JP7154244B2 - Power supply and demand plan creation device and power supply and demand plan creation method - Google Patents

Description

The present disclosure relates to an electric power supply and demand plan creation device and an electric power supply and demand plan creation method.

In recent years, deregulation of electric power has been promoted in Japan, and in 2020, implementation of unbundling of electric power generation business and power transmission business is planned. The supply and demand adjustment market is scheduled to open in 2021, and the adjustment capacity of generators, which was conventionally secured by the power generation division and the transmission and distribution division of electric power companies, will be traded on the supply and demand adjustment market. become. Here, the adjustment capacity is the surplus power of the generator that is secured in advance to match supply and demand and adjust the frequency with respect to the fluctuating power demand. Power producers can sell the surplus capacity of their own generators to power transmission and distribution companies as regulating capacity, but they lose the opportunity to trade electricity that is not intended for regulating capacity. Therefore, it is important for power producers to increase their profits to trade flexibility and power so as to maximize the economy in anticipation of opportunity losses in power trading. Since earnings are highly dependent on controllability and electricity prices, it is necessary to create a transaction plan and a generator operation plan in consideration of earnings fluctuation risks.

Patent Document 1 discloses a technique for creating a generator operation plan and an electric power trading plan that optimally maintain operating efficiency and operating economy, taking risks into consideration from the probability distribution of electric power demand forecast values and electric power market price forecast values. Proposed.

Patent Literature 2 proposes a technique of creating a generator operation plan and an electric power trading plan in consideration of factors such as the amount of rainfall, the amount of solar radiation, and the electric power trading price as factors that cause fluctuation risk of earnings.

JP-A-2005-4435 JP 2016-207070 A

However, the problem with these techniques is that they do not plan for the trading of controllability.

Therefore, the present disclosure has been made in view of the above problems, and an object thereof is to provide a technology capable of creating a trading plan of reparability.

The power supply and demand plan creation device according to the present disclosure includes one or more spot price scenario data representing the future contract price of electricity traded in the spot market in time series, and the future contract of the adjustment power traded in the supply and demand adjustment market. One or more control power price scenario data representing prices in time series, one or more control power activation ratio scenario data representing future activation ratios of the control power in time series, the electric power and power generation generating the control power an acquisition unit that acquires generator data representing the specifications of the generator and one or more fuel price data that represents the future fuel price of the generator in chronological order; the spot price scenario data acquired by the acquisition unit; Optimization of an objective function representing a profit or loss obtained by subtracting a profit of the electric power business from a loss of the electric power business based on the control power price scenario data, the control power activation ratio scenario data, the generator data, and the fuel price data. a plan creation unit that creates a generator operation plan and a trading plan in the spot market and the supply and demand adjustment market by solving an optimization problem that minimizes the profit and loss represented by the objective function, which is an optimization problem; Prepare.

According to the present disclosure, an optimization problem that minimizes the profit and loss represented by the objective function based on spot price scenario data, control power price scenario data, control power activation ratio scenario data, generator data, and fuel price data Solving creates a generator operation plan and a trading plan in the spot market and the balancing market. According to such a configuration, it is possible to create a trading plan for controllability.

1 is a schematic configuration diagram of a power supply and demand plan creation device according to Embodiment 1; FIG. 4 is a diagram for explaining spot price scenario data according to the first embodiment; FIG. 1 is a schematic configuration diagram showing a data flow of an electric power demand and supply plan creation device according to Embodiment 1; FIG. FIG. 4 is an explanatory diagram for explaining a generator operation plan according to Embodiment 1; FIG. FIG. 4 is an explanatory diagram for explaining a power trading plan and a control power trading plan according to Embodiment 1; FIG. 4 is an explanatory diagram for explaining a relationship between a power trading plan and a control power trading plan according to Embodiment 1; 1 is a schematic configuration diagram showing hardware of an electric power supply and demand plan creation apparatus according to Embodiment 1; FIG. 4 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device according to Embodiment 1; 4 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device according to Embodiment 1; FIG. 10 is a schematic configuration diagram of a power supply and demand plan creation device according to Embodiment 2; FIG. 10 is an explanatory diagram for explaining VaR used in the power supply and demand planning apparatus according to Embodiment 2; FIG. 11 is a schematic configuration diagram showing a data flow of an electric power demand and supply plan creation device according to Embodiment 2; FIG. 10 is a diagram for explaining spot price scenario data according to Embodiment 2; FIG. 9 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device according to Embodiment 2; 9 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device according to Embodiment 2; FIG. 11 is a schematic configuration diagram of a power supply and demand plan creation device according to Embodiment 3; FIG. 11 is a schematic configuration diagram showing a data flow of an electric power supply and demand plan creation device according to Embodiment 3; 11 is a flow chart showing an example of a processing procedure of an electric power supply and demand plan creation device according to Embodiment 3; FIG. 11 is a schematic configuration diagram of a power supply and demand plan creation device according to Embodiment 4; FIG. 11 is a schematic configuration diagram showing a data flow of an electric power supply and demand plan creation device according to Embodiment 4; FIG. 11 is a schematic configuration diagram of an electric power supply and demand plan creation device according to Embodiment 5; FIG. 11 is a schematic configuration diagram showing a data flow of an electric power supply and demand plan creation device according to Embodiment 5; FIG. 12 is a schematic configuration diagram of a power supply and demand plan creation device according to Embodiment 6; FIG. 11 is a schematic configuration diagram showing a data flow of an electric power supply and demand plan creation device according to Embodiment 6; FIG. 13 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device according to Embodiment 6. FIG.

<Embodiment 1>
FIG. 1 is a schematic configuration diagram of an electric power supply and demand planning apparatus according to the first embodiment. As shown in FIG. 1, the power supply and demand plan creation apparatus 100 includes a data acquisition unit 11 that acquires data necessary for calculation, a data storage unit 12 that stores data necessary for calculation and calculation results, and profit and loss of the electric power business. Created by a plan creation unit 16 that creates a generator operation plan and a trading plan in the spot market (electricity) and the supply and demand adjustment market (adjustment power) by solving the optimization problem of the objective function representing and a plan output unit 17 for outputting the generated generator operation plan and transaction plan.

Here, the profit and loss of the electric power business is the sum of the power generation cost, the profit from the power transaction, the profit from the control power transaction, and the power generation cost from the activation of the control power. Specifically, the profit and loss of the electric power business is the value obtained by subtracting the profit of the electric power business from the loss (cost) of the electric power business. , the negative value of the profit or loss represents the profit as a whole. In the first embodiment, the power generation cost means the total cost required for all power generators to be planned to generate power over the planned period.

The data acquisition unit 11, which is an acquisition unit, acquires spot price scenario data 121, control power price scenario data 122, control power activation ratio scenario data 123, generator data 114, and fuel price data 115, and converts each data into data. Stored in the storage unit 12 .

The spot price scenario data 121, as shown in FIG. 2, is data representing, in chronological order, contracted prices for future electricity assumed to be traded in the spot market. The spot price scenario data 121 may be set based on actual values of the latest execution prices published in the market, or may be set based on assumed values obtained by the user himself with reference to bidding history and the like. good too.

The reserve power price scenario data 122 is data representing, in chronological order, contract prices of future reserve power that are assumed to be traded in the supply and demand balance market. The control power price scenario data 122 may be set based on actual values of the latest execution prices published in the market, or set based on assumed values obtained by the user himself with reference to bidding histories, etc. may

The controllability activation rate scenario data 123 is data representing the expected future controllability activation rate (power generation rate) in chronological order. The adjustability activation ratio scenario data 123 may be set based on the actual values of the latest contract amount and activation amount published in the market, or the user himself/herself obtained it with reference to the bidding history and activation record. It may be set based on an assumed value.

The generator data 114 is data representing specifications of a generator that generates electric power and control power. The generator data 114 includes, for example, generator output upper and lower limit values, output change rate, fuel type, startup cost, minimum operation time, minimum stop time, fuel consumption characteristics, fuel consumption curve coefficient, upper limit number of times of start and stop, Includes failure occurrence model parameters, etc.

The fuel price data 115 is data representing, in chronological order, the estimated future fuel price per unit quantity of the generator and its fluctuations. The fuel price data 115 is calculated, for example, based on changes in past fuel prices. Fuels include, for example, coal, LNG, petroleum, and the like.

FIG. 3 is a schematic configuration diagram showing the data flow of the power supply and demand plan creation device 100 according to the first embodiment.

Based on the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, and the fuel price data 115 acquired by the data acquisition unit 11, the plan creation unit 16 We formulate an objective function that expresses the profit and loss of the electric power industry and its constraints. Then, the plan creation unit 16 creates a generator operation plan and a power and control power trading plan by solving an optimization problem that minimizes the profit and loss represented by the objective function.

The generator operation plan is calculated as start/stop plan data 124 and power generation plan data 125, the power trading plan is calculated as power trading plan data 126, and the control power trading plan is calculated as control power trading plan data 127. .

The start-up/shutdown plan data 124 is data representing the state of start-up or stoppage of the generator in the planned period in chronological order. The power generation amount plan data 125 is data representing the amount of power generated by the generator during the planning period in chronological order. The power trading plan data 126 and the control power trading plan data 127 are data representing the bid prices in the planning period in chronological order.

