Generator operation plan creation device and generator operation plan creation method

Abstract

To create a power generation plan for maximizing profit.SOLUTION: The generator operation plan creation device 100 includes an input unit 5, an in-operation output estimation unit 10, an operation state determination unit 12, and an output determination unit 14. An input unit 5 acquires power generator information data 4 having specification data of a power generator and plan input data 8 including scenario data 7 having time-series data of a predicted value of a power price. An operating output approximate calculation part 10 generates operating output approximate calculation data 11 which are approximate calculation data of the operating output of the generator based on the planning input data 8. An operation state determination unit 12 generates operation state data 13 indicating a start / stop state of the generator on the basis of the planning input data and the operation-time output approximate data 11. The output determination unit 14 generates the generator operation plan data 15 indicating the start / stop state and the output of the generator based on the plan input data 8 and the operation state data 13. The control display unit 16 displays the generator operation plan data 15.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a generator operation plan creation device and a generator operation plan creation method. [Background technology]

[0002] Power generation companies plan their power generation to maximize expected profits in response to fluctuations in power generation volume and market prices, and sell the generated electricity. Creating a power generation plan that maximizes expected profits requires solving a large-scale problem that takes into account market prices and power generator constraints, but solving the problem is difficult. Patent Document 1 proposes a method for formulating a power generation plan that maximizes expected profits by using machine learning. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-100472 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 exemplifies machine learning as a means for determining a power generation plan. However, machine learning has a problem in that it may not be possible to obtain a solution that satisfies constraints. Furthermore, no specific methods other than machine learning are described. Therefore, it is difficult to create a power generation plan that maximizes profits while satisfying constraint conditions.

[0005] The present disclosure has been made in view of the above, and aims to create a power generation plan that maximizes profits. [Means for solving the problem]

[0006] The generator operation plan creation device includes an input unit that acquires plan input data including generator information data having generator specification data and scenario data having time series data of predicted values ​​of electricity prices; an operating time output approximation unit that generates operating time output estimate data that is approximate data of the operating time output of the generator based on the plan input data; an operating state determination unit that generates operating state data that indicates a start-stop state of the generator based on the plan input data and the operating time output estimate data; an output determination unit that generates generator operation plan data that indicates the start-stop state and output of the generator based on the plan input data and the operating state data; and a control display unit that displays the generator operation plan data.

[0007] The generator operation plan creation method acquires plan input data including generator information data having generator specification data and scenario data having time series data of predicted values ​​of electricity prices, generates operating output estimate data that is approximate data of the generator's operating output based on the plan input data, generates operating status data that indicates the startup / shutdown status of the generator based on the plan input data and the operating status data, generates generator operation plan data that indicates the startup / shutdown status and output of the generator based on the plan input data and the operating status data, and displays the generator operation plan data. [Effects of the Invention]

[0008] According to the present disclosure, estimated operating output data, which is approximate data for the operating output of the generator, is generated based on generator information data having generator specification data and plan input data including scenario data having time series data for predicted values ​​of electricity prices, operating state data indicating the start / stop state of the generator is generated based on the plan input data and the estimated operating output data, and generator operation plan data indicating the start / stop state and output of the generator is generated based on the plan input data and the operating state data, thereby making it possible to create a power generation plan that maximizes profits. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an example of a hardware configuration of a generator operation plan creation device according to a first embodiment. [Figure 2] 1 is a functional block diagram showing an example of the configuration of a generator operation plan creation device according to a first embodiment. [Figure 3] 4 is a flowchart showing an example of a generator operation plan creation method executed by the generator operation plan creation device according to the first embodiment. [Figure 4] FIG. 10 is a functional block diagram showing an example of the configuration of a generator operation plan creation device according to a second embodiment. [Figure 5] 10 is a flowchart showing an example of a generator operation plan creation method executed by the generator operation plan creation device according to the second embodiment. [Figure 6] FIG. 10 is a block diagram showing a hardware configuration of a generator operation plan creation device according to another modified example. [Figure 7] FIG. 10 is a block diagram showing a hardware configuration of a generator operation plan creation device according to another modified example. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the description of each embodiment, the same or similar reference numerals will be used to designate matters described in the previously described embodiments of the present disclosure, and duplicate descriptions may be omitted.

