Scenario creating device, scenario creating method, and program

Abstract

To select an appropriate scenario candidate in a short time from among a plurality of scenario candidates including uncertain items affecting a transaction plan on a power selling / buying transaction day in a power market.SOLUTION: A scenario creation device comprising: a prediction unit configured to predict a value of an uncertain item that affects a trading plan at a power trade transaction date in an electric power market in an electric facility in which a power generation facility using renewable energy and a storage battery are combined, by using information on weather; a scenario candidate creation unit configured to create a plurality of scenario candidates including the predicted value of the uncertain item at the trade transaction date; and a scenario selection unit configured to select a smaller number of scenario candidates than the number of the plurality of scenario candidates to be used when creating the trading plan from among the plurality of scenario candidates.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a scenario creation device, a scenario creation method, and a program. [Background technology]

[0002] When creating a trading plan for a power purchase and sale transaction day in the power market for a power generation facility that uses renewable energy, a technique is known in which a scenario is prepared in advance that includes values ​​of uncertain items such as the power price and the power generation amount of the power generation device on the power purchase and sale transaction day, and a trading plan is created based on this scenario (e.g., Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-74886 [Patent Document 2] Japanese Patent Application Laid-Open No. 2005-25377 Summary of the Invention [Problem to be solved by the invention]

[0004] In the case of Patent Document 1, multiple scenarios including uncertain items are prepared in advance, all of the multiple scenarios are divided into pairs of scenario combinations, and for each pair of scenario combinations, an overlap indicating the degree of similarity between the scenarios is evaluated, and one of the pairs of scenarios for which the overlap is equal to or greater than a preset threshold is deleted, thereby reducing the number of scenarios. However, in Patent Document 1, the above-mentioned division and overlap evaluation calculation processes must be repeated until the optimal scenario can be evaluated before creating a trading plan, which could result in a long calculation time until the optimal scenario can be evaluated.

[0005] In addition, in the case of Patent Document 2, simulated results of market trading, such as electricity prices and electricity trading volumes, are calculated based on each of a plurality of scenarios, and these simulated results are displayed in comparison on a display means, thereby formulating an optimal bidding strategy. However, in Patent Document 2, scenarios are evaluated by displaying the simulated results on a display means, which may result in a long time being required to evaluate the optimal scenario.

[0006] The present invention has been made in consideration of the above-mentioned conventional problems, and its purpose is to provide a scenario creation device, a scenario creation method, and a program that can shorten the time it takes to select an appropriate scenario candidate from multiple scenario candidates that include uncertain items. [Means for solving the problem]

[0007] A first aspect of the scenario creation device of the present invention, which solves the above-mentioned problems, includes a prediction unit that uses meteorological information to predict values ​​of uncertain items that affect a trading plan for a power trading day in an electricity market for an electrical facility that combines a power generation facility that uses renewable energy with a storage battery; a scenario candidate creation unit that creates a plurality of scenario candidates including predicted values ​​of the uncertain items for the power trading day; and a scenario selection unit that selects, from the plurality of scenario candidates, a number of scenarios that is less than the number of the plurality of scenario candidates to be used when creating the trading plan. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a scenario creation device, a scenario creation method, and a program that can shorten the time required to select an appropriate scenario from multiple scenario candidates that include uncertain items regarding the date of electricity trading in the electricity market. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a market transaction planning system 100 including a scenario creating device 700 and a schematic configuration of its surroundings. [Figure 2] FIG. 1 is a functional block diagram of a market transaction planning system 100. [Figure 3] FIG. 7 is a block diagram showing the functions of a scenario creating device 700. [Figure 4] FIG. 10 is a diagram showing an example of a data group stored in a market price factor DB 710. [Figure 5] FIG. 10 is a diagram showing an example of a data group stored in a weather forecast DB 720. [Figure 6] 10 is a graph showing an example of predicted values ​​of X electricity market prices by the price prediction unit 730. [Figure 7] 10 is a graph showing an example of predicted values ​​of X amounts of power generation in the photovoltaic power generation facility 200 by the power generation prediction unit 740. [Figure 8] FIG. 10 is a diagram showing an example of a data group stored in a scenario candidate DB 760. [Figure 9] This figure shows an example of a graph in which X predicted values ​​of electricity market prices and X predicted values ​​of power generation amounts of the photovoltaic power generation facility 200 are combined for each of scenario candidates 1 to X as a data group stored in the scenario candidate DB 760. [Figure 10] 10 is a flowchart showing an example of a selection operation by the scenario selection unit 750. [Figure 11] FIG. 10 is a diagram showing an example of a data group stored in a scenario DB 770. [Figure 12] FIG. 8 is a block diagram showing the functions of a trading plan creation device 800. [Figure 13] FIG. 10 is a diagram showing examples of transaction plans A′, B′, and C′ created for three scenario candidates A, B, and C selected by the scenario selection unit 750, respectively. [Figure 14] FIG. 9 is a block diagram showing the functions of a trading plan evaluator 900. [Figure 15] FIG. 10 is a diagram showing an example of evaluating which of the trading plans A', B', and C' will have the largest profit and loss. [Figure 16]FIG. 10 is a block diagram showing an example of hardware of an information processing device 1000 that realizes the functions of a scenario creating device 700, a transaction plan creating device 800, and a transaction plan evaluating device 900. DETAILED DESCRIPTION OF THE INVENTION

[0010] At least the following matters will become apparent from the description of this specification and the accompanying drawings. Hereinafter, the present invention will be described in accordance with one embodiment thereof with reference to the accompanying drawings.

