JP2006244202A - Transaction condition evaluation system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a transaction condition evaluation system for easily evaluating respective transaction conditions in the power business of a power undertaker. <P>SOLUTION: This transaction condition evaluation system is provided with a data part 110 which stores information to be used for simulation relating to the evaluation of power transaction, a communication part 100 which acquires information to be used for simulation and a processing part 120 which executes simulation processing. The processing part 120 is provided with a simulation part 121 which calculates revenues and payment relating to power transaction several times based on information stored in a data part 110, and calculates respective profits from the several time calculation results of the revenues and payment and a risk calculation part 122 which calculates risk quantity being the fluctuating quantity of the profits based on the plurality of calculation results of the profits calculated by the simulation part 121. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a transaction condition evaluation system for evaluating electric power transaction conditions at an electric power company and the like, and more particularly to evaluation of electric power transaction conditions based on an amount of profit risk that simulates a fluctuation range of profit calculated from a demand model. It is related to effective technology when applied.

  The electric power company has predicted the amount of demand for power, and has performed a power generation plan based on the predicted value of the amount of demand.

  Traditionally, the unit price of electricity has been set to a uniform rate system that reflects fluctuations in fuel market prices, thereby stabilizing profits in the power business.

  In recent years, due to the liberalization of electricity, the principle of competition has been introduced, and it has become difficult to stabilize profits with a uniform fee structure that reflects fuel prices. For this reason, in order to realize the stabilization of profits in the power business of electric power companies, it has become necessary to evaluate each transaction from the viewpoint of stabilizing profits.

  However, in the past, demand was forecasted, but earnings were not predicted and fluctuations were not taken into account. Evaluation based on fluctuations in earnings due to individual transaction conditions, etc. Could not do.

  Then, the objective of this invention is providing the transaction condition evaluation system which can perform easily evaluation of each transaction condition in the electric power business of an electric power provider.

  Of the inventions disclosed in the present application, the outline of typical ones will be briefly described as follows.

  The transaction condition evaluation system according to the present invention includes a data unit that stores information used for simulation related to evaluation of power transactions, a communication unit that acquires information used for simulation, and a processing unit that performs simulation processing. The processing unit performs calculation of income and expenditure related to power transactions a plurality of times based on information stored in the data unit, and calculates each income from the calculation results of the plurality of incomes and expenditures, and a simulation unit And a risk calculation unit that calculates a risk amount that is a fluctuation amount of the profit based on the calculation results of the plurality of profits calculated by the above.

  Among the inventions disclosed in the present application, effects obtained by typical ones will be briefly described as follows.

  According to the present invention, as described above, the simulation considering the fluctuation of the profit is performed a plurality of times, and the evaluation for the new transaction is calculated from the simulation results of the plurality of times, thereby stabilizing the profit due to the addition of the new transaction. Evaluation can be performed easily.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that components having the same function are denoted by the same reference symbols throughout the drawings for describing the embodiment, and the repetitive description thereof will be omitted.

<Configuration of transaction condition evaluation system>
With reference to FIG. 1, the configuration of a transaction condition evaluation system according to an embodiment of the present invention will be described. FIG. 1 is a configuration diagram showing a configuration of a transaction condition evaluation system according to an embodiment of the present invention.

  In FIG. 1, the transaction condition evaluation system includes a communication unit 100, a data unit 110, a processing unit 120, a display unit 130, and an input unit 140.

  The communication unit 100 includes, for example, a communication line 101 for acquiring market information from the outside, a communication line 102 for acquiring weather information from the outside, a communication line 103 for acquiring transaction information and company information, and floppy (registered trademark). ) An information acquisition unit 104 that acquires information from an external storage device such as a disk is connected to acquire various types of information.

  The data unit 110 includes a database of a market DB 111, a weather DB 112, a customer DB 113, a company DB 114, a basic attribute DB 115, a model DB 116, and a result DB 117, and stores various types of information.

  The processing unit 120 includes a simulation unit 121, a risk calculation unit 122, and a new transaction evaluation unit 123. The processing unit 120 performs a revenue simulation, a risk amount calculation, an evaluation for a new transaction, and the like, and controls the communication unit 100 and the data unit 110. ing.

