Operation plan generation device and operation plan generation method

Abstract

The present invention provides an operational plan generation device, etc., that generates operational plans that take into account the uncertainty of future conditions regarding nuclear fuel cycle facilities. [Solution] The operation plan generation device 10 includes a constraint acquisition unit 121 that acquires combinations of multiple patterns of constraints as operational constraints for the nuclear fuel cycle facility, an operation plan search unit 122 that searches for an operation plan for the nuclear fuel cycle facility toward the optimization of a predetermined objective function under the constraints of each of the aforementioned combinations, and an output control unit 126 that outputs the search results for the operation plan for each of the aforementioned combinations.

Description

Technical Field

[0001] The present disclosure relates to an operation plan generation device and an operation plan generation method.

Background Art

[0002] Regarding the generation of an operation plan for nuclear fuel cycle facilities, for example, the techniques described in Patent Documents 1 and 2 are known. That is, Patent Document 1 describes that "the safety performance and construction cost of a geological disposal site intended for construction are estimated, and the output unit outputs the estimated safety performance and construction cost to the decision maker in a predetermined format in which the relationship between the two is made explicit." Further, Patent Document 2 describes "a method for evaluating the robustness of a proposed solution to a constraint problem."

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] For example, even when an operation plan for nuclear fuel cycle facilities is formulated, the feasibility and cost of the operation plan are often influenced by national policies, social trends, and international situations. In Patent Documents 1 and 2, the uncertainty of future situations regarding nuclear fuel cycle facilities is not particularly considered, and there is room for improvement.

[0005] Therefore, an object of the present disclosure is to provide an operation plan generation device or the like that generates an operation plan in which the uncertainty of future situations regarding nuclear fuel cycle facilities is considered.

Means for Solving the Problems

[0006] To solve the aforementioned problems, the operation plan generation device according to this disclosure includes: a constraint acquisition unit that acquires combinations of multiple patterns of constraints as operational constraints for a nuclear fuel cycle facility; an operation plan search unit that searches for an operation plan for the nuclear fuel cycle facility toward the optimization of a predetermined objective function under the constraints of each of the aforementioned combinations; and an output control unit that outputs the search results for the operation plan for each of the aforementioned combinations. [Effects of the Invention]

[0007] According to this disclosure, it is possible to provide an operational plan generation device, etc., that generates an operational plan that takes into account future uncertainties regarding nuclear fuel cycle facilities. [Brief explanation of the drawing]

[0008] [Figure 1] This is an explanatory diagram of the nuclear fuel cycle that is the target of the operation plan in the operation plan generation device according to the embodiment. [Figure 2] This is a functional block diagram of the operation plan generation device according to the embodiment. [Figure 3] This is an example of a display screen showing multiple patterns of constraint combinations in the operation plan generation device according to the embodiment. [Figure 4] This figure shows the hardware configuration of the operation plan generation device according to the embodiment. [Figure 5] This is a flowchart of the processing performed by the processing unit of the operation plan generation device according to the embodiment. [Figure 6] This is an example of a display screen showing an operation plan for the first example in the operation plan generation device according to the embodiment. [Figure 7A] This is an example of a display screen showing an operation plan for a second example in the operation plan generation device according to the embodiment. [Figure 7B] This is an example of a display screen showing the cost in the operation plan generation device according to the embodiment, for the operation plan of the second example. [Figure 8A]This is an example of a display screen showing an operation plan for a third example in the operation plan generation device according to the embodiment. [Figure 8B] This is an example of a display screen showing the cost in the operation plan generation device according to the embodiment, for the third example of the operation plan. [Modes for carrying out the invention]

[0009] <<Embodiment>> Below, we will first briefly explain nuclear fuel cycle C1 (see Figure 1), which is an example of an operational plan target generated by the operational plan generation device 10 (see Figure 2), and then provide a detailed explanation of the operational plan generation device 10.

[0010] <Nuclear Fuel Cycle> Figure 1 is an explanatory diagram of the nuclear fuel cycle C1, which is the target of the operation plan in the operation plan generation device according to the embodiment. As shown in Figure 1, the nuclear fuel cycle C1 consists of, for example, uranium fuel light water reactors 41, 42, 43, ..., interim storage facilities 51, 52, 53, ..., reprocessing plants 61, 62, ..., final disposal sites 71, 72, ..., MOX fuel fabrication plants 81, 82, ..., and plutonium thermal light water reactors 91, 92, 93, ....

[0011] Uranium-fueled light water reactors 41, 42, 43, ... are reactors configured to cause nuclear fission by bombarding uranium fuel with neutrons. The heat generated by this fission is used to rotate a turbine (not shown), and a generator (not shown) connected to this turbine generates electricity. Incidentally, in light water reactors, water (light water) is used as a moderator to slow down fast neutrons into thermal neutrons before causing nuclear fission of the uranium fuel (thermal neutron reactor). On the other hand, in fast breeder reactors (or fast reactors) described later, no moderator is used, and nuclear fission of uranium fuel is caused by fast neutrons (fast neutron reactor).

