Scenario creation device, scenario creation method, and program

Abstract

To quickly select an appropriate scenario candidate from among a plurality of scenario candidates including uncertainties that affect a trading plan for the day of power buying and selling in an electricity market. [Solution] A scenario creation device including: a prediction unit that uses meteorological information to predict values ​​of uncertain items that affect a trading plan on 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 on the power trading day; and a scenario selection unit that selects from the plurality of scenario candidates a number of scenario candidates that is less than the number of the plurality of scenario candidates to be used when creating the trading plan.

Description

[Technical field]

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

[0002] When creating a trading plan for a power purchase and sale date in the electricity 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 date, 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] JP 2023-74886 A [Patent Document 2] JP 2005-25377 A 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 scenarios, and for each pair of scenarios, an overlap indicating the degree of similarity between the scenarios is evaluated, and one of a pair of scenarios with an overlap equal to or greater than a preset threshold is deleted, thereby reducing the number of scenarios. However, in Patent Document 1, the above division and overlap evaluation calculation processes must be repeated until an optimal scenario can be evaluated before creating a trading plan, and there is a risk that the calculation time until the optimal scenario is evaluated will be long.

[0005] In addition, in the case of Patent Document 2, simulated results of market trading, that is, electricity prices and electricity trading volumes, are calculated based on each of a plurality of scenarios, and the simulated results are displayed in comparison on a display means to set up an optimal bidding strategy. However, in Patent Document 2, since the scenarios are evaluated by displaying the simulated results on a display means, there was a risk that it would take a long time to evaluate the optimal scenario.

[0006] The present invention has been made in consideration of the conventional problems described above, and its object 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 a number of scenario candidates including uncertain items. [Means for solving the problem]

[0007] A scenario creation device of a first aspect of the present invention that 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 in 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 to be used when creating the trading plan that is less than the number of the plurality of scenario candidates. Effect 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 a plurality of scenario candidates that include uncertain items regarding the date of electricity trading in the electricity market. [Brief description of the drawings]

[0009] [Figure 1] FIG. 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. [Diagram 2] FIG. 1 is a functional block diagram of a market trading planning system 100. [Diagram 3] FIG. 7 is a block diagram showing the functions of a scenario creating device 700. [Figure 4] FIG. 13 is a diagram showing an example of a data group stored in a market price factor DB 710. [Diagram 5] FIG. 11 is a diagram showing an example of a data group stored in a weather forecast DB 720. [Figure 6] 13 is a graph showing an example of predicted values ​​of X electricity market prices by the price prediction unit 730. [Figure 7] 13 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] A figure showing an example of a data group stored in a scenario candidate DB760. [Figure 9] FIG. 13 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. [Figure 10] 13 is a flowchart showing an example of a selection operation by the scenario selection unit 750. [Figure 11] FIG. 13 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] 13 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 transaction plan evaluator 900. [Figure 15] FIG. 13 is a diagram showing an example of evaluating which of the trading plans A', B', and C' will have the largest balance. [Figure 16]1 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. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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 detail based on an embodiment thereof with reference to the accompanying drawings.

[0011] <<Market Trading Planning System 100>> FIG. 1 is a block diagram showing a market transaction planning system 100 including a scenario creating device 700 and a schematic configuration of the surroundings. In this embodiment, the electricity market is a market in which electricity is traded between a power generation company, who is the owner of a power generation facility that generates electricity using renewable energy (hereinafter referred to as a "renewable energy-using power generation facility"), and a general power transmission and distribution company, and is a wholesale electricity market in which the power generation company makes a spot bid based on a transaction plan by the day before the power trading date to establish the power trading. In this embodiment, the renewable energy-using 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-using power generation facility may be 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, etc., instead of the solar power generation facility 200, or may be a combination of a wind power generation facility, a hydroelectric power generation facility, a geothermal power generation facility, a biomass power generation facility, etc. with the solar power generation facility 200. In the latter case where the solar power generation facility 200 is combined with other power generation facilities, the predicted power generation amount of the power generation facility is the sum of the predicted power generation amount of the solar power generation facility 200 and the predicted power generation amount of the other power generation facilities to be combined with it.