[0011] <<Market Transaction Planning System 100>> FIG. 1 is a block diagram showing a market transaction planning system 100 including a scenario creation device 700 and a schematic configuration of its surroundings. In this embodiment, the electricity market is a wholesale electricity market in which electricity is traded between power generation companies, which are owners of power generation facilities that generate electricity using renewable energy (hereinafter referred to as "renewable energy power generation facilities"), and general electricity transmission and distribution companies. The power generation companies submit spot bids based on a transaction plan by the day before the power trading date to conclude the electricity trade. In this embodiment, the renewable energy power generation facility is a solar power generation facility 200 that generates electricity using solar light, but is not limited to this. For example, the renewable energy power generation facility may be, instead of the solar power generation facility 200, any of a wind power generation facility that generates electricity using wind power, a hydroelectric power generation facility that generates electricity using hydroelectric power, a geothermal power generation facility that generates electricity using geothermal heat, a biomass power generation facility that generates electricity using biomass as fuel, or the like. Alternatively, the solar power generation facility 200 may be combined with any of a wind power generation facility, a hydroelectric power generation facility, a geothermal power generation facility, a biomass power generation facility, or the like. In the latter case where the solar power generation facility 200 is combined with other power generation facilities, the predicted value of the power generation amount of the power generation facility is the sum of the predicted value of the power generation amount of the solar power generation facility 200 and the predicted value of the power generation amount of the other power generation facilities to be combined.

[0012] The photovoltaic power generation facility 200 and the storage battery 400, which are electrical facilities, are connected to a power grid 300 in order to buy and sell electric power. The photovoltaic power generation facility 200 and the storage battery 400 are also connected without going through the power grid 300 so that the storage battery 400 can be directly charged with electric power generated by the photovoltaic power generation facility 200. For ease of explanation, this embodiment will be described assuming that when electric power is purchased in the electricity market, electric power is charged from the power grid 300 to the storage battery 400, and when electric power is sold in the electricity market, electric power is discharged from the storage battery 400 to the power grid 300.

[0013] The solar power generation facility 200 includes a solar power generation panel (not shown) configured using, for example, a polycrystalline silicon power generation element, a monocrystalline silicon power generation element, a thin-film power generation element, or the like. The solar power generation facility 200 also includes a power conditioner (hereinafter referred to as "PCS") 210 that converts the power generated by the solar power generation panel from direct current to alternating current and supplies the power to the power grid 300. PCS is an abbreviation for Power Conditioning Subsystem.

[0014] The storage battery 400 is, for example, a lead battery, a lithium ion battery, a sodium-sulfur battery, a nickel-metal hydride battery, a redox flow battery, a capacitor battery, etc. The storage battery 400 includes a PCS 410 for charging and discharging between the storage battery 400 and the power grid 300. When discharging the power stored in the storage battery 400 to the power grid 300, the PCS 410 converts the power stored in the storage battery 400 from direct current to alternating current, and when charging the storage battery 400 with power from the power grid 300, the PCS 410 converts the power from the power grid 300 from alternating current to direct current.

[0015] For example, in a trading plan for conducting spot bidding by the day before the power buying and selling transaction date, during a power buying period in which power purchases are planned, power purchased from the power grid 300 is charged to the storage battery 400 via the PCS 410. On the other hand, during a power selling period in which power sales are planned in this trading plan, the power charged in the storage battery 400 is discharged to the power grid 300 via the PCS 410. The power generated by the photovoltaic power generation facility 200 is charged directly to the storage battery 400 without passing through the PCSs 210 and 410.

[0016] The battery control device 420 controls the charging and discharging operation of the storage battery 400 so that, in accordance with spot bidding based on the finally determined trading plan, during the power purchasing period when power is purchased, the battery 400 is charged with power purchased from the power grid 300, and during the power selling period when power is sold, the battery control device 420 discharges the power charged in the storage battery 400 toward the power grid 300, and further charges the storage battery 400 with the power generated by the solar power generation facility 200 when the solar power generation facility 200 generates power.

[0017] The market transaction planning system 100 is a system that supports the creation of an optimal transaction plan for a power generation company, which is required for placing spot bids in the electricity market by the day before the electricity trading day. The market transaction planning system 100 is communicably connected via a communication network 500 to a computer 600 on the electricity market side that receives electricity trading information included in the spot bids and manages the electricity trading, so that the power generation company can place spot bids in accordance with the finally determined transaction plan. The communication network 500 may be, for example, a local area network (LAN), a wide area network (WAN), a dedicated line, a power line communication network, or any of various public communication networks.

[0018] FIG. 2 is a functional block diagram of the market trading planning system 100. The market trading planning system 100 includes a scenario creating device 700, a trading plan creating device 800, and a trading plan evaluating device 900 as means for realizing the above functions.

[0019] <<Scenario creating device 700>> FIG. 3 is a block diagram showing the functions of the scenario creating device 700. The amount of power generated by the solar power generation facility 200 varies depending on the amount of solar radiation irradiating the solar power generation panel. In other words, the amount of power generated by the solar power generation facility 200 is the first uncertain item that fluctuates due to the influence of the weather in the area where the solar power generation facility 200 is installed.