[0012] Note that the types of uranium-fueled light water reactors 41, 42, 43, … may be boiling water reactors or pressurized water reactors. Also, in the uranium-fueled light water reactors 41, 42, 43, …, those with different reactor shapes and fuel types may be mixed. The predetermined spent fuel generated in the uranium-fueled light water reactors 41, 42, 43, … is carried out to the interim storage facilities 51, 52, 53, ….

[0013] The interim storage facilities 51, 52, 53, … are facilities for storing and managing spent fuel. The types of such interim storage facilities 51, 52, 53, … may be wet storage facilities or dry storage facilities. In the case of wet storage facilities, the spent fuel is stored in a spent fuel pool (not shown). Also, in the case of dry storage facilities, the spent fuel is stored in a metal cask (not shown). The spent fuel stored in the interim storage facilities 51, 52, 53, … is then carried out to the reprocessing plants 61, 62, ….

[0014] The reprocessing plants 61, 62, … are facilities for performing a predetermined reprocessing on spent fuel. In the reprocessing plants 61, 62, …, as the above-mentioned reprocessing, the fuel materials such as plutonium and uranium are separated and recovered from the spent fuel. The separated and recovered fuel materials are carried out to the MOX fuel processing plants 81, 82, …. Also, a part of the substances other than the fuel materials is processed into a glass solid and then carried out to the final disposal sites 71, 72, …. The final disposal sites 71, 72, … are facilities for disposing of the glass solid.

[0015] The MOX fuel processing plants 81, 82, … are plants for processing fuel materials such as plutonium and uranium into a predetermined MOX fuel (Mixed Oxide Fuel). The MOX fuel manufactured in the MOX fuel processing plants 81, 82, … is carried out to the pressurized water reactors 91, 92, 93, ….

[0016] The pressurized thermal water reactors 91, 92, 93, … are light water reactors for generating electricity using MOX fuel. In the pressurized thermal water reactors 91, 92, 93, …, those with different reactor types and fuel types may be mixed. The spent fuel generated by the power generation of the pressurized thermal water reactors 91, 92, 93, … is transported to the interim storage facilities 51, 52, 53, … and stored in these interim storage facilities 51, 52, 53, …

[0017] In this way, a series of processes in which plutonium and uranium are separated and recovered by reprocessing the spent fuel generated in the uranium fuel light water reactors 41, 42, 43, … and then reused as nuclear fuel (fuel material) in the pressurized thermal water reactors 91, 92, 93, … is called a nuclear fuel cycle. By circulating nuclear fuel in the nuclear fuel cycle, reduction of high-level radioactive waste and effective utilization of resources are achieved.

[0018] Note that the uranium fuel light water reactors 41, 42, 43, …, the interim storage facilities 51, 52, 53, …, the reprocessing plants 61, 62, …, the final disposal sites 71, 72, …, the MOX fuel processing plants 81, 82, …, and the pressurized thermal water reactors 91, 92, 93, … shown in FIG. 1 all correspond to "nuclear fuel cycle facilities". In the following description, the symbols of these nuclear fuel cycle facilities may be appropriately omitted. Also, the nuclear fuel cycle C1 shown in FIG. 1 is an example and is not limited thereto.

[0019] <Configuration of the operation plan generation device> FIG. 2 is a functional block diagram of the operation plan generation device 10. The operation plan generation device 10 shown in Figure 2 is a device that generates the operation plan for the nuclear fuel cycle facility described above. Such an operation plan generation device 10 may be configured as a single computer, or it may be composed of multiple computers connected via signal lines or a network. For example, the functions of the operation plan generation device 10 may be distributed among multiple computers such as cloud servers or edge servers. Users of the operation plan generation device 10 include, for example, power companies that operate nuclear fuel cycle facilities and manufacturers of nuclear fuel cycle facilities.

[0020] As shown in Figure 2, the operation plan generation device 10 comprises a storage unit 11 and a processing unit 12. The storage unit 11 has predetermined programs and data stored in it beforehand. In addition, data input via the input device 20 and the calculation results of the processing unit 12 are also stored in the storage unit 11 as appropriate. The processing unit 12 performs a simulation of the nuclear fuel cycle based on the programs and data stored in the storage unit 11, and generates an operation plan for the nuclear fuel cycle facility based on the results of the simulation.

[0021] As shown in Figure 2, the processing unit 12 includes a constraint acquisition unit 121, an operation plan search unit 122, a nuclear fuel cycle logistics calculation unit 123, a cost calculation unit 124, a convergence determination unit 125, and an output control unit 126.

[0022] The constraint acquisition unit 121 accepts input of predetermined constraints (operational constraints of the nuclear fuel cycle facility) through user operation via the input device 20. The input device 20 may be a keyboard or mouse, or a touch-panel smartphone or tablet. Although not shown in Figure 2, constraint data may also be acquired from the user's terminal (not shown) via a network (not shown).