  The display unit 130 displays an information input screen and a result display screen during simulation. The input unit 140 inputs simulation information.

<Data example of each database>
Next, data examples of each database used in the transaction condition evaluation system according to the embodiment of the present invention will be described with reference to FIGS. FIG. 2 is a diagram showing an example of market DB data used in the transaction condition evaluation system according to the embodiment of the present invention. FIG. 3 is a diagram of the weather DB used in the transaction condition evaluation system according to the embodiment of the present invention. FIG. 4 is a diagram showing an example of data, FIG. 4 is a diagram showing an example of customer DB data used in the transaction condition evaluation system according to the embodiment of the present invention, and FIG. 5 is a transaction condition evaluation system according to the embodiment of the present invention. FIG. 6 is a diagram showing a data example of a basic attribute DB used in the transaction condition evaluation system according to an embodiment of the present invention, and FIG. 7 is an embodiment of the present invention. It is a figure which shows the example of data of model DB used with the transaction condition evaluation system which concerns on a form.

  In FIG. 2, the market DB 111 includes a table storing fuel price information such as spot prices (past data) for each fuel / date shown in (a), and an interest rate such as Japanese yen <period, rate> shown in (b). It has a table for storing information.

  In FIG. 3, the weather DB 112 includes a table for storing temperature information such as the average temperature for each region / month / day / hour shown in (a), and a temperature change such as a temperature change rate by year / month shown in (b). A table for storing rate information, a table for storing precipitation information such as average precipitation for each region / month / day / hour shown in (c), a wind speed for each month / day / hour shown in (d), etc. A table for storing wind speed information, a table for storing sunshine time information such as the day / month sunshine hours shown in (e), and a table for storing weather pattern information such as the weather pattern shown in (f). is doing.

  4, the customer DB 113 includes a table for storing customer information such as a customer name shown in (a), a table for storing transaction information such as a transaction period / fee system shown in (b), and (c). A table for storing demand amount information such as demand amount expected value / standard deviation for each month / holiday type, a table for storing demand fluctuation rate information such as year / month demand amount fluctuation ratio shown in (d), ( e) includes a table for storing transaction calendar information such as holiday types within the transaction period of each transaction, and a table for storing a correlation between customers such as a correlation coefficient between each customer illustrated in (f). .

  In FIG. 5, the company DB 114 includes a table for storing fixed cost information such as fixed costs for each year shown in (a), a table for storing generator information such as a generator name shown in (b), ( A table for storing operation schedule information such as the operation rate for each generator / date / time shown in c), and a table for storing fuel purchase information such as the purchase amount / cost for each fuel / purchase date shown in (d). have.

  In FIG. 6, the basic attribute DB 115 includes a table that stores industry information such as the industry name shown in (a), a table that stores area information such as the area name shown in (b), and a fuel name shown in (c). Etc., and a table for storing fuel information.

  In FIG. 7, the model DB 116 includes a table storing model formula information shown in (a), a table storing variable information of each model formula shown in (b), and a model formula for each fuel shown in (c). It has a table for storing input value information and a table for storing input value information of model formulas for the demand amount of each customer shown in (d).

<Overview of the operation of the transaction condition evaluation system>
Next, the outline of the operation of the transaction condition evaluation system according to the embodiment of the present invention will be described with reference to FIG. FIG. 8 is an explanatory diagram for explaining the outline of the operation of the transaction condition evaluation system according to the embodiment of the present invention.

  As shown in FIG. 8, first, the data in the market DB 111 is updated with the data acquired from the communication line 101, the data in the weather DB 112 is updated with the data acquired from the communication line 102, and the data in the customer DB 113 and the own DB 114 Is updated with data acquired from the communication line 103. Further, data is acquired from the external storage device by the information acquisition unit 104 and updated as necessary.

  The simulation unit 121 considers fluctuations in revenue based on the new transaction information 141 input from the input unit 140 and the like, and data of the market DB 111, the weather DB 112, the customer DB 113, the company DB 114, the basic attribute DB 115, and the model DB 116. The generated revenue is simulated a plurality of times (for example, 100 times), and the result is stored in the result DB 117.