[0020] Electricity market prices tend to decrease when fuel costs used in power generation facilities in general decrease, and increase when fuel costs increase. Furthermore, when the weather is clear and the amount of sunlight is high, the electricity market price decreases as the amount of electricity generated by the solar power generation facility 200 increases, and when the weather is rainy or cloudy and the amount of sunlight is low, the electricity market price tends to increase as the amount of electricity generated by the solar power generation facility 200 decreases. Furthermore, electricity market prices tend to increase when the amount of electricity demand exceeds the amount of electricity supply, and decrease when the amount of electricity demand falls below the amount of electricity supply. Furthermore, during the daytime, the electricity market price tends to decrease as the amount of electricity generated by the solar power generation facility 200 increases, and during the nighttime, the electricity market price tends to increase as the amount of electricity generated by the solar power generation facility 200 stops generating electricity. Thus, electricity market prices are a second uncertain item that fluctuates due to factors such as fuel costs, weather, the balance between supply and demand of electricity, and the time of day (daytime and nighttime).

[0021] The scenario creation device 700 creates multiple scenario candidates by predicting the first uncertain item, the amount of power generated by the photovoltaic power generation facility 200, and the second uncertain item, the power market price, at predetermined time intervals on a power trading day in the power market, and selects a small number of scenarios necessary to create an optimal trading plan for the power generation company from the multiple scenario candidates. In this embodiment, the predetermined time interval is, for example, one hour. That is, for one day's worth of scenario candidates, 24 predictions are made for the amount of power generated by the photovoltaic power generation facility 200 and the power market price at one-hour intervals from midnight to midnight. Note that the predetermined time interval may be other than one hour, such as 30 minutes or two hours. If the predetermined time interval is 30 minutes, 48 ​​predictions are made for the amount of power generated by the photovoltaic power generation facility 200 and the power market price at one-hour intervals from midnight to midnight for one day's worth of scenario candidates. Furthermore, if the predetermined time interval is two hours, twelve predictions are made for each of the power generation amount and the electricity market price of the photovoltaic power generation facility 200 for one day's worth of scenario candidates, at one-hour intervals from midnight to midnight.

[0022] The scenario creation device 700 includes, as means for realizing the above functions, a market price factor database (hereinafter referred to as the "market price factor DB") 710, a weather forecast database (hereinafter referred to as the "weather forecast DB") 720, a price prediction unit 730, a power generation prediction unit 740, a scenario candidate creation unit 745, a scenario selection unit 750, a scenario candidate database (hereinafter referred to as the "scenario candidate DB") 760, a scenario database (hereinafter referred to as the "scenario DB") 770, a selection number setting unit 780, an input unit 790, and an output unit 795. Here, the market price factor DB 710, the weather forecast DB 720, the scenario candidate DB 760, and the scenario DB 770 may be configured in separate storage devices, or may be configured using each of the four areas obtained by dividing the entire storage area of ​​a single storage device.

[0023] The market price factor DB 710 stores a group of data that the price prediction unit 730 references to predict the electricity market price on the day of an electricity purchase and sale transaction in the electricity market up to the previous day (spot bidding day). An example of the group of data stored in the market price factor DB 710 is shown in FIG. 4.

[0024] The market price factor DB 710 stores electricity market prices for a number of past days prior to the spot bid date. For example, the fixed electricity market prices (yen / kWh) from midnight to midnight on the electricity trading day one day prior to the spot bid date, the electricity trading day two days prior to the spot bid date, and the electricity trading day three days prior to the spot bid date are stored in hourly intervals.

[0025] The market price factor DB 710 also stores available capacities for power purchase and sale transactions in a plurality of interconnection lines in the power grid 300 to which the photovoltaic power generation facilities 200 and the storage batteries 400 are interconnected. For example, available capacities (MW) from midnight to midnight for each of the different interconnection lines 1 to 4 as of the day before the spot bidding day (the day before the day of the power purchase and sale transaction) are stored in one-hour intervals.

[0026] Furthermore, the market price factor DB 710 stores the capacities of large generators that are out of operation due to maintenance, breakdowns, etc., among the large generators of the electric power company that generate the power supplied to the power grid 300. For example, as of the day before the spot bidding date, the capacities (MW) from midnight to midnight of the large generators that affect the buying and selling of power on the interconnections 1 to 4 are stored in one-hour intervals. If multiple large generators are out of operation, the total capacity of the multiple large generators is stored.

[0027] The data groups stored in the market price factor DB 710 are not limited to those described above. If the market price factor DB 710 is a factor that has the potential to improve the accuracy with which the price prediction unit 730 predicts the electricity market price, data indicating that factor may be newly stored in addition to the data groups described above, or may be stored as a replacement for at least a portion of the data groups described above.

[0028] Furthermore, past electricity market prices and the capacity of large generators that are out of service at a given point in time can be obtained, for example, from the Japan Electric Power Exchange (JEPX), and available capacity of interconnection lines at a given point in time can be obtained, for example, from the Organization for Cross-regional Coordination of Transmission Operators, JAPAN (OCCTO).

[0029] The weather forecast DB 720 stores a group of data that is referenced by the price prediction unit 730 to predict the electricity market price up to the day before the electricity trading date, and by the power generation prediction unit 740 to predict the amount of power generated by the solar power generation facility 200 up to the day before the electricity trading date. An example of the group of data stored in the weather forecast DB 720 is shown in Fig. 5.

