Estimation device, energy storage device, and estimation program

Abstract

To provide an estimation device and an estimation program each capable of easily and quickly performing estimation with high accuracy, and to provide an energy storage device.SOLUTION: According to one embodiment, an estimation device comprises an input part for receiving first power generation time-series data that is power generation time-series data concerning a recyclable energy power generator in an investigation target area, and first power demand time-series data that is power demand time-series data in the investigation target areas each as an input. The estimation device further comprises a residual power calculation part for calculating first residual power based on the first power generation time-series data and the first power demand time-series data. The estimation device further comprises a feature amount extraction part for extracting a first feature amount as a feature amount of the first residual power. The estimation device further comprises an estimation value calculation part for calculating an estimation value of constituent device capacity or introduction cost of an energy storage device, based on the first feature amount.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Embodiments of the present invention relate to an estimation device, an energy storage device, and an estimation program.

Background Art

[0002] In recent years, with the expansion of the introduction of renewable energy, the development of an energy storage device that smooths fluctuating power and enables stable power supply has been underway.

[0003] Conventionally, the capacity of the constituent devices and the introduction cost of such an energy storage device have been calculated by operation simulation.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] The calculation of the component capacity and the introduction cost by the above-described operation simulation requires a huge amount of data for a single simulation and takes a long time.

[0007] On the other hand, there is a method to calculate approximate values ​​for the component equipment capacity and installation costs by using the scale ratio between already installed energy storage devices and those planned for installation. However, the approximate values ​​calculated using this method often differ significantly from the true values ​​calculated through operational simulations.

[0008] Therefore, embodiments of the present invention provide an estimation device and estimation program that enable simple, rapid, and highly accurate estimation. Furthermore, embodiments of the present invention provide a low-cost and highly efficient energy storage device. [Means for solving the problem]

[0009] According to one embodiment, the estimation device includes an input unit that receives as input a first power generation time series data, which is power generation time series data relating to renewable energy power generation equipment in the area under consideration, and a first power demand time series data, which is power demand time series data for the area under consideration. Furthermore, the estimation device includes a residual power calculation unit that calculates a first residual power based on the first power generation time series data and the first power demand time series data. Furthermore, the estimation device includes a feature extraction unit that extracts a first feature quantity, which is a feature quantity of the first residual power. Furthermore, the estimation device includes an estimated value calculation unit that calculates an estimated value of the component equipment capacity or installation cost of an energy storage device based on the first feature quantity. [Brief explanation of the drawing]

[0010] [Figure 1] This is a block diagram of the energy storage device to be estimated in the first embodiment. [Figure 2] This is a system block diagram of the estimation device in the first embodiment. [Figure 3] This is an example of output data in the first embodiment. [Figure 4] This is an example of the operation results of the estimation device in the first embodiment. [Figure 5] This is a flowchart (S1-S6) of the estimation device in the first embodiment. [Figure 6] It is a flowchart (S7 to S12) of the estimation device in the first embodiment. [Figure 7] It is a system block diagram of the estimation device in the second embodiment. [Figure 8] It is a system block diagram of the estimation device in the third embodiment. [Figure 9] It is a system block diagram of the estimation device in the fourth embodiment. [Figure 10] It is a flowchart (S7 to S11) of the estimation device in the fourth embodiment. [Figure 11] It is a flowchart (S21 to S23) of the estimation device in the fourth embodiment. [Figure 12] It is an example of a relationship diagram between a capacity estimation formula and characteristic quantities of a hydrogen storage device in an energy storage device of the fifth embodiment. [Figure 13] It is an example of a relationship diagram between a capacity estimation formula and characteristic quantities of a storage battery in an energy storage device of the fifth embodiment. [Figure 14] It is a hardware configuration diagram of the estimation device in the sixth embodiment.

Mode for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0012] (First Embodiment) FIG. 1 is a block diagram of an energy storage device to be estimated in the first embodiment.

[0013] In the energy storage device 20 of the present embodiment, as devices constituting the device, a storage battery 21, a fuel cell device 22, a hydrogen production device 23, a hydrogen storage device 24, and a control device 25 are provided. Hereinafter, the devices constituting the energy storage device 20 are referred to as constituent devices. The energy storage device 20 is a device that produces hydrogen or generates power using hydrogen based on the difference between the supply power generated by the renewable energy power generation device 100 and the demand power of the power consumer 200, that is, the value of surplus power or the value of deficit power. The renewable energy power generation device 100 is, for example, an arbitrary renewable energy power generation device 100 such as a solar power generation device or a wind power generation device. Hereinafter, an example of a solar power generation device will be given for explanation.

