becomes possible to shift the time when power is generated from the time when it is self-consumed or sold, and as a result, profitability can be improved. The power storage device TD is, for example, a storage battery, a device for converting hydrogen, ammonia, or other chemical substances, a battery for an electric vehicle, or the like. In this example, only one power storage device TD is illustrated, but multiple power storage devices TD may be distributed and disposed, and the power storage devices TD may be managed by a third party as a management entity. In other words, the number and location of the power storage devices TD, and even the management entity, are not important.

[0063] A user Uk (k is an arbitrary integer value from 1 to m) is an electricity supplier as described above, and supplies electricity demanded by electricity consumers to run its own business or the like. However, at least some of the users U1 to Um have facilities that consume renewable energy and electricity storage facilities. Facilities capable of producing renewable energy include, for example, facilities for solar power generation, wind power generation, biomass power generation, hydroelectric power generation, geothermal power generation, wave power generation, pumped storage power generation, and the like.

[0064] Specifically, for example, a user U1 has a user power generation system UGS1 in order to generate renewable energy. The user power generation system UGS1 has a solar power generation device SO1, a power storage device TK1, a control device CO1, and the like. For example, a user U2 has a user power generation system UGS2 for generating renewable energy. The user power generation system UGS2 has a solar power generation device SO2, a power storage device TK2, a control device CO2, and the like.

[0065] For example, in the user power generation system UGS1, the electricity generated by the solar power generation device SO1 is stored in the power storage device TK1, or sold to others (for example, the electricity markets DS1 to DSn, the power companies DK1 and DK2 that do not go through these markets, or other users Up (p is any integer value between 2 and m)) through the service provider SA, or consumed by the load FK1 in the user U11. The same applies to user power generation systems other than the user power generation system UGS1 (user power generation systems USG2 and onward).

[0066] Based on instructions from the service provider SA, the control device CO1 controls the usage of electricity, such as whether to sell the electricity generated by the solar power generation device SO1 or the electricity stored in the storage device TK1 to others through the service provider SA (for example, the electricity markets DS1 to DSn, an electric power company (supply BG) DK1 or an electric power company (retail electricity supplier) DK2 that do not go through these markets, or other users Up (p is any integer value between 2 and m)), or to consume it in the load FK1 within the user U11.

[0067] Here, the loads FK1 and FK2 are facilities (e.g., factories) for running the businesses of the users U11 and U2. That is, the consumption of power by the load FK1 in the user U11 means that the user U1 is providing a PPA (Power Purchase Agreement) service to the user U11 who is the final consumer. The consumption of power by the load FK2 in the user U2 means that the user U2 is self-consumption.

[0068] A PPA service is a service in which a business providing the service installs solar power generation equipment on the customer's roof, etc., enabling the customer to use electricity with zero carbon dioxide emissions with no initial investment.

[0069] Next, the operation of the information processing system of the second embodiment will be described with reference to FIGS. The basic operation in the second embodiment is the same as that in the first embodiment shown in the flowchart of FIG. 6, and therefore a description thereof will be omitted.

[0070] In some cases, trading is optimized on a daily basis, but there are also characteristics such as prices tending to rise on Wednesdays and fall on Sundays (because factories are not operating). Therefore, instead of operating on a daily basis, it is possible to adopt a method that aims for optimal operation within each range, such as 3 days, 1 week, 2 weeks, 1 month, or 3 months, and it is expected that the operating policy will differ significantly depending on the period (especially the way the power storage device is used, etc.). In this regard, this information processing system can select the optimal operation according to the period setting.

[0071] Here, it is assumed that user U1 uses the second service by combining self-consumption and market sales as an optimal operation on a weekly basis out of 1,314,000 kWh ( / year).

[0072] As a result, the power usage of the user U1 is controlled based on the operation mode for one week as shown in FIG. 11 and FIG. Fig. 11 is a diagram showing an example of a one-week operation mode by the service provider server of the second embodiment. Fig. 12 is a diagram showing another example of a one-week operation mode by the service provider server of the second embodiment, showing an example in which a user U1 performs an operation giving priority to personal consumption.