[0030] Ensemble weather forecasting is a well-known method for predicting future weather conditions using numerical calculations. In ensemble weather forecasting, multiple initial values ​​are prepared that have small errors comparable to the observation errors required to predict a forecast target (e.g., wind speed, temperature, atmospheric pressure, precipitation, and solar radiation), and future weather conditions are predicted by statistically processing the calculation results of the forecast target corresponding to each of the multiple initial values. In this embodiment, the weather forecast DB 720 stores the calculation results (X) of the forecast target corresponding to each of the multiple initial values ​​(X).

[0031] When the calculation results for the forecast targets are divided into groups of members 1 to X, the weather forecast DB 720 stores data for each of members 1 to X from 0:00 to 24:00, divided into hourly intervals, indicating the wind speed (m / s), temperature (℃), air pressure (hPa), precipitation (mm), and solar radiation (W / m2) on the day of the electricity trading transaction in the area where the solar power generation facility 200 is installed.

[0032] The above data group can be obtained, for example, from the Japan Meteorological Business Support Center. For each of the above members 1 to X, the forecast objects are wind speed, temperature, air pressure, precipitation, and solar radiation, but are not limited to these. Items other than wind speed, temperature, air pressure, precipitation, and solar radiation may be selected from the items obtainable from the Japan Meteorological Business Support Center as items constituting the forecast objects, and added to and stored as forecast objects, or may be rewritten and stored in place of at least a part of the forecast objects.

[0033] The price prediction unit 730 predicts the electricity market price on an electricity trading day every hour from midnight to midnight based on the data stored in the market price factor DB 710 and the weather forecast DB 720. The price prediction unit 730 can predict the electricity market price using, for example, a deep neural network or just-in-time modeling. Furthermore, since the weather forecast DB 720 stores a total of X pieces of data for prediction targets corresponding to members 1 to X, the price prediction unit 730 predicts X electricity market price values ​​corresponding to each of members 1 to X.

[0034] FIG. 6 is a graph showing an example of X electricity market price prediction values ​​calculated by the price prediction unit 730. In FIG. 6, the horizontal axis represents the time (hours) from midnight to midnight on the day of the electricity trading transaction, and the vertical axis represents the predicted value of the electricity market price (yen / kWh). The X electricity market price prediction values ​​show changes in accordance with the values ​​of the data groups stored in the market price factor DB 710 and the weather forecast DB 720. Specifically, the X electricity market price prediction values ​​show changes in accordance with the past electricity market price several days before the day of the electricity trading transaction, the unused capacity of the interconnection line, the capacity of large generators that are currently out of service, and the predicted wind speed, temperature, air pressure, precipitation, and solar radiation on the day of the electricity trading transaction. In the example shown in FIG. 6, the X electricity market price prediction values ​​show changes in accordance with the relatively low values ​​during the daytime when solar radiation is observed, and changes in accordance with the relatively high values ​​during the nighttime when solar radiation is not observed. Here, the predicted values ​​of the electricity market prices corresponding to members 1 to X are designated as scenario candidates 1 to X.

[0035] The power generation prediction unit 740 predicts the amount of power generated by the photovoltaic power generation facility 200 on the day of the power purchase transaction, hourly from midnight to midnight, based on a group of data stored in the weather forecast DB 720. As a method by which the power generation prediction unit 740 predicts the amount of power generated by the photovoltaic power generation facility 200 on the day of the power purchase transaction, various methods can be adopted, for example, to predict the amount of power generated by the photovoltaic power generation facility 200 using at least one of wind speed, temperature, atmospheric pressure, precipitation, and solar radiation for each of members 1 to X as a parameter. Furthermore, since the weather forecast DB 720 stores a total of X pieces of data for prediction targets corresponding to members 1 to X, the power generation prediction unit 740 predicts X predicted values ​​of the amount of power generated by the photovoltaic power generation facility 200, one for each of members 1 to X.

[0036] FIG. 7 is a graph showing an example of X predicted values ​​of power generation amount of the photovoltaic power generation facility 200 by the power generation prediction unit 740. In FIG. 7, the horizontal axis represents the time (hour) from midnight to midnight on the day of the power trading, and the vertical axis represents the predicted value of power generation amount (kWh) of the photovoltaic power generation facility 200. The X predicted values ​​of power generation amount of the photovoltaic power generation facility 200 show changes according to the values ​​of the data group stored in the weather forecast DB 720, specifically, changes according to the predicted wind speed, temperature, air pressure, precipitation, and solar radiation on the day of the power trading. In the example of FIG. 7, the X predicted values ​​of power generation amount of the photovoltaic power generation facility 200 show changes according to the solar radiation during the daytime when the solar radiation is observed, while showing zero during the nighttime when the solar radiation is not observed. Here, the predicted values ​​of power generation amount of the photovoltaic power generation facility 200 corresponding to members 1 to X are designated as scenario candidates 1 to X.

[0037] The scenario candidate creation unit 745 creates scenario candidates 1 to X shown in FIGS. 8 and 9 below based on the prediction results of the price prediction unit 730 and the power generation prediction unit 740. 8 is a diagram showing an example of a data group stored in the scenario candidate DB 760. The scenario candidate DB 760 stores, in one-hour intervals, X electricity market prices predicted by the price prediction unit 730 and X predicted values ​​of the amount of power generated by the photovoltaic power generation facility 200 predicted by the power generation prediction unit 740, which are combined for each of scenario candidates 1 to X and stored in the scenario candidate DB 760.