[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 directly be 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 electric power market, electric power is charged from the power grid 300 to the storage battery 400, and when electric power is sold in the electric power 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, polycrystalline silicon power generation elements, single crystal silicon power generation elements, thin film power generation elements, etc. 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 system 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 system 300. When discharging the power charged in the storage battery 400 to the power system 300, the PCS 410 converts the power charged in the storage battery 400 from direct current to alternating current, and when charging the storage battery 400 with power from the power system 300, the PCS 410 converts the power from the power system 300 from alternating current to direct current.

[0015] For example, in a trading plan for making spot bidding by the day before the power buying and selling date, during a power buying period in which power buying is 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 selling is 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 solar power generation facility 200 is charged directly to the storage battery 400 without passing through the PCSs 210, 410.

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

[0017] The market trading plan system 100 is a system that supports the creation of an optimal trading plan for power generation companies, which is necessary for placing spot bids by the day before the power trading day in the power market. The market trading plan system 100 is communicatively connected to a computer 600 on the power market side that receives power buying and selling information included in the spot bids and manages the buying and selling of power, via a communication network 500, so that the power generation companies can place spot bids according to the finally determined trading plan. The communication network 500 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), a dedicated line, a power line communication network, various public communication networks, etc.

[0018] FIG. 2 is a block diagram illustrating the functionality of the market trading planning system 100. The market trading plan 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 irradiated onto 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] The electricity market price often falls when the cost of fuel used in the power generation equipment in general becomes cheaper, and rises when the cost of fuel becomes more expensive. In addition, when the weather is clear and the amount of solar radiation is high, the electricity market price falls as the power generated by the solar power generation equipment 200 increases, and when the weather is rainy or cloudy and the amount of solar radiation is low, the electricity market price often rises as the power generated by the solar power generation equipment 200 decreases. In addition, the electricity market price often rises when the demand for electricity exceeds the supply of electricity, and falls when the demand for electricity falls below the supply of electricity. In addition, the electricity market price often falls during the daytime hours of a day as the power generated by the solar power generation equipment 200 increases, and rises during the nighttime hours of a day as the power generation by the solar power generation equipment 200 stops. In this way, the electricity market price is the second uncertain item that fluctuates due to the influence of fuel costs, weather, the balance between supply and demand of electricity, the time of day and night in a day, and the like.

[0021] The scenario creation device 700 is a device that creates multiple scenario candidates that predict the power generation amount of the photovoltaic power generation facility 200, which is a first uncertain item, and the power market price, which is a second uncertain item, on a power buying and selling transaction day in the power market at a predetermined time interval, and selects a small number of scenarios required 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 scenario candidates, 24 predictions are made for the power generation amount of the photovoltaic power generation facility 200 and the power market price at one-hour intervals from midnight to midnight. The predetermined time interval may be, for example, 30 minutes, 2 hours, etc. other than one hour. When the predetermined time interval is 30 minutes, for one day's scenario candidates, 48 ​​predictions are made for the power generation amount of the photovoltaic power generation facility 200 and the power market price at one-hour intervals from midnight to midnight. Furthermore, when the predetermined time interval is two hours, twelve predictions are made for each of the power generation amount of the photovoltaic power generation facility 200 and the power market price for one day's scenario candidates at one-hour intervals from midnight to midnight.

[0022] The scenario creating device 700 includes, as means for realizing the above functions, a market price factor database (hereinafter referred to as "market price factor DB") 710, a weather forecast database (hereinafter referred to as "weather forecast DB") 720, a price prediction unit 730, a power generation prediction unit 740, a scenario candidate creating unit 745, a scenario selection unit 750, a scenario candidate database (hereinafter referred to as "scenario candidate DB") 760, a scenario database (hereinafter referred to as "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 by utilizing each of the four areas obtained by dividing the entire storage area of ​​one storage device into four areas.

[0023] The market price factor DB 710 stores a group of data that the price prediction unit 730 refers to in order to predict the electricity market price on the day of electricity buying and selling in the electricity market by 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.

[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 electricity market prices (yen / kWh) confirmed from 0:00 to 24:00 on the electricity trading date one day prior to the spot bid date, the electricity trading date two days prior to the spot bid date, and the electricity trading date three days prior to the spot bid date are stored in hourly intervals.

