JP7810038B2 - Information processing device, information processing method, and program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and a program.

Energy storage devices are widely used in uninterruptible power supplies, DC or AC power supplies included in stabilized power supplies, etc. The use of energy storage devices is also expanding in large-scale power systems that store electricity generated by renewable energy sources or existing power generation systems.

It is known that repeated charging and discharging of energy storage elements causes deterioration, gradually reducing their full charge capacity. In order to estimate the capacity transition of an energy storage element, such as predicting the future progression of deterioration and its lifespan, it is necessary to understand the power load of the energy storage element. Patent Document 1 discloses a technology that accurately predicts the progression of deterioration and the lifespan of a storage battery by improving the accuracy of predicted deterioration rates corresponding to multiple usage conditions of the storage battery.

JP 2015-121520 A

The capacity transition of a storage element can be estimated by providing the results of measuring the power load on the storage element over a certain period of time. If the measurement results for a certain period of time are used as is, it becomes difficult to accurately estimate the capacity transition when the way the storage element is used changes, i.e., when the power load fluctuates from the measurement results for a certain period of time. To accurately estimate the capacity transition, it is necessary to generate a power load that corresponds to the usage mode of the storage element.

The purpose of this disclosure is to provide an information processing device or the like that can generate a power load according to the usage mode of the storage element.

An information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires multiple representative power loads extracted from each power load obtained by dividing a power load indicating time-series data of power of a storage element in a first period into second periods that are shorter than the first period, and a generation unit that generates a virtual power load for the first period by combining the multiple representative power loads acquired by the acquisition unit.

This disclosure makes it possible to generate a power load that corresponds to the usage pattern of the storage element.

FIG. 1 illustrates an example of the configuration of an information processing device. FIG. 1 is a diagram illustrating a configuration of a power generation system. FIG. 2 is a diagram illustrating an example of a bank configuration. FIG. 1 is a functional block diagram illustrating an example of the configuration of an information processing device. 10A and 10B are diagrams illustrating a method for extracting a representative power load and a method for generating a virtual power load. 10A and 10B are diagrams illustrating a method for extracting a representative power load and a method for generating a virtual power load. 10A and 10B are diagrams illustrating a method for extracting a representative power load and a method for generating a virtual power load. FIG. 10 is a diagram showing the estimated results of capacity transition when the power load is changed. 10 is a flowchart illustrating an example of a procedure for acquiring a representative power load. 10 is a flowchart illustrating an example of a virtual power load generation process procedure.

The information processing device includes an acquisition unit that acquires multiple representative power loads extracted from each power load obtained by dividing a power load indicating time-series data of the power of a storage element in a first period into second periods that are shorter than the first period, and a generation unit that generates a virtual power load for the first period by combining the multiple representative power loads acquired by the acquisition unit.

The information processing device generates a new virtual power load using multiple representative power loads extracted from the power loads of the storage elements during the first period. The virtual power load is a virtual power load of the storage elements during the first period.

The power load is information that indicates time-series data on the power of a storage element. The power load may be obtained by obtaining time-series data on the current and voltage values of the storage element. The power load for a first period obtained by the information processing device is divided into second periods. The first period is a relatively long period, such as one year, two years, six months, or one month. The second period is a period shorter than the first period, such as one day or one hour.

The representative power load is the representative power load for each group when the power loads for each second period are classified into multiple groups. A virtual power load is generated by combining multiple representative power loads. By generating a virtual power load using a representative power load that indicates the current usage of the storage element, it is possible to express a power load that corresponds to a variety of usage patterns.

The lifespan of a storage element is predicted using power load and temperature data for a specified period. For example, the power load for one year obtained from the storage element's operational data is input into a prediction model, and the amount of deterioration of the storage element is calculated, thereby predicting the storage element's lifespan several years from now. In this case, it is assumed that the power load obtained from the operational data will continue into the following year and beyond, and by using the same power load from that year onwards, a single lifespan prediction result is derived.

