JP2008059125A - System, method, and program for analyzing profit and loss of electrical business - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system, a method and a program for analyzing profits and losses of electrical business capable of specifying concretely the measures to reduce risks. <P>SOLUTION: A profit/loss probability distribution calculating part 501 performs statistical processing on the frequency distribution of business profit/loss data for each trial registered in a profit/loss calculation result table 711, and calculates first data consisting of its statistical data or risk index data. A derivative instrument comparing/calculating part 502 calculates for each trial the profits of a derivative instrument portfolio registered in a derivative instrument contract portfolio table 710, and calculates the profits and losses of the entire business matching the portfolio. Then statistical processing is performed on the frequency distribution of profit/loss data for all the trials thus obtained, and second data consisting of either its statistical data or risk index data is calculated. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an electric power business profit / loss analysis system, method, and program for analyzing the influence of each factor on the risk of fluctuation of future profit / loss in the electric power business, and more particularly to a technique for reducing the risk.

  When an electric utility carries out a business, profits and losses for the business plan are affected by risk factors such as fuel prices, exchange rates, and the magnitude of demand, so it is necessary to analyze this.

  As a conventional technique for grasping a risk amount, there is a method (for example, Patent Document 1) of calculating a risk index using a Monte Carlo simulation for a service business.

JP 2002-259655 A

  When an electric utility carries out a business, the profit and loss will fluctuate with respect to the business plan, affected by risk factors such as fuel prices, exchange rates, and the magnitude of demand. In carrying out management planning and management, it is necessary to grasp the impact of these risk factors and to quantitatively analyze the impact. In order to grasp the risk amount, there is a method of modeling the relevance of the risk factors and calculating the risk amount stochastically by Monte Carlo simulation, but simply calculating the risk amount can grasp the magnitude of the risk. However, there is a problem that it is not possible to clearly specify a policy for reducing the risk.

  For example, in the above-mentioned patent document 1, a means for calculating a probability distribution of profit and loss using a probability distribution of an event for increasing / decreasing income and an event for increasing / decreasing expenditure and a parameter, and a determination means by comparison with a preset target value Although described, there is no disclosure of a method that clearly specifies a measure for reducing the risk based on the profit / loss calculated here or the judgment result.

  The present invention has been made to solve the above problems, and provides an electric power business profit and loss analysis system, method, and program capable of specifically specifying a measure for reducing the risk. Objective.

  The electric power business profit / loss analysis system according to the present invention is an electric power business profit / loss analysis system for analyzing the profit / loss related to the electric business by a computer, and profit / loss calculation means for calculating the profit / loss related to the electric business by a predetermined number of trials by Monte Carlo simulation, First data calculating means for calculating first data including statistical data or risk index data by statistically processing the profit / loss calculated by the profit / loss calculating means; and the first data calculated by the first data calculating means. Second data calculating means for calculating second data including statistical data or risk index data by applying a predetermined derivative product to the data.

  Since the electric power business profit / loss analysis system according to the present invention allows derivative products to be applied to statistical data or risk index data calculated based on profit / loss, a policy for reducing risk can be specifically specified.

<Embodiment 1>
FIG. 1 is a configuration diagram of an electric power business profit / loss analysis system 1 according to Embodiment 1 of the present invention. The electric power business profit / loss analysis system 1 is an electric power business profit / loss analysis system for analyzing the profit / loss related to the electric power business by a computer, and includes a parameter setting unit 2 for setting parameters for calculating the risk of profit / loss, and a set parameter. Based on the simulation control unit 3 that controls the simulation to calculate the profit and loss related to the electricity business, the profit and loss calculation unit 4 that calculates the profit and loss, the risk amount is calculated from the profit and loss calculated by the profit and loss calculation unit 4, and calculated with different parameter settings A result comparison processing unit 5 that performs comparison with the risk amount in the case of being performed, a result display unit 6 that displays a calculation result, a parameter set in the parameter setting unit 2, profit / loss data calculated in the profit / loss calculation unit 4, And a data storage unit 7 for storing a profit / loss comparison result calculated by the result comparison processing unit 5.

  The parameter setting unit 2 includes an input device such as a keyboard and a mouse for a user of this system to input data from the outside, and a display device such as a display monitor for displaying a setting screen.

  The simulation control unit 3, the profit / loss calculation unit 4, and the result comparison processing unit 5 are constituted by a central processing unit that performs processing according to a program stored and read in a storage device of a computer.

  The result display unit 6 includes a display device such as a display monitor for displaying the profit / loss risk analysis result.

  The data storage unit 7 stores parameter data set by the parameter setting unit 2 and data necessary for the profit / loss calculation unit 4, the result comparison processing unit 5, and the result display unit 6, and is stored as necessary. It is composed of a database from which data is read, and is stored in a computer storage device.

  The data storage unit 7 includes an assumed demand table 701, a power generation facility table 702, a power generation facility constraint table 703, a customer table 704, a price model parameter table 705, a fuel transaction contract table 706, and a derivative product contract table 707. And a fuel constraint table 708, a simulation parameter table 709, a derivative product contract portfolio table 710, and a profit / loss calculation result table 711. In this specification, derivative products refer to derivatives for risk hedging against fluctuations in market prices.

  The assumed demand table 701 stores time-series data of power demand supplied by the entire target power generation facility for each simulation target time zone.

  In the power generation equipment table 702, output upper and lower limit values, output change speed, fuel type, start-up cost, minimum start-up time, minimum stop time required for making a power generation plan for the assumed demand of each generator, Generator data including a heat consumption curve coefficient, a startup / stop count upper limit value, a failure occurrence model parameter, and the like is stored for each generator. In addition, you may store another value for these parameters for every period (for example, every month).

  The power generation facility constraint table 703 is a power generation including a start / stop state of each generator, a fixed output value, a maintenance work state, an output upper limit value at work, a fixed unit price, etc., arbitrarily set as calculation conditions by a user of this system. The facility constraint data is stored for each generator by specifying the target period.

  Here, as to the data generator registered in the power generation equipment table 702 and the power generation equipment restriction table 703, only necessary items are defined according to the type of the power generator such as a thermal power generator or a pumped-storage generator.

  The customer table 704 stores customer data including customer contract power, basic charge per contract power, pay-per-use charge per used electric power, demand ratio with respect to total demand, and the like.

