Information processing apparatus, information processing method, and computer program

Abstract

To estimate the amount of generated power.SOLUTION: An information processing apparatus of the present embodiment comprises a processing unit that, on the basis of information on a plurality of power generators arranged at a plurality of positions, classifies the plurality of power generators into groups to generate first to N-th groups each including one or more power generators, adds up actual result values of the amounts of power generated by the power generators included in the first to N-th groups to calculate an actual result value of the total amount of generated power of the first to N-th groups, on the basis of attribute information of the power generators belonging to the first to N-th groups, generates group attribute information that is attribute information of the first to N-th groups, and on the basis of the actual result value of the total amount of generated power of the first to N-th groups, the group attribute information of the first to N-th groups, and actual result data on the meteorological quantities of first to N-th regions including the positions of the power generators belonging to the first to N-th groups, generates an estimation model using the group attribute information and the meteorological quantities as explanatory variables and the total amount of generated power as an objective variable.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present embodiment relates to an information processing apparatus, an information processing method, and a computer program.

Background Art

[0002] Power generation businesses are obliged to generate the same amount of electricity as the planned value at the same time, such as accurately generating the amount of electricity sold. In renewable energy power sources (renewable energy power sources) such as solar power generation, wind power generation, and hydroelectric power generation, under the FIT (Feed-in Tariff) system, the obligation to generate the same amount as the planned value at the same time was waived by the grid operator's full purchase. After the end of FIT or in the non-FIT case, the power generation business must submit its own power generation and sales plan and achieve the same amount as the planned value at the same time. Therefore, an aggregator that operates by bundling the renewable energy power sources of one or more power generation businesses bears the responsibility of generating the same amount as the planned value at the same time. The aggregator predicts the power generation amount of the renewable energy power source, creates a bidding plan for the market, a trading volume plan for bilateral trading, and an operation plan for adjustable power sources such as storage batteries, and if it approaches the actual supply and demand profile, it performs control (charging or discharging) of storage batteries and the like. Since the output of renewable energy power sources depends on the weather, it is necessary to predict the power generation amount with high accuracy in order to stably improve profits through market trading and the like.

[0003] When an aggregator handles a large number of small-scale and dispersed low-voltage solar power generation devices (low-voltage PV (Photovoltaic Power Generation) devices), the amount of data and the amount of processing (prediction, planning, control) to be handled become enormous. The low-voltage PV device is a low-capacity PV device with an equipment capacity of less than 50 kW, for example. Therefore, it is difficult to perform prediction, planning, and control for each low-voltage PV device due to problems such as processing time and the cost of computing resources.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The present embodiment provides an information processing apparatus, an information processing method, and a computer program for realizing estimation of power generation amount.

Means for Solving the Problems

[0006] The information processing apparatus according to the present embodiment groups a plurality of power generation devices based on information regarding the plurality of power generation devices arranged at a plurality of positions, and generates first to Nth (N is an integer of 1 or more) groups each including one or more power generation devices. By summing up the actual power generation amounts of the power generation devices included in the first to Nth groups, the actual power generation amounts of the first to Nth groups are calculated. Based on the attribute information of the power generation devices belonging to the first to Nth groups, group attribute information that is the attribute information of the first to Nth groups is generated. Based on the actual power generation amounts of the first to Nth groups, the group attribute information of the first to Nth groups, and the actual data regarding the weather amounts of the first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups, an estimation model is generated with the group attribute information and the weather amounts as explanatory variables and the total power generation amount as the target variable, and includes a processing unit.

Brief Description of the Drawings

[0007]

Figure 1

Figure 2

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Embodiments for Carrying Out the Invention

[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the embodiments of the present invention, an aggregator that operates by bundling renewable energy power sources of one or more power generation companies predicts the total power generation amount by these renewable energy power sources with high accuracy. In this embodiment, a PV device, particularly a low-voltage PV (Photovoltaic Power Generation) device, is assumed as the renewable energy power source, but a high-voltage PV device, a wind power generation device, a hydroelectric power generation device, etc. may also be used. Hereinafter, the low-voltage PV device will be referred to as the low-voltage PV device.

[0009] In this specification, estimating the value of a future time based on the current time (processing execution time) is called prediction. In this embodiment, the case of predicting the power generation amount is shown, but this embodiment can also be implemented when estimating the value of a past time. In this case, the prediction of the power generation amount in the following description may be read as the estimation of the power generation amount.

[0010] (First Embodiment) FIG. 1 is a block diagram of a power generation amount prediction device 1 as an information processing device according to the first embodiment. The power generation amount prediction device 1 includes a PV bulk data processing unit 100, a prediction processing unit 200, a resource cooperation unit (resource cooperation interface) 300, and a weather performance data storage unit 560. The prediction processing unit 200 can communicate with a weather prediction server 550 via a communication network (not shown). The processing unit according to this embodiment includes the PV bulk data processing unit 100 and further includes at least one of the prediction processing unit 200 and the resource cooperation unit (resource cooperation interface) 300.

[0011] The PV bulk data processing unit 100 includes a PV bulk generation unit 110, a generated energy achievement consolidation unit 120, a PV bulk facility information generation unit 130, a generated energy achievement data storage unit 140, a PV facility information storage unit 150, a PV device - PV bulk correspondence storage unit 170, and a PV bulk facility information storage unit 180.

