Electronic device for supporting power transaction and method thereof

Abstract

Disclosed is an electronic device for supporting power transaction, the electronic device comprising: a transceiver; a processor; and a memory for storing one or more instructions, wherein the one or more instructions are configured to, when executed, cause the processor to: identify a plurality of power prediction data during a first period corresponding to a plurality of energy storage systems (ESSs), respectively; on the basis of first power prediction data during the first period corresponding to a first ESS among the plurality of ESSs, identify at least one second ESS matching the first ESS; identify power transaction intention information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and determine whether to perform power exchange between the first ESS and the second ESS, on the basis of the power transaction intention information identified from the first terminal and the second terminal.

Description

Electronic device supporting power trading and method thereof

[0001] This application claims the benefit of priority to Republic of Korea Patent Application No. 2024-0090330, dated July 9, 2024, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to an electronic device and method supporting power trading.

[0003] As the adoption of self-generation facilities and energy storage systems (ESS) increases, the efficient management and utilization of household power is becoming increasingly important. In particular, storing surplus power generated by self-generation facilities for later use is becoming commonplace. However, if surplus power continues to accumulate and remains unused, it can lead to resource waste. Therefore, a platform supporting power trading between ESSs is needed to efficiently trade surplus power, supply it where it's needed, and enhance power resource utilization.

[0004] The disclosed embodiments provide an electronic device and method supporting power trading. Specifically, the primary goal is to build a platform system that accumulates data on power generation and consumption based on past climate conditions and predicts future power surpluses and shortages based on that data, thereby enabling more effective and efficient power trading between individuals who own ESS.

[0005] The technical tasks to be achieved by this embodiment are not limited to the technical tasks described above, and other technical tasks can be inferred from the following embodiments.

[0006] One aspect of the present disclosure provides an electronic device supporting power trading, comprising: a transceiver; a processor; and a memory storing one or more instructions, wherein the one or more instructions, when executed, cause the processor to: check a plurality of power prediction data for a first period corresponding to each of a plurality of energy storage systems (ESSs); check at least one second ESS matching the first ESS based on the first power prediction data for the first period corresponding to a first ESS among the plurality of ESSs; check power trading intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and determine whether to perform power exchange between the first ESS and the second ESS based on the power trading intent information checked from the first terminal and the second terminal.

[0007] In one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to control the first ESS and the second ESS so that power exchange occurs in an amount of power agreed upon between the first ESS and the second ESS when the processor determines to perform power exchange between the first ESS and the second ESS when the processor determines to perform power exchange between the first ESS and the second ESS.

[0008] In addition, in one embodiment of the present disclosure, the one or more instructions may be configured to, when executed, cause the processor to obtain the first power prediction data for the first period corresponding to the first ESS output from the artificial intelligence engine by inputting weather prediction data for the first period of the area corresponding to the location of the first ESS, which is confirmed from a weather prediction server, into the artificial intelligence engine, and the artificial intelligence engine may include an electronic device that is an artificial intelligence model constructed by learning a correlation between weather data for a second period of the area corresponding to the location of at least some of the plurality of ESSs and power data including at least some of the generation amount, the charge amount, the discharge amount, and the household power consumption amount corresponding to the second period for at least some of the plurality of ESSs.

[0009] Additionally, in one embodiment of the present disclosure, the correlation may include an electronic device including a first correlation related to the proportionality of solar power generation, which is at least a portion of the power generation, to the amount of sunlight and inversely proportional to the temperature; a second correlation related to the proportionality of wind power generation, which is at least a portion of the power generation, to the wind speed; and a third correlation related to the U-shaped relationship of the household power consumption to the temperature.

[0010] Additionally, in one embodiment of the present disclosure, the weather forecast data may include an electronic device including data on at least some of the temperature, amount of sunlight, and wind speed predicted for the first period of the region.

[0011] In addition, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to cause the processor, when executed, to check, based on the first power prediction data, power supply and demand data per unit time predicted for the first period of the first ESS, wherein the power supply and demand data per unit time includes data predicting whether household power consumption corresponding to the first ESS can be supplied and demanded with the power amount of the first ESS and the power generation amount corresponding to the first ESS per unit time, and to check, based on the power supply and demand data per unit time, at least one ESS whose power supply and demand state is predicted to be opposite to that of the first ESS during at least a portion of the unit time, and to check, based on condition information of the first ESS, at least one second ESS among the at least one ESS whose power supply and demand state is predicted to be opposite to that of the first ESS.

[0012] Additionally, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to cause the processor, when executed, to check the unit-time power supply and demand data further including a predicted value as a difference between the sum of the power amount of the first ESS and the power generation amount corresponding to the first ESS and the unit-time power consumption of the household based on the first power prediction information.

[0013] Additionally, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to cause the processor, when executed, to identify at least one second ESS that satisfies at least one of a purchase price condition, a distance condition, a compatibility condition, and a connection condition included in condition information of the first ESS among at least one ESS whose power supply and demand status is predicted to be opposite to that of the first ESS for at least a portion of the unit time.

[0014] Additionally, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to cause the processor, when executed, to determine that a size of a value predicted as a difference between the sum of the power amount of the first ESS and the power generation amount corresponding to the first ESS and the household power consumption per unit time is greater than or equal to a threshold value during at least a portion of the unit time, and to determine the at least one second ESS without applying at least a portion of the conditions included in the condition information during at least a portion of the unit time during which the size of the value predicted as the difference is greater than or equal to the threshold value.

