Information processing apparatus

Abstract

This disclosure relates to an information processing apparatus. A market electricity price forecasting unit forecasts the transaction price of electricity traded in a peer-to-peer (P2P) electricity trading market. The transaction price includes the price of renewable energy electricity and the price of non-renewable energy electricity. A renewable energy index setting unit sets a renewable energy index indicating the extent of renewable energy electricity in the electricity purchased in the P2P electricity trading market. A transaction price setting unit sets the expected transaction price in an electricity trading plan based on the forecasted transaction price of the electricity and the renewable energy index. A trading plan creation unit creates the electricity trading plan based on the expected transaction price.

Description

Technical Field

[0001] This disclosure relates to an information processing apparatus, and more particularly, to an information processing apparatus for creating an electricity trading plan for enabling electricity resources to be traded through an electricity trading market. Background Technology

[0002] With the increase of renewable energy, the use of battery-powered mobile vehicles (such as pure electric vehicles) or facilities (such as residences and factories) equipped with stationary batteries is being considered as a regulatory force needed to ensure the maintenance of a balance between electricity supply and demand. For example, Japanese Unexamined Patent Application Publication No. 2020-78124 (JP 2020-78124A) describes the creation of a charging and discharging plan for battery-powered mobile vehicles (electrical resources) based on an electricity category such as system electricity or renewable energy electricity (see JP2020-78124A). Summary of the Invention

[0003] Due to the liberalization of electricity, the introduction of peer-to-peer (P2P) electricity trading into the electricity trading market is being considered. In P2P electricity trading, electricity transactions occur directly between individuals or businesses with electricity resources and other individuals or businesses. In this type of electricity trading, transaction prices in the electricity trading market can be predicted to create electricity trading plans (bidding plans). Here, when it is desired to adjust the procurement level of renewable energy electricity in electricity trading, the question is how to incorporate the desired procurement level of renewable energy electricity into the aforementioned electricity trading plan.

[0004] Therefore, the purpose of this disclosure is to provide an information processing apparatus that can create an electricity trading plan taking into account the extent to which renewable energy electricity is procured in P2P electricity trading.

[0005] The information processing apparatus according to this disclosure is an information processing apparatus for creating an electricity trading plan for enabling the trading of electricity resources through an electricity trading market, wherein the electricity resources are configured to receive electricity from at least external sources. The information processing apparatus includes a price prediction unit, an index setting unit, a transaction price setting unit, and a transaction plan creation unit. The price prediction unit predicts the transaction price of electricity traded in the electricity trading market. The transaction price includes: a first price indicating the price of renewable energy electricity, and a second price indicating the price of non-renewable energy electricity not applicable to the renewable energy electricity. The index setting unit sets a renewable energy index indicating the extent of renewable energy electricity in the electricity purchased in the electricity trading market. The transaction price setting unit sets the expected transaction price in the electricity trading plan based on the prediction results of the price prediction unit and the renewable energy index. The transaction plan creation unit creates the electricity trading plan based on the expected transaction price.

[0006] In this information processing apparatus, the price of electricity (renewable energy electricity and non-renewable energy electricity) traded in the electricity trading market is predicted. Then, an expected trading price, taking into account the procurement level of the renewable energy electricity, is set from the predicted electricity price and a set renewable energy index, and the electricity trading plan is created based on the expected trading price. As described above, according to the aforementioned information processing apparatus, the electricity trading plan taking into account the procurement level of the renewable energy electricity can be created.

[0007] The information processing device may further include a power generation prediction unit, which predicts the power generation of the renewable energy electricity traded in the electricity trading market. Then, the price prediction unit may predict the first price based on the prediction result of the power generation prediction unit.

[0008] The power generation prediction unit can predict the power generation of the renewable energy source based on weather information of the area covered by the power trading market.

[0009] Using the above configuration, the price of the renewable energy power can be appropriately predicted based on the forecast of the renewable energy power generation. As a result, the expected trading price can be appropriately set, and an appropriate power trading plan can be created based on the expected trading price.

[0010] The information processing device may further include a power consumption forecasting unit, which forecasts the power consumption of the power resource. Then, the trading plan creation unit can create the power trading plan from the expected trading price and the forecast result of the power consumption forecasting unit.

[0011] As a result, appropriate electricity trading schemes that reflect electricity consumption of electricity resources can be created.

[0012] The renewable energy index can be the ratio of renewable energy power in the electricity purchased in the electricity trading market, and the ratio of renewable energy power is set based on input from the user.

[0013] Therefore, it is possible to create electricity trading schemes that reflect users' desire for the ratio of the aforementioned renewable energy electricity.

[0014] The index setting unit can set the renewable energy index based on the proportion of renewable energy power in the electricity purchased in the electricity trading market during a predetermined time period.

[0015] This allows the electricity trading plan to be created based on the aforementioned ratio of renewable energy power over a predetermined period of time. For example, when the aforementioned ratio of renewable energy power is low during a predetermined period of time, the electricity trading plan can be created to increase that ratio.

[0016] The transaction price setting unit can set the expected transaction price based on the prediction results of the price prediction unit and the ratio of renewable energy electricity set according to the renewable energy index.

[0017] According to the information processing device, the electricity trading plan can be created taking into account the ratio of renewable energy electricity set according to the renewable energy index.

[0018] The index setting unit can set the renewable energy index based on predetermined laws applicable to the power resources.

[0019] As a result, electricity trading schemes that reflect the predetermined laws applicable to electricity resources can be created. For example, when regulations stipulate that a certain percentage of renewable energy power is a condition for entry into a specific region, electricity trading schemes that meet the condition of a certain percentage of renewable energy power usage can be created.