[0025] The market price factor DB 710 also stores available capacities available for buying and selling electricity in a plurality of interconnection lines in the power system 300 to which the photovoltaic power generation facilities 200 and the storage batteries 400 are interconnected. For example, available capacities (MW) from 0:00 to 24:00 of each of the different interconnection lines 1 to 4 on the day before the spot bidding day (the day before the day of the electricity buying and selling day) are stored in hourly intervals.

[0026] Furthermore, the market price factor DB 710 stores the capacities of large generators that are out of power generation due to maintenance, breakdowns, etc., among the large generators of the electric power company that generate the power supplied to the power system 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 in the interconnections 1 to 4 are stored in hourly intervals. When multiple large generators are out of power generation, the total capacity of the multiple large generators is stored.

[0027] The data group stored in the market price factor DB 710 is not limited to the above. In the market price factor DB 710, if there is a factor that has the potential to improve the accuracy of the price prediction unit 730 in predicting the electricity market price, data indicating the factor may be newly stored in addition to the above data group, or the data may be rewritten and stored in place of at least a part of the above data group.

[0028] Note that 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 referenced by the price prediction unit 730 to predict the electricity market price in the electricity market 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 known as a method for predicting future weather by numerical calculation. In ensemble weather forecasting, multiple initial values ​​including small errors similar to the observation errors required to predict a forecast target (e.g., wind speed, temperature, air pressure, precipitation, solar radiation) are prepared, and the calculation results of the forecast target corresponding to each of the multiple initial values ​​are statistically processed to predict future weather. In this embodiment, the weather forecast DB 720 stores the calculation results (X items) of the forecast target corresponding to each of the multiple initial values ​​(X items).

[0031] When the calculation results of the prediction targets are divided into groups of members 1 to X, the weather forecast DB 720 stores data from 0:00 to 24:00 for each of the members 1 to X, which indicates 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, divided into hourly intervals.

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

[0033] The price prediction unit 730 predicts the electricity market price on the day of an electricity buying and selling transaction every hour from midnight to midnight based on the data groups stored in the market price factor DB 710 and the weather forecast DB 720. As a method for the price prediction unit 730 to predict the electricity market price, for example, a method using a deep neural network or just-in-time modeling can be adopted. In addition, 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 predicted values ​​of the electricity market price corresponding to each of members 1 to X.

[0034] FIG. 6 is a graph showing an example of predicted values ​​of X electric power market prices by the price prediction unit 730. In FIG. 6, the horizontal axis indicates the time (hours) from 0:00 to 24:00 on the day of the electric power trading, and the vertical axis indicates the predicted value of the electric power market price (yen / kWh). The predicted values ​​of the X electric power market prices show changes according to the values ​​of the data group stored in the market price factor DB 710 and the weather forecast DB 720, and specifically, show changes according to the past electric power market price several days before the day of the electric power trading, the available capacity in the interconnection line, the capacity of a large generator that is out of service, the predicted wind speed, temperature, air pressure, precipitation, solar radiation, etc. on the day of the electric power trading. In the example of FIG. 6, the predicted values ​​of the X electric power market prices show changes to relatively low values ​​during the daytime when solar radiation is observed, and on the other hand, show changes to relatively high values ​​during the nighttime when solar radiation is not observed. Here, the predicted values ​​of the electric power market prices corresponding to the 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 buying and selling transaction for each hour from midnight to midnight based on a group of data stored in the weather forecast DB 720. As a method for the power generation prediction unit 740 to predict the amount of power generated by the photovoltaic power generation facility 200 on the day of the power buying and selling transaction, various methods can be adopted for predicting the amount of power generated by the photovoltaic power generation facility 200 using at least one of the wind speed, temperature, air pressure, precipitation, and solar radiation for each of the members 1 to X as a parameter. In addition, since the weather forecast DB 720 stores a total of X pieces of data for prediction targets corresponding to the members 1 to X, the predicted values ​​of the amount of power generated by the photovoltaic power generation facility 200 predicted by the power generation prediction unit 740 are X pieces corresponding to each of the members 1 to X.

