JP6519215B2 - Power transaction support system, power transaction support method and program - Google Patents

Description

  The present invention relates to a power transaction support system, a power transaction support method, and a program.

  In recent years, in the power market, it has become possible to freely buy and sell power. When buying and selling the power, the trading price of the power is determined for a certain future period to buy and sell the power (for example, buying and selling every 30 minutes two days ago).

  From such a background, there has been proposed a method and the like for predicting the amount of power demand on a transaction target day (for example, two days later) so that the power supplier can manage risk in the power transaction (see Patent Document 1).

JP, 2004-252967, A

  However, the method described in Patent Document 1 does not consider the power generation cost of the power supplier, can not calculate the transaction amount in consideration of the benefit of the power supplier, and the power supply is dropped. It is also impossible to figure out how much the impact will be.

  Therefore, the present invention proposes a power transaction support system, a power transaction support method, and a program capable of presenting the influence when the power is dropped after calculating the power transaction amount so as to maximize the profit. With the goal.

The main invention of the present invention for solving the above-mentioned problems is a system for supporting power transaction, which stores data on a power generation means including minimum power generation capacity, power generation capacity and marginal cost of each of a plurality of power generation means. The first power generation unit , the second storage unit for storing transaction information for calculating the transaction amount according to the transaction amount according to the power transaction, and the operation priority of the plurality of power generation means, the lowest power generation With regard to the power generation means whose amount is larger than 0, the lower limit cost is set to be higher as the lower power generation means, and the lower power generation means is set to be higher as the lower cost is the lower power generation means. And third memory storing data in which the operation priority of the power generation unit having the minimum power generation amount larger than 0 is higher than the operation priority of the power generation unit having the minimum power generation amount of 0 And a limit corresponding to the power generation cost and the power generation cost to be generated to generate the required amount of power generation based on the data on the power generation means and the operation priority set to the plurality of power generation means. A first power generation cost necessary to generate a first power generation amount corresponding to a predicted power demand amount based on a power generation schedule calculation unit that calculates data on a cost line, and data on the marginal cost line; 1. A change cost which is the difference between the second power generation cost necessary to generate the second power generation amount obtained by adding the sales amount of power in the power transaction to the first power generation amount is determined, and is obtained based on the transaction information Among the plurality of power generation means, an optimal transaction amount calculation unit that calculates a profit amount obtained by subtracting the change cost from the income amount from the power transaction, and calculates an optimal sales amount at which the profit amount becomes maximum. drop out And dropping the power generation unit designation unit that receives designation of dropping the power generating means is a power generation means we assume that, wherein the optimum trading volume calculation unit, after the optimum sales volume is determined, the pre-SL dropout generation means The amount of profit in the case of performing the power transaction by generating power of the second power generation without using it is calculated again .

Further, in the power transaction support system according to the present invention, the data on the power generation means includes an amount of power that can be generated and a marginal cost for the standby power generation means to be operated when the dropped power generation means falls off. Calculating data on the marginal cost line when operating the standby power generation unit instead of the dropped power generation unit based on the data on the power generation unit and the priority set to the plurality of power generation units; The optimal transaction amount calculation unit may calculate the profit amount in the case of operating the standby power generation unit instead of the dropout power generation unit after the optimal sales amount is determined .

Further, in the power transaction support system according to the present invention, the data on the power generation means includes a power generation capacity and a marginal cost when using national accommodation as a virtual power generation means, and the power generation schedule calculation unit Calculating the data relating to the marginal cost line when the virtual power generation means is operated instead of the dropout power generation means based on the data on the means and the priorities set to the plurality of power generation means, and the optimum The trade volume calculation unit may calculate the profit amount when the virtual power generation unit is operated instead of the dropout power generation unit after the optimal sales volume is determined .

In addition, another aspect of the present invention is a method of supporting power transaction , comprising: a first storage unit storing data related to a power generation means, including a power generation capacity of each of a plurality of power generation means and a marginal cost ; The second storage unit storing transaction information for calculating the transaction amount according to the transaction amount by the power transaction, the operation priority of the plurality of power generation units, and the power generation unit having the minimum power generation amount larger than 0 The marginal cost is set to be higher as the cheaper power generation means, and the marginal power generation means is set to be higher as the marginal cost is lower for the power generation means with the minimum power generation amount of zero, and the minimum power generation amount is more than zero computer having a third storage unit that stores data operation priority of large the power generating means is set to be higher than operating priority of the power generating means of the minimum amount of power generation 0, a is the generator Data on marginal cost lines corresponding to generation means and generation costs to be operated to generate the required amount of generation based on data on stages and the operation priorities set for the plurality of generation means Calculating the first power generation cost necessary to generate the first power generation amount corresponding to the predicted power demand amount based on the data on the step of calculating the marginal cost line, and the power transaction to the first power generation amount Calculating the change cost which is the difference between the second power generation cost necessary to generate the second power generation amount obtained by adding the sales amount of power in the unit and the revenue from the power transaction obtained based on the transaction information from calculates the profit obtained by subtracting the change cost, calculating the optimum sales volume which the profit is maximized, among the plurality of power generating means, are power generation means we assume that fell off A step of accepting a specification of drop generating means, and performing the steps of calculating the profit value again in the case of performing the power exchange performs power generation of the second power generation amount without using the dropping power generating means Do.

Moreover, the other aspect of this invention is a program for supporting an electric power transaction, Comprising: The 1st memory | storage part which memorize | stores the data regarding a power generation means including the generation possible amount of each of several electric power generation means , and the limit cost. When, a second storage unit for storing the collected 引情 report for calculating a transaction amount in accordance with the transaction amount by the power trading, the operation priority of the plurality of power generating means, than the minimum amount of power generation 0 With regard to a large power generation means, the lower limit cost is set to be higher as the lower power generation means is set, and for the power generation means with the lowest power generation amount of 0, the lower limit cost is set to be higher as the lower power generation means computer comprising: a third storage unit for storing the power generation amount is set such that operation priority of large the power generation unit from 0 becomes higher than the operating priority of the power generating means of the minimum amount of power generation 0 data, To, on the basis of the said operating priority that is set in the data and the plurality of power generating means relating to the power generating means, it is made to correspond and the power generating means to operate the power generation cost in order to perform the power generation of the power generation amount required limit A step of calculating data relating to a cost line, and a first power generation cost necessary to generate a first power generation amount corresponding to the predicted power demand amount based on the data relating to the marginal cost line, and the first power generation amount Calculating the change cost which is the difference between the second power generation cost necessary to generate the second power generation amount obtained by adding the sales amount of power in the power transaction to the power transaction, and the power obtained based on the transaction information from revenues from transactions, calculates the profits obtained by subtracting the change cost, calculating the optimum sales volume which the profit is maximized, among the plurality of power generating means, it assumes that fell off A step of accepting a specification of dropping the power generating means is a conductive means, and wherein the step of calculating a profit again in the case of performing the power exchange performs power generation of the second power generation amount without using the dropping power generating means, the Let's run it.

  Other problems disclosed in the present application and their solutions will be made clear by the section of the embodiments of the present invention and the drawings.

  ADVANTAGE OF THE INVENTION According to this invention, after calculating the amount of power transactions which maximizes profit, the influence when a power supply drops off can be shown.

It is a figure showing a prediction system in an embodiment of the present invention. It is a figure which shows the hardware constitutions of the prediction apparatus in 1st Embodiment of this invention. It is a figure which shows the hardware constitutions of the transaction information provision apparatus in 1st Embodiment of this invention. It is a figure which shows the data table M1 regarding the electric power generation means in 1st Embodiment of this invention. It is a figure which shows the data table M2 regarding the predicted value of the electric power demand amount in 1st Embodiment of this invention. It is a figure which shows the data table M3 regarding the transaction information in 1st Embodiment of this invention. It is a figure which shows the function structure of the prediction apparatus in 1st Embodiment of this invention. It is a figure which shows the flow of operation | movement of the prediction system in 1st Embodiment of this invention. It is a figure which shows the data table M4 regarding the working priority in 1st Embodiment of this invention. It is a figure which shows the relationship of the operation rate and marginal cost in 1st Embodiment of this invention. It is a figure which shows the marginal cost line in 1st Embodiment of this invention. It is a figure which shows the marginal cost line in 1st Embodiment of this invention. It is a figure which shows the marginal cost line in 1st Embodiment of this invention. It is an image figure of probability distribution of the amount of electric power demand in a 2nd embodiment of the present invention. It is an image figure of variable conversion of a probability density function in a 2nd embodiment of the present invention. It is a figure which shows the flow of operation | movement of the demand forecasting part in 3rd Embodiment of this invention. It is a figure which shows the data table M5 regarding the past amount of electric power demand in 3rd Embodiment of this invention. It is a figure which shows the data table M6 regarding the past measurement air temperature in 3rd Embodiment of this invention. It is a figure which shows the data table M4 regarding the drop-off power supply in 4th Embodiment of this invention. It is a figure which shows the data table M5 regarding the preliminary | backup power supply in 4th Embodiment of this invention. It is a figure which shows the data table M6 regarding the nationwide accommodation in 4th Embodiment of this invention. It is a flow chart which simulates increase and decrease of profit at the time of drop-off of a power generation means in a 4th embodiment of the present invention. It is a figure which shows an example of the marginal cost line at the time of removing the drop-off power supply in 4th Embodiment of this invention. It is a figure which shows an example of a marginal cost line at the time of removing the drop-off power supply in 4th Embodiment of this invention, and incorporating a backup power supply. It is a figure which shows an example of a marginal cost line at the time of removing the drop-off power supply in 4th Embodiment of this invention, incorporating a reserve power supply, and also incorporating national flexibility. It is a figure which shows the structural example of the data table M7 for managing the upper limit and lower limit of the electric power generation amount by the electric power generation means in 4th Embodiment of this invention for every time slot | zone.