[0020] The index setting unit can set the renewable energy index based on a predetermined tax system applicable to the power resources.

[0021] As a result, electricity trading schemes that reflect a predetermined tax system applicable to electricity resources can be created. For example, when a tax rate is set based on the usage rate of the said renewable energy electricity, an electricity trading scheme that takes into account the expected rate of renewable energy electricity can be created.

[0022] Using the information processing apparatus according to this disclosure, it is possible to create an electricity trading plan that takes into account the extent of renewable energy procurement in P2P electricity trading. Attached Figure Description

[0023] The features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, wherein like reference numerals denote like elements, and wherein:

[0024] Figure 1 This is a diagram schematically illustrating an example configuration of a power transmission and distribution system that uses an information processing apparatus according to the first embodiment to conduct power transactions;

[0025] Figure 2 This is a diagram illustrating an example of a peer-to-peer (P2P) electricity trading market;

[0026] Figure 3 This is a diagram illustrating an example of bidding in a typical trading market used for P2P electricity trading;

[0027] Figure 4 This is a diagram illustrating an example of bidding in a direct trading market used for P2P electricity trading;

[0028] Figure 5 This is a diagram illustrating an example of the hardware configuration of the brokerage and trading market server;

[0029] Figure 6 This is a diagram illustrating an example of resource information;

[0030] Figure 7 This is a diagram illustrating an example of agent information;

[0031] Figure 8 This is a block diagram illustrating the configuration of the agent according to the first embodiment;

[0032] Figure 9 This is a graph showing the relationship between the projected generation of renewable energy electricity and the expected transaction price (unit price) of renewable energy electricity;

[0033] Figure 10 This is a flowchart illustrating an example of the processing procedures performed when an agent submits a bid in the P2P electricity trading market;

[0034] Figure 11 This is a block diagram illustrating the configuration of the agent according to the second embodiment; and

[0035] Figure 12 This is a flowchart illustrating an example of the processing procedure performed when an agent bids for a P2P electricity trading market according to the second embodiment. Detailed Implementation

[0036] In the following description, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Identical or corresponding parts in the drawings are indicated by the same reference numerals and will not be described again.

[0037] First Embodiment

[0038] Figure 1 This is a schematic diagram illustrating an example configuration of a power transmission and distribution system that uses the information processing apparatus according to the first embodiment to conduct power transactions. (Refer to...) Figure 1 The power transmission and distribution system 1 includes multiple power resources, charging and discharging facilities 6A to 6H, power company 9, transmission line network PL, trading market server 3, and communication network 10.

[0039] Multiple power resources include, for example, electric vehicles 5A to 5E, factories 7A, companies 7B, commercial facilities 7C, residences 7D, and shops 7E. Each power resource is configured to transmit power to and receive power from other power resources either via the transmission line network PL or directly.

[0040] The number of electric vehicles and the number of charging and discharging facilities are not limited to those shown in the attached figures. Furthermore, facilities such as factory 7A are not limited to those shown in the attached figures. In the following text, each of electric vehicles 5A to 5E may be referred to as "electric vehicle 5" without distinction, and each of charging and discharging facilities 6A to 6H may be referred to as "charging and discharging facility 6" without distinction. Furthermore, each of factory 7A, company 7B, commercial facility 7C, residential building 7D, and shop 7E may be referred to as "facility 7" without distinction.

[0041] Electric vehicle 5 is an electric vehicle that can run using electricity stored in a battery, and is, for example, a battery electric vehicle (BEV), a plug-in hybrid electric vehicle (PHEV), etc. Hereinafter, electric vehicle 5 is referred to as BEV. Electric vehicle 5 is configured to be electrically connected to charging / discharging facility 6, and can supply power to and receive power from transmission line network PL or facility 7 via charging / discharging facility 6.

[0042] Facility 7 is electrically connected to the transmission line network PL, and can supply power to and receive power from the transmission line network PL. Facility 7 is also electrically connected to the charging and discharging facility 6, and can supply power to and receive power from the electric vehicle 5 connected to the charging and discharging facility 6.

[0043] The charging / discharging facility 6 is electrically connected to the transmission line network PL or facility 7. The charging / discharging facility 6 can be electrically connected to the electric vehicle 5 via a power cable, and the electric vehicle 5 can supply power to and receive power from the transmission line network PL or facility 7 through the charging / discharging facility 6, which is the connection destination.

[0044] Electricity generated by the power plant managed by power company 9 can be supplied to various facilities 7 via the transmission line network PL (system power grid), and can also be supplied to electric vehicles 5A connected to charging and discharging facilities 6A. Conventionally, electricity is supplied specifically from the power plant of power company 9 to facilities 7 and electric vehicles 5 via the transmission line network PL as described above. Within this power transmission and distribution system 1, electricity transactions can be conducted between individuals or businesses (various facilities 7 or individual electric vehicles 5), i.e., peer-to-peer (P2P) electricity transactions.

[0045] The trading market server 3 provides a platform for such P2P electricity trading. The trading market server 3 is configured to communicate with the electric vehicle 5, the charging / discharging facility 6, and the facility 7 via the communication network 10. When facility 7 or electric vehicle 5 desires a P2P electricity transaction, facility 7 or electric vehicle 5 (specifically, the agent conducting the electricity transaction for facility 7 or electric vehicle 5 (described later)) bids on the trading market server 3. The trading market server 3 manages the P2P electricity trading market by specifying, for example, the time zone where electricity is desired to be sold or purchased, the amount of electricity to be sold or purchased in each unit time zone, and the transaction price as bidding conditions. The trading market server 3 uses any algorithm to activate contracts for electricity transactions between sellers and buyers whose bidding conditions match, and processes bids with mismatched bidding conditions as non-contractual transactions. Furthermore, "bid" refers to the act of ordering an electricity transaction (buy or sell) or the order itself. "Contract" refers to the act of determining the electricity transaction for which the agent has already bid, or the determination itself.