Next, an example of calculation of the generator operation plan and the optimization problem of the power and control capacity trading plan by the plan creation unit 16 will be described.

The plan creating unit 16 solves an optimization problem that minimizes the profit and loss represented by the objective function. Specifically, "Constraints that make the total output of generators equal to the trading volume of electricity at each time of the planning period" and "Constraints that the total surplus power of generator output is equal to or greater than the trading volume of controllability" Solve the optimization problem that minimizes the profit and loss represented by the objective function under the condition. As a result, the loss of the electric power business as a whole is minimized, and the profit of the electric power business as a whole is maximized. The plan creation unit 16 obtains the start/stop plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 when the profit and loss of the electric power business is minimized.

The planning unit 16 formulates such an optimization problem as a mixed integer quadratic programming problem, for example, and solves it by a branch-and-bound method or the like. Since this solution is publicly known such as disclosed in Patent Document 1, a detailed description of this solution is omitted. An example of formulation of the optimization problem is shown in Equations (1) to (4). Equation (1) represents an optimization problem that minimizes the profit and loss represented by the objective function. Equations (2) to (4) each represent a constraint on the objective function.

Here, F is the profit and loss of the electric power business, t is the time, and g is the generator number. ∀ is a symbol representing "all". Q _kwh (t) is a variable indicating the amount of power traded at time t, and is a number to be obtained by solving the optimization problem. V _kwh (t) is a variable indicating the price of electricity at time t and is included in the spot price scenario data 121 . Q _Δkw (t) is a variable indicating the trading volume of controllability at time t, and is a number to be obtained by solving the optimization problem. V _Δkw (t) is a variable indicating the Δkw price of control power at time t, and is included in the control power price scenario data 122 . R _usedΔkw (t) is a variable indicating the control power activation ratio at time t, and is included in the control power activation rate scenario data 123 . V _usedΔkw (t) is a variable indicating the kwh price of control power at time t, and is included in control power price scenario data 122 .

E is a function of the power generation cost of the generator with generator number g at time t. x(g, t) is a variable indicating the amount of power generated by the generator with generator number g at time t, and is a number to be obtained by solving the optimization problem. u(g, t) is a variable indicating the start/stop state of the generator with generator number g at time t, and is a number to be obtained by solving the optimization problem. As u(g, t), stop is represented by "0" and start is represented by "1". y(g, t) is the surplus capacity of the generator with generator number g at time t and is included in the generator data 114 .

As shown on the right side of equation (2), the profit and loss is the power generation cost in the first term minus the profit from the power trading in the second term and the profit from the controllability trading in the third term. It is the sum of the power generation costs in the fourth term. In other words, in the first embodiment, the profit and loss of the electric power business are the cost of power generation required over the planning period, the power trading in the spot market over the planning period, and the power trading over the planning period. It includes the income and expenditure combined with the amount of power sold by the trading of balancing power in the supply and demand adjustment market.

Here, the function E of the power generation cost of the generator with the generator number g at time t on the right side of Equation (2) is expressed, for example, by Equation (5).

fuel(g, t) is the fuel price of the generator with generator number g at time t and is included in the fuel price data 115 . a(g), b(g), and c(g) are coefficients indicating fuel consumption characteristics of the generator with generator number g, and are included in the generator data 114 . d(g) is the starting cost of the generator with the generator number g and is included in the generator data 114;

As shown in Equation (5), the power generation cost of each generator at a certain time is expressed, for example, as a value obtained by adding the starting cost of each generator to the product of the fuel price and the amount of power generation. Although an example of expressing the amount of power generation by a quadratic function has been shown in the expression (5), the expression is not limited to this and may be a linear expression.

Equation (3) represents a constraint that makes the amount of power generated by all generators equal to the sum of the amount of power traded and the amount of controllability activated at each time.

Equation (4) represents a constraint condition that makes the surplus power of all generators equal to or greater than the transaction amount of control power at each time. Trading of adjustment power includes trading of up adjustment power products and trading of down adjustment power products. In the case of trading for the increase adjustability product, the generator surplus capacity y is the increase surplus capacity and can be calculated as the difference between the generator output upper limit and the generator output. Further, in the case of trading a downward adjustability product, the generator's reserve capacity y is the downward reserve capacity and can be calculated as the difference between the generator output and the lower limit of the generator's output.

The plan creating unit 16 may add constraints other than the above formula. For example, constraint conditions such as minimum operation time constraint, minimum stop time constraint, change speed constraint, fuel consumption constraint, and power flow constraint may be added. In addition, if there is a generator whose starting and stopping cannot be quickly changed among the generators to be planned, a constraint condition may be added so that the generator is started and stopped. In addition, both up and down adjustment power products may be traded, or different types of up and down adjustment power products may be traded. In this case, we define each commodity as a different variable, and add a constraint that the upside potential is greater than or equal to the total trading volume of the upside control capacity, and a constraint condition that the downside potential is greater than or equal to the sum of the downside control power trading volumes. , and include the profit from the trading of each product and the activation of the adjustment power in the objective function.

In this way, by solving the optimization problem, the plan creation unit 16 obtains the start/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data when the profit and loss of the electric power business is minimized. 127 can be obtained as u(g,t), x(g,t), Q _kwh (t), Q _Δkw (t), respectively.

The activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 are output to the plan output unit 17 and displayed as graphs or tables, for example.

FIG. 4 is an explanatory diagram for explaining the generator operation plan of the power supply and demand planning apparatus 100 according to the first embodiment. The horizontal axis indicates time, and the vertical axis indicates the power generation amount. In the generator operation plan, for example, changes in the amount of power generated by the generators with generator numbers g1, g2, . . . , g6 are shown.

FIG. 5 is an explanatory diagram for explaining the power trading plan and the balancing power trading plan of the power supply and demand plan creation apparatus 100 according to the first embodiment. The horizontal axis indicates time, and the vertical axis indicates transaction volume. These trading plans show, for example, changes in trading volume of electric power and control power obtained by operating generators with generator numbers g1, g2, . . . , g6.

FIG. 6 is an explanatory diagram for explaining the relationship between the power trading plan and the balancing power trading plan of the power supply and demand plan creation apparatus 100 according to the first embodiment. Here, the amount of transactions for a part of the period of the generator with the generator number g3 in FIG. 5 is exemplified. The output of the generator is traded as electric power, and a part or all of the surplus power of the generator generated by the electric power trade is traded as control power. Specifically, part or all of the difference between the output of the generator and the upper limit of output, and part or all of the difference between the output of the generator and the lower limit of output, are traded as up and down control capacities, respectively. do.

FIG. 7 is a schematic configuration diagram showing the hardware of the power supply and demand plan creation device 100 according to the first embodiment. The power supply and demand plan creation device 100 is configured using a PC (Personal Computer) 10, for example. The PC 10 includes a CPU (Central Processing Unit) 1 , a main storage device 2 , an auxiliary storage device 3 , an external storage device 4 , an input device 5 and an output device 6 . Note that the external storage device 4 may be connected via the network 7 .

The main memory device 2 is, for example, a memory device such as a DRAM (Dynamic Random Access Memory). The auxiliary storage device 3 is, for example, a magnetic disk. The external storage device 4 is an optical disc such as a CD (Compact Disc)-R, a DVD (Digital Versatile Disc)-R, or a flash memory storage device such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. be. The input device 5 is, for example, a mouse and a keyboard. The output device 6 is, for example, a display or a printer. The network 7 is composed of optical communication equipment, for example.

Next, an electric power supply and demand plan creation method using the electric power supply and demand plan creation apparatus 100 according to the first embodiment will be described.

FIG. 8 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device 100 according to the first embodiment.

In step S100, the data acquisition unit 11 acquires the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, and the fuel price data 115, and stores each data in the data storage unit. 12.

In step S104, the plan creation unit 16 stores spot price scenario data 121, control power price scenario data 122, control power activation ratio scenario data 123, generator data 114, and fuel price data 115 stored in the data storage unit 12. to create a generator operation plan and a power and control capacity trading plan.

Specifically, the plan creation unit 16 calculates the profit and loss of the electric power business based on the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, and the fuel price data 115. We formulate the objective function and its constraints. Then, the plan creation unit 16 creates a generator operation plan and a power and control power trading plan by solving an optimization problem that minimizes the profit and loss represented by the objective function. A more specific explanation of the generator operation plan and the power and control power trading plan will be explained later using another flow chart.

The plan creation unit 16 generates start-stop plan data 124 and power generation plan data 125 obtained as generator operation plans, power trading plan data 126 obtained as power trading plans, and power trading plans obtained as control power trading plans. The adjusted power trading plan data 127 is stored in the data storage unit 12 .

In step S105, the plan output unit 17 acquires the activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 from the data storage unit 12. It is output to the output device 6 as a trading plan and a trading plan of control power. The output device 6, for example, prints or displays these plans to the user.

FIG. 9 is a flow chart showing an example of the optimization process in step S104 of the power supply and demand plan creation device 100 according to the first embodiment.

In step S1041, the plan creation unit 16 formulates the profit and loss minimization as shown in Equation (1).

In step S1042, the planning unit 16 formulates the constraints of the optimization problem as shown in Equations (2) to (4). In addition, the power generation cost of each power generator is formulated as in Equation (5) with respect to Equation (2).