[0036] FIG. 7 is a graph showing an example of predicted values ​​of X power generation amounts in the photovoltaic power generation facility 200 by the power generation prediction unit 740. In FIG. 7, the horizontal axis indicates the time (hours) from 0:00 to 24:00 on the day of the power trading, and the vertical axis indicates the predicted value of the power generation amount (kWh) of the photovoltaic power generation facility 200. The predicted values ​​of X power generation amounts in the photovoltaic power generation facility 200 show changes according to the values ​​of the data group stored in the weather forecast DB 720, and specifically, show 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 predicted values ​​of X power generation amounts in 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 the power generation amounts of the photovoltaic power generation facility 200 corresponding to the 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 the following FIGS. 8 and 9 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. In the scenario candidate DB 760, X electricity market prices predicted by the price prediction unit 730 and X predicted values ​​of power generation amounts of the photovoltaic power generation facility 200 predicted by the power generation prediction unit 740 from 0:00 to 24:00 are combined for each of scenario candidates 1 to X and stored in hourly intervals.

[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. Fig. 9 is a combination of, for each of scenario candidates 1 to X, a graph showing predicted values ​​of electricity market prices corresponding to each of scenario candidates 1 to X in Fig. 6 and a graph showing 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 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 of FIG. 10 described later. Here, when selecting the selected number of scenarios from among the scenario candidates 1 to X, the scenario selection unit 750 rearranges the scenario candidates 1 to X in ascending or descending order of the predicted value of the electricity market price, or in ascending or descending order of the predicted value of the power generation amount of the solar power generation facility 200. Since 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 linked one-to-one in each of the scenario candidates 1 to X, it is sufficient to perform rearrangement with respect to 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 rearranged in order according to 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 rearrangement in the memory 751.

[0041] 10 is a flowchart showing an example of the selection operation by the scenario selection unit 750. Here, the total number of scenario candidates 1 to X is m, the number of selected scenarios selected from the scenario candidates 1 to X is n, and a number indicating the ordinal number of the selected scenario out of the number n of selected scenarios is i. Furthermore, the formula ki shown in step S130 is a formula for determining the order of the scenarios to be selected from the rearranged scenario candidates 1 to X, and if the calculation result of formula 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 of the 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 judges 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 the above steps S130 to S150 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 third decimal place 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 order of scenario candidates 1 to X, the three scenarios are selected in the order of 17th, 50th, and 84th.

[0050] Further, 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 order of scenario candidates 1 to X, the four scenarios are selected in the order of 13th, 38th, 63rd, and 88th.

[0051] Further, 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 order of 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 or their neighbors for which the predicted value of the electricity market price is the lowest or highest value, or the predicted value of the power generation amount of the solar power generation facility 200 is the maximum or minimum value, 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. In the scenario DB 770, forecast values ​​from 0:00 to 24:00 of the electricity market price and the power generation amount of the photovoltaic power generation facility 200 included in n scenarios selected from the scenario candidates 1 to X by the scenario selection unit 750 are stored in one-hour intervals. 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 downstream transaction 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 creates n trading plans for power purchase and sale that maximize the profit of the power generation company that owns the solar power generation facility 200, using n scenarios selected from among scenario candidates 1 to X by the scenario selection unit 750 of the scenario creation device 700. In the following description, the number n of scenarios selected by the scenario selection unit 750 is assumed to be, for example, 3. For convenience of explanation, the time when the predicted value of the power market price is the lowest will arrive in the order of scenarios A, C, and B selected by the scenario selection unit 750, and the time when the predicted value of the power market price is the highest will arrive in the order of scenarios C, B, and A, which are different from the example shown in FIG. 6. Scenarios A, B, and C will be indicated by a solid line, a dashed line, and a dashed line, 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 is a diagram showing an example 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 side shows the scenarios A, B, and C. In the graph on the left side, the horizontal axis indicates the time (hours) from 0:00 to 24:00 on the day of the power trading, and the vertical axis indicates the power market price (yen / kWh) on the day of the power trading. In addition, in FIG. 13, the graph on the upper right side shows the trading plan A' that is considered to be optimal created using scenario A, the graph on the middle right side shows the trading plan B' that is considered to be optimal created using scenario B, and the graph on the lower right side 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 sides, the horizontal axis indicates the time (hours) from 0:00 to 24:00 on the day of the power trading, the vertical axis on the right side indicates the predicted value of the power market price (yen / kWh) on the day of the power trading, and the vertical axis on the left side indicates 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 the scenarios A, B, and C via the input unit 810. The trading plan creation unit 830 also acquires data indicating the chargeable free capacity of the storage battery 400 from the storage battery control device 420 via the input unit 810. Then, the trading plan creation unit 830 detects the optimal time slots for buying and selling electricity for each of the 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 date included in the scenarios A, B, and C, and the chargeable free capacity of the storage battery 400 at the time of spot bidding, 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 during which power is purchased in the trading plans A', B', and C' is a time period (e.g., 2 hours) including the time when the predicted value of the power market price is the lowest, while the time period during which power is sold in the trading plans A', B', and C' is a time period (e.g., 2 hours) including the time when the predicted value of the power 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 downstream trading plan evaluation device 900 .

