Optimization device, optimization method, and optimization program

Abstract

To obtain an optimization device capable of reducing a calculation time required to obtain a control parameter used for controlling a control target in a presence of a disturbance.SOLUTION: An optimization device 1 for obtaining a value of a control parameter used to control a control target 2, comprises: an information acquisition unit 11 being a disturbance information acquisition unit that acquires disturbance information indicating a magnitude of a disturbance to the control target 2; and an optimization unit 12 which obtains an optimal solution for the control parameter by performing optimization processing of a variable set in an object function among variables whose value changes according to a value of the control parameter based on the disturbance information, wherein: the optimization unit 12 limits the number of the object functions used for the optimization processing in a first period, which is a period up to a disturbance peak indicated by the disturbance information, more than in a second period, which is a period after the disturbance peak.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an optimization device, an optimization method, and an optimization program for determining, through optimization processing, values ​​of control parameters used in controlling a controlled object. [Background technology]

[0002] When determining the values ​​of control parameters used to control a controlled object, an optimization process may be used to search for values ​​of the control parameters that optimize the values ​​of the variables set as objective functions whose values ​​change according to the values ​​of the control parameters. A process that uses one objective function is called a single-objective optimization process, and a process that uses multiple objective functions is called a multi-objective optimization process.

[0003] For example, Patent Document 1 discloses a technology in which, in an optimization device that optimizes the values ​​of control parameters of a control object by multi-objective optimization processing, when the control object has an integral system, the values ​​of the control parameters are individually optimized in a transient state section and a steady state section in the middle of controlling the control toward a target value. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-10072 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in multi-objective optimization processing, the more objective functions there are, the more factors related to the values ​​of the control parameters can be considered, but there is a problem in that the calculation time required for the optimization processing increases. In particular, when a disturbance exists for the controlled object, in a period in which the influence of the disturbance is dominant, the calculation time increases even though a realistic solution cannot be obtained by executing a multi-objective optimization processing that considers many objective functions.

[0006] The present disclosure has been made in consideration of the above, and aims to provide an optimization device capable of reducing the calculation time for determining control parameters used for controlling a control object in the presence of a disturbance. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the objective, the optimization device disclosed herein is an optimization device that determines values ​​of control parameters used in controlling a control object, and includes a disturbance information acquisition unit that acquires disturbance information indicating the magnitude of a disturbance to the control object, and an optimization unit that performs an optimization process to optimize variables whose values ​​change depending on the value of the control parameter and are set as objective functions based on the disturbance information, and determines an optimal solution for the control parameters, wherein the optimization unit limits the number of objective functions to be used in the optimization process in a first period, which is the period up to the peak of the disturbance indicated by the disturbance information, more than in a second period, which is the period after the peak of the disturbance. Effect of the Invention

[0008] According to the present disclosure, it is possible to reduce the calculation time required to obtain control parameters used for controlling a control target in the presence of a disturbance. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing a configuration example of an optimization device according to a first embodiment. [Diagram 2] Explanation of the operation of the optimization unit [Diagram 3] A diagram showing an example of application when the control target is a sewage treatment system. [Figure 4] FIG. 13 is a diagram showing an example of a display screen output by the optimization device. [Diagram 5] 1 is a flowchart for explaining a first example of an operation of the optimization device according to the first embodiment. [Figure 6] 11 is a flowchart for explaining a second example of the operation of the optimization device according to the first embodiment. [Figure 7] A diagram showing an example of application when the controlled object is an air conditioning system. [Figure 8] A flowchart for explaining an example of the operation of the optimization device according to the second embodiment. [Figure 9] FIG. 1 is a diagram showing an example of the configuration of a computer system that realizes an optimization device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An optimization device, an optimization method, and an optimization program according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0011] Embodiment 1 FIG. 1 is a diagram showing a configuration example of an optimization device 1 according to a first embodiment. The optimization device 1 is a device that obtains control parameters used for controlling a control target 2 by optimization processing. The optimization device 1 has an information acquisition unit 11 capable of acquiring information used for the optimization processing from the control target 2 and an information providing server 3, an optimization unit 12 that performs optimization processing based on the information acquired by the information acquisition unit 11, and a result output unit 13 that outputs an optimization result by the optimization unit 12. The control target 2 is subject to disturbance. Here, the disturbance is an element that affects the state of the control target 2, occurs outside the control target 2, and is difficult for an operator of the control target 2 to control.

[0012] The information acquisition unit 11 acquires information used in the optimization process. The information acquisition unit 11 can acquire actual measured values ​​of state variables indicating the state of the control target 2 from the control target 2. The information acquisition unit 11 can also acquire information distributed by the information providing server 3, if necessary. For example, the information acquisition unit 11 can acquire meteorological information such as the amount of precipitation distributed by the information providing server 3. The information acquisition unit 11 outputs the acquired information to the optimization unit 12.

