JP2016148945A - Electricity transaction support system, electricity transaction support method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To calculate an amount of transaction maximizing a profit, and to enable a proposal of an influence when a power source is shut down.SOLUTION: An electricity transaction support system is configured to: specify first power generation means to be operated for implementing a power generation of a first amount of power generation serving as a prediction value of an amount of electricity demand; calculate a first power generation expense for implementing the first amount of power generation by the first power generation means; specify second power generation means to be operated for implementing a power generation of a second amount of power generation adding an optimum amount of sales in an electricity transaction to the first amount of power generation; calculate a second power generation expense for implementing the second amount of power generation by the second power generation means; calculate a profit amount subtracting an increase amount from the first power generation expense to the second power generation expense from an income amount corresponding to the optimum amount of sales; determine an optimum amount of sales so that a profit amount is maximum; thereafter, receive a specification of shut-down power generation means; specify the first and second power generation means again so as not to select the shut-down power generation means; and calculate a profit amount again for the specified first and second power generation means to output the profit amount.SELECTED DRAWING: Figure 17

Description

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

  In recent years, in the electric power market, electric power can be freely bought and sold. When buying and selling electric power, the electric power transaction price is determined for a certain period in the future, and buying and selling of electric power is carried out (for example, buying and selling in units of 30 minutes two days ago).

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

JP 2004-252967 A

  However, in the method described in Patent Document 1, the power generation cost of the power supplier is not taken into consideration, the transaction amount cannot be calculated in consideration of the interest of the power supplier, and the power source is dropped It is not possible to grasp how much it will affect.

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

  A main invention of the present invention for solving the above-described problem is a system for supporting power transactions, and is a power generation cost calculation information for calculating a power generation cost corresponding to the amount of power generated by each of a plurality of power generation means. A first storage unit that stores the transaction amount, a second storage unit that stores transaction amount calculation information for calculating a transaction amount according to the transaction amount of the power transaction, and power generation cost calculation information based on the power generation cost calculation information. A first power generation unit that is operated to generate a first power generation amount that is a predicted value of a demand amount is specified, and a first power generation unit that generates the first power generation amount by the first power generation unit. Calculating the power generation cost of 1 and specifying the second power generation means to be operated to generate power of the second power generation amount obtained by adding the optimal sales amount in the power transaction to the first power generation amount; The second power generation means A power generation cost calculation unit that calculates a second power generation cost for generating a quantity of power, and the optimal increase amount from the first power generation cost to the second power generation cost based on the transaction amount calculation information A profit amount calculation unit that calculates a profit amount subtracted from the revenue amount that is the transaction amount according to the sales amount, an optimal sales amount determination unit that determines the optimal sales amount so that the profit amount is maximized, and omission A drop-off power generation means designating unit that accepts designation of a drop-off power generation means that is assumed to be the power generation means. The power generation cost calculation unit specifies the first and second power generation units again so as not to select the drop-off power generation unit, and the profit amount calculation unit determines the first and second power generation units specified again. About the above profit amount Calculated, and to output the calculated profit.

  In addition, the power trading support system of the present invention includes a constraint condition storage unit that stores a maximum power generation amount for each power generation unit, and the power generation cost calculation unit has the total of the maximum power generation amount as the first or second. Until the power generation amount becomes equal to or greater than the power generation amount, the power generation cost may be selected as the first or second power generation unit in order from the power generation unit with the lowest power generation cost.

  In the power trading support system of the present invention, the constraint condition storage unit stores the minimum power generation amount in addition to the maximum power generation amount for each power generation means, and the power generation cost calculation unit has the minimum power generation amount of 0. The larger power generation means is selected as the first and second power generation means, and a value obtained by adding the sum of the maximum power generation amounts to the sum of the minimum power generation amounts is equal to or greater than the first or second power generation amount. Until it becomes, it may be made to choose as the 1st or 2nd power generation means in order from the power generation means with the cheapest power generation cost.

  The power trading support system of the present invention further includes a standby power generation unit storage unit that stores a standby power generation unit that is operated when the power generation unit is dropped in association with the power generation unit, and the first storage unit Stores the power generation cost calculation information for the standby power generation means, and after the optimum sales amount has been determined, the power generation means specifying unit includes the dropout power generation means of the plurality of power generation means and the standby power generation means. You may make it specify the said 1st and 2nd electric power generation means out of what excludes.

  The power trading support system of the present invention further includes a procurement cost calculation information storage unit for storing procurement cost calculation information for calculating a procurement cost when power is procured by a procurement means different from the power trading, The power generation cost calculation unit, based on the power generation cost calculation information and the procurement cost calculation information, generates power for the first power generation amount by the first power generation means or costs for procurement by the procurement means. The first power generation cost may be calculated, and the second power generation cost may be calculated as the second power generation cost by generating the second power generation amount using the second power generation unit or using the procurement unit. .

  According to another aspect of the present invention, there is provided a method for supporting a power transaction, wherein a first power generation cost calculation information for calculating a power generation cost according to a power generation amount required for power generation by each of a plurality of power generation means is stored. And a second storage unit that stores transaction amount calculation information for calculating a transaction amount according to the transaction amount of the power transaction, based on the power generation cost calculation information, A first power generation unit that is operated to generate a first power generation amount that is a predicted value of a demand amount is specified, and a first power generation unit that generates the first power generation amount by the first power generation unit. Calculating the power generation cost of 1 and specifying the second power generation means to be operated to generate power of the second power generation amount obtained by adding the optimal sales amount in the power transaction to the first power generation amount; The second power generation by two power generation means Calculating a second power generation cost for performing the power generation of the first and the amount of increase from the first power generation cost to the second power generation cost based on the transaction amount calculation information according to the optimum sales amount A step of calculating a profit amount subtracted from the amount of income that is the transaction amount, a step of determining the optimum sales amount so that the profit amount calculated by the profit amount calculation unit is maximized, A step of accepting designation of a drop-off power generation means that is assumed to be the power generation means; a step of re-specifying the first and second power generation means so as not to select the drop-off power generation means; The step of calculating the profit amount again for the second power generation means and the step of outputting the calculated profit amount are executed.