[0073] For example, in the example of FIG. 11, on Monday, the weather is fine and the amount of power generated by user U1 is 3600 kWh. In the case of optimal operation on a period basis, the power usage determination unit 104 determined a usage pattern (operation pattern) in which 3000 kWh is sold at a PPA service unit price of 17 yen (recorded as self-consumption in the table of FIG. 11) and the remaining 600 kWh is sold at a relative power selling rate of 15 yen / kWh. As a result, for user U1, the amount of power generated is 3600 kWh, the amount of PPA service power selling is 3000 kWh, and the relative power selling rate is 600 kWh, resulting in a revenue of 60,000 yen. The operation format from Tuesday to Sunday will be as shown in Figure 11. As a result, user U1's weekly earnings from Monday to Sunday will be 403,400 yen.

[0074] In addition, if user U1 performs optimal operation on a periodic basis with priority given to self-consumption, the operation pattern for one week will be as shown in Fig. 12. However, this is the case assuming that user U1 is a company-sized user that uses 3000 kWh / day. For example, in the example of Fig. 12, for Saturday, the power usage determination unit 104 determines the usage pattern (operation pattern) in which only 100 kWh of utility equipment is sold at the PPA service unit price of 17 yen on Saturday because the factory is not operating, 2000 kWh of the remaining 3500 kWh is sold at a relative price of 15 yen / kWh, and the remaining 1500 kWh is sold at the spot market price of 10 yen / kWh. As a result, for user U1, the amount of power generated is 3600 kWh, the amount of PPA service power sold is 100 kWh, the amount of relative power sold is 2000 kWh, and the amount of market power sold is 1500 kWh, and the revenue from these amounts is 46700 yen. The operation patterns for other days of the week are as shown in Fig. 12. As a result, user U1's weekly earnings from Monday to Sunday will be 353,400 yen.

[0075] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope that can achieve the object of the present invention are included in the present invention.

[0076] In the above-described embodiment, a solar power generation apparatus SO is exemplified, but other power generation apparatuses may be used as long as they are capable of producing renewable energy. In the above-described embodiment, the buying and selling price acquisition unit 101 acquired the current price at which users U1 to Um buy and sell electricity in each of the electricity markets DS1 to DSn as the current buying and selling price, but the current buying and selling price may be acquired from an electricity market other than this, and it is sufficient to acquire the current price at which users U1 to Um buy and sell electricity in each of the multiple electricity markets DS1 to DSn as the current buying and selling price.

[0077] In the above-described embodiment, the buying and selling price prediction unit 102 predicted the price at which users U1 to Um will buy and sell electricity during a specified time period in the future for each of the multiple electricity markets DS1 to DSn as the predicted buying and selling price, but the predicted buying and selling prices of other electricity markets may also be predicted, and it is sufficient to predict the price at which users will buy and sell electricity during a specified time period in the future for each of the multiple electricity markets as the predicted buying and selling price.

[0078] In the above-described embodiment, the electricity usage determination unit 104 determined the electricity usage patterns of users U1 to Um based on the current buying and selling prices, the predicted self-consumption amount, and the predicted buying and selling prices, as well as predetermined rules aimed at maximizing the profits of users U1 to Um. However, the electricity usage patterns of users U1 to Um may also be determined using other rules (such as rules that maximize self-consumption of electricity and minimize purchases of electricity from others) or parameters or conditions other than rules.

[0079] Furthermore, for example, the series of processes described above can be executed by hardware or software. In other words, the functional configuration of the service provider server in FIG. 4 is merely an example and is not particularly limited. That is, it is sufficient that the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block used to realize this function is not limited to the example of FIG. 4. Furthermore, the location of the functional blocks and databases of the service provider server is not particularly limited to that of FIG. 4 and may be arbitrary. For example, at least a part of the functional blocks and databases required for executing various processes may be transferred to another information processing device (server). Conversely, the functional blocks and databases of another information processing device (server) may be transferred to the service provider server, etc. Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination of both.

