JP2016038348A - Plan formulation method, plan formulation system and plan formulation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately set a photovoltaic power generation amount for each target area.SOLUTION: A plan formulation method includes the steps of: acquiring a predicted value and an actual value of the amount of solar radiation for each of a plurality of target areas (S12); calculating a prediction error of the amount of solar radiation on the basis of difference between the predicted value and the actual value of the amount of solar radiation (S20); calculating covariance of the prediction error of the amount of solar radiation for each combination of two target areas identified from among the plurality of target areas (S20); and on the basis of the covariance, setting a photovoltaic power generation amount for each of the plurality of target areas so that the prediction error of the amount of solar radiation for each of the plurality of target areas becomes small (S22).SELECTED DRAWING: Figure 6

Description

  The present invention relates to a plan formulation method, a plan formulation system, and a plan formulation program.

  2. Description of the Related Art In recent years, a service for bundling power generation devices of consumers to form a virtual power plant (VPP (Virtual Power Plant)) and selling power to an electric power company is known. The VPP provider notifies the power company in advance of the amount of power supplied, and obtains a reward by supplying the notified power amount to the power company, but receives a penalty if it cannot be supplied.

JP-A-8-163778 Japanese Patent No. 4848051 JP-A-10-108486

  Recently, a VPP service using photovoltaic power generation (PV) has also been studied. In such a VPP service using solar power generation, it is preferable for the VPP provider to determine the amount of power supplied in consideration of the prediction accuracy of the amount of solar radiation in order to avoid penalties as much as possible.

  In one aspect, an object of the present invention is to provide a plan development method, a plan development system, and a plan development program that can appropriately set the amount of photovoltaic power generation in each target area.

  In one aspect, the plan formulation method obtains a predicted value and an actually measured value of the solar radiation amount for each of a plurality of target areas, and based on a difference between the predicted value of the solar radiation amount and the actually measured value, Calculate a prediction error, calculate a correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from the plurality of target areas, and based on the correlation, each of the plurality of target areas This is a plan formulation method in which the computer executes the process of setting the amount of photovoltaic power generation in each of the plurality of target areas so that the prediction error of the amount of solar radiation of the above becomes small.

  In one aspect, the plan formulation system, for each of a plurality of target areas, based on the acquisition unit that acquires the predicted value and the actual measurement value of the solar radiation amount, the difference between the predicted value of the solar radiation amount and the actual measurement value, A first calculation unit that calculates a prediction error of the solar radiation amount, a second calculation unit that calculates a correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from the plurality of target areas, A setting unit configured to set the solar power generation amount of each of the plurality of target areas so that the prediction error of the solar radiation amount of each of the plurality of target areas is reduced based on the correlation.

  In one aspect, the plan formulation program acquires a predicted value and an actual value of the solar radiation amount for each of a plurality of target areas, and based on the difference between the predicted value and the actual solar radiation amount, Calculate a prediction error, calculate a correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from the plurality of target areas, and based on the correlation, each of the plurality of target areas This is a planning program for causing a computer to execute the process of setting the amount of photovoltaic power generation in each of the plurality of target areas so that the prediction error of the amount of solar radiation of the above becomes small.

  The amount of photovoltaic power generation in each target area can be set appropriately.

1 is a diagram schematically showing a configuration of a VPP system according to an embodiment. FIG. It is a hardware block diagram of a VPP provider server. It is a functional block diagram of a VPP provider server. FIG. 4A is a diagram illustrating an example of a weather pattern (forecast) on the target day, FIG. 4B is a diagram illustrating an example of solar radiation amount prediction on the target day, and FIG. FIG. 4D is a diagram illustrating an example of past solar radiation amount prediction, and FIG. 4E is an example of past solar radiation amount results. FIG. It is a figure which shows the data stored in power generation performance DB. It is a flowchart which shows the process of a VPP provider server. FIGS. 7A and 7B are diagrams for explaining steps S14 and S16 in FIG. FIGS. 8A and 8B are diagrams for explaining steps S14 and S18 in FIG. It is a figure for demonstrating the effect of one Embodiment.

