JP6711007B2 - Energy market transaction support device, energy market transaction support system, energy market transaction support method, and program - Google Patents

Description

The present invention relates to an energy market transaction support device, an energy market transaction support system, an energy market transaction support method, and a program.

Conventionally, in Japan, under the control of Japan Electric Power eXchange (JEPX), electric power companies, power producers (Power Producer and Supplier: PPS) and independent power producers (Independent Power Producer). : IPP) and other market participants conduct market transactions using electricity as a commercial product.

In Patent Document 1, a probability model of individual fuel unit price and a probability model of demand fluctuation are created as a probability distribution due to uncertain factors for profits generated by power generation and trading power, and a plurality of them are combined by combining the respective results. The technology for creating the scenario is disclosed.

Patent No. 3791503

However, in the technology described in Patent Document 1, since the fuel unit price and the demand scenario created individually by random numbers are combined, the created scenario causes a scenario that cannot exist in the relationship between the fuel unit price and the demand. There is a possibility that For example, in reality, when the demand is high, the inefficient power generator may be started, and thus the fuel unit price may increase. However, the technique described in Patent Document 1 may cause a scenario in which the fuel unit price is low. There is a problem.

Therefore, in one aspect, the purpose is to evaluate the trading plan in the energy market transaction with higher accuracy.

One option is to create a scenario in the energy market transaction support device that creates a correlation scenario that represents the correlation of multiple fluctuation factors by the Monte Carlo method using variance covariance , based on the actual values of fluctuation factors in energy market transactions. Section, and based on the above-mentioned correlation scenario, the portfolio including the trading plan of the energy market is arranged in the order of low power generation cost, optimization method or metaheuristic method with the objective function of maximizing profit, and the set power generation and trading A portfolio creation unit that creates using at least one of the patterns, and a portfolio evaluation unit that evaluates the portfolio by calculating profit from the cost and the profit obtained when the correlation scenario occurs for the portfolio. , Is provided.

According to one aspect, it is possible to evaluate trading plans in energy market transactions with higher accuracy.

It is a figure which shows the structural example of the electric power market transaction support system in embodiment. It is a figure showing an example of hardware constitutions of an information processor in an embodiment. It is a functional block diagram of the information processing apparatus in an embodiment. It is a figure which shows an example of the flowchart of a scenario creation process. It is a figure which shows an example of the flowchart of a correlation scenario creation process. It is a figure explaining the process which extracts learning data. It is a figure explaining the process which produces the scenario with respect to the combination of a some variation factor. It is a figure showing an example of a flow chart of spike scenario creation processing. It is a figure explaining the creation process of a spike coefficient. It is a figure which shows an example of a spike scenario. It is a figure which shows an example of the portfolio with respect to a scenario. It is a figure explaining the example which calculates a portfolio. It is a figure explaining the evaluation process of a portfolio. It is a figure which shows the example of a display of a risk and a profit. It is a figure explaining the spike in the market price of electric power.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a configuration example of an energy market transaction support system in an embodiment. In FIG. 1, an energy market transaction support system 1 includes an information processing device (energy market transaction support device) 10 and a database (DB) server 20.

The information processing device 10 and the DB server 20 are communicatively connected via a communication line such as the Internet.

The information processing device 10 is, for example, a terminal such as a PC (Personal Computer), a tablet terminal, or a smartphone.

The DB server 20 manages transaction information in the electric power market such as JEPX. The DB server 20 notifies the information processing apparatus 10 of transaction information in the electric power market in response to the request from the information processing apparatus 10. The processing in the DB server 20 may be performed using a known technique.

FIG. 2 is a diagram illustrating a hardware configuration example of the information processing device 10 according to the embodiment. The information processing device 10 in FIG. 2 includes a drive device 100, an auxiliary storage device 102, a memory device 103, a CPU 104, an interface device 105, a display device 106, and an input device 107, which are connected to each other by a bus B.