  At least the following matters will be made clear by the present specification and the description of the accompanying drawings.

First Embodiment
=== About the prediction system ===
The first embodiment is a transaction that maximizes the profit value in the power transaction based on the characteristics and the power transaction transaction information by grasping the characteristics regarding the power generation cost per unit power generation and the like for each power generation means. It predicts the quantity.

  The power generation cost varies depending on whether it is a mass transit power source, the operation rate of the power generation means, the fuel cost required for the power generation at that time, and the like. Therefore, the first embodiment is based on the understanding that in order to maximize the profit value in the power transaction, it is essential to grasp those characteristics. Hereinafter, the power generation cost required per unit power generation (for example, 1 MWh / h) will be referred to as the “limit cost”. Also, the amount of trading that maximizes the profit value in electricity trading is called the “optimum trading volume”.

  FIG. 1 shows an example of a prediction system for realizing the prediction of the optimal transaction volume in the power transaction in the first embodiment. The prediction system according to the first embodiment includes the prediction device 100, the transaction information providing device 200, and the like. Both devices transmit and receive data using the communication network 300 by LAN connection or the like.

  The prediction device 100 is a computer operated by the user. In addition, the transaction information providing device 200 is a computer that transmits data in response to a request from the prediction device 100. The prediction device 100 according to the first embodiment is configured to predict a profit value by acquiring transaction information on a transaction price and the like from the transaction information providing device 200.

=== Hardware configuration of the prediction device 100 ===
FIG. 2A shows an example of the hardware configuration of the prediction device 100 according to the first embodiment.

  The prediction device 100 includes a control unit 100A, a storage unit 100B, a communication unit 100C, an input unit 100D, and a display unit 100E.

  The control unit 100A is a CPU or the like, and is connected to the storage unit 100B, the communication unit 100C, the input unit 100D, and the display unit 100E via a bus or the like. Then, based on the computer program stored in the storage unit 100B, the control unit 100A performs data communication with the storage unit 100B, the communication unit 100C, the input unit 100D, and the display unit 100E, and controls their operation.

  The storage unit 100B includes volatile memory (RAM), non-volatile memory (flash memory), and the like. The storage unit 100B stores a computer program for controlling the prediction device 100, data M1 on a power generation unit to be described later, data M2 on a predicted power demand, and the like. The storage unit 100B also includes a storage unit (not shown) that stores a regression model described later, intermediate data calculated by each functional unit, final data, acquired analysis target data, and the like.

  The communication unit 100C is a communication controller or the like, and transmits / receives data to / from the transaction information providing apparatus 200 using the communication network 300 or the like by LAN connection by wire or wireless.

  The input unit 100D is a switch, a touch panel, or the like, and receives a user's operation instruction to the prediction device 100.

  The display means 100E is a liquid crystal display etc. which displays various information, and displays probability distribution etc. of the prediction air temperature mentioned later.

=== Hardware Configuration of Transaction Information Providing Device 200 ===
FIG. 2B shows a hardware configuration example of the transaction information providing apparatus 200 of the first embodiment.

  The transaction information providing apparatus 200 includes a control unit 200A, a storage unit 200B, and a communication unit 200C.

  The control unit 200A is a CPU or the like, and is connected to the storage unit 200B and the communication unit 200C via a bus or the like. Then, based on the computer program stored in the storage unit 200B, the control unit 200A performs data communication with the communication unit 200C and the storage unit 200B, and controls those operations.

  The storage unit 200B includes volatile memory (RAM), non-volatile memory (flash memory), and the like. The storage unit 200B stores a computer program for controlling the transaction information providing apparatus 200, data related to transaction information to be described later, and the like.

  The communication unit 200C is a communication controller or the like, and transmits / receives data to / from the prediction device 100 using a communication network 300 or the like by LAN connection by wire or wireless.

=== Data table M1 (power generation means) ===
FIG. 3A shows an example of a data table M1 related to power generation means stored in the storage means 100B of the prediction device 100 of the first embodiment.

  The data on the power generation means is composed of the minimum power generation amount, the power generation amount, and the marginal cost for the plurality of power generation means possessed by the power supplier.

  A power supplier has a plurality of power generation means such as hydroelectric power generation, solar power generation, nuclear power generation, and thermal power generation. The marginal cost, the amount of power that can be generated, and the like differ depending on the type of power generation means and the performance such as the power generation efficiency. For example, since nuclear power generation is difficult to stop operation, it will be scheduled as a means of power generation to always operate. On the other hand, in thermal power generation, the marginal cost may rise depending on the fuel price and the like. Besides, hydroelectric power generation is also characterized in that its marginal cost is cheaper than other power generation means.

  The power generation means A, B, C,... Shown in FIG. 3A represent one of the power generation means. In addition, the minimum amount of power generation (for example, 1 MWh / h) is the mass transit power source (as with the above-mentioned nuclear power generation, it is difficult to stop operation in operation from the viewpoint of generation cost etc.) Amount of electricity generated by Also, the power generation capacity (for example, 1 MWh / h) is a limit value of the power generation capacity of each of the power generation means. In addition, the marginal cost represents the power generation cost required per unit power generation (for example, 1 MWh) when the power generation means is operated.

  Here, the marginal cost also differs depending on the operation rate, that is, the power generation capacity being exhibited among the limit values of the power generation capacity of the power generation means. Specifically, the marginal cost of the power generation means increases as the operation rate increases. Therefore, in order to maximize profit, it is better to store in the data table M1 also the marginal cost according to the change in the operation rate. In the data table of FIG. 3A, the marginal costs of the power generation means A, B, C,... Are described as A (x1), B (x2), C (x3). This represents a marginal cost function according to the operating rates x1, x2, x3. The storage format may be a table format or the like. Also, as the marginal cost decreases linearly, the marginal cost A (Pmin) in the case of the minimum power generation (Pmin) and the marginal cost A (Pmax) in the case of the possible generation amount (Pmax) are stored. May be

=== Data table M2 (estimated power demand) ===
FIG. 3B shows an example of the data table M2 related to the predicted power demand stored in the storage unit 100B of the prediction device 100 of the first embodiment.

  The data relating to the forecasted demand is data relating to the forecasted value (for example, 100 MWh) of the power demand amount that the power supplier who is the user of the forecasting apparatus 100 needs to supply in a predetermined time slot. The power demand varies depending on the time zone. For example, although plant facilities are operating during the daytime, much power demand is expected, but it is expected that the electricity demand will decrease because there is little plant facility operation at night. As this data, for example, data calculated based on the required power demand amount on the day of the same condition in the past is used. The same conditions as in the past are conditions relating to time, season, day of the week, weather and the like.

  In the data table M2, the amount of power demand predicted for each hour is stored. The predicted value of the amount of power demand may be stored every arbitrary time (for example, 30 minutes).

=== Data Table M3 (Trading Information) ===
FIG. 3C shows an example of the data table M3 related to the transaction information stored in the storage unit 200B of the transaction information providing device 200 of the first embodiment.

