Information processing device, information processing method, and program

Abstract

To provide an information processing device capable of improving profitability in an electric power market by utilizing a power generation prediction width of a power generation facility changing with the change in the weather.SOLUTION: A server 1 comprises a weather forecast acquisition unit 411, a power generation achievement acquisition unit 412, a power generation capacity acquisition unit 413, a power generation prediction calculation unit 421, and a power generation sales plan, etc., determination unit 46. The weather forecast acquisition unit 411 acquires weather forecast data. The power generation achievement acquisition unit 412 acquires the power generation achievement in a power generation facility. The power generation capacity acquisition unit 413 acquires the power generation capacity and installation location of the power generation facility. The power generation prediction calculation unit 421 calculates a power generation prediction value and a power generation prediction minimum value of renewable energy on the basis of at least the latest weather forecasting data and the power generation achievement, power generation capacity and installation location of the power generation facility. The power generation sales plan, etc., determination unit 46 determines the power generation plan and the power sales and purchase bidding conditions of the power generation facility on the basis of the calculated power generation prediction value and power generation prediction minimum value.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Conventionally, there exists a technology relating to a system that supports trading consideration in the electricity market using data including the market price of electricity and weather forecast data (for example, see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2007-4646 A Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, there has been a demand for improved display of power generation forecast information, etc., as information provided to users for determining bidding conditions in considering transactions in the electricity market. However, such demands have not been fully met.

[0005] The present invention has been made in consideration of the above circumstances, and has an object to improve the profitability of electricity trading by utilizing the power generation forecast range of power generation facilities, which changes in response to changes in weather. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: A weather forecast acquisition means for acquiring weather forecast data; A power generation record acquiring means for acquiring a power generation record of the power generation facility; A power generation capacity acquisition means for acquiring the power generation capacity and the installation location of the power generation facility; A power generation forecast calculation means for calculating a power generation forecast value and a minimum power generation forecast value of the renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan etc. determination means for determining a power generation plan of the power generation facility and bidding conditions for selling and purchasing power based on the calculated power generation forecast value and the calculated minimum power generation forecast value; Equipped with. An information processing method and a program corresponding to the information processing device according to one aspect of the present invention are also provided as an information processing method and a program according to one aspect of the present invention. Effect of the Invention

[0007] According to the present invention, it is possible to improve profitability in the electricity market by utilizing the power generation forecast width of a power generation facility, which changes in response to changes in weather. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram showing a configuration of an information processing system including a server of an embodiment of an information processing device according to the present invention. [Diagram 2] 2 is a block diagram showing an example of a hardware configuration of a server in the information processing system of FIG. 1. [Diagram 3] 3 is a functional block diagram showing an example of a functional configuration of a server having the hardware configuration of FIG. 2. [Figure 4] 4 is a flowchart illustrating an example of an operation of a server having the functional configuration of FIG. 3. [Diagram 5] FIG. 4 is a diagram showing a table of predicted power generation amount for a certain day predicted by the server of FIGS. 2 and 3 when the power generated in a specific solar power plant is sold to the power trading market. [Figure 6] 6 is a line graph showing the values ​​in the table of FIG. 5. [Figure 7] FIG. 4 is a diagram showing a table showing an example of the results of forecasting the amount of power generation on a certain day, forecasted by the server of FIGS. 2 and 3, when the power generated in a specified solar power plant is supplied wholesale on a bilateral basis. [Figure 8] 8 is a line graph showing the values ​​in the table of FIG. 7. [Figure 9] FIG. 13 is a diagram illustrating an example of a risk tolerance for determining a submission policy. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a configuration of an information processing system including a server of an embodiment of an information processing device according to the present invention.

[0010] The information processing system shown in Fig. 1 includes a server 1 managed by a provider of this service, a user terminal 2 operated by a user, and an electricity market server 3. The server 1, the user terminal 2, and the electricity market server 3 are connected to each other so as to be able to communicate with each other via a network NW. The form of the network NW is not particularly limited, and may be, for example, Bluetooth (registered trademark), Wi-Fi, a LAN (Local Area Network), the Internet, or the like.

[0011] FIG. 2 is a block diagram showing an example of a hardware configuration of a server in the information processing system of FIG. The server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0012] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 .

[0013] The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0014] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0015] The input unit 16 is made up of various buttons and the like, and inputs various information in response to user instructions. The output unit 17 is composed of a display, a speaker, etc., and outputs images and sounds. The storage unit 18 is composed of a hard disk or a dynamic random access memory (DRAM) or the like, and stores various data such as map information, which will be described later. The communication unit 19 controls communication with other devices (for example, the user terminal 2).

[0016] Removable media 21, which may be a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is appropriately attached to the drive 20. Programs read from the removable media 21 by the drive 20 are installed in the storage unit 18 as necessary. The removable media 21 can also store various data, such as map information, stored in the storage unit 18 in the same manner as the storage unit 18. The hardware configurations of the user terminal 2 and the electricity market server 3 can be basically the same as the hardware configuration of the server 1, and therefore a description thereof will be omitted here.

[0017] FIG. 3 is a functional block diagram showing an example of a functional configuration of the server 1 of FIG. As shown in FIG. 3, the CPU 11 of the server 1 includes an acquisition unit 41 that acquires data that is prerequisite information for formulating a power generation plan, a calculation unit 42 that calculates a power generation forecast, a comparison unit 43 that compares the predicted power generation value with actual results, etc., an abnormality judgment unit 44, a display control unit 45, a power generation and sales plan determination unit 46, and a setting unit 47.