[0014] In the power system 204, when surplus power is generated, the energy storage device 20 charges the storage battery 21 with the surplus power based on the control of the control device 25. When the storage battery 21 approaches a fully charged state and cannot be charged, the hydrogen production device 23 produces hydrogen based on the control of the control device 25 and stores it in the hydrogen storage device 24. Also, in the power system 204, when deficit power occurs, the energy storage device 20 discharges the storage battery 21 based on the control of the control device 25 to compensate for the deficit power. When the storage battery 21 approaches an empty state and cannot be discharged, the fuel cell device 22 generates power using the hydrogen stored in the hydrogen storage device 24 based on the control of the control device 25.

[0015] FIG. 2 is a system block diagram of the estimation device in the first embodiment.

[0016] The estimation device 1 in the present embodiment calculates a constituent device capacity, which is the capacity of a constituent device in the energy storage device, and an introduction cost, which is the cost required for the introduction of the energy storage device 20, based on the feature amount of the residual power.

[0017] The estimation device 1 includes an input unit 2, a residual power calculation unit 3, a feature amount extraction unit 4, an estimation formula calculation unit 5, an estimated value calculation unit 6, an output unit 7, and a storage unit 8.

[0018] The input unit 2 includes a first power generation time series data input unit 31, a first power demand time series data input unit 32, a second power generation time series data input unit 33, a second power demand time series data input unit 34, a component equipment capacity data input unit 35, and an installation cost data input unit 36. The residual power calculation unit 3 includes a first residual power calculation unit 37 and a second residual power calculation unit 38. The feature extraction unit 4 includes a first feature extraction unit 39 and a second feature extraction unit 40. The estimation formula calculation unit 5 includes a capacity estimation formula calculation unit 41 and a cost estimation formula calculation unit 42. The estimated value calculation unit 6 includes a component equipment capacity estimation unit 43 and an installation cost estimation unit 44. The estimation device 1 is connected to an input device 9 and an output device 10.

[0019] Estimation device 1 can be implemented, for example, by installing a program for estimation device 1 on a PC (Personal Computer). The CPU (Central Processing Unit) within the PC executes the program for estimation device 1, thereby realizing the functions of the input unit 2, residual power calculation unit 3, feature extraction unit 4, estimation formula calculation unit 5, estimated value calculation unit 6, output unit 7, and storage unit 8 for estimation device 1. These functional blocks will be described below.

[0020] Input unit 2 receives first power generation time series data and first power demand time series data as input from the user via an input device 9, such as a mouse or keyboard. Input unit 2 also receives second power generation time series data and second power demand time series data as input. Furthermore, input unit 2 receives input of the capacity data and installation cost data of each component of the energy storage device 20 that has been considered. This data is received as input in each of the functional blocks 31 to 36 described above. For example, the user can enter values ​​into text boxes, etc. The following describes this input data.

[0021] Input unit 2 accepts, as input, a first time-series data set of power generation data for a renewable energy power generation device 100 in the area under consideration, and a first time-series data set of electricity demand data for the area under consideration. The area under consideration refers to the area where the introduction of the renewable energy power generation device 100 is being considered. Here, the area under consideration includes the area where the electricity generated by this renewable energy power generation device 100 is consumed. The first time-series data set of power generation data may be the predicted time-series data set (kW) of a solar power generation device based on average sunshine hours. The first time-series data set of electricity demand data may be the predicted time-series data set (kW) of electricity demand in the area under consideration. These time-series data can be represented, for example, as a line graph with power (kW) on the Y axis and time (date, hour, minute) on the X axis, representing the power at each time point.

[0022] Furthermore, the input unit 2 accepts, as input, a second time-series data set of power generation data for a renewable energy power generation device 100 in a studied area, and a second time-series data set of electricity demand data for the studied area. A studied area refers to an area where a renewable energy power generation device 100 has already been installed or where its installation has been considered. Here, a studied area includes the area where the electricity generated by this renewable energy power generation device 100 is consumed. The second time-series data set of power generation could, for example, be time-series data of power generated at another location where a solar power generation device has been installed or where its installation has been considered in the past. It is desirable that the second time-series data set of power generation data pertains to a renewable energy power generation device 100 with site conditions such as solar radiation that are similar to those of the area under study. The second time-series data set of electricity demand could, for example, be time-series data of electricity demand in the studied area. In addition, data obtained through operational simulations may be used for these data. Furthermore, it is desirable that these data be from multiple locations, not just one location.

[0023] Furthermore, the input unit 2 accepts input of capacity data for each component of the energy storage device 20 that has been considered. The energy storage device 20 that has been considered refers to an energy storage device 20 that has already been introduced at another location or for which introduction has been considered. The capacity data for each component of the energy storage device 20 that has been considered refers to the capacity data of the components that make up the energy storage device 20, such as the battery 21, fuel cell device 22, hydrogen production device 23, hydrogen storage device 24, and control device 25 as shown in Figure 1. In this example, the capacity of the battery 21 refers to the storage capacity expressed in units such as kWh, and the capacity of the hydrogen storage device 24 refers to the storage capacity expressed in units such as Nm3. The unit of capacity is not limited to this example, and any unit necessary to represent the performance of each component may be input.