  The data on the transaction information is data on the transaction price per unit power generation amount (for example, 1 MWh / h) in a predetermined time zone (for example, 11 o'clock to 12 o'clock) which is the object of the transaction, and data on the tradable amount. Here, the data related to the transaction information may be a fixed value or a predicted value. Power trading is conducted based on the transaction price. Specifically, when a power supplier sells a predetermined amount of power, based on the transaction price, the power supplier earns the sale instead of supplying the sale extra power. Can be obtained as Similarly, when the power supplier buys a predetermined amount of power, the power supplier uses the power supply for the buy instead of receiving the power supply for the buy from the transaction target person based on the transaction price. It is an aspect of paying the amount of money. Also, the tradable amount represents the upper limit value that the power supplier can trade in the power transaction.

  The data table M3 stores the transaction price and the tradable amount per hour.

  Further, the data on the tradable amount may be set at the operating convenience of the power generation means of the power supplier. For example, as described above, since it is premised that the mass transit power supply is always operated, the power due to the operation of the mass transit power supply may be set as untradable. Further, data on tradable amount may not necessarily be set.

=== Functional Configuration of Prediction Device 100 ===
FIG. 4 shows an example of a functional configuration of the prediction device 100 of the first embodiment.

  The prediction device 100 obtains an acquisition unit 101, a power generation schedule calculation unit 102, an optimal transaction amount calculation unit 103, and a presentation described below according to the computer program stored in the storage unit 100B and the hardware configuration (100A to 100E) described above. The functions of the unit 104 are realized.

  The acquisition unit 101 performs data communication with another device to acquire data. In the first embodiment, data on transaction information is acquired from the transaction information providing device 200.

  The power generation schedule calculation unit 102 generates a marginal cost based on data M1 on the means for generating (data relating to the power generation capacity of the means for generating, data on the marginal cost of the means for generating) and the operation priorities for the plurality of generating means set. Calculate data about the line. The data relating to the marginal cost line is data that specifies how to operate the power generation means to compensate the power generation amount in correspondence with the required power generation amount (power demand amount), and the marginal cost described later It is data corresponding to the line F (W).

  The optimum transaction amount calculation unit 103 includes data on the calculated limit cost line, change cost of the power generation cost when the power transaction of a predetermined amount calculated based on the predicted value of the power demand amount, and data on the transaction information. Based on the calculated amount of power transaction, the amount of transaction when the difference between the amount and the transaction balance is the largest is calculated as the optimum amount of transaction.

=== About the operation of the prediction system ===
Next, an example of the operation of the prediction system will be described.

  FIG. 5 shows a flowchart of the first embodiment.

  (S1) is a process in which the user of the prediction device 100 inputs a period to be predicted (for example, 7 o'clock to 8 o'clock etc. of 2013/2/1). At this time, the user of the prediction device 100 sets the operation priority of the power generation means in consideration of the situation of each power generation means.

  FIG. 6 shows a data table M4 regarding the priority regarding the mass transit power source and the priority regarding the marginal cost in the first embodiment. The power generation schedule calculation unit 102 calculates data on the marginal cost line so as to operate in order from the power generation means with the highest operation priority. The priority for the mass transit power source is set higher than the priority for the marginal cost.

  Here, the priority regarding the limit cost set to each power generation means may be set to fluctuate according to the operation rate of the power generation means. This is based on the understanding that the power generation means fluctuates in power generation efficiency according to the operation rate, and the marginal cost also fluctuates accordingly. An example of the marginal cost according to the operation rate of each power generation means is shown in FIG. As shown in FIG. 7, the power generation efficiency of many power generation means decreases according to the operation rate, and the marginal cost increases. From this, the priority may be changed at the intersection of the relation between the operating rate of each power generation means and the marginal cost.

  (S2) is a process in which the prediction device 100 acquires the acquired information from the transaction information providing device 200. Specifically, the acquisition unit 101 of the prediction device 100 requests the transaction information providing device 200 about the acquired price and the tradeable amount for the period to be predicted. Then, in response to the request, the transaction information provision device 200 transmits the acquired price and tradeable amount for the period to be predicted from the data table M3 for the transaction information to the prediction device 100. Alternatively, the data table M3 may be provided in the prediction device 100, and the transaction price and the tradable amount may be stored in advance, and (S2) may be omitted.

  In (S3), how the power generation schedule calculation unit 102 operates a plurality of power generation means in the period subject to the power transaction based on the data M1 concerning the power generation means and the data M4 concerning the operation priority, ie, It is a process of calculating data on marginal cost lines. The data relating to the marginal cost line is data specifying how to operate the power generation means in order to compensate the power generation amount in correspondence with the required power generation amount (power demand amount), and the marginal cost line described later It is data corresponding to F (W). The data relating to the marginal cost line may be any data that correlates the power generation amount with the power generation cost, and is not limited to the one represented by the functional expression, but data etc. stored in a table format that correlates the power generation amount with the power generation cost It may be

  Below, it demonstrates with reference to FIG. 8A-FIG. 8C which graphed the data regarding the marginal expense line calculated by (S3).

  FIG. 8A is a marginal cost line calculated from data M1 on the power generation means and data M2 on the operation priority of the power generation means. The horizontal axis represents the required amount of power generation (the amount of power demand), and the vertical axis represents the marginal cost of each power generation means. The rightward direction indicates that the amount of power generation (the amount of power demand) is large, and the upward direction indicates that the marginal cost is large. Also, the sections below the marginal cost line represent differences in the means of power generation to be operated (the same applies to FIGS. 8B and 8C). Here, since the priority relating to the mass transit power source is set as a higher priority than the priority relating to the marginal cost, the marginal cost line is composed of two regions, the mass transit power source priority area A and the marginal cost priority area B There is. That is, it represents that the marginal cost of the power generation means A, B, C is operated earlier than the lowest power generation means D.

  (S4) is a process in which the optimal transaction amount calculation unit 103 calculates a change cost of the power generation cost when the power transaction of the predetermined amount C is performed.

  Here, with reference to FIG. 8B and FIG. 8C, the change cost of the power generation cost when the power transaction of the predetermined amount C is performed will be described. FIG. 8B shows the power generation cost required when the amount of power demand is N0. When the predicted power demand is N0, the total value of the areas of the hatched areas in FIG. 8B is the required power generation cost. At this time, the power generation cost can be expressed by equation (1).

ここで、Ｆ（Ｗ）は、発電量と対応した限界費用を示す限界費用線に関するデータを表す。上述したとおり、Ｆ（Ｗ）は、各発電手段の稼働率と限界費用に関するデータＡ（ｘ１）、Ｂ（ｘ２）、Ｃ（ｘ３）・・、稼働優先度、発電可能量等に基づいて算出される（ｘ１、ｘ２・・・は、稼働率を表す）。

Here, F (W) represents data on a marginal cost line indicating the marginal cost corresponding to the power generation amount. As described above, F (W) is calculated based on the data A (x1), B (x2), C (x3). (X1, x2,... Represent operating rates).

  FIG. 8C shows an example of the change cost (reduction) of the power generation cost when the transaction of the transaction amount C (power purchase) is performed in the period targeted for the power transaction. From FIG. 8C, the change cost of the power generation cost at the time of buying the electric power of the transaction amount C is an area | region where F (W) is surrounded by demand forecast value N0 and post-transaction N0-C about W axis. Specifically, the change cost of the power generation cost can be expressed by equation (2).

（Ｓ５）は、最適取引量算出部１０３が、取引情報に関するデータＭ３に基づいて、電力取引の対象となる期間に所定量の取引を行った場合の取引収支を算出する工程である。具体的には、電力取引の対象となる期間の取引価格が一定値Ｒである場合、所定量Ｃの取引（電力買い）を行った場合の取引収支（支払額）は、式（３）より表すことができる。

(S5) is a process in which the optimal transaction amount calculation unit 103 calculates the transaction balance when the predetermined amount of transaction is performed in the period targeted for the power transaction based on the data M3 related to the transaction information. Specifically, when the transaction price in the period subject to the power transaction is a constant value R, the transaction balance (payment amount) in the case of performing a transaction (a power purchase) of a predetermined amount C is Can be represented.

（Ｓ６）は、最適取引量算出部１０３が、（Ｓ４）において算出した所定量の取引を行った場合の発電費用の変化費用と、（Ｓ５）において算出した所定量の取引を行った場合の取引収支の差に基づいて、最適取引量を算出する工程である。具体的には、電力買いの場合、発電費用の変化費用（減少）から取引収支（支払額）を減じた差分が大きくなるほど、電力供給者にとって最大利益となる。

(S6) is the case where the optimal transaction amount calculation unit 103 performs the transaction of the predetermined amount calculated in (S5) with the change cost of the power generation cost when the transaction of the predetermined amount calculated in (S4) is performed This is a step of calculating the optimal transaction amount based on the difference in transaction balance. Specifically, in the case of the power purchase, the greater the difference between the change cost (reduction) of the generation cost and the transaction balance (payment amount), the greater the profit for the power supplier.