The electric power market transaction support program that realizes the processing in the information processing device 10 is provided by the recording medium 101. When the recording medium 101 recording the power market transaction support program is set in the drive device 100, the power market transaction support program is installed in the auxiliary storage device 102 from the recording medium 101 via the drive device 100. However, the power market transaction support program does not necessarily have to be installed from the recording medium 101, and may be downloaded from another computer via the network. The auxiliary storage device 102 stores the installed power market transaction support program and also stores necessary files and data.

The memory device 103 reads the program from the auxiliary storage device 102 and stores the program when an instruction to activate the program is given. The CPU 104 realizes a function related to the information processing device 10 according to a program stored in the memory device 103. The interface device 105 is used as an interface for connecting to a network. The display device 106 displays a GUI (Graphical User Interface) or the like according to a program. The input device 107 includes a keyboard and a mouse or the like, or a touch panel and buttons and the like, and is used to input various operation instructions.

As an example of the recording medium 101, a portable recording medium such as a CD-ROM, a DVD disc, or a USB memory can be given. Further, as an example of the auxiliary storage device 102, an HDD (Hard Disk Drive), a flash memory, or the like can be given. Both the recording medium 101 and the auxiliary storage device 102 correspond to a computer-readable recording medium.

The hardware configuration of the DB server 20 is the hardware configuration of the server computer, and is the same as the hardware configuration example of the information processing apparatus 10 illustrated in FIG.

Next, the functional configuration of the information processing device 10 will be described with reference to FIG. FIG. 3 is a functional block diagram of the information processing device 10. The information processing device 10 includes an acquisition unit 11, a scenario creation unit 13, a portfolio creation unit 14, and a portfolio evaluation unit 15. Each of these units is realized by a process that causes the CPU 104 of the information processing device 10 to execute one or more programs installed in the information processing device 10.

The information processing device 10 also includes a storage unit 12. The storage unit 12 is realized by using, for example, the auxiliary storage device 102 or the like.

The storage unit 12 stores facility information 121, known plan information 122, and operation record information 123.

The facility information 121 includes information on power generators held by the electric power company (generator type, power generation cost, power generator characteristics) and information on customers (customer ID, customer Gr, customer electricity rate table). Store data.

The known plan information 122 includes power generation plans (generator start/stop state plan values, power generation amount plan values, renewable energy forecast values) and trading plans (relative trading plan values, etc.) that are determined in advance by the operator (electric power company). Data of the supply and demand plan (demand forecast value) is stored.

The operation record information 123 stores, for example, data of generator information (power generation record value), customer information (demand record value), and power market transaction information (contract price, fixed amount) up to the present. The electric power market transaction information is an example of actual values of fluctuation factors in the electric power market transaction.

The acquisition unit 11 acquires data from the DB server 20 and stores it in the equipment information 121, the known plan information 122, and the operation record information 123.

The scenario creation unit 13 uses data such as the operation record information 123 and the like to create a correlation scenario that represents the correlation of a plurality of fluctuation factors based on the actual values of fluctuation factors (for example, the market price and demand of electricity) in the power market transaction. create.

In addition, the scenario creating unit 13 creates a spike scenario that represents a spike of a fluctuation factor in the power market transaction, using the data such as the operation record information 123. The fluctuation factors related to power market transactions are external factors that the operator cannot decide for himself.

The spike is a sharp price change as indicated by 501 in FIG. 15, which occurs in the market price of electric power due to a big bid for a generator failure or the like. If a trading plan is formulated and evaluated in the electricity market trading system without considering this spike, there is a problem that a proper result may not be obtained.

The details of the processing by the scenario creating unit 13 will be described later.