[0024] Furthermore, the input unit 2 receives input from the user via the input device 9 for introduction cost data for the energy storage device 20 that has been considered. The introduction cost data may include, for example, the total cost (in yen) required for the introduction of the entire energy storage device 20 that has been considered, as well as the individual costs (in yen) required for the introduction of each component.

[0025] The data that input unit 2 accepts can include manual input from the user, such as using a mouse or keyboard, as well as input from configuration files, such as CSV (Comma Separated Value) files.

[0026] The residual power calculation unit 3 comprises a first residual power calculation unit 37 and a second residual power calculation unit 38. The first residual power calculation unit 37 calculates the difference between the first power generation time series data and the first power demand time series data, and calculates the time series data of the first residual power (kW), which is the residual power in the area under consideration. Similarly, the second residual power calculation unit 38 calculates the difference between the second power generation time series data and the second power demand time series data, and calculates the time series data of the second residual power (kW), which is the residual power at another location where a solar power generation system was previously installed or considered to be installed. Residual power refers to the difference between power demand and renewable energy generation power. For example, if the power demand is 40 kW and the renewable energy generation power at that time is 20 kW, the residual power will be 20 kW. For example, if the power demand is 20 kW and the renewable energy generation power is 40 kW, the residual power will be -20 kW.

[0027] The feature extraction unit 4 comprises a first feature extraction unit 39 and a second feature extraction unit 40. Based on the first residual power and the second residual power, the feature extraction unit 4 extracts a first feature quantity and a second feature quantity, which are features related to the respective residual power. The first feature quantity and the second feature quantity are extracted by the first feature extraction unit 39 and the second feature extraction unit 40, respectively. The features may be scalar quantities, such as maximum residual power, minimum residual power, or fluctuation range of the annual residual power change. Alternatively, the features may be scalar quantities related to power generation or power demand, such as maximum power generation or maximum power demand in the renewable energy power generation device 100. In addition to scalar quantities, the features may also be vector quantities.

[0028] The following describes an example of extracting the annual fluctuation range of the monthly average residual power, the minimum residual power, and the annual residual power amount as the first and second features, respectively. Here, the annual fluctuation range of the monthly average residual power refers to the range between the maximum and minimum values ​​when the average residual power for each month is calculated and its annual trend is shown. In addition to this example, other features that could be used include, for example, the annual fluctuation range of the daily average residual power, the daily fluctuation range of the daily average residual power, the maximum residual power, and the maximum power demand.

[0029] The estimation formula calculation unit 5 calculates a capacity estimation formula and an implementation cost estimation formula based on the second features obtained by the second feature extraction unit 40. The estimation formula can be calculated, for example, by regression analysis using machine learning with the second features as explanatory variables. The estimation formula calculation unit 5 can perform Lasso regression, which can reduce unnecessary variables, and Ridge regression, which adjusts the weights of each variable to avoid overfitting, to determine the coefficients for each feature extracted by the second feature extraction unit 40, and then calculate the estimation formula. The calculated estimation formula is a function with the second features obtained by the second feature extraction unit 40 as variables. The method used by the estimation formula calculation unit 5 to calculate the estimation formula may be a numerical analysis method such as regression analysis, or other methods may be used.

[0030] The capacity estimation formula calculation unit 41 calculates an estimation formula for the capacity of each component of the energy storage device 20. The capacity estimation formula calculation unit 41 calculates an estimation formula to be used to estimate the component capacity from the capacity of the components of the energy storage device 20 that has been studied and received as input by the input unit 2, and from the second feature obtained from the second feature extraction unit 40. Below, examples of estimation formula (1) for the hydrogen storage device capacity y1 and estimation formula (2) for the battery capacity y2 are shown. Here, x1 refers to the annual fluctuation range of the monthly average residual power, x2 refers to the minimum residual power, and x3 refers to the annual residual power amount. In this example, the estimation formula was calculated using regression analysis with machine learning, with these feature quantities as explanatory variables. In this way, the estimation formula is calculated for each component. y1=265.68x1-29.786x2+0.03x3+826.06 (1) y2=6.0965x1-4.1512x2+0.0033x3+852.08 (2)

[0031] The cost estimation formula calculation unit 42 calculates an introduction cost estimation formula for the energy storage device 20. The cost estimation formula calculation unit 42 calculates an estimation formula for the introduction cost y3 from the introduction cost data received by the input unit 2 and the second feature obtained from the second feature extraction unit 40. Below, an example of the introduction cost estimation formula (3) is shown. In this example, the estimation formula was calculated using regression analysis with machine learning, with the features x1 to x3 as explanatory variables. y3=0.0429x1-0.0046x2+5.2766×10 -6 x3 + 2.8543 (3)

[0032] The storage unit 8 is built on an auxiliary storage device, such as the HDD (Hard Disk Drive) of the estimation device 1. The storage unit 8 includes an estimation formula database (not shown) that stores each estimation formula calculated by the estimation formula calculation unit 5. In this embodiment, (1) to (3) are stored in the estimation formula database. By storing these estimation formulas in the storage unit 8, the estimation device 1 can utilize the calculated estimation formulas when estimating in other target areas. Alternatively, the estimation device 1 may calculate the estimation formulas in advance and store them in the storage unit 8 when calculating each estimated value.