[0038] Fig. 9 is a diagram showing an example of a graph in which X predicted values ​​of electricity market prices and X predicted values ​​of power generation amounts of the photovoltaic power generation facility 200 are combined for each of scenario candidates 1 to X as a data group stored in the scenario candidate DB 760. For example, Fig. 9 combines, for each of scenario candidates 1 to X, a graph showing the predicted values ​​of electricity market prices corresponding to each of scenario candidates 1 to X in Fig. 6 with a graph showing the predicted values ​​of power generation amounts of the photovoltaic power generation facility 200 corresponding to each of scenario candidates 1 to X in Fig. 7. In addition to the data group shown in Fig. 8, the scenario candidate DB 760 may also store the data group shown in Fig. 9.

[0039] The number of scenarios to be finally selected from the scenario candidates 1 to X shown in FIGS. 8 and 9 is set in the selection number setting unit 780 via the input unit 790.

[0040] When the number of scenarios to be selected is set in the selection number setting unit 780, the scenario selection unit 750 selects the selected number of scenarios from among the scenario candidates 1 to X according to the processing procedure in FIG. 10 , which will be described later. Here, when selecting the selected number of scenarios from among the scenario candidates 1 to X, the scenario selection unit 750 sorts the scenario candidates 1 to X in ascending or descending order of the predicted value of the electricity market price, or in descending or descending order of the predicted value of the power generation amount of the solar power generation facility 200. For each of the scenario candidates 1 to X, the predicted value of the electricity market price and the predicted value of the power generation amount of the solar power generation facility 200 are associated one-to-one. Therefore, it is sufficient to sort the scenario candidates 1 to X based on either the predicted value of the electricity market price or the predicted value of the power generation amount of the solar power generation facility 200. As a result, when the scenario candidates 1 to X are sorted in order based on the magnitude of either the predicted value of the electricity market price or the predicted value of the power generation amount of the solar power generation facility 200, the scenario selection unit 750 stores data indicating the order of the scenario candidates 1 to X after sorting in the memory 751.

[0041] 10 is a flowchart showing an example of the selection operation by the scenario selection unit 750. Here, let m be the total number of scenario candidates 1 to X, n be the number of scenarios selected from scenario candidates 1 to X, and i be a number indicating the ordinal number of the selected scenario out of the n selected scenarios. Furthermore, equation ki shown in step S130 is an equation for determining the order of the scenarios to be selected from the rearranged scenario candidates 1 to X, and if the calculation result of equation ki includes a decimal, the calculation result with the decimal rounded up is determined as the order of the scenario to be selected.

[0042] First, the scenario selection unit 750 refers to the scenario candidate DB 760 to obtain the total number m of scenario candidates 1 to X, and refers to the selection number setting unit 780 to obtain the selection number n of scenarios to be selected from the scenario candidates 1 to X (step S110).

[0043] Next, the scenario selection unit 750 sets i=1 to select the first scenario from among the scenario candidates 1 to X (S120).

[0044] Next, the scenario selection unit 750 performs calculations by substituting the numbers corresponding to m, n, and i in the formula ki, and determines the order among scenario candidates 1 to X that corresponds to the scenario to be selected first (S130).

[0045] Next, the scenario selection unit 750 stores data indicating the order of the scenario to be selected first from the rearranged scenario candidates 1 to X in the memory 751 (S140).

[0046] Next, the scenario selection unit 750 determines whether i = n (S150). If i = n is not the case (S150: NO), the scenario selection unit 750 adds 1 to i (S160) and repeatedly executes steps S130 to S150 above until i = n.

[0047] When i=n (S150: YES), the series of processes ends. By executing the above steps S110 to S160, n scenarios can be selected from the scenario candidates 1 to X.

[0048] For example, the case where m = 100 and n = 3 will be described. For ease of explanation, if the calculation result of ki includes three decimal places, the three decimal places will be rounded off and the result will be stated to two decimal places.

[0049] When i=1, k1=16.67 When i=2, k2=50 When i=3, k3=83.33 In other words, from the rearranged scenario candidates 1 to X, the three scenarios are selected in the order of 17th, 50th, and 84th.

[0050] Also, for example, a case where m=100 and n=4 will be described. When i=1, k1=12.5 When i=2, k2=37.5 When i=3, k3=62.5 When i=4, k4=87.6 In other words, from the rearranged scenario candidates 1 to X, the four scenarios are selected in the order of 13th, 38th, 63rd, and 88th.

[0051] Also, for example, a case where m=100 and n=5 will be described. When i=1, k1=10 When i=2, k2=30 When i=3, k3=50 When i=4, k4=70 When i=5, k5=90 In other words, from the rearranged scenario candidates 1 to X, five scenarios are selected in the order of 10th, 30th, 50th, 70th, and 90th.

[0052] In this way, the n scenarios selected from the rearranged scenario candidates 1 to X will not include the first and hundredth scenario candidates and their neighbors, which will result in the lowest or highest predicted value for the electricity market price, or the highest or lowest predicted value for the power generation amount of the solar power generation facility 200, but will include scenario candidates spaced at equal intervals other than those.