  The price model parameter table 705 stores parameters of a fuel market price model, a currency exchange model, and a power market price model according to the type of fuel market (for example, Dubai, WTI, etc.). Various methods are conceivable for these price models. For example, when a GBM (Geometric Brownian Motion) model is used as the exchange model, a stochastic differential equation such as Equation (1) is obtained. In the equation (1), the logarithmic function 価 格 = lnP of the price P is determined by the trend term μ, the volatility σ, and the normal random number W having an average of 0 and the variance dt, and the trend term μ and the volatility σ are parameters. Is stored as

Further, formula parameters for customs statistical prices determined by formulas using market prices as variables are also stored in the price model parameter table 705. For example, the customs statistics price JCC of crude oil is defined by a formula as shown in Equation (2) by a linear combination of fuel market prices in Dubai and Minas. In Equation (2), the customs statistical price JCC (k) of crude oil at time k is the Dubai crude price Dubai (ki) at time (ki) and Minas crude oil price Minas at time (ki). (k-i) and a Dubai weighting coefficients a _i of the time (k-i), a weight coefficient b _i of Minas point (k-i), and total coefficient a, defined by a whole constant B, The weight coefficients a _i and b _i , the overall coefficient A, and the overall constant B are stored as parameters.

  These parameters are determined by statistical analysis using past market price data, but may be changed intentionally by the system user.

  Here, the GBM model is exemplified as the market price model, but the market price model in the present invention does not depend on this model formula. Further, the customs statistics formula does not depend on the form of the formula exemplified in Formula (2). For example, when there is a correlation in price movement between markets, it is possible to use a model that takes into account the correlation between a plurality of market prices.

The fuel transaction contract table 706 stores fuel procurement unit price parameters related to a procurement contract for fuel used for power generation. For example, when the procurement unit price of a certain fuel is defined by a plurality of contracts so as to be linked to the prices of the market X and the market Y as shown in the equation (3), the weight parameters w _i , a _i , b of the contract i _i , c _i and d _i are stored as parameters. In equation (3), the fuel procurement unit price P (k) at time k is the price X (k) of the fuel market X at time k, the price Y (k) of the fuel market Y at time k, and It is determined by the exchange rate TTM (k) and the weight parameters w _i , a _i , b _i , c _i , d _{i of} the contract i.

  It should be noted that the mathematical formula for calculating the procurement price does not depend on this form, and may take a different form depending on the fuel type.

  The derived product contract table 707 stores derived product contract parameters related to the derived products for which the contract is concluded. FIG. 2 is an example of a derivative product contract table. In FIG. 2, for the contract types “fuel N1” and “fuel N2”, products WTI and Dubai are set as targets, respectively.

  The fuel constraint table 708 stores an arbitrarily set period and the total fuel consumption during the period of a generator using a predetermined fuel (for example, LNG) as a constraint value.

  The simulation parameter table 709 stores setting items of the system user for performing simulation such as the number of trials of profit / loss calculation Monte Carlo simulation and the simulation target period.

  The derivative product contract portfolio table 710 stores a flag based on the portfolio set by the user of this system for each derivative product contract registered in the derivative product contract table 707. FIG. 3 is an example of a derivative product contract portfolio table. In FIG. 3, three types of portfolios P1 to P3 are evaluated with respect to fuels N1 to N5 and exchange rates K1 to K3. As shown in FIG. 3, portfolio P1 is flagged “true” for all fuels N1-N5 and exchanges K1-K3, and portfolio P2 is flagged “false” for all fuels N1-N5 and exchanges K1-K3. Yes, the portfolio P3 has the flag “true” only for the fuels N1, N3 to N5 and the exchange rates K1, K3.

  The profit / loss calculation result table 711 includes business profit / loss calculated in each trial of the profit / loss calculation Monte Carlo simulation, its breakdown (for example, power generation cost, power charge revenue, derivative product revenue), and statistical data required for data analysis (for example, Stores annual average price of fuel procurement.

  The parameter setting unit 2 includes a simulation parameter setting unit 201 and a derivative product contract portfolio setting unit 202.

  The simulation parameter setting unit 201 is used by the user of the present system to input parameters for performing simulation such as the number of trials of the profit and loss calculation Monte Carlo simulation and the simulation target period, and register them in the simulation parameter table 709.

  The derivative product contract portfolio setting unit 202 creates a portfolio in which a user of this system assumes a contract for each derivative product contract registered in the derivative product contract table 707, and inputs the result of the creation as a derivative product. Register in the contract portfolio table 710.

  The simulation control unit 3 reads the number of simulations stored in the simulation parameter table 709 and repeats the profit / loss calculation trial by this number of times. In addition, a random number used for each trial is generated.

  The profit / loss calculation unit 4 includes an assumed demand generation unit 401, an assumed price data generation unit 402, a generator output / cost calculation processing unit 403, a power rate revenue calculation unit 404, a derivative product contract revenue calculation unit 405, The calculation processing unit 406 is a part (profit / loss calculation means) that estimates and calculates profit / loss by Monte Carlo simulation. A specific procedure of the Monte Carlo simulation will be described later.

  The assumed demand generation unit 401 generates assumed demand data for the simulation target period and registers it in the assumed demand table 701 in each trial of profit / loss calculation.

At this time, when the correlation with the temperature is not considered, for example, the power demand P (k) at the time point k is changed to the estimated planned demand Q (k) at the time point k using a model as shown in the equation (4). And normal random number ε and coefficients a, b _i , c, d. In addition, you may add and use the track record of the past demand, and the plan demand data created separately.

  The assumed price data generation unit 402 uses the price model parameters stored in the price model parameter table 705 and the random numbers generated by the simulation control unit 3 in each trial of profit / loss calculation, and calculates the fuel, exchange rate, And generate electricity market price data. Further, using this market price data and the fuel procurement unit price parameter stored in the fuel transaction contract table 706, the fuel procurement unit price data for the simulation target period is generated. In the market price calculation based on the price model, when using past market price performance data, data stored in a storage medium may be used, or data read from the Internet or a communication line may be used.

  The generator output / cost calculation processing unit 403 obtains the generator output in each time zone in the simulation target period in each trial of profit / loss calculation, using generator constraints (for example, generator output upper and lower limit constraints and minimum start-up time constraints). In addition, the power generation cost is determined with reference to the fuel procurement price while satisfying the fuel constraints and the fuel constraints, and the power generation cost is calculated. The calculation process is simulated using a mathematical programming method in the same manner as the power generation plan calculation used at a central power supply command center of a general electric utility, for example.

  The power charge revenue calculation unit 404 calculates the power charge revenue from the customer in each trial of profit / loss calculation.

  The derived product contract revenue calculation unit 405 reads the derived product contract data stored in the derived product contract table 707 and calculates the revenue for each contract in each trial of profit / loss calculation. For example, in the case of a derivative product corresponding to the fuel market price or the exchange rate, the market price data generated by the assumed price data generation unit 402 is used for calculation.