[0033] The estimated value calculation unit 6 calculates estimated values ​​for the capacity of each component in the energy storage device 20 and the installation cost of the energy storage device 20 based on each estimated formula calculated by the estimated formula calculation unit 5. The estimated value calculation unit 6 may read each estimated formula from the storage unit 8 or accept it as input from the estimated formula calculation unit 5.

[0034] Specifically, when calculating the hydrogen storage device capacity y1, the component equipment capacity estimation unit 43 can calculate it by substituting the first feature quantities (x1 to x3) extracted by the first feature quantity extraction unit 39 into equation (1). Similarly, when calculating the battery capacity y2, the component equipment capacity estimation unit 43 can calculate it by substituting the first feature quantities (x1 to x3) into equation (2).

[0035] Furthermore, when calculating the implementation cost y3, the implementation cost estimation unit 44 can similarly calculate it by substituting the first feature quantities (x1 to x3) into equation (3).

[0036] The output unit 7 creates data for output to the output device 10 for each estimated value calculated by the estimated value calculation unit 6. The output unit 7 may present the output data, for example, in a tabular format so that the user can view it on a browser.

[0037] In the above, the input unit 2, residual power calculation unit 3, feature extraction unit 4, estimation formula calculation unit 5, and estimated value calculation unit 6 were described as components of a functional block. However, each component may be classified as two functional blocks, for example, an estimation formula derivation unit 12 and an estimated value calculation unit 13. Specifically, the second power generation time series data input unit 33, the second power demand time series data input unit 34, the component equipment capacity data input unit 35, the introduction cost data input unit 36, the second residual power calculation unit 38, the second feature extraction unit 40, the capacity estimation formula calculation unit 41, and the cost estimation formula calculation unit 42 may be implemented as components of the estimation formula derivation unit 12. The first power generation time series data input unit 31, the first power demand time series data input unit 32, the first residual power calculation unit 37, the first feature extraction unit 39, the component equipment capacity estimation unit 43, and the introduction cost estimation unit 44 may be implemented as components of the estimated value calculation unit 13.

[0038] Figure 3 shows an example of output data in the first embodiment.

[0039] Figure 3 shows an example of estimated values ​​for the capacity and installation cost of each component. The components shown are examples of the capacity of the battery 21 and the hydrogen storage device 24, and other components are omitted. This example also shows a table comparing the approximate values ​​calculated using the conventional scale ratio with the estimated values ​​calculated by the estimation device 1 in this embodiment.

[0040] Figure 4 shows an example of the operation results of the estimation device in the first embodiment.

[0041] Estimation device 1 calculated an estimation formula using second time-series power generation data for 65 locations of examined renewable energy power generation equipment 100 and second time-series power demand data for those regions. Subsequently, estimation device 1 calculated an estimated value for an energy storage device 20 with the configuration shown in Figure 1, and obtained the results shown in Figure 4. As shown in Figure 4, it can be seen that estimation device 1 of this embodiment has a lower relative error to the true value compared to the conventional calculation method using scale ratio.

[0042] Figures 5 and 6 are flowcharts of the estimation device in the first embodiment.

[0043] First, let's explain the flow shown in Figure 5. In step S1, the input unit 2 receives input of second power generation time series data and second power demand time series data from the user. In step S2, the input unit 2 receives input of capacity data for each component of the energy storage device 20 and installation cost data from the user. The data received in steps S1 and S2 can be manually entered by the user, or it can be automatically collected from external systems such as a monitoring and control system like SCADA (Supervisory Control And Data Acquisition) or an equipment ledger system. In addition, this data can be received as input by importing a configuration file such as a CSV (Comma Separated Value) file.

[0044] In step S3, the second residual power calculation unit 38 calculates the second residual power time series data based on the second power generation time series data and the second power demand time series data. In step S4, the second feature extraction unit 40 extracts the second feature quantities.

[0045] In step S5, the capacity estimation formula calculation unit 41 calculates an estimation formula for the capacity of each component device from the capacity data of each component device received by the input unit 2 and the second feature quantity. In step S6, the cost estimation formula calculation unit 42 calculates an estimation formula for the introduction cost from the introduction cost data received by the input unit 2 and the second feature quantity.

[0046] Next, let's explain the flow in Figure 6. The flow in Figure 6 is a continuation of the flow in Figure 5, and the flowcharts in Figure 5 and Figure 6 are connected by connectors.

[0047] In step S7, the input unit 2 receives input of the first power generation time series data and the first power demand time series data. In step S8, the first residual power calculation unit 37 calculates the time series data of the first residual power based on the first power generation time series data and the first power demand time series data.