[0015] Additionally, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to cause the processor, when executed, to determine that the power supply / demand status of the first ESS corresponds to a power shortage for at least a portion corresponding to a peak time period during the unit time, and to determine the at least one second ESS without applying at least a portion of the conditions included in the condition information for at least a portion corresponding to a peak time period during the unit time during which the power supply / demand status of the first ESS corresponds to a power shortage.

[0016] Additionally, in one embodiment of the present disclosure, the one or more instructions may include an electronic device configured to, when executed, cause the processor to transmit, to each of the first terminal and the second terminal, transaction information related to power exchange between the first ESS and the second ESS, and to confirm, from each of the first terminal and the second terminal, the power transaction intent information including information indicating whether or not a power transaction is desired and an amount of power desired to be traded.

[0017] Another aspect of the present disclosure provides a method for supporting power trading in an electronic device, comprising: a step of confirming a plurality of power prediction data for a first period corresponding to each of a plurality of Energy Storage Systems (ESS); a step of confirming at least one second ESS matching the first ESS based on the first power prediction data for the first period corresponding to the first ESS among the plurality of ESSs; a step of confirming power trading intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and a step of determining whether to perform power exchange between the first ESS and the second ESS based on the power trading intent information confirmed from the first terminal and the second terminal.

[0018] Another aspect of the present disclosure is a computer-readable, non-transitory recording medium having recorded thereon a program for executing a power trading support method on a computer, the power trading support method comprising: a step of: confirming a plurality of power prediction data for a first period corresponding to each of a plurality of energy storage systems (ESS); a step of confirming at least one second ESS matching the first ESS based on the first power prediction data for the first period corresponding to a first ESS among the plurality of ESSs; a step of confirming power trading intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and a step of determining whether to perform power exchange between the first ESS and the second ESS based on the power trading intent information confirmed from the first terminal and the second terminal.

[0019] Specific details of other embodiments are included in the detailed description and drawings.

[0020] According to the proposed embodiment, one or more of the following effects can be expected.

[0021] According to the embodiment of this specification, a platform system can be built that accumulates data on power generation and consumption according to past climate conditions and predicts future power surplus or power shortage in advance based on the data, thereby enabling power transactions between individuals who own ESS to be conducted more effectively and efficiently.

[0022] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0023] FIG. 1 is a diagram showing an electronic device supporting power trading and an interconnection relationship between each component supporting the electronic device according to one embodiment.

[0024] Figure 2 is a flowchart for explaining a power transaction support method according to one embodiment.

[0025] FIG. 3 is a diagram illustrating an example of power data and weather data collected by a data collection server according to one embodiment.

[0026] FIG. 4 is a diagram illustrating an artificial intelligence engine according to one embodiment.

[0027] FIG. 5 is an exemplary diagram showing first power prediction data of a first ESS according to one embodiment and second power prediction data of an ESS whose power supply and demand status is predicted to be opposite to that of the first ESS.

[0028] Figure 6 shows a block diagram of an electronic device according to one embodiment.

[0029] The terms used in the examples have been selected from widely used, current terms, taking into account the functions of the present disclosure. However, these terms may vary depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, in which case their meanings will be described in detail in the relevant description. Therefore, the terms used in this disclosure should not be defined simply as names, but rather based on the meanings of the terms and the overall content of the present disclosure.

[0030] When a part of a specification is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise stated.

[0031] The expression "at least one of a, b, and c" described throughout the specification may encompass 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'all of a, b, and c'.

[0032] The "terminal" mentioned below may be implemented as a computer or portable terminal that can connect to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, laptop, etc. equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that guarantees portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0033] Below, embodiments of the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein.

[0034] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0035] FIG. 1 is a diagram showing an electronic device supporting power trading and an interconnection relationship between each component supporting the electronic device according to one embodiment.

[0036] Referring to FIG. 1, the electronic device (100) can operate in conjunction with an artificial intelligence engine (200), a data collection server (300), and a weather information server (400). In addition, the electronic device (100) can also operate in conjunction with a plurality of energy storage systems (ESS) and terminals corresponding to each ESS. For convenience of explanation, FIG. 1 only illustrates a first ESS (510), a first terminal (520) corresponding to the first ESS (510), a second ESS (610), and a second terminal (620) corresponding to the second ESS (610). Only components directly related to the present disclosure are illustrated in FIG. 1, and it will be understood by a person skilled in the art related to the present embodiment that other general-purpose components may be further included in addition to the components illustrated in FIG. 1.

[0037] Hereinafter, the operation of each component will be briefly described. The artificial intelligence engine (200) can perform an operation of receiving weather prediction data and outputting power prediction data. The data collection server (300) can obtain power data from multiple ESSs and obtain and store weather data from a weather information server (400) to support the learning of the artificial intelligence engine (200). The weather information server (400) can be a server that uses its own weather prediction model or is connected to an external weather prediction agency server to check weather data and weather prediction data. The electronic device (100), artificial intelligence engine (200), data collection server (300), and weather information server (400) described above may each operate as physically separate servers, or at least some of them may operate as logically separate servers within the same physical server. For example, the artificial intelligence engine (200) may be implemented as an artificial intelligence model built into the data collection server (300), or may be implemented as an artificial intelligence model implemented as a separate device from the data collection server (300) or another server.

[0038] Figure 2 is a flowchart for explaining a power transaction support method according to one embodiment.

