Power supply planning support device and power supply planning support program

Abstract

To provide information on a change in a profit in the case of evaluating the profitability of each power supply to maintain or abolish each of existing power supplies and newly establish a new power supply.SOLUTION: A power supply plan formulation support device includes means (a power amount profit calculation task 153) for multiplying a generated power amount of a power supply by a power amount price to calculate a power amount profit of the power supply, means (a capacity profit assumption task 154) for multiplying surplus power generation of the power supply by a supply capability price in a marketplace to calculate a capacity profit of the power supply, means (a profit calculation task 157) for subtracting the sum of fuel cost of the power supply and a fixed cost from the sum of the power amount profit and the capacity profit to calculate a profit of the power supply, means (an expected profit calculation task 158) for using the profit and the an initial investment amount of the power supply to calculate a profit rate of the power supply, and means (a determination task 159) for determining the handling of the power supply on the basis of the profit and the profit rate.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a power supply plan development support device and a power supply plan development support program, and more particularly to a technique for supporting the development of plans relating to the continuation or abolition of power sources and the establishment or cancellation of such plans based on profitability. [Background technology]

[0002] A known conventional technology for predicting the contract price of electricity traded in the electricity trading market is a system that stores environmental performance information, including past weather information, fuel price performance, etc., in association with the electricity market price, and predicts the electricity market price based on environmental forecast information, including weather information, and fuel prices, etc. (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-046281 A Summary of the Invention [Problem to be solved by the invention]

[0004] In order to formulate a mid- to long-term power supply plan, it is necessary to evaluate the profitability of each power source and to obtain information on the change in profit when each existing power source is maintained or abolished, or when a new power source is newly established. However, the system described in Patent Document 1 cannot predict the change in profit when each existing power source is maintained or abolished, or when a new power source is newly established.

[0005] Therefore, an object of the present invention is to provide a power supply plan formulation support device and a power supply plan formulation support program that are capable of evaluating the profitability of each power source and providing information regarding changes in profits when each existing power source is maintained or abolished and when a new power source is newly established. [Means for solving the problem]

[0006] In order to solve the above problem, the invention of claim 1 provides a means for calculating the energy revenue of a power source by multiplying the amount of power generated by the power source by the market price of the amount of power generated by the power source, a means for calculating the capacity revenue of the power source by multiplying the power generation surplus of the power source by the market price of the supply capacity, a means for calculating the revenue of the power source by subtracting the sum of the fuel cost and fixed cost of the power source from the sum of the energy revenue and the capacity revenue, and If is equal to or greater than a certain revenue threshold, Continued use of existing power sources If the revenue is less than the predetermined revenue threshold, abolition and and a means for determining whether or not a power supply plan has been established.

[0007] The invention of claim 2 includes means for calculating the energy revenue of the power source by multiplying the amount of power generated by the power source by the market price of the energy, means for calculating the capacity revenue of the power source by multiplying the power generation surplus of the power source by the market price of the supply capacity, means for calculating the profit of the power source by subtracting the sum of the fuel cost and fixed cost of the power source from the sum of the energy revenue and the capacity revenue, means for calculating the rate of profit of a new power source using the profit and the initial investment amount of the power source, and If is equal to or greater than a certain return threshold, Installation of the new power source If the profitability is less than the predetermined profitability threshold, the new power source is cancel and and a means for determining whether or not a power supply plan has been established.

[0008] The invention of claim 3 is the power supply plan formulation support device according to claim 1 or 2, Fluctuating the price of the electricity The present invention is characterized in that it has a means for estimating the electricity price in the market using a prediction model constructed by machine learning with electricity price factors as input and the electricity price as output.

[0009] The invention of claim 4 is the power supply plan formulation support device according to claim 2, The impact on the rate of return is significant The profitability factor is input and the profitability is output, and a prediction model constructed by machine learning is used to New The present invention is characterized by comprising a means for estimating the rate of return of a power source.

[0010] The invention of claim 5 relates to a computer including: means for calculating the energy revenue of a power source by multiplying the amount of power generated by the power source by the market price of the amount of power; means for calculating the capacity revenue of the power source by multiplying the power generation surplus of the power source by the market price of the supply capacity; means for calculating the revenue of the power source by subtracting the sum of the fuel cost and fixed cost of the power source from the sum of the energy revenue and the capacity revenue; and If is equal to or greater than a certain revenue threshold, Continued use of existing power sources If the revenue is less than the predetermined revenue threshold, abolition and The power supply plan formulation support program is characterized in that the program causes the power supply plan formulation support program to function as a means for determining whether or not a power supply is in a standby state.

[0011] The invention of claim 6 provides a computer that includes means for calculating the energy revenue of a power source by multiplying the amount of power generated by the power source by the market price of the energy, means for calculating the capacity revenue of the power source by multiplying the power generation surplus of the power source by the market price of the supply capacity, means for calculating the profit of the power source by subtracting the sum of the fuel cost and fixed cost of the power source from the sum of the energy revenue and the capacity revenue, means for calculating the rate of profit of a new power source by using the profit and the initial investment amount of the power source, and If is equal to or greater than a certain return threshold, Installation of the new power source If the profitability is less than the predetermined profitability threshold, the new power source is cancel and The power supply plan formulation support program is characterized in that the program causes the power supply plan formulation support program to function as a means for determining whether or not a power supply is in a standby state.

[0012] The invention of claim 7 is the power supply plan formulation support program according to claim 5 or 6, comprising: Fluctuating the price of the electricity The present invention is characterized in that it functions as a means for estimating the electricity price in the market by using a prediction model constructed by machine learning with electricity price factors as input and the electricity price as output.