[0080] When the series of processes is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium. The computer may be a computer implemented with dedicated hardware. Furthermore, the computer may be a computer capable of executing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

[0081] A recording medium containing such a program may be composed not only of a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to users U1 to Um, etc., but also of a recording medium that is provided to users U1 to Um, etc. in a state where it is pre-installed in the device main body.

[0082] In this specification, the steps of describing a program to be recorded on a recording medium include not only processes that are performed chronologically according to the order, but also processes that are not necessarily performed chronologically but are executed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is composed of a plurality of devices, a plurality of means, etc.

[0083] In other words, it is sufficient for an information processing device to which the present invention is applied to have the following configuration, and various embodiments can be adopted. That is, an information processing device to which the present invention is applied (for example, the service provider server 1 in FIG. 4, etc.) A buying and selling price acquisition unit (e.g., buying and selling price acquisition unit 101 in FIG. 4 ) that acquires, as a current buying and selling price, a current price at which a user (e.g., users U1 to Um in FIG. 1 ) buys and sells electricity for each of a plurality of electricity buying and selling markets (e.g., electricity markets DS1 to DSm in FIG. 1 ); A buying and selling price prediction means (e.g., buying and selling price prediction unit 102 in FIG. 4 ) for predicting, for each of the plurality of electricity buying and selling markets, the price at which the users (e.g., users U1 to Um in FIG. 1 ) will buy and sell electricity during a predetermined time period in the future as a predicted buying and selling price; A user consumption prediction unit (e.g., the user consumption prediction unit 103 in FIG. 4 ) for predicting the amount of power consumed by the user (e.g., the users U1 to Um in FIG. 1 ) in a future predetermined time period as a predicted home consumption amount; an electric power usage determination means (e.g., the electric power usage determination unit 104 in FIG. 4 ) that determines the electric power usage pattern of the user (e.g., the users U1 to Um in FIG. 1 ) based on the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and a predetermined rule for the purpose of maximizing the profit of the user (e.g., the users U1 to Um in FIG. 1 ); A control means (e.g., the control unit 105 in FIG. 4 ) that controls the use of power by the users (e.g., users U1 to Um in FIG. 1 ) based on the determined use mode (e.g., controls as described in the notes of FIG. 7 and FIG. 8 ); Equipped with. This allows users (eg, users U1 to Um in FIG. 1) to maximize their profits overall, including power supply and demand and self-consumption.

[0084] The power utilization determining means (for example, the power utilization determining unit 104 in FIG. 4 ) The transaction cost DB (e.g., the user information DB 202 in FIG. 4 ) in which information on the transaction costs related to the electricity of the users (e.g., the users U1 to Um in FIG. 1 ) is stored as transaction cost information is accessed. Furthermore, the utilization mode is determined based on the transaction cost information. In this way, by determining the usage pattern taking into account the transaction cost information, the accuracy of the calculation of the profit can be improved.

[0085] The above-mentioned usage form is: This includes a form of utilizing a power storage facility that stores electricity provided from power sources including the users (e.g., users U1 to Um in FIG. 1) and the multiple power trading markets (e.g., power markets DS1 to DSn in FIG. 1) in a predetermined power storage means (e.g., storage device TD in FIG. 4), and discharges and provides the electricity to power destinations including the users and the multiple power markets. By using a specified power storage means (such as the storage device TD in FIG. 4) in this manner, it becomes possible to shift the time when power is generated from the time when it is consumed or sold, thereby improving profitability.

[0086] The above-mentioned usage form is: This includes a form in which, in a predetermined electricity trading market (e.g., the electricity market (spot market) DS2 in Figure 1), when the current trading price is a first price, the electricity is purchased and stored in the electricity storage device (e.g., the electricity storage device TD in Figure 1), and when the predicted trading price is a second price that is higher than the first price, the electricity discharged from the electricity storage device (e.g., the electricity storage device TD in Figure 1) is used for self-consumption by the user (e.g., users U1 to Um in Figure 1) or for sale to at least some of the multiple electricity trading markets. In this way, by setting the first price and the second price in advance and trading electricity in a price range between the two prices, it is possible to ensure stable profits in that price range.