  Hereinafter, an embodiment of a VPP (Virtual Power Plant) system will be described in detail with reference to FIGS. FIG. 1 schematically shows the configuration of the VPP system 100. The VPP system 100 is a system in which a VPP provider bundles consumer photovoltaic power generation devices to form a virtual power plant (VPP), and buys and sells power generated in the VPP with an electric power company. is there.

  As shown in FIG. 1, the VPP system 100 includes a weather forecast provider server 10, an electric power company server 20, a large number of photovoltaic power generation devices 40, and a VPP provider server 30 as a plan formulation system. Each component of the VPP system 100 is connected to a network 70 such as the Internet.

  The weather forecast provider server 10 is a server installed in the Japan Meteorological Agency, a weather-related company, etc., and provides various data related to the weather (such as weather forecast information and information on the amount of solar radiation) to the VPP provider server 30 and the like. provide.

  The electric power company server 20 is a server installed in an electric power company or the like, and makes a power generation request to the VPP provider server 30. The content of the power generation request is, for example, to request R (kW) power generation on the target date (the next day or the like).

  The solar power generation device 40 is a power generation device that generates power using sunlight installed in a consumer.

  The VPP provider server 30 is a server used by the VPP provider. Here, the VPP provider promises to the power company in advance the amount of power that can be supplied on the target date (the next day, etc.), and if it supplies power as promised, it will receive a reward from the power company, while making a promise. If not, pay a penalty to the power company. The VPP provider returns a part of the reward to the customer.

  FIG. 2 shows the hardware configuration of the VPP provider server 30. As shown in FIG. 2, the VPP provider server 30 includes a CPU (Central Processing Unit) 90, a ROM (Read Only Memory) 92, a RAM (Random Access Memory) 94, and a storage unit (here, an HDD (Hard Disk Drive)). 96, a network interface 97, a portable storage medium drive 99, and the like. Each component of the VPP provider server 30 is connected to the bus 98. In the VPP provider server 30, a program (including a plan formulation program) stored in the ROM 92 or the HDD 96 or a program (including a plan formulation program) read from the portable storage medium 91 by the portable storage medium drive 99. When executed by the CPU 90, the function of each unit shown in FIG. 3 is realized. 3 also shows a weather information DB (database) 38 and a power generation result DB 39 stored in the HDD 96 of the VPP provider server 30 and the like. The specific data structure of the DBs 38 and 39 will be described later.

  FIG. 3 shows a functional block diagram of the VPP provider server 30. In the VPP provider server 30, when the CPU 90 executes the program, the weather information collection unit 31, the power generation amount collection unit 32, the power generation request reception unit 33, the first and second calculation units as the acquisition unit and the weather information acquisition unit. As a covariance calculation unit 34, a power generation amount determination unit 35 as a setting unit, and a result notification unit 36 are realized.

  The weather information collection unit 31 acquires information necessary for the VPP provider server 30 from the weather forecast provider server 10 and stores it in the weather information DB 38.

  The weather information DB 38 stores data as shown in FIGS. 4A to 4E. The data in FIG. 4A is data of the weather pattern (forecast) on the target day (the target day of the power generation request, mainly the next day). This weather pattern (forecast) data is announced by the Japan Meteorological Agency, and as an example, the weather (sunny or cloudy) of five areas (A to E), which are the areas where consumers are contracted by the VPP provider. ) Information is included. In addition, an area shall be areas, such as Tokyo, Isogo, Tsukuba, Maebashi, and Utsunomiya, for example.

  The data of FIG.4 (b) is data of the solar radiation amount prediction of a target day. Here, the amount of solar radiation is the amount of radiant energy received from the sun per unit area and unit time. The data in FIG. 4B includes, as an example, predicted values of the amount of solar radiation on the target day in areas A to E announced by the Japan Meteorological Agency.

  The data in FIG. 4C is data of past weather patterns (forecasts). The data in FIG. 4C includes weather pattern forecast data for each day and each area, which is announced by the Japan Meteorological Agency during a predetermined period (for example, one month) before the target date. The data in FIG. 4C can be said to be data obtained by accumulating the data in FIG. 4A for a predetermined period (for example, one month).