[0013] The invention of claim 8 is the power supply plan formulation support program according to claim 6, further comprising: The impact on the rate of return is significant The profitability factor is input and the profitability is output, and a prediction model constructed by machine learning is used to New The present invention is characterized in that it functions as a means for estimating the rate of return of a power source. Effect of the Invention

[0014] According to the invention of claims 1 and 5, the amount of the electricity generated is calculated by subtracting the total of the fuel cost and the fixed cost of the power source from the total of the energy revenue and the capacity revenue. Continuation or abolishment of the existing power sources Therefore, the profitability of each power source is evaluated using the profit as an evaluation index, and the decision is made as to whether to continue or discontinue each existing power source. Stop This makes it possible to provide information about the change in revenue when

[0015] According to the inventions of claims 2 and 6, the profit rate is calculated using the profit obtained by subtracting the total of the fuel cost and fixed cost of the power source from the total of the power consumption revenue and the capacity revenue, and the initial investment amount of the power source. Installation or cancellation of new power sources Therefore, the profitability of each power source is evaluated using the profitability rate as an evaluation index. New New power supply Setting This makes it possible to provide information about the change in revenue when

[0016] According to the inventions of claims 3 and 7, the market price of electricity is estimated using a prediction model constructed by machine learning with the electricity price factors as input and the electricity price as output. This makes it possible to estimate a more realistic price of electricity that reflects actual information on past electricity prices, and ultimately improves the reliability of the technology as a support for formulating power supply plans.

[0017] According to the inventions of claims 4 and 8, the profitability of a power source is estimated using a prediction model constructed by machine learning with profitability factors as input and the profitability as output. This makes it possible to estimate a more realistic profitability that reflects performance information on past profitability, and ultimately improves the reliability of the technology as a support for formulating power source plans. [Brief description of the drawings]

[0018] [Figure 1]1 is a functional block diagram showing a schematic configuration of a power supply plan development supporting device according to an embodiment of the present invention; [Diagram 2] 2 is a diagram showing an example of a processing result of the power supply plan development supporting device of FIG. 1. [Diagram 3] FIG. 2 is a conceptual diagram showing an example of the configuration of a performance database. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Hereinafter, the present invention will be described based on the illustrated embodiment.

[0020] In this invention, the determination of the continuation or abolishment of existing power sources and the establishment or cancellation of new power sources (i.e., power sources scheduled for establishment or power sources with proposed establishment plans, etc.) in a predetermined cycle is called a "power source plan," and each cycle when formulating a power source plan is called an "evaluation period." One cycle when formulating a power source plan, i.e., the evaluation period, is not limited to a specific length of time, but it can be set to, for example, about one year, six months, or three months.

[0021] A power supply plan is basically a plan for the future that is formulated with reference to past data and performance, and the length of the planning period covered by the power supply plan is not limited to a specific period, but may be set to, for example, 10 years, 20 years, or 30 years. The length of the planning period covered by the power supply plan may also be set taking into consideration the length of the evaluation period when calculating the rate of return of a new power supply by the expected rate of return calculation task 158 described later.

[0022] The power source refers to a power generation facility, and includes, for example, nuclear power plants, gas / coal / oil-fired power plants, hydroelectric power plants, and renewable energy power plants (specifically, for example, solar power plants, wind power plants, biomass power plants, geothermal power plants, pumped storage power plants, etc.).

[0023] Power supply plans are formulated for each designated region, taking into consideration factors such as the extent / integrity of the power transmission and distribution network and the area to which power is expected to be supplied by the power source. Each area covered when formulating a power supply plan is called a "planning area." Note that planning areas are not limited to designated areas within Japan, and may be designated areas in other countries.

[0024] FIG. 1 is a functional block diagram showing a schematic configuration of a power supply plan formulation support device 1 according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of a processing result of the power supply plan formulation support device 1. In the example shown in FIG. 2, for a certain planning area, there are power plants A, B, and C as existing power sources, and power plants α and β as new (newly constructed) power sources. FIG. 2 also shows that the length of each evaluation period (i.e., one cycle) is one year, and the evaluation period at the beginning of the planning period covered by the power supply plan is "20XX" and the evaluation period at the end is "20YY". A power supply plan is formulated for each planning area, and the following describes a case where processing is performed by the power supply plan formulation support device 1 for the certain planning area.

[0025] The power supply plan formulation support device 1 is a mechanism for supporting the formulation of a power supply plan by evaluating the profitability of each power source and creating information regarding changes in profits when each existing power source is maintained or abolished and when a new power source is newly established, and is mainly equipped with an input unit 11, a display unit 12, a storage unit 13, a memory 14, a main task 15, a communication unit 17, and a central processing unit 18 that controls these units.

[0026] The power supply plan development support device 1 is configured, for example, by installing and executing an application (power supply plan development support program 19) for performing various processes related to supporting the development of a power supply plan and a control program for the entire device on various computers, etc.

[0027] The input unit 11 is an interface having a function of receiving commands from a user and inputting them to the power supply plan formulation supporting device 1, and is configured by, for example, a keyboard and a mouse.

[0028] The display unit 12 has a function of displaying the contents input via the input unit 11, and displaying the processing results (see FIG. 2) of the power supply plan formulation supporting device 1, and is configured by, for example, a liquid crystal display.

[0029] The storage unit 13 is a storage area / storage device having a function of storing various information, programs, data, and the like, and is configured by, for example, a hard disk (HDD: Hard Disk Drive).

[0030] The storage unit 13 stores a control program for the entire power supply plan development support device 1 and a power supply plan development support program 19, as well as a power supply database 131 and a performance database 132.

[0031] The power source database 131 is a database in which information about each power source is recorded and accumulated, and is referred to as appropriate to obtain information required about the power source when the power source plan development support device 1 performs various processes related to supporting the development of a power source plan.

[0032] The power source database 131 specifically records and accumulates various information about each power source, such as the planned area to supply power, power generation capacity, fuel type, thermal efficiency, and fixed costs, which are required when the power source plan formulation support device 1 performs various processes related to supporting the formulation of a power source plan. The power source database 131 also records and accumulates information about each new power source in addition to information about each existing power source, and also records and accumulates the initial investment amount for each new power source (note that this is the initial investment amount required when calculating the rate of return of a new power source by the expected rate of return calculation task 158 described later, in other words, the initial business cost).

[0033] The performance database 132 will be described later.