The way energy storage elements are used is expected to change in the future. With conventional lifespan predictions, the accuracy of predictions decreases when the power load of the energy storage element changes. By generating a virtual power load, the information processing device enables lifespan predictions that reflect changes in power load, improving the accuracy of lifespan predictions. Generating a variety of virtual power loads broadens the range of lifespan predictions.

The generation unit may combine the representative power loads based on the occurrence probability of each representative power load during the first period. With the above configuration, the occurrence probability of each representative power load can be reflected in the virtual power load. By assuming how the desired storage element will be used and appropriately setting the occurrence probability, the virtual power load can be generated easily and appropriately.

The generation unit may generate multiple patterns of virtual power loads by varying the occurrence probability of each representative power load during the first period. By varying the occurrence probability of each representative power load, a variety of virtual power loads can be efficiently generated, enabling a wide range of lifespan predictions to be made based on a variety of user usage patterns.

The information processing device may include a random number generation unit that generates random numbers, and the generation unit may combine the representative power loads based on the random numbers generated by the random number generation unit. With the above configuration, the representative power loads are used to reflect the usage history of the energy storage elements in the virtual power load, and random numbers are applied to randomly change the power load, thereby efficiently generating virtual power loads that can represent a large number of power load patterns.

The random number generator may generate the random number based on the length of the first period relative to the second period, the type of representative power load, and the probability of each representative power load appearing in the first period. With the above configuration, the state of each representative power load for a single power load can be appropriately reflected in the random number. By arbitrarily varying each element of the random number, a virtual power load that meets the user's needs can be easily generated.

The representative power loads are extracted for each group obtained by dividing the power loads into second periods shorter than the first periods and classifying them into multiple groups, and the random number generator may generate random numbers based on the total amount of power supplied to each power load in each group and the frequency of each amount of power supplied to each group. With this configuration, the random numbers can appropriately reflect the distribution of the total amount of power supplied to the power loads in each group.

The information processing device may include an output unit that outputs the generated virtual power load to a lifespan prediction device that predicts the lifespan of the storage element using the virtual power load. By using the virtual power load, the lifespan prediction device can improve the accuracy of lifespan predictions and enable a wider range of lifespan predictions.

In this information processing method, a computer executes a process in which a power load indicating time-series data of the power of a storage element during a first period is divided into second periods that are shorter than the first period, and multiple representative power loads are extracted from each power load, and the multiple representative power loads are combined to generate a virtual power load for the first period.

The program causes the computer to execute a process of obtaining multiple representative power loads extracted from each power load obtained by dividing the power load indicating the time series data of the power of the storage element in a first period into second periods that are shorter than the first period, and combining the obtained multiple representative power loads to generate a virtual power load for the first period.

Specific examples of an information processing device, an information processing method, and a program according to embodiments of the present invention will be described below with reference to the drawings. Note that the present invention is not limited to these examples, but is defined by the claims, and is intended to include all modifications within the meaning and scope of the claims. Furthermore, at least some of the embodiments described below may be combined in any manner.
The sequences shown in the following embodiments are not limited, and the order of each process step may be changed within a consistent range, and multiple processes may be performed in parallel. The entity that performs each process is not limited, and the process of each device may be performed by another device within a consistent range. Furthermore, while the present invention discloses examples and analysis examples regarding power loads, the present invention is not limited to power loads, and can be applied to parameters such as current loads and polarization amounts, as well as other electrical and physical quantities.

Figure 1 is a diagram showing an example configuration of an information processing device 50. The information processing device 50 is communicatively connected to a network 1 such as the Internet. The information processing device 50 is capable of sending and receiving data to and from multiple power generation systems 100 via the network 1. The information processing device 50 may be integrated into any of the power generation systems 100.

The information processing device 50 is, for example, a server computer, personal computer, quantum computer, etc., and performs various types of information processing and information transmission and reception. Details of the information processing device 50 will be described later.