[0012] The resource cooperation unit 300 is capable of communicating with a plurality of measurement devices (low - voltage PV measurement devices) that measure a plurality of low - voltage PV devices managed by one or more power generation operators. The plurality of low - voltage PV devices are respectively arranged at different positions and are connected to the power grid either via a transformer or directly. The capabilities (specifications), altitudes, azimuths, inclinations (tilt angles), etc. of each low - voltage PV device vary. The resource cooperation unit 300 acquires generated energy achievement data including the achievement values (generated energy achievement values) of the generated energy of the plurality of low - voltage PV devices from these low - voltage PV measurement devices.

[0013] The generated energy achievement data includes, as an example, attribute information such as the identifier (resource ID) of the low - voltage PV device, the date and time (time) of power generation, and the generated energy achievement value.

[0014] The resource cooperation unit 300 acquires the generated energy achievement data at regular intervals. The regular interval can be arbitrary, such as every hour, every 10 minutes, or every day. The acquisition of the generated energy achievement data may also be performed at one or more predetermined times within a day instead of at regular intervals.

[0015] In addition to the method of acquiring the generated energy achievement data from the low - voltage PV measurement devices, if there is a management server that centrally manages the generated energy achievements of each low - voltage PV device, the generated energy achievement data may be acquired from the management server. The management server may be a network service center (NSC) (see Figure 7) that centrally manages operations such as power retail consignment.

[0016] The generated energy achievement data storage unit 140 stores the generated energy achievement data acquired by the resource cooperation unit 300 internally.

[0017] The PV equipment information storage unit 150 stores the equipment information of the low-voltage PV device (low-voltage PV equipment information). The low-voltage PV equipment information is information representing the attributes of the low-voltage PV device and is stored in a database such as a table.

[0018] FIG. 2 shows an example of a table storing the low-voltage PV equipment information. In the table, as attributes of the low-voltage PV device, an aggregator name, an identifier (resource ID) of the low-voltage PV device, a weather measurement location (e.g., AMeDAS) for measuring the weather related to the installation of the low-voltage PV device, longitude, latitude, output capacity (power generation capacity), PCS output which is the output of the PCS (power conditioner) provided in the low-voltage PV, inclination (tilt angle), azimuth, and terrain are included. These attributes are just examples, and some of the illustrated attributes may be deleted or other attributes not shown may be added.

[0019] The PV bulk generation unit 110 groups the low-voltage PV devices based on the low-voltage PV equipment information in the PV equipment information storage unit 150. This process is called the PV bulk generation process. A set of grouped low-voltage PV devices is called a PV bulk. A PV bulk is a group including one or more low-voltage PV devices, and the first to Nth (N is an integer of 1 or more) groups, that is, one or more PV bulks are generated. One PV bulk (group) includes one or a plurality of low-voltage PV devices. The PV bulk generation unit 110 may perform the PV bulk generation process at regular intervals or arbitrary intervals, or every time it receives an instruction from the user to update the PV bulk. The user may give an instruction when there is an addition or removal of a low-voltage PV device to the low-voltage PV devices managed by the power generation business operator.

[0020] [Example of grouping of low-voltage PV devices] Details of the process in which the PV bulk generation unit 110 groups the low-voltage PV devices to generate a PV bulk are shown.

[0021] Figure 3 is a flowchart of an example of the operation of the PV bulk generation unit 110. Determine the number of groups (number of clusters) for dividing the low-voltage PV devices (S101). Let the number of clusters be represented by K. In the first processing of step S101, 1, which is the initial value, is set as K. The initial value may be an integer of 2 or more.

[0022] Using the K-means method, cluster (group) the low-voltage PV devices into K clusters (S102). As the clustering algorithm, any method such as the K-means method, the Ward method, the group average method, or the method using a neural network can be used. In this flowchart, the case of using the K-means method is shown. The K-means method is an algorithm for non-hierarchical clustering. It uses the average of the data belonging to the clusters to classify a plurality of data into K clusters. Here, at least one of the attribute information included in the low-voltage PV facility information is used as the data for clustering. The details of the data for clustering will be described later.

[0023] In all the generated clusters (groups), calculate the centroid of each cluster based on the data belonging to the cluster. For each cluster, calculate the distance from all the data belonging to the cluster to the centroid, and determine whether the distance of all the data is less than or equal to the threshold value (S103). For all the clusters, if the distance from all the data to the centroid is less than or equal to the threshold value, determine each cluster at this time as a PV bulk (S105), and end the process. The above threshold value may be specified by the user, who is the operator of this information processing device, as an argument when this process is executed.

[0024] On the other hand, if there is data for which the distance to the centroid is greater than the threshold value in at least one cluster, increment K by 1 (S104), and return to step S101.

[0025] An example of the attribute information of the low-voltage PV devices used as the data for the above-described grouping (clustering) is shown. Note that the clustering algorithm may be different depending on the type of attribute information used.

[0026] (1) Grouping is performed using the location information of the low-voltage PV device. As the location information, one or any combination of latitude, longitude, and altitude is used. Examples of the combination include, for example, latitude·longitude, or longitude·latitude·altitude.

[0027] (2) Grouping is performed using the specification information or installation conditions of the low-voltage PV device. Examples of the specification information include output capacity or PCS output. Examples of the installation conditions include inclination (tilt angle) or azimuth.

[0028] (3) Grouping is performed using the terrain information of the location where the low-voltage PV device is installed. Examples of the terrain information include coastal, mountainous, urban, etc.

[0029] (4) Grouping is performed using the correlation coefficient of the actual power generation values of the low-voltage PV device. In this case, while collecting the power generation performance data, the group (cluster) to which the low-voltage PV device belongs may be determined dynamically.

[0030] (5) Grouping is performed using two or more of the above (1) to (4) methods respectively. When two or more of these grouping results contain the same grouping result, the most frequent grouping result is adopted. When all the grouping results are different, the grouping result of the method with the highest predetermined priority may be adopted, or the grouping results may be presented to the user to prompt the user to input the desired grouping result and the input grouping result may be adopted.