[0039] Referring to FIG. 2, in step S210, the electronic device (100) may check a plurality of power prediction data for a first period corresponding to each of the plurality of ESSs. Here, each of the plurality of ESSs may have an owner who has subscribed to a power trading platform, and thus may be connected to the electronic device (100) via a network. As will be described in detail later, the first period may be a future period from that point in time, and the power prediction data may correspond to the amount of power generated by the generator corresponding to each ESS, the amount of charge of each ESS, the amount of discharge of each ESS, and the household power consumption of the household to which each ESS is connected, which are expected to be measured in the first period for each ESS. Examples of such respective values ​​can also be found later in FIG. 5.

[0040] In step S220, the electronic device (100) can identify at least one second ESS (610) matching the first ESS (510) based on first power prediction data for a first period corresponding to the first ESS (510) among the plurality of ESSs. Here, the first ESS (510) is an example among the plurality of ESSs, and the second ESS (610) can be at least one of the ESSs that has a power supply and demand state opposite to that of the first ESS (510) in a unit time during the first period, as will be described in detail later.

[0041] In step S230, the electronic device (100) can check power transaction intent information from each of the first terminal (520) associated with the first ESS (510) and the second terminal (620) associated with the second ESS (610). Here, the power transaction intent information is information input into the first terminal (520) and the second terminal (620) by the respective owners of the first ESS (510) and the second ESS (610), respectively, and may be information including the content that each owner agrees to the power transaction according to the transaction information transmitted to the first terminal (520) and the second terminal (620), which will be described later.

[0042] In step S240, the electronic device (100) may determine whether to perform power exchange between the first ESS (510) and the second ESS (610) based on the power transaction intent information confirmed from the first terminal (520) and the second terminal (620). Each step and each component associated with each step will be described below.

[0043] First, the electronic device (100) can verify multiple power prediction data for a first period corresponding to each of multiple ESSs by operating in conjunction with the artificial intelligence engine (200). The artificial intelligence engine (200) may be trained using power data and weather data from the data collection server (300), as described above. First, the power data and weather data acquired by the data collection server (300) will be described with reference to FIG. 3.

[0044] FIG. 3 is a diagram illustrating an example of power data and weather data collected by a data collection server according to one embodiment.

[0045] Referring to FIG. 3, basic data (511) and power data (512) can be collected from a first ESS (510), which is one of a plurality of ESSs. The first ESS (510) is provided as an example, and the data collection server (300) can similarly obtain basic data and power data from other plurality of ESSs.

[0046] First, as basic data, the data collection server (300) can obtain latitude, longitude, ESS installation capacity, and minimum State-of-Charge (SoC) setting information of the first ESS (510). The latitude and longitude information of the first ESS (510) is obtained as location information of the first ESS, and the basic data can also be obtained in other ways, for example, in the form of an address. Next, the ESS installation capacity can include information on the capacity of the first ESS (510). The minimum SoC setting information can mean a minimum SoC value set for the first ESS (510), and a setting value that allows only charging to occur without discharging when the SoC value of the first ESS (510) falls below the SoC value. According to the example of FIG. 3, the data collection server (300) can acquire basic data of latitude 127 degrees, longitude 37.56 degrees, ESS installation capacity 17.2 kWh, and minimum SoC setting 20% ​​for the first ESS (510). Such basic data can be collected by the data collection server (300) at the time when multiple ESSs are initially installed or at any time thereafter.

[0047] Next, as power data, the data collection server (300) can obtain information on household power consumption, ESS charging power, ESS discharge power, ESS power, and generation power from the first ESS (510). Such power data can be transmitted to the data collection server (300) from multiple ESSs at set unit times. For example, the power data can be transmitted to the data collection server (300) from multiple ESSs at intervals of one minute or several minutes. Among the power data, household power consumption can refer to power consumed by a household connected to the first ESS (510) during a unit time corresponding to the corresponding point in time. The ESS charging power can refer to the amount of power charged by the ESS by power generated by the generator connected to the first ESS (510) at the corresponding point in time, and the ESS discharge power can refer to the amount of power being discharged from the first ESS (510) at the corresponding point in time. The ESS power can be a value obtained by subtracting the ESS discharge power from the ESS charging power. The amount of power generated may refer to the amount generated by the generator connected to the first ESS (510) at the given time, and may include, but is not limited to, solar power generation and wind power generation, for example. According to the example of FIG. 3, the data collection server (300) may confirm the household power consumption indicating that 0.486 kW was consumed by the household during the unit time corresponding to the given time from the first ESS (510), the ESS charging power indicating that the first ESS (510) was not charged at all during the unit time, the ESS discharge power indicating that the first ESS (510) discharged 0.213 kW during the unit time, the ESS power indicating -0.213 kW, and the power generation indicating that the generator corresponding to the first ESS (510) generated 0.109 kW. At this time, the amount discharged from the first ESS (510) and the power generated are combined to be less than the household power consumption, which may indicate that the household corresponding to the first ESS (510) at that time received and used power from an external grid.

[0048] Referring back to FIG. 3, the data collection server (300) can obtain weather data from the weather information server (400). According to one embodiment, the weather data can be transmitted to the data collection server (300) at intervals of one hour or several hours. The weather data can include information on a reference time, latitude, longitude, temperature, solar radiation, wind speed, probability of precipitation, humidity, and weather classification information. First, the reference time and latitude and longitude information included in the weather data can indicate the weather measured at a certain location at a certain time. The latitude and longitude can be replaced with other formats used to indicate locations, such as administrative districts. The weather classification information can be information indicating the weather condition at a certain time and location in a broad sense, for example, whether it is rainy, cloudy, clear, or foggy. According to the example of FIG. 3, the data collection server (300) can check weather data indicating that the temperature is 23.6 degrees Celsius, the solar radiation per square meter is 154.1 W, the wind speed is 1.4 m / s, the probability of precipitation is 60%, the humidity is 40%, and the weather condition is cloudy at 12:00 on June 17, 2024, in the area around the location of latitude 127 degrees and longitude 37.56 degrees.