[0013] The optimization unit 12 performs optimization processing based on the information acquired by the information acquisition unit 11 to obtain values ​​of control parameters of the control target 2. The control parameters are variables used for controlling the control target 2, and are set values ​​for the control target 2, etc. The optimization processing is a method of searching for values ​​of control parameters such that the value of a state variable, which is a variable whose value changes according to the value of the control parameter of the control target 2 and is set in an objective function, is optimized. The state variable is, for example, a variable indicating the state of an object related to the control target 2, such as a variable indicating the state of the control target 2. When the control target 2 is an object that affects the state of a user of the control target 2, such as an air conditioner, the state variable may be a variable indicating the state of the user of the control target 2. The optimization unit 12 has a first optimization unit 121 and a second optimization unit 122. The first optimization unit 121 executes optimization processing with a more limited number of objective functions than the second optimization unit 122. For example, the first optimization unit 121 executes single-objective optimization processing using one objective function. The number of objective functions used by the first optimization unit 121 may be two or more objective functions as long as it is smaller than the number of objective functions used by the second optimization unit 122. The second optimization unit 122 performs a multi-objective optimization process using more objective functions than the first optimization unit 121.

[0014] Multi-objective optimization is a method for finding a solution that optimizes multiple objective functions. In multi-objective optimization, multiple objective functions are optimized within a range that satisfies constraint conditions. Here, the objective function is set so that the value of the objective function is small when the state of the control target 2 is in a favorable state. There may be a trade-off relationship between the objective functions, and it is difficult to minimize all of the objective functions. For this reason, in multi-objective optimization, when the objective functions are f1, f2, and f3 and the weights for each objective function are α, β, and γ, a Pareto solution is found so that F(X), which is a weighted linear sum of multiple objective functions as shown in the following formula (1), is minimized. Note that although the case where the number of objective functions is three is shown here, the same applies when the number of objective functions is two or four or more, and F(X) can be a weighted linear sum of multiple objective functions.

[0015]

number

[0016] FIG. 2 is an explanatory diagram of the operation of the optimization unit 12. The horizontal axis of FIG. 2 indicates time, and the vertical axis of FIG. 2 indicates the magnitude of the disturbance. The optimization unit 12 divides the processing target period into a first period, which is the period up to the peak of the disturbance, and a second period, which is the period after the peak of the disturbance, based on disturbance information indicating the magnitude of the disturbance. Then, in the first period, an optimization process, for example, a single-objective optimization process, is performed by the first optimization unit 121, and in the second period, a multi-objective optimization process is performed by the second optimization unit 122, thereby limiting the number of objective functions used in the optimization process in the first period compared to the second period. In FIG. 2, the time t p The period up to time t p The subsequent period is defined as the second period. The optimization unit 12 can further divide each of the first period and the second period into a plurality of periods and perform optimization processing for each target period to obtain an optimal solution for the control parameters. In FIG. 2, each of the plurality of periods indicated by straight lines parallel to the horizontal axis is a target period for one optimization processing. As shown in FIG. 2, the optimization unit 12 can make the length D1 of the target period for one optimization processing in the first period longer than the length D2 of the target period in the second period. Here, the length D1 of the target period in the first period is set longer than the length D2 of the target period in the second period, but D1 may be equal to D2. Also, although it is not prohibited to make D1 shorter than D2, it is difficult to obtain a realistic solution in the first period because the influence of disturbances is dominant. For this reason, by making D1 longer than D2, the calculation time required for the optimization processing can be further reduced. Further, although it has been stated here that the length of the target period is constant within each of the first period and the second period, the length of the target period is not limited to this example and does not have to be constant.

[0017] Returning to the description of FIG. 1, the result output unit 13 outputs the optimization result, which is the result of the optimization process of the optimization unit 12. The method in which the result output unit 13 outputs the optimization result is not particularly limited. For example, the result output unit 13 may support the work of the operator by outputting the optimization result to the operator of the controlled object 2 as guidance information regarding the operation of the controlled object 2 by a display screen, a voice, lighting of a lamp, or the like, or may directly output the optimization result to the controlled object 2 as a setting value for controlling the controlled object 2 without the intervention of the operator. In addition, the result output unit 13 may be configured to output the value of the control parameter as a setting value to the controlled object 2 when the operator performs an operation to adopt a control parameter of the optimization result after providing the optimization result to the operator by a display screen or the like.