[0011] In each embodiment, when only a part of the configuration is described, other parts of the configuration that are not described can be applied to other embodiments previously described in this disclosure. In addition, in each embodiment and modified example, in addition to the combinations between parts that are specifically indicated as being possible to combine, it is also possible to partially combine embodiments without specifying it, as long as there are no particular problems with the combination.

[0012] <First Embodiment> FIG. 1 is a block diagram showing an example of a hardware configuration of a generator operation plan creation device 100 according to the first embodiment. The generator operation plan creation device 100 creates a generator operation plan for a power generation system. The power generation system includes one or more (e.g., multiple) generators. The generators generate power using various methods such as thermal power and nuclear power. The power generation system may also include one or more (e.g., multiple) renewable energy power sources. The renewable energy power sources generate renewable energy using various methods such as solar power and wind power.

[0013] As shown in FIG. 1 , the generator operation plan creation device 100 may be configured by a computing device (e.g., a personal computer) 40. The computing device 40 includes, for example, a central processing unit (CPU) 41, a main memory device 42, an auxiliary memory device 43, an external memory device 44, an input device 45, and an output device 46. The external memory device 44 may be connected to the CPU 41 via a network 47. The computing device 40 may be connected to an external computer 48 via the network 47. The main memory device 42 is, for example, a memory device such as a dynamic random access memory (DRAM). The auxiliary memory device 43 is, for example, a magnetic disk. The external memory device 44 is, for example, an optical disk such as a compact disc recordable (CD-R) or a digital versatile disc recordable (DVD-R), or a flash memory storage device such as a universal serial bus (USB) memory or a secure digital (SD) card. The input device 45 includes, for example, at least one of a mouse and a keyboard. The output device 46 includes, for example, a display or a printer. The network 47 includes, for example, optical communication equipment.

[0014] Fig. 2 is a functional block diagram showing an example of the configuration of the generator operation plan creation device 100 according to the first embodiment. As shown in Fig. 2, the generator operation plan creation device 100 includes an input unit 5, an operating output approximation unit 10, an operating state determination unit 12, an output determination unit 14, and a control display unit 16. These functional units are realized, for example, by the CPU 41 executing a program stored in the main storage device 42.

[0015] The input unit 5 acquires scenario data 7 and generator information data 4, which will be described later. The operating output approximation unit 10 generates operating output approximate data 11, which will be described later, based on plan input data 8 including the generator information data 4 and the scenario data 7. The output determination unit 14 generates generator operation plan data 15, which will be described later, based on the plan input data 8 and operating state data 13. The control display unit 16 displays the generator operation plan data 15.

[0016] The input unit 5 acquires scenario data 7 and generator information data 4. The generator information data 4 is data that represents the specifications of the generator, and includes, for example, at least one of the generator's upper output limit, lower output limit, fuel cost characteristic function, minimum operating time, minimum stop time, startup cost, and output change rate threshold (upper limit). The generator information data 4 is input to the input unit 5, for example, by an operator.

[0017] The scenario data 7 is data that represents the predicted power price value and the amount of power generation (predicted value) from renewable energy at a fixed time interval for the period to be planned. The fixed time interval is, for example, 30 minutes. The scenario data 7 can also be said to include time series data of the predicted power price value and time series data of the predicted amount of power generation from renewable energy. The scenario data 7 may include multiple scenarios for the predicted power price value and the predicted amount of power generation. Although the planning period for each scenario is the same, the predicted power price value and the amount of power generation from renewable energy may differ between the multiple scenarios.