Then, the plan creation unit 16 acquires the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the power generator data 114, and the fuel price data 115 from the data storage unit 12, and formula ( 2) to set as parameters of equation (5). For example, the plan creating unit 16 sets the startup cost and fuel consumption characteristics of the generator data 114 and the fuel price of the fuel price data 115 as parameters of the equation (5). In addition, the plan creation unit 16 converts the spot price of the spot price scenario data 121, the ΔkW price and kWh price of the control power price scenario data 122, and the activation ratio of the control power activation ratio scenario data 123 into formula (2). Set as a parameter.

In step S1403, the plan creating unit 16 solves the optimization problem of minimizing the profit and loss represented by the objective function so as to satisfy the constraint conditions. Equation (3) reserves power for trading power, and Equation (4) reserves controllability for trading controllability. Then, the plan creation unit 16 converts the variables u(g, t), x(g, t), Q _kwh (t), and Q _Δkw (t) when the profit/loss is the minimum into the activation/shutdown plan data 124, They are stored in the data storage unit 12 as power generation plan data 125, power trading plan data 126, and control power trading plan data 127, respectively.

<Summary of Embodiment 1>
As described above, in the power supply and demand plan creation device and the power supply and demand plan creation method according to the first embodiment, by solving the optimization problem for minimizing profit and loss, adjustment is made in addition to the generator operation plan and the power trading plan. Can create power trading plans. Therefore, it can contribute to the improvement of the evaluation of the profit of the power generation company.

For example, consider a schedule for one generator at a time. Here, the maximum output of the generator is 500 [MWh], the power generation unit price of the generator is 9 [yen/kWh], the price of electricity is 10 [yen/kWh], and the price of the adjustment power is 5 [yen/kW]. , the price for activating the increase control power is 8 [yen/kWh], and the rate of activation of the control power is 40%.

At this time, if only electric power is considered in the trading plan, the electric power price of 10 [yen/kWh] is higher than the power generation unit price of 9 [yen/kWh], so it is advantageous to generate power as much as possible. In this case, the balance of the power transaction is 5000000 yen (=500 [MWh] x 10 [yen/kWh]), and the power generation cost is 4500000 yen (=500 [MWh] x 9 [yen/kWh]). It becomes a surplus of 500,000 yen.

On the other hand, as a case of considering trading of controllability in addition to electric power, a case of trading 100 [MWh] of electric power and 400 [MW] of controllability out of the above 500 [MWh] is assumed. In this case, the balance of the electricity transaction is 1000000 yen (=100 [MWh] x 10 [yen/kWh]), and the balance of the control power transaction is 3280000 yen (=400 [MW] x 5 [yen/kW] + 400 [ MW] x 40 [%] x 8 [yen/kWh]). Then, the power generation cost is 2,340,000 yen (=(transaction amount of electric power (100 [MWh]) + activation amount of control power (400 [MW] x 40 [%])) x 9 [yen/kWh]). Therefore, a profit of 1,940,000 yen will be generated, and the profit will be improved by 1,440,000 yen compared to the transaction of electric power alone.

Note that an example assuming a trading volume has been described here. In the first embodiment, not only restrictions on the transaction volume but also restrictions on the operation of generators are taken into consideration, so that the loss of the electric power business as a whole is minimized (the profit of the electric power business as a whole is maximized). ), the trading volume of power and scalability can be determined.

<Embodiment 2>
FIG. 10 is a schematic configuration diagram of an electric power supply and demand planning apparatus 100 according to the second embodiment. In the following, description of points that are the same as in Embodiment 1 will be omitted, and differences will be mainly described.

The configuration of the power supply and demand plan creation device 100 in FIG. 10 is the same as the configuration of the power supply and demand plan creation device 100 in FIG. It is the same as the configuration added as a part.

The spot price prediction unit 13 predicts a plurality of spot prices, each of which represents a scenario of the contracted price of electricity, based on the spot market transaction record data 111 representing the actual contracted price of electricity traded in the spot market in time series. Predict scenario data 121 . The reserve power price forecasting unit 14 predicts a scenario of the contract price of the reserve power based on the supply and demand market transaction record data 112 representing the actual value of the contract price of the reserve power traded in the supply and demand market in time series. Predict multiple control power price scenario data 122 to represent. The controllability activation ratio prediction unit 15 predicts scenarios of activation ratios based on actual supply and demand control market transaction data 112 and controllability activation amount actual data 113 representing actual values of the controllability activation amount in chronological order. A plurality of control force activation ratio scenario data 123 representing

Each of the plurality of spot price scenario data 121 is the same as the spot price scenario data 121 explained in the first embodiment. Each of the multiple control power price scenario data 122 is the same as the control power price scenario data 122 described in the first embodiment. Each of the plurality of control power activation ratio scenario data 123 is the same as the control power activation ratio scenario data 123 described in the first embodiment.

As in Embodiment 1, the profit and loss of the electric power business in the second embodiment is the sum of the power generation cost, the profit from the power transaction, the profit from the control power transaction, and the power generation cost from the activation of the control power. income and expenditure. The power generation cost means the total cost required to generate power by all power generators covered by the plan over the plan period.

The data acquisition unit 11, which is an acquisition unit, acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, and reliability level data 116. Each data is acquired and stored in the data storage unit 12 .

The spot market transaction performance data 111 is data representing, in chronological order, the actual contract price of electricity traded in the spot market. The spot market transaction performance data 111 may be set based on the actual value of contract prices published in the market, or may be set based on an assumed value obtained by the user by referring to the bidding history etc. good.

The supply and demand adjustment market transaction performance data 112 is data representing, in chronological order, the actual value of the contracted price and the actual value of the total contract amount of the balancing power traded in the supply and demand adjustment market. The contract price of controllability includes the ΔkW price, which is the price for securing controllability, and the kWh price, which is the price for activating controllability. The supply and demand adjustment market transaction performance data 112 may be set based on the actual contract price and total contract volume published in the market, or based on an assumed value obtained by the user himself referring to the bidding history etc. may be set to

The control power activation amount performance data 113 is data representing the actual value of the control power activation amount in time series. The adjustability activation amount actual data 113 may be set based on the actual value of the activation amount published in the market, or based on an assumed value obtained by the user by referring to the history of the activation amount. may be The generator data 114 and fuel price data 115 are the same as the generator data 114 and fuel price data 115 described in the first embodiment.

The reliability level data 116 reflects the degree to which the user cannot tolerate the risk of profit and loss in the electric power business that can occur in the plan created by the power supply and demand planning device 100, that is, the degree to which the user wants to avoid the risk. value, and a value between 0 and 100% is set. In other words, the confidence level data 116 represents the confidence level of the risk indicator regarding the profit and loss of the electric power business. In Embodiment 2, the risk index represented by the confidence level data 116 is VaR (Value at Risk). Here, VaR is the maximum profit/loss (loss) that can occur at a certain probability, that is, at a certain confidence level. The greater the user's tolerance for risk, the lower the confidence level, which reflects the degree to which he/she wishes to avoid risk, and the lower the user's tolerance for risk, the higher the confidence level.

FIG. 11 is an explanatory diagram for explaining VaR used in the power supply and demand planning apparatus 100 according to the second embodiment. In FIG. 11, the horizontal axis indicates the profit and loss of the electric power business, and the vertical axis indicates the frequency of the power generation cost. A plurality of spot price scenarios represented by a plurality of spot price scenario data 121, a plurality of controllability price scenarios represented by a plurality of controllability price scenario data 122, and a control amount activation ratio scenario represented by a plurality of controllability activation ratio scenario data 123. , the power generation cost is represented by a frequency distribution as shown in FIG.

VaR is the maximum power generation cost that can occur in a power generation cost section in which the ratio of the frequency accumulated from the lowest power generation cost to the total frequency in the frequency distribution is α%. In other words, VaR is the power generation cost corresponding to the percentile of the confidence level α. For example, if VaR with a confidence level of 90% is 100 million yen, it means that the power generation cost will be within 100 million yen with a 90% probability. The user presumes that the power generation cost of the generator operation plan created based on various predictions of the power supply and demand plan creating apparatus 100 has a frequency distribution, and inputs the reliability level to the power supply and demand plan creating apparatus 100 in advance.

FIG. 12 is a schematic configuration diagram showing the data flow of the power supply and demand planning apparatus according to the second embodiment.

The spot price prediction unit 13 creates a plurality of spot price scenario data 121 based on the spot market transaction record data 111 . The spot price scenario data 121 is data that represents an assumed future contracted price of electricity in chronological order. FIG. 13 is an explanatory diagram for explaining a plurality of spot price scenario data 121 of the power supply and demand planning apparatus 100 according to the second embodiment, that is, a plurality of power demand scenarios.

After calculating the model parameters of the time series model by, for example, maximum likelihood estimation, the spot price prediction unit 13 uses the Monte Carlo method for the model parameters, and uses the spot price scenario data 121-1 to 121- for the number of trials of the Monte Carlo method. create n. The time series model may use an autoregressive integrated moving average or a vector autoregressive model, or may use other models. Further, the spot price prediction unit 13 may create the spot price scenario data 121 by clustering the spot market transaction record data 111 by day of the week or season, for example.

The control power price prediction unit 14 creates a plurality of control power price scenario data 122 based on the actual supply and demand control market transaction data 112 . The controllability price scenario data 122 is data representing contract prices of assumed future controllability in chronological order. The contracted price of control power includes a ΔkW price and a kWh price. Like the spot price prediction unit 13, the control power price prediction unit 14 creates control power price scenario data 122 by, for example, the Monte Carlo method or clustering.