  Then, the risk calculation unit 122 calculates the risk amount that is the maximum loss amount of the revenue using the data of the simulation results of the plurality of revenues taking account of the fluctuation of the revenue stored in the result DB 117, and the new transaction evaluation unit 123 Based on the amount of risk in the case of existing transactions only and in the case of adding new transactions, the evaluation for new transactions is calculated.

  In this way, it is easy to evaluate revenue stabilization by adding new transactions by performing simulations that take into account fluctuations in revenues multiple times, and calculating evaluations for new transactions from the results of the multiple times of simulation. Is possible.

<Operation of simulation unit>
Next, the operation of the simulation unit of the transaction condition evaluation system according to an embodiment of the present invention will be described with reference to FIG. FIG. 9 is a flowchart showing the operation of the simulation unit of the transaction condition evaluation system according to the embodiment of the present invention.

  As shown in FIG. 9, the operation of the simulation unit 121 first sets the number of simulations N (S100), and substitutes 1 for I to initialize the number of simulations (S101).

  Then, income calculation processing for calculating income is performed (S102), expenditure calculation processing for calculating expenditure is performed (S103), and income calculation processing for calculating revenue from the income calculated in S102 and the expenditure calculated in S103 is performed. This is performed (S104). Details of the processing of S102 to S104 will be described later.

  Then, the profit PR (I) obtained by the simulation of S102 to S104 is stored in the result DB 117 (S105), I + 1 is substituted into I (S106), and it is determined whether or not the value of I is larger than N. (S107), if it is determined in S107 that the value of I is not greater than N, the process returns to S102 and the processes of S102 to S106 are repeated, and if it is determined in S107 that the value of I is greater than N, the simulation unit 121 End the process.

  As a result of the above processing, the simulation result of the N earnings is stored in the result DB 117, and the risk amount is calculated based on the simulation result.

<Operation of each process in the simulation unit>
Next, the operation of each process in the simulation unit of the transaction condition evaluation system according to the embodiment of the present invention will be described with reference to FIGS. FIG. 10 is a flowchart showing an operation of income calculation processing in the simulation unit of the transaction condition evaluation system according to one embodiment of the present invention, and FIG. 11 shows income in the simulation unit of the transaction condition evaluation system according to one embodiment of the present invention. The figure which shows an example of the data of a calculation process, FIG. 12 is a flowchart which shows an example of the operation | movement of the expenditure calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention, FIG. 13 is one embodiment of this invention The flowchart which shows the other example of the operation | movement of the expenditure calculation process in the simulation part of the transaction condition evaluation system which concerns on a form, FIG. 14 is the data of the expense calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. FIG. 15 shows an example, and FIG. 15 shows a transaction condition evaluation system according to an embodiment of the present invention. Flowchart illustrating the operation of the revenue calculation processing in the simulation unit arm, FIG. 16 is a diagram showing an example of data revenue calculation processing in the simulation unit transaction condition evaluation system according to an embodiment of the present invention.

  First, as shown in FIG. 10, the operation of the income calculation process in S102 of FIG. 9 first selects a demand amount prediction model to be used from the model DB 116 (S110), and stores information necessary for the demand amount prediction model in the market. Obtained from the DB 111, the weather DB 112, and the customer DB 113 (S111).

Then, the required number of standard normal random numbers are generated (S112), converted into standard normal random numbers that take into account the correlation between customers (S113), and the demand amount after the transaction based on the prediction model, information of each DB, and random numbers. The dispersion amount _dj is calculated (S114).

Then, the amount of fluctuation in revenue r _i = d _i × UP _i after the transaction is calculated from the variance d _{i of} each transaction and the power unit price UP _i , and the amount of fluctuation R of all transactions is calculated (S115).

Then, the income I is calculated from the expected demand value E _{i of} each transaction, the power unit price UP _i , the fixed charge FP _i and the fluctuation amount R, for example, by the following equation (1) (S116).

  Here, an example of random number generation and random number conversion in S112 and S113 of FIG. 10 will be described.

  First, as a premise for explanation, the number of customers, the correlation coefficient, and the standard normal random number are set as the following conditions.

-Number of customers = 3 (customer A, customer B, customer C)
-Correlation coefficient (customer A, customer B) → 0.8
(Customer B, Customer C) → 0.9
(Customer C, Customer A) → 0.7
Standard normal random numbers for a certain time zone on a certain day (customer A: a0, customer B: b0, customer C: c0)
Under this assumption, a specific method for converting into random numbers (customer A: a1, customer B: b1, customer C: c1) in consideration of the correlation coefficient is as follows.