[0018] As shown in FIG. 2, the power generator operation plan creation device 100 may include a scenario generation unit 6. The scenario generation unit 6 generates scenario data 7 based on, for example, weather information data 1, price information data 2, and renewable energy data 3. The weather information data 1 is data representing weather information such as temperature and weather, the price information data 2 is data representing electricity prices in the electricity market, and the renewable energy data 3 is data representing past amounts of power generation from renewable energy sources. The scenario generation unit 6 generates the scenario data 7 using a prediction method such as machine learning. Alternatively, data determined by other methods may be input by an operator, for example, using an input device 45.

[0019] The operation time output approximation unit 10 generates operation time output approximate data 11 based on the generator information data 4 and the planning input data 8 including the scenario data 7. The operation time output approximate data 11 is the value of the generator output that can obtain the most profit if the generator is put into operation. Specifically, the operation time output approximation unit 10 solves the optimization problem shown in equations (1) and (2) for each generator g (belonging to G), time t (belonging to T: T={1, 2, , |T|}) within the planning period, and scenario s (belonging to S), to generate operation time output approximate data 11. In other words, the operation time output approximate data 11 is the output x of each generator g for each time t and scenario s g,t,s Solution of the optimization problem for x ~ g,t,s The driving output approximation unit 10 solves the optimization problem using a solution method such as the interior point method. The driving output approximation unit 10 stores driving output approximate data 11. As a specific example, the driving output approximation unit 10 stores the driving output approximate data 11 in the main memory device 42, the auxiliary memory device 43, or the external memory device 44.

[0020]

number

[0021] Here, equation (1) represents the profit generated by each generator g in scenario s when the generator g is operating at time t. P t,s is the electricity price (i.e., the predicted electricity price) at time t and scenario s within the planning period. F g Q and F g L are the quadratic and linear coefficients of the fuel cost characteristic function, respectively. Equation (2) is the lower and upper output constraints of generator g, and X g MIN represents the lower output limit, and X g MAX represents the upper limit of output.

[0022] The operating state determination unit 12 generates operating state data 13 based on the planning input data 8 and the operating output rough estimate data 11. The operating state data 13 is data that represents the start / stop state of the generator g at each time t during the planning period. Specifically, the operating state determination unit 12 generates the operating state data 13 by solving the optimization problems shown in equations (3) to (7). In other words, the operating state data 13 represents the start / stop state u of the generator g at time t. g,t (u g,t is 1 to indicate start, 0 to indicate stop) ~ g,t The operating state determination unit 12 solves the optimization problem using a solution method such as a branch and bound method and a branch and cut method. The operating state determination unit 12 stores the operating state data 13. As a specific example, the operating state determination unit 12 stores the operating state data 13 in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0023]

number

[0024] where π s is the occurrence probability of scenario s, and Y r,t,s is the output of renewable energy source r (belonging to R) at time t and scenario s, and F g cis the constant term of the fuel cost characteristic function of generator g, and F g ON is the start-up cost, and T g MUT is the minimum driving time, and T g MDT is the minimum stopping time, and E g represents the start / stop status at the initial time of the plan (t=1). t1,t2 represents a symbol that is 1 when t1 = t2 holds and is 0 when t1 = t2 does not hold. g,t,s1,s2 represents a variable that is 1 if the activation / shutdown state transitions from s1 to s2, and is 0 otherwise. Equation (3) represents the expected value of the revenue of the power generation system. Equation (4) represents the variable e g,t,s1,s2 From the start / stop state g,t is a formula for calculating the equation. Equation (4) is substituted into equation (3) when solving the optimization problem. Equations (5) and (6) represent the conservation of flow rate in the network flow, and minimum operating time constraints and minimum downtime constraints are imposed. Equation (7) represents the domain of definition for each decision variable. Note that while an example of a formulation for maximizing the expected value of revenue is shown here, the minimum value of revenue may also be used as the objective function, or risk indicators such as VaR (Value at Risk) and CVaR (Conditional Value at Risk) may also be used as the objective function. Here, the revenue and risk indicators used in the objective function are collectively referred to as the objective quantity. Equation (3) can also be said to be the sum of the values ​​obtained by multiplying the objective quantity (e.g., revenue) for each scenario s by the probability of occurrence of that scenario s.