[0059] <<Transaction Plan Evaluator 900>> FIG. 14 is a block diagram showing the functions of the trading plan evaluator 900. As shown in FIG. 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 generate profits.

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

[0061] FIG. 15 is a diagram showing an example of evaluating which of the trading plans A', B', and C' has the largest balance. The upper part of FIG. 15 is a diagram explaining the expected values ​​EA1, EB1, and EC1 of the balance when the trading plan A' is executed for each of the scenarios A, B, and C, and the expected value AV1 of the average balance of the expected values ​​EA1, EB1, and EC1 of the balance. The middle part of FIG. 15 is a diagram explaining the expected values ​​EA2, EB2, and EC2 of the balance when the trading plan B' is executed for each of the scenarios A, B, and C, and the expected value AV2 of the average balance of the expected values ​​EA2, EB2, and EC2 of the balance. The lower part of FIG. 15 is a diagram explaining the expected values ​​EA3, EB3, and EC3 of the balance when the trading plan C' is executed for each of the scenarios A, B, and C, and the expected value AV3 of the average balance of the expected values ​​EA3, EB3, and EC3 of the balance.

[0062] The transaction plan evaluation device 900 acquires data indicating the 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 the scenarios A, B, and C transmitted 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 balance expectation value EA1, the balance expectation value EB1, the balance expectation value EC1, and the average balance expectation value AV1 of the balance expectation values ​​EA1, EB1, and EC1 in order to evaluate whether the scenario A is an optimal plan. For example, the balance expectation value EA1 is calculated to be 90 (yen), the balance expectation value EB1 is calculated to be 0 (yen), the balance expectation value EC1 is calculated to be 0 (yen), and the average balance expectation value AV1 is calculated to be 30 (yen).

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

[0065] Next, the trading plan evaluation unit 930 calculates the balance expectation EA3, the balance expectation EB3, the balance expectation EC3, and the average balance expectation AV3 of the balance expectations EA3, EB3, and EC3 in order to evaluate whether scenario C is an optimal plan. For example, the balance expectation EA3 is -25 (yen), the balance expectation EB3 is -10 (yen), the balance expectation EC3 is 20 (yen), and the average balance expectation AV3 is -5 (yen).

[0066] The above calculation results are stored in the 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 EA1 of the balance is maximized, and the expected value AV1 of the average balance is also maximized, so it can be seen that executing trading plan A' for scenario A is likely to maximize profits.

[0067] The expected values ​​of balance EA1, EB1, EC1, EA2, EB2, EC2, EA3, EB3, and EC3 and the expected values ​​of average balance AV1, AV2, and AV3 are output from 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 solar power generation facility 200 in selecting one of 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, by using virtual information processing resources provided by using a virtualization technology, such as a virtual server provided by a cloud system. The scenario creating device 700, the trading plan creating device 800, and the trading plan evaluating device 900 may be realized by using a plurality of 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 Central Processing Unit (CPU), a Micro Processing Unit (MPU), a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Artificial Intelligence (AI) 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 (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, an IC card, an SD card, a reading / writing device for a recording medium such as an optical recording medium, a storage area of ​​a cloud server, etc. Programs and data can be read into the auxiliary storage device 1030 via a recording medium reading device 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), a graphic card, etc.) that visualizes the various information described above, a device that converts the various information described above into voice (voice output device (speaker, etc.)), or a device that converts the various information described above 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.