The control reserve activation ratio prediction unit 15 creates a plurality of control reserve activation ratio scenario data 123 based on the supply and demand adjustment market transaction performance data 112 and the control reserve activation amount performance data 113 . The controllability activation ratio scenario data 123 is data representing expected future controllability activation ratios in chronological order. After calculating the ratio of the controllability activation amount to the total contract amount in the supply and demand adjustment market, the controllability activation rate prediction unit 15, like the spot price prediction unit 13, predicts the controllability activation rate scenario by, for example, the Monte Carlo method or clustering. Create data 123 .

Based on the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel price data 115, and the confidence level data 116, the plan creation unit 16 calculates the objective function and The constraints are formulated and an optimization problem is solved to create a generator operation plan and a power and control power trading plan.

The generator operation plan is calculated as start/stop plan data 124 and power generation plan data 125, the power trading plan is calculated as power trading plan data 126, and the control power trading plan is calculated as control power trading plan data 127. .

The start-up/shutdown plan data 124 is data representing the state of start-up or stoppage of the generator in the planned period in chronological order. The power generation amount plan data 125 is data representing the amount of power generated by the generator during the planning period in chronological order. These activation/shutdown plan data 124 and power generation plan data 125 depend on the spot price scenario, the control power price scenario, and the control power activation rate scenario. However, in cases such as when the generator cannot quickly change its activation/shutdown, the activation/shutdown plan data 124 need not depend on all of the spot price scenario, the controllability price scenario, and the controllability activation rate scenario.

The power trading plan data 126 and the controllability trading plan data 127 are data representing the bid prices in the planning period in chronological order, and depend on the spot price scenario, the controllability price scenario, and the controllability activation rate scenario, respectively.

Next, an example of the optimization problem of the generator operation plan calculated by the plan creation unit 16 and the power and control power trading plan will be described. In the second embodiment, a case will be described in which the profit and loss represented by the objective function is VaR of profit and loss based on the confidence level α for various predictions. In this case, the plan creating unit 16 solves the optimization problem of minimizing VaR as the optimization problem of the objective function representing the profit and loss VaR based on the confidence level α. Specifically, "the ratio of the power generation cost in each scenario of the spot price scenario data 121, the control power price scenario data 122, and the control power activation ratio scenario data 123 to VaR or less is equal to or higher than the confidence level α of the confidence level data 116. "Constraints to make the total output of generators equal to the trading volume of electricity at each time of the planning period", and "The total surplus output of generators must be greater than or equal to the trading volume of controllability." Solve the optimization problem of minimizing the profit and loss VaR based on the confidence level α represented by the objective function under the constraint. Then, the plan creation unit 16 obtains the activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 when VaR is minimized.

The planning unit 16 formulates such an optimization problem as a mixed integer quadratic programming problem, for example, and solves it by a branch-and-bound method or the like. An example of formulation of the optimization problem is shown in Equations (6) to (10). Equation (6) represents an optimization problem for minimizing the profit and loss VaR represented by the objective function. Each of Equations (7) to (10) represents constraints on the objective function.

Here, F is the profit and loss VaR of the electric power business, t is the time, s is the scenario, and g is the generator number. Scenarios are all combinations of spot price scenarios, control power price scenarios, and control power activation rate scenarios. M is a very large number, and is set so that, for example, F when the optimization problem is solved with a confidence level of 100% is the maximum profit or loss that the user can tolerate. n(s) is an auxiliary variable, eg a binary variable.

Q _kwh (t, s) is a variable indicating the power transaction volume at time t and scenario s, and is a number to be obtained by solving the optimization problem. V _kwh (t, s) is a variable indicating the power price at time t and scenario s, and is included in the spot price scenario data 121 . Q _Δkw (t, s) is a variable indicating the trading volume of controllability at time t and scenario s, and is a number to be obtained by solving the optimization problem. V _Δkw (t, s) is a variable indicating the Δkw price of control power at time t and scenario s, and is included in the control power price scenario data 122 . R _usedΔkw (t, s) is a variable indicating the control power activation ratio at time t and scenario s, and is included in the control power activation rate scenario data 123 . V _usedΔkw (t, s) is a variable indicating the kwh price of control power at time t and scenario s, and is included in control power price scenario data 122 .

E is a function of the power generation cost of the generator with generator number g at time t and scenario s. x(g, t, s) is a variable indicating the amount of power generated by the generator with generator number g at time t and scenario s, and is a number to be obtained by solving the optimization problem. u(g, t, s) is a variable indicating the start/stop state of the generator with generator number g at time t and scenario s, and is a number to be obtained by solving the optimization problem. As u(g, t, s), stop is represented by "0" and start is represented by "1". α is the confidence level and |S| is the total number of scenarios. y(g, t, s) is the surplus capacity of the generator with generator number g at time t and scenario s, and is included in generator data 114 .

As shown on the right side of equation (7), the profit and loss is the power generation cost in the first term minus the income from the electricity transaction in the second term and the income from the controllability trading in the third term. It is the sum of the power generation costs in the fourth term. The auxiliary variable n(s) in Equation (7) is used to determine whether or not the profit/loss in each scenario is less than or equal to VaR. If the auxiliary variable n(s) is 0, the profit/loss in each scenario is less than VaR, and if the auxiliary variable n(s) is 1, it means that the profit/loss in each scenario is greater than VaR.

Here, the function E of the power generation cost of the generator with the generator number g in the time t on the right side of the equation (7) and the scenario s is expressed as in the equation (11), for example.

fuel(g, t) is the fuel price of the generator with generator number g at time t and is included in the fuel price data 115 . a(g), b(g), and c(g) are coefficients indicating fuel consumption characteristics of the generator with generator number g, and are included in the generator data 114 . d(g) is the starting cost of the generator with the generator number g and is included in the generator data 114;

As shown in Equation (11), the power generation cost of each generator at a certain time is expressed, for example, as a value obtained by adding the starting cost of each generator to the product of the fuel price and the power generation amount. Although an example of expressing the amount of power generation by a quadratic function is shown in the expression (11), the expression is not limited to this and may be a linear expression.

The left side of equation (8) indicates the sum of the auxiliary variables n(s) in equation (7) divided by the number of scenarios. Equation (8) represents a constraint that sets the probability that the power generation cost in each power demand scenario will be less than or equal to VaR to be greater than or equal to the confidence level α.

Equation (9) represents a constraint that makes the amount of power generated by all generators equal to the sum of the amount of power traded and the amount of controllability activated at each time and in each scenario.

Equation (10) represents a constraint that makes the spare capacity of all generators equal to or greater than the trading volume of controllability at each time and in each scenario. Trading of adjustment power includes trading of up adjustment power products and trading of down adjustment power products. In the case of trading for the increase adjustability product, the generator surplus capacity y is the increase surplus capacity and can be calculated as the difference between the generator output upper limit and the generator output. Further, in the case of trading a downward adjustability product, the generator's reserve capacity y is the downward reserve capacity and can be calculated as the difference between the generator output and the lower limit of the generator's output.

The plan creating unit 16 may add constraints other than the above formula. For example, constraint conditions such as minimum operation time constraint, minimum stop time constraint, change speed constraint, fuel consumption constraint, and power flow constraint may be added. In addition, if there is a generator that cannot be started and stopped quickly among the generators to be planned, a constraint condition may be added to the generator so that the starting and stopping are the same in all scenarios. In addition, both up and down adjustment power products may be traded, or different types of up and down adjustment power products may be traded. In this case, we define each commodity as a different variable, and add a constraint that the upside potential is greater than or equal to the total trading volume of the upside control capacity, and a constraint condition that the downside potential is greater than or equal to the sum of the downside control power trading volumes. , and include the profit from the trading of each product and the activation of the adjustment power in the objective function.

In this way, by solving the optimization problem, the plan creation unit 16 obtains the activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, the adjustment The force trading plan data 127 can be obtained as u(g, t, s), x(g, t, s), Q _kwh (t, s), Q _Δkw (t, s), respectively.

The activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 are output to the plan output unit 17 and displayed as graphs or tables, for example.

FIG. 4 is an explanatory diagram for explaining the generator operation plan of the power supply and demand planning apparatus 100 according to the second embodiment. The horizontal axis indicates time, and the vertical axis indicates the power generation amount. In the generator operation plan, for example, changes in the amount of power generated by the generators with generator numbers g1, g2, . . . , g6 are shown.

FIG. 5 is an explanatory diagram for explaining the power trading plan and the balancing power trading plan of the power supply and demand plan creation apparatus 100 according to the second embodiment. The horizontal axis indicates time, and the vertical axis indicates transaction volume. These trading plans show, for example, changes in trading volume of electric power and control power obtained by operating generators with generator numbers g1, g2, . . . , g6.

FIG. 6 is an explanatory diagram for explaining the relationship between the power trading plan and the balancing power trading plan of the power supply and demand plan creation apparatus 100 according to the second embodiment. Here, the amount of transactions for a part of the period of the generator with the generator number g3 in FIG. 5 is exemplified. The output of the generator is traded as electric power, and a part or all of the surplus power of the generator generated by the electric power trade is traded as control power. Specifically, part or all of the difference between the output of the generator and the upper limit of output, and part or all of the difference between the output of the generator and the lower limit of output, are traded as up and down control capacities, respectively. do.