[0023] Examples of operational constraints for nuclear fuel cycle facilities include the following: Specifically, a constraint may be included requiring that the volume of spent fuel be reduced through reprocessing so that the total weight (or total volume) of spent fuel in Japan does not exceed the total capacity of the interim storage facilities. Furthermore, a constraint may be included requiring that plutonium separated and recovered through reprocessing be used at MOX fuel fabrication plants so as not to increase Japan's total stockpile.

[0024] Furthermore, with regard to spent fuel delivered to the reprocessing plant, the constraint may include that its cooling period and burnup meet the reprocessing plant's acceptance requirements. Also, with regard to vitrified waste delivered to the final disposal site, the constraint may include that its calorific value and toxicity meet specified specifications.

[0025] Other constraints may include, for example, ensuring that the amount of spent fuel generated does not exceed the operating limit of the reprocessing plant. Similarly, constraints may include ensuring that the amount of MOX fuel supplied to the plutonium-thermal light water reactor does not exceed the operating limit of that reactor. Furthermore, constraints may include the timing of the introduction of fast breeder reactors, etc. Note that the constraints mentioned above are examples and are not limiting.

[0026] Incidentally, under the current legal system, the costs associated with implementing the nuclear fuel cycle (costs required for reprocessing spent fuel, manufacturing MOX fuel, and disposing of radioactive waste, etc.) are recovered in the form of contributions from the power companies that operate the nuclear power plants. Power companies formulate operational plans to ensure the reliability of the nuclear fuel cycle while minimizing unnecessary costs.

[0027] However, even if a power company formulates an operational plan for nuclear fuel cycle facilities, it is not guaranteed that the operation will proceed as planned. This is because the feasibility and cost of the operational plan for nuclear fuel cycle facilities may be influenced by national policies, social trends, and international circumstances. For example, the capacity of future interim storage facilities, the area of ​​final disposal sites, and the price of uranium may fluctuate depending on national policies, social trends, and international circumstances. Therefore, in this embodiment, taking into account the uncertainty of future circumstances, the user is allowed to set multiple patterns (constraints for the first pattern, constraints for the second pattern, etc.) as combinations of constraints.

[0028] Figure 3 shows an example of a display screen illustrating multiple patterns related to the combination of constraint conditions. In the example in Figure 3, the constraints for the first pattern are set by the user through input operations, with combinations of constraints 1a, 2a, ... being used. Constraint 1a states that "the total capacity of domestic interim storage facilities will be 10,000 tons or less in 2040." For example, from the perspective of "total capacity of interim storage facilities," several candidate values ​​could be displayed in a pull-down menu, and one of them (10,000 tons in the first pattern) could be selected through user input operations.

[0029] Furthermore, constraint 2a states that "a fast breeder reactor will be introduced in 2050." For example, in terms of "timing of introduction of a fast breeder reactor," several candidate values ​​could be displayed in a pull-down menu, and one of them (2050 in the first pattern) could be selected by the user's input.

[0030] The second, third, and fourth patterns shown in Figure 3 differ from the first pattern in the combination of the total capacity of the interim storage facility and the timing of the introduction of the fast breeder reactor. These combinations of constraints are set by user input operations to appropriately cover the future expected state of the nuclear fuel cycle facility. The search start button B1 shown in Figure 3 is a button operated via the input device 20 (see Figure 2) when the search for the operation plan of the nuclear fuel cycle facility is started, and is displayed on the display screen as predetermined.

[0031] The constraint acquisition unit 121 shown in Figure 2 acquires combinations of multiple patterns of constraints as operational constraints for the nuclear fuel cycle facility. As explained in the example in Figure 3, the constraint acquisition unit 121 acquires data for the first pattern, the second pattern, the third pattern, the fourth pattern, and so on, as combinations of constraints. As will be described in detail later, in this embodiment, the processing unit 12 individually generates an operational plan for the nuclear fuel cycle facility (an operational plan aimed at minimizing costs) for each of these multiple pattern combinations.

[0032] The operation plan search unit 122 shown in Figure 2 is a simulator for searching for an operation plan for a nuclear fuel cycle facility. That is, for each combination of constraints (for example, multiple patterns of constraint combinations as shown in Figure 3), the operation plan search unit 122 searches for an operation plan for a nuclear fuel cycle facility to optimize a predetermined objective function under the constraints of that combination. In this embodiment, the cost required to operate the nuclear fuel cycle facility (cost borne by the power company) is used as the objective function when searching for an operation plan. In other words, the operation plan search unit 122 solves a constrained optimization problem with cost as the objective function and the components of the operation plan for the nuclear fuel cycle facility as the decision variables.

[0033] Methods for solving such constrained optimization problems may include, for example, random search, or methods such as genetic algorithms or Bayesian optimization. It is also possible to narrow the search range for operational plans to a discrete finite number of decision variable combinations (combinations of decision variables pre-input via the input device 20). Decision variables in a constrained optimization problem include, for example, what type of fuel to use, how much to use and when, and what to invest in and when.