[0034] Either or both of the power supply database 131 and the performance database 132 may be stored in an external storage device such as a server. In this case, the power supply plan formulation support device 1 may access the external storage device via the communication unit 17 to acquire various data and information, or may be provided with a connection interface for transmitting / receiving / inputting / outputting signals such as data and control commands to / from the external storage device via various signal lines, and may access the external storage device via the connection interface to acquire various data and information.

[0035] The memory 14 is a storage area / storage device that has the function of serving as a working area for temporarily storing data and information generated when the central processing unit 18 executes calculations related to supporting the formulation of power supply plans, and is composed of, for example, RAM (short for Random Access Memory).

[0036] The main task 15 is a group of tasks for executing various processes related to supporting the formulation of a power supply plan, which are realized by executing a power supply plan formulation support program 19 stored in the storage unit 13 .

[0037] The communication unit 17 is a communication interface that has the function of transmitting / receiving / inputting / outputting signals and information transmitted via various wireless / wired communication networks, including, for example, LAN (short for Local Area Network) and WAN (short for Wide Area Network).

[0038] The central processing unit 18 has a function of coordinating and controlling each part that constitutes the power supply plan formulation support device 1, and is configured to include, for example, a central processing unit (CPU: abbreviation for Central Processing Unit), and realizes each function in accordance with the control program and power supply plan formulation support program 19 stored in the memory unit 13.

[0039] The power supply plan formulation support device 1 has a means for calculating the power supply's energy revenue by multiplying the power generation amount of the power supply by the market power price (power volume revenue calculation task 153), a means for calculating the power supply's capacity revenue by multiplying the power supply's excess power generation capacity by the market supply capacity price (capacity revenue estimation task 154), a means for calculating the power supply's revenue by subtracting the sum of the power supply's fuel costs and fixed costs from the sum of the power volume revenue and the capacity revenue (revenue calculation task 157), a means for calculating the power supply's rate of profit using the profit and the power supply's initial investment amount (expected rate of profit calculation task 158), and a means for determining the handling of the power supply based on the profit and the rate of profit (determination task 159).

[0040] In order to execute various processes related to the support for the formulation of a power supply plan, a power supply plan formulation support program 19 stored in the storage unit 13 is executed, thereby forming a main task 15. The main task 15 includes an electric energy estimation task 151, a price estimation task 152, an electric energy revenue calculation task 153, a capacity revenue estimation task 154, a fuel cost estimation task 155, a fixed cost estimation task 156, a revenue calculation task 157, an expected rate of return calculation task 158, a judgment task 159, and a learning task 160.

[0041] The power amount estimation task 151 is a task for estimating the amount of power generated (MWh) from each power source based on the power demand (MW) for each evaluation period. Specifically, the power demand is estimated as, for example, hourly power demand.

[0042] Specifically, the power demand estimation task 151 first estimates future power demand (called "base power demand") for each evaluation period based on the future population and industrial location conditions predicted in consideration of future development plans, etc., in accordance with a regression equation that is determined in advance based on the relationship between the power demand and the past and present population and industrial location conditions, for example.

[0043] The power amount estimation task 151 then corrects the base power demand according to factors that cause fluctuations in the base power demand as necessary to estimate the final power demand. That is, if there is a factor that reduces the power demand in each evaluation period, the base power demand is revised downward according to the degree of impact of the factor on the power demand. Also, if there is a factor that increases the power demand in each evaluation period, the base power demand is revised upward according to the degree of impact of the factor on the power demand.

[0044] Factors that may affect electricity demand include, for example, changes in natural conditions such as climate and temperature, changes in industrial structure, and economic trends.

[0045] Note that data on future power demand that has already been estimated may be used, or the current power demand may be used as the future power demand assuming that the current situation will continue. Here, "assuming" in the description of this invention includes setting a value estimated in the power source plan formulation supporting device 1, a value that has already been estimated, and a value that remains the current value.

[0046] The power amount estimation task 151 further calculates the amount of power generated by each power source in the target planning area of ​​the power source plan, which is required according to the power demand for each evaluation period, based on the power demand for each evaluation period estimated above. At this time, the constraints are that the amount of power required to cover the power demand is supplied by the sum of the amounts of power generated by each power source supplying power to the target planning area of ​​the power source plan and does not exceed the power generation capability (power generation capacity) of each power source, and for example, a schedule of long-term maintenance work for the power source may be taken into consideration.

[0047] The power amount estimation task 151 may also add the amount of generated power to be supplied to other areas, taking into consideration the situation of supplying / selling power to other areas other than the planned area targeted by the power source plan (in other words, the planned area where the power source under consideration exists). In this case, the transmission constraints of the interconnection / transmission system between the planned area and other areas (for example, the transmission length, voltage, capacity, etc. between the areas) may be taken into consideration.

[0048] Then, the power amount estimation task 151 outputs the amount of power generated by each power source for each evaluation period.

[0049] The price assumption task 152 is a task for assuming the market price of electricity (specifically, for example, the market price in trading of electricity in the wholesale electricity market) [yen / MWh] for each evaluation period.

[0050] Here, the unit price of the amount of energy common to the planning areas, ignoring the transmission loss and capacity between the planning areas, is called the "system price," while the unit price of the amount of energy that takes into account the transmission loss and capacity between the planning areas is called the "energy price."The energy price usually differs by area due to factors such as uneven distribution of electricity demand and differences in power source composition between the planning areas, as well as constraints on the transmission system between the planning areas (specifically, transmission loss, transmission capacity, etc.).

[0051] Specifically, the price estimation task 152 estimates an electricity price (referred to as a "base electricity price") for each evaluation period according to a regression equation (i.e., a trend regression equation or trend estimation equation in the trend estimation method) that is determined in advance based on the market price trend in electricity transactions in the wholesale electricity market from the past to the present.

[0052] The price estimation task 152 then estimates the final energy price by correcting the base energy price according to factors that cause fluctuations in the base energy price as necessary. That is, if there are factors that push down the energy price in each evaluation period, the base energy price is revised downward according to the degree of impact of the factors on the energy price. Also, if there are factors that push up the energy price in each evaluation period, the base energy price is revised upward according to the degree of impact of the factors on the energy price.