[0018] In one area of ​​the storage unit 18 of the server 1, a weather forecast data DB 91, a power generation record data DB 92, a power plant information DB 93, a power generation forecast data DB 94, a transaction data DB 95, and a user information DB 96 are stored. The weather forecast data DB 91 stores weather forecast data for a specific time period at a specific location, etc. Specifically, such data includes, for example, weather, temperature, cloud cover, wind direction, wind speed, and solar radiation. The power generation record data DB 92 stores data on the power generation record for each specific power plant and for each specific time period. Specifically, such data includes, for example, the amount of DC power generation, the amount of AC power generation, whether or not output is suppressed, whether or not various devices are broken, and operation status information. The power plant information DB 93 stores information on the power generation facilities of each specific power plant, such as the address, latitude, longitude, altitude, etc. of the power plant's location, as well as the power plant's DC capacity, AC capacity, number of PCS units, PCS manufacturer, and start of operation. The power generation prediction data DB 94 stores power generation prediction results for each specific power plant and for each specific time period. Specifically, the information includes, for example, a power generation prediction value, a maximum and minimum value of an expected prediction error range, and a prediction error from the actual power generation performance. The transaction data DB 95 stores electricity transaction data for each specific power plant and for each specific time period. Specifically, the information includes, for example, the electricity purchase and sale price unit price, total purchase and sale volume, total purchase and sale amount, spot market price, imbalance price, bidding deadline or bidding start time for electricity transactions, and bidding conditions for electricity sales and purchases. In addition, the information may include forecast information for the spot market price and imbalance price for each specific time period. The user information DB 96 stores user information (such as the names of members or users of the service, and login information (accounts) for the website of the service published by the server 1).

[0019] The acquisition unit 41 includes a weather forecast acquisition unit 411 , a power generation record acquisition unit 412 , and a power generation capacity acquisition unit 413 .

[0020] The weather forecast acquisition unit 411 acquires weather forecast data from an external source, for example, a weather information publishing organization, and stores the data in the weather forecast data DB 91 . The weather forecast acquisition unit 411 acquires the latest weather forecast data based on at least the bidding deadline or bidding start time for the power trading, and the time required to determine the bidding conditions for the power sales and purchase. Here, the bidding deadline is, for example, gate closing in the pre-hours market, and the bidding start time is, for example, 5 p.m. the day before in the pre-hours market. The 5 p.m. the day before shown here is just an example, and other times may be used. Also, it may be changed in the future.

[0021] The power generation record acquiring unit 412 acquires power generation records from the power plant (power generation facility) and stores the power generation records in the power generation record data DB 92. The power generation record acquisition unit 412 acquires, for example, the amount of DC power generation, the amount of AC power generation, whether or not output has been suppressed, whether or not various devices have malfunctioned, operation status information, and the like for each power plant.

[0022] The power generation capacity acquisition unit 413 acquires information such as power generation capacity and installation location from the power plant (power generation facility) and stores it in the power plant information DB93. The power generation capacity of a power generation facility is, for example, in the case of a solar power plant, the design capacity of the solar panels, the degree of deterioration, and other performance information. The installation location is, for example, location information such as latitude, longitude, and altitude that identifies a location on the earth.

[0023] The calculation unit 42 calculates the predicted minimum power generation value based on the prediction accuracy set by the setting unit 47 (using at least the prediction accuracy and also including data used by a power generation prediction calculation unit 421 described below). The calculation unit 42 includes a power generation prediction calculation unit 421 . The power generation forecast calculation unit 421 calculates a power generation forecast value and a minimum power generation forecast value of renewable energy based on at least the latest weather forecast data, and the power generation record, power generation capacity, and installation location of the power generation facility acquired by the acquisition unit. Renewable energy refers to electricity generated by, for example, solar power generation, as well as geothermal, wind, hydroelectric, tidal, and other sources of power generation. The power generation prediction calculation unit 421 further calculates a power generation prediction range defined by a power generation prediction minimum value and a power generation prediction maximum value. The power generation prediction range refers to, for example, an interval in which prediction is reliable. Information such as the power generation forecast value of renewable energy, the power generation forecast range defined by the power generation forecast minimum value and the power generation forecast maximum value calculated by the power generation forecast calculation unit 421 is stored in the power generation forecast data DB 94 as needed.

[0024] The comparison unit 43 compares the power generation prediction value calculated by the power generation prediction calculation unit 421 or stored in the power generation prediction data DB 94 with the power generation record stored in the power generation record data DB 92 . The power generation record is data including the amount of power actually generated at the power plant over the past few years, the date, the weather, and the like. In addition, the power generation record includes data such as the difference between the predicted value of the amount of power generation on the same day in the past and the actual amount of power generation, and the prediction accuracy obtained from these.

[0025] The abnormality determination unit 44 determines whether or not an abnormality has occurred in the photovoltaic power plant based on the comparison result of the comparison unit 43.

[0026] The display control unit 45 includes a display range designation unit 451 , a graph display portion specification unit 452 , a graph generation unit 453 , an association unit 454 , and a display data generation unit 455 .

[0027] The display range designation unit 451 designates the display range of the graph by one or more unit times (for example, six frames from 12:00 to 15:00, etc.). The display range designation unit 451 accepts an operation to change the display range of the graph.

[0028] The graph display portion specifying unit 452 specifies, as the graph display portion, a portion of the graph that includes the predicted power generation values ​​and the predicted minimum power generation values ​​associated with each of the one or more specified unit times. When the display range designation unit 451 accepts an operation to change the display range of the graph, the graph display portion identification unit 452 changes the graph display portion in conjunction with the operation to change the display range of the graph, and the display data generation unit 455 generates data for displaying the changed graph display portion as display data. Specifically, for example, a slide bar or the like for varying the calculation date and time is arranged on the screen, and by varying the slide bar by the user's operation, the calculation date and time changes in conjunction with the slide bar display, and the graphs showing the temporal changes in the power generation forecast value and power generation forecast range also change in conjunction with the change in the calculation date and time.