[0018] 3 is a diagram showing an application example in which the control target 2 is a sewage treatment system 2A. Here, the configuration of a typical sewage treatment system 2A is described. The sewage treatment system 2A has a sewer pipe 21 that carries sewage, an inflow culvert 22 into which the sewage flows from the sewer pipe 21, a grit basin 23 that is a reservoir for settling and removing soil and garbage in the sewage from the inflow culvert 22, and a pump well 24 into which the sewage flows from the grit basin 23.

[0019] The sewage that reaches the pump well 24 is pumped up by the lift pump 25 so that it flows into the next treatment facility, the primary settling basin 26. The lift pump 25 pumps up the sewage at a set pumping rate. Since the sewage accumulates in the pump well 24 until it is pumped up by the lift pump 25, the sewage treatment rate of the sewage treatment system 2A is controlled by the pumping rate set for the lift pump 25.

[0020] The primary settling tank 26 gently flows dirty sewage, settling small garbage and mud that were not settled in the grit tank 23, and removing dirt from the sewage. The sewage after treatment in the primary settling tank 26 flows into the reaction tank 27. The reaction tank 27 is a facility that purifies the sewage by making microorganisms eat the dirt in the sewage. In the reaction tank 27, mud containing microorganisms called activated sludge is mixed with the sewage from the primary settling tank 26 for treatment, so the sewage after treatment in the reaction tank 27 contains activated sludge. The sewage containing this activated sludge is sent from the reaction tank 27 to the final settling tank 28. The final settling tank 28 slowly flows the sewage from the reaction tank 27, settles the activated sludge, and extracts clean water. The clean water extracted in the final settling tank 28 is sent to the disinfection equipment 29. The disinfection equipment 29 disinfects the clean water after treatment from the final settling tank 28, and then discharges it into a river or other location as treated water. The disinfection facility 29 disinfects the water using chemicals.

[0021] In the case where the sewage treatment system 2A as described above is the control object 2, for example, the control parameter that the optimization device 1 obtains as a solution is the pumping amount of the lifting pump 25. The disturbance of the sewage treatment system 2A is the inflow amount of sewage into the sewage treatment system 2A. The inflow amount of sewage into the sewage treatment system 2A varies depending on, for example, the amount of precipitation. A skilled operator of the sewage treatment system 2A takes into consideration a number of factors when determining the pumping amount. For example, factors that a skilled operator takes into consideration include reducing the risk of flooding, ensuring the quality of the discharged water, and reducing the treatment cost. In order to reduce the risk of flooding in the sewage treatment system 2A, it is preferable to increase the pumping amount in order to lower the water level of the inflow culvert 22, which is the water level of the sewage before treatment stored in the sewage treatment system 2A. In addition, in order to ensure the quality of the discharged water, it is preferable to lower the total water quality value and the sludge interface, and therefore it is preferable to lower the pumping amount. In order to further reduce the treatment cost, it is preferable to lower the pumping amount so as not to use up too much electricity. Therefore, when the control target 2 is a sewage treatment system 2A, the objective function used by the second optimization unit 122 can be the water level of the inflow culvert 22, which is the water level of the untreated sewage stored in the sewage treatment system 2A, the water quality of the treated water discharged from the sewage treatment system 2A, and the power consumption of the sewage treatment system 2A. The power consumption is an example of consumed energy. In addition, the second optimization unit 122 can use constraint conditions using the demand peak, water level, cost, and water quality.

[0022] The information acquisition unit 11 can acquire state variables indicating the state of the sewage treatment system 2A. Examples of state variables include the water level of the inflow conduit 22 and effluent water quality, which is the water quality of treated water discharged by the sewage treatment system 2A. The information acquisition unit 11 can also acquire an actual measurement value of the inflow amount indicating the magnitude of a disturbance to the sewage treatment system 2A from the sewage treatment system 2A. Furthermore, the information acquisition unit 11 may acquire rainfall data at the location where the sewage treatment system 2A is located from the information providing server 3, and estimate a predicted value of the inflow amount of sewage to the sewage treatment system 2A based on the rainfall data. Here, the information acquisition unit 11 acquires a predicted value of the inflow amount of sewage as disturbance information, and the optimization unit 12 can obtain an optimal solution of the pumping amount in a future target period, which is a prediction period of the acquired predicted value of the inflow amount. The information acquisition unit 11 is an example of a disturbance information acquisition unit that acquires disturbance information.

[0023] As described above, in a situation where the influence of disturbance is dominant, even if optimization processing is performed using many objective functions, a realistic solution may not be obtained. For this reason, the optimization unit 12 of the optimization device 1 performs single-objective optimization processing in a first period, which is a period until the peak of the disturbance, and performs multi-objective optimization processing in a second period, which is a period after the peak of the disturbance. In this case, the optimization unit 12 performs single-objective optimization processing with power consumption as the objective function in the first period.