[0025] The output determination unit 14 generates generator operation plan data 15 based on the plan input data 8 and the operation state data 13. The generator operation plan data 15 is data that indicates the start / stop state and output of the generator at each time during the planning period, and the amount of electricity sold. Specifically, the output determination unit 14 solves the optimization problems shown in equations (8) to (12) to generate the generator operation plan data 15. In other words, the generator operation plan data 15 is generated by solving the optimization problems shown in equations (8) to (12) for the output x g,t,s and the amount of electricity sold q g,t and the acquired start / stop status u ~ g,tThe generator operation plan data 15 is data including the above. The generator operation plan data 15 is obtained by solving an optimization problem using a solution method such as a branch and bound method or a branch and cut method. The output determination unit 14 stores the generator operation plan data 15. As a specific example, the output determination unit 14 stores the generator operation plan data 15 in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0026]

number

[0027] Here, equation (8) represents the expected value of the revenue of the power generation system. Equation (9) represents the calculation formula for the amount of electricity sold. Equation (10) represents the output lower limit constraint and the output upper limit constraint. Equation (11) represents the change rate constraint, and R g represents the threshold for the rate of change of generator g. Equation (12) represents the domain of definition of each decision variable. Note that while an example of a formulation for maximizing the expected value of profit has been shown here, the minimum value of profit may also be used as the objective function, or risk indicators such as VaR (Value at Risk) and CVaR (Conditional Value at Risk) may also be used as the objective function. Equation (8) can also be said to be the sum of values ​​obtained by multiplying the objective quantity (e.g., profit) for each scenario s by the occurrence probability of that scenario s.

[0028] The control display unit 16 outputs the generator operation plan data 15 to the output device 46. When the output device 46 is a display, the output device 46 displays the generator operation plan data 15. When the output device 46 is a printer, the output device 46 prints the generator operation plan data 15 on paper.

[0029] 3 is a flowchart showing an example of a generator operation plan creation method executed by the generator operation plan creation device 100 according to Embodiment 1. First, the input unit 5 executes input processing S101 to store the generator information data 4 and the scenario data 7 in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0030] Next, the driving output approximation unit 10 executes driving output approximation processing S102. The driving output approximation processing S102 is processing that solves the optimization problem shown in, for example, equations (1) and (2) to generate driving output approximate data 11. The driving output approximation unit 10 stores the driving output approximate data 11, which is the result of the driving output approximation processing S102, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0031] Next, the operating state determination unit 12 executes an operating state determination process S103. The operating state determination process S103 is a process of solving an optimization problem shown in, for example, equations (3) to (7) to generate operating state data 13. The operating state determination unit 12 stores the operating state data 13, which is the result of the operating state determination process S103, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0032] Next, the output determination unit 14 executes the output determination process S104. The output determination process S104 is a process for solving the optimization problem shown in, for example, equations (8) to (12) to generate the generator operation plan data 15. The output determination unit 14 stores the generator operation plan data 15, which is the result of the output determination process S104, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0033] Finally, the control display unit 16 executes a control display process S105. The control display process S105 is a process for displaying the generator operation plan data 15 by the output device 46. For example, the control display unit 16 outputs the generator operation plan data 15 to the output device 46. The output device 46 displays the generator operation plan data 15.

[0034] As described above, according to the first embodiment, it is possible to create a power generator operation plan that maximizes expected profits (or minimizes risk indexes) so as to satisfy the constraint equations. Furthermore, when determining the start / stop state of the power generators, it is not necessary to simultaneously optimize the output for each scenario (see equation (3)). In other words, by roughly estimating the output for each scenario and provisionally determining it in advance, it is possible to reduce the number of decision variables to be optimized in equation (3).