  In each trial of profit / loss calculation, the profit / loss calculation processing unit 406 adds the revenues of all derivative product contracts generated by the derivative product contract revenue calculation unit 405 to the power charge revenue generated by the power rate revenue calculation unit 404 to generate power generation. The power generation cost generated by the machine output / cost calculation processing unit 403 is subtracted to calculate the profit / loss of the business, and the breakdown of the business profit / loss, power generation cost, power charge revenue, and contract revenue of each derivative product is calculated. Register in table 711.

  The result comparison processing unit 5 includes a profit / loss probability distribution calculation unit 501 and a derived product comparison calculation unit 502.

  The profit / loss probability distribution calculation unit 501 performs statistical processing on the frequency distribution of the business profit / loss data of each trial registered in the profit / loss calculation result table 711, and calculates first data including the statistical data or risk index data ( First data calculation means). The risk index data is defined as a profit / loss value that becomes 95% or 99% in a probability distribution obtained by normalizing the frequency distribution of business profit / loss. The statistical data to be calculated is, for example, the average value of profit and loss, median value, variance, standard deviation, skewness, kurtosis, percentile value, ratio of 95% value to the average value (risk on return), etc. .

  The derived product comparison calculation unit 502 calculates the profit of the derived product portfolio registered in the derived product contract portfolio table 710 for each trial, and calculates the profit and loss of the entire business according to the portfolio. Then, statistical processing is performed on the frequency distribution of the profit / loss data of all trials obtained in this way, and second data including the statistical data or risk index data is calculated (second data calculating means). The second data calculated here has the same format as the first data calculated by the profit / loss probability distribution calculation unit 501, but the content is as much as the derivative product whose flag is “true” is applied. Is different.

  The result display unit 6 displays the frequency distribution or probability distribution calculated by the profit / loss probability distribution calculation unit 501 and the derived product comparison calculation unit 502, statistical data, and risk index on the screen. In addition, for example, a result analysis screen such as a correlation diagram between the average market price of fuel or exchange rate and profit and loss, or a correlation diagram between demand amount and profit and loss in the simulation target period is displayed.

  Next, a procedure for executing a program for performing profit / loss risk analysis in the electricity business profit / loss analysis system 1 of FIG. 1 will be described. FIG. 4 is an example of a processing flowchart of Monte Carlo simulation to be executed by a computer that performs profit / loss risk analysis, and a procedure for executing a program for performing profit / loss risk analysis using this is described (that is, the program relates to the present invention). This is for causing a computer to execute each of the above-described steps in the electric power business profit / loss analysis method).

  In step S100, data initialization processing is performed. That is, the number of trials of Monte Carlo simulation is set to zero.

  In step S101, a process for generating a random number used in the Monte Carlo simulation is performed.

  In step S102, a process for calculating an assumed demand is performed. For example, the assumed demand data may be created by adding perturbation with random numbers to the demand data prepared in advance by the system user. Alternatively, the assumed demand data may be created using a statistical model and random numbers with respect to separately prepared planned demand data and past demand record data. The assumed demand data is registered in the assumed demand table 701.

  In step S103, processing is performed to calculate future prices in markets that affect profit and loss, such as fuel, currency exchange, and power markets. In this calculation, a preset price model and a random number are used.

  In step S104, the unit price of fuel procurement is calculated using the future market price calculated in step S103. At this time, as the fuel procurement unit price at the time point k, data at a time point before the time point k (for example, market price data such as the time point (k-1) and the time point (k-2)) may be used.

  In step S105, using the estimated demand calculated in step S102 and the fuel procurement unit price calculated in step S104, the generator start / stop state and generator output at each time point are calculated so that the total power generation cost is minimized. Perform the process. As the power generation facility data and the power generation facility constraint data used for the power generation plan calculation, data stored in the power generation facility table 702 and the power generation facility constraint table 703 are read and used, respectively. The demand data for the simulation target period uses the assumed demand registered in the assumed demand table 701. If there is a generator whose output is determined in advance for each time period, the demand for this output is subtracted. It may be calculated later. In addition, in order to handle the case where the power generation amount changes due to uncontrollable factors such as weather, such as a hydroelectric generator, for example, the power generation amount is calculated using random numbers based on past power generation result data or water discharge rate data. It may be determined and calculated by subtracting the demand for this output. Alternatively, a generator failure model may be prepared separately, and the generator failure period may be set using a random number, and this period may be calculated assuming that the generator stops. In addition, when there is power procurement or sales through power market transactions, the estimated demand is adjusted according to a preset transaction volume, power generation plan calculation is performed, and income (loss) from market transactions is generated in step S103. Calculated from the electricity market price data and the transaction volume, and subtracted (added) from the power generation cost.

  In addition, when the output-fuel cost characteristic of each generator used in the power generation plan calculation changes depending on the fuel price, the fuel procurement unit price data generated in step S104 and the heat consumption specific to the power generation equipment are obtained for each time zone. Calculate from the curve coefficient.

  In step S106, the demand of the consumer is calculated from the assumed demand calculated in step S102, and the fee income is calculated. At this time, data such as the unit price of the consumer is read from the consumer table 704. Further, the amount of electric power used by each consumer is calculated by reading the estimated demand data at each time point from the estimated demand table 701 and apportioning it at a preset ratio, and the power rate revenue corresponding to this is calculated. However, at this time, in consideration of power transmission loss in the power system, a process of reducing the assumed demand data according to a preset ratio may be performed. Also, as in the fuel cost adjustment system, when the electricity rate changes depending on the market price of fuel (customs statistics price), the unit price read from the customer table 704 using the future customs statistics price calculated in step S103. The calculation may be performed by adjusting the data.

  In step S107, a process for calculating the profit related to the derivative product contract is performed. For example, in the case of a derivative product corresponding to the fuel market price or the exchange rate, the market price data calculated in step S103 is used.

  In step S108, the profit / loss of the business is calculated from the power generation cost calculated in step S105, the power charge revenue calculated in step S106, and the derived product revenue calculated in step S107, and the profit / loss and breakdown are calculated in the profit / loss calculation result table 711. Perform the registration process.

  In step S109, it is determined whether or not the processing in steps S102 to S108 has been repeated for a preset number of trials. If the number of trials has not been repeated, the process proceeds to step S102. If the number of trials has been repeated, the process proceeds to step S110.