  First, as a procedure 1, a conversion formula (for creating a standard normal random number considering the correlation) is created from the correlation coefficient between customers.

  (1) The random number a0 generated by the standard normal random number is used as the random number a1 after the correlation of the customer A is considered.

a1 = a0 (Formula 1)
(2) The y and z values of b1 = y × a0 + z × b0 are determined so that the correlation coefficient between a1 and b1 is 0.8.

Since the correlation coefficient between a1 and b1 is 0.8, y = 0.8
Since the dispersion of b1 is 1, z = √ (1−0.8 ² ) = 0.6
b1 = 0.8 × a0 + 0.6 × b0 (Expression 2)
(3) The values of u, v, and w of c1 = u × a0 + v × b0 + w × c0 are determined so that the correlation coefficient between a1 and c1 is 0.7 and the correlation coefficient between b1 and c1 is 0.9. To do.

Since the correlation coefficient between a1 and c1 is 0.7, u = 0.7
Since the correlation coefficient of b1 and c1 is 0.9, v = (0.9−0.8 × 0.7) /0.6=0.57
Since the dispersion of c1 is 1, w = √ (1−0.7 ² −0.57 ² ) = 0.43
c1 = 0.7 × a0 + 0.57 × b0 + 0.43 × c0 (Formula 3)
In step 2, the acquired standard normal random number is converted using the conversion formula created in step 1. That is, the standard normal random number which considered the correlation between customers is generated using the above (Formula 1) to (Formula 3).

  Moreover, as an example of the data of income calculation processing, required data is acquired from each DB by the demand amount prediction model used as shown in FIG. 11, for example. For example, in the example shown in FIG. 11, D = E + σdω1 + c (T_REAL−T_AVE) dω2 is used as the demand amount prediction model, and the demand amount expected value E, the demand amount standard deviation σ, the constant c, and the predicted temperature T_REAL are used as the data portion. The average temperature T_AVE is used.

  Further, the values of the random part data dω1 and dω2 use standard normal random numbers converted in consideration of the above-mentioned correlation between customers, and the values of the random numbers are different for each simulation.

Further, in D = E + σdω1 + c (T_REAL−T_AVE) dω2 in the demand amount prediction model shown in FIG. 11, σdω1 + c (T_REAL−T_AVE) dω2 is used as the demand amount dispersion amount d _j calculated in S114 of FIG. . In addition, the demand amount prediction value D for each transaction is calculated using the demand amount prediction value D of the demand amount prediction model, not the calculation method of the income I in S116 of FIG. Revenue I may be calculated from the unit price and fixed fee.

Next, operation of the expenditure calculation process at S103 in FIG. 9, as shown in FIG. 12, first, the information necessary market DB 111, obtains from its DB 114, and calculates a fuel cost per P _i (S120).

Then, the fuel cost V = V _i × P _i is calculated from the fuel usage V _i for each generator and the fuel unit price P _i (S121), and the expenditure C is calculated from the fuel cost V and the fixed cost F (S122). ).

  Here, an example of fuel unit price calculation in S120 of FIG. 12 will be described.

  First, as a premise for explanation, the following conditions are assumed.

・ Current time: t ₀
Total fuel amount V ₀ of fuel F, total fuel cost C ₀
Time: t ₁ (t ₀ <t ₁ )
Fuel consumption V_ (remaining fuel: V ₀ −V_) at time t ₀ → time t ₁
Implementation of a fuel transaction in which the fuel F is purchased at the fuel amount V ₁ and the fuel cost C _{1 From the} above conditions, the cost is calculated as follows: total cost ÷ total fuel amount.

・ Current time: t ₀
Fuel unit price of fuel F P ₀ = C ₀ / V ₀
Time: t ₁ (t ₀ <t ₁ )
Fuel unit price P _{1 of} fuel F = (P ₀ (V ₀ −V _) + C ₁ ) / ((V ₀ −V _) + V ₁ )
Note that only the fuel cost may be taken into consideration with the fixed cost = 0.