[0035] The generator operation plan creation device 100 may be realized by hardware as shown in Fig. 1, or may be realized as software such as a program that embodies each function on a computer, or as a recording medium on which the software is recorded. The recording medium may be any type, such as a hard disk, an optical disk, or a magneto-optical disk.

[0036] <Embodiment 2> Fig. 4 is a functional block diagram showing an example of the configuration of the generator operation plan creation device 100 according to embodiment 2. The generator operation plan creation device 100 according to embodiment 2 includes a Lagrangian relaxation unit 21, a constraint satisfaction determination unit 23, and a Lagrangian multiplier update unit 25 in addition to the configuration of embodiment 1 shown in Fig. 2.

[0037] The Lagrangian relaxation unit 21 generates Lagrangian relaxation data 22 based on the planning input data 8. The Lagrangian relaxation data 22 indicates a mathematical formula representing a penalty to be imposed on the objective function of the optimization problem expressed by equations (1) and (2). A penalty is generated for each constraint condition, and when the operational output estimating unit 10 solves the optimization problem expressed by equations (1) and (2), all of the penalties are imposed on the objective function. For example, an example is shown in which, for the power demand Dt at each time t, no power trading is conducted and power is supplied by power generated by the company's own generator G and renewable energy power source R. In this case, the Lagrangian relaxation data 22 for the supply and demand balance constraint is expressed by equation (13).

[0038]

number

[0039] where λ t,s is the Lagrange multiplier. Lagrange multiplier λ t,s The initial value may be set arbitrarily by the operator.

[0040] The operating power output approximation unit 10 solves the optimization problem expressed by equations (1) and (2) using the Lagrangian relaxation data 22 of equation (13). Specifically, the operating power output approximation unit 10 solves the optimization problem obtained by adding equation (13) to equation (1).

[0041] The constraint satisfaction determination unit 23 calculates the violation amount of the constraint conditions based on the generator operation plan data 15, and stores the violation amount as constraint violation data 24. If there is no violation of the constraint, the violation amount becomes zero. Here, an example is shown in which, in response to the power demand Dt, no power trading is conducted and power is supplied by power generation from the owned generator G and renewable energy power source R. In this case, the violation amount v of the supply and demand balance constraint is t,s is expressed by equation (14).

[0042]

number

[0043] When a constraint violation occurs, that is, when the violation amount is greater than zero, the Lagrangian multiplier update unit 25 updates the Lagrangian relaxation data 22 based on the Lagrangian relaxation data 22 and the constraint violation data 24. Specifically, the Lagrangian multiplier update unit 25 updates the Lagrangian multiplier λ by a method such as the subgradient method. t,s and the violation amount ν t,s Based on the Lagrange multiplier λ t,s Then, the Lagrange multiplier update unit 25 updates the updated Lagrange multiplier λ t,s The Lagrangian relaxation data 22 is updated using the above formula. Note that although an example of Lagrangian relaxation of the supply-demand balance constraint and updating of the Lagrangian multiplier has been shown here, Lagrangian relaxation can also be applied to other constraint conditions such as reserve capacity constraints and fuel consumption constraints in the same way.

[0044] When there is no constraint violation, the control display unit 16 displays the generator operation plan data 15. On the other hand, when there is a constraint violation, the control display unit 16 may display the constraint violation data 24 and the generator operation plan data 15.

[0045] 5 is a flowchart showing an example of a generator operation plan creation method executed by the generator operation plan creation device 100 according to Embodiment 2. First, as in Embodiment 1, the input unit 5 executes input processing S101.

[0046] Next, the Lagrangian relaxation unit 21 executes the Lagrangian relaxation process S201. The Lagrangian relaxation process S201 is performed by using the Lagrangian multiplier λ t,s The Lagrangian relaxation unit 21 stores the Lagrangian relaxation data 22, which is the result of the Lagrangian relaxation process S201, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0047] Next, the driving output approximation unit 10 executes driving output approximation processing S102. The driving output approximation processing S102 is processing that solves an optimization problem in which, for example, equation (1) is added to equation (13) to generate driving output approximate data 11. The driving output approximation unit 10 stores the driving output approximate data 11, which is the result of the driving output approximation processing S102, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44.