  The data shown in FIG. 4D is data for estimating the amount of solar radiation in the past. The data shown in FIG. 4D includes the predicted value of the amount of solar radiation for each day and each area, which is announced by the Japan Meteorological Agency during a predetermined period (for example, one month) before the target date. It can be said that the data in FIG. 4D is data obtained by accumulating the data in FIG. 4B for a predetermined period (for example, one month). The data of FIG.4 (e) is data of the past solar radiation results. The data shown in FIG. 4 (e) includes an actual measurement value of the amount of solar radiation for each day and each area, which is announced by the Japan Meteorological Agency in a predetermined period (for example, one month) before the target date.

  Returning to FIG. 3, the power generation amount collection unit 32 collects data on the power generation results from the solar power generation devices 40 of each customer and stores the data in the power generation result DB 39. As shown in FIG. 5, the power generation record DB 39 stores the record of the amount of power generation (kWh) per hour of each customer (each solar power generation device 40).

  The power generation request receiving unit 33 receives a power generation request (R (kW) or the like) on the target date (such as the next day) transmitted from the power company server 20 and transmits it to the covariance calculation unit 34.

  The covariance calculation unit 34 uses the data stored in the weather information DB 38 to create the covariance matrix H of the solar radiation amount prediction error. The specific processing of the covariance calculation unit 34 will be described later.

  The power generation amount determination unit 35 determines the optimum solar power generation amount for each area based on the power generation request amount (R), the covariance matrix (H), the solar radiation amount forecast, and the data stored in the power generation result DB 39. To do. A specific process of the power generation amount determination unit 35 will also be described later.

  The result notification unit 36 transmits (notifies) the determination result of the power generation amount determination unit 35 (answer to the power generation request) to the power company server 20.

(Processing of VPP provider server 30)
Next, processing in the VPP provider server 30 will be described in detail along the flowchart of FIG. 6 with reference to other drawings as appropriate. In the present embodiment, the VPP provider server 30 considers that the change in weather greatly affects the amount of solar radiation, and the past in which the weather pattern of all areas or two areas matches the weather pattern of the target day. Use the day data. As a premise of the processing in FIG. 6, the past weather pattern (forecast) as shown in FIG. 4C regarding a predetermined period (for example, one month) before the target date, as shown in FIG. It is assumed that the past solar radiation amount prediction and the past solar radiation amount actual as shown in FIG. 4E are already accumulated in the weather information DB 38.

  In the process of FIG. 6, first, in step S <b> 10, the power generation request reception unit 33 stands by until a power generation request is received from the power company server 20. When a power generation request (in this embodiment, the target date is the next day and the power generation amount is R (kW)) is transmitted from the power company server 20, the process proceeds to step S12.

  When the process proceeds to step S12, the weather information collecting unit 31 determines the weather pattern (forecast) (see FIG. 4 (a)) and the solar radiation amount forecast (FIG. 4 (b)) of the target area (AE) on the target day (next day). Reference). The weather information collection unit 31 stores the acquired weather pattern (forecast) and solar radiation amount prediction in the weather information DB 38.

  Next, in step S14, the covariance calculation unit 34 determines whether there is a day in which the weather pattern matches the weather pattern of the target day during a predetermined period (for example, one month) before the target date.

  For example, it is assumed that the past weather pattern (forecast) is data as shown in FIG. In this case, if the weather pattern (forecast) on the target day is “fine sunny cloudy” as shown in FIG. 4A, the past weather pattern (forecast) in FIG. Search for the weather pattern of "Sunny Sunny Cloudy". When the weather pattern for the target day is included as indicated by the bold frame, the determination in step S14 is affirmed.

  On the other hand, it is assumed that the past weather pattern (forecast) is data as shown in FIG. In this case, the weather pattern (forecast) of the target day is “sunny sunny cloudy” as shown in FIG. 4A, and “sunny sunny cloudy” in the past weather pattern (forecast) of FIG. If the weather pattern is not included, the determination in step S14 is negative.