[0049] As shown in FIG. 3, after power data and weather data are acquired, the data collection server (300) can map them. That is, the data collection server (300) can check weather data at locations where multiple ESSs are installed based on basic data, and map power data acquired from multiple ESSs and weather data at the corresponding locations. At this time, the mapping can be performed by aligning the time at which the power data was acquired with the reference time information included in the weather data. For example, the power data of the first ESS (510) from 3:00 PM to 4:00 PM on June 26, 2024 can be mapped with weather data of the area where the first ESS (510) is installed from 3:00 PM to 4:00 PM on June 26, 2024. Here, if the acquisition intervals of the power data and weather data are the same, each data can be mapped as is. In contrast, when the acquisition intervals are different, for example, when power data is acquired at intervals of one minute or several minutes as described above and meteorological data is acquired at intervals of one hour or several hours, the power data can be accumulated and mapped in a manner corresponding to the acquisition interval of the meteorological data. For example, the power data of the first ESS (510) acquired every three minutes from 15:00 to 16:00 on June 26, 2024 can be accumulated for the corresponding hour, and then mapped with the meteorological data for the area where the first ESS (510) is installed at 15:00 on June 26, 2024, which is acquired every hour. Here, when the power data is in kWh units, i.e., in hourly units, as shown in FIG. 3, an average value can be used to accumulate the power data acquired in minutes in hourly units. Of course, other methods of accumulating power data are also possible.

[0050] The data collection server (300) that collects data in this manner can transmit the data to the artificial intelligence engine (200) to support learning of the artificial intelligence engine (200). The artificial intelligence engine (200) will be described below with reference to FIG. 4.

[0051] FIG. 4 is a diagram illustrating an artificial intelligence engine according to one embodiment.

[0052] Referring to FIG. 4, the artificial intelligence engine (200) can store power data and weather data acquired from the data collection server (300) in its database (210). According to one embodiment, the database (210) can be implemented in a time-series DB method or a NoSQL method, but is not limited thereto. Thereafter, the artificial intelligence engine (200) can preprocess the power data and weather data stored in the database (210). For example, the power data and weather data can be processed as learning data by performing a preprocessing operation such as excluding data that appears to be a measurement error or data with extreme values, or processing missing values. Next, the artificial intelligence engine (200) can extract patterns of the weather data and power data using a data analysis tool (220). For example, the artificial intelligence engine (200) can extract basic statistical characteristics such as the average, standard deviation, maximum value, or minimum value for each item of the weather data and power data, and can aggregate hourly data to analyze seasonality and trends. Of course, the pattern extraction process is not limited to the process described above. Next, the artificial intelligence engine (200) can train a recurrent neural network (RNN) included in the engine using a deep learning framework (230). For example, the artificial intelligence engine (200) can train the RNN by inputting a pattern of weather data as training data into the RNN using the deep learning framework (230), comparing the value output from the RNN with the correct answer value confirmed based on the pattern of the power data, and backpropagating the calculated loss. For example, backpropagation for the RNN can also be performed as backpropagation through time.

[0053] According to one embodiment, the artificial intelligence engine (200) may receive data corresponding to a second period in the past from among the weather data and power data stored in the data collection server (300) and learn as described above. Here, the second period may be appropriately set in consideration of the efficiency of learning. Through the learning process, the artificial intelligence engine (200) may learn a correlation between weather data for a second period in an area corresponding to the location of at least some of the plurality of ESSs and power data including at least some of the generation amount, the charge amount, the discharge amount, and the household power consumption corresponding to the second period for at least some of the plurality of ESSs. For example, the correlation may include a first correlation related to the proportionality of solar power generation, which is at least some of the generation amount, to the amount of sunlight and the inverse proportionality to the temperature, a second correlation related to the proportionality of wind power generation to the wind speed, and a third correlation related to the U-shaped relationship of household power consumption to the temperature. Here, the U-shaped relationship may refer to a relationship in which household electricity consumption increases in high-temperature and low-temperature sections when temperature sections are divided into high-temperature, medium-temperature, and low-temperature sections. The artificial intelligence engine (200) can learn this relationship and perform the function of receiving weather forecast data and outputting electricity forecast data, as described below.

[0054] According to one embodiment, the artificial intelligence engine (200) may be trained to output power prediction data that reflects previous household power usage patterns for each of a plurality of ESSs. The household power usage pattern may be, for example, if household A often has household members staying at home during the day, and household B has household members not staying at home during the day, household A may have high power usage due to high air conditioning usage during hot summer days, but household B may have low power usage even during hot summer days. In order for the artificial intelligence engine (200) to output power prediction data that reflects such household power usage patterns, according to one embodiment, the artificial intelligence engine (200) may be implemented in a manner such as training weather data and power data by dividing them by each of a plurality of ESSs, training and using models that are differentiated by each of a plurality of ESSs, or using identification information for each of a plurality of ESSs together as input, but is not limited to the above-described manner.