  Another aspect of the present invention is a program for supporting power trading, and stores power generation cost calculation information for calculating a power generation cost corresponding to the amount of power generated by each of a plurality of power generation means. Based on the power generation cost calculation information in a computer comprising: a first storage unit that performs a second storage unit that stores transaction amount calculation information for calculating a transaction amount according to the transaction amount of the power transaction Then, the first power generation unit that is operated to generate the first power generation amount that is the predicted value of the power demand amount is specified, and the first power generation unit generates the first power generation amount. Calculating the first power generation cost for the first power generation, and specifying the second power generation means to be operated to generate the second power generation amount obtained by adding the optimal sales amount in the power transaction to the first power generation amount , By the second power generation means Based on the step of calculating the second power generation cost for generating the second power generation amount and the transaction amount calculation information, an increase from the first power generation cost to the second power generation cost is calculated. Calculating an amount of profit subtracted from the amount of revenue that is the transaction amount according to the optimal sales amount, and determining the optimal sales amount so that the profit amount calculated by the profit amount calculation unit is maximized; Receiving a designation of a drop-off power generation means that is the power generation means assumed to have dropped, re-specifying the first and second power generation means so as not to select the drop-off power generation means, A step of calculating the profit amount again for the identified first and second power generation means and a step of outputting the calculated profit amount are executed.

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

  According to the present invention, after calculating the amount of power trade that maximizes profits, it is possible to present the effect when the power supply is dropped.

It is a figure which shows the prediction system in embodiment of this 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 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 operation priority in 1st Embodiment of this invention. It is a figure which shows the relationship between 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 the probability distribution of the electric power demand in 2nd Embodiment of this invention. It is an image figure of variable conversion of the probability density function in 2nd Embodiment of this invention. It is a figure which shows the flow of operation | movement of the demand prediction part in 3rd Embodiment of this invention. It is a figure which shows the data table M5 regarding the past power demand in 3rd Embodiment of this invention. It is a figure which shows the data table M6 regarding the past measured 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 standby power supply in 4th Embodiment of this invention. It is a figure which shows the data table M6 regarding national accommodation in 4th Embodiment of this invention. It is a flowchart which simulates the increase / decrease in the profit at the time of the omission of a power generation means in 4th Embodiment of this 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 a 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 a drop-off power supply in 4th Embodiment of this invention, incorporating a backup power supply, and incorporating national accommodation. 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 become apparent from the description of this specification and the accompanying drawings.

<First Embodiment>
=== About the prediction system ===
In the first embodiment, for each power generation means, the characteristics related to the power generation cost per unit power generation, etc. are grasped, and the transaction that maximizes the profit value in the power transaction based on the characteristics and the transaction information of the power transaction The amount is to be predicted.

  The power generation cost varies variously depending on whether or not it is a masstran power supply, the operating rate of the power generation means, the fuel cost required for 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 indispensable to grasp these characteristics. Hereinafter, the power generation cost required per unit power generation amount (for example, 1 MWh / h) is referred to as “marginal cost”. In addition, the transaction volume that maximizes the profit value in power trading is called “optimum transaction volume”.

  In FIG. 1, an example of the prediction system which implement | achieves prediction of the optimal transaction amount in the case of an electric power transaction in this 1st Embodiment is shown. The prediction system according to the first embodiment includes a prediction device 100, a transaction information providing device 200, and the like. Both devices transmit and receive data using a communication network 300 such as a LAN connection.

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

=== Hardware Configuration of Prediction Device 100 ===
FIG. 2A shows a hardware configuration example of the prediction apparatus 100 of 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, 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 based on the computer program stored in the storage unit 100B, and controls their operations.

  The storage unit 100B includes a volatile memory (RAM), a nonvolatile memory (flash memory), and the like. The storage unit 100B stores a computer program for controlling the prediction device 100, data M1 related to power generation unit (to be described later), data M2 related to the predicted power demand amount, and the like. The storage unit 100B also includes a storage unit (not shown) that stores a regression model, which will be 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 a communication network 300 or the like by wired or wireless LAN connection.

  The input unit 100 </ b> D is a switch, a touch panel, or the like, and receives a user operation instruction for the prediction device 100.

  The display unit 100E is a liquid crystal display or the like that displays various types of information, and displays a probability distribution or the like of a predicted temperature described later.

=== Hardware Configuration of Transaction Information Providing Device 200 ===
FIG. 2B shows a hardware configuration example of the transaction information providing apparatus 200 according to 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, the control unit 200A performs data communication with the communication unit 200C and the storage unit 200B based on the computer program stored in the storage unit 200B, and controls their operation.

  The storage unit 200B includes a volatile memory (RAM), a nonvolatile memory (flash memory), and the like. And the memory | storage means 200B has memorize | stored the computer program for controlling the transaction information provision apparatus 200, the data regarding the transaction information mentioned later, etc.

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

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

  The data relating to the power generation means includes a minimum power generation amount, a power generation possible amount, and a marginal cost for a plurality of power generation means possessed by the power supplier.

  The 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 generation, and the like vary depending on the type of power generation means and the performance such as power generation efficiency. For example, since it is difficult to stop the operation of nuclear power generation, it is planned as a power generation means that is always operated. On the other hand, in the case of thermal power generation, the marginal cost may rise depending on the fuel price or the like. In addition, hydroelectric power generation also has a feature that the marginal cost is cheaper than other power generation means.

  The power generation means A, B, C... Shown in FIG. 3A represents one of those power generation means. In addition, the minimum power generation amount (for example, 1 MWh / h) is a Mastran power supply (power generation means that is difficult to stop in operation from the viewpoint of power generation costs, as in the case of the above-mentioned nuclear power generation, and that needs to be operated constantly). The amount of power generated by The power generation possible amount (for example, 1 MWh / h) is a limit value of the power generation capacity of each of the power generation means. The marginal cost represents the power generation cost required per unit power generation amount (for example, 1 MWh) when operating the power generation means.

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

=== Data table M2 (forecast 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 predicted demand is data relating to a predicted value (for example, 100 MWh) of the electric power demand that needs to be supplied in a predetermined time zone by the power supplier who is the user of the prediction device 100. The said electric power demand amount changes with time zones. For example, since the factory facilities are operating in the daytime, a large amount of power demand is predicted. However, since the factory facilities are not operated at night, the power demand is predicted to decrease. As this data, for example, data calculated based on the required power demand amount on the same day in the past is used. The past similar conditions are conditions relating to time, season, day of the week, weather, and the like.

  The data table M2 stores the power demand estimated every hour. You may make it memorize | store the predicted value of the electric power demand amount for every arbitrary time (for example, 30 minutes etc.).

=== Data Table M3 (Transaction 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 apparatus 200 according to the first embodiment.