  If the determination in step S14 is affirmative, the process proceeds to step S16, and the covariance calculation unit 34 identifies the day in which the weather pattern matches the weather pattern of the target day in all areas, and extracts data on the identified day. Execute. Specifically, as shown in FIG. 7B, the day (July 3,..., July x day) in which the weather pattern matches the weather pattern of the target day is specified, and each area of the specified day is specified. (Data on solar radiation amount prediction and solar radiation performance data) are extracted. Thereafter, the process proceeds to step S20.

  On the other hand, if the determination in step S14 is negative, the process proceeds to step S18, and the covariance calculation unit 34 specifies the day on which the weather patterns in the two areas match the weather pattern on the target day, and the specified day The data extraction of two areas in is performed. Specifically, as shown in FIG. 8B, the covariance calculation unit 34 specifies past days in which the weather pattern matches the weather pattern of the target day for each combination of the two areas. For example, as shown in FIG. 8B, the covariance calculation unit 34 specifies a day in which the area A and the area B are “sunny” weather patterns, and the area A and the area C are “sunny”. The day that is the weather pattern of “A” and the area A and the area D specify the day that is the weather pattern of “fine cloudy”, and so on, and all the combinations of the areas are processed. And the data of all the specified days (data of solar radiation amount prediction of each area and solar radiation results) are extracted. Thereafter, the process proceeds to step S20.

  If transfering it to step S20, the covariance calculation part 34 will produce the covariance matrix of a solar radiation amount prediction error using the extracted data. In this case, the covariance calculation unit 34 specifies a combination of the two areas and obtains the covariance of the solar radiation amount prediction error.

  Note that the combination of the two areas includes a combination of the same areas (area A and area A). In the case of a combination of the same area, the variance of the solar radiation amount prediction error is obtained. In the following, for convenience of explanation, it will be expressed as “covariance” as in the case of a combination of different areas.

As an example, the following equation (1) shows an equation for obtaining the covariance of the solar radiation amount prediction error in the areas A and B. In equation (1), the solar radiation amount prediction error of area A on a certain day is x _{1, i} , the solar radiation amount prediction error of area B on a certain day is x _{2, i,} and the average amount of solar radiation prediction error in area A is the are x ₁ bar, the average amount of solar radiation prediction error in the area B and x ₂ bar. The covariance calculation unit 34 calculates, for example, the solar radiation amount prediction error of the area A of a certain day, the solar radiation amount prediction of the area A of the day (FIG. 4D), and the solar radiation amount actual result of the area A of the day (FIG. 4). (E)) is calculated from the difference. Note that n means the number of days specified in steps S16 and S18.

In addition, the solar radiation amount prediction errors of the areas C, D, and E are represented by x _{3, i} , x _{4, i} , and x _{5, i} , and the solar radiation amount prediction errors of the areas C, D, and E are x _{3, i} bar, x _{4, i} bars, and those represented by x _{5, i} bar. The covariance calculation unit 34 calculates covariances S _x1x1 , S _x1x2 ,..., S _x2x1 , S _x2x2 ,..., S _x5x5 using a formula similar to the above formula (1). Note that, when calculating the covariance (variance) S _x1x1 , the solar radiation amount prediction error is calculated using data on a day in which the weather in the area A matches the weather on the target day. The same _{applies to} the covariances S _x2x2 , S _x3x3 , S _x4x4 , and S _x5x5 .

  Then, the covariance calculation unit 34 uses the calculated covariances to create a covariance matrix as represented by the following equation (2).

  In the present embodiment, the covariance matrix is a 5 × 5 matrix.

Next, in step S22, the power generation amount determination unit 35 calculates the amount of solar power generation in each area. Specifically, the power generation amount determination unit 35 is based on the power generation request amount R, the covariance matrix H, the solar radiation amount forecast sr (MJ / m ² ), etc. Calculate the amount of photovoltaic power generation.