[0087] a power generation amount acquiring means (e.g., the power generation amount acquiring unit 106 in FIG. 4 ) for acquiring a predicted result of the amount of power generated in a predetermined time period in the future by the power generation equipment owned by the user (e.g., users U1 to Um in FIG. 1 ), and calculating a predicted power generation amount as an index to be used for predicting the predicted buying and selling price by the buying and selling price predicting means (e.g., the buying and selling price predicting unit 102 in FIG. 4 ) and for determining the usage mode by the power usage determining means (e.g., the power usage determining unit 104 in FIG. 4 ), based on the predicted result; It further comprises: In this way, the predicted amount of electricity generated is calculated based on the predicted amount of electricity generated during a specified time period in the future by the power generation equipment owned by the user (e.g., users U1 to Um in Figure 1), and the predicted buying and selling prices and the electricity usage pattern are determined using the predicted amount of electricity generated, thereby improving the accuracy of overall profit maximization.

[0088] The power generation prediction means (e.g., the power generation acquisition unit 106 in FIG. 4 ) calculates the predicted power generation amount by multiplying the prediction result by the width of the confidence interval of the predicted power generation amount (e.g., a confidence interval of ±10% for the predicted value of the predicted power generation amount in a time period when the reliability (accuracy) of the prediction is high, and a confidence interval of ±30% for the predicted value of the predicted power generation amount in a time period when the reliability (accuracy) of the prediction is low) and a safety coefficient based on the possibility of imbalance costs (e.g., fines, penalties, etc.). This makes it possible to obtain a predicted power generation amount that takes safety into consideration so as to avoid incurring imbalance costs.

[0089] The power usage decision means (e.g., power usage decision unit 104 in FIG. 4) adopts as the predetermined rule either a first rule aimed at maximizing the profits of each of the multiple bases (each of the factories) of the user (e.g., users U1 to Um in FIG. 1), or a second rule aimed at maximizing the profits of a group consisting of the multiple bases, and determines the usage pattern. This allows you to choose the usage pattern that best suits your needs at the time, between maximizing the profits of each of your multiple locations (individually each factory) and maximizing the profits of the group consisting of those multiple locations.

[0090] The user consumption prediction means (e.g., user consumption prediction unit 103 in FIG. 4, etc.) predicts the predicted home consumption amount taking into account the operation plans of the users (e.g., users U1 to Um in FIG. 1, etc.) and the predicted results of future weather. As a result, the predicted home consumption forecast amount takes into account the operation plans of actual users (e.g., users U1 to Um in Figure 1) and future weather forecast results, making it possible to predict the home consumption forecast amount with high accuracy.

[0091] The power usage determination means includes: The usage pattern is determined based on the purchase price and useful life of the power generation equipment and storage equipment by the user (e.g., users U1 to Um in Figure 1), and a rule that gives top priority to obtaining revenue equal to the purchase price is adopted as the specified rule. In this way, by adopting as a predetermined rule the priority given to obtaining revenues equal to the purchase price of power generation equipment (e.g., solar power generation equipment SO in FIG. 1) and energy storage equipment (e.g., energy storage equipment TK in FIG. 1) based on the purchase price and useful life of the power generation equipment (e.g., users U1 to Um in FIG. 1) by a user (e.g., users U1 to Um in FIG. 1), it is possible to give priority to depreciation of the power generation equipment (e.g., solar power generation equipment SO in FIG. 1) and energy storage equipment (e.g., energy storage equipment TK in FIG. 1).