[0048] Next, the driving state determination unit 12 executes the driving state determination process S103 to generate driving state data 13, in the same manner as in the first embodiment.

[0049] Next, the output determination unit 14 executes the output determination process S104 in the same manner as in the first embodiment, and generates the generator operation plan data 15.

[0050] Next, the constraint satisfaction determination unit 23 executes the constraint satisfaction determination process S202. The constraint satisfaction determination process S202 determines whether or not the violation amount v t,sis a process of calculating the constraint violation data 24. The constraint satisfaction determination unit 23 stores the constraint violation data 24, which is the result of the constraint satisfaction determination process S202, in the main storage device 42, the auxiliary storage device 43, or the external storage device 44. The constraint satisfaction determination unit 23 determines whether or not there is a constraint violation in the constraint satisfaction determination process S202. The constraint satisfaction determination unit 23 calculates the violation amount v t,s is greater than zero, it is determined that a constraint violation exists. At this time, the Lagrangian multiplier update unit 25 executes the Lagrangian update process S203. The Lagrangian update process S203 is, for example, t,s and the violation amount ν t,s Based on this, the Lagrange multiplier λ can be calculated by methods such as the subgradient method. t,s The Lagrange multiplier update unit 25 updates the updated Lagrange multiplier λ t,s The Lagrangian multiplier update unit 25 uses the above to update the Lagrangian relaxation data 22. The Lagrangian multiplier update unit 25 stores the Lagrangian relaxation data 22, which is the result of the Lagrangian update process S203, in the main storage device 42, the auxiliary storage device 43 or the external storage device 44.

[0051] Then, the operating power output approximation unit 10 executes the operating power output approximation process S102 using the updated Lagrangian relaxation data 22. In the example of Fig. 5, the Lagrangian update process S203, the operating power output approximation process S102, the operating state determination process S103, and the power output determination process S104 are repeated until there are no more constraint violations.

[0052] If there is no violation of the constraints, the control display unit 16 executes the control display process S105 in the same manner as in the first embodiment.

[0053] 5 shows a flowchart in which the output device 46 displays the generator operation plan data 15 when there is no constraint violation, but this is not necessarily limited to this. When there is a constraint violation, the control display unit 16 may cause the output device 46 to display the constraint violation data 24 and the generator operation plan data 15.

[0054] According to the second embodiment, the generator operation plan creation device 100 can efficiently create a generator operation plan that takes into consideration operational constraints such as supply and demand balance constraints, reserve power constraints, and fuel consumption amount constraints.

[0055] <Other variations> The input unit 5, the operating output approximation unit 10, the operating state determination unit 12, the output determination unit 14, and the control and display unit 16 described above can be realized by a processing circuit 81 shown in Fig. 6. That is, the processing circuit 81 includes an input unit 5 that acquires plan input data 8 including generator information data 4 and scenario data 7, an operating output approximation unit 19 that generates operating output estimate data 11, which is approximate data of the operating output of the generator, based on the plan input data 8, an operating state determination unit 12 that generates operating state data 13 indicating the start / stop state of the generator based on the plan input data 8 and the operating output estimate data 11, an output determination unit 14 that generates generator operation plan data 15 having the start / stop state and output of the generator based on the plan input data 8 and the operating state data 13, and a control and display unit 16 that displays the generator operation plan data 15. The processing circuit 81 may further include a scenario generation unit 6 that generates scenario data 7 based on meteorological information data 1, electricity market price information data 2, and renewable energy data 3. The processing circuit 81 may further include a Lagrangian relaxation unit 21 that generates Lagrangian relaxation data 22 based on the plan input data 8, a constraint satisfaction determination unit 23 that calculates the amount of violation of constraint conditions based on the generator operation plan data 15 and generates constraint violation data 24, and a Lagrangian multiplier update unit 25 that updates the Lagrangian multiplier data based on the Lagrangian multiplier data included in the Lagrangian relaxation data 22 and the constraint violation data 24.