  The data related to the transaction information is data related to the transaction price per unit power generation amount (for example, 1 MWh / h) in a predetermined time zone (for example, 11:00 to 12:00) that is the object of the transaction, and data related to the transaction possible amount. Here, the data relating to the transaction information may be a confirmed value or a predicted value. The electric power transaction is performed 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 sold amount instead of supplying additional power. Can be obtained as Similarly, when a power supplier purchases a predetermined amount of power, based on the transaction price, the power supplier receives the power supply for the purchase instead of receiving the power supply from the transaction target. This is a mode of paying an amount. Further, the tradeable amount represents an upper limit value at which the power supplier can trade in the power trade.

  The data table M3 stores a transaction price and a tradeable amount every hour.

  Further, the data relating to the tradeable amount may be set for the convenience of operation of the power generation means of the power supplier. For example, as described above, it is assumed that the Mastran power supply is always operated, and therefore, the electric power generated by the operation of the Mastran power supply may be set as inoperable. Moreover, the data regarding the tradeable 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 according to the first embodiment.

  The prediction device 100 includes an acquisition unit 101, a power generation schedule calculation unit 102, an optimum transaction amount calculation unit 103, and a presentation described below, based on the computer program stored in the storage unit 100B and the hardware configuration (100A to 100E) described above. The function of the unit 104 is realized.

  The acquisition unit 101 acquires data by performing data communication with another device. In the first embodiment, data related to transaction information is acquired from the transaction information providing apparatus 200.

  The power generation schedule calculation unit 102 determines the marginal cost based on the data M1 regarding the power generation means (data regarding the power generation possible amount of the power generation means, data regarding the marginal cost of the power generation means) and the operation priorities for the plurality of power generation means set. Calculate data about the line. The data related to the marginal cost line is data that specifies how to operate the power generation means in order to compensate for the power generation amount in correspondence with the required power generation amount (power demand amount). Data corresponding to the line F (W).

  The optimum transaction amount calculation unit 103 uses the data related to the calculated marginal cost line, the change cost of the power generation cost when performing a predetermined amount of power transaction calculated based on the predicted value of the power demand amount, and the data related to the transaction information. The transaction amount when the difference from the transaction balance when performing a predetermined amount of power transaction calculated based on the maximum is calculated as the optimum transaction amount.

=== 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 step in which the user of the prediction device 100 inputs a prediction target period (for example, 2013/2/1 from 7:00 to 8:00). At this time, the user of the prediction device 100 sets the operation priority of the power generation means in consideration of the status of each power generation means.

  FIG. 6 shows a data table M4 related to the priority related to the mass-tran power supply and the priority related to the marginal cost in the first embodiment. The power generation schedule calculation unit 102 calculates data related to the marginal cost line so as to operate in order from the power generation means with the highest operation priority. In addition, the priority regarding the mastrun power supply is set higher than the priority regarding the marginal cost.

  Here, the priority related to the marginal cost set for each power generation means may be set so as to vary according to the operating rate of the power generation means. This is based on the understanding that the power generation efficiency of the power generation means varies depending on the operating rate, and the marginal cost also varies accordingly. FIG. 7 shows an example of the marginal cost according to the operating rate of each power generation means. 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, it is good also as what changes a priority at the intersection of the relationship line of the operation rate of each electric power generation means and a marginal cost.

  (S2) is a step in which the prediction device 100 acquires acquisition 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 for an acquisition price and a transaction possible amount related to a prediction target period. And the transaction information provision apparatus 200 receives the said request, and transmits the acquisition price and transaction possible quantity regarding the period of prediction object to the prediction apparatus 100 from the data table M3 regarding transaction information. In addition, the data table M3 may be provided in the prediction device 100, and the transaction price and the transaction possible amount may be stored in advance, and (S2) may be omitted.

  (S3) is how the power generation schedule calculation unit 102 operates a plurality of power generation means in a period subject to a power transaction based on the data M1 related to the power generation means and the data M4 related to the operation priority. This is a process of calculating data related to the marginal cost line. The data related to the marginal cost line is data that specifies how to operate the power generation means in order to compensate for the power generation amount in correspondence with the required power generation amount (power demand amount). Data corresponding to F (W). The data related to the marginal cost line may be any data that associates the power generation amount with the power generation cost, and is not limited to a function expression, but is stored in a table format corresponding to the power generation amount and the power generation cost. It may be.

  Hereinafter, the data related to the marginal cost line calculated in (S3) will be described with reference to FIGS. 8A to 8C which are graphed.

  FIG. 8A is a marginal cost line calculated from the data M1 regarding the power generation means and the data M2 regarding the operation priority of the power generation means. The horizontal axis represents the required power generation amount (power demand), and the vertical axis represents the marginal cost of each power generation means. The further to the right, the greater the amount of power generation (power demand), and the more upward, the greater the marginal cost. The section below the marginal cost line represents the difference in power generation means to be operated (the same applies to FIGS. 8B and 8C). Here, since the priority related to the mastrun power supply is set as a higher priority than the priority related to the marginal cost, the marginal cost line is composed of two areas, a masstran power supply priority area A and a marginal cost priority area B. Yes. In other words, it represents that the marginal cost of the power generation means A, B, C is operated before the lowest power generation means D.

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

  Here, with reference to FIG. 8B and FIG. 8C, the change cost of the power generation cost when a predetermined amount C of power transaction is performed will be described. FIG. 8B shows the power generation cost required when the power demand is N0. When the predicted power demand is N0, the total value of the areas indicated by the oblique lines in FIG. 8B is the necessary 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 related to the marginal cost line indicating the marginal cost corresponding to the power generation amount. As described above, F (W) is calculated on the basis of data A (x1), B (x2), C (x3),. (X1, x2,... Represent operating rates).

  FIG. 8C shows an example of the change cost (decrease) in the power generation cost when a transaction with the transaction amount C (electric power purchase) is performed during the period of power transaction. From FIG. 8C, the change cost of the power generation cost when the power purchase of the transaction amount C is performed is an area in which F (W) is surrounded by the demand predicted value N0 and the post-transaction N0-C on the W axis. Specifically, the change cost of the power generation cost can be expressed by Expression (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 step in which the optimal transaction amount calculation unit 103 calculates a transaction balance when a predetermined amount of transaction is performed during a period subject to power transaction based on the data M3 related to transaction information. Specifically, when the transaction price for a period subject to power trading is a constant value R, the transaction balance (payment amount) when a transaction of a predetermined amount C (power purchase) is performed is given by Equation (3) 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 a case where the optimal transaction amount calculation unit 103 performs a predetermined amount of transaction calculated in (S4) and a change amount of power generation cost when the predetermined amount of transaction calculated in (S5) is performed. This is a process of calculating the optimum transaction volume based on the difference in transaction balance. Specifically, in the case of power purchase, the larger the difference obtained by subtracting the transaction balance (payment amount) from the change cost (decrease) in power generation cost, the maximum profit is for the power supplier.