The portfolio creation unit 14 creates a portfolio including a trading plan from the energy market based on the correlation scenario created by the scenario creation unit 13. The portfolio creation unit 14 refers to, for example, the facility information 121 and the known plan information 122, and with respect to each scenario created by the scenario creation unit 13, the power generation plan and the trading plan (optimum) that maximizes the profit of the electric power company. Portfolio). The portfolio creation unit 14 creates, for example, one portfolio for one scenario. The details of the process of creating the portfolio by the portfolio creating unit 14 will be described later.

The portfolio evaluation unit 15 provides a statistical amount of profit distribution (for example, an expected value or a percentage point) when a scenario changes variously (a different scenario occurs) for each portfolio created by the portfolio creation unit 14. Calculate and display the relationship between market transaction risk and profit to the operator. The details of the processing by the portfolio evaluation unit 15 will be described later.

<Scenario creation processing>
Next, with reference to FIG. 4, a scenario creating process by the scenario creating unit 13 will be described. FIG. 4 is a diagram showing an example of a flowchart of the scenario creating process.

The scenario creating unit 13 creates a correlation scenario creating process that creates a scenario in consideration of the correlation of the fluctuation factors related to the power market transaction (step S1).

The scenario creating unit 13 performs a scenario creating process in consideration of a spike in the market price of electric power (step S2).

<<Creation of correlation scenario>>
Next, with reference to FIG. 5, a detailed example of a process of creating a scenario (correlation scenario) in step S1 of FIG. 4 in which the scenario creation unit 13 considers the correlation of the fluctuation factors related to the power market transaction will be described. To do. FIG. 5 is a diagram showing an example of a flowchart of the correlation scenario creation processing.

First, the scenario creating unit 13 extracts learning data for a combination of a plurality of fluctuation factors related to power market transactions (for example, market price and demand of power) (step S11).

Here, with reference to FIG. 6, a process of extracting learning data for a combination of a plurality of fluctuation factors related to the power market transaction in step S11 of FIG. 5 will be described. FIG. 6 is a diagram illustrating a process of extracting learning data. As the variable factor, any plural (two or more) variable factors may be used among the variable factors relating to profit and risk such as the market price of electricity, demand, renewable energy output, fuel cost and the like. In FIG. 6, the case where the market price of electricity and the demand are used as the fluctuation factors will be described.

The learning data is the actual value of the fluctuation factor obtained up to immediately before, or the error value obtained by subtracting the predicted value from the actual value. The operator may set whether to use the actual value or the error for each variation factor. When the actual value is set, learning data using the actual value is created as shown in FIG. 6B from the operation actual information 123 in FIG. 6A. If the error is set, learning data using the error is created as shown in FIG.

Returning to the explanation of FIG.

Subsequently, the scenario creating unit 13 creates a scenario for a combination of a plurality of fluctuation factors related to the power market transaction based on the learning data (step S12), and ends the process.

Next, with reference to FIG. 7, a process of creating a scenario for a combination of a plurality of fluctuation factors related to the power market transaction in step S12 will be described. FIG. 7 is a diagram illustrating a process of creating a scenario for a combination of a plurality of fluctuation factors. Similar to FIG. 6, FIG. 7 illustrates a case where the market price of electricity and demand are used as the fluctuation factors.

In the example of FIG. 7, the demand scenario is the actual value of the demand of the learning data. When the error value obtained by subtracting the predicted value from the actual value is used as the learning data, the demand scenario may be obtained by adding the error of the demand to the predicted value of the demand.

In the example of FIG. 7, the market price scenario is created by adding the market price error to the market price predicted value of 15 yen/kW at the trading target time (for example, tomorrow's 5:00 to 5:30).

The scenario creating unit 13 uses a Monte Carlo method using a variance-covariance method such as a multidimensional normal random number or a moment adjustment method in order to consider the correlation of a plurality of fluctuation factors (for example, the market price of electricity and demand). To create market price and demand scenarios for electricity.

In this case, the learning data matrix l is represented by the following equation.

また、この場合の、シナリオの行列yは、以下の式で表される。

Further, the matrix y of the scenario in this case is represented by the following formula.