FIG. 7 is a schematic configuration diagram of the power supply and demand plan creation device 100 according to the second embodiment. The power supply and demand plan creation device 100 is configured using the PC 10, for example. The PC 10 includes a CPU 1 , a main storage device 2 , an auxiliary storage device 3 , an external storage device 4 , an input device 5 and an output device 6 . The external storage device 4 may be connected via the network 7 .

The main memory device 2 is, for example, a memory device such as a DRAM. The auxiliary storage device 3 is, for example, a magnetic disk. The external storage device 4 is an optical disk such as CD-R or DVD-R, or a flash memory storage device such as USB memory or SD card. The input device 5 is, for example, a mouse and a keyboard. The output device 6 is, for example, a display or a printer. The network 7 is composed of optical communication equipment, for example.

Next, a power supply and demand plan creation method using the power supply and demand plan creation apparatus 100 according to the second embodiment will be described.

FIG. 14 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device 100 according to the second embodiment. The flowchart of the second embodiment shown in FIG. 14 is substantially the same as the flowchart of the first embodiment shown in FIG. 8 with steps S101, S102, and S103 added.

In step S100, the data acquisition unit 11 acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, and reliability level data 116. , each data is stored in the data storage unit 12 . The data acquisition unit 11 may acquire each data via the input device 5 or may acquire data stored in the external storage device 4 via the network 7 . Further, when each data is stored in the data storage unit 12 in advance, this process may be omitted.

In step S101, the spot price prediction unit 13 acquires the spot market transaction data 111 from the data storage unit 12, and based on the spot market transaction data 111, the spot price scenario data 121 representing changes in the contract price of electricity. Create multiple The spot price prediction unit 13 stores the created plurality of spot price scenario data 121 in the data storage unit 12 .

In step S102, the control reserve price prediction unit 14 acquires the supply and demand adjustment market transaction performance data 112 from the data storage unit 12, and based on the supply and demand adjustment market transaction performance data 112, an adjustment indicating fluctuations in the contract price of the control reserve. A plurality of force price scenario data 122 are created. The control power price prediction unit 14 stores the created multiple control power price scenario data 122 in the data storage unit 12 .

In step S103, the control reserve activation ratio prediction unit 15 acquires the supply and demand adjustment market transaction performance data 112 and the control reserve activation amount performance data 113 from the data storage unit 12, and obtains the supply and demand adjustment market transaction performance data 112 and the control reserve activation amount. Based on the performance data 113, a plurality of control power activation ratio scenario data 123 are created that show fluctuations in the control power activation ratio. The control force activation ratio prediction unit 15 stores the created plural control force activation ratio scenario data 123 in the data storage unit 12 .

In step S104, the plan creation unit 16 stores the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel price data 115, and the reliability data stored in the data storage unit 12. Based on the level data 116, a generator operation plan and a power and control power trading plan are created.

Specifically, based on the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel price data 115, and the confidence level data 116, An objective function representing profit and loss VaR and its constraints are formulated. Then, the plan creation unit 16 creates a generator operation plan and a power and control power trading plan by solving an optimization problem that minimizes the profit and loss VaR represented by the objective function. A more specific explanation of the generator operation plan and the power and control power trading plan will be explained later using another flow chart.

The plan creation unit 16 generates start-stop plan data 124 and power generation plan data 125 obtained as generator operation plans, power trading plan data 126 obtained as power trading plans, and power trading plans obtained as control power trading plans. The adjusted power trading plan data 127 is stored in the data storage unit 12 .

In step S105, the plan output unit 17 acquires the activation/shutdown plan data 124, the power generation plan data 125, the power trading plan data 126, and the control power trading plan data 127 from the data storage unit 12. It is output to the output device 6 as a trading plan and a trading plan of control power. The output device 6, for example, prints or displays these plans to the user.

FIG. 15 is a flow chart showing an example of the optimization process in step S104 of the power supply and demand plan creation device 100 according to the second embodiment.

In step S1046, the plan creation unit 16 formulates minimization of the profit and loss VaR based on the confidence level α as shown in Equation (6).

In step S1041, the planning section 16 formulates the constraints of the optimization problem as shown in Equations (7) to (10). Moreover, the power generation cost of each generator is formulated as in Equation (11) for Equation (7).

The plan creation unit 16 acquires spot price scenario data 121, control power price scenario data 122, control power activation ratio scenario data 123, generator data 114, fuel price data 115, and confidence level data 116 from the data storage unit 12. , are set as parameters of equations (7) to (11). For example, the plan creating unit 16 sets the startup cost and fuel consumption characteristics of the generator data 114 and the fuel price of the fuel price data 115 as parameters of the equation (11). In addition, the plan creation unit 16 converts the spot price of the spot price scenario data 121, the ΔkW price and kWh price of the control power price scenario data 122, and the activation ratio of the control power activation ratio scenario data 123 into equation (7). Set as a parameter.

In step S1402, the plan creating unit 16 solves the optimization problem of minimizing the profit and loss VaR represented by the objective function so as to satisfy the constraint. Equation (7) determines whether the profit and loss in each scenario of the electricity price, the control power price, and the control power activation rate is above the profit and loss VaR, and the profit and loss in each scenario is below the profit and loss VaR solution is selected. Also, according to the formula (8), the ratio of the profit and loss in each scenario to VaR or lower is equal to or higher than the confidence level. Also, power is reserved for power trading by equation (9), and controllability is reserved for controllability trading by equation (10). Then, the planning unit 16 calculates the variables u (g, t, s), x (g, t, s), Q _kwh (t, s), Q _Δkw (t, s) when VaR is the minimum are stored in the data storage unit 12 as activation/shutdown plan data 124, power generation plan data 125, power trading plan data 126, and control power trading plan data 127, respectively.

<Summary of Embodiment 2>
As described above, in the power supply and demand plan creation apparatus and the power supply and demand plan creation method according to the second embodiment, the profit and loss VaR corresponding to the percentage point of the confidence level α% of the confidence level data 116 is used as the objective function. Then, the optimization problem of minimizing VaR is solved under the constraint that the ratio of profit and loss below VaR in each scenario of power price, control power price, and control power activation rate is equal to or greater than the confidence level α%. As a result, it is possible to create a plan that is optimized to minimize the maximum profit and loss occurring at the confidence level α% while reflecting the degree to which the user cannot tolerate risk.

For example, if the confidence level is set to a large value such as 100% to create a plan that minimizes VaR, the overall loss of the electric power business will be large, but the loss will be greater than the expected loss, so the risk of generator operation is low. Plans, power trading plans, and balancing power trading plans can be created. In other words, the profit of the electric power business as a whole is small, but since the profit is less than the expected profit, a low-risk plan can be created.

For example, consider a schedule for one generator at a time. In order to simplify the explanation here, only electricity price scenarios are considered. There are 10 scenarios where the electricity price is 6 [yen/kWh], 70 cases where the electricity price is 10 [yen/kWh], and 14 [yen/kWh]. kWh] is assumed to be 20 cases. Also assume that the confidence level is 80%. As in Embodiment 1, the maximum output of the generator is 500 [MWh], the power generation unit price of the generator is 9 [yen/kWh], the price of the increase control power is 5 [yen/kW], and the increase control power is activated. Assume that the price is 8 [yen/kWh] and the control power activation ratio is 40%.

At this time, for example, if electricity is traded at 100 [MWh] and control power is traded at 400 [MW], the same calculation as in Embodiment 1 yields a profit of 1,540,000 yen for 10 cases, 1,940,000 yen for 70 cases, and 20 The case costs 2,340,000 yen. At this time, the profit/loss VaR based on the confidence level of 80% is 1940000 yen because it is the 80% value counting from the side with the largest profit/loss (loss).

On the other hand, for example, if power is traded at 50 [MWh] and control power is traded at 500 [MW], the same calculation as in Embodiment 1 yields a profit of 1,920,000 yen in 10 cases and 2,120,000 yen in 70 cases. , 20 cases for 2,320,000 yen. At this time, the profit and loss VaR based on the confidence level of 80% is 2,120,000 yen. A trade to avoid is realized.

Note that an example has been described here assuming a trading volume. In the second embodiment, after considering the restrictions on the transaction volume and the operation restrictions of the generator, the transaction volume of power and control power is minimized so that the profit and loss VaR corresponding to the percentage point of the confidence level α% is minimized. can be determined.

Also, if you set a small value such as 10% for the confidence level and create a plan that minimizes the profit and loss VaR, the risk that the profit and loss will exceed the expected profit and loss is high, but the generator operation plan with small profit and loss, the power A trading plan, a trading plan of adjustment power can be created. In other words, although there is a high risk that the profit of the electric power business as a whole will fall below the expected profit, it is possible to create a plan with a large profit. If the expected price of electricity is higher than that of controllability, the amount of electricity traded should be increased.