[0024] The processing of the second optimization unit 122 when applied to the sewage treatment system 2A will be described below. When using an objective function f1 indicating the water level, an objective function f2 indicating the power consumption, and an objective function f3 indicating the water quality, the second optimization unit 122 optimizes the objective functions so as to minimize F(X) shown in the above formula (1). In this case, each objective function is expressed, for example, by the following formulas (2) to (4).

[0025]

number

[0026] In formula (2), y1 is the water level of the inflow culvert 22. The objective function f1 indicating the water level is the average value during the target period of the square of the value obtained by subtracting 2 from the water level of the inflow culvert 22 and dividing the value by "4-2.3".

[0027]

number

[0028] In the formula (3), y2 is the power consumption, and the objective function f2 indicating the power consumption is the average value of the power consumption y2 in the target period.

[0029]

number

[0030] COD (Chemical Oxygen Demand) in formula (4) is the amount of pollutants in water, expressed as the amount of oxygen consumed when they are chemically oxidized by an oxidizing agent. TN (Total Nitrogen) is the sum of nitrogen compounds such as ammonia nitrogen and nitrate nitrogen. TP (Total Phosphorus) is the sum of phosphate ions and organic phosphorus. Q Total is an index that represents the total water quality value, and is expressed as a percentage of the total quantity regulated value.

[0031] Moreover, the second optimization unit 122 can perform the optimization process under constraint conditions g1 to g5 expressed by the following formulas (5) to (9), for example.

[0032]

number

[0033] The constraint condition g1 shown in formula (5) defines the upper limit value, which is the management reference value of the water level y1, and here, optimization processing is performed so that the water level y1 does not exceed 5.8 m.

[0034]

number

[0035] The constraint g2 in formula (6) defines the upper limit of the pumping volume u(t), which is 4000 m 3 Optimization processing is performed so that / h is not exceeded.

[0036]

number

[0037] The constraint g3 in equation (7) defines the upper limit of the pumping volume u(t), which is 6000 m 3 The optimization process is performed so that the maximum value of g2 does not exceed / h. Either the constraint g2 or the constraint g3 is used.

[0038]

number

[0039] Demand(u(t)) of the constraint g4 shown in formula (8) is the demand value of power consumption. The constraint g4 defines the upper limit of the demand value, and optimization processing is performed so that the demand value does not exceed the contracted power value, which is 1990 kW in this case.

[0040]

number

[0041] The constraint g5 shown in formula (9) is the index that represents the total water quality value, Q total Here, the optimization process is performed so that the upper limit of Q is not exceeded. total Since the total water quality value is expressed as a percentage of the total quantity regulation value, optimization processing is performed so that the total water quality value of the discharged water is 80% or less of the total quantity regulation value.

[0042] The result output unit 13 can output the optimization result to the operator using the display screen.

[0043] Fig. 4 is a diagram showing an example of a display screen output by the optimization device 1. Fig. 4 shows, from the top, changes over time in the pumping amount, power, and water level obtained as a result of the optimization process. By displaying such a display screen, it is possible to assist the operator in setting the pumping amount. In addition, it becomes possible for the operator to set the pumping amount while checking how the water level and water quality will change depending on the pumping amount to be set.

[0044] 5 is a flowchart for explaining a first example of the operation of the optimization device 1 according to the first embodiment. The optimization unit 12 of the optimization device 1 judges whether it is time to execute the optimization process (step S101). There is no particular restriction on the criteria for judging the execution timing of the optimization process. For example, the optimization unit 12 may execute the optimization process at a fixed time interval, may execute the optimization process when a predetermined time arrives, or may execute the optimization process when a predetermined condition is satisfied.

[0045] When it is determined that it is not time to execute the optimization process (step S101: No), the optimization unit 12 repeats the process of step S101.

[0046] When it is determined that it is time to execute the optimization process (step S101: Yes), the optimization unit 12 first executes a single-objective optimization process for the first period, which is the period up to the peak of the disturbance, using information on the first period, which is the period up to the peak of the disturbance, from the information acquired by the information acquisition unit 11 based on the disturbance information (step S102) using the first optimization unit 121.

[0047] Next, based on the disturbance information, the optimization unit 12 uses information on a second period, which is a period after the peak of the disturbance, from the information acquired by the information acquisition unit 11, to execute a multi-objective optimization process for the second period using the second optimization unit 122 (step S103).

[0048] When the optimization unit 12 outputs the optimization result to the result output unit 13, the result output unit 13 outputs the optimization result using a means such as a display screen (step S104).