[0046] Figure 2 This is a schematic diagram illustrating an example of a P2P electricity trading market. (See reference...) Figure 2 In the P2P electricity trading market, "agents" for bidding in the P2P electricity trading market create and execute bidding plans, manage contracts, and create charge and discharge plans based on contracts. Agents are set up for each facility 7 or electric vehicle 5, and in the first embodiment, there are multiple mobile agents 2A to 2D corresponding to electric vehicle 5, multiple operator agents 2E to 2H corresponding to factory 7A, etc., and multiple residential agents 2I and 2J corresponding to residences. For example, taking electric vehicle 5 as an example, the mobile agent of electric vehicle 5 creates an electricity trading plan (bidding plan) for the P2P electricity trading market, and the mobile agent bids for the P2P electricity trading market (trading market server 3).

[0047] In the following text, mobile agents 2A to 2D, operator agents 2E to 2H, and residential agents 2I and 2J may be referred to as "Agent 2" without distinction. Agent 2 is an "information processing device" that creates an electricity trading plan to enable the corresponding electricity resources to be traded through the P2P electricity trading market.

[0048] In the P2P electricity trading market, there are "general trading markets" and "direct trading markets." The general trading market is where electricity transmitted through the transmission line network (PL) is traded, and a large number of non-specific agents (2) can participate in electricity trading. In the general trading market, the administrator managing the P2P electricity trading market formulates electricity trading contracts (matching) according to appropriately set rules. As a matching rule, for example, when the seller's asking price and the buyer's asking price match within a predetermined unit time zone, there is a method to complete the transaction on a first-come, first-served basis. Another matching rule can also be to organize temporary bidding (ordering) between sellers and buyers within a unit time zone and then complete the transaction at an appropriate price.

[0049] Figure 3 This is a diagram illustrating an example of bidding in a typical trading market used for P2P electricity trading. (See reference...) Figure 3 In a general trading market, for each predetermined time zone (1, 2, ... n), there are numerous unspecified combinations of selling prices and electrical energy (p, q) from sellers, and numerous unspecified combinations of buying prices and electrical energy (P, Q) from buyers. A time zone is a time width (e.g., 30 minutes) set in the general trading market. Electrical energy trading takes place for each unit of electrical energy (power × time zone length) delivered within that time zone.

[0050] Refer again Figure 2 The direct trading market is a market where electric vehicles 5 move to the location of facility 7 and trade electricity without transmission through the power line network PL. Only agents with a direct trading market identifier (ID) can participate in electricity trading. In the direct trading market, one market is configured for facility 7 where charging and discharging facilities 6 are installed. Servers set up for each facility 7 can manage one direct trading market, or a common server can manage multiple direct trading markets for multiple facilities 7. In the direct trading market, contracts (matching) for electricity trading are formulated according to unique rules appropriately set by the managers in each market. The matching rules can employ the methods described above in general trading markets.

[0051] Figure 4 This is a diagram illustrating an example of bidding in a direct trading market used for P2P electricity trading. (See reference...) Figure 4In a direct trading market, for each predetermined unit time zone (1, 2, ... n), the seller proposes a combination of selling price and electrical energy (p, q), and multiple buyers with a direct trading market ID bid for a combination of purchasing price and electrical energy (P, Q). A unit time zone is a time width set separately in the direct trading market. Electricity trading takes place for each unit of electrical energy (power × unit time zone length) transmitted within that unit time zone.

[0052] Figure 5 This is a diagram illustrating an example of the hardware configuration of Agent 2 and Trading Market Server 3. (Refer to...) Figure 5 Agent 2 includes a processor 21, a memory 22, and a communication device 23. Agent 2 is configured for various power resources such as electric vehicles 5 and facilities 7 (factory 7A, residence 7D, etc.). Agent 2 can be configured within the corresponding power resource or in a cloud capable of communicating with the corresponding power resource.

[0053] The processor 21 is an arithmetic unit (computer) that performs various processes by executing various programs. The processor 21 consists of a central processing unit (CPU), a field-programmable gate array (FPGA), a graphics processing unit (GPU), etc. The processor 21 can be configured by processing circuitry.

[0054] Memory 22 stores programs and data for various processes performed by processor 21. Memory 22 consists of storage media such as read-only memory (ROM) and random access memory (RAM). Memory 22 stores arithmetic programs 221, resource information 222, and external information 223.

[0055] Arithmetic program 221 specifies the processing to be performed by processor 21. For example, arithmetic program 221 includes a program for bidding on electricity transactions in a P2P electricity trading market managed by trading market server 3.

[0056] Resource information 222 includes information about the power resources (e.g., electric vehicles 5) corresponding to agent 2, and in particular, information about bidding and contracts for power transactions.

[0057] Figure 6 This is a diagram illustrating an example of resource information 222. Figure 6 As an example, resource information 222 in agent 2 (mobile agent) of electric vehicle 5 is shown.

[0058] Reference Figure 6 Resource information 222 includes ID, category information, trip information, state of charge (SOC) information, connection information, bidding information, contract information, charge and discharge plan, and charge and discharge records.