Figure 2 is a diagram showing the configuration of a power generation system 100. The power generation system 100 includes a communication device 10, a server device 20 connected to the communication device 10 via a network 2, a domain management device 30, and a power storage unit (domain) 40. The power storage unit 40 may include multiple banks 41. The power storage unit 40 is housed in a battery panel and used, for example, in thermal power generation systems, mega solar power generation systems, wind power generation systems, uninterruptible power supplies (UPS), and stabilized power supply systems for railways. The portion of the power storage unit 40 excluding the power conditioner (not shown) is sometimes referred to as a storage battery system. The power storage unit 40 is not limited to industrial use, and may also be for home use.

The information processing device 50 and multiple power generation systems 100 constitute a remote monitoring system. The remote monitoring system enables remote access to information related to the energy storage elements included in the power generation systems 100. A business operator designs, installs, operates, and maintains an energy storage system including a communication device 10, a domain management device 30, and an energy storage unit 40, and can remotely monitor the energy storage system using the remote monitoring system.

The communication device 10 includes a control unit 11, a memory unit 12, a first communication unit 13, and a second communication unit 14. The control unit 11 is composed of a CPU (Central Processing Unit) and other components, and controls the entire communication device 10 using built-in memory such as ROM (Read Only Memory) and RAM (Random Access Memory).

The memory unit 12 includes a non-volatile storage device such as a flash memory. The memory unit 12 can store required information, such as information obtained through processing by the control unit 11.

The first communication unit 13 has a communication interface that enables communication with the domain management device 30 (or the battery management device 44 shown in Figure 3). The control unit 11 can communicate with the domain management device 30 through the first communication unit 13.

The second communication unit 14 has a communication interface that enables communication via the network 2. The control unit 11 can communicate with the server device 20 through the second communication unit 14.

The domain management device 30 sends and receives information to and from each bank 41 using a specified communication interface. The memory unit 12 can store operational data acquired via the domain management device 30.

The server device 20 can collect operational data of the power storage system from the communication device 10. The operational data includes time-series data such as current values, voltage values, and temperature data for each storage element in the power storage system. The server device 20 separates and stores the collected operational data for each storage element. The server device 20 can transmit the operational data to the information processing device 50 via the networks 2 and 1. Note that the networks 2 and 1 may be a single communication network.

Figure 3 shows an example configuration of the bank 41. The bank 41 consists of multiple power storage modules connected in series, and includes a battery management unit (BMU) 44, multiple power storage modules 42, and a measurement board (CMU: Cell Management Unit) 43 provided in each power storage module 42.

The energy storage module 42 has multiple energy storage cells connected in series. In this specification, "energy storage element" may refer to an energy storage cell, an energy storage module 42, a bank 41, or a domain in which the bank 41 is connected in parallel. In this embodiment, the measurement board 43 acquires energy storage element information related to the state of each energy storage cell in the energy storage module 42. The energy storage element information includes, for example, the voltage, current, temperature, SOC (state of charge), and SOH of the energy storage cell. The energy storage element information can be acquired repeatedly at appropriate intervals, such as 0.1 seconds, 0.5 seconds, or 1 second. The accumulated data of the energy storage element information becomes part of the operational data. The "energy storage element" is preferably a secondary battery such as a lead-acid battery or a lithium-ion battery, or a rechargeable battery such as a capacitor. Some of the energy storage elements may be non-rechargeable primary batteries.

The battery management device 44 can communicate via serial communication with a measurement board 43 equipped with communication functions, and can acquire information about the energy storage elements detected by the measurement board 43. The battery management device 44 can send and receive information to and from the domain management device 30. The domain management device 30 aggregates the energy storage element information from the battery management devices 44 of the banks belonging to the domain. The domain management device 30 outputs the aggregated energy storage element information to the communication device 10. In this way, the communication device 10 can acquire operational data about the energy storage unit 40 via the domain management device 30. The communication device 10 transmits the acquired operational data to the information processing device 50 via the server device 20.