[0034] The nuclear fuel cycle logistics calculation unit 123 shown in Figure 2 calculates the amount of predetermined substances produced in the nuclear fuel cycle facilities. Specifically, the nuclear fuel cycle logistics calculation unit 123 calculates the mass balance (so-called mass balance) of things like the weight of spent fuel produced in a uranium fuel light water reactor, the number of MOX fuels produced at the MOX fuel fabrication plant, and the number of vitrified waste products produced at the reprocessing plant, in a predetermined operation plan searched by the operation plan search unit 122.

[0035] The cost calculation unit 124 calculates the cost required to operate the nuclear fuel cycle equipment (the cost borne by the power company) based on the amount of predetermined substances calculated by the nuclear fuel cycle logistics calculation unit 123. Specifically, the cost calculation unit 124 calculates the cost based on the calculation result of the nuclear fuel cycle logistics calculation unit 123 and a predetermined cost unit price set in advance. In addition, a predetermined discount rate (the rate at which future money received is discounted to its present value) may be used in the cost calculation, taking into account when the cost is incurred. Furthermore, the cost of capital investment in the nuclear fuel cycle equipment may be included in the cost.

[0036] The convergence determination unit 125 determines whether the objective function (i.e., the cost value) has converged based on the calculation results of the cost calculation unit 124. If the objective function has not converged, the convergence determination unit 125 ensures that the search for the operational plan and other operations are repeated as appropriate. In other words, the search for the operational plan by the operational plan search unit 122, the logistics calculation by the nuclear fuel cycle logistics calculation unit 123, and the cost calculation by the cost calculation unit 124 are repeated a predetermined number of times until the convergence determination unit 125 determines that the objective function has converged.

[0037] In cases where the search for an operational plan is repeated in this manner, the convergence determination unit 125 may determine that the objective function has converged if the difference between the cost of the operational plan based on the previous search and the cost of the operational plan based on the current search falls below a predetermined value. When the objective function converges, the convergence determination unit 125 outputs the operational plan data at the time the objective function converged to the output control unit 126.

[0038] The output control unit 126 outputs the search results for operation plans for each combination of constraint conditions (for example, multiple patterns of constraint condition combinations as shown in Figure 3). For example, the operation plan for the nuclear fuel cycle facility may be displayed on the display device 30, or it may be output in a predetermined file format. In the example in Figure 2, the "first operation plan" is output as the operation plan corresponding to the "first pattern of constraint conditions". Also, the "second operation plan" is output as another operation plan corresponding to the "second pattern of constraint conditions". In this way, multiple operation plans are output as appropriate to correspond one-to-one with multiple patterns of constraint condition combinations. These operation plans can be used as appropriate as decision-making material when power companies formulate nuclear fuel cycle plans, for example.

[0039] Figure 4 shows the hardware configuration of the operation plan generation device 10. The operation plan generation device 10 has a hardware configuration comprising a processor 10a, RAM 10b (Random Access Memory), ROM 10c (Read Only Memory), HDD 10d (Hard Disk Drive), a communication interface 10e, and an input / output interface 10f, all of which are predeterminedly connected via an internal bus 10g.

[0040] The processor 10a is hardware that constitutes the processing unit 12 (see Figure 2) of the operation plan generation device 10. The RAM 10b, ROM 10c, and HDD 10d are hardware that constitute the storage unit 11 (see Figure 2) of the operation plan generation device 10. The processor 10a reads a predetermined program stored in the ROM 10c or HDD 10d and loads it into the RAM 10b, thereby executing a predetermined process.

[0041] The communication interface 10e shown in Figure 4 communicates with a user's terminal (not shown) or the cloud (not shown) via a network (not shown) as required. The input / output interface 10f receives data from the input device 20 and outputs data to the display device 30. The input / output interface 10f functions as the constraint condition acquisition unit 121 (see Figure 2) and also functions as the output control unit 126 (see Figure 2). Furthermore, when acquiring constraint condition data from a user's terminal (not shown) via a network (not shown), the communication interface 10e functions as the constraint condition acquisition unit 121 (see Figure 2).

[0042] Figure 5 is a flowchart of the processes performed by the processing unit of the operation plan generation device (see also Figure 2 as appropriate). In step S101, the processing unit 12 acquires multiple patterns (n ​​patterns: n is a natural number greater than or equal to 2) of constraint condition combinations using the constraint condition acquisition unit 121 (constraint condition acquisition step). As described above, the multiple patterns of constraint condition combinations are set by user operation via the input device 20.

[0043] In step S102, the processing unit 12 sets the value of k to 1. Here, the value of k is used to specify the pattern to be processed in steps S103 to S106 from among the n possible patterns of constraint condition combinations obtained in step S101. For example, if k=1, the processing in steps S103 to S106 is performed on the first pattern out of the n possible patterns of constraint condition combinations.