  In addition, since the random numbers generated in step S101 are used for the processing in steps S102 to S108, the assumed demand and the market price are different for each trial, and thereby the final profit / loss is also different for each trial. Is done. For example, if the number of trials is 5000, 5000 types of profits / losses are calculated, and if the number of trials is 1, only one type of profit / loss is calculated.

  In step S110, the profit and loss data obtained by repeating the number of trials is calculated for the profit and loss frequency distribution, and is divided by the number of trials to normalize the profit and loss probability distribution. Ask. In addition, the statistics of the frequency distribution are calculated.

  In step S111, the profit / loss in the case where the derivative product contract state is different is calculated by considering the derivative product revenue whose flag is “true” based on the derivative product portfolio data read from the derivative product contract portfolio table 710. Similar to step S110, the frequency distribution of this profit / loss is calculated, and the probability distribution of each profit / loss is obtained by dividing this by the number of trials, and the statistics of this frequency distribution are calculated.

  That is, steps S101 to S109 function as a profit and loss calculation process according to the present invention, step S110 functions as a first data calculation process according to the present invention, and step S111 functions as a second data calculation process according to the present invention. It is.

  In step S112, each frequency distribution graph calculated in step S110 and step S111, a comparison table of these statistics, and a difference value are output to the screen. Further, statistical data such as a correlation diagram between market prices and profit / loss may be output on the screen. FIG. 5 shows an example of statistical data display, and FIG. 6 shows a frequency distribution graph corresponding to FIG. In the frequency distribution graph shown in FIG. 6, the horizontal axis indicates profit and loss (unit: billion yen), and the vertical axis indicates the number of trials (unit: times).

  FIGS. 7 to 8 are examples of a statistical data comparison table of profit and loss and a frequency distribution comparison chart due to differences in derivative product portfolios. FIG. 7 shows a statistical data comparison table, and FIG. 8 shows a frequency distribution comparison chart corresponding to FIG. 7 to 8 show a case where no derivative product is contracted, a case where a combination of the first derivative products is contracted (portfolio P1), and a case where a combination of the second derivative products is contracted (portfolio P2). Has been. 7 to 8, it can be seen that the variance is the smallest when the combination of the second derivative products is contracted, and the profit and loss is stable.

  FIG. 9 is an example in which a correlation diagram between the average value of the fuel procurement unit price calculated in step S104 and the profit and loss is displayed. In FIG. 9, one point corresponds to one trial.

  In FIG. 1, the program executed by the simulation control unit 3, the profit / loss calculation unit 4, and the result comparison processing unit 5 is stored and stored in a recording medium, or is read and stored via a transmission medium. The operator can call up the process stably at any time and can use it repeatedly.

  The program is stored in a recording medium, and the recording medium may be a computer-readable medium such as a hard disk, a CD-ROM, or an MO.

  As described above, the electricity business profit and loss analysis system, method, and program according to the present embodiment calculate the average value and volatility of the profit and loss when the assumed demand and market price fluctuate, and the amount of risk. It is possible to easily grasp how the risk amount and the average value of profit and loss change depending on whether or not the derivative product contract assumed by the person is concluded. For example, if it is found that the risk amount can be reduced by a combination of a certain derivative product contract, it can contribute to management judgment such as conclusion of the contract. Thereby, a risk can be reduced.

<Embodiment 2>
FIG. 10 is a configuration diagram of the electric power business profit / loss analysis system 1a according to Embodiment 2 of the present invention. The electric power business profit / loss analysis system 1a of FIG. 10 adds the future temperature generation unit 407 to the profit / loss calculation part 4 (the profit / loss calculation part 4a) in the electric power profit / loss analysis system 1 of FIG. A temperature model parameter table 712 and a demand prediction model parameter table 713 are added to the unit 7 (data storage unit 7a). The added future air temperature generation unit 407, the air temperature model parameter table 712, and the demand prediction model parameter table 713 have a function of calculating the assumed demand using a model having a correlation with the air temperature such as a demand area based on a well-known method. It is what you have. In FIG. 10, the same parts as those in FIG. 1 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In the data storage unit 7a, the air temperature model parameter table 712 stores parameters of a model for predicting the air temperature in the area where the customer exists.

  The demand prediction model parameter table 713 stores parameters of a model for calculating an assumed demand value.

  In the present embodiment, in order to calculate predicted future temperature data, a derivative product contract for the temperature can be set in the derivative product contract table 707.

  The derived product contract portfolio setting unit 202 of the parameter setting unit 2 also sets a portfolio including a derived product contract for the temperature.

  In the profit / loss calculation unit 4a, the future temperature generation unit 407 uses a temperature prediction model parameter stored in the temperature model parameter table 712 and a random number generated by the simulation control unit 3, and there are consumers in the simulation target period. The future temperature data of the area is calculated and generated on the left (future temperature data calculation means).

  At this time, for example, using a model shown in Equation (5) (proposed by B.Dischel), the temperature T (k) at the time point k is normalized with the normal temperature Θ (k) at the time point k. It can be determined using the random number ε and the coefficients a and b.

  Further, by transforming Equation (4) according to Embodiment 1 as shown in Equation (6), for example, the power demand P (k) at time point k is determined in consideration of the correlation with the temperature. Can do.

  The assumed demand generation unit 401 uses the temperature calculated by the future temperature generation unit 407, the demand prediction model parameter stored in the demand prediction model parameter table 713, and the random number generated by the simulation control unit 3. Calculate the expected demand. In addition, you may add and use the track record of the past demand, and the plan demand data created separately.

  When the derivative product contract for the temperature is set in the derivative product contract table 707, the derivative product contract revenue calculation unit 405 generates the revenue of the derivative product based on the temperature data generated by the future temperature generation unit 407. Calculate

  Accordingly, the business profit / loss calculated in the profit / loss calculation processing unit 406 can take into account the change in the assumed demand due to the temperature change and the profit from the derivative product contract for the temperature.

  Next, a procedure for executing the profit / loss risk analysis program in the electric utility profit / loss analysis system 1a of FIG. 10 will be described with reference to FIG. In addition, FIG. 11 adds step S101-1 which produces | generates future temperature using an air temperature model between step S101-S102 in FIG. 4 which concerns on Embodiment 1, and it replaces with step S102, S107, S111. Steps S102-1, S107-1, and S111-1 are performed, respectively. In FIG. 11, the same steps as those in FIG. 4 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In step S101-1, a process of calculating the future temperature at which the customer exists is performed. A temperature model parameter is read from the temperature model parameter table 712 for a preset temperature model formula, a temperature model is set, and future temperature data is calculated using past temperature record data and random numbers.