[0053] Factors that may affect electricity prices include, for example, the following: 1) Electricity demand A) If the electricity demand (i.e., the total electricity demand by planning area) is large, the system price will be high.

[0054] 2) Power supply information by area a) If nuclear power generation capacity is large, the system price will be lower. C) If the coal-fired power generation capacity is large, the system price will be high. E) If the renewable energy generating capacity is large, the system price will be lower. E) If the thermal efficiency is high, the amount of electricity generated by the power source will be large.

[0055] 3) Fuel prices by planning area F) If the price of fuel for generating electricity in each planning area increases, the system price will become higher.

[0056] 4) Power grid information (k) If the transmission length is long, the difference between the system price and the electricity price for each planning area will become larger (this is due to the large transmission loss between planning areas). h) When the transmission voltage is high, the difference between the system price and the electricity price for each planning area becomes smaller (this is due to smaller transmission losses between planning areas). i) If the transmission capacity between planning areas is small, the difference between the system price and the electricity price for each planning area will be large (this is because the amount of electricity transmitted will reach the transmission capacity and economical power plants will no longer be able to operate).

[0057] 5) Power demand and power source information j) When there is a large difference in the uneven distribution of electricity demand among planning areas or in the power source information among planning areas (specifically, for example, power generation capacity, fuel type, thermal efficiency, fixed costs, etc.), the difference between the system price and the electricity price for each planning area will become large. (Note: This is due to the fact that an increase in the amount of electricity transmitted between planning areas increases transmission losses, and the amount of electricity transmitted reaches the transmission capacity, making it impossible to operate economical power plants.)

[0058] The price estimation task 152 may estimate the energy price for each evaluation period according to a multiple regression equation with the energy price factors as explanatory variables and the energy price as a response variable. In this case, the energy price is estimated according to a multiple regression equation that is determined in advance using a group of combined data of the energy price factors and the energy price at multiple points in time from the past to the present for the target planning area of ​​the power source plan.

[0059] The power price factors, which are explanatory variables of the multiple regression equation, are not limited to specific items, but are appropriately selected and set, taking into consideration whether or not the impact on the power price is significant. Examples of power price factors, which are explanatory variables of the multiple regression equation, include the fluctuation factors of the power price listed as A to J above, such as power demand, nuclear power generation capacity, coal-fired power generation capacity, renewable energy power generation capacity, thermal efficiency, price of fuel for power generation, transmission length, transmission voltage, transmission capacity between planning areas, power demand by planning area, and power source information by planning area (in other words, the contents of the power source configuration).

[0060] Alternatively, the price estimation task 152 may estimate the energy price for each evaluation period according to a prediction model that uses the energy price factors as input and the energy price as output. In this case, the energy price is estimated according to a prediction model (learning model) that is constructed in advance by machine learning using a group of combined data of the energy price factors and the energy price at multiple points in time from the past to the present for the planning area targeted by the power source plan.

[0061] As a predictive model (learning model), a machine learning technique that is particularly suitable for predicting continuous values ​​may be used. Specifically, for example, an extreme learning machine (ELM: short for Extreme Learning Machine), a deep neural network (DNN: short for Deep Neural Network), or a support vector machine (SVM: short for Support Vector Machine) may be used.

[0062] The power price factors that are inputs to the prediction model constructed by machine learning are not limited to specific items, but are appropriately selected and set, taking into consideration whether the impact on the power price is significant or not. Examples of the power price factors that are inputs to the prediction model include the power demand, nuclear power generation capacity, coal-fired power generation capacity, renewable energy power generation capacity, thermal efficiency, price of fuel for power generation, transmission length, transmission voltage, transmission capacity between planning areas, power demand by planning area, and power source information by planning area (in other words, the contents of the power source configuration), which are the fluctuation factors of the power price listed above as A to J.

[0063] It should be noted that data on future electricity prices that have already been estimated may be used, or the current electricity price may be used as the future electricity price assuming that the current situation will remain unchanged.

[0064] Then, the price assumption task 152 outputs the power price for each evaluation period.

[0065] The power income calculation task 153 is a task for calculating the power income (yen) of each power source for each evaluation period.

[0066] Specifically, the energy revenue calculation task 153 receives input of the amount of energy generated by each power source for each evaluation period output from the energy estimation task 151, and receives input of the energy price for each evaluation period output from the price estimation task 152, and multiplies the amount of energy generated by each of the power sources by the energy price for each evaluation period to calculate the energy revenue of each power source (which is the integral value (in other words, the accumulated value) for the entire evaluation period).

[0067] Then, the power income calculation task 153 outputs the power income (Re in FIG. 2) of each power source for each evaluation period.

[0068] Note that "by evaluation period" means, as in the example shown in Figure 2, that for existing power sources (power plants A, B, and C in the figure), the evaluation period at the start of the planning period covered by the power source plan (20XX in the figure) is included as the subject of processing, and for new power sources (power plants α and β in the figure), each evaluation period between the evaluation period at the start and the evaluation period at the end of the planning period covered by the power source plan (20YY in the figure) is included as the subject of processing.

[0069] In the example shown in FIG. 2, for existing power sources, only the evaluation period at the start of the planning period covered by the power source plan (20XX in the figure) is displayed; however, it is also possible to display evaluation periods between the evaluation period at the start and the evaluation period at the end of the planning period covered by the power source plan (20YY in the figure).

[0070] In addition, in the example shown in FIG. 2, for new (newly installed) power sources, only the evaluation period at the start of the planning period covered by the power source plan (in the figure, 20XX) and the evaluation period at the end of the planning period covered by the power source plan (in the figure, 20YY) are displayed, but it is also possible to display the evaluation periods between the evaluation period at the start and the evaluation period at the end of the planning period covered by the power source plan.

[0071] The capacity revenue estimation task 154 is a task for estimating the capacity revenue (yen) of each power source for each evaluation period.

[0072] Capacity revenue is revenue obtained by trading "power generation capacity" (specifically, supply capacity (MW) equivalent to the amount of electricity (demand) expected to be required in the future). In Japan, "power generation capacity" is traded in the capacity market, where the Organization for Cross-regional Coordination of Transmission Operators (OCCO) serves as the market administrator.