In addition, when the confidence level is set at 50% to create a plan that minimizes profit and loss VaR, the impact of low-probability extreme scenarios and outliers is reduced compared to when expected values are used as risk indicators. be able to. Here, unlike the second embodiment, in a configuration using an expected value as a risk index, there is a possibility that a plan will be created to increase the amount of power generation in anticipation of an increase in the spot price. In such a case, there is a high probability that the profit and loss will deteriorate, which may adversely affect the user's cash flow. On the other hand, the profit and loss VaR with a confidence level of 50% is the median value of the probability distribution of profit and loss assumed based on the scenario of the electricity price, the control power price, and the activation rate of the control power. The median is less sensitive to extreme scenarios with low probabilities of occurrence. For this reason, according to the second embodiment, a generator operation plan, an electric power trading plan, and a flexible power trading plan that are less likely to adversely affect the cash flow of an electric power company than in a configuration using an expected value as a risk index. can get.

In addition, the spot price prediction unit 13, the control power price prediction unit 14, and the control power activation rate prediction unit 15 create scenarios of various assumed electricity prices, control power prices, and control power activation rates. As a result, it is possible to create a generator operation plan, a power trading plan, and a control power trading plan that correspond to profit and loss fluctuations caused by these fluctuations. VaR is also expressed using a single parameter, the confidence level. Therefore, compared to methods that set parameters for each of multiple items that have a trade-off relationship between risk and profit and loss, repetitive calculations based on user experience and trial and error can be suppressed, making it easier for users to make decisions. It will be possible to create a plan to

<Embodiment 3>
FIG. 16 is a schematic configuration diagram of the power supply and demand plan creation device 100 according to the third embodiment. In the following, the description of the points that are the same as in the second embodiment will be omitted, and the description will focus on the points of difference.

The configuration of the power demand and supply plan creation device 100 in FIG. 16 is the same as the configuration of the power demand and demand plan creation device 100 in FIG. In the third embodiment, assuming a case where a part of the generated power is sold to a retailer in negotiated transactions without going through an exchange, when the profit and loss VaR of the electric power business is minimized Prepare generator operation plans and power and control capacity trading plans. In Embodiment 3, the profit and loss of the electric power business is the sum of the power generation cost, the loss such as the amount of power purchased from the power transaction, and the profit from the amount of power sold from the power transaction throughout the planning period.

In addition to spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, confidence level data 116, the data acquisition unit 11 also obtains bilateral transaction data. 117, the actual demand data 118 is further acquired.

FIG. 17 is a schematic configuration diagram showing the data flow of the power supply and demand plan creation device 100 according to the third embodiment. The bilateral transaction data 117 is used for calculations in the plan creation section 16 . The actual demand data 118 is used for calculation of the demand scenario data 128 in the demand forecasting section 18 . The demand scenario data 128 is used for calculations in the plan creating section 16 .

The negotiated transaction data 117 is data on negotiated contracts with retailers, and is electricity selling prices to retailers.

The actual demand data 118 is time-series data of electric power sold to retailers (hereinafter also referred to as “demand”). The actual demand data 118 may be set based on the demand in the past contract, or may be set based on the area demand obtained from the electricity forecast or the like.

The demand forecasting unit 18 creates a plurality of demand scenario data 128 based on the actual demand data 118 . The demand scenario data 128 is data representing expected future demand in chronological order.

After calculating the model parameters of the time-series model by, for example, maximum likelihood estimation, the demand forecasting unit 18 uses the Monte Carlo method for the model parameters to create demand scenario data 128 for the number of trials of the Monte Carlo method. The time series model may use an autoregressive integrated moving average or a vector autoregressive model, or may use other models. Further, the demand forecasting unit 18 may create the demand scenario data 128 by clustering the actual demand data 118 by day of the week or season, for example.

The plan creation unit 16 generates spot price scenario data 121, control power price scenario data 122, control power activation ratio scenario data 123, generator data 114, fuel price data 115, confidence level data 116, negotiated transaction data 117, and demand Based on the scenario data 128, an objective function and its constraints are formulated, and an optimization problem is solved to create a generator operation plan and a power and control capacity trading plan.

Next, an example of the optimization problem of various plans calculated by the plan creating unit 16 will be described. In the third embodiment, the plan creating unit 16 solves the optimization problem by changing the equation (7) of the second embodiment to the equation (12) and the equation (9) to the equation (13).

Here, D(t, s) is the demand at time t and scenario s and is included in the demand scenario data 128 . P is the electricity selling price to the retailer and is included in the bilateral transaction data 117 . The difference between the third embodiment and the second embodiment is that the profit and loss of the power generation company is added to the income and expenditure of the negotiated transaction, and that the demand is included in the power amount balance formula.

The right-hand side of equation (12) is the profit and loss, which is obtained by subtracting the profit from electricity trading, the profit from control power trading, and the profit from bilateral trading from the power generation cost, and adding the profit from activating control power. In other words, the profit and loss of the electric power business in the third embodiment are the power generation costs required over the planning period, the power trading in the spot market over the planning period, and the supply and demand adjustment over the planning period. It includes the income and expenditure combined with the trading of controllability in the market and the amount of power sold through negotiated trading of electricity during the plan period.

The auxiliary variable n(s) in equation (12) is used to determine whether or not the profit/loss in each scenario is less than or equal to VaR. If the auxiliary variable n(s) is 0, the profit/loss in each scenario is less than VaR, and if the auxiliary variable n(s) is 1, it means that the profit/loss in each scenario is greater than VaR.

Equation (13) represents a constraint that makes the amount of power generated by all power generators equal to the sum of the amount of power traded, the amount of controllability exercised, and the demand at each time and in each scenario.

Next, a power supply and demand plan creation method using the power supply and demand plan creation apparatus 100 according to the third embodiment will be described.

FIG. 18 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device 100 according to the third embodiment. The flowchart of the third embodiment shown in FIG. 18 differs from the flowchart of the second embodiment shown in FIG. 14 in steps S100, S106, and S104, as described below.

In step S100, which is a data acquisition step, the data acquisition unit 11 acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, confidence level In addition to the data 116 , bilateral transaction data 117 and actual demand data 118 are stored in the data storage unit 12 .

In step S106, which is a demand forecasting step, the demand forecasting unit 18 acquires the actual demand data 118 from the data storage unit 12, and based on the actual demand data 118, creates a plurality of demand scenario data 128 representing changes in demand. . The demand forecasting unit 18 stores the created plurality of demand scenario data 128 in the data storage unit 12 .

In step S104, which is a plan creation step, the plan creation unit 16 stores the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel Based on price data 115, confidence level data 116, bilateral transaction data 117, and demand scenario data 128, generator operation plans and power and control capacity trading plans are created.

<Summary of Embodiment 3>
As described above, in the power supply and demand plan creation apparatus and the power supply and demand plan creation method according to the third embodiment, the profit and loss VaR corresponding to the percentage point of the confidence level α% of the confidence level data 116 is used as the objective function. Then, the optimization problem of minimizing VaR under the constraint condition that the ratio of profit and loss below VaR in each scenario of power price, control power price, control power activation ratio, and demand scenario is above the confidence level α%. solve. As a result, it is possible to create a plan that is optimized to minimize the maximum profit and loss occurring at the confidence level α% while reflecting the degree to which the user cannot tolerate risk.

In addition, since the demand forecasting unit 18 creates various assumed demand scenarios, it creates a generator operation plan, an electric power trading plan, and a regulating power trading plan that correspond to fluctuations in profit and loss caused by fluctuations in demand. can. Fluctuations in demand are caused, for example, by changes in heating and cooling demand associated with rising and falling temperatures. For this reason, for example, when demand is expected to increase due to extreme heat, etc., by setting a large confidence interval such as the confidence level, it is possible to avoid risks by increasing the output of the generator and selling the controllability. can be created.

<Embodiment 4>
FIG. 19 is a schematic configuration diagram of an electric power supply and demand plan creating apparatus 100 according to the fourth embodiment. In the following, the description of the points that are the same as in the second embodiment will be omitted, and the description will focus on the points of difference.

In the fourth embodiment, assuming a case where a part of the generated power is sold in a forward transaction, the generator operation plan and the power and control capacity when the profit and loss VaR of the electric power business is minimized Create a trading plan.

In addition to spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, and reliability level data 116, the data acquisition unit 11 Further data 119 are obtained.

FIG. 20 is a schematic configuration diagram showing the data flow of the power supply and demand plan creation device 100 according to the fourth embodiment. The forward market data 119 is used for calculations in the plan creation section 16 .

The forward market data 119 is data of buy bids that have not yet been contracted among bids in the forward market, and includes bid price and bid volume data. The forward market data 119 may be set based on board information of the forward market, or may be set based on expected buy bid data.

Next, an example of the optimization problem of various plans calculated by the plan creating unit 16 will be described. In the fourth embodiment, the plan creating unit 16 solves the optimization problem by changing the equation (7) of the second embodiment to the equation (14) and the equation (9) to the equation (15).

Here, Q _forward (t, n) is the bid volume at time t of the n-th bid when serial numbers are assigned to buy bids that have not yet been executed in the forward market. included. V _forward (n) is the bid price of the nth bid and is included in forward market data 119 . B _forward (n) is a binary variable to be found by solving the optimization problem and is 1 if the sell bid is for the nth buy bid and 0 otherwise. The fourth embodiment differs from the second embodiment in that the profit and loss of the power generation company is added to the income and expenditure of the forward transaction, and that the formula for balancing the amount of electric power includes the forward contract.