  That is, step S101-1 functions as a future air temperature data calculation step according to the present invention.

  In step S102-1, a process for calculating an assumed demand is performed. A demand forecast model parameter is read from the demand forecast model parameter table 713 with respect to a preset demand forecast model formula, and assumed demand data is generated using the temperature data and random numbers calculated in step S101-1, and the assumed demand table. 701 is registered. Note that the demand prediction model may include data other than temperature data, such as past demand records and planned demand data created separately.

  In step S107-1, not only the derivative products whose original products are fuel and currency exchange as described in the first embodiment, but also the profits of the derivative products targeted at the temperature are calculated.

  In step S111-1, the profit / loss when the derivative product contract state is different is calculated based on the derivative product portfolio data read from the derivative product contract portfolio table 710. This is also the fuel as described in the first embodiment. In addition to derivative products based on currency and exchange rates, derivative products targeting temperature are added to the portfolio.

  As described above, the electric power business profit / loss analysis system, method, and program according to the present embodiment can calculate the average value, volatility, and risk amount of profit / loss for the future temperature fluctuation risk. In addition, it is possible to easily grasp the risk hedging effect on the derivative products targeting the temperature, and it is possible to contribute to the management judgment such as how to combine such derivative product contracts. That is, compared to the first embodiment, the overall risk can be reduced in consideration of the influence of the temperature.

<Embodiment 3>
FIG. 12 is a configuration diagram of the electric power business profit / loss analysis system 1b according to Embodiment 3 of the present invention. The electric power business profit / loss analysis system 1b shown in FIG. 12 is the same as the electric power business profit / loss analysis system 1a shown in FIG. is there. The added demand withdrawal amount generation unit 408 has a function of simulating the withdrawal of a customer that affects profit and loss. In FIG. 12, the same components as those in FIG. 10 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  The demand detachment amount generation unit 408 calculates and generates a demand detachment amount for a consumer using a preset detachment model based on a known method (demand detachment amount calculation means). For example, by defining a mathematical model that calculates the rate at which customers leave (the rate of withdrawal) according to price differences, calculate the withdrawal rate for each customer for the price difference data entered by the system user, and assume the demand Based on the assumed demand value registered in the table 701, the demand withdrawal amount of each consumer is calculated. The estimated demand value registered in the estimated demand table 701 is updated with the demand withdrawal amount, and the demand after each customer's withdrawal is calculated. The power generation cost calculated by the generator output / cost calculation processing unit 403 is changed by this demand separation. Furthermore, the electricity fee income calculated by the electricity fee income calculation unit 404 also changes, and finally affects the value of business profit / loss.

  For example, using a model (logistic curve) as shown in Equation (7), the departure rate Y can be determined using the price difference x (user setting) and the coefficients a, b, and c.

  The departure model parameter is defined by being added to the customer table 704 of the data storage unit 7.

  Next, a procedure for executing the profit / loss risk analysis program in the electric utility profit / loss analysis system 1b of FIG. 12 will be described with reference to FIG. In addition, FIG. 13 adds step S102-2 which simulates a customer detachment | leave and updates an estimated demand between steps S102-1 to S103 in FIG. Step S106-1 is performed instead of S106. In FIG. 13, the same steps as those in FIG. 11 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In step S102-2, processing for calculating the amount of withdrawal of the customer is performed. The departure model parameter is read from the customer table 704 with respect to a preset departure model formula, and the departure rate of each customer is calculated using the variable value of this departure model set by the system user, for example, price difference data. To do. Based on this withdrawal rate, the amount of withdrawal for each customer is calculated, and the assumed demand value registered in the assumed demand table 701 is updated.

  That is, step S102-2 functions as a demand withdrawal amount calculation step according to the present invention.

  In step S106-1, the demand of each customer is calculated and the fee income is calculated in the same manner as in step S106 described in the first embodiment. At this time, the withdrawal of each consumer calculated in step S102-2 is calculated. It is different from step S106 that the amount is additionally used.

  In the above description, the case where the function for calculating the amount of withdrawal of the customer is added to the second embodiment has been described. However, the present invention is not limited to this and may be added to the first embodiment. Good.

  Thus, the electric power business profit / loss analysis system, method, and program according to the present embodiment can calculate the average value, volatility, and risk amount of profit / loss with respect to the risk associated with the withdrawal of the customer. Therefore, it is possible to contribute to a management decision such as how to set a power price for a consumer for profit improvement and risk hedging. That is, as compared with the first and second embodiments, the overall risk can be reduced in consideration of the influence of the withdrawal of the customer.

<Embodiment 4>
FIG. 14 is a configuration diagram of an electric power business profit / loss analysis system 1c according to Embodiment 4 of the present invention. The electric utility profit / loss analysis system 1c of FIG. 14 adds a comparison parameter setting unit 203 (parameter setting part 2a) to the parameter setting part 2 in the electric utility profit / loss analysis system 1b of FIG. A comparison parameter setting table 714 is added to the storage unit 7a, and a profit / loss calculation result table 711a is provided in place of the profit / loss calculation result table 711 (data storage unit 7b). The added comparison parameter setting unit 203 and comparison parameter setting table 714 have a function capable of changing risk factor data and a function of observing a difference in profit / loss and risk amount associated with the change. In FIG. 14, the same parts as those in FIG. 12 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In the data storage unit 7b, the comparison parameter setting table 714 stores the amount of change of the risk factor that is analyzed by the system user. For example, in order to analyze the sensitivity of the price of the fuel market X and the volatility of the exchange rate, the amount to be changed in the analysis simulation is stored.

  The profit / loss calculation result table 711a includes a plurality of profit / loss calculation result tables 711 (for the types of comparison parameters described above), and registers the profit / loss calculation results in the profit / loss calculation result table 711 corresponding to the selected comparison parameter.

  In the parameter setting unit 2a, the comparison parameter setting unit 203 is for the user of this system to input the amount of change of the risk factor to be analyzed and register it in the comparison parameter setting table 714 (parameter changing means) ).

  The simulation control unit 3 reads the function described in the first embodiment (the function of repeating the trial of profit and loss calculation by this number of times stored in the simulation parameter table 709, and the random number used for each trial. In addition to the function, the number of types of change parameters set in the comparison parameter setting table 714 is read, and a Monte Carlo simulation of profit and loss is repeated by this number of times. For example, if the comparison parameter setting table 714 is set so that the prices of the fuel markets X and Y are changed by 1 dollar, the price of the fuel market X is changed by 1 dollar and the price of the fuel market Y is 1 A Monte Carlo simulation of the set number of trials is performed for each case of changing the dollar.