[0049] FIG. 6 is a flowchart for explaining a second example of the operation of the optimization device 1 according to the first embodiment. Hereinafter, differences from the first example shown in FIG. 5 will be mainly explained. In the second example, when it is determined that it is time to execute the optimization process (step S101: Yes), the optimization unit 12 determines whether or not to limit the number of objective functions (step S201). For example, in the process of step S201, the optimization unit 12 determines whether or not to limit the number of objective functions based on the absolute value of the magnitude of the disturbance based on the disturbance information. For example, the optimization unit 12 can determine to limit the number of objective functions when the absolute value of the magnitude of the disturbance is equal to or greater than a predetermined threshold, and determine not to limit the number of objective functions when the absolute value is less than the threshold.

[0050] When it is determined that the number of objective functions is to be limited (step S201: Yes), the same processing as that from step S102 in Fig. 5 is performed. When it is determined that the number of objective functions is not to be limited (step S201: No), the optimization unit 12 executes a multi-objective optimization process for the entire period regardless of whether it is before or after the peak of the disturbance, by the second optimization unit 122 (step S202). The multi-objective optimization process performed here is the same as the process of step S103, except that the period to be processed is the entire period including the first period and the second period. After the process of step S103 or step S202 is performed, the process of step S104 is performed.

[0051] As described above, according to the first embodiment, the optimization device 1 for determining the value of the control parameter used for controlling the control target 2 includes an information acquisition unit 11 that is a disturbance information acquisition unit that acquires disturbance information indicating the magnitude of the disturbance to the control target 2, and an optimization unit 12 that performs an optimization process to optimize variables set as objective functions among the variables whose values ​​change according to the value of the control parameter based on the disturbance information to determine an optimal solution for the control parameter, and the optimization unit 12 limits the number of objective functions used in a first period that is a period until the peak of the disturbance indicated by the disturbance information, more than in a second period that is a period after the peak of the disturbance. This makes it possible to reduce the calculation time in the period until the peak of the disturbance compared to the period after the peak of the disturbance, and to reduce the calculation time for determining the control parameter of the control target 2 in which a disturbance exists.

[0052] Furthermore, the optimization unit 12 can perform a single-objective optimization process using one objective function in a first period, and a multi-objective optimization process using a plurality of objective functions in a second period.

[0053] Furthermore, the optimization unit 12 can exclude some of the state variables set in the objective function in the second time period from the state variables set in the objective function in the first time period, and obtain an optimal solution for the control parameters under constraint conditions using the excluded state variables. This makes it possible to obtain control parameters that satisfy certain conditions while excluding specific state variables from the optimization targets.

[0054] The objective function used by the optimization unit 12 in the first period may include an objective function indicating the energy consumption of the controlled object 2. In this case, the objective function indicating the energy consumption of the controlled object 2 may include at least the power consumption of the controlled object 2. In this case, in the period until the peak of the disturbance, it becomes possible to exclude from the optimization targets the other objective functions for which it is difficult to obtain a realistic optimal solution due to the influence of the disturbance, while targeting the energy consumption, particularly the power consumption.

[0055] In the first embodiment, the controlled object 2 is a sewage treatment system 2A. In this case, the control parameter is the pumping amount of the pumping pump 25 of the sewage treatment system 2A, and the inflow amount of sewage to the sewage treatment system 2A can be treated as a disturbance. In this case, the optimization unit 12 can obtain an optimal solution for the pumping amount by performing multi-objective optimization in the second period, with the objective functions being the power consumption of the sewage treatment system 2A, the water level of the sewage before treatment stored in the sewage treatment system 2A, and the water quality of the treated water discharged from the sewage treatment system 2A. In addition, the optimization unit 12 can obtain an optimal solution for the pumping amount by performing single-objective optimization to minimize the power consumption of the sewage treatment system 2A in the first period.

[0056] The optimization unit 12 may limit the number of objective functions for the entire target period up to the peak of the disturbance, or may determine whether to limit the number of objective functions based on disturbance information, and when it is determined not to limit the number of objective functions, may use the same number of objective functions in the first period as in the second period. For example, when the magnitude of the disturbance is small, the influence of the disturbance on the control target 2 may be limited. Therefore, by changing whether to limit the number of objective functions to be used depending on the magnitude of the influence of the disturbance, it is possible to obtain a more reliable and realistic optimal solution.

[0057] The information acquiring unit 11, which is a disturbance information acquiring unit, can acquire a predicted value of the magnitude of the disturbance as disturbance information. This makes it possible to obtain an optimal solution of the control parameters for a future period. Note that, although the disturbance information is a predicted value of the magnitude of the disturbance in the above, it is also possible to obtain an optimal solution of the control parameters for a past period by using an actual measured value.