[0059] The ID includes identification information for identifying the power resource (electric vehicle 5 in this example). Category information includes information about the category of the power resource, and includes information such as identifying the electric vehicle, operator, residence, etc. Trip information includes information about driving history, such as past routes and driving times. SOC information includes information about the electrical energy currently stored in the power storage device. Connectivity information includes information for identifying whether electric vehicle 5 is currently connected to charging / discharging facility 6. Bidding information includes information about past bidding history and information about currently ongoing bidding. Contract information includes information about past contract history and information for identifying whether the contract was made for the currently ongoing bidding. The charging / discharging plan includes information about the charging / discharging plan for the power resource based on the contract. The charging / discharging record includes information about the charging / discharging results of the above charging / discharging plan.

[0060] The trip information and connection information are information used by the agent 2 (mobile agent) of the electric vehicle 5, and for facilities 7 (factory 7A, residence 7D, etc.), the columns for this information are blank.

[0061] Refer again Figure 5 External information 223 includes the price of system electricity provided by power company 9, weather information (solar radiation, weather, wind speed, etc.) for the region of the P2P electricity trading market where the bidding takes place, and information about renewable energy power generation facilities (solar power generation facilities, wind power generation facilities, hydropower generation facilities, etc.) in the market. Agent 2 obtains external information 223 from an external server device (which may be the trading market server 3) through communication network 10.

[0062] The communication device 23 sends various data to the trading market server 3 through the communication network 10 and receives various data from the trading market server 3.

[0063] The trading market server 3 includes a processor 31, a memory 32, and a communication device 33. The trading market server 3 is a device that manages P2P electricity transactions between electricity resources in the P2P electricity trading market (general trading market and direct trading market) and performs processing related to electricity transactions.

[0064] The processor 31 is an arithmetic unit (computer) that performs various processes by executing various programs. The processor 31 consists of a CPU, FPGA, GPU, etc. The processor 31 can be configured by processing circuitry.

[0065] Memory 32 stores programs and data for various processes performed by processor 31. Memory 32 consists of storage media such as ROM and RAM. Memory 32 stores arithmetic programs 321 and agent information 322.

[0066] Arithmetic program 321 specifies the processes to be executed by processor 31. For example, arithmetic program 321 includes procedures for performing bid processing for accepting bids from multiple agents 2 and bid-based contract processing. Contract processing is the process of activating a contract for an electricity transaction between a seller and a buyer whose bids match, and treating bids whose bids do not match as non-contractual transactions.

[0067] Agent information 322 includes information about agents 2 participating in the P2P electricity trading market managed by the trading market server 3, and specifically includes information about the bidding and contracts for electricity trading by each agent 2.

[0068] Figure 7 This is a diagram illustrating an example of agent information 322. (See reference...) Figure 7 The agency information 322 includes ID, category information, bidding information, and contract information.

[0069] The ID includes identification information used to identify agents 2 participating in the P2P electricity trading market managed by the trading market server 3. Category information includes information about the category of electricity resources, and includes information such as that for identifying electric vehicles, operators, residential properties, etc. Bidding information includes information about the past bidding history of each agent 2 and information about currently ongoing bidding. Contract information includes information about the past contract history of each agent 2 and information for identifying whether a contract was drafted for a currently ongoing bidding.

[0070] Refer again Figure 5 The communication device 33 sends various data to the agent 2 and receives various data from the agent 2 through the communication network 10.

[0071] In the P2P electricity trading market comprised of Agent 2 and Trading Market Server 3, Agent 2 predicts the usage of the corresponding electricity resources (in the case of electric vehicle 5, the user predicts the usage of electric vehicle 5), and predicts the electricity trading price in the electricity trading market during the time zone in which Agent 2 can participate in electricity trading. Then, under the constraints of the upper and lower limits of the State of Charge (SOC) to be met in the electricity storage device of the corresponding electricity resources (chargeable and dischargeable range), Agent 2 creates a cost-optimized electricity trading plan (bidding plan) and submits a bid for electricity trading to Trading Market Server 3.

[0072] Here, when Agent 2 creates an electricity trading plan, in addition to the perspective of cost optimization, there is also the expectation of adjusting the procurement level of renewable energy power in the context of social demand to increase the utilization of renewable energy. For example, consider increasing the procurement level of renewable energy power (power from solar power, wind power, etc.) when there is a surplus of renewable energy power (power from solar power, wind power, etc.), and decreasing the procurement level of renewable energy power that may increase costs when the generation of renewable energy power is low.

[0073] Therefore, in Agent 2 according to the first embodiment, in addition to the viewpoint of cost optimization, a power trading scheme that can reflect the degree of procurement of renewable energy electricity is also created. This will be described in detail below.

[0074] Figure 8 This is a block diagram illustrating the configuration of agent 2 according to the first embodiment. (Refer to...) Figure 8 Agent 2 includes an external information acquisition unit 102, a renewable energy power generation forecasting unit 104, a market electricity price forecasting unit 106, a renewable energy index setting unit 108, a transaction price setting unit 110, an electricity consumption forecasting unit 112, and an electricity transaction plan creation unit 114.

[0075] The external information acquisition unit 102 acquires weather information for the region of the bidding P2P electricity trading market from an external server device (not shown). The weather information is a weather forecast for the aforementioned region and includes forecast information such as solar radiation, weather conditions, and wind speed for each time zone. The bidding P2P electricity trading market is the electricity trading market in the region where the corresponding electricity resources are expected to be charged and discharged based on P2P electricity trading, and is determined based on the usage forecast of the corresponding electricity resources. For example, in the case of agent 2 (mobile agent) of electric vehicle 5, the bidding P2P electricity trading market can be determined based on the predicted location (e.g., destination) of electric vehicle 5.