[0073] Specifically, the capacity revenue estimation task 154 receives the amount of power generated by each power source for each evaluation period output from the power amount estimation task 151, and calculates the power generation reserve (MW) of each power source for each evaluation period, with the constraint that the power generation capacity (power generation capacity) of each power source is not exceeded.

[0074] The capacity revenue estimation task 154 also estimates the supply capacity price in the market [yen / MW] (referred to as the "base supply capacity price") for each evaluation period, for example, according to a regression equation (i.e., the trend regression equation or trend estimation equation in the trend estimation method) that is determined in advance based on the trend of market prices in future supply capacity transactions in the capacity market from the past to the present.

[0075] The capacity revenue estimation task 154 then estimates the final supply capacity price by correcting the base supply capacity price according to factors that cause fluctuations in the base supply capacity price as necessary. In other words, if there are factors that push down the supply capacity price in each evaluation period, the base supply capacity price is revised downward according to the degree of impact of the factors on the supply capacity price. Also, if there are factors that push up the supply capacity price in each evaluation period, the base supply capacity price is revised upward according to the degree of impact of the factors on the supply capacity price.

[0076] The capacity revenue estimation task 154 may take into account "Net CONE" (short for Net Cost Of New Entry), which is an index price in the capacity market, when estimating the base supply capacity price or as a variable factor of the supply capacity price. Net CONE is the level at which a new power source can recover fixed costs (for example, fixed costs recorded over multiple years, mainly construction costs), and specifically, it is the amount obtained by subtracting the profits obtained in the wholesale electricity market, etc. (i.e., cash flow after deducting variable costs such as fuel costs from wholesale electricity revenue) from the fixed costs per year of a typical newly constructed power source.

[0077] In addition, data on future supply capacity prices that have already been estimated may be used, or the current supply capacity price may be used as the future supply capacity price assuming that the current situation will remain unchanged.

[0078] Then, the capacity revenue estimation task 154 calculates the capacity revenue of each power source (which is the integral value (in other words, the accumulated value) for each evaluation period) by multiplying the power generation reserve of each power source for each evaluation period calculated above by the supply capacity price assumed above.

[0079] The capacity revenue estimation task 154 may estimate the capacity revenue of each power source for each evaluation period according to a multiple regression equation with the capacity revenue factors as explanatory variables and the capacity revenue as the objective variable. In this case, the capacity revenue is estimated according to a multiple regression equation that is determined in advance using a group of combined data of the capacity revenue factors and the actual capacity revenue at multiple points in time from the past to the present for the planning area targeted by the power source plan.

[0080] The capacity revenue factors, which are explanatory variables in the multiple regression equation, are not limited to specific items, but are appropriately selected and set, taking into consideration whether or not the impact on capacity revenue is significant. Examples of capacity revenue factors, which are explanatory variables in the multiple regression equation, include the power generation capacity (power generation capacity), generated energy amount, and power generation surplus of each power source, as well as the market price in the trading of supply capacity in the capacity market, and "Net CONE", which is the index price in the capacity market.

[0081] Alternatively, the capacity revenue estimation task 154 may estimate the capacity revenue of each power source for each evaluation period according to a prediction model that uses capacity revenue factors as inputs and capacity revenue as output. In this case, the capacity revenue is estimated according to a prediction model (learning model) that is constructed in advance by machine learning using a group of combined data of capacity revenue factors and actual capacity revenues at multiple points in time from the past to the present for the planning area targeted by the power source plan.

[0082] As a predictive model (learning model), it is possible to use machine learning techniques that are particularly suitable for predicting continuous values. Specifically, for example, extreme learning machines (ELM), deep neural networks (DNN), support vector machines (SVM), etc. may be used.

[0083] The capacity revenue factors, which are inputs to the forecasting model built by machine learning, are not limited to specific items, but are appropriately selected and set, taking into consideration whether the impact on capacity revenue is significant or not. Examples of capacity revenue factors, which are inputs to the forecasting model, include the power generation capacity (power generation capacity), generated energy amount, and power generation surplus of each power source, as well as the market price in the trading of supply capacity in the capacity market, and "Net CONE", which is the index price in the capacity market.

[0084] Then, the capacity revenue estimation task 154 outputs the capacity revenue (Rc in FIG. 2) of each power source for each evaluation period.

[0085] The fuel cost estimation task 155 is a task for estimating the fuel cost (yen) of each power source in accordance with the type, specifications, characteristics, etc. of each power source for each evaluation period.

[0086] Specifically, the fuel cost estimation task 155 estimates the price (unit price; called the "base fuel price") of fuel for power generation for each evaluation period, for example, for each fuel of each power source, in accordance with a regression equation (i.e., a trend regression equation or trend estimation equation in the trend estimation method) that is determined in advance based on the trend in the price (unit price) of fuel for power generation from the past to the present.

[0087] The fuel cost estimation task 155 then estimates the final power generation fuel price by correcting the base fuel price according to factors that cause the base fuel price to fluctuate as necessary. In other words, if there is a factor that pushes down the power generation fuel price in each evaluation period, the base fuel price is revised downward according to the degree of impact of that factor on the power generation fuel price. Also, if there is a factor that pushes up the power generation fuel price in each evaluation period, the base fuel price is revised upward according to the degree of impact of that factor on the power generation fuel price.

[0088] Factors that may affect the price of fuel for power generation include, for example, changes in the prices of fossil fuels such as gas, coal, and oil, and changes in the prices of biofuels such as woody biomass.

[0089] It is also possible to use data on the future price of fuel for power generation that has already been estimated, or to use the current price of fuel for power generation as the future price assuming that the current price will remain unchanged.

[0090] Then, the fuel cost estimation task 155 calculates the fuel cost of each power source (which is an integrated value (in other words, an accumulated value) for each evaluation period) for each evaluation period based on the price (unit price) of the power generation fuel for each evaluation period estimated above and the amount of power generated by each power source for each evaluation period output from the power amount estimation task 151.