[0058] In addition, the optimization unit 12 can obtain an optimal solution of the control parameters for each target period of the optimization process, and can make the length of the target period of the optimization process in the first period longer than the length of the target period in the second period. This makes it possible to obtain the optimal solution less frequently in the first period than in the second period. In the period in which the influence of disturbances is dominant, the number of objective functions is limited, so that even if the optimal solution is frequently obtained, the change in the optimal solution of the control parameters is small. Therefore, by reducing the frequency of execution of the optimization process, it is possible to further reduce the calculation time.

[0059] Moreover, according to the first embodiment, an optimization method can be provided in which the optimization device 1 determines values ​​of control parameters used in controlling the control target 2. This optimization method includes a step of acquiring disturbance information indicating the magnitude of disturbance to the control target 2, and a step of performing an optimization process for optimizing variables set as objective functions among variables whose values ​​change according to the values ​​of the control parameters based on the disturbance information to determine an optimal solution for the control parameters, in which the number of objective functions used is limited in a first period, which is a period until the peak of the disturbance indicated by the disturbance information, compared to a second period, which is a period after the peak of the disturbance.

[0060] Moreover, according to the first embodiment, an optimization program can also be provided. This optimization program causes a computer system to execute the steps of acquiring disturbance information indicating the magnitude of disturbance to the control target 2, and performing an optimization process for optimizing variables set as objective functions among variables whose values ​​change according to values ​​of control parameters used to control the control target 2, based on the disturbance information, to obtain an optimal solution for the control parameters, and in the step of obtaining the optimal solution, the number of objective functions used is limited in a first period, which is a period until the peak of the disturbance indicated by the disturbance information, compared to a second period, which is a period after the peak of the disturbance.

[0061] Embodiment 2 In the above-mentioned first embodiment, a case where the controlled object 2 is a sewage treatment system 2A has been described, but in the second embodiment, a case where the controlled object 2 is an air conditioning system 2B will be described.

[0062] 7 is a diagram showing an application example in which the controlled object 2 is an air conditioning system 2B. The air conditioning system 2B has a primary air conditioner 4 and multiple air conditioners 5-1 to 5-4. The primary air conditioner 4 has an air-cooled chiller 41 that uses outside air to cool liquid, and an absorption type chiller / heater 42 that produces chilled or hot water and supplies the chilled or hot water to the multiple air conditioners 5-1 to 5-4.

[0063] When the air conditioning system 2B as described above is the control target 2, for example, the control parameter that the optimization device 1B finds as a solution is the set temperature of the air conditioning system 2B. Furthermore, examples of disturbances to the air conditioning system 2B include solar radiation, outside temperature, and internal heat generated by people or equipment. The optimization device 1B has the same functions as the optimization device 1 according to the first embodiment. Below, the differences from the optimization device 1 will be mainly described.

[0064] The information acquisition unit 11B can acquire room information including state variables indicating the state of the space conditioned by the air conditioning system 2 B. The room information is generated, for example, for each room conditioned by each of the air conditioners 5-1 to 5-4, and can include room temperature data, indoor humidity data, actual values ​​and planned values ​​for data on the number of people in the room, etc.

[0065] Furthermore, the information acquisition unit 11B can acquire external measurement values ​​measured by a pyranometer 61, a thermometer 62, and a hygrometer 63 installed outside the locations where the air conditioners 5-1 to 5-4 are installed. The pyranometer 61 measures the amount of solar radiation, the thermometer 62 measures the outside air temperature, and the hygrometer 63 measures the outside air humidity.

[0066] Furthermore, the information acquisition unit 11B can acquire information such as the past operating record of the air conditioning system 2B, external forecast values ​​such as weather information, and air conditioner specifications indicating the specifications of the air conditioners 5-1 to 5-4.

[0067] Like the optimization unit 12, the optimization unit 12B may perform a single-objective optimization process in a first period and a multi-objective optimization process in a second period, or may perform a multi-objective optimization process in a first period using fewer objective functions than in a second period. Here, the optimization unit 12B performs a multi-objective optimization process using two objective functions in a first period and a multi-objective optimization process using three or more objective functions in a second period.

[0068] For example, the optimization unit 12B can use state variables such as room temperature, indoor humidity, energy consumption, and comfort as the objective function. The energy consumption indicates the amount of energy consumption such as electricity and gas, and is, for example, a gas usage rate and an electricity usage rate. The optimization unit 12B can also use a condition using a demand peak value of the energy consumption as a constraint condition. The optimization unit 12B uses three or more of the above state variables as the objective function in the second period, and uses two of the above state variables as the objective function in the first period. The state variables used as the objective function in the first period can be, for example, the gas usage rate and the electricity usage rate. The optimization unit 12B can exclude some of the state variables set in the objective function in the second period from the state variables set in the objective function, and can obtain an optimal solution of the set temperature, which is a control parameter, under a constraint condition using the excluded state variables. For example, the optimization unit 12B can set the room temperature as the objective function during the second period and make it the subject of optimization, and can exclude the room temperature from the state variables set in the objective function during the first period and make it out of the subject of optimization, and can find an optimal solution for the set temperature under constraint conditions using the room temperature, for example, so that the room temperature is within a predetermined range of values.