[0029] The graph display portion specifying unit 452 has a specific date and time specifying unit 51 . The specific date and time designation unit 51 designates a specific date and time or a specific prediction interval. Specifically, a specific date and time or a specific prediction interval is specified by operating the slide bar, but may also be specified by direct key input in an input field or input frame.

[0030] The graph generating unit 453 generates a graph showing the change over time in the predicted power generation value and the predicted minimum power generation value calculated by the predicted power generation calculation unit 421 or stored in the predicted power generation data DB 94 . The graph generating unit 453 generates a continuous curve showing the change over time of at least one of the predicted power generation value, the predicted maximum power generation value, the predicted minimum power generation value, and the actual power generation value. The graph generating unit 453 reads out the power generation result value stored in the power generation result data DB 92 in addition to the power generation prediction value and the power generation prediction minimum value, and generates a graph showing the change over time in the power generation result value.

[0031] The graph generating unit 453 includes a graph selecting unit 61 and a frame changing unit 62 . The graph selection unit 61 accepts the selection of any one of a step-like, a continuous line-like, and a scatter-like as the trajectory of the graph. The frame changing unit 62 changes the time unit (for example, 30-minute units) showing the change over time in the graph according to a predetermined bidding unit (electricity sales in the spot market, wholesale supply to retail electricity companies, etc.).

[0032] The associating unit 454 associates the unit time (for example, 30 minutes, such as 10:00 to 10:30) for which the predicted power generation value and the predicted minimum power generation value were calculated with the calculated predicted power generation value and the calculated minimum power generation value.

[0033] The display data generating unit 455 generates display data for displaying the graph generated by the graph generating unit 453 . The display data generating unit 455 generates data for displaying the graph display portion as display data. The display data generating unit 455 generates display data for displaying, on a graph, at least one of the predicted power generation value, the predicted minimum power generation value, and the actual power generation value at the specific date and time or in the specific prediction interval specified by the display range specifying unit 451.

[0034] The power generation sales plan determination unit 46 determines the power generation plan (e.g., time and power generation amount) of the power generation facility and the bidding conditions for power sales and purchase based on the power generation forecast value and the minimum power generation forecast value calculated by the power generation forecast calculation unit 421. The bidding policy for determining the bidding conditions is, for example, a radio button with a pair of items and check boxes such as "maximize the bidding amount (use the intermediate value between the upper limit of the power generation forecast range and the power generation forecast value)", "minimize the surplus and shortage imbalance (use the power generation forecast value)", "reduce the shortage imbalance risk (use the intermediate value between the power generation forecast value and the lower limit of the power generation forecast range)", and "minimize the shortage imbalance risk (use the lower limit of the power generation forecast range)" according to the risk tolerance of each user, and the user can specify the bidding policy by simply checking the check box of the desired radio button. The specific "value" etc. may be specified by a mouse. Bidding conditions include, for example, the price and quantity of electricity sales and purchases, and the buyer and seller. When deciding bidding conditions, for example, they may be decided sequentially and fully automatically without human judgment, or they may be assisted in making manual decisions (such as providing information that will help a human make a decision). By deciding fully automatically, man-months of labor can be reduced. Also, bidding accuracy can be improved. In addition, by displaying the power generation forecast range for determining bidding conditions, it becomes possible to consider bidding policies for maximizing profitability, for example. Furthermore, the display and analysis function for power generation forecast ranges can contribute to improving the accuracy of power generation forecasts. The bidding conditions for selling and purchasing electricity include, for example, the bidding time, the bidding price, the bidding amount, and the like. The power generation and sales plan determination unit 46 determines the power generation plan of the power generation facility and the bidding conditions for selling and purchasing electricity based on the power generation forecast value and the minimum power generation forecast value calculated by the power generation forecast calculation unit 421, as well as the forecast accuracy, and also on the imbalance fee and market price and their price forecast data stored in the transaction data DB95. The power generation and sales plan determination unit 46 determines the power generation plan of the power generation facility and the bidding conditions for selling and purchasing power based on the power generation forecast value and the power generation forecast range calculated by the power generation forecast calculation unit 421. The power generation and sales plan determination unit 46 adopts, as the user's designation of the bidding policy, either a designation to be applied to multiple decisions or a designation to be applied to an individual decision, whichever is selected by the user, and determines the power generation plan for the power generation facility and the bidding conditions for the sale and purchase of electricity based on the bidding policy based on the adopted designation. In addition, the bidding policy can be selected to be applied to multiple decisions (e.g., specified as a default policy) or to be applied to an individual decision (e.g., specified only once).

[0035] The power generation and sales plan determining unit 46 includes a power generation amount setting unit 71 . The power generation amount setting unit 71 determines the power generation plan of the power generation facility and the bidding conditions for selling and purchasing electricity based on the set power generation amount, and when the position of the displayed graph is specified, for example by clicking with a mouse, it sets the set power generation amount based on that position.

[0036] The setting unit 47 sets a threshold value related to the prediction accuracy of power generation. The power generation prediction accuracy means the size of the power generation prediction range relative to the power generation prediction value calculated by the power generation prediction calculation unit 421. For example, in the case of a solar power plant, the size of the power generation prediction range is small during the day on a clear, cloudless day, and the power generation prediction accuracy is high. On the other hand, on a rainy day or a day with a lot of clouds, the size of the power generation prediction range is large and the power generation prediction accuracy is low. The threshold value for the power generation prediction accuracy set by the setting unit 47 is a value set according to the risk tolerance of each user, and is used in the form of adopting the minimum power generation prediction value as the decision point for the power generation sales plan when the value is exceeded (the size of the power generation prediction range is large), for example.