  That is, the profit amount Y of the power supplier can be expressed as Expression (4) in the case of purchasing power 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 ′ that maximizes Y is the optimum transaction amount. For example, the optimal transaction amount C ′ can be calculated by a differential equation relating to C. Note that 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 sets the maximum C ′ in the direction in which the difference Y obtained by subtracting the transaction balance (payment amount) from the change cost (decrease) in the power generation cost becomes a positive amount for the power supplier. Select as the optimal trading volume.

  Moreover, when the transaction possible quantity is set as transaction information, the optimal transaction quantity is calculated by using the transaction possible quantity as an 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 regarding the power purchase of the power transaction.

  (S7) is a step in which the presentation unit 104 performs predetermined image processing and presents the optimum transaction amount in the power transaction calculated in (S5) so that the user of the prediction device 100 can recognize it. For example, the presentation unit 104 outputs the power generation means, the operation rate, and the optimum transaction amount in the time zone of the transaction target as text data.

  Thus, 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 buying power has been described, but the same applies to the case of selling power. That is, in the case of power sales, when the difference obtained by subtracting the change cost (increased) of the power generation cost from the transaction balance (income) is maximized, the profit is maximized for the power supplier. At this time, the profit amount Y can be expressed as Equation (5). Then, C ′ that maximizes Y can be calculated as the optimum 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 power purchase and power sale is possible has been described. However, when both power purchase and power sale are possible, Y for both power purchase and power sale. And C ′ when Y is maximized may be set as the optimum transaction amount.

  Further, in the first embodiment, the data related to the priority of each power generation means is set by the user of the prediction device 100 in consideration of the status of each power generation means, but these data are optimal. Each time the transaction amount is calculated, it may be set by the power generation schedule calculation unit 102 or may be stored in the data table M4 in advance.

  Moreover, in the said 1st Embodiment, although the priority regarding a mastrand power supply and the priority regarding a marginal cost were described as data regarding a priority, the aspect which sets only the priority regarding a marginal cost may be sufficient. In addition to those priorities, data regarding 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 the use refusal level as another item so as 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 vary depending on the transaction amount C. In that case, R in Equation (4) can be expressed as a function R (C) of C. Then, the optimum transaction amount calculation unit 103 calculates the optimum transaction amount C ′ that maximizes Y in a manner 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, the prediction system is not limited to the profit amount and may be a predetermined point or the like.

  Note that, due to the influence of lightning or the like, the predetermined power generation means may stop and it may not be possible to supply power. In this case, the marginal cost line calculated by the power generation schedule calculation unit 102 varies. Therefore, in (S3), in consideration of such fluctuation factors, the respective power generation possible amounts on the marginal cost line may be calculated as expected values considering the possibility of dropping out. In this case, data relating to the probability distribution relating to dropout is stored in the predetermined power generation means of the data table M1. Then, an expected value of the power generation possible amount of the power generation means is calculated based on the data relating to the probability distribution regarding the dropout of the predetermined power generation means and the power generation possible amount of the 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 possible amount of the power generation means. Further, instead of changing the marginal cost line, assuming that the demand forecast value N has virtually increased, the expected value due to the dropout of each power generation means is added to the demand forecast value N, and the subsequent processing is executed. Good.

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

  Therefore, in the second embodiment, based on the data related to the probability distribution of the power demand, data related to the probability distribution of the profit amount is calculated together with the optimal transaction 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 thus the description of 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 related to the probability distribution of the profit amount.

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

  As an example, the optimum 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, and uses the expected value N1 as the predicted value of the power demand amount as an optimum transaction. The optimal transaction amount and the probability distribution data of the profit amount are 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, based on the probability distribution of the power demand, the step of calculating the expected value N1 of the power demand is based on the probability distribution of the power demand by the probability density function f ( 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 optimum transaction amount using the expected value N1 as the predicted value of the power demand is as shown in (S4) to (S6) of FIG. In other words, the optimal transaction amount calculation unit 103 ′ sets the predicted value of the power demand amount as the expected value N1, and changes the power generation cost when performing a power transaction for a predetermined transaction amount C based on the data on the marginal cost line. Is calculated (S4). And optimal transaction amount calculation part 103 'calculates the transaction balance at the time of performing a predetermined amount of transaction in the period used as the object of electric power transaction based on the data M3 regarding transaction information (S5). Then, the optimal transaction amount calculation unit 103 ′ performs the change in power generation cost when the predetermined amount of transaction calculated in (S4) is performed, and the transaction balance when the predetermined amount of transaction calculated in (S5) is performed. The optimal transaction amount C ′ is calculated so that the difference between the two is maximized (S6).

  The step of reflecting the probability distribution of the power demand amount with respect to the profit value for the optimum transaction amount calculates the confidence interval of the profit amount based on the confidence interval of the power demand amount, for example. 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 the equation (4) (the function equation (4) of the profit amount Y is It is a monotonically increasing function with respect to power demand N when C is constant). In addition, by selecting 5 points included in the confidence interval of the power demand amount and calculating the profit amount Y corresponding to each, it is a method of imitating the upper limit value and lower limit value of the confidence interval of the profit amount. Also good.

  The confidence interval means a numerical range that can actually occur within a certain probability range. FIG. 9 represents a confidence interval that falls within a range of 68.3% when the probability density function f (Nf) of the power demand 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 in the confidence interval of the power demand amount N is sampled and converted into a confidence interval of the profit amount Y. The probability distribution of the profit amount Y is a 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 converting the probability density function f (N) of the power demand N into the probability density function g (Y) of the profit amount Y, the probability density function g (Y) of Y is obtained by modifying the formula (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。

As a result, the optimum transaction amount calculation unit 103 ′ can variable-convert the probability density function f (N) of the power demand amount N into the probability density function g (Y) of the profit amount.

  Thus, according to the second embodiment, it is possible to calculate the optimum transaction 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, the method of calculating the optimum transaction amount based on the expected value N1 of the probability distribution of the power demand amount has been described. However, the optimum transaction amount is calculated based on the average value instead of the expected value. A calculation method may be used.