[0031] Figure 4 shows a specific example of grouping. The left diagram of Figure 4 schematically shows a state where low-voltage PV devices are located at a plurality of different positions. The PV bulk generation process described in Figure 3 is performed on these low-voltage PV devices. As a result, three PV bulks 191, 192, and 193 are generated as shown in the right diagram. The frames surrounding the PV bulks 191, 192, and 193 schematically represent regions 191A, 192A, and 193A that include the positions of a plurality of low-voltage PV devices belonging to the PV bulks 191, 192, and 193. When the PV bulks 191, 192, and 193 correspond to the first to Nth groups, the regions 191A to 193C correspond to the low-voltage PV devices (corresponding to the first to Nth regions including the installation positions of the power generation devices) belonging to the first to Nth groups.

[0032] The PV device - PV bulk correspondence storage unit 170 internally stores, as PV device - PV bulk correspondence information, information associating a plurality of PV bulks generated by the PV bulk generation unit 110 with the low-voltage PV devices belonging to the plurality of PV bulks.

[0033] Figure 5 shows an example of PV device - PV bulk correspondence information. The identifier of the PV bulk (bulk ID) and the identifier of the low-voltage PV device (resource ID) are associated. For example, low-voltage PV devices with resource IDs such as 267, 286, 107 belong to the PV bulk with the bulk ID BK_1.

[0034] The PV bulk facility information generation unit 130 determines the attribute information of the PV bulk based on the PV device - bulk correspondence information in the PV device - PV bulk correspondence storage unit 170, and generates PV bulk facility information including the attribute information of the PV bulk. The attribute information of the PV bulk corresponds to the group attribute information, which is the attribute information of the group including the low-voltage PV devices.

[0035] The PV bulk facility information storage unit 180 internally stores the PV bulk facility information generated by the PV bulk facility information generation unit 130.

[0036] [Example of generation of facility information of PV bulk] An example of the PV bulk facility information generation unit 130 determining the attribute information of the PV bulk is shown below. The types of the attribute information are the same as or at least partially the same as the low-voltage PV facility information.

[0037] (Position (latitude and longitude) of the PV bulk) The latitude and longitude of the PV bulk are calculated by the average of the longitudes and the average of the latitudes of the low-voltage PV devices belonging to the PV bulk. When the altitude of the low-voltage PV device is included as the attribute information, the altitude of the PV bulk is also calculated by the average in the same way. The average may be a weighted average weighted by the output capacity or the PCS output.

[0038] (Output capacity of the PV bulk) The output capacity of the PV bulk is calculated by the sum of the output capacities of the low-voltage PV devices belonging to the PV bulk.

[0039] (PCS output of the PV bulk) The PCS output of the PV bulk is calculated by the sum of the PCS outputs of the low-voltage PV devices belonging to the PV bulk.

[0040] (Tilt angle of the PV bulk) The tilt angle of the PV bulk is calculated by the average of the tilt angles of the low-voltage PV devices belonging to the PV bulk. The average may be a weighted average weighted by the power generation capacity or the PCS output.

[0041] (Azimuth angle of the PV bulk) The azimuth angle of the PV bulk is calculated by the average of the azimuth angles of the low-voltage PV devices belonging to the PV bulk. The average may be a weighted average weighted by the power generation capacity or the PCS output.

[0042] (Meteorological measurement point of the PV bulk) The meteorological measurement point of the PV bulk is set as the meteorological measurement point at the position closest to the longitude and latitude of the PV bulk (for example, the average of the longitudes and the average of the latitudes of the low-voltage PV devices belonging to the PV bulk described above). It can be said that the meteorological measurement point of the PV bulk is the point for measuring the meteorological quantity in the area including the positions of the low-voltage PV devices belonging to the PV bulk.

[0043] (Terrain of the PV bulk) The terrain of the PV bulk is determined by, for example, the most common terrain among the terrains of the low-voltage PV devices belonging to the PV bulk, or the terrain of the low-voltage PV device with the largest output capacity.

[0044] The actual power generation amount summing unit 120 sums up the actual power generation amount values of the low-voltage PV devices belonging to the PV bulk by using the actual power generation data in the actual power generation data storage unit 140 and the PV device-bulk correspondence information in the PV device-PV bulk correspondence storage unit 170. Thereby, the actual value of the total power generation amount for each PV bulk (total power generation amount actual value) is obtained. The actual power generation amount summing unit 120 sends the PV bulk power generation actual data indicating the total power generation amount actual value of each PV bulk to the prediction processing unit 200.

[0045] The weather actual data storage unit 560 stores the actual data of the weather amount for each weather measurement point. The actual data of the weather amount is the data of the weather amount measured (observed) in the past (weather amount observation data), or the data of the weather amount acquired (predicted) in the past by the weather prediction server 550 (past weather prediction data).

[0046] Based on the PV bulk power generation actual data received from the actual power generation amount summing unit 120, the PV bulk facility information in the PV bulk facility information storage unit 180, and the actual data of the weather amount in the weather actual data storage unit 560, the prediction processing unit 200 generates a prediction model (estimation model) of the total power generation amount of the PV bulk by machine learning.

[0047] Here, as an example of the prediction model, a regression model with one or more weather amounts and attribute information (group attribute information) of the PV bulk as explanatory variables and the total power generation amount as the target variable can be used. The type of the regression model is not limited to specific ones such as linear regression, multiple regression, neural network, and logistic regression. As the weather amount used when generating the prediction model of the PV bulk, the weather amount at the weather measurement point of the PV bulk in the actual data of the weather amount is used.