The right-hand side of equation (14) is the profit and loss, which is obtained by subtracting the profit from electricity trading, the profit from control power trading, and the profit from forward trading from the power generation cost, and adding the profit from activating control power. In other words, the profit and loss of the electric power business in the fourth embodiment is the power generation cost required over the planning period, the power trading in the spot market over the planning period, and the supply and demand adjustment over the planning period. It includes the income and expenditure combined with the amount of power sold by the trading of power in the market and the power trading in the forward market during the plan period.

The auxiliary variable n(s) in equation (14) is used to determine whether or not the profit/loss in each scenario is less than or equal to VaR. If the auxiliary variable n(s) is 0, the profit/loss in each scenario is less than VaR, and if the auxiliary variable n(s) is 1, it means that the profit/loss in each scenario is greater than VaR.

Equation (15) is a constraint that the amount of power generated by all generators in each scenario at each time is equal to the sum of the trading volume in the spot market, the amount of controllability activated, and the trading volume in the forward market. Indicates a condition.

Next, a power supply and demand plan creation method using the power supply and demand plan creation apparatus 100 according to the fourth embodiment will be described.

The flowchart showing the processing procedure of the fourth embodiment is almost the same as the flowchart (FIG. 14) of the second embodiment. However, as described below, there is a difference in the data acquired and stored in step S100, which is the data acquisition step, and step S104, which is the plan creation step, between the two.

In step S100, which is a data acquisition step, the data acquisition unit 11 acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, confidence level In addition to data 116 , forward market data 119 is stored in data storage unit 12 .

In step S104, which is a plan creation step, the plan creation unit 16 stores the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel Based on price data 115, confidence level data 116, and forward market data 119, generator operating plans and power and reserve trading plans are developed.

Specifically, the planning unit 16 solves the optimization problem, and calculates variables u (g, t, s), x (g, t, s), Q _kwh (t, s) when VaR is minimized. ), Q _Δkw (t, s), and B _forward (n) are used as activation/shutdown plan data 124, power generation plan data 125, power trading plan data 126, controllability trading plan data 127, and forward transaction plan data 129, respectively. Stored in the data storage unit 12 .

<Summary of Embodiment 4>
As described above, in the power supply and demand plan creation apparatus and the power supply and demand plan creation method according to the fourth embodiment, the profit and loss VaR corresponding to the percentage point of the confidence level α% of the confidence level data 116 is used as the objective function. Then, the optimization problem of minimizing VaR is solved under the constraint that the ratio of profit and loss below VaR in each scenario of power price, control power price, and control power activation rate is equal to or greater than the confidence level α%. As a result, it is possible to create a plan that is optimized to minimize the maximum profit and loss occurring at the confidence level α% while reflecting the degree to which the user cannot tolerate risk.

In addition, based on the forward market data 119, the plan creation unit 16 formulates transactions and creates plans, thereby reducing fluctuations in profit and loss through transactions in the forward market. , can create a trading plan for the adjustment power. For this reason, for example, when the uncertainty of spot price forecasts is large, by setting a large confidence interval such as the confidence level, transactions in the forward market are increased and the volume of transactions in the spot market is decreased. You can create a risk-avoidable plan.

<Embodiment 5>
FIG. 21 is a schematic configuration diagram of an electric power supply and demand plan creating apparatus 100 according to the fifth embodiment. In the following, the description of the points that are the same as in the second embodiment will be omitted, and the description will focus on the points of difference.

In the fifth embodiment, a generator operation plan and a power and control power trading plan when the profit and loss VaR of the electric power business is minimized are created. It is assumed that fluctuations in profit and loss caused by power trading can be suppressed by operating futures trading, which is a derivative product.

In addition to spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, confidence level data 116, futures market data 120 is further obtained.

FIG. 22 is a schematic configuration diagram showing the data flow of the power supply and demand plan creation device 100 according to the fifth embodiment. The futures market data 120 is used for calculations in the planning unit 16 .

The futures market data 120 is data of uncontracted buy bids and data of final settlement prices among bids in the futures market, and includes data of bid prices and bid amounts, and data of final settlement prices. The futures market data 120 may be set based on depth information of the futures market, or may be set based on expected buy bid data.

The final settlement price data may be set based on the monthly average price of the spot trading market for the previous month, which is the data used for clearing the net settlement, or it may be operated in consideration of the trend of spot price fluctuations. It may be set based on a value predicted by an operator.

An example of the optimization problem of various plans calculated by the plan creating unit 16 will be described below. In the fifth embodiment, the plan creating unit 16 solves the optimization problem in which the equation (7) of the second embodiment is changed to the equation (16).

where Q _future (m) is the bid volume of the m-th bid when serializing unfilled buy bids in the futures market, included in the futures market data 120 . V _future (m) is the bid price of the mth bid, included in futures market data 120 . V _final is the final settlement price and is included in futures market data 120 . B _future (m) is a binary variable to be found by solving the optimization problem and is 1 if the m th buy bid is for a sell bid and 0 otherwise. The fifth embodiment differs from the second embodiment in that the income and expenditure of futures trading is added to the profit and loss of the power generation company.

The right-hand side of the equation (16) is the profit and loss, which is obtained by subtracting the profit from the electricity trading, the profit from the control power trading, and the futures trading from the power generation cost, and adding the profit from the activation of the control power. In other words, the profit and loss of the electric power business in the fifth embodiment is the power generation cost required over the planning period, the power trading in the spot market over the planning period, and the power trading over the planning period. It includes the balance of the amount of electricity sold by trading of balancing power in the supply and demand adjustment market, the amount of electricity sold by electricity trading in the futures market during the plan period, and the amount of difference settlement.

The auxiliary variable n(s) in equation (16) is used to determine whether or not the profit/loss in each scenario is less than or equal to VaR. If the auxiliary variable n(s) is 0, the profit/loss in each scenario is less than VaR, and if the auxiliary variable n(s) is 1, it means that the profit/loss in each scenario is greater than VaR.

Next, a power supply and demand plan creation method using the power supply and demand plan creation apparatus 100 according to the fifth embodiment will be described.

The flowchart showing the processing procedure of the fifth embodiment is almost the same as the flowchart (FIG. 14) of the second embodiment. However, as described below, there is a difference in the data acquired and stored in step S100, which is the data acquisition step, and step S104, which is the plan creation step, between the two.

In step S100, which is a data acquisition step, the data acquisition unit 11 acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, fuel price data 115, confidence level In addition to data 116 , futures market data 120 is stored in data store 12 .

In step S104, which is a plan creation step, the plan creation unit 16 stores the spot price scenario data 121, the control power price scenario data 122, the control power activation ratio scenario data 123, the generator data 114, the fuel Based on price data 115, confidence level data 116, and futures market data 120, generator operating plans and power and reserve trading plans are developed.

Specifically, the planning unit 16 solves the optimization problem, and calculates variables u (g, t, s), x (g, t, s), Q _kwh (t, s) when VaR is minimized. ), Q _Δkw (t, s), and B _future (m) are used as start/stop plan data 124, power generation plan data 125, power trading plan data 126, control power trading plan data 127, and futures trading plan data 130, respectively. Stored in the storage unit 12 .

<Summary of Embodiment 5>
As described above, in the power supply and demand plan creation apparatus and the power supply and demand plan creation method according to the fifth embodiment, the profit and loss VaR corresponding to the percentage point of the confidence level α% of the confidence level data 116 is used as the objective function. Then, the optimization problem of minimizing VaR is solved under the constraint that the ratio of profit and loss below VaR in each scenario of power price, control power price, and control power activation rate is equal to or greater than the confidence level α%. As a result, it is possible to create a plan that is optimized to minimize the maximum profit and loss occurring at the confidence level α% while reflecting the degree to which the user cannot tolerate risk.

In addition, based on the futures market data 120, the plan creation unit 16 formulates transactions and creates plans to reduce fluctuations in profit and loss through transactions in the futures market. Can create power trading plans. For this reason, for example, if the uncertainty of spot price forecasts is high, by setting a large confidence interval such as the confidence level, transactions in the futures market are increased and price fluctuations due to transactions in the spot market are reduced. It is possible to create a risk avoidable plan such as offsetting.

<Embodiment 6>
FIG. 23 is a schematic configuration diagram of an electric power supply and demand planning apparatus 100 according to the sixth embodiment. In the following, the description of the points that are the same as in the second embodiment will be omitted, and the description will focus on the points of difference.

The configuration of the power supply and demand plan creation device 100 in FIG. 23 is the same as the configuration of the power supply and demand plan creation device 100 in FIG. In the sixth embodiment, assuming fluctuations in fuel prices due to fluctuations in exchange rates, etc., a generator operation plan and a power and control power trading plan for minimizing the profit and loss VaR of the electric power business are created.

The data acquisition unit 11 acquires spot market transaction performance data 111 , supply and demand adjustment market transaction performance data 112 , control power activation amount performance data 113 , generator data 114 , and reliability level data 116 . Also, the data acquisition unit 11 acquires the actual fuel price data 131 instead of the fuel price data 115 .

FIG. 24 is a schematic configuration diagram showing the data flow of the power supply and demand plan creation device 100 according to the sixth embodiment. The actual fuel price data 131 is used for calculation of the fuel price scenario data 132 in the fuel price prediction section 19 . The fuel price scenario data 132 is used for calculations in the plan creating section 16 .