[0092] The control means (for example, the control unit 105 in FIG. 4 ) Executes control to output a storage request to one or more of registered power storage facilities (e.g., the power storage device TD in FIG. 1 ) capable of storing power, as at least a part of control of power usage by the user (e.g., users U1 to Um in FIG. 1 ); The power usage determination means (e.g., the power usage determination unit 104 in FIG. 4, etc.) determines the usage mode based on the power storage status (e.g., the charge rate and the available battery capacity in consideration of the capacity reduction status from the initial capacity, etc.) of the power storage facility (e.g., the power storage device TD in FIG. 1, etc.) corresponding to the storage request, The user consumption prediction means (eg, user consumption prediction unit 103 in FIG. 4) predicts the predicted personal consumption amount based on the accumulation request, the user's operation plan, and prediction results of future weather. This makes it possible to maximize profits by taking into consideration the power storage state of the power storage facility (such as the power storage device TD in FIG. 1) that responds to the storage request.

[0093] In an information processing method executed by an information processing device (for example, the service provider server 1 in FIG. 4), A buying and selling price acquisition step of acquiring, as a current buying and selling price, a current price at which a user (e.g., users U1 to Um in FIG. 1 ) buys and sells electricity for each of a plurality of electricity buying and selling markets (e.g., electricity markets DS1 to DSm in FIG. 1 ); a buying and selling price prediction step of predicting, as a predicted buying and selling price, the price at which the users (e.g., users U1 to Um in FIG. 1 ) will buy and sell electricity during a predetermined time period in the future for each of the plurality of electricity buying and selling markets (e.g., electricity markets DS1 to DSm in FIG. 1 ); A user consumption prediction step of predicting the amount of power consumed by the user (e.g., users U1 to Um in FIG. 1) in a future predetermined time period as a predicted self-consumption amount; an electricity usage determination step of determining an electricity usage pattern of the user (e.g., users U1 to Um in FIG. 1, etc.) based on the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and a predetermined rule for the purpose of maximizing the profit of the user (e.g., users U1 to Um in FIG. 1, etc.); A control step of controlling the use of power by the user based on the determined use mode (for example, control as described in the notes of FIG. 7 or FIG. 8 ); Includes. This allows users (eg, users U1 to Um in FIG. 1) to maximize their profits overall, including power supply and demand and self-consumption.

[0094] On the computer, A buying and selling price acquisition step of acquiring, as a current buying and selling price, a current price at which a user (e.g., users U1 to Um in FIG. 1 ) buys and sells electricity for each of a plurality of electricity buying and selling markets (e.g., electricity markets DS1 to DSm in FIG. 1 ); a buying and selling price prediction step of predicting, as a predicted buying and selling price, the price at which the users (e.g., users U1 to Um in FIG. 1 ) will buy and sell electricity during a predetermined time period in the future for each of the plurality of electricity buying and selling markets (e.g., electricity markets DS1 to DSm in FIG. 1 ); A user consumption prediction step of predicting the amount of power consumed by the user (e.g., users U1 to Um in FIG. 1) in a future predetermined time period as a predicted self-consumption amount; an electricity usage determination step of determining a usage pattern of the user (e.g., users U1 to Um in FIG. 1 ) based on the current buying and selling price, the predicted self-consumption amount, the predicted buying and selling price, and a predetermined rule for the purpose of maximizing the profit of the user; A control step of controlling (for example, as described in the notes of FIG. 7 and FIG. 8 ) the use of power by the user (for example, users U1 to Um in FIG. 1 ) based on the determined use mode; The control process includes: This allows users (eg, users U1 to Um in FIG. 1) to maximize their profits overall, including power supply and demand and self-consumption. [Explanation of symbols]

[0095] 1···Service provider server, 2···Power company server 2, 3-n···Power market server, 11···CPU, 12···ROM, 13···RAM, 14···Bus, 15···Input / output interface, 16···Output section, 17···Input section, 18···Memory section, 19···Communication section, 101···Buying and selling price acquisition section, 102···Buying and selling price prediction section, 103···User consumption prediction section, 104···Power usage decision section, 105···Control section, 106···Power generation amount acquisition section, 201···Power information DB, 202···User information DB