ここで、tは学習データの数、dは変動要因の数、nはシナリオの数である。

Here, t is the number of learning data, d is the number of fluctuation factors, and n is the number of scenarios.

As shown in FIG. 7, when the actual value of the demand of the learning data is used as the demand scenario, and the market price scenario is set to a value obtained by adding the error of the market price to the market price forecast value of 15 yen/kW at the trading target time. , Is calculated as follows.

By substituting the actual value of the demand of FIG. 6B and the error of the market price of FIG. 6C into the matrix l of the learning data, the following is obtained.

図７の例では、シナリオの行列yは、以下となる。

In the example of FIG. 7, the scenario matrix y is as follows.

シナリオ作成部１３は、このシナリオの行列ｙを、例えば、分散共分散を用いたモンテカルロ手法（多次元正規乱数や一次・二次モーメント調整法）を用いて作成する。

The scenario creating unit 13 creates the matrix y of this scenario using, for example, a Monte Carlo method (multidimensional normal random number or first-second moment adjustment method) using the variance-covariance.

-Scenario creation method using first-order multidimensional normal random number Multidimensional normal random number is a random number generation method that follows the variance-covariance of multiple factors. It can be obtained by linearly transforming the covariance matrix of multiple factors by Cholesky decomposition. The scenario creation method using multidimensional normal random numbers will be described below.

First, the scenario creating unit 13 calculates the variance-covariance matrix Σ and the average vector μ from the matrix l of the learning data.

Subsequently, the scenario creating unit 13 calculates the lower triangular matrix C by Cholesky decomposition of the variance-covariance matrix Σ of the matrix l of the learning data using the following formula.

続いて、シナリオ作成部１３は、シナリオ数分の変動要因の標準正規乱数x作成し、以下の式に代入することでシナリオの行列yを作成する。

Subsequently, the scenario creating unit 13 creates a standard normal random number x of fluctuation factors corresponding to the number of scenarios, and creates a scenario matrix y by substituting it into the following formula.

なお、Cは下三角行列、Σは学習データの行列lの分散共分散行列、x=(x₁、x₂、x₃…、x_n)^Tは標準正規乱数、μは学習データの平均ベクトルである。

Note that C is a lower triangular matrix, Σ is a variance-covariance matrix of the learning data matrix l, x=(x ₁ , x ₂ , x ₃ ..., x _n ) ^T is a standard normal random number, and μ is an average vector of learning data. Is.

-Scenario creation method using the first and second moment adjustment method The first and second moment adjustment method is a random number generation method that adjusts the first and second moments. The scenario creation method by the first/second moment adjustment method will be described below.

First, the scenario creating unit 13 calculates the variance-covariance matrix Σ and the average vector μ from the matrix l of the learning data.

Subsequently, the scenario creating unit 13 calculates the lower triangular matrix C by Cholesky decomposition of the variance-covariance matrix Σ of the matrix l of the learning data by using the above-mentioned formula [5].

Subsequently, the scenario creating unit 13 calculates the variance-covariance matrix S and the average vector m from the matrix of multidimensional standard normal random numbers.

Next, the scenario creating unit 13 calculates the lower triangular matrix C 1 ^through Cholesky decomposition of the variance-covariance matrix S of the multidimensional standard normal random number using the following formula.

続いて、シナリオ作成部１３は、下三角行列C^〜の逆行列C^〜-1を算出する。

Then, the scenario creating section 13 calculates a lower triangular matrix C inverse matrix C ^{~-1 of.}

Subsequently, the scenario creating unit 13 creates a standard normal random number x of fluctuation factors corresponding to the number of scenarios, and creates a scenario matrix y by substituting it into the following formula.