  Further, instead of the method of variable-converting the probability density function f (N) of the power demand amount N into the probability density function g (Y) of the profit amount, the variance coefficient of the power demand amount N is changed to the variance coefficient of the profit amount. A variable conversion method may be used.

Further, the second embodiment is not limited to the case where the probability density function f (N) of the power demand N is a normal distribution, but is applied to an arbitrary distribution function such as a t distribution, a χ ² distribution, an F distribution, or the like. Can do.

  In the second embodiment, the case where the power demand is calculated as a probability distribution has been described. However, even when the transaction price is calculated as a probability distribution, the profit probability distribution can be calculated by performing variable conversion in the same manner as described above. In addition, when both the power demand and the transaction price are calculated as probability distributions, a 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. Hereinafter, aspects of the present embodiment will be described. In addition, about the structure which is common in 1st Embodiment, it abbreviate | omits.

  In the third embodiment, based on the understanding that one of the factors that cause the actual power demand to fluctuate from the predicted value of power demand is fluctuations in the demand for air conditioning caused by temperature fluctuations, Make a prediction. That is, the probability distribution of power demand is calculated from the probability distribution of predicted temperature calculated by a known method. In the third embodiment, the demand prediction unit 105 calculates a relational expression between the temperature and the electric power demand based on the past actual measurement value of the air temperature and the actual measurement value of the electric power demand, and predicts the predicted temperature from the relational expression. 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 a LAN connection (not shown in FIG. 1) to measure the actual temperature value. And, the power demand is predicted by transmitting and receiving past data and the like regarding the power demand 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 apparatus 400 and the weather information providing apparatus 500 have the same hardware configuration as the transaction information providing apparatus 200 shown in FIG. 2B.

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

  FIG. 12B shows an example of the data table M6 related to the temperature actually measured in the past, stored in the storage unit of the weather information providing apparatus 500. In this data table M6, the measured value of the temperature in a predetermined time zone is associated with the date and time and stored in units of one hour.

  (S41) in FIG. 11 is a step in which the acquisition unit 101 of the prediction device 100 requests the demand information providing device 400 for a past power demand amount related to a future predetermined time zone P.

  (S42) is a step in which the demand information providing apparatus 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 apparatus 100. It is.

  As an example, when the predetermined time zone P in the future is 7 o'clock, the acquisition unit 101 acquires data from 7 o'clock to 8 o'clock in the data M <b> 5 related to the power demand in the past (the same month last year).

  (S43) is a step in which the acquisition unit 101 of the prediction device 100 requests the weather information providing device 500 for an actual measured value of the past temperature related to the time of the power demand acquired in (S42).

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

  As an example, when the time zone of the power demand acquired in (S42) is from 7:00 to 8:00, the acquisition unit 101 acquires data at 7:00 or 8:00 of the data M6 related to the measured temperature.

  (S45) is a step 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 device 100 performs regression analysis for calculating a relational expression between the electric power demand amount and the air temperature based on the measured value T1 of the air temperature in the predetermined time zone acquired in (S44) and the electric power demand amount N1. I do.

  In the regression analysis, for example, for the regression model of Equation (9), the power demand amount N is an objective variable, the difference between the standard temperature 18 ° C. and the measured value, and the square of the difference between the standard temperature 18 ° C. and the measured value is the explanatory variable. The least squares method is used.

（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. , Each measured value T1 of the past data acquired as a sample, and power demand amount N1 are represented.)
Here, the difference between the standard temperature of 18 ° C. and the actually measured value is used as an explanatory variable because the fluctuation amount of the power demand due to the heating / cooling demand can be regarded as substantially zero when the standard temperature is 18 ° C. It is. In addition, by using the square of the difference between the standard temperature of 18 ° C and the measured value as an explanatory variable, the demand for air conditioning will more accurately reflect the general social phenomenon that the temperature will increase rapidly as the temperature departs from the standard temperature of 18 ° C. Can be made.

  Further, the equation (9) divides the equation into two depending on whether it is 18 ° C. or higher and 18 ° C. or lower, and separates the cooling demand from the heating demand. Although omitted in 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 , δ ₂ of the regression model are determined, and the regression equation (10) is calculated as a demand prediction formula.

（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 step in which the demand prediction unit 105 of the prediction device 100 calculates the probability distribution of the power demand based on the regression equation (10) and the probability distribution data of the predicted temperature.

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

That is, the expected value of the power demand can be calculated by substituting the expected value T2 of the probability distribution of the predicted temperature with respect to the temperature T in Expression (10). Then, the upper limit value and lower limit value of the confidence interval of the predicted value of the power demand can be calculated by substituting T2 + S and T2-S for equation (10). (Equation (10) can be regarded as a monotonically increasing function for temperature T)
Thus, according to the third embodiment, the probability distribution of the predicted temperature can be reflected in the probability distribution of the power demand amount, and the probability distribution of the power demand amount can be calculated with high accuracy.

  In the third embodiment, a regression model is used in which the difference between the standard temperature of 18 ° C. and the measured value and the square of the difference between the standard temperature of 18 ° C. and the measured value are used as explanatory variables. However, the regression model need not be limited to the above if the probability distribution of the power demand can be calculated from the probability distribution of the predicted temperature with a certain degree of accuracy. For example, regarding the square of the difference between the standard temperature 18 ° C. and the actual measurement value, an explanatory variable may be omitted, or a normal actual measurement value may be used as the explanatory variable instead of the difference between the standard temperature 18 ° C. and the actual measurement value. Also, the standard temperature may be set to 17 ° C. or 19 ° C. instead of 18 ° C. Further, a predicted maximum temperature, predicted minimum temperature, weather information, regional information, etc. may be added as explanatory variables. In order to stabilize the variance of the sample, the regression model may be applied by performing variance stabilization conversion.

  Further, in the third embodiment, the process of calculating the demand prediction formula is performed in accordance with the setting of the prediction target date and time. However, the demand prediction formula is generated in advance and the prediction target date and time is set. In response, the predicted value of the power demand amount for the corresponding date and time may be calculated.

  Further, in the third embodiment, the variation in the power demand is calculated by assuming that the fluctuation in the cooling / heating demand caused by the temperature fluctuation is the largest factor that causes the actual power demand to fluctuate from the predicted value of the power demand. In this case, the error term of Equation (9) is not considered. However, regarding the error term in Equation (9), a standard deviation of the error term may be calculated and taken into consideration when calculating the probability distribution of the power demand. In that case, the standard deviations may be integrated according to the known law of error propagation.