The actual fuel price data 131 is data representing actual values of fuel prices in chronological order. The actual fuel price data 131 may be set based on an average value of fuel prices over a certain period in the past, or may be set based on a predicted value in consideration of fluctuations in exchange rates. The actual fuel price data 131 may be data for each fuel type (coal, petroleum, LNG, etc.) or data for each power generator.

The fuel price scenario data 132 is data representing a fuel price scenario.

After calculating the model parameters of the time series model by, for example, maximum likelihood estimation, the fuel price prediction unit 19 uses the Monte Carlo method for the model parameters to create fuel price scenario data 132 for the number of trials of the Monte Carlo method. The time series model may use an autoregressive integrated moving average or a vector autoregressive model, or may use other models.

Next, an example of the optimization problem of various plans calculated by the plan creating unit 16 will be described. In the sixth embodiment, the plan creating unit 16 solves the optimization problem by changing the equation (7) of the second embodiment to the equation (17) and the equation (11) to the equation (18).

Here, fuel(s, g, t) is the fuel price of generator number g at time t and scenario s, and is included in fuel price scenario data 132 . A difference from the second embodiment is that the generator cost (E) and fuel price (fuel) are different for each scenario s.

Next, a power supply and demand plan creation method using the power supply and demand plan creation apparatus 100 according to the sixth embodiment will be described.

FIG. 25 is a flow chart showing an example of a processing procedure of the power supply and demand plan creation device 100 according to the sixth embodiment. The flowchart of the sixth embodiment in FIG. 25 differs from the flowchart of the second embodiment in FIG. 14 in steps S100, S107, and S104, as described below.

In step S100, which is a data acquisition step, the data acquisition unit 11 acquires spot market transaction performance data 111, supply and demand adjustment market transaction performance data 112, control power activation amount performance data 113, generator data 114, confidence level data 116, and The actual fuel price data 131 is stored in the data storage unit 12 .

In step S107, which is a fuel price prediction step, the fuel price prediction unit 19 acquires the actual fuel price data 131 from the data storage unit 12, and based on the actual fuel price data 131, generates a fuel price scenario indicating fuel price fluctuations. A plurality of data 132 are created. The fuel price prediction unit 19 stores the created plurality of fuel price scenario data 132 in the data storage unit 12 .

In step S104, which is a plan creation step, the plan creation unit 16 stores spot price scenario data 121, control power price scenario data 122, control power activation ratio scenario data 123, generator data 114, reliability Based on level data 116 and fuel price scenario data 132, a generator operation plan and a power and control power trading plan are created.

<Summary of Embodiment 6>
As described above, in the power supply and demand plan creation apparatus and the power supply and demand plan creation method according to the sixth embodiment, the profit and loss VaR corresponding to the percentage point of the confidence level α% of the confidence level data 116 is used as the objective function. Then, an optimization problem that minimizes VaR under the constraint that the ratio of profit and loss below VaR in each scenario of electricity price, control power price, control power activation ratio, and fuel price is above the confidence level α%. Solve As a result, it is possible to create a plan that is optimized to minimize the maximum profit and loss occurring at the confidence level α% while reflecting the degree to which the user cannot tolerate risk.

In addition, based on the fuel price scenario data 132, the plan creation unit 16 creates a plan, which includes a generator operation plan, an electric power trading plan, and a control power plan that take into account fluctuations in power generation costs due to fluctuations in fuel prices. Create a trading plan. For this reason, for example, when fuel prices fluctuate greatly, by setting a large confidence interval such as the confidence level, electricity trading in the spot market is reduced and trading in the supply and demand adjustment market is increased. You can create a risk-avoidable plan.

<Modification>
In the above explanation, the profit and loss of the electric power business is the value obtained by subtracting the profit of the electric power business from the loss (cost) of the electric power business. It may be the obtained value. In this case, the plan creation unit 16 may create a generator operation plan and a power and control power trading plan by solving an optimization problem that maximizes the profit and loss represented by the objective function.

It should be noted that it is possible to freely combine each embodiment and each modification, and to modify or omit each embodiment and each modification as appropriate.

11 data acquisition unit, 13 spot price prediction unit, 14 control power price prediction unit, 15 control power activation ratio prediction unit, 16 plan creation unit, 19 fuel price prediction unit, 100 power supply and demand plan creation device.

Claims (10)

One or more spot price scenario data representing the future contract price of electricity traded in the spot market in time series, and one or more control powers representing the future contract price of the control power traded in the supply and demand adjustment market in time series Price scenario data, one or more control power activation rate scenario data representing a future activation rate of the control power in a time series, generator data representing specifications of a generator that generates the electric power and the control power, and an acquisition unit that acquires one or more fuel price data representing future fuel prices of generators in chronological order;
Based on the spot price scenario data, the controllability price scenario data, the controllability activation ratio scenario data, the generator data, and the fuel price data acquired by the acquisition unit, By solving the optimization problem of the objective function representing the profit and loss minus the profit of the business and minimizing the profit and loss represented by the objective function, the generator operation plan, the spot market and the supply and demand adjustment and a plan creating unit for creating a market transaction plan. The power supply and demand plan creation device according to claim 1,
The profit and loss of the electricity business is
Power generation cost over the planning period, electricity sales in the spot market over the planning period, and power sales amount in the supply and demand adjustment market over the planning period. A power supply and demand planning device that includes the combined balance of The power supply and demand plan creation device according to claim 1 or claim 2,
The acquisition unit
predicting the one or more spot price scenario data based on the spot market transaction performance data representing the actual value of the contracted price of electricity traded in the spot market in chronological order;
predicting the one or more control power price scenario data based on the supply and demand control market transaction performance data representing the actual value of the contract price of the control power traded on the supply and demand control market in chronological order;
An electric power supply and demand plan that predicts the one or more controllability activation ratio scenario data based on the supply and demand control market transaction performance data and the controllability activation amount performance data representing the actual value of the controllability activation amount in time series. creation device. The power supply and demand plan creation device according to claim 3,
The one or more spot price scenario data are a plurality of spot price scenario data,
The one or more control power price scenario data are a plurality of control power price scenario data,
The one or more control power activation ratio scenario data is a plurality of control power activation ratio scenario data,
The profit and loss represented by the objective function is
A power supply and demand plan creation device, which is VaR (Value at Risk) of the profit and loss based on a level of confidence in the prediction of the acquisition unit. The power supply and demand plan creation device according to any one of claims 1 to 4,
The profit and loss of the electricity business is
Power generation cost over the planning period, electricity trading in the spot market over the planning period, controllability trading in the supply and demand adjustment market over the planning period, and the planning period An electric power supply and demand plan creation device including the balance including the amount of electric power sold by bilateral trading of electric power. The power supply and demand plan creation device according to any one of claims 1 to 4,
The profit and loss of the electricity business is
Power generation cost over the planning period, electricity trading in the spot market over the planning period, controllability trading in the supply and demand adjustment market over the planning period, and the planning period An electric power supply and demand plan creation device including the balance including the amount of electric power sold by the electric power transaction in the forward market. The power supply and demand plan creation device according to any one of claims 1 to 4,
The profit and loss of the electricity business is
Power generation cost over the planning period, electricity sales in the spot market over the planning period, and power sales amount in the supply and demand adjustment market over the planning period. , an electric power supply and demand plan creation apparatus, including the income and expenditure combined with the electric power sales amount and the difference settlement amount due to electric power trading on the futures market during the planning period. The power supply and demand plan creation device according to any one of claims 1 to 4,
The acquisition unit
Predicting a plurality of fuel price scenario data, which are the one or more fuel price data, based on the actual fuel price data representing the actual value of the fuel price of the generator in time series,
The profit and loss represented by the objective function is
A power supply and demand plan creation device, which is VaR (Value at Risk) of the profit and loss based on a level of confidence in the prediction of the acquisition unit. One or more spot price scenario data representing the future contract price of electricity traded in the spot market in time series, and one or more control powers representing the future contract price of the control power traded in the supply and demand adjustment market in time series Price scenario data, one or more control power activation rate scenario data representing a future activation rate of the control power in a time series, generator data representing specifications of a generator that generates the electric power and the control power, and an acquisition unit that acquires one or more fuel price data representing future fuel prices of generators in chronological order;
Based on the spot price scenario data, the controllability price scenario data, the controllability activation ratio scenario data, the generator data, and the fuel price data acquired by the acquisition unit, the electricity By solving the optimization problem of the objective function representing the profit and loss minus the loss of the business and maximizing the profit and loss represented by the objective function, the generator operation plan, the spot market and the supply and demand adjustment and a plan creating unit for creating a market transaction plan. One or more spot price scenario data representing the future contract price of electricity traded in the spot market in time series, and one or more control powers representing the future contract price of the control power traded in the supply and demand adjustment market in time series Price scenario data, one or more control power activation rate scenario data representing a future activation rate of the control power in a time series, generator data representing specifications of a generator that generates the electric power and the control power, and obtaining one or more fuel price data representing a time series of future fuel prices for the generator;
Based on the acquired spot price scenario data, controllability price scenario data, controllability activation ratio scenario data, generator data, and fuel price data, profit of the electric power business is converted from loss of the electric power business. By solving an optimization problem of an objective function representing the subtracted profit and loss and minimizing the profit and loss represented by the objective function, the generator operation plan and trading in the spot market and the supply and demand adjustment market A power supply and demand plan creation method for creating a plan.

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