  Further, the fuel unit price may be calculated not by calculation based on the actual fuel purchase price but by prediction using a model formula.

  The operation of the expenditure calculation process in S103 of FIG. 9 when the unit price of fuel is calculated by predicting using a model formula is as follows. First, as shown in FIG. (S130), necessary information is acquired from the market DB 111, the weather DB 112, and the company DB 114 (S131).

The predictive model, based on information of each DB, creates a fuel forward curve consisting of the fuel forward price P _i of each fuel (S132), the fuel consumption V _i and fuel forward price P _i for each generator, Fuel cost V = V _i × P _i is calculated (S133), and expenditure C is calculated from fuel cost V and fixed cost F (S134).

  Moreover, as an example of the data of expenditure calculation processing, required data are acquired from each DB by the fuel unit price prediction model used as shown in FIG. 14, for example. For example, in the example shown in FIG. 14, dS = a (S−μ) + bdω3 is used as the fuel unit price prediction model, and constant a, fuel unit price S, fuel price average μ, and constant b are used as the data part. Yes.

  Further, the value of the data dω3 in the random number portion is different for each simulation.

  The fuel unit price increment dS calculated by the fuel unit price prediction model is calculated based on the fuel unit price S of the previous month, and becomes the fuel unit price data of the current month based on the fuel unit price S + dS of the previous month. For example, in the example shown in FIG. 14, when the target month of fuel number 1 is 2004/3, the unit price of fuel number 1 at 2004/2 is 38.45, and the fuel unit price increment calculated by the fuel unit price prediction model Since dS is -0.4025919, the fuel unit price S is 38.45-0.4255919, and 38.05 is the fuel unit price data of the current month as the fuel unit price with two digits after the decimal point.

Then, to calculate the fuel forward price P _i of each fuel from the data of the fuel unit price, it is creating a fuel forward curve.

  Next, as shown in FIG. 15, the operation of the revenue calculation process in S104 of FIG. 9 is output by the income I output by the income calculation process shown in FIG. 10 and the expenditure calculation process shown in FIG. 12 or FIG. Revenue PR is calculated from revenue I-expenditure C based on the spent expenditure C (S140).

  Further, as data of the revenue calculation process, for example, as shown in FIG. 16, data of income I output in the income calculation process and data of income C output in the expenditure calculation process are used. Then, the value calculated from the income I-expenditure C becomes the data of the profit PR, and the processing in the risk calculation unit 122 and the new transaction evaluation unit 123 is performed using the data of the profit PR for each simulation.

<Operation of risk calculator>
Next, the operation of the risk calculation unit of the transaction condition evaluation system according to the embodiment of the present invention will be described with reference to FIGS. FIG. 17 is a flowchart showing the operation of the risk calculation unit of the transaction condition evaluation system according to the embodiment of the present invention. FIG. 18 is an example of data of the risk calculation unit of the transaction condition evaluation system according to the embodiment of the present invention. FIG.

  As shown in FIG. 17, the operation of the risk calculation unit 122 is as follows. First, from the result DB 117, the profit result PR = {PR (1), PR (2),..., PR (N )} Is acquired (S150).

  Then, an expected value EX and a risk amount RI are calculated based on the profit results PR for the number of simulations acquired in S150 (S151).

  Next, a method for calculating the risk amount RI calculated in S151 of FIG. 17 will be described.

  First, as illustrated in FIG. 18, for example, data of a profit result PR having a total simulation count (total SIM count) of 100 is acquired in S <b> 150 of FIG. 17.

  Then, the results of the 100 simulation results are arranged in order from the better to the worse as shown in FIG. 18, for example, the value obtained by subtracting the expected value from the value of the 95% point VaR is the risk amount RI. And

  Here, the definition of the A% point VaR will be described.

  As shown in FIG. 18, the A% point VaR is the (total SIM count) × (1−A / 100) th value from the bottom of the profit result data arranged in order from the better to the worse. means.

  That is, in the case of the 95% point VaR, the value from the bottom (from the worse) to the fifth, that is, the 96th from the better, becomes the 95% point VaR from 100 × (1-95 / 100) = 5. In the example shown in FIG. 18, the value of −4,704,861 is the value of the 95% point VaR, and this value is a value calculated as the risk amount RI.