  That is, the profit amount Y of the power supplier can be expressed as Expression (4) in the case of power purchase from Expression (2) and Expression (3).

ここで、Ｙが最大となるＣ’が最適取引量となる。例えば、最適取引量Ｃ’は、Ｃに係る微分方程式により算出することができる。尚、（Ｓ４）、（Ｓ５）の工程は、実質的に（Ｓ６）の工程に集約されるから省略してもよい。

Here, C ′ at which Y is maximum is the optimal transaction volume. For example, the optimal transaction amount C ′ can be calculated by a differential equation related to C. The steps (S4) and (S5) may be omitted because they are substantially integrated into the step (S6).

  Note that the optimal transaction amount calculation unit 103 calculates the difference C, which is obtained by subtracting the transaction balance (payment amount) from the change cost (reduction) of the generation cost, from the change cost (payment amount) to the maximum value C 'for the power supplier. Choose as the optimal trading volume.

  In addition, when the tradable amount is set as the transaction information, the optimal trade amount is calculated with the tradable amount as the upper limit value.

  Thus, the optimum transaction amount calculation unit 103 calculates the optimum transaction amount so that the power supplier can obtain the maximum profit with respect to the power purchase of the power transaction.

  (S7) is a process in which the presentation unit 104 performs predetermined image processing and presents the optimal transaction amount in the power transaction calculated in (S5) so that the user of the prediction apparatus 100 can recognize. For example, the presentation unit 104 outputs, as text data, the power generation means and the operation rate in the time zone to be traded, and the optimal trading volume.

  As described above, according to the first embodiment, it is possible to calculate the optimum transaction amount of the power transaction that maximizes the profit value in consideration of the power generation cost.

  In the first embodiment, the case of the power purchase has been described, but the same applies to the case of the power sale. That is, in the case of power selling, when the difference obtained by subtracting the change cost (increase) of the power generation cost from the transaction balance (income amount) becomes the maximum, the maximum profit is for the power supplier. At this time, the profit amount Y can be expressed as Expression (5). And C 'which Y becomes the largest can be calculated as the optimal transaction amount.

尚、本第１実施形態では、電力買い、電力売りの一方のみ可能な場合について説明したが、電力買い、電力売りのいずれも可能である場合には、電力買い、電力売りのいずれについてもＹを算出し、Ｙが最大となるときのＣ’を最適取引量とすればよい。

In the first embodiment, the case where only one of the power purchase and the power sale is possible has been described. However, when both the power purchase and the power sale are possible, Y for both the power purchase and the power sale Is calculated, and C ′ at which Y becomes maximum may be taken as the optimum transaction volume.

  Further, in the first embodiment, the data on the priority of each power generation means is set in consideration of the situation of each power generation means by the user of the prediction device 100, but these data are optimum. When calculating the transaction amount, it may be set by the power generation schedule calculation unit 102 or may be stored in advance in the data table M4.

  Further, in the first embodiment, the priority relating to the mass transit power source and the priority relating to the marginal cost are described as data relating to the priority, but it may be an aspect in which only the priority relating to the marginal cost is set. In addition to these priorities, data relating to other priorities may be set. For example, when there is a request to reduce the amount of power supplied by nuclear power generation, the power generation means related to nuclear power generation sets data on moderation as another item to lower the priority.

  In the first embodiment, the case where the transaction price R is constant regardless of the transaction amount C has been described. However, the transaction price R may be variable according to the transaction amount C. In that case, R in equation (4) can be expressed as a function R (C) of C. Then, the optimal transaction amount calculation unit 103 calculates the optimal transaction amount C ′ at which Y becomes maximum, in a mode in which the equation (4) is replaced with the function R (C).

  In the first embodiment, the prediction system calculates the profit amount Y. However, as long as the data is related to the profit value, not only the profit amount but also predetermined points may be used.

  In addition, a predetermined electric power generation means may stop by the influence of lightning etc., and it may become impossible to supply electric power. In this case, the marginal cost line calculated by the power generation schedule calculation unit 102 will fluctuate. Therefore, in (S3), in consideration of such a variation factor, each power generation possible amount in the marginal cost line may be calculated as an expected value in consideration of the possibility of dropping out. In this case, data relating to the probability distribution concerning dropout is stored in the predetermined power generation means of the data table M1. And the expected value of the power generation possible amount of the said power generation means is calculated based on the data regarding the probability distribution regarding drop-off | omission of the said predetermined power generation means, and the power generation possible amount of the said power generation means. Then, the power generation schedule calculation unit 102 can reflect the risk of power loss by calculating data related to the marginal cost line based on the expected value of the power generation capacity of the power generation means. Also, instead of changing the marginal cost line, assuming that the demand forecast value N has been virtually increased, the expected value due to the dropout of each power generation means is added to the demand forecast value N, and subsequent processing is executed. Good.

Second Embodiment
The second embodiment is different from the first embodiment in that data regarding probability distribution of profit as well as optimal transaction amount is calculated when the predicted value of the amount of power demand is calculated as probability distribution. In the first embodiment, the case where the power demand is a constant forecast value has been described, but in reality, the forecast value of the power demand has a probability distribution that fluctuates in a certain range. For example, although the amount of power demand used in a factory facility or the like can be predicted in advance, it is difficult to predict the amount of power demand due to the cooling and heating demand due to fluctuations in air temperature in advance.

  Therefore, in the second embodiment, data on the probability distribution of the profit amount as well as the optimal transaction amount is calculated based on the data on the probability distribution of the power demand amount.

  Hereinafter, aspects of the second embodiment will be described. In the second embodiment, only the configuration of the optimum transaction amount calculation unit 103 'is different from that of the first embodiment, and therefore the description of the other configurations is omitted.

  In the second embodiment, the optimum transaction amount calculation unit 103 ′ calculates the optimum transaction amount based on the probability distribution of the power demand amount, and calculates data on the probability distribution of the profit amount.

  The data on the probability distribution in the second embodiment (hereinafter referred to as “probability distribution data”) indicates the variation of the value that can be realized from the predicted value, and, for example, a predetermined time zone in the future (for example, , The power demand amount that can be realized with a probability of 95% at 7 o'clock in 2013/2/1), which means the range of the amount of profit (confidence interval). In addition, the probability distribution may be data representing a probability density function related to the predicted value, data indicating the correspondence between the probability that can be realized and the width of the predicted value, a dispersion coefficient, or the like.

  The optimal transaction amount calculation unit 103 ′ calculates the expected value N1 of the power demand amount based on the probability distribution of the power demand amount as an example, and the optimal transaction using the expected value N1 as a predicted value of the power demand amount The optimal transaction amount is calculated and probability distribution data of the profit amount is calculated by the step of calculating the amount and the step of reflecting the probability distribution of the power demand amount with respect to the profit value for the optimal transaction amount.

  Specifically, in the step of calculating the expected value N1 of the power demand based on the probability distribution of the power demand, the expected value N1 of the power demand has a probability density function f (probability distribution of the power demand). In the case of N), it can be calculated from equation (6).

当該期待値Ｎ１を電力需要量の予測値として、最適取引量を算出する工程は、図５の（Ｓ４）〜（Ｓ６）に示すとおりである。すなわち、最適取引量算出部１０３’は、電力需要量の予測値を期待値Ｎ１として、限界費用線に関するデータに基づいて、所定の取引量Ｃの電力取引を行った場合の発電費用の変化費用を算出する（Ｓ４）。そして、最適取引量算出部１０３’は、取引情報に関するデータＭ３に基づいて、電力取引の対象となる期間に所定量の取引を行った場合の取引収支を算出する（Ｓ５）。そして、最適取引量算出部１０３’は、（Ｓ４）において算出した所定量の取引を行った場合の発電費用の変化費用と、（Ｓ５）において算出した所定量の取引を行った場合の取引収支の差が最大となるように、最適取引量Ｃ’を算出する（Ｓ６）。

The process of calculating the optimal transaction amount using the expected value N1 as the predicted value of the power demand amount is as shown in (S4) to (S6) of FIG. That is, the optimum transaction amount calculation unit 103 ′ sets the predicted value of the power demand amount as the expected value N1, and changes the generation cost of the power generation cost when performing the power transaction of the predetermined transaction amount C based on the data on the marginal cost line. Is calculated (S4). Then, the optimum transaction amount calculation unit 103 ′ calculates the transaction balance in the case where a predetermined amount of transaction is performed in the period targeted for the power transaction, based on the data M3 related to the transaction information (S5). Then, the optimum transaction amount calculation unit 103 ′ changes the cost of changing the power generation cost in the case of performing the transaction of the predetermined amount calculated in (S4) and the transaction balance in the case of performing the transaction of the predetermined amount calculated in (S5). The optimal transaction amount C 'is calculated such that the difference between the two is the largest (S6).