[0076] In addition, the external information acquisition unit 102 acquires information about renewable energy power generation facilities in the bidding P2P electricity trading market from an external server device (not shown). Renewable energy power generation facilities include solar power generation facilities, wind power generation facilities, hydropower generation facilities, etc. The information about renewable energy power generation facilities includes information about the type and number of power generation facilities in the area covered by the P2P electricity trading market, the rated output or maximum output of each power generation facility, etc. The information acquired by the external information acquisition unit 102 is stored in the memory 22 as external information 223.

[0077] The renewable energy generation prediction unit 104 predicts the generation of renewable energy in the bidding P2P electricity trading market based on weather information and power generation facility information acquired by the external information acquisition unit 102. For example, in the case of solar power generation facilities, when the time zone is daytime, the weather is sunny, and there are many solar power generation facilities, the renewable energy generation prediction unit 104 predicts a large generation of renewable energy from solar power. On the other hand, when the weather is rainy or the time zone is nighttime, the renewable energy generation prediction unit 104 predicts a small generation of renewable energy from solar power. Furthermore, in the case of wind power generation facilities, when there are strong winds and many wind power generation facilities, the renewable energy generation prediction unit 104 predicts a large generation of renewable energy from wind power.

[0078] The market electricity price forecasting unit 106 forecasts the price (unit price) of electricity traded (buying and selling electricity) in the bidding P2P electricity trading market. In the P2P electricity trading market according to the first embodiment, renewable energy electricity from renewable energy generation facilities and non-renewable energy electricity (such as electricity from thermal power plants) that are not applicable to renewable energy electricity are traded separately. For example, when trading renewable energy electricity, a label indicating that the electricity being traded is renewable energy electricity (renewable energy label) is attached, making it possible to distinguish between renewable energy electricity and non-renewable energy electricity. Alternatively, within the P2P electricity trading market, the market for handling renewable energy electricity transactions and the market for handling non-renewable energy electricity transactions can be separated.

[0079] Then, in the first embodiment, for the purchase price, the market electricity price prediction unit 106 predicts the price of renewable energy electricity (first price) and the price of non-renewable energy electricity (second price), respectively. The price of renewable energy electricity is predicted based on the prediction results of the renewable energy generation prediction unit 104.

[0080] For example, when the renewable energy generation forecasting unit 104 predicts a large amount of renewable energy generation, a surplus of renewable energy electricity is expected. Therefore, the market electricity price forecasting unit 106 predicts a low price for renewable energy electricity. On the other hand, when the renewable energy generation forecasting unit 104 predicts a small amount of renewable energy generation, the recycling volume of renewable energy electricity is limited. Therefore, the market electricity price forecasting unit 106 predicts a high price for renewable energy electricity. A high price (low price) may be high (low) relative to a certain standard price, or it may be high (low) relative to the price of non-renewable energy electricity.

[0081] The price of non-renewable energy electricity can be based on, for example, a price forecast of system electricity available from power company 9.

[0082] The renewable energy index setting unit 108 sets a "renewable energy index" that indicates the extent of renewable energy in the electricity purchased in the P2P electricity trading market (purchased electricity). In the P2P electricity trading market according to the first embodiment, as described above, renewable energy and non-renewable energy can be traded separately, and the renewable energy index setting unit 108 can adjust the extent of renewable energy included when purchasing electricity.

[0083] In the first embodiment, the user of the corresponding electricity resource can set the ratio of renewable energy power in the purchased electricity (the user's desired renewable energy ratio) from an input device (not shown). For the user's desired renewable energy ratio, the renewable energy power ratio value can be itself, or the renewable energy power ratio can be increased or decreased relative to a certain reference ratio. For example, when a user wishes to increase the amount of renewable energy power purchased, the user can set a higher desired renewable energy ratio from the input device.

[0084] The renewable energy index is defined as an index that increases, for example, as the proportion of renewable energy in purchased electricity increases, and decreases as the proportion of renewable energy in purchased electricity decreases. The renewable energy index setting unit 108 sets the renewable energy index based on the desired renewable energy ratio set by the user.

[0085] The transaction price setting unit 110 sets the expected transaction price for P2P electricity trading market bids based on the electricity transaction price forecast results from the market electricity price forecasting unit 106 and the renewable energy index set by the renewable energy index setting unit 108. Specifically, the transaction price setting unit 110 changes the price of renewable energy electricity (first price) predicted by the market electricity price forecasting unit 106 according to the renewable energy index. (Referring to the following text...) Figure 9 Describe it.

[0086] Figure 9 This is a graph showing the relationship between projected renewable energy power generation and expected transaction prices (unit price) for renewable energy power. (See reference...) Figure 9 Line k1 shows the price of renewable energy predicted by the market electricity price prediction unit 106 based on the renewable energy power generation predicted by the renewable energy power generation prediction unit 104. As mentioned above, when the predicted renewable energy power generation is large, the predicted price of renewable energy power is low. On the other hand, when the predicted renewable energy power generation is small, the predicted price of renewable energy power is high.

[0087] Line k2 indicates the expected transaction price of renewable energy electricity set by the transaction price setting unit 110 when the renewable energy index is high (expected renewable energy ratio is high). Line k2 is obtained by changing line k1 so that the price fluctuation based on the predicted renewable energy power generation increases relative to the predicted price of renewable energy electricity indicated by line k1. That is, relative to the predicted price of renewable energy electricity indicated by line k1, when the predicted renewable energy power generation is high, the transaction price setting unit 110 sets the expected transaction price of renewable energy electricity lower than the predicted price. When the predicted renewable energy power generation is low, the transaction price setting unit 110 sets the expected transaction price of renewable energy electricity higher than the predicted price. As a result, in the electricity trading scheme described below, a trading scheme for actively purchasing renewable energy electricity is created during time zones with high renewable energy power generation.