  In this embodiment, the 95% point VaR is used. However, the 98% point VaR and the 99% point VaR can be used when considering the maximum loss amount when the probability of occurrence is small. Further, a 95% point CVaR that calculates the average of values worse than the 95% point VaR (all values from the bottom to the 95% point VaR) can also be used.

  Further, although the number of simulations has been described as an example of 100 times, the greater the number of simulations, the more uneven the result values (the smaller the variance), and thus a more accurate value (VaR). Can be requested. However, if the number of simulations is increased, processing time is required. Therefore, in practice, it is necessary to determine the optimum number of simulations in consideration of an allowable error.

<Operation of New Transaction Evaluation Department>
Next, the operation of the new transaction evaluation unit of the transaction condition evaluation system according to one embodiment of the present invention will be described with reference to FIGS. FIG. 19 is a flowchart showing the operation of the new transaction evaluation unit of the transaction condition evaluation system according to one embodiment of the present invention, and FIG. 20 is the output of the new transaction evaluation unit of the transaction condition evaluation system according to one embodiment of the present invention. It is a figure which shows an example of data.

As shown in FIG. 19, the operation of the new transaction evaluation unit 123 first obtains the expected value EX ₀ and the risk amount RI ₀ of the existing transaction portfolio PO ₀ from the risk calculation unit 122 (S160).

Then, to obtain the expected value EX ₁ and risk amount RI ₁ existing trading portfolio PO ₀ new portfolio PO ₁ = PO ₀ ∪ adding a new transaction ND to {ND} Risk calculation unit 122 (S161).

Then, the evaluation of the new transaction is calculated from RI ₁ -RI ₀ (S162).

  By this process, a change in the amount of risk due to the addition of a new transaction can be calculated as an evaluation for the new transaction.

  Further, as output data of the new transaction evaluation unit 123, for example, as shown in FIG. 20, an expected value of only an existing transaction and a value of 95% point VaR, an expected value obtained by adding a new transaction, and a value of 95% point VaR In addition, the expected value of only the existing transaction, the value of 95% point VaR, the expected value obtained by adding a new transaction, and the value of new transaction evaluation calculated from the value of 95% point VaR are output.

  Based on the value of this new transaction evaluation, whether or not a new transaction is possible can be determined.

  For example, in the example shown in FIG. 20, since the value of the new transaction evaluation is ¥ −227,284, the risk amount is increased in the negative direction by adding a new transaction. It can be judged that the profit will be stabilized if the new transaction is not added.

<Example of screen display on display>
Next, an example of a screen display on the display unit of the transaction condition evaluation system according to one embodiment of the present invention will be described with reference to FIGS. FIG. 21 is a diagram illustrating an example of a screen display of the transaction condition evaluation system according to an embodiment of the present invention, and FIG. 22 illustrates another example of a screen display of the transaction condition evaluation system according to an embodiment of the present invention. FIG.

  First, as a screen display for setting a new transaction condition when adding a new transaction, for example, as shown in FIG. 21 (a), a transaction period, a trading category, a contract fee, a fixed fee, a power unit price [daytime], power A screen for inputting the unit price [night], the target period, and the total number of SIMs is displayed.

  Then, after inputting necessary values for each item, the demand amount setting button and the evaluation start button are pressed to start the evaluation, and the evaluation result output from the new transaction evaluation unit 123 as shown in FIG. 21B is displayed. Is done.

  In addition, in the case where a simulation is performed for a plurality of contract quantities within the contract quantity range by giving a range to the contract quantity, as shown in FIG. Enter a value with a width of 500 to 700, enter the required value for each item, then press the demand setting button and the evaluation start button to start the evaluation, and the new as shown in FIG. 22 (b) A list of evaluation results for each contract amount output from the transaction evaluation unit 123 is displayed, and a graph of the evaluation results for each contract amount as shown in FIG. 22C is also displayed.

  As shown in FIG. 22, in the case where a simulation is performed for a plurality of contract quantities within the contract quantity range by giving a range to the contract quantity, the result of new transaction evaluation for each contract quantity is obtained. For example, in the example shown in FIGS. 22B and 22C, when a customer wants to make a contract of 500 kW as a new transaction, the contract of 600 kW with the maximum new transaction evaluation is obtained from the simulation result. Because the risk of new transactions is reduced, if a 600 kW contract is made to the customer, it is possible to deal with it by reducing the charge, and it is possible to stabilize profits.