[0055] According to one embodiment, by operating in conjunction with the learned artificial intelligence engine (200), the electronic device (100) can check a plurality of power prediction data for a first period corresponding to each of the plurality of ESSs. Here, the first period may be a future period from the corresponding point in time, and the artificial intelligence engine (200) can obtain weather prediction data, which is data predicting future weather conditions from the corresponding point in time, from the weather information server (400). For example, the first period may be a range up to 48 hours after the corresponding point in time. The weather prediction data may include predicted information for the first period with respect to items similar to the aforementioned weather data, such as temperature, solar irradiance, wind speed, probability of precipitation, humidity, and weather classification information. In addition, the weather prediction data can be mapped for each of the plurality of ESSs using latitude and longitude information included in such weather prediction data. The artificial intelligence engine (200) can calculate such weather prediction data and output power prediction data. Here, since the artificial intelligence engine (200) has learned the correlation between weather data and power data as described above, when weather prediction data for each of the plurality of ESSs is input, power prediction data for each of the plurality of ESSs can be output.

[0056] According to one embodiment, the power prediction data may include predicted information for the second period regarding items similar to power data, such as power generation, charging, discharging, and household power consumption. For example, the power prediction data may include predicted information for power generation, charging, discharging, and household power consumption for each unit of time in the second period. For example, if the second period corresponds to 00:00 to 24:00 on August 3, 2024, and the unit of time corresponds to 1 hour, the power prediction data for the first ESS (510) may include predicted information for power generation, charging, discharging, and household power consumption for each hour of each 1-hour interval on August 3, 2024.

[0057] According to one embodiment, the electronic device (100) can check first power prediction data corresponding to the first ESS (510). The electronic device (100) can check at least one second ESS (610) matching the first ESS (510) based on the first power prediction data. To this end, the electronic device (100) can check hourly power supply and demand data predicted for a first period of the first ESS (510) based on the first power prediction data. Here, the hourly power supply and demand data can include a power supply and demand value as information on whether household power consumption of a household in which a plurality of ESSs are installed can be supplied or demanded based on the amount of power stored in the first ESS (510) and the amount of power generated corresponding to the first ESS (510). Specifically, the power supply and demand value may be a predicted value of whether the household power consumption of a household in which multiple ESSs are installed can be supplied and supplied based on the amount of power stored in each of the multiple ESSs and the corresponding amount of power generated by each of the multiple ESSs, which are calculated as the difference between the amount of charge and the amount of discharge for the unit time. For example, the unit hourly power supply and demand value for the first ESS (510) may be a value corresponding to the difference between the sum of the amount of power and the amount of power generated by the first ESS (510) predicted for each unit time and the household power consumption corresponding to the first ESS (510). The fact that the power supply and demand value is calculated as a negative number may mean that the first ESS (510) and the generator connected thereto are predicted not to be able to supply power to the household for the unit time, and therefore, the household is predicted to receive and use power from an external grid in an amount equal to the absolute value of the power supply and demand value. Conversely, if the power supply and demand value is positive, it may mean that the first ESS (510) and the generator connected thereto are expected to be able to supply power to the household for that unit of time, and therefore, the household is expected to have a surplus of power equal to the absolute value of the power supply and demand value.The power supply and demand data for each unit of time may include the power supply and demand value for each unit of time itself and information on whether the power supply and demand value for each unit of time is positive or negative, which is confirmed through whether the power supply and demand value is positive or negative. For example, the power supply and demand data for a specific unit of time corresponding to the first power prediction data may include a power supply and demand value corresponding to 14.8 kWh and information indicating a power surplus, which may indicate that the first ESS (510) is expected to have a surplus power of 14.8 kWh in the specific unit of time. Conversely, the power supply and demand data for a specific unit of time corresponding to the first power prediction data may include a power supply and demand value corresponding to -6.5 kWh and information indicating a power shortage, which may indicate that the first ESS (510) is expected to have a power shortage of 6.5 kWh in the specific unit of time.

[0058] Alternatively, according to another embodiment, the artificial intelligence engine (200) may directly output the power supply and demand value per unit time as power prediction data. In this case, the electronic device (100) may not perform the process of calculating the difference between the charge and discharge amounts as described above and calculating the difference between the difference value and the household power consumption value to determine the power supply and demand value, but may instead determine the value output from the artificial intelligence engine (200) as the power supply and demand value per unit time.

[0059] The electronic device (100) can identify at least one ESS whose power supply and demand status is predicted to be opposite to that of the first ESS (510) for at least part of the unit time, based on the power supply and demand data per unit time. For example, if the first ESS (510) is predicted to have a positive power supply and demand value for a specific unit time, that is, a power surplus, the electronic device (100) can identify at least one ESS whose power supply and demand value is predicted to be negative, that is, a power shortage, for the corresponding unit time. Conversely, if the first ESS (510) is predicted to have a negative power supply and demand value for a specific unit time, that is, a power shortage, the electronic device (100) can identify at least one ESS whose power supply and demand value is predicted to be positive, that is, a power surplus.

[0060] An example of identifying at least one ESS whose power supply and demand status is predicted to be reversed is described with reference to Fig. 5.

[0061] FIG. 5 is an exemplary diagram showing first power prediction data of a first ESS according to one embodiment and second power prediction data of an ESS whose power supply and demand status is predicted to be opposite to that of the first ESS.