<Fourth embodiment>
The fourth embodiment assumes a situation where the power generation means (power supply) falls into a state where power generation is not possible due to an accident or a failure (referred to as a dropout) on the basis of the optimum power transaction volume created in the first to third embodiments. However, it is intended to simulate the increase or decrease in revenue when the power generation means is dropped. In the fourth embodiment, simulation is performed only for a power sale transaction.

  Specifically, the following three balance calculations are performed.

  (1) The prediction apparatus 100 calculates the balance with the power generation possible amount of the power generation means (hereinafter referred to as “dropped power supply”) assumed to have been dropped as “0”. That is, as a power generation plan that satisfies the power demand by operating the power generation means handled as not operating at the time of the above-described optimal power transaction amount prediction process, the balance in consideration of the increase in power generation cost in that case is calculated.

  (2) In addition, the prediction apparatus 100 calculates a balance after incorporating a standby power generation means (hereinafter referred to as a standby power supply) that is operated when the power supply is dropped as a power generation means. This is a simulation assuming that the marginal power generation cost of the standby power supply is lower than that of the power generation means that has not been operated, and the case where there is no power generation capacity to meet the power demand even if all power generation means are operated. It is.

  (3) In addition, the balance of payment is calculated after virtually incorporating the power interchange in an emergency (referred to as nationwide interchange), which is different from the electricity trading market, as a power generation means. This is a simulation that assumes a case where there is no power generation capacity to meet the power demand even if a standby power supply is incorporated.

  In 4th Embodiment, the prediction apparatus 100 shall be provided with the balance calculation part (not shown; it corresponds to the profit amount calculation part of this invention) which performs the above simulation.

=== Data table M4 (dropped parallel) ===
FIG. 13 shows an example of the data table M4 related to the dropped power 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 standby 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 in FIG. 3A. The dropout flag is a flag indicating whether or not simulation is performed as a dropout power source. The prediction device 100 can accept the designation of the drop-off power supply by receiving the input of the drop-off flag in the data table M4 (drop-off power generation means designation unit). The parallel flag is a flag (standby power generation means storage unit) that indicates whether or not to operate the standby power supply when the standby power supply is disconnected for each standby power supply.

=== Data table M5 (standby power supply) ===
FIG. 14 shows an example of the data table M5 related to the standby power source stored in the storage unit 100B of the prediction device 100 according to 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 will be described later, when calculation is performed with a standby power supply incorporated as a power generation means, the record of the data table M5 may be incorporated into the data table M1.

=== Data table M6 (national interchange) ===
FIG. 15 shows an example of a data table M6 related to national accommodation stored in the storage unit 100B of the prediction device 100 according to the fourth embodiment. The data table M6 includes the time zone (in the example of FIG. 15, the time required for national accommodation and the lowest transaction for national accommodation for each of the 48-hour time zones 1A, 1B, 2A, 2B,. In the case of nationwide accommodation, the price changes when the amount of electric power to be procured (accommodation amount) exceeds a predetermined threshold.In FIG. “Stage”, the case where the amount of accommodation exceeds the threshold is indicated as “Two steps”.

=== Simulation ===
FIG. 16 is a flowchart for simulating increase / decrease in profit when the power generation means is dropped in the fourth embodiment.

  (S51) is a process in which the balance calculation unit identifies a dropped power supply. Specifically, the balance calculation unit reads the record with the drop flag = 1 from the data table M4.

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

  (S521) is a step of excluding the dropped power source from the power generation cost calculation target. The balance calculation unit sets “0” as the possible power generation amount of the dropped power supply in the data table M1. The minimum power generation amount is also set to “0”. As a result, the dropped power supply is not used for the power generation plan.

  (S522) is a step of calculating the power generation cost when the dropped power supply is removed. Specifically, the balance calculation unit calculates the second term of Equation (5) described above. FIG. 17 is a diagram illustrating an example of a marginal cost line when the power supply is removed. FIG. 17A shows the power generation cost (lane portion) when the power generation amount W is increased by the transaction amount C of power sales from the demand forecast value N0 in the marginal cost line shown in FIG. 8A. Here, when the power generation means B is designated as a drop-off power supply, the power generation possible amount of the power generation means B becomes 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. 17B, the power generation means H that was not scheduled to be operated in FIG. 17A is operated. Since the marginal cost of the power generation means H is high, the overall power generation cost also increases.

  (S523) is a step of calculating the profit when the dropped power supply is removed. Specifically, the balance calculation unit calculates Equation (5) using the marginal cost line F (W) with the dropped power supply removed as described above. The balance calculation unit stores the calculation result as profit 1 corresponding to the designated dropped power supply.

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

  (S531) is a step for setting the standby power source to be the calculation target. Specifically, the balance calculation unit identifies the standby power supply with the parallel flag = 1 in the record, reads the standby power supply data record corresponding to the standby power supply from the data table M5, and reads the read standby power supply. The data record is registered in the data table M1.

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

  (S533) is a step of calculating the power generation cost when the power supply is removed and the standby power supply is incorporated. Similarly to (S522), the balance calculation unit calculates the second term of Equation (5) described above. FIG. 18 is a diagram showing an example of a marginal cost line when a drop-off power supply is removed and a standby power supply is incorporated. FIG. 18A is the same as FIG. Here, when the power generation means B is designated as a drop-off power supply and the power generation means b is designated as a standby power supply, as shown in FIG. A marginal cost line in which the amount of power generated by the standby power source b is increased can be created. In the example of FIG. 18 (b), it is not necessary to operate the power generation means H unlike the case of FIG. 17 (b) by incorporating the standby power supply b, but the marginal cost of the standby power supply b is the limit of the power generation means B. It is higher than the cost, and it can be seen that the power generation cost as a whole increases.

  (S534) is a step of calculating profits when the dropped power supply is removed and a standby power supply is incorporated. Specifically, the balance calculator calculates the formula (5) using the marginal cost line F (W) with the standby power supply removed, with the dropped power supply removed. The balance calculation unit stores the calculation result as profit 2 corresponding to the designated dropped power supply.