[0044] In step S103, the processing unit 12 uses the operation plan search unit 122 to search for an operation plan for the nuclear fuel cycle facility that optimizes the objective function (operation plan search step). As described above, in this embodiment, the cost required to operate the nuclear fuel cycle facility is used as the objective function. For each combination of constraints, the operation plan search unit 122 searches for an operation plan that minimizes the cost required to operate the nuclear fuel cycle facility under the constraints of that combination.

[0045] Although not shown in Figure 5, if the constraint acquisition unit 121 acquires multiple combinations of constraints, the operation plan search unit 122 may be configured to start searching for an operation plan when the operation plan search start button B1 (see Figure 3) on the display screen is operated via the input device 20.

[0046] Furthermore, even if the number of constraint combination patterns acquired by the constraint acquisition unit 121 is only one, the operation plan search start button B1 (see Figure 3) on the display screen may be operated via the input device 20. In such a case, the output control unit 126 may display a message indicating that it is possible to set the number of constraint combination patterns to multiple. This will make the user aware that it is possible to set multiple constraint patterns.

[0047] In step S104, the processing unit 12 performs logistics calculations for the nuclear fuel cycle using the nuclear fuel cycle logistics calculation unit 123. Specifically, the processing unit 12 calculates the amount of predetermined substances (for example, spent fuel, MOX fuel, or vitrified waste) generated at the nuclear fuel cycle facility, based on patterns of combinations of constraints specified in step S102 (or step S108 described later). In step S105, the processing unit 12 calculates the costs for the power company using the cost calculation unit 124. Specifically, the processing unit 12 calculates the costs required for the operation plan of the nuclear fuel cycle facility (the operation plan explored in step S103) based on the calculation results from step S104 and a predetermined cost unit price.

[0048] In step S106, the processing unit 12 uses the convergence determination unit 125 to determine whether the objective function (i.e., the cost value) has converged. If the objective function has not converged in step S106 (S106: No), the processing unit 12 returns to step S103. In this case, steps S103 to S106 are repeated based on a predetermined algorithm for mathematical optimization. If the objective function has converged in step S106 (S106: Yes), the processing unit 12 proceeds to step S107.

[0049] In step S107, the processing unit 12 determines whether the value of k has reached n. As mentioned above, n is the number of combinations of constraint conditions entered in step S101. If the value of k has not reached n in step S107 (S107: No), the processing unit 12 proceeds to step 108.

[0050] In step S108, the processing unit 12 increments the value of k and then returns to the process in step S103. For example, if the value of k was 1 (S102) when the process in step S108 was performed, the processing unit 12 increments this value of k (S108) to k=2. As a result, the processes in steps 103 to S106 are performed targeting the second of the n possible patterns regarding the constraint conditions. Also, if the value of k reaches n in step S107 (S107: Yes), the processing unit 12 proceeds to step 109.

[0051] In step S109, the processing unit 12 outputs the operation plan for the nuclear fuel cycle facilities via the output control unit 126 (output control step). For example, the processing unit 12 displays the operation plan for the nuclear fuel cycle on the display device 30 in a table format, with the nuclear fuel cycle facilities in the vertical direction (column direction) and the year, month, and day in the horizontal direction (row direction). After performing the processing in step S109, the processing unit 12 terminates the series of processes (END).

[0052] Next, we will sequentially explain the first to third examples as concrete examples. For simplicity, the first and second examples will explain the problem of which of the two types of MOX fuel (MOX fuel A and MOX fuel B) to use in a plutonium thermal light water reactor. The third example will explain the problem of choosing whether to invest in the development of MOX fuel or in the expansion of the interim storage facility.

[0053] <Example 1> Figure 6 shows an example of a display screen illustrating the operational plan for the first example. Furthermore, the decision variables used to generate the operational plan for the nuclear fuel cycle facility include the types of MOX fuel and uranium fuel. The constraints may be, for example, the first or second pattern shown in Figure 3, or any other predetermined pattern. Then, the processing unit 12 (see Figure 2) searches for an operational plan for a plutonium thermal light water reactor, and the screen shown in Figure 6 is displayed on the display device 30 (see Figure 2).

[0054] In the example in Figure 6, the result of minimizing the power company's costs under the constraints of the first pattern is shown as the first operational plan. The first operational plan involves using MOX fuel A in the plutonium-thermal light water reactor and uranium fuel A in the uranium-fuel light water reactor. Incidentally, although not shown in the figure, there are other candidate types of MOX fuel besides MOX fuel A, such as MOX fuel B (the same applies to uranium fuel).

[0055] Furthermore, the result of minimizing the costs for power companies under the constraints of the second pattern is shown as the second operational plan. The second operational plan involves using MOX fuel A in plutonium-thermal light water reactors and uranium fuel B in uranium-fuel light water reactors. Although not shown in Figure 6, it is assumed that in the other operational plans (third operational plan, fourth operational plan, etc.), the type of MOX fuel is MOX fuel A as a result of minimizing costs.