  In the step of reflecting the probability distribution of the power demand amount on the profit value for the optimal transaction amount, for example, the confidence interval of the profit amount is calculated based on the trust interval of the power demand amount. The confidence interval of the profit amount can be calculated by substituting the upper limit value N2 and the lower limit value N3 of the confidence interval of the power demand amount N into equation (4) (function equation (4) of benefit amount Y is When C is constant, it is a monotonically increasing function with respect to the power demand N). In addition, it is a method of simulating the upper limit value and the lower limit value of the confidence interval of the profit amount by selecting five N included in the confidence interval of the power demand amount and calculating the profit amount Y corresponding to each. It is also good.

  The confidence interval means a numerical range that can actually occur within the range of a certain probability. FIG. 9 shows a confidence interval that falls within the range of 68.3% when the probability density function f (Nf) of the power demand amount is a normal distribution of N (N1, σ).

  As described above, according to the second embodiment, the probability distribution of the profit amount can be calculated by using the probability distribution of the power demand amount N, and the power transaction can be performed while considering the risk.

  In the second embodiment, a predetermined point is sampled out of the confidence intervals of the power demand amount N and converted into the confidence intervals of the profit amount Y. However, the probability distribution of the profit amount Y is the probability of f (Nf) The density function may be converted into a variable and expressed as a probability density function of the profit amount Y.

  FIG. 10 is an image diagram of variable conversion of the probability density function. When the probability density function f (N) of the power demand amount N is converted to the probability density function g (Y) of the profit Y, the probability density function g (Y) of Y is obtained by modifying the equation (7) It can be expressed as (8).

これによって、最適取引量算出部１０３’は、電力需要量Ｎの確率密度関数ｆ（Ｎ）を、利益額の確率密度関数ｇ（Ｙ）に変数変換することができる。

By this, the optimal transaction amount calculation unit 103 ′ can perform variable conversion of the probability density function f (N) of the power demand amount N into the probability density function g (Y) of the profit.

  As described above, according to the second embodiment, it is possible to calculate the optimal trading amount of the power transaction that maximizes the profit value, in consideration of the probability distribution of the power demand amount.

  In the second embodiment, although the method of calculating the optimal transaction amount based on the expected value N1 of the probability distribution of the power demand amount has been described, the optimal transaction amount is calculated based on the average value instead of the expected value. It may be a method of calculating.

  Also, instead of converting the probability density function f (N) of the power demand amount N to the probability density function g (Y) of the profit amount, the variance coefficient of the power demand amount N is used as the variance coefficient of the profit amount It may be a method of converting variables.

In the second embodiment, the probability density function f (N) of the power demand N is not limited to the case of normal distribution, and may be applied to any distribution function such as t distribution, 分布² distribution, F distribution, etc. Can.

  In the second embodiment, the case where the power demand amount is calculated as the probability distribution has been described. However, even in the case where the transaction price is calculated as the probability distribution, the probability distribution of the amount of profit can be calculated by performing variable conversion in the same manner as described above. In addition, when both the power demand amount and the transaction price are calculated as probability distributions, known two-dimensional variable conversion may be performed.

Third Embodiment
The third embodiment is different from the third embodiment in that the prediction device 100 further includes a demand prediction unit 105 (not shown) that calculates the probability distribution of the power demand amount. Hereinafter, aspects of the present embodiment will be described. The configuration common to the first embodiment is omitted.

  In the third embodiment, based on the understanding that the actual amount of power demand fluctuates from the predicted value of the amount of power demand based on the understanding that there is a change in the demand for air conditioning caused by fluctuations in air temperature. Make predictions. That is, the probability distribution of the power demand amount is calculated from the probability distribution of the predicted air temperature calculated by the known method. In the third embodiment, the demand prediction unit 105 calculates a relational expression between the air temperature and the power demand amount based on the past actual measurement value of the air temperature and the actual measurement value of the power demand amount, and the predicted air temperature from the relational expression The probability distribution of power demand is reflected in the probability distribution of power demand.

  FIG. 11 shows an example of a flowchart of the third embodiment.

  In the third embodiment, the prediction device 100 uses the demand information providing device 400, the weather information providing device 500, and the communication network 300 such as LAN connection (not shown in FIG. 1) to measure the measured temperature. The power demand amount is predicted by transmitting and receiving past data and the like regarding the power demand amount at that time. The demand information providing device 400 and the weather information providing device 500 are computers that transmit data in response to a request from the prediction device 100. Further, the demand information providing device 400 and the weather information providing device 500 have the same hardware configuration as the transaction information providing device 200 shown in FIG. 2B.

  FIG. 12A shows an example of a data table M5 related to the amount of power demand actually measured and stored in the storage means of the demand information providing device 400. In the data table M5, the power demand amount (W) in a predetermined time zone is stored in an hour unit in association with the date and time.

  FIG. 12B shows an example of a data table M6 related to the temperature actually measured in the past, which is stored in the storage means of the weather information providing device 500. In the data table M6, the measured values of the air temperature in the predetermined time zone are stored in one-hour units in association with the date and time.

  (S41) in FIG. 11 is a process in which the acquiring unit 101 of the prediction device 100 requests the demand information providing device 400 for the past electric power demand amount for the future predetermined time period P.

  (S42) is a step in which the demand information providing device 400 receives the request, acquires the power demand from the data table M5 related to the past power demand, and transmits the data to the prediction device 100. It is.

  As an example, when the predetermined time zone P of the future is 7 o'clock, the acquiring unit 101 acquires data of 7 o'clock to 8 o'clock of data M5 related to the amount of power demand in the past (the same month last year).

  (S43) is a process in which the acquiring unit 101 of the prediction device 100 requests the weather information providing device 500 to measure the past air temperature regarding the time of the power demand amount acquired in (S42).

  (S44) is a step in which the weather information providing device 500 receives the request, acquires an actual measurement value from the data table M6 related to the past measured air temperature, and transmits the data to the prediction device 100. .

  As an example, when the time zone of the amount of power demand acquired in (S42) is 7 o'clock to 8 o'clock, the acquiring unit 101 acquires 7 o'clock or 8 o'clock data of the data M6 related to the measured air temperature.

  (S45) is a process in which the demand prediction unit 105 of the prediction device 100 calculates a demand prediction formula based on the acquired past data. Specifically, the prediction apparatus 100 is a regression analysis for calculating the relational expression between the amount of power demand and the temperature based on the actual value T1 of the temperature in the predetermined time zone and the amount of power demand N1 acquired in (S44). I do.

  The regression analysis is, for example, the regression model of the equation (9) using the power demand amount N as an objective variable, the difference between the standard temperature 18 ° C. and the actual measurement value, and the square of the difference between the standard air temperature 18 ° C. and the actual measurement value , By the least squares method.

（γ₀、δ₀は母切片、γ₁、γ₂、δ₁、δ₂は母回帰係数、Ｅ_iは誤差項を表す。また、各変数の末尾のｉは、各観測点ｉを表し、サンプルとして取得した過去のデータの各実測値Ｔ１、電力需要量Ｎ１を表す。）
ここで、標準気温１８℃と実測値の差を説明変数としているのは、冷暖房需要による電力需要量の変動量は、標準気温１８℃のときには、冷暖房需要は実質的に０であるとみなせるためである。また、標準気温１８℃と実測値の差の二乗を説明変数とすることによって、冷暖房の需要は、気温が標準気温１８℃から離れるにつれて、急激に増加するという一般的社会現象をより正確に反映させることができる。

(Γ ₀ , δ ₀ are population intercepts, γ ₁ , γ ₂ , δ ₁ , δ ₂ are population regression coefficients, E _i is an error term, and i at the end of each variable represents each observation point i , Represents each actual measurement value T1 of the past data acquired as a sample, and the amount of power demand N1.)
Here, the reason why the difference between the standard temperature 18 ° C and the actual measurement value is used as an explanatory variable is that the amount of fluctuation of the electricity demand due to the heating and cooling demand can be regarded as substantially zero when the standard temperature is 18 ° C. It is. In addition, by taking the square of the difference between the standard temperature 18 ° C and the actual measurement value as an explanatory variable, the demand for heating and cooling reflects the general social phenomenon that the temperature rapidly increases as the temperature deviates from the standard temperature 18 ° C. It can be done.