[0091] Furthermore, the fuel cost estimation task 155 may estimate the fuel cost of each power source for each evaluation period according to a multiple regression equation with the fuel cost factors as explanatory variables and the fuel cost as the objective variable. In this case, the fuel cost is estimated according to a multiple regression equation that is determined in advance using a group of combined data of the fuel cost factors and the actual fuel cost at multiple points in time from the past to the present for the target planning area of ​​the power source plan.

[0092] The fuel cost factors, which are explanatory variables in the multiple regression equation, are not limited to specific items, but are appropriately selected and set, taking into consideration whether or not the impact on fuel costs is significant. Examples of fuel cost factors, which are explanatory variables in the multiple regression equation, include the amount of power generated by each power source, as well as the prices of fossil fuels such as gas, coal, and oil, and the prices of biofuels such as woody biomass.

[0093] Alternatively, the fuel cost estimation task 155 may estimate the fuel cost of each power source for each evaluation period according to a prediction model that uses fuel cost factors as input and fuel costs as output. In this case, the fuel cost is estimated according to a prediction model (learning model) that is constructed in advance by machine learning using a group of combined data of fuel cost factors and actual fuel costs at multiple points in time from the past to the present for the planning area targeted by the power source plan.

[0094] As a predictive model (learning model), it is possible to use machine learning techniques that are particularly suitable for predicting continuous values. Specifically, for example, extreme learning machines (ELM), deep neural networks (DNN), support vector machines (SVM), etc. may be used.

[0095] The fuel cost factors that are inputs to the prediction model constructed by machine learning are not limited to specific items, but are appropriately selected and set, taking into consideration whether or not the impact on fuel costs is significant. Examples of fuel cost factors that are inputs to the prediction model include the amount of power generated by each power source, as well as the prices of fossil fuels such as gas, coal, and oil, and the prices of biofuels such as woody biomass.

[0096] Then, the fuel cost estimation task 155 outputs the fuel cost (Cf in FIG. 2) of each power source for each evaluation period.

[0097] The fixed cost estimation task 156 is a task for estimating the fixed cost (yen) of each power source for each evaluation period.

[0098] Specifically, the fixed cost estimation task 156 estimates fixed costs to be recorded for each evaluation period, taking into account the type, specifications, and characteristics of each power source. For fixed costs such as construction costs, the total amount is discounted to the amount per evaluation period. In addition, costs required for operating and maintaining the power source may be estimated as fixed costs as appropriate.

[0099] Then, the fixed cost estimation task 156 outputs the fixed cost (Cc in FIG. 2) of each power source for each evaluation period.

[0100] The revenue calculation task 157 is a task for calculating the revenue (yen) of each power source for each evaluation period.

[0101] Specifically, the revenue calculation task 157 receives as input the energy revenue of each power source for each evaluation period output from the energy revenue calculation task 153, the capacity revenue of each power source for each evaluation period output from the capacity revenue estimation task 154, the fuel cost of each power source for each evaluation period output from the fuel cost estimation task 155, and the fixed cost of each power source for each evaluation period output from the fixed cost estimation task 156, and calculates the revenue of each power source (which is the integral value (in other words, the accumulated value) for the entire evaluation period) by subtracting the sum of the fuel cost and the fixed cost from the sum of the energy revenue and the capacity revenue of each power source for each evaluation period.

[0102] Then, the revenue calculation task 157 outputs the revenue (R in FIG. 2) of each power source for each evaluation period.

[0103] The expected return rate calculation task 158 is a task for calculating the return rate of each new power source.

[0104] Specifically, the expected return calculation task 158 receives the return of each new power source for each evaluation period output from the return calculation task 157, and calculates the return (also called the "expected return") for each new power source using the return for each evaluation period of the planning period covered by the power source plan (in the example shown in FIG. 2, the return for each year between 20XX and 20YY) and the initial investment amount of the power source.

[0105] The rate of return is calculated by setting the length of the evaluation period while taking into consideration the life cycle of the power source, etc. The length of the planning period covered by the power source plan may be set by taking into consideration the length of the evaluation period when calculating the rate of return.

[0106] Specifically, the expected rate of return calculation task 158 may calculate an index (unit: %) also known as the internal rate of return (IRR), the return on investment, or the project IRR (PIRR).

[0107] Then, the expected return calculation task 158 outputs the return (ER in FIG. 2) of each new power source.

[0108] The judgment task 159 is a task for judging whether each of the existing power sources should be continued or abolished, and for judging whether each of the new power sources should be newly installed or discontinued. Here, the continued or discontinued status of a power source and the newly installed or discontinued status are also referred to as "power source handling."

[0109] Specifically, the judgment task 159 receives the revenue for each evaluation period of each existing power source output from the revenue calculation task 157 for each existing power source, and judges the power source to continue if the revenue is equal to or greater than a predetermined revenue threshold, and judges the power source to be discontinued if the revenue is less than the predetermined revenue threshold.

[0110] The above-mentioned predetermined revenue threshold is not limited to a specific value, but may be set to, for example, 0. In this case, if the revenue is not in the red, the power source is determined to continue operating, and if the revenue is in the red, the power source is determined to be discontinued.

[0111] The judgment task 159 also receives the profit rate of each new power source output from the expected profit rate calculation task 158 for each new power source, and judges it to be new installation if the profit rate is equal to or greater than a predetermined profit rate threshold, and judges it to be canceled if the profit rate is less than the predetermined profit rate threshold.

[0112] The above-mentioned specified return threshold is not limited to a specific value and may be set to an appropriate value taking into consideration the type of return indicator. For example, if the internal rate of return (IRR) is used, it may be set to around 8%.

[0113] Then, the judgment task 159 outputs the judgment result of whether each of the existing power sources should be continued or abolished (J in FIG. 2), and also outputs the judgment result of whether each of the new power sources should be newly installed or canceled (J in FIG. 2).

[0114] The result of the judgment on whether to build or cancel each new power source output from the judgment task 159 can also be used as an indicator for deciding whether to invest in a power source developed by an independent power producer (IPP).