[0056] In this case, in each combination of constraints, including the first and second patterns, the type of MOX fuel is common (MOX fuel A in all cases) as a result of minimizing costs. In other words, even when future conditions are uncertain and constraints may change, users can know in advance that costs can be reduced with almost certainty when using MOX fuel A.

[0057] Furthermore, if there is a decision variable (in the example of Figure 6, the type of MOX fuel) whose value is common to multiple operational plans corresponding to each combination of constraints (in the example of Figure 6, the first operational plan, the second operational plan, etc.), it is desirable to display that decision variable in a way that distinguishes it from other decision variables. Here, the "value" of a decision variable refers to a predetermined value used in the calculations of the processing unit 12 to indicate the content of that decision variable (for example, the type of MOX fuel). In the example of Figure 6, among the multiple decision variables when searching for an operational plan, the decision variable indicating the type of MOX fuel has a common value (the value corresponding to MOX fuel A).

[0058] For example, as shown in Figure 6, the text of decision variables whose values ​​(i.e., content) are common may be enclosed in a dashed frame F1, or they may be highlighted, or their size and color may be made different from others. In addition, a predetermined mark or animation may be displayed next to the text of the decision variables. Furthermore, a message such as "MOX fuel A is common to all operational plans" may be displayed. This allows the user to quickly grasp the decision variables that are common to all operational plans.

[0059] In the example shown in Figure 6, there are no common constraints regarding the type of uranium fuel across the multiple patterns. In such cases, the type of uranium fuel is determined by the power company based on future costs, etc. Furthermore, there may be cases where no common decision variables (i.e., their content) exist in the first operational plan, the second operational plan, etc. In such cases, the power company will appropriately determine the operational plan from among multiple patterns of constraints, taking into account factors such as future probability and cost.

[0060] <Example 2> Figure 7A is an example of a display screen showing the operational plan for the second example. In the second example, the operational plans are displayed separately for two cases: one where MOX fuel A is used, and another where MOX fuel B is used, out of two candidate MOX fuels (MOX fuel A and MOX fuel B). The first operational plan shown in Figure 7A shows the results of searching for an operational plan that minimizes costs under the constraints of the first pattern, assuming that MOX fuel A is used in a plutonium thermal light water reactor.

[0061] Furthermore, the second operational plan shows the results of searching for an operational plan that minimizes costs under the constraints of the second pattern, assuming the use of MOX fuel A. The third operational plan shows the results of searching for an operational plan that minimizes costs under the constraints of the first pattern, assuming the use of MOX fuel B. Furthermore, the fourth operational plan shows the results of searching for an operational plan that minimizes costs under the constraints of the second pattern, assuming the use of MOX fuel B.

[0062] It is assumed that the user has pre-configured the search for operational plans for both the case using MOX fuel A and the case using MOX fuel B. In addition, in the example in Figure 7A, the cost value of the power company is also displayed along with the operational plan as a search result. In this way, the output control unit 126 (see Figure 2) displays the search results for the operational plan on the display device 30 (see Figure 2) for each combination of constraint conditions, and also displays the calculation result of the cost (objective function). This allows the user to understand how much each operational plan will cost.

[0063] Figure 7B is an example of a display screen showing the costs in the operational plan for the second example. Note that the graph in Figure 7B corresponds to Figure 7A. Alternatively, Figures 7A and 7B may be displayed together on a single screen. In the graph of Figure 7B, the horizontal axis represents the constraints, and the vertical axis represents the power company's costs. The output control unit 126 (see Figure 2) arranges each pattern representing the different combinations of constraints on the horizontal axis, plots the cost (objective function) calculation results in the direction of the vertical axis, and displays them in graph format on the display device 30 (see Figure 2).

[0064] In Figure 7B, the points marked with a circle (○) indicate the cost when the first pattern of constraints is applied, assuming the use of MOX fuel A. The points marked with a square (□) indicate the cost when the second pattern of constraints is applied, assuming the use of MOX fuel A. The points marked with a triangle (△) indicate the cost when the first pattern of constraints is applied, assuming the use of MOX fuel B. The points marked with a black triangle (▽) indicate the cost when the second pattern of constraints is applied, assuming the use of MOX fuel B.

[0065] In the example in Figure 7B, the points corresponding to the use of MOX fuel A (marked with circles and squares) are connected by line segments (dashed lines). Similarly, the points corresponding to the use of MOX fuel B (marked with triangles and inverted triangles) are connected by another line segment (dotted-dotted line). By graphing the points where the type of MOX fuel (one of the decision variables) is common with line segments, it becomes easier for users to visually compare the case of using MOX fuel A with the case of using MOX fuel B.

[0066] In the example in Figure 7B, using MOX fuel A results in lower costs for the power company in both the first and second constraint patterns (the dashed line in Figure 7B is below the dotted line). Therefore, even if national policies, social trends, and international situations are uncertain, users can understand in advance that using MOX fuel A will result in lower costs. Furthermore, by also displaying the costs when using MOX fuel B, users can more easily understand how much cost reduction can be achieved by using MOX fuel A compared to using MOX fuel B.