As shown in FIG. 1, the information processing device 50 includes a control unit 51, a memory unit 52, and a communication unit 53. The information processing device 50 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed using software.

The control unit 51 is an arithmetic circuit equipped with a CPU, GPU (Graphics Processing Unit), ROM, RAM, etc. The CPU or GPU included in the control unit 51 executes various computer programs stored in the ROM or memory unit 52, and controls the operation of each of the hardware components described above. The control unit 51 may also have functions such as a timer that measures the elapsed time from when an instruction to start measurement is given until an instruction to end measurement is given, a counter that counts numbers, and a clock that outputs date and time information.

The memory unit 52 is a non-volatile storage device such as a flash memory. The memory unit 52 stores programs and data referenced by the control unit 51. The computer programs stored in the memory unit 52 include a program 521 for executing processing related to the power load of the storage element.

The data stored in the memory unit 52 includes operational data received from the power generation system 100. As described above, the operational data includes time-series data on the current and voltage values of the storage elements within the power generation system 100. The control unit 51 collects operational data from each power generation system 100 and stores it in the memory unit 52 as big data. The control unit 51 performs processes to obtain representative power loads for the storage elements and generate virtual power loads based on the actual operational data of the storage elements that has been stored.

The computer program (computer program product) stored in the storage unit 52 may be provided by a non-transitory storage medium M on which the computer program is readably recorded. The storage medium M is a portable memory such as a CD-ROM, USB memory, or SD (Secure Digital) card. The control unit 51 reads the desired computer program from the storage medium M using a reading device (not shown) and stores the read computer program in the storage unit 52. Alternatively, the computer program may be provided via communications. The program 521 may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or multiple computers interconnected by a communications network.

The communication unit 53 has a communication interface that enables communication via the network 1. The control unit 51 can communicate with external devices through the communication unit 53. Examples of external devices that can be communicatively connected to the communication unit 53 include the power generation system 100 and a lifespan prediction device that predicts the lifespan of a storage element. When the control unit 51 obtains information regarding the power load of the storage element, it transmits the obtained information from the communication unit 53 to the lifespan prediction device. The lifespan prediction device receives the information transmitted from the communication unit 53 and predicts the lifespan of the storage element based on the received information.

The information processing device 50 may further include, for example, an input unit that accepts operational input, a display unit that displays images, etc.

Figure 4 is a functional block diagram showing an example configuration of an information processing device 50. The control unit 51 of the information processing device 50 reads and executes a program 521 stored in the memory unit 52, thereby functioning as a first acquisition unit 511, a classification unit 512, an extraction unit 513, a second acquisition unit 514, a generation unit 515, a random number generation unit 516, and an output unit 517.

Figures 5 to 7 are diagrams illustrating a method for extracting a representative power load and a method for generating a virtual power load. Using Figures 5 to 7, we will specifically explain the method for extracting a representative power load and the method for generating a virtual power load in this embodiment, as well as the functions of each functional unit of the control unit 51.

The first acquisition unit 511 receives time-series data of current values, voltage values, and temperature within the power generation system 100 over a first period from the server device 20 via the communication unit 53. The time-series data of current values and voltage values is data obtained when the storage elements are being charged or discharged.

The first acquisition unit 511 calculates power, which is the product of the current value and voltage value, based on the acquired time-series data of the current value and voltage value, and acquires the power load, which indicates the time-series data of power. The first acquisition unit 511 may acquire the power load in the form of a graph, for example, plotting the time-series data of the power of the storage element over the first period. The upper part of Figure 5 shows an example of a graph of the power load for one year. In the graph of Figure 5, the vertical axis represents power and the horizontal axis represents time (period). The positive side of the vertical axis represents charging, and the negative side represents discharging.

The first acquisition unit 511 may acquire the power load over the first period all at once, or may acquire the power load for the first period by continuously collecting the power load for each predetermined period. The first acquisition unit 511 stores the acquired power load and temperature data in the memory unit 52. The first acquisition unit 511 may also acquire the power load (time series power data) directly from the server device 20.