[0062] Referring to the first power prediction data (501) according to the example of FIG. 5, it can be confirmed that the first ESS (510) can sufficiently handle the household power consumption with only the power generated from 7:00 to 17:00, and the remaining power is continuously charged to the first ESS (510), so that the ESS power of the first ESS (510) from 7:00 to 17:00 continuously increases. Therefore, for a unit time included in the daytime time zone of the first ESS (510), the power supply and demand status can be confirmed as a power surplus. In contrast, referring to the example of the second power prediction data (601) in FIG. 5, it can be confirmed that the power of the ESS is continuously close to 0, and the power generated is less than the household power consumption. In this case, it can be confirmed that the power supply and demand status of the ESS is a power shortage, that is, the power supply and demand status is opposite to that of the first ESS (510).

[0063] Thereafter, the electronic device (100) can identify at least one second ESS (610) among at least one ESS whose power supply and demand status is predicted to be opposite to that of the first ESS (510) based on the condition information of the first ESS (510). Here, the condition information of the first ESS (510) can satisfy at least one of various conditions, such as a purchase price condition, a distance condition, a compatibility condition, and a connection condition, according to one embodiment.

[0064] According to one embodiment, the purchase price condition may refer to the price to be applied when selling or purchasing the power of the first ESS (510), set by the owner of the first ESS (510) through the first terminal (520) corresponding to the first ESS (510). For example, the condition may be related to the minimum price per unit or the minimum total amount when selling the power of the first ESS (510), or the maximum price per unit or the maximum total amount when purchasing the power to be stored in the first ESS (510). In this case, such purchase price condition may be set differently by time zone or may be set differently according to the distance from the trading partner ESS. For example, since it is predicted that the closer an ESS is, the better the efficiency in power exchange, it is also possible to diversify the conditions, such as applying a relatively high price when purchasing from a nearby ESS and a relatively low price when selling.

[0065] In one embodiment, the distance condition may refer to a condition related to the maximum distance to be applied when selling or purchasing power of the first ESS (510), set by the owner of the first ESS (510) through the first terminal (520) corresponding to the first ESS (510). As described above, since the efficiency of power exchange decreases as the distance increases, the owner of the first ESS (510) may set such a condition through the first terminal (520) taking this into consideration.

[0066] In one embodiment, the compatibility conditions may be compatibility-related conditions determined based on the basic characteristics of the first ESS (510), such as the type of the first ESS (510) or the method of connection to the power grid. Furthermore, the connection conditions may be conditions related to whether the first ESS (510) is connected to a power grid capable of exchanging power. Such compatibility conditions and connection conditions may be conditions determined based on the basic characteristics of the first ESS (510) or infrastructure environmental characteristics.

[0067] Some of these conditions may not be applied depending on the situation. According to one embodiment, if the absolute value of the power supply and demand value, i.e., the value predicted by the difference between the sum of the power amount of the first ESS (510) and the power generation amount corresponding to the first ESS (510) and the household power consumption per unit time is greater than a threshold value, the electronic device (100) may not apply at least some of the conditions included in the aforementioned condition information. For example, if the amount of power remaining in the first ESS (510) is too large or too insufficient, the electronic device (100) may not apply the conditions set by the first terminal (520), such as the purchase price condition or the distance condition, for the corresponding unit time. This means that if there is too much surplus power left in 1 ESS (510), it may be advantageous for the owner of the first ESS (510) to sell power even if the conditions are slightly unmet, and if there is too little power, it may be advantageous for the owner of the first ESS (510) to purchase power even if the conditions are slightly unmet. Therefore, the electronic device (100) can check the second ESS (610) without applying some of the conditions.

[0068] In addition, as an example of a situation in which the conditions set by the first terminal (520), such as a purchase price condition or a distance condition, are not applied, there may be a case in which the power supply and demand status of the first ESS (510) corresponds to a power shortage in at least a part corresponding to a peak time zone during a unit of time. For example, the daytime on a day when a heat wave is forecast or the nighttime on a day when a cold wave is forecast may be set as a peak time zone, and if the power supply and demand status of the first ESS (510) corresponds to a power shortage during a unit of time included in the peak time zone during a unit of time, it may be more economical to purchase power from another ESS even if the conditions are slightly unsuitable rather than to receive power from an external grid, so even in such a case, the electronic device (100) may check the second ESS (610) without applying at least a part of the conditions included in the condition information.

[0069] Based on at least some of the conditions included in the aforementioned condition information, the electronic device (100) can identify at least one second ESS (610) for which it is determined whether to perform power exchange with the first ESS (510). To this end, specifically, the electronic device (100) can transmit transaction information related to power exchange to a first terminal (520) corresponding to the first ESS (510) and a second terminal (620) corresponding to the second ESS (610), together with a notification indicating that power exchange between the two is possible.

[0070] Specifically, the electronic device (100) can transmit transaction information related to power exchange, along with a notification indicating that power trading is possible between the first terminal (520) corresponding to the first ESS (510) and the second terminal (620) corresponding to the second ESS (610). Here, the transaction information can include various information that can assist in decision-making and transaction conclusion, such as the time corresponding to the corresponding unit time, the predicted power supply and demand value of the own ESS for the corresponding unit time, the power supply and demand value of the ESS of the transaction counterparty, the unit price, and the total amount information. In the above description, the power trading between the first ESS (510) and the second ESS (610) was described as reference, but according to one embodiment, the electronic device (100) can provide the first terminal (520) with information related to a plurality of candidate unit times in which power trading is possible, and when one of them is selected, transmit transaction information related to the corresponding unit time.