[0056] Dedicated hardware or a processor that executes a program stored in memory may be applied to the processing circuit 81. Examples of processors include a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, and a DSP (Digital Signal Processor).

[0057] When the processing circuitry 81 is dedicated hardware, the processing circuitry 81 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each of the functions of the above-mentioned units of the generator operation plan creation device 100 may be realized by a circuit in which the processing circuits are distributed, or the functions of the units may be realized together by a single processing circuit.

[0058] When the processing circuitry 81 is a processor, the functions of the above-described units of the generator operation plan creation device 100 are realized in combination with software or the like. The software or the like includes, for example, software, firmware, or software and firmware. The software or the like is written as a program and stored in a memory. As shown in FIG. 7 , a processor 82 applied to the processing circuit 81 realizes the functions of the units by reading and executing a program stored in a memory 83 connected via a bus 84 and functioning as a work memory. That is, when executed by the processing circuit 81, the generator operation plan creation device 100 includes the steps of: acquiring plan input data 8 including generator information data 4 and scenario data 7; generating, based on the plan input data 8, approximate operating output data 11, which is approximate data of the operating output of the generator; generating, based on the plan input data 8 and the approximate operating output data 11, operating status data 13 indicating the start / stop status of the generator; generating, based on the plan input data 8 and the operating status data 13, generator operation plan data 15 having the start / stop status and output of the generator; and displaying the generator operation plan data 15. In other words, this program causes a computer to execute the procedures and methods of the above-mentioned components of the generator operation plan creation device 100. Here, the memory 83 may be, for example, a non-volatile or volatile semiconductor memory such as a random access memory (RAM), a read-only memory (ROM), a flash memory, an erasable programmable read-only memory (EPROM), or an electrically erasable programmable read-only memory (EEPROM), a hard disk drive (HDD), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a digital versatile disk (DVD), a drive device for any of these, or any storage medium that will be used in the future.

[0059] The above has described a configuration in which each functional unit of the generator operation plan creation device 100 is realized either by hardware or software, etc. However, the present invention is not limited to this, and a configuration in which some of the functional units are realized by dedicated hardware and other parts are realized by software, etc.

[0060] As described above, the processing circuitry 81 can realize the above-described functions by hardware, software, or a combination of these.

[0061] It is possible to freely combine the embodiments, and to modify or omit the embodiments as appropriate.

[0062] Various aspects of the present disclosure are summarized below as appendices.

[0063] (Appendix 1) an input unit that acquires generator information data having specification data of the generator and plan input data including scenario data having time-series data of predicted values ​​of electricity prices; an operating output approximation unit that generates operating output approximate data, which is approximate data of an operating output of the generator, based on the plan input data; an operating state determination unit that generates operating state data indicating a start / stop state of the generator based on the plan input data and the operating output approximate data; an output determination unit that generates generator operation plan data indicating the start / stop state and output of the generator based on the plan input data and the operation state data; a control display unit that displays the generator operation plan data; A generator operation plan creation device comprising:

[0064] (Appendix 2) A generator operation plan creation device according to (Supplementary Note 1), the operating output approximation unit generates the operating output approximate data by solving an optimization problem that maximizes the profit generated in each scenario of the scenario data when the generator is in an operating state; The power generator operation plan creation device, wherein the operating state determination unit generates the operating state data by solving an optimization problem including an expected value of profit or a risk measure calculated from a target quantity for each scenario and the occurrence probability of the scenario.