In this case, the power generation amount determination unit 35 calculates the optimum value of the ratio p of the solar power generation amount in each area by the secondary planning method. Specifically, using the expressions (3) to (5) as constraints,
R ≦ p ^T r (3)
0 ≦ p _i ≦ 1 (4)
r _i = sr _i (5)
P that satisfies the following equation (6) is calculated.
min: p ^T Hp (6)

Incidentally, sr _i refers to solar radiation forecast for each area, r _i denotes the power generation amount prediction of each area. Actually, the solar radiation amount and the power generation amount are not equal, but it is assumed that the power generation amount increases or decreases in proportion to the solar radiation amount. Therefore, although r _i = k · sr _i is correct expression, since even remove the nature coefficient k of this optimization the same result, omitted for convenience. The matrix p means the ratio of the amount of photovoltaic power generation (corresponding to the amount of photovoltaic power generation) and is expressed by the following equation (7). _{Incidentally, p 1, p 2, ...} , p 5 means the percentage of solar power generation amount in the area A-E.
p = (p ₁ p ₂ ... p ₅ ) (7)

The matrix r is expressed by the following equation (8). _{Incidentally, r 1, r 2, ...} , r 5 means a power generation amount prediction in areas A-E.
r = (r ₁ r ₂ ... r ₅ ) (8)

Then, the power generation amount determining unit 35 calculates the solar power generation amount that promises to target day (= p ^T · x) using the ratio p of the calculated solar power generation amount. Note that the power generation amount prediction x on the target day for each area is calculated from the power generation results (the power generation results of the consumers in FIG. 5 summarized for each area).

  Next, in step S24, the result notifying unit 36 notifies the electric power company server 20 of the amount of photovoltaic power generation calculated in step S22 as an answer to the power generation request.

  As described above in detail, according to the present embodiment, the weather information collection unit 31 obtains the predicted value and the actual measurement value of the solar radiation amount for each of the plurality of areas (target areas A to E). The variance calculating unit 34 calculates a prediction error of the solar radiation amount based on the difference between the predicted value of the solar radiation amount and the actual measurement value, and shares the prediction error of the solar radiation amount for each combination of two areas specified from a plurality of areas. Calculate the variance. Then, the power generation amount determination unit 35 sets the solar power generation amount of each of the plurality of areas so that the prediction error of the solar radiation amount of each of the plurality of areas is minimized based on the calculated covariance. As described above, in this embodiment, by setting the solar power generation amount so that the prediction error of the solar radiation amount of each area is minimized, the solar power generation amount notified to the electric power company is paid to an appropriate value (penalty is paid). Can be set to a low value).

  In the present embodiment, the covariance calculation unit 34 includes, for each of the plurality of areas, weather history information (past weather pattern (forecast)) and weather prediction information (target day weather pattern (forecast)). And the covariance of the prediction error of the solar radiation amount between the two areas is calculated based on the weather history information and the weather prediction information. Thereby, the photovoltaic power generation amount notified to the electric power company can be set to an appropriate value in consideration of the weather history (weather pattern).

  Further, in the present embodiment, the covariance calculation unit 34 matches the weather history information (past weather pattern (forecast)) in at least two areas with the weather forecast information (weather pattern (forecast) on the target day). Calculate the covariance of the prediction error of the amount of solar radiation. Thus, by setting the solar power generation amount using the covariance of the prediction error of the solar radiation amount of the two areas on the day where the weather pattern matches, the relationship between the weather between the regions and the solar radiation amount is considered. The amount of photovoltaic power generation to be notified to the electric power company can be set to an appropriate value. FIG. 9 shows the standard deviation of the solar radiation amount prediction error when the method of the present embodiment (the present method) is used, and the standard deviation of the solar radiation amount prediction error when the average method for equally allocating power to each region ( (Improvement rate) and standard deviation (improvement rate) of the solar radiation amount prediction error in the case of using the lowest method that preferentially selects a place with a large prediction error. In FIG. 9, for example, various weather patterns are set for five areas, and the power generation request amount R is set to 80% of the next-day solar radiation amount prediction. In addition, (improvement rate) of FIG. 9 has shown the improvement rate with respect to each method at the time of using the method of this embodiment. As can be seen from FIG. 9, when the method of this embodiment is used, it is possible to set a more appropriate amount of photovoltaic power generation than the average method or the minimum method.

  In the present embodiment, even if the weather patterns for all the areas do not match, if there is a date that matches in the two areas, the data for the two areas on that day is used. Thereby, even if it is a case where the day where the weather pattern regarding all the areas corresponds is not present, an appropriate amount of photovoltaic power generation can be set. In particular, as the number of areas and the number of types of weather (sunny, cloudy,...) Increase, the possibility of matching weather patterns decreases, so the effect of adopting the method of this embodiment is great. .