[0037] As described above, according to the server 1 having the functional configuration shown in FIG. 3, it is possible to calculate the power generation forecast value and power generation forecast range of renewable energy such as a solar power plant based on weather forecast data and power generation facility information, and to determine the bidding timing, bidding price, bidding amount, etc. as bidding conditions for selling and purchasing electricity. In this way, by utilizing the power generation forecast range of the solar power plant, and particularly when the power generation forecast range for a certain time period is wider than a specified range, a power generation and sales plan for the electricity generated by the solar power plant can be submitted for that time period at the lower limit of the forecast range (minimum power generation forecast value), thereby making it possible to fully automatically determine the power generation plan of the solar power plant and the bidding conditions for power sales and purchases that maximize profitability, for example.

[0038] Next, the operation of the server having the functional configuration of FIG. 3 will be described with reference to FIG. FIG. 4 is a flowchart illustrating an example of the operation of the server having the functional configuration of FIG.

[0039] In step S1, the weather forecast acquisition unit 411 acquires weather forecast data. In step S2, the power generation result acquisition unit 412 acquires the power generation result of the power generation facility. In step S3, the power generation capacity acquisition unit 413 acquires the power generation capacity and the installation location of the power generation facility. In step S4, the power generation forecast calculation unit 421 calculates a power generation forecast value and a minimum power generation forecast value of renewable energy based on at least the latest weather forecast data, and the power generation record, power generation capacity, and installation location of the power generation facility. In step S5, the power generation and sales plan determination unit 46 determines the power generation plan of the power generation facility and the bidding conditions for power sales and purchases based on the calculated power generation forecast value and the minimum power generation forecast value.

[0040] According to the operation of the server 1 as described above, by utilizing the power generation forecast range in the solar power plant, it is possible to automatically determine, for example, a power generation plan for the power generation equipment and bidding conditions for the sale and purchase of power that maximizes profitability.

[0041] Hereinafter, the operation of the server will be specifically described in accordance with data predicting the amount of power generated by the photovoltaic power plant with reference to Figs. FIG. 5 is a diagram showing a table of the results of power generation forecast for a certain day, which is predicted by the server of FIG. 2 and FIG. 3 when the power generated in a specific photovoltaic power plant is sold to the spot market. FIG. 6 is a graph showing the values ​​in the table of FIG. 5 as a line graph. FIG. 7 is a diagram showing a table showing an example of the predicted power generation amount for a certain day predicted by the server of FIG. 2 and FIG. 3 when the power generated in a specific photovoltaic power plant is supplied on a bilateral wholesale basis. FIG. 8 is a graph showing the values ​​in the table of FIG. 7 as a line graph. FIG. 9 is a diagram showing an example of risk tolerance for determining a submission policy.

[0042] First, an example of submitting a power generation sales plan based on the result of power generation amount forecast when selling power to the spot market will be described with reference to Figs.

[0043] In this embodiment, a photovoltaic power plant will be taken as an example of a power generation facility for explanation. The entire amount of electricity generated by the photovoltaic power plant is sold in the spot market through the grid. Under the current system, the deadline for bidding on electricity sales in the spot market is 10:00 a.m. the day before actual supply and demand. Therefore, for example, as of 6:00 a.m. the day before actual supply and demand, the amount of electricity generated for 24 hours on the day of actual supply and demand is predicted. Since transactions in the spot market are bid in 30-minute units, for example, the prediction target is 48 24-hour periods on the day of actual supply and demand. The setting of 48 frames per 24 hours is just one example based on the current system, and future changes to the system may result in changes to the bidding deadline and frame unit.

[0044] When predicting the amount of power generation, the server 1 calculates an error margin for the power generation prediction in addition to the predicted value. The margin of error in the power generation prediction is estimated by a power generation prediction calculation unit 421 (AI) from a huge amount of accumulated data. At this time, the power generation prediction calculation unit 421 determines the error range of the power generation prediction based on the accuracy of its own prediction learned from a huge amount of accumulated data. Therefore, the width of the prediction error range changes (expands or contracts) depending on the time of day, even within the same day. For example, on cloudy days or days with unstable weather, the accuracy of the prediction is relatively low, and the margin of error in the prediction automatically increases on days and during times of such unstable weather. For example, assume that the amount of power generation for 24 hours and 48 frames on the following day, Tuesday, at 6:00 a.m. is predicted as shown in the table of FIG. 5 and the graph of FIG. 6, the vertical axis indicates the amount of power generation, and the horizontal axis indicates 30-minute intervals. In this graph 110, as power generation prediction values, for example, a line 111 indicating the median value of the power generation prediction, a line 112 indicating the upper limit value, and a line 113 indicating the lower limit value are shown.

[0045] Considering the forecast values ​​in FIG. 5 and the graph in FIG. 6, it is estimated that the forecast accuracy for the following day, Tuesday, is high, so the power generation sales plan determination unit 46 decides to submit the power generation forecast value as the median value of the power generation forecast (line 111 in FIG. 6) as the planned value for the power generation sales plan to be submitted in advance to a specified submission institution for selling electricity.

[0046] Next, an example of submitting a power generation and sales plan based on the result of power generation amount forecast in the case of wholesale supply will be described with reference to Figs.

[0047] In this example, the power generation facility is assumed to be, for example, a ground-based solar power plant. The generated electricity is assumed to be supplied wholesale to a retail electricity supplier through a grid. When supplying wholesale to a retail electricity supplier, in order to facilitate coordination with the retail electricity supplier, the amount of electricity generated for 24 hours on the day of actual demand is to be forecast at 5:00 p.m. two days prior to the actual demand, and a power generation sales plan is to be submitted. Since transactions are conducted in 30-minute units, for example, the forecast covers 48 24-hour periods on the day of actual supply and demand. In this case, as above, future changes to the system may result in changes to the bidding deadline and the unit size, so the above is just one example based on the current system.