The classification unit 512 divides the power load for the first period acquired by the first acquisition unit 511 into second periods that are shorter than the first period. In the following, as an example, the first period is assumed to be one year and the second period is assumed to be one day. In the following, the power load for each second period, i.e., the power load in each divided area obtained by dividing the second period, is also referred to as the short-term power load. The classification unit 512 classifies the short-term power load into multiple groups.

The classification unit 512 classifies multiple short-term power loads obtained by dividing the annual power load into one or more groups (patterns). There are no particular limitations on the method for classifying the short-term power loads, but classification models such as k-means and Gaussian mixture models can be used. The classification model is a machine learning model based on a clustering algorithm. The classification model classifies multiple short-term power loads into multiple clusters based on the correlation between their features. Note that the classification model may also be based on other learning algorithms, such as neural networks, SVMs (Support Vector Machines), decision trees, etc.

The classification unit 512 may classify short-term power loads using a rule-based method. The classification unit 512 classifies each short-term power load into a predetermined group based on, for example, the number of times the charge/discharge direction of the power load is switched over in one day, the total amount of change in power value (absolute value of power value along the vertical axis), the total amount of charge/discharge time, etc.

An example of a classification pattern is shown at the bottom of Figure 5. In the example of Figure 5, each short-term power load is classified into five groups corresponding to patterns 1 to 5. Pattern 1 classifies short-term power load data with relatively little change in power amount, and as the pattern number increases, the change in power amount becomes greater.

The pie chart on the lower right of Figure 5 shows the probability of occurrence of each pattern. The probability of occurrence of each pattern refers to the probability that short-term power load belonging to each pattern will occur (probability of existence) among the total power load over the course of a year. In the example shown in Figure 5, pattern 5 has the highest probability of occurrence, and pattern 1 has the lowest probability of occurrence.

For each group classified by the classification unit 512, the extraction unit 513 extracts a representative power load that represents the power load pattern of the group from the short-term power loads included in the same group. The extraction unit 513 extracts the representative power load using a histogram of the total amount of current-carrying power, as shown in Figure 6.

The extraction unit 513 calculates the total daily energy flow for all short-term power loads belonging to the group to be extracted (e.g., pattern 1). The total energy flow is calculated by integrating the power for one day over time based on the time series data of the power in the short-term power loads. The extraction unit 513 generates a histogram showing the distribution of the total energy flow, as shown in the lower part of Figure 6, based on the calculated total energy flow. The extraction unit 513 may use, for example, kernel density estimation to estimate the distribution of the total energy flow. This makes it possible to generate a histogram shown as a continuous curve, as shown in Figure 6.

The extraction unit 513 extracts a representative power load based on the most frequent value of the total amount of power supplied in the group of pattern 1. Specifically, the extraction unit 513 identifies the short-term power load corresponding to the peak of the histogram of pattern 1, and extracts the identified short-term power load as the representative power load of pattern 1. The extraction unit 513 only needs to extract the representative power load based on the most frequent value of the total amount of power supplied, and may estimate the distribution of the total amount of power supplied using a method other than a histogram.

The extraction unit 513 normalizes the frequency values on the vertical axis of the histogram so that the frequency corresponding to the identified representative power load is 1. The extraction unit 513 performs the above-described process for each group to obtain a histogram and representative power load for each group, as shown in FIG. 6. The extraction unit 513 stores the extracted representative power load and the generated histogram in the storage unit 52.

The above process allows a representative power load for the storage element to be obtained. The obtained representative power load can be effectively used in analyzing the storage element as data that accurately represents how the storage element is currently being used. Furthermore, in this embodiment, a virtual power load is generated based on the obtained representative power load, assuming a change in how the storage element is being used. The method for generating a virtual power load is explained below using Figure 7.

The second acquisition unit 514 acquires the representative power load for each group extracted by the extraction unit 513 by reading the information stored in the memory unit 52.