  In the above embodiment, when the weather pattern does not match the weather pattern of the target day in the past predetermined period, the weather pattern of the two areas specifies the day that matches the weather pattern of the target day. The case of performing (step S18) has been described. However, the present invention is not limited to this, and for example, a day in which the weather pattern of four areas or the weather pattern of three areas matches the weather pattern of the target day may be specified. In this case, the area where the four areas match is searched. If it does not exist, the day where the three areas match is searched. If not, the area where the two areas match is searched. You may make it decrease the number of gradual gradually. Note that the more the number of areas with matching weather patterns, the more appropriately the amount of solar power generated in each area can be set.

  In addition, although the case where a weather pattern was considered was demonstrated in the said embodiment, it does not necessarily need to consider a weather pattern not only this but. In this case, the covariance and the covariance matrix may be calculated using the solar radiation amount prediction error in the past predetermined period of each area. In the above embodiment, the case where covariance is calculated as the correlation of the prediction error of the solar radiation amount for each combination of the two areas has been described. However, the present invention is not limited to this, and other correlations may be calculated. Good.

  In the above embodiment, the case where the VPP provider server 30 executes the process of FIG. 6 has been described. However, the present invention is not limited to this. For example, a server other than the VPP provider server 30 may execute the processing of FIG. 6 and provide the processing result to the VPP provider server 30.

  In the above embodiment, data used when calculating the covariance may be specified in consideration of other events (for example, temperature, wind speed, etc.) instead of or together with the weather pattern.

In the above embodiment, the case of obtaining p that minimizes p ^T Hp in the above equation (6) has been described. However, the present invention is not limited to this, and p is obtained such that p ^T Hp is smaller than a predetermined threshold. It is good as well.

  The above processing functions can be realized by a computer. In that case, a program describing the processing contents of the functions that the processing apparatus should have is provided. By executing the program on a computer, the above processing functions are realized on the computer. The program describing the processing contents can be recorded on a computer-readable recording medium (except for a carrier wave).

  When the program is distributed, for example, it is sold in the form of a portable recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. It is also possible to store the program in a storage device of a server computer and transfer the program from the server computer to another computer via a network.

  The computer that executes the program stores, for example, the program recorded on the portable recording medium or the program transferred from the server computer in its own storage device. Then, the computer reads the program from its own storage device and executes processing according to the program. The computer can also read the program directly from the portable recording medium and execute processing according to the program. Further, each time the program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

  The above-described embodiment is an example of a preferred embodiment of the present invention. However, the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.