[0088] Line k3 indicates the expected trading price of renewable energy electricity set by the trading price setting unit 110 when the renewable energy index is low (expected renewable energy ratio is low). Line k3 is obtained by changing line k1 to reduce the price fluctuation of the generated renewable energy electricity based on the predicted renewable energy electricity output relative to the predicted price of renewable energy electricity indicated by line k1. In other words, the trading price setting unit 110 sets the expected trading price so that the generation of renewable energy electricity is less dependent on the trading price of renewable energy electricity. As a result, the electricity trading scheme described below does not create a trading scheme that actively purchases renewable energy electricity when the renewable energy index is high.

[0089] The expected trading price of non-renewable energy electricity is set as the predicted price predicted by the market electricity price prediction unit 106.

[0090] Refer again Figure 8 The power consumption prediction unit 112 predicts the power consumption in the power storage device of the corresponding power resource. For example, in the case of agent 2 (mobile agent) of electric vehicle 5, the power consumption prediction unit 112 predicts the power consumption of the battery installed on electric vehicle 5 due to the use (driving) of electric vehicle 5. The prediction results of the power consumption prediction unit 112 are used to set upper and lower limits of the SOC to be satisfied in the power storage device of the corresponding power resource when creating the power trading plan described below.

[0091] The power trading plan creation unit 114 creates a power trading plan (bidding plan) for conducting power trading in the P2P power trading market based on the expected trading price set by the trading price setting unit 110 and the power consumption of the power resources predicted by the power consumption prediction unit 112. As an example, the power trading plan creation unit 114 creates a cost-optimized power trading plan under the constraints (chargeable and dischargeable range) of the upper and lower limits of the State of Charge (SOC) to be met in the power storage device of the corresponding power resource.

[0092] Specifically, an objective function Fcost is set to calculate the cost in electricity trading, and under the constraints of the upper and lower limits of the State of Charge (SOC) (chargeable and dischargeable range) to be satisfied in the power storage device of the corresponding power resource, the energy to be traded that minimizes the objective function Fcost is searched. As an example, the objective function Fcost can be set as shown in the following formula.

[0093] [Formula 1]

[0094]

[0095] Here, k (from i to i+n, where i is the current unit time zone) is the code for the unit time zone, and r(k) is a variable that becomes "1" when the power resource purchases electricity through the P2P power trading market in unit time zone k and becomes "0" otherwise. F(k) is the cost of the power resource purchasing electricity through the P2P power trading market in unit time zone k, and is given by the following formula.

[0096] [Formula 2]

[0097] F(k)={Qbr(k)-Qsr(k)}·Pr(k)+{Qbn(k)-Qsn(k)}·Pn(k)…(2)

[0098] Here, Pr(k) is the expected transaction price (unit price) of renewable energy electricity set by the transaction price setting unit 110, and Qbr(k) and Qsr(k) are the expected purchase and sale of renewable energy electricity in a unit time zone k, respectively. Pn(k) is the expected transaction price (unit price) of non-renewable energy electricity set by the transaction price setting unit 110; that is, Pn(k) is the predicted price of non-renewable energy electricity predicted by the market electricity price prediction unit 106. Qbn(k) and Qsn(k) are the expected purchase and sale of non-renewable energy electricity in a unit time zone k, respectively.

[0099] On the other hand, the State of Charge (SOC) of the power storage device for power resources is given by the following formula.

[0100] [Formula 3]

[0101]

[0102] Here, Qtrip(k) is the predicted power consumption of the power resource in a unit time zone k. When the power resource is electric vehicle 5, Qtrip(k) is the predicted power consumption due to the operation of electric vehicle 5 in a unit time zone k, and is calculated from the power cost of electric vehicle 5, etc. C is the conversion factor used to convert electrical energy into SOC.

[0103] Then, set an upper limit value SOC_upper and a lower limit value SOC_lower for SOC, and set constraints to make SOC satisfy the following formula.

[0104] [Formula 4]

[0105] SOC_lower≤SOC(k)≤SOC_upper…(4) As mentioned above, for the objective function Fcost represented by Equation (1), while satisfying the SOC constraint condition represented by Equation (4), the condition for minimizing the objective function Fcost is searched (optimization of the objective function Fcost). For the optimization calculation of the objective function Fcost, any arithmetic method such as linear programming or convex optimization can be used.

[0106] As described above, in the first embodiment, a renewable energy index indicating the level of renewable energy power procurement is established, and an expected transaction price taking into account the renewable energy index is set. Then, based on the objective function Fcost, an electricity trading plan is created using the expected transaction price taking into account the renewable energy index. As a result, the level of renewable energy power procurement can be reflected in the electricity trading plan, and an electricity trading plan that takes into account the level of renewable energy power procurement can be created.

[0107] Figure 10 This is a flowchart illustrating an example of the processing procedures performed when Agent 2 submits a bid in the P2P electricity trading market. (See also...) Figure 10 Agent 2 (processor 21) obtains external information about the P2P electricity trading market for bidding from an external server device via communication network 10 (step S10). The external information includes weather information (solar radiation, weather, wind speed, etc.) for the area covered by the market, information about renewable energy power generation facilities in the market (solar power generation facilities, wind power generation facilities, hydropower generation facilities, etc.), and the cost of system electricity provided by power company 9, etc.

[0108] Next, Agent 2, based on the weather information and information about renewable energy power generation facilities obtained in step S10, predicts the amount of renewable energy power generated in the bidding P2P electricity trading market (step S20).