  In the process of the profit / loss calculation unit 4b, the simulation control unit 3 changes the data to be generated according to the type of parameter for which the change instruction is given. For example, when a parameter that changes the temperature in August once is set, the data of August in the future temperature data generated by the future temperature generation unit 407 is changed once. As a result, the value of the assumed demand that is affected by the temperature changes, and the revenue of derivative products for the fee income, power generation cost, and temperature changes, and the profit / loss eventually changes.

  Similarly, when the volatility of the price of the fuel market X is set to be increased by 1%, the assumed price data generation unit 402 increases the data of the volatility term of the price model of the fuel market X by 1%, Generate market prices. As a result, the price of other fuels affected by the price of the fuel X changes, the power generation cost and derivative product revenue change, and eventually the profit / loss changes.

  The profit / loss calculation processing unit 406 registers the profit / loss calculated in the profit / loss calculation result table 711 classified for each comparison parameter.

  In addition to the display contents shown in the first embodiment, the result display unit 6 also displays the expected value of profit and loss and the change in risk amount due to the change of the comparison parameter.

  Next, the procedure for executing the profit / loss risk analysis program in the electric utility profit / loss analysis system 1c of FIG. 14 will be described with reference to FIG. FIG. 15 shows step S101-2 for selecting a parameter to be compared and selected parameter in steps S101 and S101-1, and in steps S109 and S110 in FIG. 13 according to the third embodiment. Step S109-1 for changing the data every time and repeatedly calculating is added, and step S101-3 is performed instead of steps S101-1, S102-2, S103, S105, S108, S110, and S112, respectively. , S102-3, S103-1, S105-1, S108-1, S110-1, and S112-1. In FIG. 15, the same steps as those in FIG. 13 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In FIG. 15, the loop (steps S101-2 to S109-1) relating to each comparison parameter is outside the respective trial loops (steps S101-3 to S109) of the Monte Carlo simulation, or alternatively, relating to the Monte Carlo simulation. Trials for each comparison parameter may be performed inside the loop, and the order of these may be appropriately changed according to the number of trials, the number of parameter types, and the like.

  In step S101-2, a process for selecting a parameter for analyzing a case where the risk factor has changed is performed. In order to repeatedly execute the Monte Carlo simulation for each of the comparison parameters, the comparison parameter set in the comparison parameter setting table 714 is selected. In this Monte Carlo simulation, which comparison parameter is to be changed is selected and the simulation is controlled. .

  In step S101-3, the same processing as in step S101-1 shown in the second embodiment is performed. However, when temperature data is selected as the item of the comparison parameter, this comparison is performed on the generated temperature data. It is different that only the value set by the parameter is changed.

  In step S102-3, the same processing as in step S102-2 shown in the third embodiment is performed. However, for example, when price difference data is selected as a comparison parameter item, the price difference data is stored in the system. The difference is that the amount of withdrawal is calculated by changing only the value of the comparison parameter from the data set by the user.

  In step S103-1, the same process as in step S103 shown in the first embodiment is performed, but the data of the future market price, such as the volatility of the fuel market X, is affected as an item of the comparison parameter. When the parameter is selected, the process of changing the generated market price data by the value set by this comparison parameter, or changing the market price model volatility data to generate the market price It is different to have.

  In step S105-1, the same processing as in step S105 shown in the first embodiment is performed. However, as a comparison parameter item, for example, a failure value of the generator or a variable value that determines the output of the hydroelectric generator is used. When parameters that affect the output distribution of various generators are selected, the power generation plan calculation is performed by changing these values by the values set in this comparison parameter, and the generator output and power generation cost are calculated. Different to do.

  Note that here, as comparative parameters, parameters such as price differences and market prices that affect future temperatures and withdrawals have been described as examples. It is also possible to analyze parameter changes related to risk factors.

  In step S108-1, the same processing as in step S108 shown in the first embodiment is performed, but the profit / loss result is stored in the prepared profit / loss calculation result table 711 for the comparison parameter in accordance with the selected comparison parameter. It is different to register.

  In step S109-1, it is determined whether or not the processing of steps S101-2 to S109 has been performed for all comparison controls (that is, for all comparison parameters). If not performed for all comparison controls, the process proceeds to step S101-2, and if performed for all comparison controls, the process proceeds to step S110-1.

  In step S110-1, processing similar to that in step S110 described in the first embodiment is performed, but statistical processing for the profit / loss result is different for each comparison parameter.

  In step S112-1, the same process as in step S112 shown in the first embodiment is performed, except that it further has a function of displaying a profit / loss average value and a change amount of the risk amount due to the comparison parameter change.

  For example, the average amount of change in profit / loss for each change of the comparison parameter is displayed by statistical data as shown in FIG. 16 and a tornado graph as shown in FIG. FIG. 17 corresponds to FIG. 16 and shows the average amount of change in profit and loss when the comparison parameters are temperature, exchange rate, fuel, and water discharge rate.

  In the above description, the case where the function of repeatedly executing the Monte Carlo simulation while changing the parameter value is added to the third embodiment has been described. However, the present invention is not limited to this. 2 may be added.

  As described above, the electric power business profit / loss analysis system, method, and program according to the present embodiment can calculate the profit / loss and the risk amount change with respect to the parameter change amount of the model set by the user of the system. It is possible to clearly grasp the impact on profit and loss due to changes in In addition, this can contribute to management decisions such as which risk factors to focus on in order to reduce business risk, or what to do with the contract contents of derivative products. That is, as compared with the first to third embodiments, since the factors effective in reducing the risk can be grasped, the risk can be efficiently reduced.

<Embodiment 5>
As described above in the first embodiment, as the price model parameter table 705, a model that takes into account the correlation between a plurality of market prices can be used. Therefore, in the first to fourth embodiments, when such a model is used, for example, when there is a correlation between the price fluctuation of a certain market X and the price fluctuation of the market Y, as a comparison parameter, for example, When the price is increased by 1 yen, the correlation is simulated, so the price of the market Y also changes accordingly. However, in the future market trend, the correlation between markets is not always maintained as in the past, so it is not possible to simulate a special state in which the price changes only at a certain market price.