In addition, the following additional remarks are disclosed regarding description of the above embodiment.
(Supplementary note 1) For each of a plurality of target areas, obtain a predicted value and an actually measured value of solar radiation,
Based on the difference between the predicted value of the solar radiation amount and the actual measurement value, the prediction error of the solar radiation amount is calculated,
Calculating the correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, the solar power generation amount of each of the plurality of target areas is set so that the prediction error of the solar radiation amount of each of the plurality of target areas is reduced.
A planning method characterized in that a computer executes a process.
(Additional remark 2) The said computer further performs the process which acquires the historical information of the past weather, and the forecast information of the weather on the setting object day of photovoltaic power generation about each of the said some target area,
In the process of calculating the correlation, a prediction error of the solar radiation amount used for calculating the correlation is determined based on the past weather history information and the weather prediction information of the setting target day. The planning method described in Appendix 1.
(Supplementary Note 3) In the process of calculating the correlation, the prediction error of the solar radiation amount on the day when the historical information of the past weather and the prediction information of the weather of the setting target day coincide in at least two areas is calculated as the correlation. The method for formulating a plan according to appendix 2, wherein the method is used for calculating a relationship.
(Supplementary Note 4) For each of a plurality of target areas, an acquisition unit that acquires a predicted value and an actual measurement value of solar radiation amount;
A first calculation unit that calculates a prediction error of the solar radiation amount based on a difference between the predicted value of the solar radiation amount and the actual measurement value;
A second calculation unit that calculates a correlation between the prediction errors of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, a setting unit that sets the photovoltaic power generation amount of each of the plurality of target areas so that the prediction error of the solar radiation amount of each of the plurality of target areas becomes small,
Planning system with
(Supplementary Note 5) For each of the plurality of target areas, it further includes a weather information acquisition unit that acquires historical weather history information and weather prediction information on the setting target day of the photovoltaic power generation amount,
The second calculation unit determines a prediction error of the solar radiation amount used for calculating the correlation based on history information of the past weather and prediction information of the weather on the setting target day. The planning system described in appendix 4.
(Additional remark 6) The said 2nd calculation part calculates the prediction error of the said solar radiation amount on the day when the historical information of the said past weather and the prediction information of the weather of the said setting object day correspond in an at least 2 area of the said correlation. The planning system according to appendix 5, which is used for calculation.
(Supplementary note 7) For each of a plurality of target areas, obtain a predicted value and an actually measured value of solar radiation,
Based on the difference between the predicted value of the solar radiation amount and the actual measurement value, the prediction error of the solar radiation amount is calculated,
Calculating the correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, the solar power generation amount of each of the plurality of target areas is set so that the prediction error of the solar radiation amount of each of the plurality of target areas is reduced.
A planning program characterized by causing a computer to execute processing.
(Supplementary Note 8) For each of the plurality of target areas, the computer further executes a process of acquiring past weather history information and weather forecast information on a setting target day of photovoltaic power generation,
In the process of calculating the correlation, a prediction error of the solar radiation amount used for calculating the correlation is determined based on the past weather history information and the weather prediction information of the setting target day. The planning program described in Appendix 7.
(Supplementary Note 9) In the process of calculating the correlation, the prediction error of the solar radiation amount on the day when the past weather history information and the prediction information of the weather on the setting target day coincide in at least two areas is calculated as the correlation. The planning program according to appendix 8, which is used for calculating a relationship.

30 VPP provider server (planning system)
31 Weather information collection unit (acquisition unit, weather information acquisition unit)
34 Covariance calculation unit (first calculation unit, second calculation unit)
35 Electricity generation determination unit (setting unit)

Claims (5)

For each of the target areas, obtain a predicted value and actual measurement of solar radiation,
Based on the difference between the predicted value of the solar radiation amount and the actual measurement value, the prediction error of the solar radiation amount is calculated,
Calculating the correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, the solar power generation amount of each of the plurality of target areas is set so that the prediction error of the solar radiation amount of each of the plurality of target areas is reduced.
A planning method characterized in that a computer executes a process. For each of the plurality of target areas, the computer further executes a process of acquiring past weather history information and weather forecast information on a setting target day of solar power generation amount,
In the process of calculating the correlation, a prediction error of the solar radiation amount used for calculating the correlation is determined based on the past weather history information and the weather prediction information of the setting target day. The planning method according to claim 1.   In the process of calculating the correlation, the prediction error of the solar radiation amount on the day when the past weather history information and the weather prediction information of the setting target day coincide in at least two areas is used to calculate the correlation. The planning method according to claim 2, wherein the planning method is used. For each of a plurality of target areas, an acquisition unit that acquires a predicted value and an actual measurement value of solar radiation amount;
A first calculation unit that calculates a prediction error of the solar radiation amount based on a difference between the predicted value of the solar radiation amount and the actual measurement value;
A second calculation unit that calculates a correlation between the prediction errors of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, a setting unit that sets the photovoltaic power generation amount of each of the plurality of target areas so that the prediction error of the solar radiation amount of each of the plurality of target areas becomes small,
Planning system with For each of the target areas, obtain a predicted value and actual measurement of solar radiation,
Based on the difference between the predicted value of the solar radiation amount and the actual measurement value, the prediction error of the solar radiation amount is calculated,
Calculating the correlation of the prediction error of the solar radiation amount for each combination of two target areas specified from a plurality of the target areas;
Based on the correlation, the solar power generation amount of each of the plurality of target areas is set so that the prediction error of the solar radiation amount of each of the plurality of target areas is reduced.
A planning program characterized by causing a computer to execute processing.

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