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

  (S541) is a step of setting so that the procurement of electric power through national accommodation is regarded as a virtual power generation means. Specifically, the balance calculation unit reads the record from the data table M6, uses the information indicating nationwide accommodation as the power generation means, sets the minimum transaction amount at the first stage as the minimum generation amount, and generates the maximum transaction volume at the second stage as the power generation unit. For the amount of power generation that is greater than the minimum transaction volume of the first stage and less than the maximum transaction volume of the first stage, the price of the first stage is assumed and the maximum transaction of the second stage or more For a power generation amount less than or equal to the amount, a record in which a function that becomes the price of the second stage is set as a marginal cost is registered in the data table M1. As a result, power procurement through national accommodation will be incorporated into the marginal cost line.

  (S542) is a step of calculating the power generation cost when removing the drop-off power source and incorporating the standby power source and incorporating the national accommodation. Similarly to (S522), the balance calculation unit calculates the second term of Equation (5) described above. FIG. 19 is a diagram illustrating an example of a marginal cost line when a drop-off power supply is removed and a spare power supply is incorporated, and nationwide accommodation is also incorporated. FIG. 19A is the same as FIGS. 17A and 18A. Here, when the power generation means F is designated as a drop-off power supply and the power generation means f is designated as a standby power supply, as shown in FIG. A marginal cost line in which the amount of power generated by the standby power supply f is increased can be created. In addition, the procurement amount and transaction costs from national accommodation are included in the marginal cost line. In the example of FIG. 19B, although the standby power supply f is incorporated in place of the power generation means F, even if the power generation amount of the standby power supply f is small and the power generation means H is also operated, the optimum transaction amount is set to the demand forecast value N0. Power generation sufficient to satisfy the demand with C added is not possible, and part of the power is procured through nationwide accommodation. The procurement price through nationwide accommodation is high, which increases the overall power generation cost. Is shown.

  (S543) is a step of calculating profits when removing the drop-off power source and incorporating the standby power source and incorporating the national accommodation. Specifically, the balance calculator calculates the formula (5) using the marginal cost line F (W) that removes the dropped power supply and incorporates the standby power supply and nationwide accommodation. The balance calculation unit stores the calculation result as the profit 3 corresponding to the designated dropped power supply.

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

  The above steps S521 to S543 are performed for each record of the drop flag = 1. Thereby, the user can easily grasp the change in profit due to the power generation means designated as the drop-off power supply being dropped.

<Other variations>
FIG. 20 is a diagram illustrating a configuration example of the data table M7 for managing the upper limit and the lower limit of the power generation amount by the power generation unit for each time zone. The data table M1 stores a fixed minimum power generation amount and possible power generation amount for each power generation means. In the data table M7, the data table M1 is associated with the power generation means in a time zone (30-minute unit time in the example of FIG. 20). For each band, the upper limit (capable power generation) and the lower limit (minimum power generation) of the power generation are stored. The data table M7 can be used, for example, when a generator start and stop plan is created in advance according to a generator maintenance schedule or the like.

  When the data table M7 is used, the marginal cost line may be created for each time zone, and in that 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 embodiments, 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 each of the above embodiments, the prediction device 100 has the power generation schedule calculation unit 102 and the optimal transaction amount calculation unit 103 as functional units. However, these functional units or a part thereof may be distributed to other devices. Similarly, the storage area of the data stored in each storage means may be an arbitrary place. For example, it may be aggregated in the prediction device 100, or may be configured to be distributed and stored on a cloud system composed of a plurality of computers.

=== Conclusion ===
From the above, the above embodiments 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 power trading, and a first storage unit that stores the power generation possible amount of a plurality of power generation means (the above embodiment) Then, it corresponds to the data table M1), a second storage unit (corresponding to the data table M1 in the above embodiment) that stores the power generation cost per unit power generation amount of a plurality of power generation means, and the target of power transaction A third storage unit (corresponding to the data table M2 in the above embodiment) that stores the predicted value (N) of the power demand amount during the period and power generation means with low power generation cost per unit power generation operate preferentially. A fourth storage unit (in the above embodiment, corresponding to the data table M4) that stores operation priorities for a plurality of power generation means, and a unit power generation amount in a period that is a target of power trading. A fifth storage unit (which corresponds to the data table M3 in the above embodiment), a power generation capability of the power generation means, a power generation cost per unit power generation amount of the power generation means, and an operation priority for the power generation means Based on the power generation schedule calculation unit (102) for calculating data on the marginal cost line indicating the correspondence between the power demand and the power generation cost, and based on the data on the marginal cost line and the predicted value of the power demand The predetermined amount that maximizes the difference between the change cost of the power generation cost caused by the predetermined amount of power transaction (C) and the transaction balance generated by the predetermined amount of power transaction calculated based on the transaction price data is calculated. The prediction system characterized by having the optimal transaction amount calculation part (103, 103 ') to perform is disclosed.

  Thus, the power supplier can calculate the optimum transaction amount of the power transaction that maximizes the profit value in consideration of the power generation cost.

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

  As a result, it becomes possible to grasp the power generation cost in detail, and it is possible to calculate the optimal transaction amount of the power transaction that maximizes the profit value more accurately.

  Here, the predicted value of the power demand amount is the probability distribution data of the power demand amount in the period targeted for the power transaction, and the optimum transaction amount calculation unit is based on the probability distribution data of the power demand amount in the power transaction. The probability distribution data of the optimal transaction volume and 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.

  As a result, in addition to the optimal transaction volume, it is possible to calculate the profit fluctuation (risk) in the power transaction.

  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 includes a predetermined power generation unit. Based on the probability distribution data of the power loss 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 power generation possible amount of the predetermined power generation means is calculated. Based on this, data related to the marginal cost line may be calculated.

  Accordingly, it is possible to calculate the optimal transaction amount of the power transaction that maximizes the profit value in consideration of the risk of power loss.

  Here, the transaction price is the probability distribution data of the transaction price in the target period of the power transaction, and the optimal transaction amount calculation unit is configured to determine the optimal transaction amount in the power transaction based on the probability distribution data of the transaction price, and Probability value probability distribution data may be calculated.

  As a result, in addition to the optimal transaction volume, it is possible to calculate the profit fluctuation (risk) in the power transaction.

  Here, the fourth storage unit further has a priority related to the mastrand power supply, and the operation priority for the plurality of power generation means is a priority related to the mastrand power supply 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 for which the degree is set operates preferentially.

  As a result, it becomes possible to grasp the power generation cost in detail, and it is possible to calculate the optimal transaction amount of the power transaction that maximizes the profit value more accurately.