  Further, the equation (9) divides the equation into two depending on whether it is 18 ° C. or more or 18 ° C. or less, and the cooling demand and the heating demand are separated. Although omitted in the equation (9), regression analysis may be performed by adding explanatory variables such as weather information and day information.

From this, γ ₀ , γ ₁ , γ ₂ , δ ₀ , δ ₁ and δ ₂ of the regression model are determined, and regression equation (10) is calculated as a demand forecast equation.

（Ｓ４６）は、予測装置１００の需要予測部１０５が、上記回帰式（１０）と、予測気温の確率分布データに基づいて、電力需要量の確率分布を算出する工程である。

(S46) is a process in which the demand prediction unit 105 of the prediction device 100 calculates the probability distribution of the power demand based on the above regression equation (10) and the probability distribution data of the predicted air temperature.

  As an example, when the probability distribution data of the predicted air temperature is represented by the expected value T2 of the predicted air temperature and the air temperature T2 ± S as the 68.2% confidence interval, the probability distribution (reliable interval) of the power demand amount is It becomes as follows.

That is, the expected value of the amount of power demand can be calculated by substituting the expected value T2 of the probability distribution of predicted air temperatures into the air temperature T of equation (10). Then, the upper limit value and the lower limit value of the confidence interval of the predicted value of the power demand amount can be calculated by substituting T2 + S and T2-S into equation (10). (Eq. (10) can be regarded as a monotonically increasing function of temperature T)
As described above, according to the third embodiment, the probability distribution of predicted air temperature can be reflected on the probability distribution of power demand, and the probability distribution of power demand can be calculated with high accuracy.

  In the third embodiment, a regression model is used in which the difference between the standard temperature 18 ° C. and the actual measurement value and the square of the difference between the standard temperature 18 ° C. and the actual measurement value are used as explanatory variables. However, if the probability distribution of the power demand can be calculated with a certain degree of accuracy from the probability distribution of the predicted temperature, the regression model does not have to be limited to the above. For example, for the square of the difference between the standard temperature 18 ° C. and the actual measurement value, the explanatory variable may be omitted, or instead of the difference between the standard air temperature 18 ° C. and the actual measurement value, a normal actual measurement value may be used as the explanatory variable. Also, the standard temperature may be set to 17 ° C. or 19 ° C. instead of 18 ° C. In addition, predicted maximum temperature, predicted minimum temperature, weather information, regional information and the like may be added as explanatory variables. Also, in order to stabilize the dispersion of the sample, a dispersion stabilization transformation may be performed to adapt the regression model.

  In the third embodiment, although the process of calculating the demand forecasting equation is performed according to the date and time of the forecasting date being set, the demand forecasting equation is generated in advance, and the date and time of the forecasting date is set. According to the calculation, the predicted value of the amount of power demand on the corresponding date may be calculated.

  Further, in the third embodiment, as a factor that the actual power demand fluctuates from the predicted value of the power demand, the fluctuation of the air conditioning demand due to the air temperature fluctuation is regarded as the largest, and the dispersion of the power demand is calculated In this case, the error term of equation (9) is not considered. However, the standard deviation of the error term of the equation (9) may also be calculated to be taken into consideration when calculating the probability distribution of the power demand. In that case, the standard deviation may be integrated according to the well-known error propagation law.

Fourth Embodiment
In the fourth embodiment, it is assumed that the power generation means (power source) is in a state where power generation can not be performed due to an accident or a failure (referred to as dropout) on the premise of the optimal power transaction amount created in the first to third embodiments. To simulate the increase and decrease of the revenue when the means of In the fourth embodiment, simulation is performed only for the power selling transaction.

  Specifically, the following three balance calculations are performed.

  (1) The prediction device 100 calculates the balance by setting the potential generation amount of the power generation means (hereinafter referred to as a dropout power source) assumed to be dropout as “0”. That is, as the power generation plan which operates the power generation means treated as not to be operated at the time of the prediction processing of the optimal power transaction amount mentioned above and satisfies the power demand, the balance which considered the increase of the power generation cost in that case is calculated.

  (2) In addition, the prediction device 100 calculates a balance after incorporating a standby power generation unit (hereinafter referred to as a standby power supply) that is operated when the dropped power supply is dropped as a power generation unit. This is a simulation that assumes that the marginal power generation cost of the backup power source is lower than the power generation means that was not operated, and that there is not enough power generation capacity to meet the power demand even if all the power generation means are operated. It is.

  (3) Furthermore, balance calculation is performed after virtually incorporating power interchange (called national accommodation) in an emergency different from the power exchange market as a power generation means. This is a simulation that assumes that there is not enough power generation capacity to meet the power demand even if a backup power supply is incorporated.

  In the fourth embodiment, the prediction device 100 includes a balance calculation unit (not shown; corresponds to a profit amount calculation unit of the present invention) that performs the above simulation.

=== Data table M4 (dropout parallel) ===
FIG. 13 shows an example of the data table M4 related to the dropped power source stored in the storage unit 100B of the prediction device 100 of the fourth embodiment.

  The data table M4 stores a dropout flag and a parallel flag for each spare power supply (parallel unit) in association with the power generation means. The power generation means registered in the data table M4 is the same as the power generation means registered in the data table M1 of FIG. 3A. The dropout flag is a flag indicating whether or not to perform simulation as a dropout power source. The prediction device 100 can receive the specification of the dropout power source by receiving the input of the dropout flag of the data table M4 (dropout power generation means specification unit). The parallel flag is a flag (preliminary power generation means storage unit) indicating whether or not the standby power supply is operated when the dropped power supply falls off for each standby power supply.

=== Data table M5 (spare power supply) ===
FIG. 14 shows an example of the data table M5 related to the backup power source stored in the storage unit 100B of the prediction device 100 of the fourth embodiment. The configuration of the data table M5 is the same as that of the data table M1 shown in FIG. 3A, but the power generation means a, b, c. As described later, in the case where calculation is performed by incorporating a spare power supply as a power generation means, the record of the data table M5 may be incorporated into the data table M1.

=== Data table M6 (nationally flexible) ===
FIG. 15 shows an example of a data table M6 related to nationwide accommodation stored in the storage means 100B of the prediction device 100 of the fourth embodiment. The data table M6 is the price for nationwide accommodation and the minimum transaction for the national accommodation for each of the 48-hour zones of time zones 1A, 1B, 2A, 2B,... 24B in units of 30 minutes in the example of FIG. Store the volume and the maximum transaction volume: National flexibility changes the price when the amount of power to be procured (flexibility amount) exceeds a predetermined threshold. The case of “the stage” and the amount of accommodation exceeding the threshold is expressed as “two stages”.

=== Simulation ===
FIG. 16 shows a flowchart for simulating increase and decrease of the profit when the power generation means is disconnected in the fourth embodiment.

  (S51) is a process in which the balance calculation unit specifies the dropout power source. Specifically, the balance calculation unit reads a record of dropout flag = 1 from the data table M4.

  (S521) to (S523) are steps of performing the above-described simulation (1).

  (S521) is a step of excluding the power-off source from the calculation target of the power generation cost. The balance calculation unit sets the power generation possible amount of the dropout power source of the data table M1 to “0”. The minimum power generation amount is also set to “0”. As a result, the disconnected power source will not be used for power generation planning.

  (S522) is a step of calculating the power generation cost when the dropped power supply is removed. Specifically, the balance calculator calculates the second term of the above-mentioned equation (5). FIG. 17 is a diagram showing an example of the marginal cost line when the dropped power supply is removed. FIG. 17A shows the power generation cost (lane section) when the power generation amount W is increased from the demand forecast value N0 by the transaction amount C of the power seller on the marginal cost line shown in FIG. 8A. Here, when the power generation means B is designated as a dropout power source, the power generation possible amount of the power generation means B is 0, so that the power generation means B is removed from the marginal cost line as shown in FIG. become. In the example of FIG. 17 (b), the power generation means H which is not scheduled to be operated in FIG. 17 (a) is operated. Since the marginal cost of the power generation means H is high, the overall power generation cost is also increased.

  (S523) is a process of calculating the profit when the dropout power source is removed. Specifically, the balance calculation unit performs the calculation of Expression (5) using the marginal cost line F (W) from which the dropout power source is removed as described above. The balance calculation unit stores the calculation result as the profit 1 corresponding to the specified dropout power source.

  (S531) to (S534) are steps of performing the above-described simulation (2).