[0048] Examples of meteorological quantities include solar radiation amount, rainfall amount, snowfall amount, snow depth, temperature, humidity, etc. The attribute information (group attribute information) of the PV bulk includes at least one of the latitude and longitude of the PV bulk, power generation capacity, PCS output, tilt angle, azimuth angle, meteorological measurement location, and terrain, as described above.

[0049] An example of the formula of the prediction model is shown in the following formula (1).

Number

[0050] i corresponds to the number of meteorological quantities used. When using three meteorological quantities, i = 1, 2, 3. j is equal to the number of the attribute information of the PV bulk used. When using five pieces of attribute information, j = 1, 2, 3, 4, 5.

[0051] The formula for summing the total power generation amounts of each PV bulk is shown in the following formula (2).

[0052] The prediction processing unit 200 acquires prediction data of meteorological quantities (weather prediction data) from a weather prediction server. The weather prediction data includes, for example, predicted values of meteorological quantities at the target time for each meteorological measurement point. Based on the weather prediction data, the attribute information of each PV bulk, and a prediction model, the total power generation amount at the target time is predicted for each PV bulk. As the meteorological quantity used for predicting the power generation amount of the PV bulk, the meteorological quantity at the meteorological measurement point of the PV bulk in the weather prediction data is used. The target time is, for example, a time after a certain period from the current time (processing execution time), for example, 10:00 the next day. The prediction processing unit 200 sums up the total power generation amounts of each PV bulk, thereby calculating the predicted total power generation amount. The calculation formula for the predicted total power generation amount is shown below as Equation (2). [Number] P sum : Sum of the total power generation amounts of the PV bulk (predicted total power generation amount)

[0053] The calculated predicted total power generation amount can be used, for example, to create a bidding plan for the power trading market. A detailed usage example of the predicted total power generation amount will be described later in the explanation of the third embodiment.

[0054] FIG. 6 is a flowchart of an overall operation example of the power generation prediction device 1 in FIG. 1.

[0055] The resource cooperation unit 300 acquires power generation performance data from each low-voltage PV device and stores it in the power generation performance data storage unit 140 (S101). The PV bulk generation unit 110 groups the low-voltage PV devices based on the facility information of each low-voltage PV device in the PV facility information storage unit 150, and generates one or more PV bulks, each of which is a group including one low-voltage PV device (S102). The PV bulk facility information generation unit 130 generates PV bulk facility information (group facility information), which is the facility information of the PV bulk, based on the facility information of the low-voltage PV devices belonging to the PV bulk (S103). The power generation amount actual value summing unit 120 obtains the actual value of the total power generation amount for each PV bulk by summing the actual power generation amount values of the low-voltage PV devices belonging to the PV bulk (S104). The prediction processing unit 200 generates a prediction model for the total power generation amount of the PV bulk based on the actual weather data in the weather actual data storage unit 560, each PV bulk facility information, and the actual value of the total power generation amount of each PV bulk (S105). The prediction processing unit 200 acquires weather prediction data from the weather prediction server 550, and predicts the total power generation amount of each PV bulk at the target time based on the weather prediction data, each PV bulk facility information, and the prediction model (S106). The prediction processing unit 200 sums up the predicted total power generation amounts of each PV bulk to obtain the predicted total sum of power generation amounts (S107).

[0056] As described above, according to the present embodiment, instead of predicting the power generation amount for each low-voltage PV device, by predicting the total power generation amount for each PV bulk obtained by grouping the low-voltage PV devices, it is possible to efficiently predict the overall power generation amount (prediction of the sum of the total power generation amounts of each PV bulk) with a small amount of data processing. As a result, it is possible to quickly create an appropriate bidding plan while fulfilling the obligation of the same amount at the same time as the planned value.

[0057] (Second Embodiment) FIG. 7 is a block diagram of the power generation amount prediction device 1A as an information processing device according to the second embodiment. The PV bulk data processing unit 100 additionally includes a prediction result storage unit 160. Hereinafter, only the differences from the first embodiment will be described.

[0058] The prediction result storage unit 160 stores internally data (prediction results) associating the total power generation amount of each PV bulk calculated by the prediction processing unit 200 with the target time. Each time the prediction processing unit 200 performs prediction processing, the prediction result is stored in the prediction result storage unit 160. That is, a history of prediction results is stored in the prediction result storage unit 160.

[0059] The PV bulk generation unit 110 groups the PV bulks such that the average of the prediction errors of the total power generation amount of the PV bulks is minimized or becomes equal to or less than a predetermined value, using the prediction results of the prediction processing unit 200 and the power generation performance data of the low-voltage PV devices. Alternatively, the grouping may be performed such that the error of the total predicted power generation amount is minimized or becomes equal to or less than a predetermined value.

[0060] The prediction error of the total power generation amount of the PV bulks can be evaluated by the difference between the predicted value of the total power generation amount of the PV bulks and the total of the actual power generation amounts of the low-voltage PV devices belonging to the PV bulks. The error of the total predicted power generation amount can be evaluated by the difference between the total of the predicted values of the total power generation amounts of the respective PV bulks and the total of the actual power generation amounts of the low-voltage PV devices belonging to these PV bulks. The prediction processing unit 200 or the PV bulk generation unit 110 may have a function of calculating the prediction error of the total power generation amount of the PV bulks or the error of the total predicted power generation amount.

[0061] It is also possible to combine with the methods (1) to (4) of the first embodiment. For example, grouping may be performed in each of (1) to (4), and among the grouping results by (1) to (4), the grouping result with the minimum average of the prediction errors of the total power generation amount of the PV bulks may be adopted.