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

  Thus, the power supplier can calculate the optimum transaction amount of the power transaction that maximizes the profit value in consideration of the power generation cost.

  As mentioned above, although the specific example of this invention was demonstrated in detail, these are only illustrations and do not limit a claim. The technology described in the claims includes various modifications and changes of the specific examples illustrated above.

DESCRIPTION OF SYMBOLS 100 Prediction apparatus 101 Acquisition part 102 Power generation plan 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 (7)

A system that supports power trading,
A first storage unit that stores power generation cost calculation information for calculating a power generation cost according to the amount of power generated by each of the plurality of power generation means;
A second storage unit for storing transaction amount calculation information for calculating a transaction amount according to a transaction amount by the power transaction;
Based on the power generation cost calculation information, the first power generation unit to be operated to generate the first power generation amount that is a predicted value of the power demand amount is specified, and the first power generation unit performs the first power generation unit. The first power generation cost for generating the power generation amount of the second power is calculated, and the second power generation amount is generated to generate the second power generation amount by adding the optimal sales amount in the power transaction to the first power generation amount. A power generation cost calculation unit that identifies the power generation means and calculates a second power generation cost for generating the second power generation amount by the second power generation means;
Based on the transaction amount calculation information, a profit for calculating a profit amount obtained by subtracting an increase amount from the first power generation cost to the second power generation cost from the revenue amount that is the transaction amount according to the optimum sales amount An amount calculation unit;
An optimal sales volume determination unit that determines the optimal sales volume so that the profit amount is maximized;
A drop-off power generation means designating unit that accepts designation of a drop-off power generation means that is the power generation means assumed to have dropped out;
With
After the optimum sales amount is determined, the drop-off power generation means designating unit accepts designation of the drop-off power generation means, and the power generation cost calculation unit sets the first and second power generation means so as not to select the drop-off power generation means. Re-specifying, and the profit amount calculation unit re-calculates the profit amount for the first and second power generation means specified again, and outputs the calculated profit amount,
Power trading support system characterized by The power trading support system according to claim 1,
A constraint storage unit that stores a maximum power generation amount for each power generation unit;
The power generation cost calculation unit selects, as the first or second power generation unit, in order from the power generation unit with the lowest power generation cost until the total of the maximum power generation amount becomes equal to or greater than the first or second power generation amount. To go,
Power trading support system characterized by The power trading support system according to claim 2,
The constraint storage unit stores a minimum power generation amount in addition to the maximum power generation amount for each power generation unit,
The power generation cost calculation unit adds the total of the maximum power generation amount to the total of the minimum power generation amount after selecting the power generation unit having the minimum power generation amount greater than 0 as the first and second power generation units. Until the value is equal to or greater than the first or second power generation amount, the power generation cost is selected as the first or second power generation means in order from the power generation means at a low cost,
Power trading support system characterized by The power trading support system according to any one of claims 1 to 3,
In correspondence with the power generation means, further comprising a standby power generation means storage unit for storing standby power generation means to be operated when the power generation means falls off,
The first storage unit stores the power generation cost calculation information for the standby power generation means,
After the optimum sales amount is determined, the power generation means specifying unit specifies the first and second power generation means from among the plurality of power generation means and the standby power generation means excluding the dropped power generation means. To do,
Power trading support system characterized by The power trading support system according to any one of claims 1 to 4,
A procurement cost calculation information storage unit for storing procurement cost calculation information for calculating a procurement cost when power is procured by means of procurement different from the power transaction,
The power generation cost calculation unit calculates a cost for generating the first power generation amount by the first power generation means or for procurement by the procurement means based on the power generation cost calculation information and the procurement cost calculation information. Calculating the first power generation cost, and calculating the second power generation cost as the second power generation cost, the power generation by the second power generation means or the cost for procurement by the procurement means,
Power trading support system characterized by A method for supporting power trading,
A first storage unit that stores power generation cost calculation information for calculating a power generation cost corresponding to the power generation amount generated by each of the plurality of power generation means, and a transaction amount corresponding to the transaction amount by the power transaction is calculated. A second storage unit for storing transaction amount calculation information for
A computer comprising
Based on the power generation cost calculation information, the first power generation unit to be operated to generate the first power generation amount that is a predicted value of the power demand amount is specified, and the first power generation unit performs the first power generation unit. The first power generation cost for generating the power generation amount of the second power is calculated, and the second power generation amount is generated to generate the second power generation amount by adding the optimal sales amount in the power transaction to the first power generation amount. Identifying the power generation means, and calculating a second power generation cost for generating the second power generation amount by the second power generation means;
Based on the transaction amount calculation information, calculating a profit amount obtained by subtracting an increase amount from the first power generation cost to the second power generation cost from the revenue amount which is the transaction amount corresponding to the optimum sales amount When,
Determining the optimum sales amount so that the profit amount calculated by the profit amount calculation unit is maximized;
Receiving a designation of a drop-off power generation means that is the power generation means assumed to have dropped out;
Re-identifying the first and second power generation means so as not to select the drop-off power generation means;
Recalculating the profit amount for the first and second power generation means identified again;
Outputting the calculated profit amount;
A power trading support method characterized in that A program for supporting electric power transactions,
A first storage unit that stores power generation cost calculation information for calculating a power generation cost corresponding to the power generation amount generated by each of the plurality of power generation means, and a transaction amount corresponding to the transaction amount by the power transaction is calculated. A second storage unit for storing transaction amount calculation information for
On a computer with
Based on the power generation cost calculation information, the first power generation unit to be operated to generate the first power generation amount that is a predicted value of the power demand amount is specified, and the first power generation unit performs the first power generation unit. The first power generation cost for generating the power generation amount of the second power is calculated, and the second power generation amount is generated to generate the second power generation amount by adding the optimal sales amount in the power transaction to the first power generation amount. Identifying the power generation means, and calculating a second power generation cost for generating the second power generation amount by the second power generation means;
Based on the transaction amount calculation information, calculating a profit amount obtained by subtracting an increase amount from the first power generation cost to the second power generation cost from the revenue amount which is the transaction amount corresponding to the optimum sales amount When,
Determining the optimum sales amount so that the profit amount calculated by the profit amount calculation unit is maximized;
Receiving a designation of a drop-off power generation means that is the power generation means assumed to have dropped out;
Re-identifying the first and second power generation means so as not to select the drop-off power generation means;
Recalculating the profit amount for the first and second power generation means identified again;
Outputting the calculated profit amount;
A program for running

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