  FIG. 18 is a configuration diagram of an electric power business profit / loss analysis system 1d according to Embodiment 4 of the present invention. The electric power business profit / loss analysis system 1d in FIG. 18 is different from the electric power profit / loss analysis system 1c in FIG. 14 according to the fourth embodiment in that an estimated price data generation unit 402a is provided in the profit / loss calculation unit 4b instead of the assumed price data generation unit 402. (Profit and loss calculation section 4c). In FIG. 18, the same parts as those in FIG. 14 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  As with the assumed price data generation unit 402, the assumed price data generation unit 402 a uses the price model parameters stored in the price model parameter table 705 and the random number generated by the simulation control unit 3 in each trial of profit / loss calculation. Then, fuel price, exchange rate, and electricity market price data for the simulation target period are generated, and the fuel procurement unit price parameter stored in the fuel transaction contract table 706 is used to generate fuel for the simulation target period. Unit price data is generated, but the following points are different from the assumed price data generation unit 402. That is, if the price model parameter table 705 uses a model that takes into account the correlation between a plurality of market prices, the assumed price data generation unit 402a generates future market price data in consideration of the correlation. Correction is performed by updating the data registered in the comparison parameter setting table 714 or data separately prepared as a scenario of the market price (correction means).

  Next, a procedure for executing the profit / loss risk analysis program in the electric utility profit / loss analysis system 1d of FIG. 18 will be described with reference to FIG. FIG. 19 shows step S103-2 performed in place of step S103-1 in FIG. 15 according to the fourth embodiment. In FIG. 19, steps similar to those in FIG. 15 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In step S103-2, similarly to step S103-1 shown in the fourth embodiment, a process of calculating the future price of the market using a preset price model (price model parameter table 705) and a random number is performed. However, after the future market price data is generated in consideration of the correlation, the correction as described above is different.

  In the above description, the case where the function of performing correction according to the change in correlation is added to the fourth embodiment has been described. However, the present invention is not limited to this and is added to the first to third embodiments. May be.

  As described above, the electric power business profit and loss analysis system, method, and program according to the present embodiment can be applied to the special case where the correlation is lost when the simulation is performed using the model assuming the correlation. It is possible to easily set the case where the correlation state of the market price is changed by the user. Therefore, in addition to the effects of the first to fourth embodiments, there is an effect that the degree of influence on the profit and loss and the risk amount due to the change in the market structure can be grasped.

<Embodiment 6>
FIG. 20 is a configuration diagram of an electric power business profit / loss analysis system 1e according to Embodiment 6 of the present invention. The electric power business profit / loss analysis system 1e of FIG. 20 omits the derivative product contract portfolio setting unit 202 from the parameter setting unit 2 (parameter setting unit 2b) in the electric power business profit / loss analysis system 1b of FIG. The derived product contract portfolio table 710 is omitted from the data storage unit 7a (data storage unit 7c), and an effective frontier calculation unit 8 is provided instead of the result comparison processing unit 5. The provided effective frontier calculation unit 8 has a function of calculating an optimal derivative product portfolio from the relationship between risk and profit / loss. In FIG. 20, the same components as those in FIG. 12 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  The effective frontier calculation unit 8 calculates the risk amount and the average value of profit and loss and the volatility of the profit and loss distribution for all combinations of the derivative product contracts for the derivative product contracts set in the derivative product contract table 707 by the system user. The profit / loss calculation unit 4 calculates the calculation result data stored in the profit / loss calculation result table 711.

  The result display unit 6 displays the relationship between the average profit / loss for the volatility of the profit / loss distribution and the average profit / loss for the risk amount. The smaller the volatility or risk amount and the larger the average profit / loss, the better the portfolio. Therefore, the solution set of Pareto optimal solutions when these are considered multi-objective optimization problems, that is, the effective frontier is displayed and the portfolio is displayed. Present the list to the system user.

  Next, a procedure for executing the profit / loss risk analysis program in the electric utility profit / loss analysis system 1e of FIG. 20 will be described with reference to FIG. In addition, FIG. 21 adds step S110-2 and step S111-3 between step S110, S111-1, and between step S111-1, S112 in FIG. 13 which concerns on Embodiment 3, respectively, Instead of S111-1 and S112, steps S111-2 and S112-1 are performed, respectively. In FIG. 21, the same steps as those in FIG. 13 are denoted by the same reference numerals, and detailed description thereof is omitted here.

  In step S110-2, combinations of derived products are generated in order. For example, if there are derivative products A that are based on fuel, derivative products B that are based on foreign exchange, and derivative products C that are based on temperature, the derivative products that should be concluded are (A, B, C) = (×, ×, ×), (×, ×, ○), (×, ○, ×), (×, ○, ○), (○, ×, ×), (○, ×, ○), ( Eight types of combinations (○, ○, ×), (○, ○, ○) are generated in order (○ indicates that the contract is concluded and the profit is added to the profit and loss calculation).

  In step S111-2, the profit from the combination of the derivative products generated in step S110-2 is calculated for each trial of the Monte Carlo simulation, and the frequency distribution, statistics, and risk amount of the profit and loss are calculated as in step S111-1. Calculate in the same manner (in step S110-2, 2 N power portfolios are generated for N contracts of derivative products, and therefore the processing of step S111-2 is performed for each of them. Will be repeated).

  In step S111-3, it is determined whether or not the processing of steps S110-2 to S111-2 has been performed for all portfolio combinations (that is, N times 2). If not implemented for all portfolio combinations, the process proceeds to step S110-2, and if implemented for all portfolio combinations, the process proceeds to step S112-1.

  In step S112-1, the same processing as in step S112 shown in the first embodiment is performed, but further, the volatility and average value of the profit and loss distribution calculated in step S110-2, and the average value of risk amount and profit and loss It is different to show the related diagram For example, a graph as shown in FIG. 22 shows the relationship between these data, and displays a Pareto solution portfolio with objective functions of minimizing volatility and maximizing average profit and loss.

  In the above description, a case has been described in which the function of calculating a plurality of types of second data by applying a plurality of types of derived products to the first data is added to the third embodiment. Or may be added to the first to second and fourth to fifth embodiments.

  As described above, the electric power business profit and loss analysis system, method, and program according to the present embodiment allow the user of this system to take into account the expected derivative product in consideration of the balance between the expected value of profit and loss and the future uncertainty. This can contribute to management decisions such as how to make a contract and make the best choice. That is, a measure for further reducing the risk can be obtained as compared with the first to fifth embodiments.