The generation unit 515 generates a virtual power load for one year by combining the representative power loads acquired by the second acquisition unit 514. In generating the virtual power load, the generation unit 515 combines the representative power loads using the virtual occurrence probability of each representative power load and a random number received from the random number generation unit 516, which will be described later.

The virtual occurrence probability for each representative power load can be set arbitrarily. For example, the occurrence probability of each pattern in the operational data may be increased or decreased as appropriate, assuming future changes in how the energy storage element will be used. The generation unit 515 may obtain the occurrence probability by accepting input from a user or by receiving occurrence probabilities transmitted from an external device. The generation unit 515 may automatically generate occurrence probabilities by changing the operational data in accordance with predetermined rules. The generation unit 515 may use the occurrence probabilities in the operational data as the virtual occurrence probabilities without changing them.

The random number generator 516 generates two types of random numbers: a first random number N and a second random number K. The random number generator 516 generates the first random number N based on the classification results of each short-term power load. As an example, the first random number N can be expressed as follows:
First random number N=[4, 1, 3, 5, 2, 4, ..., 1]

The length of the first random number N corresponds to the ratio of the first period to the second period (first period/second period). In this embodiment, the second period is one year (365 days) and the second period is one day, so the length of the first random number N is 365/1 = 365. The elements of the first random number N correspond to data indicating the group type. In this embodiment, the elements of the first random number N are 1 to 5, and correspond in numerical order to patterns 1 to 5. The proportion of each element among all elements in the first random number N corresponds to the occurrence probability of the representative power load belonging to the pattern corresponding to that element. The occurrence probability of the representative power load is the virtual occurrence probability obtained when generating the virtual power load.

The random number generation unit 516 further generates a second random number K based on the representative power load selected using the first random number N as an argument. For example, if a representative power load of pattern 4 is selected as the first representative power load based on the first random number, the random number generation unit 516 reads the histogram of pattern 4. The random number generation unit 516 generates a second random number K based on the histogram.

The length of the second random number K is 1. The elements of the second random number K correspond to the values of the total amount of electric power supplied in the histogram. As an example, the elements of the second random number K can be values in increments of a predetermined value (e.g., 0.1 kW) between the minimum and maximum values of the total amount of electric power supplied. The proportion of each element among all elements in the second random number K corresponds to the frequency in the histogram of the total amount of electric power supplied corresponding to each element. The frequency of each amount of electric power supplied is normalized in advance from 0 to 1, as described above.

The random number generation unit 516 generates a second random number K for each element of the first random number N. The random number generation unit 516 outputs the generated first random number N and second random number K to the generation unit 515.

The generation unit 515 combines representative power loads using the first random number N and second random number K received from the random number generation unit 516. For example, suppose the first first random number N = 4 and the second random number K corresponding to the first first random number = 9.3. The generation unit 515 uses the first random number N as an argument to select a representative power load of pattern 4 corresponding to first random number N = 4. The generation unit 515 reads out the representative power load and histogram of the selected pattern 4.

The generation unit 515 generates a new power load (hereinafter also referred to as a reproduced power load) by changing the representative power load of pattern 4 based on the second random number K. As shown in the lower part of Figure 7, the reproduced power load is obtained by expanding or contracting the representative power load according to a ratio calculated based on the second random number K. The ratio can be the ratio between the total amount of power supplied by the second random number K and the total amount of power supplied by the representative power load (total amount of power supplied by the second random number K / total amount of power supplied by the representative power load). The generation unit 515 calculates the ratio based on the histogram of pattern 4. For example, assume that the calculated ratio is 0.75. The generation unit 515 generates a reproduced load by contracting the representative power load by 0.75 times in the power direction.

The generation unit 515 generates a reproduced load for one year by repeating the above process for each element of the first random number N. The generation unit 515 generates a virtual power load for one year by concatenating all the generated reproduced loads.