なお、Cは下三角行列、Σは学習データの行列lの分散共分散行列、x=(x₁、x₂、x₃…、x_n)^Tは標準正規乱数、C^〜は下三角行列、Sは多次元標準正規乱数の分散共分散行列、x=(x₁、x₂、x₃…、x_n)^Tは標準正規乱数、mは多次元標準正規乱数の平均ベクトル、μは学習データの平均ベクトルである。

C is a lower triangular matrix, Σ is a variance-covariance matrix of the learning data matrix l, x=(x ₁ , x ₂ , x ₃ ..., x _n ) ^T is a standard normal random number, C ^~ is a lower triangular matrix, S is the variance-covariance matrix of the multidimensional standard normal random numbers, x=(x ₁ , x ₂ , x ₃ …, x _n ) ^T is the standard normal random numbers, m is the average vector of the multidimensional standard normal random numbers, and μ is the training data Is the average vector of.

<Creation of spike scenario>
Next, with reference to FIG. 8, a detailed example of the process of creating the scenario (spike scenario) in consideration of the spike of the market price by the scenario creating unit 13 in step S2 of FIG. 4 will be described. FIG. 8 is a diagram illustrating an example of a flowchart of spike scenario creation processing.

First, the scenario creating unit 13 creates a spike coefficient (step S21).

For the spike coefficient, for example, the spike coefficient is created from the market price of the past power market transaction. Here, the spike coefficient creation processing will be described with reference to FIG. FIG. 9 is a diagram illustrating a spike coefficient creation process.

Spikes are assumed to be caused by a large bid for a generator failure, for example, and it is difficult to understand the factors of fluctuation. Therefore, as shown in FIG. 8, a spike value is calculated by the following formula from the actual value 502 of the market price on the day when the spike has occurred in the past and the actual value 503 of the market price (average market price) on the day when the spike has not occurred. Calculate the coefficient.

Spike coefficient=market price on day of spike/average market price The spike coefficient may be set by an operator or the like.

Returning to the explanation of FIG.

Subsequently, the scenario creating unit 13 creates a spike scenario for the trading target day using the spike coefficient (step S22), and ends the process.

The scenario creating unit 13 uses, for example, the market price forecast value on the trading target day and the spike coefficient to calculate, for example, by the following formula.

Spike scenario = Market price prediction value x Spike increase/decrease coefficient The market price at the time of a spike may be created as a spike scenario from the past actual value of the market price of electricity.

FIG. 10 is a diagram showing an example of a spike scenario. The spike scenario is stored in association with a fluctuation factor other than the market price, such as a demand scenario. In that case, the predicted value or the average value of the fluctuation factors is used as the scenario value of the fluctuation factors.

<Portfolio creation processing>
Next, with reference to FIG. 11, details of the process of creating a portfolio by the portfolio creating unit 14 will be described. FIG. 11 is a diagram showing an example of a portfolio for a scenario.

The portfolio includes data of decision-making factors for various fluctuation factors related to electricity market transactions in each scenario.

The decision-making factor is an internal factor that can be decided by the operator, and includes, for example, a power generation plan and a trading plan. It should be noted that, as the decision-making factor, even if the generators that the operator can use and the consumers are added individually, or the known plan information such as the power generation plan and the trading plan that the operator has decided in advance is added. Good.

The portfolio creation unit 14 may use a logic method such as a merit order (comparing the generator unit price and the market price and making a plan in ascending order), or an optimization method or a meta method in which profit maximization is used as an objective function. The portfolio may be created from a heuristic method or a power generation/trading pattern preset by the electric power company.

An example of calculating a portfolio of a power generation plan and a trading plan, which are decision-making factors, will be described with reference to FIG. 12 using a merit order. FIG. 12 is a diagram illustrating an example of calculating a portfolio. In the example of FIG. 12, in the scenario 1 of FIG. 11, the power generation plan and the trading plan are calculated. In scenario 1 of FIG. 11, the demand scenario is 10,000 kW and the market price scenario is 19 yen/kW. In the facility information 121, the generators owned by the operator are G1, G2, and G3, the power generation costs are 17 yen/kW, 19 yen/kW, 25 yen/kW, and the maximum power of the generator characteristics (rated ) Are 4500 kW, 4500 kW, and 3000 kW, respectively.