  In the present embodiment, the random numbers used in the simulation are described as random numbers having a standard normal distribution. However, the random numbers in the standard normal distribution are only examples, and other random numbers such as a uniform random number may be used. However, it is necessary to use a random number that takes into account the characteristics of the object to be used.

  As mentioned above, the invention made by the present inventor has been specifically described based on the embodiment. However, the present invention is not limited to the embodiment, and various modifications can be made without departing from the scope of the invention. Needless to say.

It is a block diagram which shows the structure of the transaction condition evaluation system which concerns on one embodiment of this invention. (A), (b) is a figure which shows the example of data of market DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(f) is a figure which shows the example of data of weather DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(f) is a figure which shows the example of data of customer DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(d) is a figure which shows the example of data of own company DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(c) is a figure which shows the example of data of basic attribute DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(d) is a figure which shows the example of data of model DB used with the transaction condition evaluation system which concerns on one embodiment of this invention. It is explanatory drawing for demonstrating the outline | summary of operation | movement of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows operation | movement of the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows the operation | movement of the income calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a figure which shows an example of the data of the income calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows an example of operation | movement of the expenditure calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows the other example of operation | movement of the expenditure calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a figure which shows an example of the data of the expenditure calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows the operation | movement of the profit calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a figure which shows an example of the data of the profit calculation process in the simulation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows operation | movement of the risk calculation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a figure which shows an example of the data of the risk calculation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a flowchart which shows operation | movement of the new transaction evaluation part of the transaction condition evaluation system which concerns on one embodiment of this invention. It is a figure which shows an example of the output data of the new transaction evaluation part of the transaction condition evaluation system which concerns on one embodiment of this invention. (A), (b) is a figure which shows an example of the screen display of the transaction condition evaluation system which concerns on one embodiment of this invention. (A)-(c) is a figure which shows the other example of the screen display of the transaction condition evaluation system which concerns on one embodiment of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 100 ... Communication part, 101-103 ... Communication line, 110 ... Data part, 111 ... Market DB, 112 ... Weather DB, 113 ... Customer DB, 114 ... Own DB, 115 ... Basic attribute DB, 116 ... Model DB, 117 ... Result DB, 120 ... processing unit, 121 ... simulation unit, 122 ... risk calculation unit, 123 ... new transaction evaluation unit, 130 ... display unit, 140 ... input unit, 141 ... new transaction information.

Claims (5)

A data section for storing information used in simulations relating to the evaluation of power transactions;
A communication unit for acquiring information used for the simulation;
A processing unit for performing the simulation process,
The processor is
Based on the information stored in the data portion, calculation of revenue and expenditure related to the power transaction is performed a plurality of times, and a simulation unit that calculates each revenue from the calculation results of the plurality of revenues and expenditures;
A transaction condition evaluation system, comprising: a risk calculation unit that calculates a risk amount that is a fluctuation amount of the profit based on a plurality of profit calculation results calculated by the simulation unit. In the transaction condition evaluation system according to claim 1,
The processing unit further calculates a new transaction evaluation based on the risk amount of the existing power transaction calculated by the risk calculation unit and the risk amount of the power transaction to which the new power transaction is added. The transaction condition evaluation system characterized by having a section. In the transaction condition evaluation system according to claim 1 or 2,
The new transaction evaluation unit calculates the evaluation of the new power transaction for a plurality of different power contract amounts, and calculates a change in the evaluation of the new power transaction with respect to a change in the contract amount. Evaluation system. In the transaction condition evaluation system according to claim 1, 2, or 3,
The simulation unit calculates a demand amount of electric power used for calculating the income by a demand amount prediction model, and calculates a fuel unit price used for calculating the expenditure by a fuel unit price prediction model. system. In the transaction condition evaluation system according to claim 1, 2, 3 or 4,
The said simulation part calculates the revenue which considered the correlation between the said customers based on the information of the correlation between the customers of the said power transaction, when calculating the said revenue, The transaction condition evaluation system characterized by the above-mentioned.

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