[0048] In step S9, the first feature extraction unit 39 extracts the first feature. In step S10, the component device capacity estimation unit 43 substitutes the first feature into the estimation formula calculated by the capacity estimation formula calculation unit 41 and calculates an estimated value of the capacity of each component device.

[0049] In step S11, the introduction cost estimation unit 44 substitutes the first feature quantity into the introduction cost estimation formula for the energy storage device 20 calculated by the cost estimation formula calculation unit 42, and calculates an estimated value of the introduction cost of the energy storage device 20. In step S12, the output unit 7 creates data for outputting each estimated value to the output device 10 and displays it on the output device 10.

[0050] According to this embodiment, based on the first residual power, the estimated capacity of each component and the installation cost in the energy storage device 20 can be estimated simply, quickly, and with high accuracy.

[0051] Furthermore, the estimation device 1 may pre-calculate each estimation formula and store it in the storage unit 8. After receiving input such as the first power generation time series data for the area under consideration, the estimation device 1 can calculate the estimated values ​​even more quickly by reading each estimation formula from the storage unit 8. In addition, the estimation formula calculated once can be used in the second and subsequent areas under consideration to quickly calculate the estimated values. As a result, the estimation device 1 can calculate each estimated value more simply and quickly than the conventional method of calculating the estimated capacity and installation cost of each component of the energy storage device 20 through operation simulations.

[0052] Furthermore, the estimation device 1 can calculate the estimated capacity of each component and the installation cost of the energy storage device 20 with higher accuracy than calculating them using a scale ratio.

[0053] (Second Embodiment) Figure 7 is a system block diagram of the estimation device in the second embodiment.

[0054] In this embodiment, the estimation device 1 has a configuration that does not include the cost estimation formula calculation unit 42 and the introduction cost estimation unit 44, compared to the estimation device 1 in Figure 1.

[0055] According to this embodiment, the estimation device 1 can narrow down the estimated values ​​to be calculated to only the capacity of each component device, thereby enabling the acquisition of estimated values ​​more quickly and with higher accuracy.

[0056] (Third embodiment) Figure 8 is a system block diagram of the estimation device in the third embodiment.

[0057] In this embodiment, the estimation device 1 has a configuration that does not include the capacity estimation formula calculation unit 41 and the component equipment capacity estimation unit 43, compared to the estimation device 1 in Figure 1.

[0058] According to this embodiment, the estimation device 1 can focus solely on the implementation cost when calculating the estimated value, and can obtain the estimated value more quickly and with higher accuracy.

[0059] (Fourth Embodiment) Figure 9 is a system block diagram of the estimation device in the fourth embodiment.

[0060] In this embodiment, the estimation device 1, compared to the estimation device 1 in Figure 1, further includes an estimation unit 11 for calculating construction costs.

[0061] Furthermore, the storage unit 8 in this embodiment includes a construction cost database (not shown) that stores construction cost data such as the price of the equipment per unit capacity of the constituent equipment and the installation cost per unit capacity of the constituent equipment. This unit cost data is not limited to the data described above, and various types of unit cost data for calculating construction costs can be used, such as unit cost data created by the user, unit cost data created by a market price research company, or labor cost data issued by a government agency.

[0062] In this embodiment, we will explain an example of calculating the equipment costs of the constituent equipment and the installation costs of the constituent equipment when estimating the costs related to construction work.

[0063] The calculation unit 11 reads construction unit price data from the storage unit 8. The calculation unit 11 can calculate the equipment cost of the components by multiplying the estimated capacity of the components obtained from the component equipment capacity estimation unit 43 by the equipment price per unit capacity of the components. The calculation unit 11 can also calculate the installation cost of the components by multiplying the estimated capacity of the components by the installation cost per unit capacity of the components. By summing up (calculating) each of these costs, the total cost of construction related to the energy storage device 20 can be calculated.

[0064] Furthermore, the cost estimation unit 11 may have a function to calculate expenses based on a predetermined calculation method, such as common temporary expenses and general administrative expenses, when calculating the total cost of the construction work.

[0065] Furthermore, the calculation unit 11 may also calculate the costs related to maintenance and operation. For example, the construction cost database stores the maintenance and operation costs per unit capacity of the constituent equipment in advance as maintenance and operation labor cost data. The calculation unit 11 can calculate the maintenance and operation costs of the constituent equipment by multiplying the estimated capacity of the constituent equipment by the maintenance and operation costs per unit capacity of the constituent equipment. The total cost may also be calculated by further calculating the maintenance and operation costs in addition to the construction costs.

[0066] Furthermore, the calculation unit 11 may provide installation conditions for the components. For example, the construction cost database pre-stores the installation area per unit capacity of the components. The calculation unit 11 can calculate the installation area of ​​the components by multiplying the estimated value of the components capacity obtained from the components capacity estimation unit 43 by the installation area per unit capacity of the components.