[0071] After the transaction information is transmitted in this manner, if the power transaction intent information is confirmed from the first terminal (520) and the second terminal (620), the electronic device (100) can confirm that the transaction has been completed and decide to perform the power exchange between the first ESS (510) and the second ESS (610). If the power transaction intent information is not confirmed, the electronic device (100) can confirm that the transaction has not been completed and decide not to perform the power exchange between the first ESS (510) and the second ESS (610).

[0072] When the electronic device (100) determines to perform power exchange between the first ESS (510) and the second ESS (610), the electronic device (100) can control the first ESS (510) and the second ESS (610) so that power exchange occurs in an amount of power agreed upon between the first ESS (510) and the second ESS (610) at an appropriate time. Here, the appropriate time can be determined by taking various situations into consideration. For example, in a case where power must be supplied to the first ESS (510) from the second ESS (610), if the second ESS (610) has a spare capacity long before the first ESS (510) runs out of power, and if the power grid between the first ESS (510) and the second ESS (610) has no spare capacity at the time when the first ESS (510) runs out of power, but has spare capacity at a time before that, the electronic device (100) can control power to be supplied to the first ESS (510) from the second ESS (610) before the time when the first ESS (510) runs out of power. Alternatively, if the second ESS (610) has a power surplus just before the first ESS (510) runs out of power, the electronic device (100) can control power to flow from the second ESS (610) to the first ESS (510) just before the first ESS (510) runs out of power. In this way, the timing of power exchange can be determined by considering various situational information related to the capacity of the ESS from which power is to flow in and out, or whether the power grid is available.

[0073] In one embodiment, the power forecast data may differ from the corresponding power data at the corresponding point in time. In this case, the concluded transaction may be canceled. For example, if an ESS power supply is expected to have 10 kWh of remaining power and is intended to be sold, but only 5 kWh is actually remaining, the transaction may be automatically canceled. Such cases are likely to occur frequently when the weather forecast is inaccurate. In seasons where the weather forecast is likely to be inaccurate, the electronic device (100) may provide related information when transmitting transaction information to the terminal corresponding to each ESS. For example, the electronic device (100) may notify that the weather forecast is likely to be inaccurate and that the transaction may be canceled if the power data differs due to the inaccurate weather forecast.

[0074] Once a transaction is completed, the electronic device (100) can transmit the transaction ledger to a distributed database. The distributed database may be connected to a blockchain via a P2P network. By storing the transaction ledger in such a distributed database, the speed, integrity, and security of the transaction can be guaranteed.

[0075] FIG. 6 shows a block diagram of an electronic device (100) according to one embodiment.

[0076] According to one embodiment, the electronic device (100) may include a memory (101) and a processor (103). The electronic device (100) illustrated in FIG. 6 only illustrates components related to the present embodiment. Therefore, those skilled in the art will appreciate that, in addition to the components illustrated in FIG. 6, other general-purpose components may be included. In one embodiment, the processor (103) may be included in a controller.

[0077] The processor (103) can control the overall operation of the electronic device (100) and process data and signals. The processor (103) may be composed of at least one hardware unit. In addition, the processor (103) may be configured to execute one or more instructions stored in the memory (101).

[0078] For example, one or more instructions may be configured to cause the processor (103) to, when executed, check a plurality of power prediction data for a first period corresponding to each of the plurality of ESSs, check at least one second ESS matching the first ESS based on the first power prediction data for the first period corresponding to a first ESS among the plurality of ESSs, check power transaction intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS, and determine whether to perform power exchange between the first ESS and the second ESS based on the power transaction intent information checked from the first terminal and the second terminal.

[0079] The various operations described as being performed by the electronic device (100) in the present disclosure may be performed by setting the processor (103) to perform the various operations described above upon execution of one or more instructions stored in the memory (101).

[0080] According to an embodiment, the electronic device (100) may additionally include a transceiver (102) for performing wired / wireless communication. The electronic device (100) may communicate with an external electronic device using the transceiver (102). The external electronic device may be a terminal or a server. In addition, the communication technologies used by the transceiver (102) may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc.

[0081] The electronic device according to the above-described embodiments may include a processor, a memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, a user interface device such as a touch panel, a key, a button, etc. The methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable codes or program instructions executable on the processor. Here, the computer-readable recording medium includes a magnetic storage medium (e.g., read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optical reading medium (e.g., CD-ROM, DVD: Digital Versatile Disc)). The computer-readable recording medium may be distributed to computer systems connected to a network, so that the computer-readable code may be stored and executed in a distributed manner. The medium may be readable by a computer, stored in a memory, and executed by a processor.

[0082] The present embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various hardware and / or software configurations that perform specific functions. For example, the embodiment may employ direct circuit configurations such as memory, processing, logic, look-up tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. Similarly, the present embodiment may be implemented in a programming or scripting language such as C, C++, Java, assembler, etc., including various algorithms implemented as a combination of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms that execute on one or more processors. Furthermore, the present embodiment may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms like "mechanism," "element," "means," and "composition" can be used broadly and are not limited to mechanical or physical components. These terms can also encompass a series of software routines, such as those associated with a processor.

[0083] The above-described embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

[0084]

Claims

1. In an electronic device that supports power transactions, transceiver; processor; and Contains memory that stores one or more instructions, The one or more instructions, when executed, cause the processor to: Check multiple power forecast data for the first period corresponding to each of multiple energy storage systems (ESS), Based on the first power prediction data for the first period corresponding to the first ESS among the plurality of ESSs, at least one second ESS matching the first ESS is identified, Confirming power transaction intent information from each of the first terminal associated with the first ESS and the second terminal associated with the second ESS, An electronic device configured to determine whether to perform power exchange between the first ESS and the second ESS based on power transaction intent information confirmed from the first terminal and the second terminal.