[0053] 11 is a diagram showing an example of a data group stored in the scenario DB 770. The scenario DB 770 stores, in hourly intervals, forecast values ​​from midnight to midnight of the electricity market price and the power generation amount of the photovoltaic power generation facility 200 included in n scenarios selected by the scenario selection unit 750 from among scenario candidates 1 to X. For example, when n=3, the 17th, 50th, and 84th scenarios A, B, and C are extracted from the rearranged scenario candidates 1 to X from the scenario candidate DB 760 and stored in the scenario DB 770. The data group stored in the scenario DB 770 is provided to the subsequent trading plan creation device 800 via the output unit 795.

[0054] <<Trading plan creation device 800>> FIG. 12 is a block diagram showing the functions of the trading plan creating device 800. The trading plan creation device 800 is a device that uses n scenarios selected from scenario candidates 1 to X by the scenario selection unit 750 of the scenario creation device 700 to create n trading plans for power purchases and sales that maximize the profits of the power generation company that owns the solar power generation facility 200. In the following explanation, the number n of scenarios selected by the scenario selection unit 750 is assumed to be three, for example. For ease of explanation, it is assumed that the time when the predicted value of the power market price will be lowest will occur in the order of scenarios A, C, and B selected by the scenario selection unit 750, and that the time when the predicted value of the power market price will be highest will occur in the order of scenarios C, B, and A, which differ from the example shown in FIG. 6. It is assumed that scenarios A, B, and C are represented by solid lines, dashed lines, and dashed-dotted lines, respectively.

[0055] The trading plan creation device 800 includes an input unit 810, an output unit 820, and a trading plan creation unit 830 as means for realizing the above functions.

[0056] FIG. 13 shows examples of trading plans A', B', and C' created for each of three scenarios A, B, and C selected by the scenario selection unit 750. In FIG. 13, the graph on the left shows scenarios A, B, and C. In the graph on the left, the horizontal axis represents the time (hours) from midnight to midnight on the day of the power trading, and the vertical axis represents the electricity market price (yen / kWh) on the day of the power trading. In addition, in FIG. 13, the graph on the upper right shows the trading plan A' that is considered to be optimal created using scenario A, the graph on the middle right shows the trading plan B' that is considered to be optimal created using scenario B, and the graph on the lower right shows the trading plan C' that is considered to be optimal created using scenario C. In each of the graphs on the upper, middle, and lower right, the horizontal axis represents the time (hours) from midnight to midnight on the day of the power trading, the vertical axis on the right represents the predicted value of the electricity market price (yen / kWh) on the day of the power trading, and the vertical axis on the left represents the planned value of the power trading volume (kWh) on the day of the power trading.

[0057] The trading plan creation unit 830 acquires data indicating scenarios A, B, and C via the input unit 810. The trading plan creation unit 830 also acquires data indicating the chargeable available capacity of the storage battery 400 from the storage battery control device 420 via the input unit 810. The trading plan creation unit 830 then detects the optimal time slots for buying and selling electricity for each of scenarios A, B, and C based on the predicted values ​​of the electricity market price and the predicted values ​​of the power generation amount of the photovoltaic power generation facility 200 on the electricity buying and selling trading day included in scenarios A, B, and C, and the chargeable available capacity of the storage battery 400 at the time of placing the spot bid, and creates trading plans A', B', and C' for buying and selling electricity during those time slots. 13, for convenience of explanation, the time period for purchasing electricity in the trading plans A', B', and C' is assumed to be a time period (e.g., 2 hours) that includes the time when the predicted value of the electricity market price is the lowest, while the time period for selling electricity in the trading plans A', B', and C' is assumed to be a time period (e.g., 2 hours) that includes the time when the predicted value of the electricity market price is the highest. The trading plan creation unit 830 stores data indicating the created trading plans A', B', and C' in the memory 831.

[0058] The data group stored in the memory 831 is provided via the output unit 820 to the subsequent trading plan evaluation device 900 .

[0059] <<Trading Plan Evaluator 900>> FIG. 14 is a block diagram showing the functions of the trading plan evaluator 900. The trading plan evaluation device 900 is a device that evaluates which of the trading plans A', B', and C' created by the trading plan creation device 800 is most likely to bring about profits.

[0060] The trading plan evaluation device 900 includes an input unit 910, an output unit 920, and a trading plan evaluation unit 930 as means for realizing the above functions.

[0061] FIG. 15 illustrates an example of evaluating which of trading plans A', B', and C' will have the largest balance. The top panel of FIG. 15 illustrates the expected balances EA1, EB1, and EC1 when trading plan A' is executed for each of scenarios A, B, and C, and the expected average balance AV1 of the expected balances EA1, EB1, and EC1. The middle panel of FIG. 15 illustrates the expected balances EA2, EB2, and EC2 when trading plan B' is executed for each of scenarios A, B, and C, and the expected average balance AV2 of the expected balances EA2, EB2, and EC2. The bottom panel of FIG. 15 illustrates the expected balances EA3, EB3, and EC3 when trading plan C' is executed for each of scenarios A, B, and C, and the expected average balance AV3 of the expected balances EA3, EB3, and EC3.

[0062] The transaction plan evaluation device 900 acquires data indicating scenarios A, B, and C from the scenario DB 770 of the scenario creation device 700. Specifically, the transaction plan evaluation unit 930 acquires data indicating scenarios A, B, and C sent from the scenario DB 770 via the input unit 910 upon starting evaluation of the transaction plans A', B', and C'.

[0063] First, the trading plan evaluation unit 930 calculates the expected balance EA1, the expected balance EB1, the expected balance EC1, and the expected average balance AV1 of the expected balances EA1, EB1, and EC1 in order to evaluate whether scenario A is the optimal plan. For example, the expected balance EA1 is 90 yen, the expected balance EB1 is 0 yen, the expected balance EC1 is 0 yen, and the expected average balance AV1 is 30 yen.