[0062] The PV bulk facility information generation unit 130 may generate PV bulk facility information by determining the attribute information (group attribute information) of the PV bulks such that the average or the total of the prediction errors of the total power generation amount of the PV bulks is minimized or becomes equal to or less than a predetermined value.

[0063] As described above, according to the present embodiment, by grouping low-voltage PV devices using the power generation performance data of low-voltage PV devices and the prediction results of the total power generation amount of the PV bulk, the prediction accuracy of the total power generation amount of the PV bulk can be improved. Further, by determining the attribute information (group attribute information) of the PV bulk using the power generation performance data of the low-voltage PV devices and the prediction results of the total power generation amount of the PV bulk, the prediction accuracy of the total power generation amount of the PV bulk can be improved.

[0064] (Third Embodiment) FIG. 8 shows an aggregation system 10 including an aggregation device 400 incorporating the functions of the power generation amount prediction device 1A according to the second embodiment of FIG. 7 as an information processing device according to the third embodiment. The aggregation system 10 corresponds to the information processing system according to the present embodiment. Instead of the functions of the power generation amount prediction device 1A according to the second embodiment, an aggregation device incorporating the functions of the information processing device 1 according to the first embodiment may be configured.

[0065] The resource cooperation unit 300 acquires the power generation performance data of each of a plurality of low-voltage PV devices, other PV devices, and wind power generation devices. The plurality of low-voltage PV devices are the low-voltage PV devices that were the target of grouping in the first or second embodiment. The other PV devices are PV devices different from the plurality of low-voltage PV devices, and are high-voltage PV devices or low-voltage PV devices. The other PV devices and the wind power generation devices are connected to the power grid. The resource cooperation unit 300 acquires the power generation performance data of the low-voltage PV devices from a plurality of low-voltage PV measurement devices 310, the power generation performance data of the other PV devices from the PV measurement device 320, and the power generation performance data of the wind power generation devices from the wind measurement device 330. Alternatively, the resource cooperation unit 300 may acquire the power generation performance data of the low-voltage PV devices, other PV devices, and wind power generation devices from the NSC 350. When there are other types of power generation devices (for example, hydroelectric power generation devices) for the power generation operator, the power generation performance data of the other types of power generation devices may be further acquired.

[0066] The PV bulk data processing unit 100 groups low-voltage PV devices and generates one or more PV bulks based on at least one of the power generation performance data of low-voltage PV devices, the PV facility information of low-voltage PV devices, and the history of the prediction results of the prediction processing unit 200. For each PV bulk, the PV bulk data processing unit 100 sums up the actual power generation values of the low-voltage PV devices belonging to the PV bulk to calculate the power generation performance data of the PV bulk. Also, for each PV bulk, the PV bulk data processing unit 100 generates PV bulk facility information including the attribute information (group attribute information) of the PV bulk based on the PV facility information of the low-voltage PV devices belonging to the PV bulk. The prediction processing unit 200 generates a prediction model for the total power generation of the PV bulk based on the PV bulk facility information, the power generation performance data of the PV bulk, and the actual meteorological data. The prediction processing unit 200 predicts the total power generation at the target time for each PV bulk based on the prediction model, the attribute information (group attribute information) of each PV bulk, and the meteorological prediction data from the meteorological prediction server 550.

[0067] Similarly, the prediction processing unit 200 generates a prediction model for the power generation of other PV devices based on the power generation performance data of other PV devices, the actual meteorological data, and the attribute information of other PV devices, and predicts the power generation of other PV devices at the target time based on the prediction model, the attribute information of other PV devices, and the meteorological prediction data obtained from the meteorological prediction server 550. Also, the prediction processing unit 200 generates a prediction model for the power generation of the wind power generation device based on the power generation performance data of the wind power generation device, the attribute information of the wind power generation device, and the actual meteorological data. The prediction processing unit 200 predicts the power generation of the wind power generation device at the target time based on the prediction model, the attribute information of the wind power generation device, and the meteorological prediction data obtained from the meteorological prediction server 550.

[0068] The prediction processing unit 200 calculates the predicted total power generation at the target time by summing up the predicted values of the total power generation of each PV bulk, the predicted values of the power generation of other PV devices, and the predicted values of the power generation of the wind power generation device.

[0069] The market bidding plan creation unit 420 determines the bidding volume for one or more power markets 500 based on the predicted total power generation. Examples of the power market 500 include the day-ahead market (spot market) where power is traded for the target time on the day before the target time, the same-day market (time-ahead market) where trading is conducted until a certain time before the target time on the day of the target time, and other wholesale power trading markets. There are also capacity markets or supply-demand adjustment markets, etc.

[0070] The power market cooperation unit 430 transmits bidding data instructing the bid for the determined bidding volume to the power market 500 and acquires the transaction result data from the power market 500.

[0071] The planned value submission unit 460 transmits data regarding the transaction result (assuming power sales here) and the power generation plan (predicted total power generation) at the target time to the wide-area power operation promotion organization (wide-area organization) 600. The wide-area organization 600 monitors the power supply and demand of each electric utility company that is a member and instructs other members to supply power to members with a deteriorating supply-demand situation. When a member acquires or generates the transaction result and the power generation plan, it is necessary for the member to submit these transaction results and power generation plans to the wide-area organization 600.

[0072] The adjustable power source operation planning unit 440 creates an operation plan for the adjustable power sources 530 such as storage batteries and thermal power generation connected to the power grid based on the power generation plan, the transaction result, and the power supply plan for relative transactions with other predetermined operators. For example, it creates an operation plan to output the insufficient power or charge the surplus power. Note that a part of the aforementioned predicted total power generation may be allocated for power supply for relative transactions.