2. In paragraph 1, The one or more instructions, when executed, cause the processor to: An electronic device configured to control the first ESS and the second ESS so that power exchange occurs in an amount of power agreed upon between the first ESS and the second ESS when it is determined to perform power exchange between the first ESS and the second ESS.

3. In paragraph 1, The one or more instructions, when executed, cause the processor to: The weather prediction data for the first period of the region corresponding to the location of the first ESS, which is confirmed from the weather prediction server, is input into the artificial intelligence engine, thereby obtaining the first power prediction data for the first period corresponding to the first ESS output from the artificial intelligence engine. The above artificial intelligence engine, An electronic device, wherein the artificial intelligence model is constructed by learning a correlation between weather data for a second period of an area corresponding to the location of at least some of the plurality of ESSs and power data including at least some of the generation amount, the charge amount, the discharge amount, and the household power consumption corresponding to the second period for at least some of the plurality of ESSs.

4. In paragraph 3, The above correlation is, A first correlation relating to the proportionality of solar power generation, which is at least a portion of the above power generation, to the amount of sunlight and the inverse proportionality to the temperature; A second correlation relating to the proportionality of wind power generation, which is at least a portion of the above power generation, to wind speed; and An electronic device comprising a third correlation related to the U-shaped relationship between the above furniture power consumption and temperature.

5. In paragraph 3, The above weather forecast data is, An electronic device comprising data for at least some of the temperature, amount of sunlight and wind speed predicted for the first period of time in the region.

6. In paragraph 1, The one or more instructions, when executed, cause the processor to: Based on the first power prediction data, the predicted unit time power supply and demand data for the first period of the first ESS is confirmed. The power supply and demand data for each unit time includes data predicting whether the household power consumption corresponding to the first ESS can be supplied and demanded with the power amount of the first ESS and the power generation amount corresponding to the first ESS for each unit time. Based on the power supply and demand data for each unit time, at least one ESS is identified for which the power supply and demand status is expected to be opposite to that of the first ESS for at least a portion of the unit time, An electronic device configured to identify at least one second ESS among at least one ESS whose power supply and demand status is predicted to be opposite to that of the first ESS, based on condition information of the first ESS.

7. In paragraph 6, The one or more instructions, when executed, cause the processor to: An electronic device configured to check the power supply and demand data per unit time, which further includes a value predicted as a difference between the sum of the power amount of the first ESS and the power generation amount corresponding to the first ESS and the household power consumption per unit time, based on the first power prediction information.

8. In paragraph 7, The one or more instructions, when executed, cause the processor to: An electronic device configured to identify at least one second ESS that satisfies at least one of a purchase amount condition, a distance condition, a compatibility condition, and a connection condition included in the condition information of at least one ESS among at least one ESS whose power supply and demand status is predicted to be opposite to that of the first ESS at least for a portion of the unit time.

9. In paragraph 8, The one or more instructions, when executed, cause the processor to: It is confirmed that the size of the predicted value of the difference between the sum of the power amount of the first ESS and the power generation amount corresponding to the first ESS and the household power consumption per unit time is greater than a threshold value during at least some of the above unit time, An electronic device configured to verify at least one second ESS without applying at least some of the conditions included in the condition information for at least some of the unit times during which the size of the value predicted by the difference is greater than or equal to a threshold.

10. In paragraph 9, The one or more instructions, when executed, cause the processor to: It is confirmed that the power supply and demand status of the first ESS corresponds to a power shortage during at least a portion of the peak time period during the above unit time, An electronic device, wherein the power supply and demand status of the first ESS is set to check the at least one second ESS without applying at least some of the conditions included in the condition information for at least a portion of the unit time corresponding to a peak time period corresponding to a power shortage.

11. In paragraph 1, The one or more instructions, when executed, cause the processor to: Transmitting transaction information related to power exchange between the first ESS and the second ESS to each of the first terminal and the second terminal, An electronic device configured to confirm, from each of the first terminal and the second terminal, the power transaction intent information including information indicating whether or not a power transaction is desired and the amount of power desired to be transacted.

12. In a method for supporting power trading of electronic devices, A step of checking a plurality of power prediction data for a first period corresponding to each of a plurality of energy storage systems (ESS); A step of identifying at least one second ESS matching the first ESS based on first power prediction data for the first period corresponding to the first ESS among a plurality of ESSs; A step of confirming power transaction intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and A power transaction support method, comprising a step of determining whether to perform power exchange between the first ESS and the second ESS based on power transaction intent information confirmed from the first terminal and the second terminal.

13. A non-transitory computer-readable recording medium recording a program for executing a power transaction support method on a computer, The above power transaction support method is, A step of checking a plurality of power prediction data for a first period corresponding to each of a plurality of energy storage systems (ESS); A step of identifying at least one second ESS matching the first ESS based on first power prediction data for the first period corresponding to the first ESS among a plurality of ESSs; A step of confirming power transaction intent information from each of a first terminal associated with the first ESS and a second terminal associated with the second ESS; and A non-transitory recording medium, comprising a step of determining whether to perform power exchange between the first ESS and the second ESS based on power transaction intent information confirmed from the first terminal and the second terminal.

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