  (S531) is a step of setting to set the backup power supply as a calculation target. Specifically, the balance calculation unit specifies the spare power supply in which the parallel flag is 1 in the above record, reads the data record of the spare power supply corresponding to the spare power supply from the data table M5, Data records are registered in data table M1.

  (S532) is a step of registering the priority of the backup power supply. The balance calculation unit registers the record of the backup power supply in the data table M4. The balance calculation unit may receive, for example, an input of the priority of the backup power supply, and may register the input priority in the data table M4 in association with the backup power supply, or the marginal cost of the backup power supply (average value , Median) and the marginal cost of other power generation means, the priority of the spare power source is lower than the priority of the means of generation cheaper than the marginal cost of the auxiliary power source, and the marginal cost of the auxiliary power source The priority may be set or the priority of the other power generation means in the data table M4 may be updated so that the priority of the spare power supply is higher than the priority of the high power generation means.

  (S533) is a step of calculating the power generation cost when the dropout power source is removed and the backup power source is incorporated. Similar to (S522), the balance calculator calculates the second term of the above-mentioned equation (5). FIG. 18 is a diagram showing an example of a marginal cost line in the case where a drop-off power supply is removed and a backup power supply is incorporated. FIG. 18 (a) is the same as FIG. 17 (a). Here, when the power generation means B is designated as a dropout power source and the power generation means b is designated as a backup power source, as shown in FIG. 18 (b), the power generation possible amount of the power generation means B becomes zero. A marginal cost line can be created in which the amount of power generation by the backup power supply b is increased. In the example of FIG. 18 (b), although it is not necessary to operate the power generation means H unlike FIG. 17 (b) by incorporating the backup power supply b, the marginal cost of the backup power supply b is the limit of the power generation means B. It can be seen that the cost of power generation as a whole is higher than the cost.

  (S534) is a process of calculating the profit when the dropout power source is removed and the backup power source is incorporated. Specifically, the balance calculation unit performs the calculation of the equation (5) using a marginal cost line F (W) incorporating a reserve power supply while removing the dropout power supply. The balance calculation unit stores the calculation result as the profit 2 corresponding to the specified dropout power source.

  (S541) to (S543) are steps of performing the above-described simulation (3).

  (S541) is a step of setting procurement of electric power through national accommodation as a virtual power generation means. Specifically, the balance calculation unit reads a record from the data table M6, uses information indicating national accommodation as the power generation means, sets the minimum transaction amount of the first stage as the minimum power generation amount, and generates the maximum transaction amount of the second stage. The potential volume is the minimum trading volume of the first tier and the maximum trading volume of the first tier. The first tier price is the maximum trading volume of the second tier and the second tier. For the amount of power generation equal to or less than the amount, a record in which a function serving as the second stage price is set as the marginal cost is registered in the data table M1. As a result, the power supply through national flexibility will be incorporated into marginal cost lines.

  (S542) is a process of calculating the power generation cost when the dropout power source is removed and the reserve power source is incorporated and the national flexibility is also incorporated. Similar to (S522), the balance calculator calculates the second term of the above-mentioned equation (5). FIG. 19 is a diagram showing an example of a marginal cost line in the case where a dropout power source is removed and a backup power source is incorporated and also national flexibility is incorporated. FIG. 19 (a) is the same as FIG. 17 (a) and FIG. 18 (a). Here, when the power generation means F is designated as a dropout power source and the power generation means f is designated as a backup power source, as shown in FIG. 19 (b), the power generation possible amount of the power generation means F becomes zero. A marginal cost line can be created in which the amount of power generation by the backup power source f is increased. In addition, national flexible procurement volumes and transaction costs are incorporated into marginal cost lines. In the example of FIG. 19 (b), although the spare power supply f is incorporated instead of the power generation means F, the amount of power generation of the spare power supply f is small, and even if the power generation means H is also operated It is not possible to generate enough power to meet the demand added with C, and some power is procured by the country, but since the procurement price by the country is high, the power generation cost as a whole rises. Is shown.

  (S543) is a process of calculating the profit when the dropout power source is removed and the reserve power source is incorporated and the national flexibility is also incorporated. Specifically, the balance calculation unit performs calculation of equation (5) by using the marginal cost line F (W) incorporating the reserve power supply and the national flexibility, with the dropout power supply removed. The balance calculation unit stores the calculation result as the profit 3 corresponding to the specified dropout power source.

  (S55) is a step of outputting the revenue recalculated as described above. The balance calculation unit outputs, for example, the revenue 1 calculated in (S523), the revenue 2 calculated in (S534), and the revenue 3 calculated in (S543) in association with the dropped power supply to the screen.

  The above steps S521 to S543 are performed for each record of dropout flag = 1. As a result, the user can easily grasp the change in the profit due to the drop of the power generation means designated as the dropout power source.

<Other Modifications>
FIG. 20 is a diagram showing a configuration example of a data table M7 for managing the upper limit and the lower limit of the power generation amount by the power generation means for each time zone. While the data table M1 stores a certain minimum amount of power generation and a possible generation amount for each power generation means, the data table M7 associates a time zone (30 minutes in the example of FIG. 20) with the power generation means. The upper limit (power generation possible amount) and the lower limit (minimum power generation amount) of the power generation amount are stored for each band. The data table M7 can be used, for example, in the case of creating in advance a plan for starting and stopping the generator in accordance with a schedule for maintenance of the generator.

  When the data table M7 is used, the marginal cost line may be created for each time zone, in which case the lower limit and the upper limit of the data table M7 are referred to instead of the minimum power generation amount and the power generation possible amount of the data table M1. do it. Further, in (S521) of FIG. 16, both the upper limit and the lower limit of the data table M7 corresponding to the dropped power supply may be set to “0”.

  As described above, according to each of the above-described embodiments, it is possible to calculate the optimal trading volume of the power transaction that maximizes the profit value, in consideration of the power generation cost.

  In each of the above embodiments, the prediction device 100 is configured to include the power generation schedule calculation unit 102 and the optimal transaction amount calculation unit 103 as functional units. However, these functional units, or parts thereof, may be distributed to other devices. Similarly, the storage area of data stored in each storage means may be any place. For example, it may be integrated in the prediction device 100, or may be distributed and stored on a cloud system composed of a plurality of computers.

=== Conclusion ====
As mentioned above, said each embodiment can be described as follows.

  Each of the above embodiments is a prediction system that predicts the optimum transaction amount that maximizes the profit value (Y) in the power transaction, and the first storage unit that stores the possible generation amount of a plurality of power generation means (the above embodiment Then, the second storage unit (corresponding to the data table M1 in the above embodiment) storing power generation costs per unit power generation amount of a plurality of power generation means The third storage unit (in the above embodiment, corresponding to the data table M2) storing the predicted value (N) of the power demand in the period and the power generation means with low power generation cost per unit power generation are preferentially operated And a fourth storage unit (in the above embodiment, corresponding to the data table M4) for storing operation priorities for a plurality of power generation means, and a unit power generation amount in a period targeted for the power transaction. A fifth storage unit (in the above embodiment, corresponding to the data table M3) for storing the transaction price of the unit, the power generation capacity of the power generation means, the power generation cost per unit power generation amount of the power generation means, and the operation priority for the power generation means Calculated based on the power generation schedule calculation unit (102) that calculates data on the marginal cost line indicating the correspondence between the amount of power demand and the power generation cost based on the degree and the data on the marginal cost line and the predicted value of the power demand amount Calculation of the change in generation costs caused by the specified amount of power transaction (C) and the balance of transactions caused by the predetermined amount of power transaction calculated based on the data on the transaction price And a forecasting system characterized by comprising:

  This allows the power supplier to calculate the optimal trading volume of the power transaction that maximizes the profit value, taking into account the generation costs.

  Here, the power generation cost per unit power generation amount of the plurality of power generation units may be data stored by associating the operation rate of each of the plurality of power generation units with the power generation cost per unit power generation amount.

  This makes it possible to grasp the power generation cost in detail, and to calculate the optimum trading volume of the power transaction which maximizes the profit value more accurately.

  Here, the predicted value of the power demand amount is probability distribution data of the power demand amount in a period targeted for the power transaction, and the optimum trade amount calculation unit calculates the power demand amount based on the probability distribution data of the power demand amount. The probability distribution data of the optimal transaction volume and the profit value may be calculated. At this time, the optimal transaction amount calculation unit may calculate the optimal transaction amount in the power transaction based on the expected value of the probability distribution of the power demand amount.

  By this, it is possible to calculate the fluctuation range (risk) of profit in the power transaction in addition to the optimal transaction volume.