[0073] The control unit 450 generates and transmits a control instruction value for avoiding the imbalance between at least one of the operation plan and the power generation plan and the actual power generation to at least one of the adjustable power source 530 and the PCS (power conditioner) 540 of any PV device. Any PV device is the PCS of another PV device or the PCS of the plurality of low-voltage PV devices. Thereby, the output of the adjustable power source 530 and the output of the PCS 540 are remotely controlled in real time. An example of controlling the adjustable power source 530 in real time may include controlling the power output from the adjustable power source not included in the operation plan. By the operation of the control unit 450, the simultaneous operation of the planned values of the renewable energy sources is realized.

[0074] (Hardware Configuration) FIG. 9 shows the hardware configuration of the information processing apparatus according to the foregoing embodiment. The information processing apparatus is constituted by a computer apparatus 900. The computer apparatus 900 includes a CPU 901, an input interface 902, a display device 903, a communication device 904, a main storage device 905, and an external storage device 906, which are mutually connected by a bus 907.

[0075] The CPU (Central Processing Unit) 901 executes an information processing program, which is a computer program, on the main storage device 905. The information processing program is a program for realizing each of the above-described functional configurations of the information processing apparatus. The information processing program may be realized not by a single program but by a combination of a plurality of programs and scripts. By the CPU 901 executing the information processing program, each functional configuration is realized.

[0076] The input interface 902 is a circuit for inputting operation signals from input devices such as a keyboard, a mouse, and a touch panel into the information processing apparatus. The input interface 902 corresponds to the input unit of the information processing apparatus according to the foregoing embodiment.

[0077] The display device 903 displays the data output from the information processing device. The display device 903 is, for example, an LCD (Liquid Crystal Display), an organic electroluminescence display, a CRT (Cathode Ray Tube), or a PDP (Plasma Display Panel), but is not limited thereto. The data output from the computer device 900 can be displayed on this display device 903. The display device 903 corresponds to the output unit of the information processing device according to the foregoing embodiment.

[0078] The communication device 904 is a circuit for the information processing device to communicate with an external device wirelessly or by wire. Data can be input from the external device via the communication device 904. The data input from the external device can be stored in the main storage device 905 or the external storage device 906. The communication device 904 corresponds to the communication unit of the information processing device according to the foregoing embodiment.

[0079] The main storage device 905 stores an information processing program, data necessary for the execution of the information processing program, and data generated by the execution of the information processing program. The information processing program is expanded and executed on the main storage device 905. The main storage device 905 is, for example, a RAM, a DRAM, or an SRAM, but is not limited thereto. Each storage unit or database of the information processing device according to the foregoing embodiment may be constructed on the main storage device 905.

[0080] The external storage device 906 stores an information processing program, data necessary for the execution of the information processing program, and data generated by the execution of the information processing program. These information processing programs and data are read into the main storage device 905 when the information processing program is executed. The external storage device 906 is, for example, a hard disk, an optical disk, a flash memory, and a magnetic tape, but is not limited thereto. Each storage unit or database of the information processing device according to the foregoing embodiment may be constructed on the external storage device 906.

[0081] Note that the information processing program may be pre-installed in the computer device 900, or may be stored in a storage medium such as a CD-ROM. Also, the information processing program may be uploaded on the Internet.

[0082] Also, the information processing device may be configured by a single computer device 900, or may be configured as a system composed of a plurality of computer devices 900 connected to each other.

[0083] Note that the present invention is not limited to the above-described embodiments as they are, and at the implementation stage, the components can be modified and embodied without departing from the gist thereof. Also, various inventions can be formed by appropriately combining the plurality of components disclosed in the above-described embodiments. Further, for example, a configuration in which some components are deleted from all the components shown in each embodiment is also conceivable. Furthermore, the components described in different embodiments may be appropriately combined.