[0067] The integrating unit 11 may also calculate the price of electricity. For example, the price of electricity per unit of power can be calculated using the first time-series electricity demand data input to the input unit 2. The integrating unit 11 can calculate the amount of electricity demand for a certain period by finding the area that the graph of the first time-series electricity demand data forms with the X and Y axes, and then calculate the price of electricity per unit of power by dividing this amount of electricity demand by the total cost.

[0068] The output unit 7 outputs the construction cost values ​​calculated by the calculation unit 11 to the output device 10. The output unit 7 may output this data to the output device 10 as, for example, calculation data for each item.

[0069] Figures 10 and 11 are flowcharts of the estimation device in the fourth embodiment.

[0070] The flow from steps S1 to S6 is the same as in Figure 5, and the flow from steps S7 to S11 in Figure 10 is the same as steps S7 to S11 in Figure 6, so the explanation is omitted. Also, the flow in Figure 11 is a continuation of the flow in Figure 10, and the flowchart in Figure 11 and the flowchart in Figure 10 are connected by connectors.

[0071] In step S21 of Figure 11, the estimation unit 11 reads the construction unit price data from the storage unit 8. In step S22, the estimation unit 11 calculates the equipment cost and installation cost of the constituent equipment by multiplying the estimated value obtained in step S10 by the construction unit price data.

[0072] In step S23, the output unit 7 outputs the equipment costs and installation costs of the constituent equipment to the output device 10.

[0073] According to this embodiment, the estimation device 1 can calculate the equipment cost, installation cost, and maintenance and operation cost of the constituent equipment based on the estimated capacity of the constituent equipment.

[0074] Furthermore, according to this embodiment, the installation conditions for the constituent equipment can be calculated based on estimated values ​​of the constituent equipment capacity, etc.

[0075] Furthermore, according to this embodiment, the price of electricity per unit power can be calculated based on estimated values ​​of the component equipment capacity, etc.

[0076] Furthermore, according to this embodiment, the estimation device 1 does not require long-term operation simulations and calculates installation costs etc. using highly accurate estimated values, thus enabling rapid and highly accurate estimation. In addition, it is possible to reduce the time and cost of consideration before introducing the device.

[0077] (Fifth embodiment) Figure 12 shows an example of a diagram illustrating the relationship between the capacity estimation formula and characteristic quantities of the hydrogen storage device in the fifth embodiment of the energy storage device.

[0078] Similar to the first embodiment, the estimation formula in this embodiment uses the annual fluctuation range x1 of the monthly average residual power, the minimum residual power x2, and the annual residual power amount x3 as first features. Figure 12 shows the relationship between the capacity estimation formula for the hydrogen storage device 24 and the feature x1. For an energy storage device 20 having the configuration of Figure 1, the estimated capacity of the hydrogen storage device 24 is expressed within the range enclosed by equations (4) and (5). Equation (4) represents the upper limit y4 of the estimated capacity of the hydrogen storage device 24, and equation (5) represents the lower limit y5. In other words, the capacity range expressed between equations (4) and (5) realizes the capacity of the hydrogen storage device 24 that constitutes a low-cost and highly efficient energy storage device 20. In this embodiment, the capacity estimation formula calculation unit 41 calculated the estimation formula by regression analysis using machine learning, with these features as explanatory variables. Equations (4) and (5) are shown below. y4 = 280x1 - 32x2 + 0.02x3 + 1500 (4) y5 = 250x1 - 28x2 + 0.04x3(5)

[0079] Furthermore, as a comparative example, the capacity estimation formula for the hydrogen storage device 24 calculated by the capacity estimation formula calculation unit 41 in the first embodiment is shown. Also, as a comparative example, the estimated capacity values ​​of the hydrogen storage device 24 calculated by the component equipment capacity estimation unit 43 using various capacity estimation formulas are shown as black circles. Note that the relationship diagram between the minimum residual power x2 and the annual residual power x3 and the capacity estimation formula for the hydrogen storage device 24 is omitted.

[0080] Figure 13 shows an example of a diagram illustrating the relationship between the battery capacity estimation formula and feature quantities in the energy storage device of the fifth embodiment.