[0048] When predicting the amount of power generation, the power generation prediction calculation unit 421 calculates an error margin of the power generation prediction in addition to the predicted value. The prediction error range is determined by the power generation prediction calculation unit 421 based on the accuracy of its own prediction estimated from a huge amount of past accumulated data (corresponding data between estimated power generation over the course of a year and actual power generation), so the width of the prediction error range changes (expands or shrinks) depending on the time of day, even within the same day. For example, the margin of error for predictions is automatically increased on days and times when the accuracy of predictions is relatively low, such as on cloudy days or days with unstable weather. For example, assume that as of 5 p.m. on Tuesday evening, the amount of power generation for 24 hours and 48 frames on the following day, Thursday, is predicted as shown in the values ​​in the table of FIG. 7 and the graph of FIG.

[0049] Considering the prediction results by the power generation prediction calculation unit 421, it was estimated that the prediction accuracy for the day after tomorrow, Thursday, would temporarily decrease due to the influence of clouds and rain, especially from the afternoon. Therefore, the power generation and sales plan determination unit 46 decided to submit the forecast value of the lower limit of the power generation forecast range (line 123 in Figure 8) only for the six periods from 12:00 to 15:00, as shown in dashed frame 103 in Figure 7 and dashed frame 124 in Figure 8, as the power generation and sales plan to be submitted to the retail electricity supplier and the designated submission institution, and decided to submit the median value of the power generation forecast value (line 121 in Figure 8) as the planned value for the other time periods.

[0050] Next, an example of submitting a power generation sales plan based on the power generation amount forecast result when selling power to the spot market and wholesale supply will be described with reference to Figs.

[0051] In this example, the power generation facility is assumed to be, for example, a solar power plant on the roof of a building. The generated electricity, except for the amount of electricity consumed within the building, is assumed to be sold to the spot market through the grid and / or supplied wholesale to a retail electricity supplier. For example, as of 5 p.m. on a Wednesday evening, the amount of power generated over 24 hours and 48 frames on Friday is predicted as shown in the table in Fig. 7 and the graph in Fig. 9. In addition, since the store operating in the building is closed on that date and time, no power will be consumed in the building (although standby power consumption does occur in reality, it is small and will not be considered here). Therefore, all power generated on that Friday is sold to the spot market through the grid and / or supplied wholesale to retail electricity suppliers.

[0052] Considering the prediction results by the power generation prediction calculation unit 421, it was estimated that the prediction accuracy on the following day, Friday, would temporarily decrease due to the influence of clouds and rain, especially from the afternoon. On the other hand, the power generation forecast calculation unit 421 predicted that the forecast price in the afternoon of the spot market on the following Friday would be 21 to 25 yen / kWh, higher than the average price of that day of 12 yen / kWh, while the power generation forecast calculation unit 421 predicted that the imbalance price would be within the range of approximately 7 to 9 yen / kWh.

[0053] Therefore, it was originally expected that the forecast accuracy on Friday afternoon would temporarily decrease due to the influence of clouds and rain, but in this case, the power generation sales plan determination unit 46 decided to adopt a bidding policy that allows for a certain amount of risk (for example, the risk that the actual power generation sales volume will be short of the submitted power generation sales plan, and a shortage imbalance fee will be charged) based on the spot market price forecast and the imbalance price forecast.

[0054] Specifically, the power generation and sales plan determination unit 46 sets risk tolerance (how to take risk) using multiple curves 131 to 134 (risk characteristics) as shown in Figure 9, and determines a policy for submitting a power generation plan at one of the risk tolerances. Curve 131 shows risk tolerance 1 for generating a sales plan using the lower forecast limit (lowest risk of loss due to shortage imbalance, but lowest possibility of profit). Curve 132 shows risk tolerance 2 for generating a sales plan using the midpoint between the forecast value and the lower forecast limit (second lowest risk of loss due to shortage imbalance, but second lowest possibility of profit). Curve 133 shows risk tolerance 3 for generating a sales plan using the forecast value (second highest risk of loss due to shortage imbalance, but second highest possibility of profit). Curve 134 shows risk tolerance 4 for generating a sales plan using the midpoint between the upper forecast limit and the forecast value (highest risk of loss due to shortage imbalance, but highest possibility of profit).

[0055] The power generation sales plan determination unit 46 decided to determine and submit the lower limit of the power generation forecast range (curve 131 in Figure 9) and the average value of the forecast values ​​as the power generation sales plan only for the six time periods from 12:00 p.m. to 3:00 p.m., and to submit the power generation forecast value (curve 133 in Figure 9) as is as the planned value for the other time periods. This enables power generation companies to maximize their final profits, calculated from electricity sales revenue minus imbalance costs.

[0056] As described above, the server 1 of this embodiment utilizes the power generation forecast range to fully automatically determine, for example, a power generation plan for a power generation facility that maximizes profitability, as well as bidding conditions for selling and purchasing electricity. This makes it possible to reduce man-months of labor and improve bidding accuracy. Furthermore, by displaying the power generation forecast range for determining the bidding conditions, it becomes possible to consider a bidding policy for maximizing profitability, for example. Furthermore, the function of displaying the power generation forecast range can contribute to improving the accuracy of the power generation forecast by displaying the power generation forecast range and supporting the analysis of the power generation forecast.

[0057] In the above-described embodiment, the proposal policy was determined based on the broken lines of the upper limit, median, and lower limit shown in FIG. 6 and FIG. 8, and the four curves 131 to 134 shown in FIG. 9, but the proposal policy is not limited to this and may be determined based on various characteristics (indicators). In the above embodiment, an example was given in which a time period was divided into 30-minute intervals, but other divisions could also be used, for example, an hour could be divided into one interval, or it could be divided into half-day intervals or 10-minute intervals, etc.

[0058] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope that can achieve the object of the present invention are included in the present invention.