1 is a system configuration diagram illustrating an electricity business profit / loss analysis system according to Embodiment 1. FIG. It is a figure which shows the example of the derivative goods contract table by Embodiment 1. FIG. It is a figure which shows the example of the derivative goods contract portfolio table by Embodiment 1. FIG. 3 is a flowchart illustrating an electric power business profit / loss analysis method according to the first embodiment. It is an output of the electric power business profit and loss analysis program by Embodiment 1, and is a figure which shows the example of a statistical data display. It is an output of the electric power business profit and loss analysis program by Embodiment 1, and is a graph which shows the example of the frequency distribution of profit and loss. It is an output of the electric power business profit and loss analysis program by Embodiment 1, and is a figure which shows the example of the comparison display of the statistical data of the profit and loss based on a derivative product portfolio. It is an output of the electric power business profit and loss analysis program by Embodiment 1, and is a graph which shows the example of the comparison display of the frequency distribution of the profit and loss based on a derivative product portfolio. It is an output of the electric power business profit and loss analysis program by Embodiment 1, and is a graph which shows the example of the correlation of a fuel procurement price and profit and loss. It is a system block diagram which shows the electric power business profit and loss analysis system by Embodiment 2. FIG. 6 is a flowchart illustrating an electric power business profit and loss analysis method according to a second embodiment. FIG. 10 is a system configuration diagram showing an electric power business profit / loss analysis system according to a third embodiment. 10 is a flowchart illustrating an electric power business profit / loss analysis method according to a third embodiment. It is a system configuration | structure figure which shows the electric power business profit and loss analysis system by Embodiment 4. 10 is a flowchart illustrating an electricity business profit / loss analysis method according to a fourth embodiment. It is a figure which shows the example of a display screen of the profit-and-loss average value change of the electric power business profit and loss analysis program by Embodiment 4. FIG. It is a graph which shows the example of a display screen of the profit-and-loss average value change of the electric power business profit and loss analysis program by Embodiment 4. It is a system configuration | structure figure which shows the electric power business profit and loss analysis system by Embodiment 5. 10 is a flowchart illustrating an electric power business profit / loss analysis method according to a fifth embodiment. It is a system configuration | structure figure which shows the electric power business profit and loss analysis system by Embodiment 6. 12 is a flowchart illustrating an electric power business profit / loss analysis method according to a sixth embodiment. It is a graph which shows the example of a display screen of the related figure of the volatility of the profit and loss of the electric utility profit and loss analysis program by Embodiment 6, and an average value.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Electricity business profit and loss analysis system 2 Parameter setting part 3 Simulation control part 4 Profit and loss calculation part 5 Result comparison process part 6 Result display part 7 Data storage part 8 Effective frontier calculation part 201 Simulation parameter setting part 202 Derived product contract portfolio setting unit, 401 Assumed demand generating unit, 402 Assumed price data generating unit, 403 Generator output / cost calculation processing unit, 404 Electricity fee revenue calculation unit, 405 Derived product contract revenue calculation unit, 406 Profit / loss calculation processing 407 future temperature generation unit, 408 demand withdrawal amount generation unit, 501 profit / loss probability distribution calculation unit, 502 derivative product comparison calculation unit, 701 assumed demand table, 702 power generation facility table, 703 power generation facility constraint table, 704 customer table, 705 Price model parameter table, 706 Fuel Contract Contract Table, 707 Derivative Commodity Contract Table, 708 Fuel Constraint Table, 709 Simulation Parameter Table, 710 Derivative Contract Contract Portfolio Table, 711 Profit / Loss Calculation Result Table, 712 Temperature Model Parameter Table, 713 Demand Forecast Model Parameter Table, 714 table.

Claims (13)

An electricity business profit and loss analysis system for analyzing profits and losses related to the electricity business using a computer,
Profit and loss calculation means for calculating a predetermined number of trials and gains related to the electric power business by Monte Carlo simulation,
First data calculation means for calculating first data including statistical data or risk index data by statistically processing the profit / loss calculated by the profit / loss calculation means;
Electricity business profit / loss analysis comprising: second data calculating means for calculating second data including statistical data or risk index data by applying predetermined derivative products to the first data calculated by the first data calculating means system. An electricity business profit and loss analysis system according to claim 1,
The profit / loss calculation means includes a future temperature data calculation means for calculating future temperature data. An electric power business profit and loss analysis system according to claim 1 or claim 2,
The profit / loss calculation means comprises an electricity business profit / loss analysis system having demand withdrawal amount calculation means for calculating a customer withdrawal amount. An electric power business profit and loss analysis system according to any one of claims 1 to 3,
An electric utility profit / loss analysis system further comprising parameter changing means for calculating a plurality of types of profit / loss by repeatedly executing the Monte Carlo simulation while changing parameter values. An electric power business profit and loss analysis system according to any one of claims 1 to 4,
A price model parameter table that stores parameters of the model to calculate the profit and loss based on a model that takes into account the correlation of fuel prices in multiple markets;
The profit / loss calculation system according to claim 1, wherein the profit / loss calculation means includes correcting the profit / loss calculated based on the model according to the change in the correlation. An electric power business profit and loss analysis system according to any one of claims 1 to 5,
The electric data profit and loss analysis system, wherein the second data calculating means calculates a plurality of types of the second data by applying a plurality of types of the predetermined derivative products to the first data. Electricity business profit / loss analysis method for analyzing profit / loss related to electricity business by computer,
A profit and loss calculation process for calculating profit and loss related to the electric power business by a predetermined number of trials by Monte Carlo simulation,
A first data calculating step of calculating first data including statistical data or risk index data by statistically processing the profit and loss calculated in the profit and loss calculating step;
A second data calculation step of calculating second data including statistical data or risk index data by applying a predetermined derivative product to the first data calculated in the first data calculation step; Method. The electric power business profit and loss analysis method according to claim 7,
The profit / loss calculation step is an electric business profit / loss analysis method including a future temperature data calculation step of calculating future temperature data. The electric power business profit and loss analysis method according to claim 7 or claim 8,
The profit / loss calculation step is an electricity business profit / loss analysis method including a demand withdrawal amount calculation step of calculating a customer withdrawal amount. An electricity business profit and loss analysis method according to any one of claims 7 to 9,
The profit / loss calculation step includes a step of calculating a plurality of types of profit / loss by repeatedly executing the Monte Carlo simulation while changing a parameter value. An electricity business profit and loss analysis method according to any one of claims 7 to 10,
The profit / loss calculation step includes:
Calculating the profit and loss based on a model that takes into account the correlation of fuel prices in multiple markets;
And correcting the profit and loss calculated based on the model according to the change in the correlation. An electricity business profit and loss analysis method according to any one of claims 7 to 11,
The electric data profit and loss analysis method, wherein the second data calculation step includes a step of sequentially calculating a plurality of types of the second data by sequentially applying a plurality of types of the predetermined derivative products to the first data. 13. An electric power business profit / loss analysis program for causing a computer to execute each step in the electric power business profit / loss analysis method according to claim 7.

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