In this case, among generators G1, G2, G3 and the electricity market, generators G1 and G2 are used to generate the rated 4500 kW and 4500 kW, respectively, in order from the lowest electricity generation cost, and the remaining 1000 kW should be purchased from the electricity market. , Calculate power generation plan and sales plan. The power generation cost in this case is 17 yen/kW×4500 kW+19 yen/kW×4500 kW+19 yen/kW×1000 kW=181000 yen.

<Portfolio evaluation processing>
Next, the details of the processing by the portfolio evaluation unit 15 will be described with reference to FIG. FIG. 13 is a diagram illustrating a portfolio evaluation process.

In the example of FIG. 13, the profit distribution is calculated with the transaction amount (the amount bought from the power market) as “risk”. The “risk” is a data item that can be specified when bidding for buying and selling in the electric power market, and is, for example, a transaction amount, a transaction price (unit price), or the like.

The portfolio evaluation unit 15 calculates a statistic of profit distribution (for example, an expected value or a percentage point) when different scenarios occur for each portfolio, and displays the relationship between the risk and profit of the market transaction to the manager. To do.

In the example of FIG. 13, even in the case of different scenarios and portfolios, if the transaction amount of “risk” is the same amount, the profit is plotted on the same “risk” coordinate.

The portfolio evaluation unit 15 calculates, for example, a statistic (expected value, percentage point, etc.) of the profit distribution in the portfolio from the profit distribution histogram.

The portfolio evaluation unit 15 calculates profit based on, for example, sales revenue, power sale revenue, imbalance revenue, power generation cost, power purchase cost, imbalance cost, consignment cost, constant backup cost, and the like.

The profit of the electric power company can be calculated by the following formula.

Profit = Revenue-Cost The profit can be calculated by the following formula, for example.

Income = Sales income + Electricity sales income + Imbalance income The cost can be calculated by the following formula, for example.

Cost = Power generation cost + Power purchase cost
+ Imbalance cost + Consignment cost + Regular backup cost Next, with reference to FIG. 14, the risks and benefits displayed by the portfolio evaluation unit 15 will be described. FIG. 14 is a diagram showing a display example of risks and benefits.

In the example of FIG. 14, the profit distribution for the risks of all portfolios is shown. From this relationship between risk and profit, the manager can select the optimal portfolio or risk by comparing the statistics of profit distribution for each risk. For example, it is possible to select a portfolio or risk in which the distribution of profit is not negative, or to select a portfolio or risk in which the expected value of profit is highest. Also, by displaying the market price spike scenario, it is possible to understand the risk when the spike occurs.

Thereby, it is possible to support the operator's decision making in the power market transaction.

It is expected that JEPX will be further activated when the planned simultaneous amount equalization system, which is scheduled to be introduced in FY2016, comes into effect. When the same amount of planned values is enforced simultaneously, the electric power company is obliged to declare the planned values for both power generation and demand, and the difference from the actual value is calculated as the imbalance amount, and the penalty (imbalance) for that amount is calculated. The fee will be settled. Therefore, electric power companies need to respond to the electric power market as a place for economical power supply procurement, a place for avoiding various risks, and a place for procurement of adjustment power.

According to the above-described embodiment, in order to create a portfolio (sales plan, etc.) that considers the correlation of a plurality of fluctuation factors in the energy market transaction, the trading plan in the energy market transaction is evaluated with higher accuracy.

In addition, according to the above-described embodiment, the market participant (operator) of the power purchase can compare the power generation cost, the penalty charge, and the market price of the power generation by himself to make a power generation/purchase plan with low cost, and The profit can be maximized.