[0115] Learning task 160 is a task that predicts the profitability of each new power source according to a prediction model that takes profitability factors as input and profitability as output, and determines whether to handle the power source as a new installation or cancellation based on the profitability.

[0116] Specifically, the learning task 160 predicts the rate of return according to a prediction model (learning model) constructed by machine learning using a group of combined data of the rate of return factors and the rate of return at multiple points in time from the past to the present for the planning area targeted by the power source plan. The rate of return predicted by the learning task 160 is set to the same type of rate of return as the rate of return calculated in the rate of return calculation process by the expected rate of return calculation task 158.

[0117] As a predictive model (learning model), it is possible to use machine learning techniques that are particularly suitable for predicting continuous values. Specifically, for example, extreme learning machines (ELM), deep neural networks (DNN), support vector machines (SVM), etc. may be used.

[0118] The profitability factors that are inputs to the prediction model constructed by machine learning are not limited to specific items, but are appropriately selected and set, taking into consideration whether the impact on the profitability is significant or not. As the profitability factors that are inputs to the prediction model, for example, the planning area and type of power generation of each power source, the generated power amount, power revenue, capacity revenue, fuel cost, fixed cost, and revenue of each power source output from the above tasks 151 to 157, and some or all of the power price in the planning area in which the power source exists are appropriately selected and set, taking into consideration whether the impact on the profitability is significant or not. In addition, items other than the items output from the above tasks 151 to 157 may be set as the profitability factors.

[0119] The learning task 160 is configured to execute a learning process for predicting a rate of return on actual data of the rate of return using the actual data of the rate of return recorded and accumulated in the actual data database 132 stored in the memory unit 13, to construct a prediction model (learning model) based on the actual data of the rate of return, and to obtain an accurate prediction result of the rate of return when predetermined information and conditions are input.

[0120] The performance database 132 is a database in which performance information on the profitability of past power source development is recorded and accumulated, and data on combination data of profitability factors for each power plant for power source development carried out in the past, the profitability calculated based on the combination data, and the actual profitability are associated with each other, are recorded and accumulated as past performance information (see FIG. 3).

[0121] Regarding profitability factors, for items whose values ​​may vary over time (in other words, items whose values ​​may differ depending on the evaluation period; specifically, for example, the amount of generated energy, energy revenue, capacity revenue, fuel costs, fixed costs, and revenue of each power source, as well as the energy price in the planning area in which the power source in question is located), data for each evaluation period from the start of the planning period covered by the power source plan to the end of the planning period is recorded and accumulated as actual information.

[0122] The learning task 160 uses, for example, machine learning using a neural network to create a neural network including an intermediate layer, in which the profit rate factors are used as an input layer and the profit rate is used as an output layer, based on data obtained from performance information recorded and accumulated in the performance database 132.

[0123] The learning task 160 creates an intermediate layer using actual profit rates as track records of power source developments implemented in the past as learning data (teaching data), and learns various parameters in the intermediate layer. The learning task 160 learns various parameters in the intermediate layer so as to minimize the error between the predicted profit rate and the actual profit rate included in the learning data (teaching data).

[0124] The learning task 160 predicts the profitability of each new power source based on some or all of the items output from the above tasks 151 to 157 or profitability factors for which other items are set, in accordance with the prediction model constructed as described above.

[0125] The learning task 160 further determines whether to build or discontinue each new generation based on the rate of return predicted as described above.

[0126] Specifically, for each new power source, learning task 160 determines that the project should be newly installed if the profitability predicted as described above is equal to or greater than a predetermined profitability threshold, and determines that the project should be canceled if the profitability is less than the predetermined profitability threshold.

[0127] The above-mentioned predetermined profit rate threshold is set to the same value as the profit rate threshold used in the determination process by the determination task 159 .

[0128] Then, the learning task 160 outputs the rate of return (ERm in FIG. 2) for each new power source, and also outputs the decision result of whether to install or cancel (Jm in FIG. 2).

[0129] 2, the value of the rate of return calculated by the expected rate of return calculation task 158 (ER in the figure) may differ from the value of the rate of return predicted by the learning task 160 (ERm in the figure). In addition, the judgment results (J and Jm in the figure) may differ due to the difference in the value of the rate of return.

[0130] For power plant α in the example shown in Figure 2, the rate of return (ER) calculated by the expected rate of return calculation task 158 is 9.2, and the rate of return (ERm) predicted by the learning task 160 is 8.9. If the rate of return threshold is set to 8, both the judgment result (J) by the judgment task 159 based on the calculation result by the expected rate of return calculation task 158 and the judgment result (Jm) based on the prediction result by the learning task 160 will be “new construction.”

[0131] For power plant β in the example shown in Figure 2, the rate of return (ER) calculated by the expected rate of return calculation task 158 is 8.4, and the rate of return (ERm) predicted by the learning task 160 is 7.8. If the rate of return threshold is set to 8, the judgment result (J) by the judgment task 159 based on the calculation result by the expected rate of return calculation task 158 will be “new construction,” while the judgment result (Jm) based on the prediction result by the learning task 160 will be “cancelled.”

[0132] In this way, by providing the judgment result (J) by the judgment task 159 based on the calculation result by the expected return calculation task 158 and the judgment result (Jm) based on the prediction result by the learning task 160, the two results can be interpreted as complementary and used as useful information in formulating power supply plans.

[0133] As described above, according to the power supply plan formulation support device 1 and the power supply plan formulation support program 19, the handling of a power supply is determined based on the profit calculated by subtracting the sum of the fuel cost and fixed cost of the power supply from the sum of the electricity volume revenue and the capacity revenue. Therefore, it is possible to evaluate the profitability of each power supply using the profit as an evaluation index and provide information on the change in profit when each existing power supply is maintained or abolished and when a new power supply is newly established.

[0134] According to this power supply plan formulation support device 1 and power supply plan formulation support program 19, the handling of a power supply is determined based on the rate of return calculated using the revenue obtained by subtracting the total of the fuel cost and fixed cost of the power supply from the total of the electricity volume revenue and the capacity revenue and the initial investment amount of the power supply. Therefore, it is possible to evaluate the profitability of each power supply using the rate of return as an evaluation index and provide information on the change in profit when each existing power supply is maintained or abolished or when a new power supply is newly established.