  Here, the prediction system further includes a sixth storage unit (corresponding to the data table M1 in the above embodiment) that stores probability distribution data of power loss of a predetermined power generation unit, and the power generation schedule calculation unit Based on the probability distribution data of electric power dropout of the power generation means and the power generation possible amount of the predetermined power generation means, the expected value of the power generation possible amount of the predetermined power generation means is calculated, and the expected value of the power generation possible amount of the predetermined power generation means Based on the data on marginal cost lines may be calculated.

  By this, it is possible to calculate the optimal trading volume of the power transaction that maximizes the profit value, taking into consideration the risk of power loss.

  Here, the transaction price is probability distribution data of the transaction price in the target period of the power transaction, and the optimum transaction amount calculation unit calculates the optimum transaction amount in the power transaction based on the probability distribution data of the transaction price, and The probability distribution data of the profit value may be calculated.

  By this, it is possible to calculate the fluctuation range (risk) of profit in the power transaction in addition to the optimal transaction volume.

  Here, the fourth storage unit further has a priority relating to the mass transit power source, and the operation priorities for the plurality of power generation means are priority relating to the mass transit power source over the power generation means having a low power generation cost per unit power generation amount. It may be data set so that the power generation means with the set degree operates preferentially.

  This makes it possible to grasp the power generation cost in detail, and to calculate the optimum trading volume of the power transaction which maximizes the profit value more accurately.

  Further, each of the above embodiments is a prediction method for predicting the transaction amount that maximizes the profit value in the power transaction, and the power generation possible amount of the plurality of power generation means and the power generation cost per unit power generation amount of the plurality of power generation means And a power generation schedule calculating step of calculating data on the marginal cost line indicating the correspondence between the amount of power demand and the generation cost based on the operation priorities for the plurality of power generation means, and data on the marginal cost line and prediction of the power demand amount A predetermined amount that maximizes the difference between the cost of change in generation costs caused by the specified amount of power transactions calculated based on the value and the balance of transactions caused by the specified amount of power transactions calculated based on data on the transaction price And an optimal trading amount calculating step of calculating

  This allows the power supplier to calculate the optimal trading volume of the power transaction that maximizes the profit value, taking into account the generation costs.

  Although the specific examples of the present invention have been described above in detail, these are merely examples and do not limit the scope of the claims. The art set forth in the claims includes various variations and modifications of the specific examples illustrated above.

DESCRIPTION OF SYMBOLS 100 Prediction apparatus 101 Acquisition part 102 Power generation schedule calculation part 103 Optimal transaction amount calculation part 104 Presentation part 105 Demand prediction part 200 Transaction information provision apparatus 300 Communication network 400 Demand information provision apparatus 500 Weather information provision apparatus

Claims (5)

A system that supports power trading,
A first storage unit for storing data relating to the power generation means, including minimum power generation amount, power generation capacity and marginal cost of each of the plurality of power generation means;
A second storage unit storing transaction information for calculating a transaction amount according to the transaction amount by the power transaction;
The operation priority of the plurality of power generation means is set to be higher for the power generation means with lower minimum cost for the power generation means with the minimum power generation amount larger than 0, and the power generation means with the minimum power generation amount of 0 is also increased. The lower cost is set to be higher as the cheaper power generation means, and the operation priority of the power generation means whose minimum power generation amount is larger than 0 is higher than the operation priority of the power generation means whose minimum power generation amount is 0 A third storage unit for storing the set data;
A marginal cost line in which the power generation means to be operated to generate the required amount of power generation is associated with the power generation cost based on the data on the power generation means and the operation priority set to the plurality of power generation means Power generation schedule calculation unit that calculates data related to
The first power generation cost necessary to generate the first power generation amount corresponding to the predicted power demand amount based on the data on the marginal cost line, and the sales amount of power in the power transaction to the first power generation amount The change cost, which is the difference between the second power generation cost necessary to generate the added second power generation amount, is determined, and the change cost is calculated from the income from the power transaction obtained on the basis of the transaction information. An optimal transaction amount calculation unit that calculates the amount of profit subtracted and calculates the optimal sales amount at which the amount of profit is maximum;
Among the plurality of power generating means, and dropout generating means designation unit that receives designation of dropping the power generating means is a power generation means we assume dropped,
Equipped with
The optimum trading volume calculation unit calculates the after optimum sales volume is determined, the profit when without a prior SL falling power generator performs power generation of the second power generation amount performs the power trading again Do,
Power transaction support system. The power transaction support system according to claim 1 , wherein
The data relating to the power generation means includes the minimum amount of power generation, the amount of power that can be generated, and the marginal cost for the standby power generation means that is operated when the dropped power generation means falls off,
The power generation schedule calculation unit, based on the data on the power generation means and the operation priority set to the plurality of power generation means, the marginal cost line when the spare power generation means is operated instead of the dropout power generation means Calculate data on
The optimal transaction amount calculation unit calculates the profit amount when the standby power generation unit is operated instead of the dropout power generation unit after the optimal sales amount is determined.
Power transaction support system. The power transaction support system according to claim 1 or 2 , wherein
The data relating to the power generation means includes the minimum amount of power generation, the amount of power generation and the marginal cost when using national flexibility as a virtual power generation means,
The power generation schedule calculation unit is configured to operate the virtual power generation unit in place of the dropped power generation unit based on the data on the power generation unit and the operation priority set to the plurality of power generation units. Calculate data on cost lines,
The optimal transaction amount calculation unit calculates the profit amount in the case of operating the virtual power generation means in place of the dropout power generation means after the optimal sales amount is determined.
Power transaction support system. A method to support electricity trading,
A first storage unit for storing data relating to the power generation means, including a minimum power generation amount, a power generation possible amount and a marginal cost of each of the plurality of power generation means , and for calculating a transaction amount according to the transaction amount by the power transaction The second storage unit for storing transaction information and the operation priorities of the plurality of power generation means are set to be higher for the power generation means whose cost is lower than the minimum power generation amount. The minimum cost is set to be higher for the power generation means whose cost is lower than the minimum power generation amount, and the operation priority of the power generation means for which the minimum power generation amount is larger than 0 is zero. A third storage unit storing data set so as to be higher than the operation priority of the power generation unit;
A computer equipped with
A marginal cost line in which the power generation means to be operated to generate the required amount of power generation is associated with the power generation cost based on the data on the power generation means and the operation priority set to the plurality of power generation means Calculating data about the
The first power generation cost necessary to generate the first power generation amount corresponding to the predicted power demand amount based on the data on the marginal cost line, and the sales amount of power in the power transaction to the first power generation amount The change cost, which is the difference between the second power generation cost necessary to generate the added second power generation amount, is determined, and the change cost is calculated from the income from the power transaction obtained on the basis of the transaction information. Calculating an amount of profit subtracted and calculating an optimal sales volume for maximizing the amount of profit;
Among the plurality of power generating means, a step of accepting a specification of dropping the power generating means is a power generation means we assume dropped,
Recalculating the profit amount when performing the power transaction by generating the second amount of power generation without using the dropout power generation means ;
A power transaction support method characterized by performing. A program to support electricity trading,
A first storage unit for storing data relating to the power generation means, including a minimum power generation amount, a power generation possible amount and a marginal cost of each of the plurality of power generation means , and for calculating a transaction amount according to the transaction amount by the power transaction The second storage unit for storing transaction information and the operation priorities of the plurality of power generation means are set to be higher for the power generation means whose cost is lower than the minimum power generation amount. The minimum cost is set to be higher for the power generation means whose cost is lower than the minimum power generation amount, and the operation priority of the power generation means for which the minimum power generation amount is larger than 0 is zero. A third storage unit storing data set so as to be higher than the operation priority of the power generation unit;
On a computer equipped with
A marginal cost line in which the power generation means to be operated to generate the required amount of power generation is associated with the power generation cost based on the data on the power generation means and the operation priority set to the plurality of power generation means Calculating data about the
The first power generation cost necessary to generate the first power generation amount corresponding to the predicted power demand amount based on the data on the marginal cost line, and the sales amount of power in the power transaction to the first power generation amount The change cost, which is the difference between the second power generation cost necessary to generate the added second power generation amount, is determined, and the change cost is calculated from the income from the power transaction obtained on the basis of the transaction information. Calculating an amount of profit subtracted and calculating an optimal sales volume for maximizing the amount of profit;
Among the plurality of power generating means, a step of accepting a specification of dropping the power generating means is a power generation means we assume dropped,
Recalculating the profit amount when performing the power transaction by generating the second amount of power generation without using the dropout power generation means ;
A program to run a program.

Images