[0081] Similar to the first embodiment, the estimation formula in this embodiment uses the annual fluctuation range x1 of the monthly average residual power, the minimum residual power x2, and the annual residual power amount x3 as first features. Figure 13 shows the relationship between the battery capacity estimation formula and the feature x1. For an energy storage device 20 having the configuration of Figure 1, the estimated value of the battery capacity 21 is expressed within the range enclosed by equations (6) and (7). Equation (6) represents the upper limit y6 of the estimated value of the battery capacity 21, and equation (7) represents the addition or subtraction y7. In other words, the capacity range expressed between equations (6) and (7) realizes the capacity of the battery 21 that constitutes a low-cost and highly efficient energy storage device 20. In this embodiment, the capacity estimation formula calculation unit 41 calculated the estimation formula by regression analysis using machine learning, with these features as explanatory variables. Equations (6) and (7) are shown below. y6 = 7x1 - 4.5x2 + 0.003x3 + 1500 (6) y7 = 5x1 - 3.8x2 + 0.004x3 + 500 (7)

[0082] Furthermore, as a comparative example, the capacity estimation formula for the storage battery 21 calculated by the capacity estimation formula calculation unit 41 in the first embodiment is shown. Also, as a comparative example, the estimated capacity values ​​of the storage battery 21 calculated by the component equipment capacity estimation unit 43 using various capacity estimation formulas are shown as black circles. Note that the relationship diagram between the minimum residual power x2 and the annual residual energy x3 and the capacity estimation formula for the storage battery 21 is omitted.

[0083] According to this embodiment, a user can introduce an energy storage device 20 by combining a hydrogen storage device 24 having a capacity range represented by formulas (5) and (6) with a storage battery 21 having a capacity range represented by formulas (6) and (7), thereby achieving a low-cost and highly efficient energy storage device 20. Furthermore, the user can construct an energy storage device 20 that can operate energy efficiently by minimizing losses.

[0084] (Sixth Embodiment) Figure 14 is a hardware configuration diagram of the estimation device in the sixth embodiment.

[0085] The estimated device 1 in Figure 14 comprises a processor 52 such as a CPU, a main memory 53 such as RAM, an auxiliary storage device 54 such as an HDD, a network interface 55 such as a LAN (Local Area Network) board, a device interface 56 such as memory slots and memory ports, and a bus 57 that connects these devices to each other. The estimated device 1 is, for example, a computer such as a PC (Personal Computer) and is equipped with input devices 9 such as a keyboard and mouse, and output devices 10 such as an LCD (Liquid Crystal Display) monitor.

[0086] In this embodiment, a program for causing a computer to perform the information processing of the estimation device 1 in any of the first to fifth embodiments is installed in the auxiliary storage device 54. The estimation device 1 loads this program into the main memory 53 and executes it using the processor 52. This enables the functions of each block shown in Figures 3, 7, or 10 to be realized within the estimation device 1, making it possible to estimate the capacity as described in the first to fifth embodiments. The data generated by this information processing is temporarily held in the main memory 53 or stored in the auxiliary storage device 54.

[0087] This program can be installed, for example, by connecting an external device 58 containing this program to the device interface 56, and then storing the program from the external device 58 in the auxiliary storage device 54. An example of the external device 58 is a computer-readable recording medium or a recording device that incorporates such a recording medium. Examples of recording media include CD-ROM (Compact Disk Read Only Memory), CD-R (Compact Disk Recordable), flexible disk, DVD-ROM (Digital Versatile Disk Read Only Memory), and DVD-R (Digital Versatile Disk Recordable), while an example of a recording device is an HDD. Alternatively, this program can be installed, for example, by downloading it via the network interface 55.

[0088] According to this embodiment, the functions of the estimation device 1 in any of the first to fifth embodiments can be realized by software.

[0089] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel estimation device 1 described herein can be implemented in a variety of other forms. Furthermore, various omissions, substitutions, and modifications can be made to the embodiments of estimation device 1 described herein without departing from the spirit of the invention. The appended claims and equivalents are intended to include such forms and modifications included in the scope and spirit of the invention. [Explanation of Symbols]

[0090] 1: Estimation device, 2: Input unit, 3: Residual power calculation unit, 4: Feature extraction unit, 5: Estimation formula calculation unit, 6: Estimated value calculation unit, 7: Output unit, 8: Memory unit, 9: Input device, 10: Output device, 11: Summarizing unit, 12: Estimation formula derivation unit, 13: Estimated value calculation unit, 20: Energy storage devices, 21: Storage batteries, 22: Fuel cell devices, 23: Hydrogen production equipment, 24: Hydrogen storage equipment, 25: Control device, 31: First power generation time series data input unit, 32: First power demand time series data input unit, 33: Second power generation time series data input unit, 34: Second power demand time series data input unit, 35: Equipment capacity data input section, 36: Introduction cost data input section, 37: First residual power calculation unit, 38: Second residual power calculation unit, 39: First feature extraction unit, 40: Second feature extraction unit, 41: Capacity estimation formula calculation unit, 42: Cost estimation formula calculation unit, 43: Component equipment capacity estimation unit, 44: Implementation cost estimation unit, 52: Processor, 53: Main memory, 54: Secondary memory, 55: Network interface, 56: Device interface, 57: Bus, 58: External device, 100: Renewable energy power generation equipment, 200: Electricity consumers, 204: Power system

Claims

1. An input unit that receives, as input, a first power generation time series data which is power generation time series data for renewable energy power generation equipment in the area under consideration, and a first power demand time series data which is power demand time series data for the same area under consideration. A residual power calculation unit calculates a first residual power based on the first power generation time series data and the first power demand time series data, A feature extraction unit that extracts a first feature quantity, which is a feature quantity of the first residual power, An estimation unit calculates an estimated value of the component equipment capacity or installation cost of an energy storage device based on a predetermined estimation formula that uses the first feature quantity as an explanatory variable, An estimation device equipped with the following features.