The generation unit 515 may obtain multiple virtual occurrence probabilities and generate multiple virtual power loads with different occurrence probabilities for each representative power load. By setting occurrence probabilities that correspond to various ways of using power, it becomes possible to generate virtual power loads that correspond to various usage patterns.

The output unit 517 transmits the virtual power load generated by the generation unit 515 to an external device such as a lifespan prediction device via the communication unit 53. Alternatively, the information processing device 50 and the lifespan prediction device may be configured as a single common processing device, and the output unit 517 may output the virtual power load to the lifespan prediction device within the information processing device 50.

Figure 8 shows the estimated results of capacity changes when the power load is changed. The vertical axis of the graph shown in Figure 8 represents the capacity (Ah) of the storage element, and the horizontal axis represents time. The curves shown in the graph represent the estimated results from the first year to the tenth year for Case 1, Case 2, and Case 3, respectively, from top to bottom.

In Case 1, the one-year measured power load data obtained from operational data was used unchanged for the entire period from Year 1 to Year 10. In Case 2, the above measured data was used from Year 1 to Year 3, and from Year 4 to Year 10, the probability of occurrence of Pattern 1 and Pattern 2 in the measured data was reduced by 5% each, and the probability of occurrence of Pattern 3 and Pattern 4 was increased by 5% each. In Case 3, the above measured data was used from Year 1 to Year 3, and from Year 4 to Year 10, the probability of occurrence of Pattern 1 and Pattern 2 in the measured data was set to 0%, and the probability of occurrence of Pattern 4 and Pattern 5 was increased. In Case 3, the increase in Pattern 4 corresponds to the decrease in Pattern 2, and the increase in Pattern 5 corresponds to the decrease in Pattern 1.

The estimated capacity in the 10th year was 0.05 (Ah) lower in Case 2 than in Case 1, and 0.6 (Ah) lower in Case 3 than in Case 1. As mentioned above, by changing the occurrence probability in the virtual power load, the estimated capacity can be changed, making it possible to estimate a wide range of capacity trends.

Figure 9 is a flowchart showing an example of the process steps for obtaining a representative power load. The processes in each of the following flowcharts may be executed by the control unit 51 in accordance with a program 521 stored in the memory unit 52 of the information processing device 50, or may be implemented by a dedicated hardware circuit (e.g., an FPGA or ASIC) provided in the control unit 51, or may be implemented by a combination of these.

The control unit 51 of the information processing device 50 acquires time series data of the current and voltage of the storage element during the first period, and acquires a power load indicating time series data of power during the first period based on the acquired time series data of current and voltage (step S11).

The control unit 51 divides the acquired power load for the first period into second periods (step S12) and generates multiple short-term power loads. The control unit 51 classifies each short-term power load into multiple groups, for example, using a classification model (step S13).

The control unit 51 calculates, for each group, the total amount of energized electric energy during the second period for all short-term electric loads belonging to the group, thereby generating a histogram showing the distribution of the total amount of energized electric energy (step S14).

The control unit 51 extracts a representative power load for each group (step S15). Specifically, the control unit 51 identifies the short-term power load that corresponds to the most frequent value of the total amount of current-carrying power in each group, i.e., the peak of the histogram, and extracts the identified short-term power load as the representative power load. The control unit 51 normalizes the frequency value on the vertical axis of the histogram so that the frequency corresponding to the identified representative power load is 1.

The control unit 51 stores the extracted representative power load for each group and the generated histogram in the memory unit 52 (step S16), and ends the series of processes. The control unit 51 may output the extracted representative power load to an external device, etc.

FIG. 10 is a flowchart illustrating an example of a virtual power load generation process.
The control unit 51 of the information processing device 50 acquires a representative power load for each group based on the information stored in the storage unit 52 (step S21).

The control unit 51 acquires the virtual occurrence probability for each representative power load during the first period (step S22). The control unit 51 may acquire the virtual occurrence probability by, for example, accepting input from a user. The control unit 51 may acquire multiple patterns of virtual occurrence probability.