[0059] Furthermore, for example, the above-described series of processes can be executed by hardware or software. In other words, the functional configuration in FIG. 3 is merely an example and is not particularly limited. That is, it is sufficient for the power generation prediction system to have a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block used to realize this function is not limited to the example of FIG. 3. Furthermore, the location of the functional blocks and database is not particularly limited to that of FIG. 3 and may be arbitrary. For example, at least a part of the functional blocks and database required for executing various processes may be transferred to a user terminal or the like. Conversely, the functional blocks and database of the user terminal may be transferred to a server or the like. Furthermore, one functional block may be configured as a single piece of hardware, a single piece of software, or a combination of both.

[0060] When the series of processes is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium. The computer may be a computer implemented with dedicated hardware. Furthermore, the computer may be a computer capable of executing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

[0061] A recording medium containing such a program may be composed not only of a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to a user, etc., but also of a recording medium that is provided to a user, etc. in a state where it is pre-installed in the device main body.

[0062] In this specification, the steps of describing a program to be recorded on a recording medium include not only processes that are performed chronologically according to the order, but also processes that are not necessarily performed chronologically but are executed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is composed of a plurality of devices, a plurality of means, etc.

[0063] In other words, it is sufficient for an information processing device to which the present invention is applied to have the following configuration, and various embodiments can be adopted. (1) That is, an information processing device to which the present invention is applied (for example, the server 1 in FIG. 3) A weather forecast acquisition unit (e.g., the weather forecast acquisition unit 411 in FIG. 3) for acquiring weather forecast data; A power generation record acquiring unit (e.g., the power generation record acquiring unit 412 in FIG. 3) for acquiring the power generation record in the power generation facility; A power generation capacity acquisition unit (e.g., the power generation capacity acquisition unit 413 in FIG. 3 ) that acquires the power generation capacity (e.g., the performance of a solar panel) and the installation location (e.g., latitude, longitude, altitude) of the power generation facility; A power generation forecast calculation means (e.g., the power generation forecast calculation unit 421 in FIG. 3) that calculates a power generation forecast value and a power generation forecast minimum value of renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan etc. determination unit (e.g., the power generation sales plan etc. determination unit 46 in FIG. 3, a fully automatic determination unit, or a manual determination assistance unit) that determines a power generation plan of the power generation facility and bidding conditions for power sales and purchase (e.g., bidding time, bidding price, bidding amount, etc.) based on the calculated power generation forecast value and the minimum power generation forecast value; Equipped with. This makes it possible to fully automatically determine, for example, a power generation plan for a power generation facility that maximizes profitability, as well as bidding conditions for selling and purchasing electricity, by utilizing the power generation forecast range. In other words, it is possible to reduce man-months of labor. It is also possible to improve bidding accuracy. Furthermore, by displaying the power generation forecast range for determining the bidding conditions, it becomes possible to consider a bidding policy for maximizing profitability, for example. Furthermore, the display and analysis function for power generation forecast ranges can contribute to improving the accuracy of power generation forecasts. As a result of the above, it is possible to improve profitability in the electricity market by utilizing the power generation forecast range (e.g., confidence interval) of power generation facilities (e.g., solar power plants, etc.) that changes in response to changes in weather. (2) The power generation forecast calculation means (e.g., the power generation forecast calculation unit 421 in FIG. 3) further calculates a power generation forecast range defined by the power generation forecast minimum value and the power generation forecast maximum value, The power generation plan etc. determination means (e.g., the power generation sales plan etc. determination unit 46 in FIG. 3) determines the power generation plan of the power generation facility and the bidding conditions for the power sales and purchase based on the calculated power generation forecast value and the power generation forecast range. It is possible. (3) The power generation plan etc. determination means (e.g., the power generation sales plan etc. determination unit 46 in FIG. 3) determines the power generation plan of the power generation facility and the bidding conditions for the power sales and purchase based on the power generation forecast value and the power generation forecast minimum value, as well as the forecast accuracy, the imbalance charge and the market price. It is possible. (4) When a bidding policy for determining the power generation plan (e.g., timing and amount of power generation) of the power generation facility and the bidding conditions for the power sale and purchase (e.g., price and amount of power sale and purchase, buyer and seller) is specified by the user, the power generation plan etc. determination means (e.g., power generation and sales plan etc. determination unit 46 in FIG. 3) further determines the power generation plan of the power generation facility and the bidding conditions for the power sale and purchase based on the bidding policy. It is possible. The user may specify, for example, the "power generation forecast value," "upper limit of the power generation forecast range," "lower limit of the power generation forecast range," etc., using a radio button or by specifying a "value" with the mouse. (5) The power generation plan etc. determination means (for example, the power generation and sales plan etc. determination unit 46 in FIG. 3 ) As the user's designation of the bidding policy, a designation that applies to multiple decisions (e.g., designation as a default policy) or a designation that applies to an individual decision (e.g., designation only once) is adopted, whichever is selected by the user; Determine the power generation plan of the power generation facility and the bidding conditions for the sale and purchase of electricity based on the adopted bidding policy of the designation; It is possible. (6) a graph generating means (e.g., the graph generating unit 453 in FIG. 3) for generating a graph showing a change over time of the calculated power generation forecast value and the calculated power generation forecast minimum value; A display data generating means (e.g., the display data generating unit 455 in FIG. 3) for generating display