[0109] Then, Agent 2 predicts the price (unit price) of renewable energy electricity traded in the market based on the renewable energy electricity generation predicted in step S20 (step S30). Specifically, as... Figure 9 As shown by line k1, when the predicted renewable energy generation is large, Agent 2 predicts that the price of renewable energy electricity is low, and when the predicted renewable energy generation is small, Agent 2 predicts that the price of renewable energy electricity is high.

[0110] Next, Agent 2 obtains the expected renewable energy ratio set by the user of the corresponding power resource from the input device, and sets the renewable energy index based on the expected renewable energy ratio (step S40). As mentioned above, the renewable energy index is an index that indicates the extent of renewable energy in the electricity purchased in the bidding P2P electricity trading market (purchased electricity).

[0111] Then, Agent 2 sets the expected transaction price in the P2P electricity trading market for bidding based on the renewable energy electricity price predicted in step S30 and the renewable energy index set in step S40 (step S50). Specifically, as referred to Figure 9 The price of renewable energy electricity predicted in step S30 is changed according to the renewable energy index, thereby setting the expected trading price of renewable energy electricity. The expected trading price of non-renewable energy electricity is set based on the system electricity price obtained in step S10.

[0112] Next, Agent 2 predicts the power consumption in the power storage device of the corresponding power resource (step S60). When Agent 2 is an agent of the electric vehicle 5 (mobile agent), Agent 2 predicts the power consumption of the battery due to the operation of the electric vehicle 5 based on past power costs.

[0113] Then, Agent 2 searches for the condition that minimizes the objective function Fcost expressed by Formula (1) under the constraint of the SOC in the power storage device of the corresponding power resource, as expressed by Formula (4) above, so as to create an optimized power trading plan (step S70). That is, Agent 2 performs cost optimization calculations using Formulas (1) to (4) and creates a bidding plan for the P2P power trading market.

[0114] When a power trading plan (bidding plan) is created in step S70, Agent 2 bids on the P2P power trading market (trading market server 3) based on the power trading plan (step S80).

[0115] As described above, in the first embodiment, the price of electricity (renewable energy electricity / non-renewable energy electricity) traded in the P2P electricity trading market is predicted. Furthermore, a renewable energy index is set to indicate the extent of renewable energy electricity in the electricity purchased in the P2P electricity trading market. Then, an expected trading price taking into account the procurement level of renewable energy electricity is set from the predicted electricity price and the set renewable energy index, and an electricity trading plan is created based on the expected trading price. As described above, according to the first embodiment, an electricity trading plan taking into account the procurement level of renewable energy electricity can be created.

[0116] Furthermore, in the first embodiment, the generation of renewable energy electricity traded in the market is predicted based on weather information of the area covered by the P2P electricity trading market, and the price of renewable energy electricity is predicted based on the prediction results. Therefore, the price of renewable energy electricity can be appropriately predicted. As a result, a suitable expected trading price is set, and an appropriate electricity trading plan can be created based on the expected trading price.

[0117] Furthermore, in the first embodiment, the electricity consumption of the power resources is predicted, and an electricity trading plan is created from the prediction results and the expected trading price. Therefore, an appropriate electricity trading plan can be created based on the prediction of the electricity consumption of the power resources.

[0118] Furthermore, in the first embodiment, the renewable energy index is the ratio of renewable energy power in the electricity purchased in the electricity trading market, and this ratio is set based on input from users. Therefore, an electricity trading price reflecting users' desired ratio of renewable energy power can be created.

[0119] Second Embodiment

[0120] In the first embodiment described above, the user of the corresponding electricity resource sets the ratio of renewable energy power in the purchased electricity (desired renewable energy ratio) from the input device, and sets a renewable energy index based on the desired renewable energy ratio. In the second embodiment, the renewable energy index is set based on the ratio of renewable energy power in the electricity purchased in the past in the P2P electricity trading market (renewable energy usage ratio).

[0121] Figure 11 This is a block diagram illustrating the configuration of the agent according to the second embodiment. (Refer to...) Figure 11 Agent #2 is based on Figure 8 The configuration of agent 2 in the first embodiment shown further includes a renewable energy usage ratio calculation unit 116, and instead of renewable energy index setting unit 108, includes a renewable energy index setting unit 108A.

[0122] The renewable energy utilization ratio calculation unit 116 calculates the percentage of renewable energy power (renewable energy utilization ratio) in electricity purchased through the P2P electricity trading market over a predetermined period of time. The predetermined period can be appropriately set, for example, several months. Then, based on the calculated past renewable energy utilization ratio, the renewable energy utilization ratio calculation unit 116 sets the percentage of renewable energy power in the electricity transaction to be tendered (traded renewable energy ratio). For example, when the past renewable energy utilization ratio is lower than an appropriately set reference ratio, the renewable energy utilization ratio calculation unit 116 sets the traded renewable energy utilization ratio to a higher value than the past renewable energy utilization ratio to increase the renewable energy utilization ratio.

[0123] Then, the renewable energy index setting unit 108A sets the renewable energy index based on the traded renewable energy ratio set by the renewable energy usage ratio calculation unit 116. Specifically, the renewable energy index is set appropriately such that the higher the traded renewable energy ratio, the larger the renewable energy index, and the lower the traded renewable energy ratio, the smaller the renewable energy index.

[0124] Other functions of Agent 2# and Figure 8 The agent 2 shown in the first embodiment has the same function.

[0125] Figure 12 This is a flowchart illustrating an example of the processing procedures performed when agent 2# submits a bid in the P2P electricity trading market according to the second embodiment. This flowchart is similar to that described in the first embodiment. Figure 10 The flowchart corresponds to this.