[0067] Although not shown in the diagram, it is possible that under the constraints of the first pattern, MOX fuel A has a lower cost, while under the constraints of the second pattern, MOX fuel B has a lower cost. In such cases, for example, the processing unit 12 may calculate the expected cost (the sum of the product of cost and probability) for each constraint pattern and display this expected value as well. The expected cost value can be used as appropriate by the user when formulating an operational plan. The probabilities used in calculating the expected value (the probability of each constraint pattern occurring) are set by the user's input.

[0068] <Third example> Figure 8A is an example of a display screen showing the operational plan for the third example. The third example describes the problem of choosing between investing in the development of MOX fuel or investing in the expansion of the interim storage facility. The first operational plan shown in Figure 8A is the operational plan in which investment is made in the development of a new MOX fuel A, but no investment is made in expanding the interim storage facility (small interim storage facility capacity). The second operational plan is the operational plan in which investment is made in the development of a new MOX fuel A, and also in expanding the interim storage facility (large interim storage facility capacity).

[0069] The third operational plan assumes no investment in the development of MOX fuel, instead using the existing fuel, MOX fuel B, and no investment in expanding the interim storage facility (resulting in a smaller interim storage facility capacity). The fourth operational plan assumes no investment in the development of MOX fuel, but does invest in the interim storage facility (resulting in a larger interim storage facility capacity). In the example in Figure 8A, the cost values ​​for the power company in each operational plan are also shown.

[0070] Figure 8B is an example of a display screen showing the costs in the operational plan for the third example. Note that the graph in Figure 8B corresponds to Figure 8A. Figures 8A and 8B may also be displayed together on a single screen. In the graph of Figure 8B, the horizontal axis represents the constraints, and the vertical axis represents the cost for the power company. Such a graph allows for a visual comparison of costs between, for example, investing in MOX fuel without investing in interim storage facilities (indicated by the circle in Figure 8B) and investing in interim storage facilities without investing in MOX fuel (indicated by the triangle in Figure 8B). In the example in Figure 8B, it can be seen that, regardless of the constraints, investing in MOX fuel results in lower costs for the power company (the circle indicates lower costs than the triangle).

[0071] <Effects> According to this embodiment, the user inputs multiple combinations of operational constraints for the nuclear fuel cycle facility, and the processing unit 12 searches for an operational plan that minimizes the power company's costs for each pattern. This allows the user to, for example, find combinations of constraints where the values ​​of the decision variables (i.e., the content of the decision variables) are common. As a result, even when there is uncertainty about future conditions in the operation of the nuclear fuel cycle facility, an operational plan that reduces the power company's costs can be generated.

[0072] ≪Variations≫ Although the operational plan generation device 10 and operational plan generation method related to this disclosure have been described above in the form of embodiments, this disclosure is not limited to these descriptions and various modifications can be made. For example, in the embodiment, the case in which the cost of the power company is used as the objective function when generating the operational plan for the nuclear fuel cycle facility was described, but it is not limited to this. For example, the cost required to operate the nuclear fuel cycle facility and an evaluation index (correction term) other than this cost may be included in the objective function. In this case, the objective function shall be expressed by a predetermined mathematical formula that includes cost and the other evaluation index. As the other evaluation index mentioned above, for example, the total capacity of interim storage facilities in Japan or a designated area may be used. In this case, the operational plan is searched so as to reduce the cost for the power company and reduce the total capacity of the interim storage facility. Alternatively, as another evaluation index, the total area of ​​final disposal sites in Japan or a designated area may be used. In this case, the operational plan is searched so as to reduce the cost for the power company and reduce the total area of ​​final disposal sites. Other evaluation indexes that may be used include the total amount of spent fuel generated in Japan or a designated area, or the cost of constructing a new fast breeder reactor. In this case, the operation plan search unit 122 (see Figure 2) searches for an operation plan for each combination of constraints, aiming to minimize the costs required for operating the nuclear fuel cycle facility and improve other evaluation indicators under the constraints of that combination.

[0073] Furthermore, while the embodiment described a case in which the convergence determination unit 125 determines whether or not the objective function (e.g., the cost of the power company) converges in the search for an operational plan for a nuclear fuel cycle facility, the embodiment is not limited to this. For example, the processing unit 12 may terminate the search for an operational plan when the number of iterations in the search for an operational plan reaches a predetermined value. Alternatively, the processing unit 12 may sequentially display the search results for the operational plan, and the user who views the display screen may decide whether or not to terminate the search.

[0074] Furthermore, while the first and second examples in the embodiments described which of MOX fuels A and B would result in lower costs, the embodiments are not limited to this. For example, the embodiments can also be applied to the problem of how to set the ratio of the amounts (weight or volume) of MOX fuels A and B when both MOX fuels A and B are used in a given nuclear fuel cycle facility.