[0069] FIG. 8 is a flowchart for explaining an operation example of the optimization device 1B according to the second embodiment. Here, differences from the second operation example of the optimization device 1 shown in FIG. 6 will be mainly explained. The operation example shown in FIG. 8 differs from the second operation example of FIG. 6 in that step S301 is executed instead of step S102 in the second operation example of FIG. 6. In step S102, a single-objective optimization process for the first period is performed, whereas in step S301, a multi-objective optimization process in which the number of objective functions for the first period is limited is performed. That is, in the first embodiment, when the number of objective functions is limited, a single-objective optimization process using one objective function is performed in the first period, whereas in the second embodiment, a multi-objective optimization process using two or more objective functions that are less than the number of objective functions used in the second period is performed.

[0070] As described above, according to the second embodiment, even when the control object 2 is an air conditioning system 2B, similarly to the first embodiment, the optimization device 1B is capable of reducing the calculation time for determining the control parameters used to control the control object 2 in the presence of an external disturbance.

[0071] Furthermore, the optimization unit 12B can perform a multi-objective optimization process using two or more objective functions in the first period and fewer than the number of objective functions used in the second period. In the first embodiment, the case where the number of objective functions after the restriction is one has been described, but the number of objective functions used in the first period may be two or more as long as it is fewer than the number of objective functions used in the second period. In this case, the objective function used by the optimization unit 12B in the first period can be, for example, one that indicates the energy consumption of each of the multiple types of energy used by the air conditioning system 2B, which is the control target 2, such as the power usage rate and the gas usage rate.

[0072] In the second embodiment, the case where the controlled object 2 is an air conditioning system 2B and the number of objective functions used in the first period is two has been described, but the present invention is not limited to this example. For example, when the controlled object 2 shown in the first embodiment is a sewage treatment system 2A, the number of objective functions used in the first period can be two or more. In addition, the controlled object 2 is not limited to the above example, and can be widely applied to a system in which a disturbance exists and in a period in which the influence of the disturbance is dominant, a realistic solution cannot be obtained even if many objective functions are considered.

[0073] Next, the hardware configuration of the optimization device 1, 1B will be described. The optimization device 1, 1B is realized, for example, by a computer system. The optimization device 1, 1B may be realized by one computer system or by multiple computer systems. For example, the optimization device 1, 1B may be realized by a cloud system. In a cloud system, the hardware of the computer system and devices such as servers for each function can be arbitrarily set. For example, one computer system may have the functions of multiple devices, or multiple computer systems may have the functions of one device.

[0074] An example of the configuration of a computer system that realizes the optimization devices 1 and 1B will be described. Fig. 9 is a diagram showing an example of the configuration of a computer system that realizes the optimization devices 1 and 1B. As shown in Fig. 9, this computer system includes a control unit 101, an input unit 102, a storage unit 103, a display unit 104, a communication unit 105, and an output unit 106, which are connected via a system bus 107.

[0075] In FIG. 9, the control unit 101 is, for example, a CPU (Central Processing Unit) or the like. The control unit 101 executes an optimization program in which each process to be performed by the optimization device 1, 1B is described. The input unit 102 is, for example, composed of a keyboard, a mouse, etc., and is used by a user of the computer system to input various information. The storage unit 103 includes various memories such as a RAM (Random Access Memory) and a ROM (Read Only Memory) and a storage device such as a hard disk, and stores the program to be executed by the control unit 101, necessary data obtained in the process of the process, etc. The storage unit 103 is also used as a temporary storage area for the program. The display unit 104 is composed of an LCD (Liquid Crystal Display: liquid crystal display panel) or the like, and displays various screens to the user of the computer system. The communication unit 105 is, for example, a communication circuit that performs communication processing. The communication unit 105 may be composed of a plurality of communication circuits corresponding to a plurality of communication methods. The output unit 106 is an output interface that outputs data to an external device such as a printer or an external storage device.

[0076] Note that Fig. 9 is an example, and the configuration of the computer system is not limited to the example of Fig. 9. For example, the computer system may not include the output unit 106. In addition, when the optimization device 1, 1B is realized by a plurality of computer systems, all of these computer systems may not be the computer systems shown in Fig. 9. For example, some computer systems may not include at least one of the display unit 104, the output unit 106, and the input unit 102 shown in Fig. 9.