[0126] Reference Figure 12 The processes in steps S110 to S130 and S150 to S180 are respectively related to Figure 10 The processes in steps S10 to S30 and S50 to S80 of the flowchart shown are the same.

[0127] In this flowchart, when predicting the price (unit price) of renewable energy electricity traded in the P2P electricity trading market in step S130, Agent 2# calculates the transaction renewable energy ratio in the electricity transaction to be bid based on the renewable energy usage ratio in the past predetermined time period, and sets the renewable energy index based on the transaction renewable energy ratio (step S140).

[0128] Then, when the renewable energy index is set, Agent 2# will transfer the processing to step S150 and set the expected transaction price in the P2P electricity trading market for bidding based on the price of renewable energy electricity predicted in step S130 and the renewable energy index set in step S140.

[0129] As described above, in the second embodiment, the renewable energy ratio in the electricity transaction to be tendered is calculated based on the renewable energy usage ratio over a predetermined past period, and a renewable energy index is set based on this ratio. Therefore, an electricity trading plan can be created based on the renewable energy usage ratio over a predetermined past period. For example, when the aforementioned ratio of renewable energy electricity is low during the predetermined period, an electricity trading plan can be created to increase that ratio.

[0130] Variation Example

[0131] As described above, in the first embodiment, the renewable energy index, which indicates the extent of renewable energy in the electricity purchased in the P2P electricity trading market (purchased electricity), is set based on the user's expected renewable energy ratio. In the second embodiment, the renewable energy index, which indicates the extent of renewable energy in the electricity purchased in the P2P electricity trading market (purchased electricity), is set based on the transaction renewable energy ratio according to the renewable energy usage ratio over a predetermined past period. However, the renewable energy index can be set based on other parameters.

[0132] Specifically, a renewable energy index can be set based on a predetermined law applicable to the corresponding power resource. For example, if the corresponding power resource is an electric vehicle 5, and the renewable energy usage rate of the electric vehicle 5 when entering a specific area (such as a nature reserve) is stipulated by regulations, a renewable energy index can be set based on the renewable energy usage rate stipulated in the regulations.

[0133] Alternatively, a renewable energy index can be set based on a predetermined tax system applied to the corresponding power resource. For example, when the corresponding power resource is electric vehicle 5, and a tax rate such as an environmental tax is set based on the renewable energy usage rate determined according to the year of manufacture of electric vehicle 5, a renewable energy index can be set based on the renewable energy usage rate applied to electric vehicle 5.

[0134] The embodiments disclosed herein should be considered exemplary and not restrictive in all respects. The scope of this disclosure is determined based on the statements within the scope of the claims rather than the description of the embodiments described above, and is intended to include all variations within the meaning and scope equivalent to the scope of the claims.

Claims

1. An information processing apparatus for creating an electricity trading plan for enabling electricity resources to be traded through an electricity trading market, the electricity resources being configured to receive electricity from at least an external source, the information processing apparatus comprising: A price forecasting unit that forecasts the trading price of electricity traded in the electricity trading market, the trading price including... The first price, which indicates the price of renewable energy electricity, and The second price indicates the price of non-renewable energy power that is not applicable to the renewable energy power; An index setting unit sets a renewable energy index, which indicates the extent of renewable energy power in the electricity purchased in the electricity trading market, based on the expected renewable energy ratio set by the user. The transaction price setting unit sets the expected transaction price in the electricity trading plan based on the forecast results of the price forecasting unit and the renewable energy index; A transaction plan creation unit, which creates the electricity transaction plan based on the expected transaction price; as well as A power generation forecasting unit that forecasts the power generation of the renewable energy electricity traded in the electricity trading market. The price prediction unit predicts the first price based on the prediction results of the power generation prediction unit; Compared to when the renewable energy index is low, when the renewable energy index is high, and when the predicted renewable energy power generation is high, the transaction price setting unit sets the expected transaction price of the renewable energy power to be lower than the first price; and when the predicted renewable energy power generation is low, the transaction price setting unit sets the expected transaction price of the renewable energy power to be higher than the first price.

2. The information processing apparatus according to claim 1, wherein, Compared to when the renewable energy index is large, when the renewable energy index is small, and when the predicted generation of renewable energy power is large, the transaction price setting unit sets the expected transaction price of the renewable energy power to be higher than the first price; and when the predicted generation of renewable energy power is small, the transaction price setting unit sets the expected transaction price of the renewable energy power to be lower than the first price.

3. The information processing apparatus according to claim 2, wherein, The power generation forecasting unit forecasts the power generation of the renewable energy source based on weather information from the area covered by the electricity trading market.

4. The information processing apparatus according to claim 1, further comprising a power consumption prediction unit, wherein the power consumption prediction unit predicts the power consumption of the power resources, wherein, The transaction plan creation unit creates the power transaction plan from the expected transaction price and the prediction results of the power consumption prediction unit.

5. The information processing apparatus according to claim 1, wherein, The renewable energy index is the ratio of renewable energy power in the electricity purchased in the electricity trading market.

6. The information processing apparatus according to claim 1, wherein, The index setting unit sets the renewable energy index based on the proportion of renewable energy power in the electricity purchased in the electricity trading market during a predetermined time period.

7. The information processing apparatus according to claim 1, wherein, The index setting unit sets the renewable energy index based on predetermined laws applicable to the power resources.

8. The information processing apparatus according to any one of claims 1 to 7, wherein, The index setting unit sets the renewable energy index based on a predetermined tax system applicable to the electricity resource.

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