[0075] Furthermore, while the embodiments described the case in which a uranium-fueled light water reactor is used in the nuclear fuel cycle, the invention is not limited to this. For example, a uranium-fueled heavy water reactor may be used instead of a uranium-fueled light water reactor. Furthermore, at least a portion of the processing performed by the processing unit 12 of the operation plan generation device 10 may be performed by AI (Artificial Intelligence).

[0076] Furthermore, the processing (operation plan generation method) in the operation plan generation device 10 may be executed as a predetermined program on a computer. The aforementioned program can be provided via a communication line, or it can be written to a recording medium such as a CD-ROM and distributed.

[0077] Furthermore, this disclosure is not limited to the embodiments and includes various modifications. For example, the embodiments are described in detail for the purpose of clearly illustrating this disclosure and are not necessarily limited to having all the configurations described. Also, some of the configurations of the embodiments can be added, deleted, or replaced with other configurations.

[0078] Furthermore, each of the aforementioned configurations, functions, processing units, processing means, etc., may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the aforementioned configurations, functions, etc., may be implemented in software by having the processor interpret and execute programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0079] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0080] 10 Operation Plan Generator 11 Storage section 12 Processing Units 20 Input devices 30 Display device 41, 42, 43, ... Uranium-fueled light water reactors (nuclear fuel cycle facilities) 51, 52, 53, ... Interim storage facilities (nuclear fuel cycle facilities) 61, 62, ... Reprocessing plant (nuclear fuel cycle facility) 71, 72, ... Final disposal sites (nuclear fuel cycle facilities) 81, 82, ... MOX fuel fabrication plant (nuclear fuel cycle facility) 91, 92, 93, ... Plutonium-thermal light water reactor (nuclear fuel cycle facility) 121 Constraint condition acquisition part 122 Operations Planning and Search Department 123 Nuclear Fuel Cycle Logistics Calculation Department 124 Cost Accounting Department 125 Convergence determination unit 126 Output Control Unit B1 Start Search Button C1 Nuclear Fuel Cycle S101 Step (Constraint Acquisition Step) S103 Step (Operational Plan Search Step) S109 Step (Output control step)

Claims

1. A constraint acquisition unit acquires combinations of multiple patterns of operational constraints for nuclear fuel cycle equipment, For each of the aforementioned combinations, an operation plan search unit searches for an operation plan for the nuclear fuel cycle equipment to optimize a predetermined objective function under the constraints of that combination, An operation plan generation device comprising: an output control unit that outputs the search result of the operation plan for each of the aforementioned combinations.

2. The output control unit, when there is a decision variable whose value is common in the multiple operation plans corresponding to each of the above combinations, displays that decision variable in a way that distinguishes it from other decision variables. The operation plan generation device according to claim 1, characterized by the following:

3. The output control unit displays the search results of the operation plan for each of the above combinations, and also displays the calculation results of the objective function. The operation plan generation device according to claim 1, characterized by the following:

4. The output control unit arranges the patterns representing each combination on the horizontal axis and plots the calculation results of the objective function in the direction of the vertical axis, displaying them in graph format. The operation plan generation device according to claim 3, characterized by the following:

5. For each of the aforementioned combinations, a nuclear fuel cycle logistics calculation unit calculates the amount of a predetermined substance generated in the nuclear fuel cycle facility, The system includes a cost calculation unit that calculates the cost required to operate the nuclear fuel cycle facility based on the amount of the predetermined substance, The aforementioned objective function includes the aforementioned cost, The operation plan search unit searches for the operation plan for each of the above combinations in order to minimize the cost under the constraints of that combination. The operation plan generation device according to claim 1, characterized by the following:

6. For each of the aforementioned combinations, a nuclear fuel cycle logistics calculation unit calculates the amount of a predetermined substance produced in the nuclear fuel cycle facility, The system includes a cost calculation unit that calculates the cost required to operate the nuclear fuel cycle facility based on the amount of the predetermined substance, The aforementioned objective function includes the cost and an evaluation metric other than the said cost. The operation plan search unit searches for the operation plan for each of the above combinations, under the constraints of that combination, in order to minimize the cost and improve the other evaluation indicators. The operation plan generation device according to claim 1, characterized by the following:

7. When the constraint acquisition unit acquires multiple patterns of the above combination, and the operation plan search start button on the display screen is operated via the input device, the operation plan search unit starts searching for the operation plan. The operation plan generation device according to claim 1, characterized by the following:

8. If the number of combination patterns acquired by the constraint acquisition unit is one, and the operation plan search start button on the display screen is operated via the input device, the output control unit shall display a message indicating that it is also possible to set the number of combination patterns to multiple. The operation plan generation device according to claim 1, characterized by the following:

9. The constraint acquisition step involves obtaining combinations of multiple patterns of operational constraints for nuclear fuel cycle equipment, For each of the aforementioned combinations, an operational plan search step is performed to search for an operational plan for the nuclear fuel cycle equipment toward the optimization of a predetermined objective function under the constraints of that combination, A method for generating an operational plan, comprising: an output control step that outputs the search result for the operational plan for each of the aforementioned combinations.

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