data for displaying the generated graph; Further comprising: It is possible. (7) The graph generating means (e.g., the graph generating unit 453 in FIG. 3) generates a continuous curve showing a change over time of at least one of the predicted power generation value, the predicted power generation maximum value, the predicted power generation minimum value, and the actual power generation value. It is possible. (8) The graph generating means (for example, the graph generating unit 453 in FIG. 3) A graph selection means (for example, the graph selection unit 61 in FIG. 3) that accepts a selection of a step-like, continuous line-like, or scatter-like locus of the graph; A time unit changing means (for example, the frame changing unit 62 in FIG. 3) for changing the time unit (for example, 30-minute unit) showing the time change in the graph according to a predetermined bidding unit; having It is possible. (9) The power generation plan etc. determination means (e.g., the power generation and sales plan etc. determination unit 46 in FIG. 3) further determines a power generation plan of the power generation facility and bidding conditions for power sales and purchases based on the set power generation amount, a power generation amount setting unit (e.g., power generation amount setting unit 71 in FIG. 3 ) that sets the set power generation amount based on a specified position on the displayed graph (e.g., by clicking with a mouse); Further comprising: It is possible. (10) A correlation unit (e.g., correlation unit 454 in FIG. 3 ) for correlating the calculated power generation forecast value and the calculated power generation forecast minimum value with a unit time (e.g., one frame from 10:00 to 11:30) for which the power generation forecast value and the power generation forecast minimum value were calculated; A display range designation means (for example, the display range designation unit 451 in FIG. 3) for designating the display range of the graph by one or more unit times (for example, two frames from 10:00 to 12:00, etc.); a graph display portion specifying unit (e.g., the graph display portion specifying unit 452 in FIG. 3 ) for specifying, as a graph display portion, a portion of the graph including the power generation forecast value and the power generation forecast minimum value associated with each of the one or more specified unit times; Further equipped with The display data generating means (e.g., the display data generating unit 455 in FIG. 3) generates data for displaying the graph display portion as the display data. It is possible. (11) The display range designation means (e.g., the display range designation unit 451 in FIG. 3) accepts an operation for changing the display range of the graph (e.g., a slide operation of a slide bar for varying the calculation date and time, etc.), The graph display portion specifying means (for example, the graph display portion specifying unit 452 in FIG. 3) changes the graph display portion in response to the operation for changing the display range of the graph, The display data generating means (e.g., the display data generating unit 455 in FIG. 3) generates data for displaying the changed graph display portion as the display data. It is possible. (12) A specific date and time designation means (for example, the specific date and time designation unit 51 in FIG. 3) for designating a specific date and time or a specific prediction interval is further provided, The display data generating means (e.g., the display data generating unit 455 in FIG. 3 ) generates the display data for displaying at least one of the power generation forecast value, the power generation forecast minimum value, and the power generation actual value at the specified specific date and time or the specified prediction interval, superimposed on the graph. It is possible. (13) The graph generating means (e.g., the graph generating unit 453 in FIG. 3) generates a graph showing a change over time in the power generation result value in addition to the power generation forecast value and the power generation forecast minimum value. It is possible. (14) A weather forecast acquisition means (e.g., the weather forecast acquisition unit 411 in FIG. 3) acquire the latest weather forecast data based on at least the bidding deadline for electricity trading (e.g., gate close (one hour before actual supply and demand) in the case of an hourly market) or the bidding start time (e.g., 5 p.m. on the previous day in the case of an hourly market), and the time required to determine the bidding conditions for the electricity sales and purchases; It is possible. (15) A setting means (e.g., the setting unit 47 in FIG. 3) for setting the prediction accuracy of power generation; A calculation means (e.g., 2 in FIG. 3 ) for calculating the minimum predicted power generation value based on the set prediction accuracy; Further comprising: It is possible. (16) A comparison means (e.g., the comparison unit 43 in FIG. 3 ) for comparing the power generation prediction value with the power generation record; an abnormality determination means (e.g., the abnormality determination unit 44 in FIG. 3) that determines whether or not there is an abnormality in the power generation facility based on the comparison result of the comparison means; Further comprising: It is possible. (17) An information processing method to which the present invention is applied comprises: An information processing method executed by an information processing device, A weather forecast acquisition step (e.g., step S1 in FIG. 4) of acquiring weather forecast data; A power generation record acquisition step (e.g., step S2 in FIG. 4) for acquiring a power generation record in the power generation facility; A power generation capacity acquisition step (e.g., step S3 in FIG. 4) for acquiring the power generation capacity and the installation location of the power generation facility; A power generation forecast calculation step (e.g., step S4 in FIG. 4) of calculating a power generation forecast value and a power generation forecast minimum value of renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan determination step (e.g., step S5 in FIG. 4) of determining a power generation plan of the power generation facility and bidding conditions for power sales and purchases based on the calculated power generation forecast value and the minimum power generation forecast value; Including, It is possible. (18) The program to which the present invention is applied is On the computer, A weather forecast acquisition step (e.g., step S1 in FIG. 4) of acquiring weather forecast data; A power generation record acquisition step (e.g., step S2 in FIG. 4) for acquiring a power generation record in the power generation facility; A power generation capacity acquisition step (e.g., step S3 in FIG. 4) for acquiring the power generation capacity and the installation location of the power generation facility; A power generation forecast calculation step (e.g., step S4 in FIG. 4) of calculating a power generation forecast value and a power generation forecast minimum value of renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan determination step (e.g., step S5 in FIG. 4) of determining a power generation plan of the power generation facility and bidding conditions for power sales and purchases based on the calculated power generation forecast value and the minimum power generation forecast value; Executing a control process including It is possible. [Explanation of symbols]