Claims

1. A trading price prediction method that predicts the price at which users will buy and sell electricity during a predetermined time period in the future for each of multiple electricity trading markets, and A power generation prediction means obtains a prediction result of the amount of power generated by the power generation equipment owned by the user during a predetermined time period in the future, and calculates a predicted amount of power generation as an indicator to be used by the buying and selling price prediction means to predict the predicted buying and selling price based on the prediction result, Equipped with, The power generation prediction means calculates the predicted power generation amount by multiplying the prediction result by a safety factor. Information processing device.

2. The power generation prediction means is The prediction result is multiplied by a safety factor based on at least one of the following: the width of the confidence interval for the predicted power generation amount and the probability of imbalance costs occurring. The information processing apparatus according to claim 1.

3. A means for acquiring the current price at which the user buys or sells electricity in each of the multiple electricity trading markets, as the current trading price, A power usage determination means that determines the user's power usage pattern based on at least the current buying and selling price, the predicted buying and selling price, and predetermined rules aimed at maximizing the user's profits, A control means for controlling the user's use of electricity based on the determined usage pattern, The information processing apparatus according to claim 2, further comprising:

4. The aforementioned usage patterns are, This includes a configuration in which a power storage facility is used that stores power supplied from power sources including the user and the multiple power trading markets in a predetermined power storage means, and discharges and provides power to the user and the multiple power trading markets. The information processing apparatus according to claim 3.

5. The aforementioned usage patterns are, This includes a configuration in which, in a predetermined electricity trading market, electricity is purchased when the current trading price is a first price and stored in the energy storage facility, and when the predicted trading price is a second price higher than the first price, the electricity discharged from the energy storage facility is used for the user's own consumption or for sale to at least a portion of the multiple electricity trading markets, The information processing apparatus according to claim 4.

6. The power usage determination means determines the usage pattern by adopting either a first rule aimed at maximizing the revenue of each of the user's multiple locations, or a second rule aimed at maximizing the revenue of the group of those multiple locations as a whole, as a predetermined rule. The information processing apparatus according to claim 3.

7. The power utilization determination means determines the utilization pattern by adopting a predetermined rule that prioritizes obtaining revenue equal to the purchase price of the power generation equipment and energy storage equipment purchased by the user and based on the user's purchase price and useful life. The information processing apparatus according to claim 3.

8. The control means is As at least part of the control of the user's power usage, control is performed to output a storage request to one or more power storage facilities registered as capable of storing power. The power utilization determination means further determines the utilization method based on the energy storage status of the power storage equipment in response to the storage request. The user consumption forecasting means forecasts the amount of self-consumption based on the storage request, the user's operational plan, and the forecast results for future weather. The information processing apparatus according to claim 3.

9. In an information processing method executed by an information processing device, A trading price prediction step is performed to predict the price at which users will buy and sell electricity during a predetermined time period in the future for each of the multiple electricity trading markets, and A power generation prediction step that obtains a prediction result of the amount of power generated by the power generation equipment owned by the user during a predetermined time period in the future, and calculates a predicted amount of power generation as an indicator to be used in predicting the predicted buying and selling price in the buying and selling price prediction step based on the prediction result, Includes, The power generation prediction step includes a step of calculating the predicted power generation amount by multiplying the prediction result by a safety factor. Information processing methods.

10. On the computer, A trading price prediction step is performed to predict the price at which users will buy and sell electricity during a predetermined time period in the future for each of the multiple electricity trading markets, and A power generation prediction step that obtains a prediction result of the amount of power generated by the power generation equipment owned by the user during a predetermined time period in the future, and calculates a predicted amount of power generation as an indicator to be used in predicting the predicted buying and selling price in the buying and selling price prediction step based on the prediction result, The control process including this is executed, The power generation prediction step includes executing a control process that calculates the predicted power generation amount by multiplying the prediction result by a safety factor. program.

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