[0135] Furthermore, when the power supply plan formulation support device 1 and the power supply plan formulation support program 19 are configured to estimate the market electricity price using a prediction model constructed by machine learning with electricity price factors as input and electricity prices as output, it becomes possible to estimate a more realistic electricity price that reflects actual information on past electricity prices, which in turn makes it possible to improve the reliability of the technology for supporting the formulation of power supply plans.

[0136] Furthermore, when the power supply plan formulation support device 1 and the power supply plan formulation support program 19 are configured to estimate the profitability of a power source using a prediction model constructed by machine learning with profitability factors as input and the profitability as output, it becomes possible to estimate a more realistic profitability that reflects actual information on past profitability, which in turn makes it possible to improve the reliability of the technology for supporting the formulation of power supply plans.

[0137] Although the embodiment of the present invention has been described above, the specific configuration is not limited to the above embodiment, and even if there are design changes and the like within the scope of the gist of the present invention, they are included in the present invention.

[0138] For example, in the above embodiment, both the judgment for the existing power source and the judgment for the new power source are performed, but only one of the judgment for the existing power source and the judgment for the new power source may be performed. Note that, when only the judgment for the existing power source is performed, the configuration does not include the expected return rate calculation task 158.

[0139] Furthermore, in the above embodiment, in addition to the judgment task 159, the learning task 160 is provided, but it is also possible to provide only one of the judgment task 159 and the learning task 160. By providing the judgment result for a new power source by the judgment task 159 and the judgment result for a new power source by the learning task 160 in a mutually complementary manner, the accuracy of assumptions and estimates related to the formulation of a power source plan is improved, and the profitability of the power source can be evaluated with a higher accuracy, thereby further improving the reliability of the technology for supporting the formulation of a power source plan. However, it is also possible to support the formulation of a power source plan by providing only the judgment result of either the judgment task 159 or the learning task 160. [Explanation of symbols]

[0140] 1. Power supply planning support device 11 Input section 12 Display section 13 Storage section 131 Power Database 132 Performance Database 14. Memory 15 Main Task 151 Power Estimation Task 152 Price Assumption Task 153 Electricity Revenue Calculation Task 154 Capacity Revenue Estimation Task 155 Fuel Cost Estimation Task 156 Fixed Cost Assumption Task 157 Revenue Calculation Task 158 Expected Return Calculation Task 159 Judgment Task 160 Learning Tasks 17 Communications Department 18 Central Processing Unit 19. Power supply planning support program

Claims

1. A means for calculating the power revenue of a power source by multiplying the power generation amount of the power source by the power price in the market; A means for calculating a capacity revenue of the power source by multiplying the power generation surplus of the power source by a supply capacity price in the market; means for calculating revenue of the power source by subtracting a sum of fuel costs and fixed costs of the power source from a sum of the energy revenue and the capacity revenue; A means for determining that the existing power source should be continued if the revenue is equal to or greater than a predetermined revenue threshold, and for determining that the existing power source should be discontinued if the revenue is less than the predetermined revenue threshold. A power supply plan formulation support device characterized by:

2. A means for calculating the power revenue of a power source by multiplying the power generation amount of the power source by the power price in the market; A means for calculating a capacity revenue of the power source by multiplying the power generation surplus of the power source by a supply capacity price in the market; means for calculating revenue of the power source by subtracting a sum of fuel costs and fixed costs of the power source from a sum of the energy revenue and the capacity revenue; means for calculating a rate of return for a new power source using the return and an initial investment amount of the power source; means for determining that the new power source should be installed when the rate of return is equal to or greater than a predetermined rate of return threshold, and for determining that the new power source should be discontinued when the rate of return is less than the predetermined rate of return threshold; A power supply plan formulation support device characterized by:

3. a means for estimating the electric energy price in the market using a prediction model constructed by machine learning with an electric energy price factor that fluctuates the electric energy price as an input and an electric energy price as an output; 3. The power supply plan formulation assistance device according to claim 1 or 2.

4. a means for estimating the rate of return of the new power source using a prediction model constructed by machine learning with a rate of return factor having a significant effect on the rate of return as an input and a rate of return as an output; 3. The power supply plan formulation assistance device according to claim 2.

5. Computer, A means for calculating the power revenue of a power source by multiplying the amount of power generated by the power source by the power price in the market; A means for calculating a capacity revenue of the power source by multiplying the power generation surplus of the power source by the supply capacity price in the market; means for calculating revenue of the power source by subtracting a sum of fuel costs and fixed costs of the power source from a sum of the energy revenue and the capacity revenue; and When the revenue is equal to or greater than a predetermined revenue threshold, the method determines that the existing power source should be continued, and when the revenue is less than the predetermined revenue threshold, the method determines that the existing power source should be discontinued. A power supply planning support program characterized by:

6. Computer, A means for calculating the power revenue of a power source by multiplying the amount of power generated by the power source by the power price in the market; A means for calculating a capacity revenue of the power source by multiplying the power generation surplus of the power source by the supply capacity price in the market; means for calculating revenue of the power source by subtracting a sum of fuel costs and fixed costs of the power source from a sum of the energy revenue and the capacity revenue; means for calculating a rate of return for a new power source using the revenue and an initial investment amount of the power source; and when the rate of return is equal to or greater than a predetermined rate of return threshold, it is determined that the new power source should be newly installed, and when the rate of return is less than the predetermined rate of return threshold, it is determined that the new power source should be discontinued. A power supply planning support program characterized by:

7. Computer, a prediction model constructed by machine learning using an input of an electric power price factor that fluctuates the electric power price and an output of the electric power price as a means for estimating the electric power price in the market; 7. The power supply plan formulation support program according to claim 5 or 6.

8. Computer, a prediction model constructed by machine learning using a profitability factor having a significant effect on the profitability as an input and a profitability as an output, the prediction model functioning as a means for estimating the profitability of the new power source; 7. The power supply plan formulation support program according to claim 6.

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