The control unit 51 generates a first random number N based on the length of the first period relative to the length of the second period, the type of representative power load, and the occurrence probability of each representative power load obtained in step S22 (step S23).

The control unit 51 generates a second random number K based on the distribution of the total amount of current-carrying electric energy of the electric loads in each group and the frequency of each total amount of current-carrying electric energy in each group (step S24).

The control unit 51 generates a virtual power load for the first period by combining each representative power load using the first random number N and the second random number K (step S25). In detail, the control unit 51 sequentially selects representative power loads using the first random number N as an argument. The control unit 51 generates a reproduced load by varying the selected representative power load at a predetermined ratio in the power direction based on the second random number K. The control unit 51 generates a virtual power load for the first period by combining the reproduced loads generated for each second period.

In step S25, if the control unit 51 acquires setting values for the occurrence probability of multiple patterns, it generates multiple patterns of virtual power loads corresponding to the setting values for each occurrence probability.

The control unit 51 outputs the generated virtual power load to the life prediction device, etc. (step S26), and ends the series of processes.

According to this embodiment, it is possible to acquire and provide a representative power load that is useful for analyzing energy storage elements. Using the representative power load to generate a virtual power load enables efficient and highly accurate lifespan prediction. Changing the occurrence probability of each representative power load in the virtual power load makes it possible to generate virtual power loads that correspond to a variety of usage patterns. Using a virtual power load makes it possible to appropriately represent a variety of power loads that occur when the way the energy storage element is used changes, enabling a wide range of lifespan predictions.

REFERENCE SIGNS LIST 100 Power generation system 10 Communication device 11 Control unit 12 Memory unit 13 First communication unit 14 Second communication unit 20 Server device 30 Domain management device 40 Power storage unit 41 Bank 42 Power storage module 43 Measurement board 44 Battery management device 50 Information processing device 51 Control unit 52 Memory unit 53 Communication unit 511 First acquisition unit 512 Classification unit 513 Extraction unit 514 Second acquisition unit 515 Generation unit 516 Random number generation unit 517 Output unit 521 Program M Recording medium

Claims (9)

an acquisition unit that acquires a plurality of representative power loads extracted from each power load obtained by dividing a power load indicating time series data of power of the storage element in a first period into second periods that are shorter than the first period;
a generating unit that generates a virtual power load for a first time period by combining the representative power loads acquired by the acquiring unit. The information processing device according to claim 1 , wherein the generation unit combines the representative power loads based on the occurrence probability of each representative power load in the first period. The information processing apparatus according to claim 1 , wherein the generating unit generates a plurality of patterns of virtual power loads by varying the occurrence probability of each representative power load in the first period. A random number generating unit is provided to generate random numbers,
The information processing device according to claim 1 , wherein the generation unit combines the representative power loads based on random numbers generated by the random number generation unit. The information processing device according to claim 4 , wherein the random number generator generates the random number based on a length of the first period relative to a length of the second period, a type of representative power load, and an occurrence probability of each representative power load in the first period. The representative power loads are extracted for each group obtained by dividing the power loads into a second period shorter than the first period and classifying the power loads into a plurality of groups,
The information processing device according to claim 4 or 5, wherein the random number generator generates the random number based on a total amount of energized power of each power load in each group and a frequency of each amount of energized power in each group. The information processing device according to claim 1 , further comprising an output unit that outputs the generated virtual power load to a life prediction device that predicts a life of the energy storage element using the virtual power load. A power load indicating time-series data of the power of the storage element in a first period is divided into second periods shorter than the first period, and a plurality of representative power loads are extracted from the power loads;
An information processing method in which a computer executes a process of generating a virtual power load for a first period by combining the acquired representative power loads. A power load indicating time-series data of the power of the storage element in a first period is divided into second periods shorter than the first period, and a plurality of representative power loads are extracted from the power loads;
A program for causing a computer to execute a process of generating a virtual power load for a first period by combining the acquired representative power loads.