2. The estimation device according to claim 1, wherein the estimation unit calculates estimated values ​​of the component equipment capacity and installation cost of the energy storage device based on a component equipment capacity estimation formula and a cost estimation formula that use the first feature quantity as explanatory variables.

3. The energy storage device further comprises a capacity estimation formula calculation unit that calculates a capacity estimation formula for the constituent components of the energy storage device, The input unit further accepts as input a second time-series data set of power generation data for renewable energy power generation equipment in the area under consideration, a second time-series data set of power demand data for the area under consideration, and the component equipment capacity of the energy storage equipment under consideration. The residual power calculation unit further calculates a second residual power based on the second power generation time series data and the second power demand time series data. The feature extraction unit further extracts a second feature, which is a feature of the second residual power. The capacity estimation formula calculation unit calculates the capacity estimation formula based on the capacity of the constituent equipment and the second characteristic quantity. The estimated value calculation unit calculates an estimated value of the component equipment capacity based on the capacity estimation formula. The estimation device according to claim 1.

4. The system further comprises a cost estimation formula calculation unit that calculates a cost estimation formula for the introduction costs of the components of the energy storage device, The input unit further accepts, as input, a second time-series data of power generation, which is time-series data of power generation for renewable energy power generation equipment in the area under consideration, a second time-series data of power demand, which is time-series data of power demand in the area under consideration, and data on the introduction cost of the energy storage equipment under consideration. The residual power calculation unit further calculates a second residual power based on the second power generation time series data and the second power demand time series data. The feature extraction unit further extracts a second feature, which is a feature of the second residual power. The cost estimation formula calculation unit calculates the introduction cost estimation formula based on the introduction cost data and the second feature quantity. The estimated value calculation unit calculates an estimated value of the introduction cost based on the introduction cost estimation formula. The estimation device according to claim 1.

5. A capacity estimation formula calculation unit that calculates a capacity estimation formula for the components of the energy storage device, The system further comprises a cost estimation formula calculation unit that calculates a cost estimation formula for the introduction costs of the components of the energy storage device, The input unit further accepts as input: a second time-series power generation data, which is time-series power generation data for renewable energy power generation equipment in the area under consideration; a second time-series power demand data, which is time-series power demand data for the area under consideration; the component equipment capacity of the energy storage equipment under consideration; and data on the introduction cost of the energy storage equipment under consideration. The residual power calculation unit further calculates a second residual power based on the second power generation time series data and the second power demand time series data. The feature extraction unit further extracts a second feature, which is a feature of the second residual power. The capacity estimation formula calculation unit calculates the capacity estimation formula based on the capacity of the constituent equipment and the second characteristic quantity. The cost estimation formula calculation unit calculates the introduction cost estimation formula based on the introduction cost data and the second feature quantity. The estimation unit calculates an estimated value of the component equipment capacity based on the capacity estimation formula, and calculates an estimated value of the installation cost based on the installation cost estimation formula. The estimation device according to claim 1.

6. The estimation device according to claim 1, further comprising an output unit that outputs an estimated value of the capacity of the aforementioned components or the installation cost.

7. Furthermore, it includes an estimation department that calculates the costs related to construction work. The estimation device according to claim 1, wherein the estimation unit calculates the costs related to the construction work based on construction unit price data and estimated values ​​of the component equipment capacity.

8. The estimation device according to claim 7, wherein the estimation unit further estimates the costs related to maintenance and operation based on the maintenance and operation labor cost data and the estimated capacity of the constituent equipment.

9. The estimation device according to claim 8, wherein the estimation unit calculates the electricity price based on the costs related to the construction work, the costs related to maintenance and operation, and the first time-series data of electricity demand.

10. The estimation device according to claim 7, further comprising an output unit that outputs a value for the cost of the aforementioned construction work.

11. The estimation device according to claim 1, wherein the first characteristic quantity is at least one of the following: the annual fluctuation range of the monthly average residual power, the minimum residual power, and the annual residual power amount.

12. The input unit receives, as input, a first power generation time series data which is power generation time series data for renewable energy power generation equipment in the area under consideration, and a first power demand time series data which is power demand time series data for the same area under consideration. The residual power calculation unit calculates the first residual power based on the first power generation time series data and the first power demand time series data. The feature extraction unit extracts a first feature, which is the feature of the first residual power. The estimation unit calculates an estimated value of the component equipment capacity or installation cost of the energy storage device based on a predetermined estimation formula that uses the first feature quantity as an explanatory variable. An estimation program that causes a computer to perform an estimation method that includes the following.

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