[0064] Next, the trading plan evaluation unit 930 calculates the expected balance EA2, the expected balance EB2, the expected balance EC2, and the expected average balance AV2 of the expected balances EA2, EB2, and EC2 to evaluate whether scenario B is the optimal plan. For example, the expected balance EA2 is 0 (yen), the expected balance EB2 is 75 (yen), the expected balance EC2 is -30 (yen), and the expected average balance AV2 is calculated to be 15 (yen).

[0065] Next, the trading plan evaluation unit 930 calculates the expected balance EA3, the expected balance EB3, the expected balance EC3, and the expected average balance AV3 of the expected balances EA3, EB3, and EC3 to evaluate whether scenario C is the optimal plan. For example, the expected balance EA3 is −25 yen, the expected balance EB3 is −10 yen, the expected balance EC3 is 20 yen, and the expected average balance AV3 is −5 yen.

[0066] The above calculation results are stored in memory 931. In this way, the trading plan evaluation unit 930 can quantify which of the trading plans A', B', and C' is most likely to generate profits. In this example, the expected value of the balance EA1 is maximized, and the expected value of the average balance AV1 is also maximized. Therefore, it can be seen that executing trading plan A' for scenario A is likely to maximize profits.

[0067] The expected values ​​of income and expenditure EA1, EB1, EC1, EA2, EB2, EC2, EA3, EB3, and EC3 and the expected values ​​of average income and expenditure AV1, AV2, and AV3 are output from the output unit 920 in a form (for example, a form in which the content shown in Figure 15 is displayed on a display) that can assist the owner of the solar power generation facility 200 in selecting one of the trading plans A', B', or C' as the optimal trading plan.

[0068] FIG. 16 is a block diagram showing an example of hardware of an information processing device 1000 that realizes the functions of the scenario creating device 700, the transaction plan creating device 800, and the transaction plan evaluating device 900.

[0069] The information processing device 1000 includes a processor 1010 , a main memory device 1020 , an auxiliary memory device 1030 , an input device 1040 , an output device 1050 , and a communication device 1060 .

[0070] The information processing device 1000 is, for example, a personal computer, an office computer, various server devices, a general-purpose machine, etc. The information processing device 1000 may be realized, in whole or in part, using virtual information processing resources provided using virtualization technology, such as a virtual server provided by a cloud system. The scenario creation device 700, the trading plan creation device 800, and the trading plan evaluation device 900 may be realized using multiple information processing devices 10 connected to each other so as to be able to communicate with each other.

[0071] The processor 1010 is configured using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), an AI (Artificial Intelligence) chip, etc.

[0072] The main memory device 1020 is a device that stores programs and data, and is, for example, a read-only memory (ROM), a random access memory (RAM), or a non-volatile memory (NVRAM (Non-Volatile RAM)).

[0073] The auxiliary storage device 1030 is, for example, a solid state drive (SSD), a hard disk drive, an optical storage device (such as a compact disc (CD) or a digital versatile disc (DVD)), a storage system, a read / write device for a recording medium such as an IC card, an SD card, or an optical recording medium, or a storage area of ​​a cloud server. Programs and data can be read into the auxiliary storage device 1030 via a recording medium reader or a communication device 1060. The programs and data stored in the auxiliary storage device 1030 are read into the main storage device 1020 as needed.

[0074] The input device 1040 is an interface that accepts input from the outside, and is, for example, a keyboard, a mouse, a touch panel, a card reader, a pen-input tablet, a voice input device, or the like.

[0075] The output device 1050 is an interface that outputs various information such as the progress of processing and the results of processing. The output device 1050 is, for example, a display device (LCD (Liquid Crystal Display), graphic card, etc.) that visualizes the various information, a device that converts the various information into audio (audio output device (speaker, etc.)), or a device that converts the various information into text (printer, etc.). The information processing device 1000 may be configured to input and output information to and from other devices via the communication device 1060.

[0076] The input device 1040 and the output device 1050 constitute a user interface that receives information from the user and presents information to the user.

[0077] The communication device 1060 is a device that realizes communication (wired communication or wireless communication) with other devices via a communication infrastructure, and is configured using, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, and the like.

[0078] The information processing device 1000 may be equipped with, for example, an operating system, a file system, a DBMS (Data Base Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc.

[0079] The functions of the scenario creation device 700, the transaction plan creation device 800, and the transaction plan evaluation device 900 are realized by a processor 1010 of the information processing device 1000 reading and executing a control program stored in a main memory device 1020. For example, the functions of the price prediction unit 730, the power generation prediction unit 740, the scenario selection unit 750, and the selection number setting unit 780 in the scenario creation device 700, the transaction plan creation unit 830 in the transaction plan creation device 800, and the transaction plan evaluation unit 930 in the transaction plan evaluation device 900 are realized by the processor 1010. In addition, the market price factor DB710, weather forecast DB720, scenario candidate DB760, scenario DB770, and memory 751 in the scenario creation device 700, the memory 831 in the trading plan creation device 800, and the memory 931 in the trading plan evaluation device 900 are realized by the main memory device 1020 and the auxiliary memory device 1030, the input unit 790 in the scenario creation device 700, the input unit 810 in the trading plan creation device 800, and the input unit 910 in the trading plan evaluation device 900 are realized by the input device 1040, and the output unit 795 in the scenario creation device 700, the output unit 820 in the trading plan creation device 800, and the output unit 920 in the trading plan evaluation device 900 are realized by the output device 1050.