[0084] This embodiment can also be configured as follows. [Item 1] Based on information on a plurality of power generation devices arranged at a plurality of positions, the plurality of power generation devices are grouped to generate first to Nth (N is an integer of 1 or more) groups each including one or more power generation devices, By summing up the actual power generation amounts of the power generation devices included in the first to Nth groups, the actual total power generation amounts of the first to Nth groups are calculated, Based on the attribute information of the power generation devices belonging to the first to Nth groups, group attribute information that is the attribute information of the first to Nth groups is generated, Based on the actual total power generation amounts of the first to Nth groups, the group attribute information of the first to Nth groups, and the actual data on the meteorological amounts of the first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups, an estimation model is generated with the group attribute information and the meteorological amounts as explanatory variables and the total power generation amount as the target variable, a processing unit, An information processing device comprising the same. [Item 2] Based on the estimation model, the attribute information of the first to Nth groups, and the weather prediction data which is the predicted data of the weather quantity in the first to Nth regions, the processing unit estimates the total power generation amount of each of the first to Nth groups. The information processing apparatus according to Item 1. [Item 3] The processing unit calculates an estimated value of the total power generation amount by summing up the estimated total power generation amounts of the first to Nth groups. The information processing apparatus according to Item 2. [Item 4] The information regarding the plurality of power generation devices used for the grouping includes the attribute information of the plurality of power generation devices. The information processing apparatus according to any one of Items 1 to 3. [Item 5] Based on the position information of the plurality of power generation devices as the attribute information of the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to Item 4. [Item 6] Based on the information regarding the output capacity of the plurality of power generation devices as the attribute information of the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to Item 4 or 5. [Item 7] Based on the inclination angles of the plurality of power generation devices as the attribute information of the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to any one of Items 4 to 6. [Item 8] Based on the information of the terrain where the plurality of power generation devices are installed as the attribute information of the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to any one of Items 4 to 7. [Item 9] Based on the correlation relationship between the actual power generation values of each two of the plurality of power generation devices as the information regarding the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to any one of Items 1 to 8. [Item 10] Based on the actual values of the power generation amounts of the plurality of power generation devices, the processing unit calculates the estimation errors of the power generation amounts of the first to Nth groups, and performs the grouping so as to minimize or make the average or total of the errors equal to or less than a predetermined value. The information processing apparatus according to Item 2 or Item 3. [Item 11] The processing unit performs the grouping by using a plurality of grouping methods, and adopts the grouping result in which the number of most matching ones among the grouping results of the plurality of grouping methods is the largest. The information processing apparatus according to any one of Items 1 to 10. [Item 12] Based on the actual values of the power generation amounts of the plurality of power generation devices, the processing unit calculates the estimation errors of the power generation amounts of the first to Nth groups, and determines the attribute information of the first to Nth groups so that the errors are minimized or equal to or less than a predetermined value. The information processing apparatus according to Item 2 or 3. [Item 13] The plurality of power generation devices are connected to a power grid. Based on the estimated value of the total power generation amount, the processing unit determines the bidding amount for the power market, transmits bidding data including the bidding amount to the power market, and receives the contract result data of the bidding from the power market. Based on the power generation plan based on the estimated value of the total power generation amount and the contract result data, the processing unit creates an operation plan for the adjustable power source connected to the power grid. Based on the power generation plan and the operation plan, it is provided with a control unit that controls the output of the adjustable power source, at least one of the plurality of power generation devices, or another solar power generation device connected to the power grid. The information processing apparatus according to Item 3. [Item 14] The adjustable power source includes at least one of a power storage device and a thermal power generation device. The information processing apparatus according to Item 13. [Item 15] Each of the plurality of power generation devices is a low-voltage solar power generation device. The information processing apparatus according to any one of Items 1 to 14. [Item 16] The processing unit, wherein the weather amounts in the first to Nth regions are the weather amounts corresponding to the average positions of the power generation devices included in the first to Nth regions. The information processing apparatus according to Item 2 or 3. [Item 17] The weather amount corresponding to the average position is the weather amount measured by the weather measurement device closest to the average position among the plurality of weather measurement devices. The information processing apparatus according to Item 16. [Item 18] The performance data regarding the weather amounts in the first to Nth regions is the observed data or predicted data of the weather amounts at the times when the power generation amounts of the performance values of the power generation devices belonging to the first to Nth regions are generated. The information processing apparatus according to any one of Items 1 to 17. [Item 19] Based on information regarding a plurality of power generation devices arranged at a plurality of positions, the plurality of power generation devices are grouped to generate first to Nth groups each including one or more power generation devices. By summing the performance values of the power generation amounts of the power generation devices included in the first to Nth groups, the performance value of the total power generation amount of the first to Nth groups is calculated. Based on the attribute information of the power generation devices belonging to the first to Nth groups, group attribute information that is the attribute information of the first to Nth groups is generated. Based on the performance value of the total power generation amount of the first to Nth groups, the group attribute information of the first to Nth groups, and the performance data regarding the weather amounts in the first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups, an estimation model is generated with the group attribute information and the weather amount as explanatory variables and the total power generation amount as the target variable. Information processing method. [Item 20] Based on information regarding a plurality of power generation devices arranged at a plurality of positions, grouping the plurality of power generation devices to generate first to Nth groups each including one or more power generation devices; calculating an actual value of the total power generation amount of the first to Nth groups by summing up the actual values of the power generation amounts of the power generation devices included in the first to Nth groups; generating group attribute information, which is the attribute information of the first to Nth groups, based on the attribute information of the power generation devices belonging to the first to Nth groups; generating an estimation model having the group attribute information and the weather amount as explanatory variables and the total power generation amount as an objective variable, based on the actual value of the total power generation amount of the first to Nth groups, the group attribute information of the first to Nth groups, and actual data regarding the weather amount of first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups; A computer program for causing a computer to execute the above.

Explanation of Signs

[0085] 1 Power generation amount prediction device (information processing device) 1A Power generation amount prediction device (information processing device) 10 Aggregation system 100 PV bulk data processing unit 110 PV bulk generation unit 120 Power generation amount actual value summing unit 130 PV bulk facility information generation unit 140 Power generation actual data storage unit 150 PV facility information storage unit 160 Prediction result storage unit 170 PV device - PV bulk correspondence storage unit 180 PV bulk facility information storage unit 191 PV bulk 191A~191C Regions 200 Prediction processing unit 300 Resource cooperation unit (resource cooperation interface) 310 Low - voltage PV measurement device 320 PV Measurement Device 330 Wind Measurement Device 400 Aggregation Device 420 Market Bidding Plan Making Section 430 Power Market Linkage Section 440 Adjustable Power Operation Planning Section 450 Control Section 460 Planned Value Submission Section 500 Power Market 530 Adjustable Power Source 550 Weather Forecast Server 560 Weather Performance Data Storage Section 600 Electric Power Wide-Area Operation Promotion Organization (Wide-Area Organization) 600 Wide-Area Organization 900 Computer Device 902 Input Interface 903 Display Device 904 Communication Device 905 Main Memory Device 906 External Memory Device 907 Bus

Claims

1. Based on information regarding a plurality of power generation devices arranged at a plurality of positions, grouping the plurality of power generation devices to generate first to Nth (N is an integer of 1 or more) groups each including one or more power generation devices, By summing the actual power generation amounts of the power generation devices included in the first to Nth groups, calculating the actual total power generation amount of the first to Nth groups, Based on the attribute information of the power generation devices belonging to the first to Nth groups, generating group attribute information that is the attribute information of the first to Nth groups, Based on the actual total power generation amount of the first to Nth groups, the group attribute information of the first to Nth groups, and the actual data regarding the weather amount in the first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups, generating an estimation model with the group attribute information and the weather amount as explanatory variables and the total power generation amount as the target variable, a processing unit An information processing apparatus comprising the same.