Although the examples of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications are possible within the scope of the gist of the present invention described in the claims. -Can be changed.

For example, the above-described embodiment is applicable not only to electric power but also to a device that supports market transactions of energy such as gas.

The information processing device 10 may be realized as an energy market transaction support system by cloud computing including, for example, one or more computers. Further, the information processing device 10 and the DB server 20 may be configured as an integrated device.

10 Information Processing Device 11 Acquisition Unit 121 Facility Information 122 Known Plan Information 123 Operation Performance Information 13 Scenario Creation Unit 14 Portfolio Creation Unit 15 Portfolio Evaluation Unit 20 DB Server

Claims (8)

A scenario creation unit that creates a correlation scenario that represents the correlation of multiple fluctuation factors by the Monte Carlo method using variance covariance , based on the actual values of fluctuation factors in energy market transactions.
Based on the correlation scenario, a portfolio including a trading plan of the energy market is arranged at least in order of power generation cost, optimization method or metaheuristic method with the objective function of maximizing profit, and set power generation and trading patterns. A portfolio creation department that creates using one ,
A portfolio evaluation unit that evaluates the portfolio by calculating profit from the cost and the profit obtained when the correlation scenario occurs for the portfolio ,
An energy market transaction support device comprising: The plurality of fluctuation factors are the amount of energy bought and sold in the energy market, and the price of energy in the energy market,
The energy market transaction support device according to claim 1 , wherein the portfolio evaluation unit evaluates profit according to the amount of energy sold and bought in the energy market. The scenario creating unit creates a spike scenario representing a spike of the fluctuation factor based on the actual value of the fluctuation factor in the energy market transaction,
The energy market transaction support device according to claim 1 or 2 , wherein the portfolio evaluation unit evaluates a profit value according to an amount of energy bought and sold in the energy market based on the spike scenario. .. The scenario creating unit creates the spike scenario based on a ratio between an energy price in the energy market when a spike occurs and an energy price in the energy market when the spike does not occur. The energy market transaction support device according to claim 3, wherein: The energy market transaction support device according to claim 1, wherein the portfolio evaluation unit displays the profit of the portfolio. An energy market transaction support system including one or more information processing devices,
A scenario creation unit that creates a correlation scenario that represents the correlation of multiple fluctuation factors by the Monte Carlo method using variance covariance , based on the actual values of fluctuation factors in energy market transactions.
Based on the correlation scenario, a portfolio including a trading plan of the energy market is arranged at least in order of power generation cost, optimization method or metaheuristic method with the objective function of maximizing profit, and set power generation and trading patterns. A portfolio creation department that creates using one ,
A portfolio evaluation unit that evaluates the portfolio by calculating profit from the cost and the profit obtained when the correlation scenario occurs for the portfolio ,
An energy market transaction support system comprising: Computer
Creating a correlation scenario representing the correlation of a plurality of fluctuation factors based on the actual value of the fluctuation factors in the energy market transaction by the Monte Carlo method using variance covariance ,
Based on the correlation scenario, a portfolio including a trading plan of the energy market is arranged at least in order of power generation cost, optimization method or metaheuristic method with the objective function of maximizing profit, and set power generation and trading patterns. Steps to create using one ,
Evaluating the portfolio by calculating a profit from the cost and the revenue obtained if the correlation scenario occurs for the portfolio ;
Energy market trading support method to execute. On the computer,
Creating a correlation scenario representing the correlation of a plurality of fluctuation factors based on the actual value of the fluctuation factors in the energy market transaction by the Monte Carlo method using variance covariance ,
Based on the correlation scenario, a portfolio including a trading plan of the energy market is arranged at least in order of power generation cost, optimization method or metaheuristic method with the objective function of maximizing profit, and set power generation and trading patterns. Steps to create using one ,
Evaluating the portfolio by calculating a profit from the cost and the revenue obtained if the correlation scenario occurs for the portfolio ;
A program to execute.

Images