[0080] <<Summary>> As described above, the scenario creation device 700 includes a price prediction unit 730 that predicts the value of the electricity market price on the day of electricity trading in the electricity market, which is the first of two uncertain items that affect the trading plan for the day of electricity trading in the electricity market for an electrical facility that combines a solar power generation facility 200 and a storage battery 400, using a group of data stored in the market price factor DB710 and the weather forecast DB720; a power generation prediction unit 740 that predicts the value of the power generation amount of the solar power generation facility 200, which is the second uncertain item, using a group of data stored in the weather forecast DB720; a scenario candidate creation unit 745 that creates multiple scenario candidates 1 to X that include predicted values ​​of the above uncertain items on the day of electricity trading; and a scenario selection unit 750 that selects from the multiple scenario candidates 1 to X a number (n) of scenarios that is less than the number of the multiple scenario candidates 1 to X to be used when creating the trading plan.

[0081] The scenario generation device 700 can select an appropriate scenario in a short time from among multiple scenario candidates 1 to X that include the above-mentioned uncertain items. Furthermore, the scenario generation device 700 can select, from among multiple scenario candidates 1 to X that include the above-mentioned uncertain items, scenarios with high diversity in which the predicted values ​​are not similar to each other but vary to a certain extent.

[0082] Furthermore, as the renewable energy power generation facility, for example, a wind power generation facility may be used instead of the solar power generation facility 200, or both the solar power generation facility 200 and a wind power generation facility may be used. Since the power generation efficiency of wind power generation facilities is relatively high at 30 to 40%, it is possible to improve the profits of the power generation business operator based on the selected scenario.

[0083] In addition, the data group stored in the weather forecast DB720 is, for example, an ensemble weather forecast data group that statistically processes the results of multiple numerical forecasts, and it is possible to create a number of scenario candidates 1 to X corresponding to the number of members, thereby expanding the options for scenarios to select from among the scenario candidates 1 to X.

[0084] It should be noted that the present embodiment is provided to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention. [Explanation of symbols]

[0085] 100 Market Trading Planning System 200 solar power generation facilities 210,410 PCS 300 Power system 400 storage battery 500 Communication Network 600 computers 700 Scenario Creation Device 710 Market Price Factors DB 720 Weather Forecast DB 730 Price Forecasting Department 740 Power Generation Forecasting Department 745 Scenario Candidate Creation Department 750 Scenario Selection Department 751,831,931 memory 760 Scenario Candidate DB 770 Scenario DB 780 Selection Number Setting Unit 790,810,910 Input section 795,820,920 Output section 800 Trading Plan Creation Device 830 Trading Planning Department 900 Trading Plan Evaluation Device 930 Trading Plan Evaluation Department

Claims

1. a prediction unit that predicts, using meteorological information, values ​​of uncertain items that affect a trading plan for a trading day for electricity in an electricity market in an electrical facility that combines a power generation facility that uses renewable energy and a storage battery; a scenario candidate creation unit that creates a plurality of scenario candidates including predicted values ​​of the uncertain items on the electricity trading date; a scenario selection unit that selects, from the plurality of scenario candidates, scenarios to be used when creating the trading plan, the number of scenarios being less than the number of the plurality of scenario candidates; A scenario creation device including:

2. 2. The scenario creation device according to claim 1, The uncertain items include the electricity market price of the electricity market. Scenario creation device.

3. 3. The scenario creation device according to claim 2, The uncertain items further include the amount of power generated by the power generation facility. Scenario creation device.

4. 4. The scenario creation device according to claim 3, the power generation facility is a solar power generation facility or a wind power generation facility, The amount of power generation is the amount of power generation by the solar power generation facility or the amount of power generation by the wind power generation facility. Scenario creation device.

5. 4. The scenario creation device according to claim 3, The power generation facility is a solar power generation facility and a wind power generation facility, The amount of power generation is the total value of the amount of power generation of the solar power generation facility and the amount of power generation of the wind power generation facility. Scenario creation device.

6. 2. The scenario creation device according to claim 1, The weather information is information related to an ensemble weather forecast. Scenario creation device.

7. A scenario creation method for a scenario creation device, comprising: a prediction step of predicting, using meteorological information, values ​​of uncertain items that affect a trading plan for a trading day for electricity purchase and sale in an electricity market for an electrical facility that combines a power generation facility that uses renewable energy with a storage battery; a creation step of creating a plurality of candidate scenarios including predicted values ​​of the uncertain items on the electricity trading date; a selection step of selecting, from the plurality of scenario candidates, scenarios to be used when creating the trading plan, the number of scenarios being less than the number of the plurality of scenario candidates; How to create a scenario including:

8. On the computer, A prediction process that predicts, using meteorological information, values ​​of uncertain items that affect a trading plan for a trading day for electricity in an electricity market for an electrical facility that combines a power generation facility that uses renewable energy and a storage battery; a generation process for generating a plurality of candidate scenarios including predicted values ​​of the uncertain items on the electricity trading date; a selection process for selecting, from the plurality of scenario candidates, scenarios to be used when creating the trading plan, the number of scenarios being less than the number of the plurality of scenario candidates; A program that executes the following.

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