2. Based on the estimation model, the attribute information of the first to Nth groups, and weather prediction data that is prediction data of the weather amount in the first to Nth regions, the processing unit estimates the total power generation amount of each of the first to Nth groups. The information processing apparatus according to claim 1.

3. The processing unit calculates an estimated value of the total power generation amount by summing the estimated total power generation amounts of the first to Nth groups. The information processing apparatus according to claim 2.

4. The information regarding the plurality of power generation devices used for the grouping includes the attribute information of the plurality of power generation devices. The information processing apparatus according to claim 1.

5. Based on the position information of the plurality of power generation devices as the attribute information of the plurality of power generation devices, the processing unit performs the grouping. The information processing apparatus according to claim 4.

6. The processing unit performs the grouping based on information regarding the output capabilities of the plurality of power generation devices as the attribute information of the plurality of power generation devices. The information processing apparatus according to claim 4.

7. The processing unit performs the grouping based on the tilt angles of the plurality of power generation devices as the attribute information of the plurality of power generation devices. The information processing apparatus according to claim 4.

8. The processing unit performs the grouping based on information on the terrain where the plurality of power generation devices are installed as the attribute information of the plurality of power generation devices. The information processing apparatus according to claim 4.

9. The processing unit performs the grouping based on the correlation relationship between the actual power generation values of each two power generation devices among the plurality of power generation devices as the information regarding the plurality of power generation devices. The information processing apparatus according to claim 1.

10. The processing unit calculates the estimation error of the power generation amount of the first to Nth groups based on the actual power generation values of the plurality of power generation devices, and performs the grouping so as to minimize or make the average or total of the errors equal to or less than a predetermined value. The information processing apparatus according to claim 2.

11. The processing unit performs the grouping using a plurality of grouping methods, and adopts the grouping result with the largest number of matching among the grouping results of the plurality of grouping methods. The information processing apparatus according to claim 1.

12. The processing unit calculates the estimation error of the power generation amount of the first to Nth groups based on the actual power generation values of the plurality of power generation devices, and determines the attribute information of the first to Nth groups so that the error is minimized or equal to or less than a predetermined value. The information processing apparatus according to claim 2.

13. The plurality of power generation devices are connected to a power grid, Based on the estimated total power generation amount, the processing unit determines the bidding amount for the power market, transmits bidding data including the bidding amount to the power market, and receives contract result data of the bid from the power market. Based on the power generation plan based on the estimated total power generation amount and the contract result data, the processing unit creates an operation plan for the adjustable power source connected to the power grid. Based on the power generation plan and the operation plan, it includes a control unit that controls the output of the adjustable power source, at least one of the plurality of power generation devices, or another solar power generation device connected to the power grid. The information processing device according to claim 3.

14. The adjustable power source includes at least one of a power storage device and a thermal power generation device. The information processing device according to claim 13.

15. Each of the plurality of power generation devices is a low-voltage solar power generation device. The information processing device according to any one of claims 1 to 14.

16. The processing unit determines that the weather amounts in the first to Nth regions are the weather amounts corresponding to the average positions of the power generation devices included in the first to Nth regions. The information processing device according to claim 2.

17. The weather amount corresponding to the average position is the weather amount measured by the weather measurement device closest to the average position among the plurality of weather measurement devices. The information processing device according to claim 16.

18. The performance data regarding the weather amounts in the first to Nth regions is the observed data or predicted data of the weather amounts at the time when the actual generated power of the power generation devices belonging to the first to Nth regions was generated. The information processing device according to claim 1.

19. Based on information regarding a plurality of power generation devices arranged at a plurality of positions, group the plurality of power generation devices to generate first to Nth groups each including one or more power generation devices, calculate the actual value of the total power generation amount of the first to Nth groups by summing the actual values of the power generation amounts of the power generation devices included in the first to Nth groups, generate group attribute information, which is the attribute information of the first to Nth groups, based on the attribute information of the power generation devices belonging to the first to Nth groups, generate an estimation model having the group attribute information and the weather amount as explanatory variables and the total power generation amount as an objective variable based on the actual value of the total power generation amount of the first to Nth groups, the group attribute information of the first to Nth groups, and actual data regarding the weather amount in first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups, An information processing method.

20. a step of grouping the plurality of power generation devices based on information regarding the plurality of power generation devices arranged at a plurality of positions to generate first to Nth groups each including one or more power generation devices; a step of calculating the actual value of the total power generation amount of the first to Nth groups by summing the actual values of the power generation amounts of the power generation devices included in the first to Nth groups; a step of generating group attribute information, which is the attribute information of the first to Nth groups, based on the attribute information of the power generation devices belonging to the first to Nth groups; a step of generating an estimation model having the group attribute information and the weather amount as explanatory variables and the total power generation amount as an objective variable based on the actual value of the total power generation amount of the first to Nth groups, the group attribute information of the first to Nth groups, and actual data regarding the weather amount in first to Nth regions including the positions of the power generation devices belonging to the first to Nth groups; A computer program for causing a computer to execute the above.

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