[0064] 1: Server, 2: User terminal, 3: Electricity market server, 11: CPU, 12: ROM, 13: RAM, 14: Bus, 15: Input / output interface, 16: Input section, 17: Output section, 18: Memory section, 19: Communication section, 20: Drive, 21: Removable media, 41: Acquisition section, 42: Calculation section, 43: Comparison section, 44: Anomaly determination section, 45: Display control section, 46: Power generation and sales plan decision section, 47: Setting section, 51: Specific date and time designation section, 61: Graph selection section, 62: Frame change change unit, 71: power generation amount setting unit, 91: weather forecast DB, 92: power generation record DB, 93: power plant information DB, 94: power generation forecast DB, 95: transaction data DB, 96: user information DB, 411: weather forecast acquisition unit, 412: power generation record acquisition unit, 413: power generation capacity acquisition unit, 421: power generation forecast calculation unit, 451: display range designation unit, 452: graph display portion identification unit, 453: graph generation unit, 454: association unit, 455: display data generation unit, S: administrator, NW: network, U: user

Claims

1. A weather forecast acquisition means for acquiring weather forecast data; A power generation record acquiring means for acquiring a power generation record of the power generation facility; A power generation capacity acquisition means for acquiring the power generation capacity and the installation location of the power generation facility; A power generation forecast calculation means for calculating a power generation forecast value and a minimum power generation forecast value of the renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan etc. determination means for determining a power generation plan of the power generation facility and bidding conditions for selling and purchasing power based on the calculated power generation forecast value and the calculated minimum power generation forecast value; An information processing device comprising:

2. The power generation forecast calculation means further calculates a power generation forecast range defined by the power generation forecast minimum value and the power generation forecast maximum value, the power generation plan etc. determination means determines the power generation plan of the power generation facility and bidding conditions for the power sale and purchase based on the calculated power generation forecast value and the power generation forecast range. The information processing device according to claim 1 .

3. the power generation plan etc. determination means determines the power generation plan of the power generation facility and the bidding conditions for the power sale and purchase based on the power generation forecast value and the power generation forecast minimum value, as well as on a forecast accuracy, an imbalance charge, and a market price; The information processing device according to claim 1 .

4. the power generation plan etc. determination means, when a bidding policy for determining the power generation plan of the power generation facility and the bidding conditions for the power sale and purchase is specified by a user, further determines the power generation plan of the power generation facility and the bidding conditions for the power sale and purchase based on the specified bidding policy; The information processing device according to claim 1 .

5. The power generation plan etc. determination means As the user's designation of the bidding policy, a designation to be applied to multiple decisions or a designation to be applied to an individual decision, whichever is selected by the user, is adopted; Determine the power generation plan of the power generation facility and the bidding conditions for the sale and purchase of electricity based on the adopted bidding policy of the designation; The information processing device according to claim 4.

6. a graph generating means for generating a graph showing a change over time of the calculated predicted power generation value and the predicted minimum power generation value; a display data generating means for generating display data for displaying the generated graph; The information processing device according to claim 2 , further comprising:

7. the graph generating means generates a continuous curve showing a change over time of at least one of the predicted power generation value, the predicted power generation maximum value, the predicted power generation minimum value, and the actual power generation value. The information processing device according to claim 6.

8. The graph generating means a graph selection means for accepting a selection of a step-like, continuous line-like, or scatter-like locus of the graph; a time unit changing means for changing a time unit showing a change over time in the graph in accordance with a predetermined bidding unit; having The information processing device according to claim 6.

9. The power generation plan etc. determination means further determines a power generation plan of the power generation facility and bidding conditions for selling and purchasing power based on the set power generation amount, a power generation amount setting means for setting the set power generation amount based on a specified position of the displayed graph when the specified position is reached; The information processing device according to claim 6 , further comprising:

10. an association means for associating the unit time during which the power generation prediction value and the power generation prediction minimum value were calculated with the calculated power generation prediction value and the power generation prediction minimum value; a display range designation means for designating a display range of the graph by one or more of the unit times; a graph display portion specifying means for specifying, in the graph, a portion including the predicted power generation value and the predicted minimum power generation value associated with each of the one or more specified unit times as a graph display portion; Further equipped with the display data generating means generates, as the display data, data for displaying the graph display portion. The information processing device according to claim 6.

11. the display range designation means accepts an operation for changing the display range of the graph, the graph display portion specifying means changes the graph display portion in response to the operation of changing the display range of the graph; the display data generating means generates, as the display data, data for displaying the changed graph display portion. The information processing device according to claim 10.

12. A specific date and time designation means for designating a specific date and time or a specific prediction interval is further provided, the display data generating means generates the display data for displaying, on the graph, at least one of the power generation forecast value, the power generation forecast minimum value, and the power generation actual value at the specified specific date and time or the specified prediction interval. The information processing device according to claim 6.

13. the graph generating means generates the graph showing a change over time in the actual power generation value in addition to the predicted power generation value and the predicted minimum power generation value. The information processing device according to claim 7.

14. The means of obtaining weather forecasts are The latest weather forecast data is acquired based on at least the bidding deadline or bidding start time for the power trading and the time required to determine the bidding conditions for the power sales and purchase. The information processing device according to claim 1 .

15. A setting means for setting a prediction accuracy of power generation; a calculation means for calculating the minimum predicted power generation value based on the set prediction accuracy; The information processing device according to claim 1 , further comprising:

16. A comparison means for comparing the power generation prediction value with the power generation record; an abnormality determination means for determining whether or not an abnormality exists in the power generation facility based on a comparison result of the comparison means; The information processing device according to claim 1 , further comprising:

17. An information processing method executed by an information processing device, A weather forecast acquisition step of acquiring weather forecast data; A power generation record acquisition step for acquiring a power generation record in the power generation facility; A power generation capacity acquisition step of acquiring a power generation capacity and an installation location of the power generation facility; A power generation forecast calculation step of calculating a power generation forecast value and a power generation forecast minimum value of the renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan determination step of determining a power generation plan of the power generation facility and bidding conditions for selling and purchasing power based on the calculated power generation forecast value and the calculated minimum power generation forecast value; An information processing method comprising:

18. On the computer, A weather forecast acquisition step of acquiring weather forecast data; A power generation record acquisition step for acquiring a power generation record in the power generation facility; A power generation capacity acquisition step of acquiring a power generation capacity and an installation location of the power generation facility; A power generation forecast calculation step of calculating a power generation forecast value and a power generation forecast minimum value of the renewable energy based on at least the latest weather forecast data, the power generation record of the power generation facility, the power generation capacity, and the installation location; a power generation plan determination step of determining a power generation plan of the power generation facility and bidding conditions for selling and purchasing power based on the calculated power generation forecast value and the calculated minimum power generation forecast value; A program that executes control processing including:

Images