[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 include, for example, an operating system, a file system, a data base management system (DBMS) (relational database, NoSQL, etc.), a key-value store (KVS), 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 electricity trading day in the electricity market, which is the first of two uncertain items that affect the trading plan for the electricity trading day in the electricity market for an electrical facility combining a solar power generation facility 200 and a storage battery 400, using a group of data stored in the market price factor DB 710 and a weather forecast DB 720; 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 DB 720; a scenario candidate creation unit 745 that creates multiple scenario candidates 1 to X including predicted values ​​of the above uncertain items on the electricity trading day; 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 a trading plan.

[0081] The scenario creation 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. Also, the scenario creation device 700 can select highly diverse scenarios from among multiple scenario candidates 1 to X that include the above-mentioned uncertain items, in which the predicted values ​​are not similar to each other and vary to a certain extent.

[0082] Furthermore, as a 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 a wind power generation facility 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 is generated by statistically processing the results of multiple numerical forecasts, and it is possible to create scenario candidates 1 to X in a number corresponding to the number of members, thereby expanding the options for scenarios to be selected from the scenario candidates 1 to X.

[0084] It should be noted that the present embodiment is provided for the purpose of facilitating 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 equipment 210,410 PCS 300 Power system 400 Battery 500 Communication Network 600 Computers 700 Scenario Creation Device 710 Market Price Factors DB 720 Weather Forecast DB 730 Price Prediction 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 Evaluator 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 day of electricity buying and selling in an electricity market in 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 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 smaller than the number of the plurality of scenario candidates; Including, The uncertain items include an electricity market price in the electricity market and an amount of power generated by the power generation facility; The scenario selection unit selects, from the multiple scenario candidates arranged in order of magnitude of the predicted value of the electricity market price or in order of magnitude of the predicted value of the power generation amount, the scenario candidates spaced at equal intervals as the scenario, excluding the scenario candidates in which the predicted value of the electricity market price is a minimum value, a maximum value, or a value close to the minimum value and the maximum value, or excluding the scenario candidates in which the predicted value of the power generation amount is a value close to the minimum value, a maximum value, a minimum value and the maximum value. Scenario creation device.

2. 2. The scenario creation device according to claim 1, 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 of the solar power generation facility or the amount of power generation of the wind power generation facility. Scenario creation device.

3. 2. The scenario creation device according to claim 1, The power generation facility is a solar power generation facility and a wind power generation facility, The amount of power generation is a 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.

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

5. 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 day of electricity purchase and sale in an electricity market in an electrical facility that combines a power generation facility that uses renewable energy with a storage battery; 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; Including, The uncertain items include an electricity market price in the electricity market and an amount of power generated by the power generation facility; In the selection step, from among the multiple scenario candidates arranged in order of magnitude of the predicted value of the electricity market price or in order of magnitude of the predicted value of the power generation amount, the scenario candidates spaced at equal intervals are selected as the scenarios, excluding the scenario candidates in which the predicted value of the electricity market price is a minimum value, a maximum value, or a value close to the minimum and maximum values, or excluding the scenario candidates in which the predicted value of the power generation amount is a value close to the minimum value, a maximum value, or a value close to the minimum and maximum values. How to create a scenario.

6. On the computer, A prediction process for predicting, using meteorological information, values ​​of uncertain items that affect a trading plan for a day of electricity purchase and sale in an electricity market in an electrical facility that combines a power generation facility that uses renewable energy with a storage battery; A process of creating 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 smaller than the number of the plurality of scenario candidates; Run the command, The uncertain items include an electricity market price in the electricity market and an amount of power generated by the power generation facility; In the selection process, from among the multiple scenario candidates arranged in order of magnitude of the predicted value of the electricity market price or in order of magnitude of the predicted value of the power generation amount, the scenario candidates spaced at equal intervals are selected as the scenario, excluding the scenario candidates in which the predicted value of the electricity market price is a minimum value, a maximum value, or a value close to the minimum and maximum values, or excluding the scenario candidates in which the predicted value of the power generation amount is a value close to the minimum value, a maximum value, or a value close to the minimum and maximum values. program.

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