[0065] (Appendix 3) A generator operation plan creation device according to (Supplementary Note 1) or (Supplementary Note 2), a Lagrangian relaxation unit that generates Lagrangian relaxation data based on the design input data; a constraint satisfaction determination unit that calculates a violation amount of a constraint condition based on the generator operation plan data and generates constraint violation data; a Lagrangian multiplier update unit that updates the Lagrangian multiplier data based on the Lagrangian multiplier data included in the Lagrangian relaxation data and the constraint violation data; A generator operation plan creation device comprising:

[0066] (Appendix 4) A generator operation plan creation device according to (Supplementary Note 3), The power generation system includes a generator operation plan generation device, wherein the operating output approximation unit solves an optimization problem including the Lagrangian relaxation data to generate the operating output approximate data.

[0067] (Appendix 5) A generator operation plan creation device according to any one of (Supplementary Note 1) to (Supplementary Note 4), The generator operation plan creation device further comprises a scenario generation unit that generates the scenario data based on meteorological data, electricity market price information data, and renewable energy data.

[0068] (Appendix 6) Acquire planning input data including generator information data having generator specification data and scenario data having time-series data of predicted values ​​of electricity prices; generating approximate operating output data, which is approximate data of an operating output of the generator, based on the planning input data; generating operational status data indicating a start / stop status of the generator based on the plan input data and the operation output approximate data; generating generator operation plan data indicating the start / stop state and output of the generator based on the plan input data and the operating state data; A generator operation plan creating method that displays the generator operation plan data. [Explanation of symbols]

[0069] 1 Weather information data, 2 Price information data, 3 Renewable energy data, 4 Generator information data, 5 Input section, 6 Scenario generation section, 7 Scenario data, 8 Plan input data, 10 Operational output estimation section, 11 Operational output estimation data, 12 Operational state determination section, 13 Operational state data, 14 Output determination section, 15 Generator operation plan data, 16 Control display section, 21 Lagrangian relaxation section, 22 Lagrangian relaxation data, 23 Constraint satisfaction determination section, 24 Constraint violation data, 25 Lagrangian multiplier update section.

Claims

1. an input unit that acquires generator information data having specification data of the generator and plan input data including scenario data having time-series data of predicted values ​​of electricity prices; an operating output approximation unit that generates operating output approximate data, which is approximate data of an operating output of the generator, based on the plan input data; an operating state determination unit that generates operating state data indicating a start / stop state of the generator based on the plan input data and the operating output approximate data; an output determination unit that generates generator operation plan data indicating the start / stop state and output of the generator based on the plan input data and the operation state data; a control display unit that displays the generator operation plan data; A generator operation plan creation device comprising:

2. the operating output approximation unit generates the operating output approximate data by solving an optimization problem that maximizes the profit generated in each scenario of the scenario data when the generator is in an operating state; 2. The generator operation plan creation device according to claim 1, wherein the operational state determination unit generates the operational state data by solving an optimization problem including an expected value of profit or a risk measure calculated from a target quantity for each scenario and a probability of occurrence of the scenario.

3. a Lagrangian relaxation unit that generates Lagrangian relaxation data based on the design input data; a constraint satisfaction determination unit that calculates a violation amount of a constraint condition based on the generator operation plan data and generates constraint violation data; a Lagrangian multiplier update unit that updates the Lagrangian multiplier data based on the Lagrangian multiplier data included in the Lagrangian relaxation data and the constraint violation data; The generator operation plan creation device according to claim 1 or 2, comprising:

4. The generator operation plan creating device according to claim 3 , wherein the operating output approximation unit generates the operating output approximate data by solving an optimization problem including the Lagrangian relaxation data.

5. The generator operation plan creation device according to claim 1 or 2, further comprising a scenario generation unit that generates the scenario data based on meteorological data, price information data in the electricity market, and renewable energy data.

6. Acquire planning input data including generator information data having generator specification data and scenario data having time-series data of predicted values ​​of electricity prices; generating approximate operating output data, which is approximate data of an operating output of the generator, based on the planning input data; generating operational status data indicating a start / stop status of the generator based on the plan input data and the operation output approximate data; generating generator operation plan data indicating the start / stop state and output of the generator based on the plan input data and the operating state data; A generator operation plan creating method that displays the generator operation plan data.

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