[0077] Here, an example of the operation of the computer system until the optimization program in which the processing of the optimization devices 1, 1B is described is in an executable state will be described. In the computer system having the above-mentioned configuration, for example, the optimization program is installed in the storage unit 103 from a CD-ROM or DVD-ROM set in a CD (Compact Disc)-ROM drive or DVD (Digital Versatile Disc)-ROM drive (not shown). Then, when the optimization program is executed, the optimization program read from the storage unit 103 is stored in an area of ​​the storage unit 103 that becomes the main storage device. In this state, the control unit 101 executes the processing as the optimization device 1, 1B according to the optimization program stored in the storage unit 103.

[0078] In the above description, a program describing the processing in the optimization device 1, 1B is provided on a CD-ROM or DVD-ROM as a recording medium. However, this is not limiting, and depending on the configuration of the computer system, the capacity of the program to be provided, etc., it is also possible to use a program provided over a transmission medium such as the Internet via the communication unit 105.

[0079] The optimization program causes a computer to execute the steps of acquiring disturbance information indicating the magnitude of the disturbance to the controlled object 2, and performing an optimization process to optimize variables whose values ​​change according to the values ​​of control parameters used to control the controlled object 2 and that are set as objective functions based on the disturbance information, to obtain an optimal solution for the control parameters. In the step of obtaining the optimal solution, the number of objective functions used is limited in a first period, which is the period up to the peak of the disturbance indicated by the disturbance information, compared to a second period, which is the period after the peak of the disturbance.

[0080] The information acquisition unit 11 shown in FIG. 1 is realized by the control unit 101, communication unit 105 and memory unit 103 shown in FIG. 9, the optimization unit 12 shown in FIG. 1 is realized by the control unit 101 shown in FIG. 9, and the result output unit 13 shown in FIG. 1 is realized by the display unit 104, output unit 106, etc. shown in FIG. 9.

[0081] The configurations shown in the above embodiments are examples of the contents of the present disclosure, and may be combined with other known technologies, and parts of the configurations may be omitted or modified without departing from the gist of the present disclosure. [Explanation of symbols]

[0082] 1,1B Optimization device, 2 Control object, 2A Sewage treatment system, 2B Air conditioning system, 3 Information providing server, 4 Primary air conditioner, 5-1 to 5-4 Air conditioner, 11,11B Information acquisition unit, 12,12B Optimization unit, 13 Result output unit, 21 Sewer pipe, 22 Inlet conduit, 23 Grit basin, 24 Pump well, 25 Lifting pump, 26 Primary sedimentation basin, 27 Reaction tank, 28 Final sedimentation basin, 29 Disinfection equipment, 41 Air-cooled chiller, 42 Absorption type hot and cold water heater, 61 Pyranometer, 62 Thermometer, 63 Hygrometer, 101 Control unit, 102 Input unit, 103 Memory unit, 104 Display unit, 105 Communication unit, 106 Output unit, 121 First optimization unit, 122 Second optimization unit.

Claims

1. An optimization device for determining values ​​of control parameters used in controlling a controlled object, comprising: a disturbance information acquisition unit that acquires disturbance information indicating a magnitude of a disturbance to the control target; an optimization unit that performs an optimization process to optimize variables that change in value according to the value of the control parameter and are set as an objective function based on the disturbance information, to obtain an optimal solution for the control parameter; Equipped with The optimization device is characterized in that the optimization unit limits the number of objective functions to be used in the optimization process in a first period, which is a period up to the peak of the disturbance indicated by the disturbance information, more than in a second period, which is a period after the peak of the disturbance.

2. 2. The optimization device according to claim 1 , wherein the optimization unit performs a single-objective optimization process using one of the objective functions in the first period, and performs a multi-objective optimization process using a plurality of the objective functions in the second period.

3. 2. The optimization device according to claim 1, wherein the optimization unit excludes a portion of the variables set in the objective function in the second time period from the variables set in the objective function in the first time period, and finds the optimal solution of the control parameters under constraint conditions using the excluded variables.

4. 2. The optimization device according to claim 1, wherein the objective function used by the optimization unit in the first period includes an objective function indicating energy consumption of the controlled object.

5. 5. The optimization device according to claim 4, wherein the objective function indicating the energy consumption of the controlled object includes at least the power consumption of the controlled object.

6. the controlled object is a sewage treatment system, the control parameter is a pumping amount of a pumping pump of the sewage treatment system; 2. The optimization apparatus according to claim 1, wherein the disturbance is an inflow of sewage into the sewage treatment system.

7. The optimization device described in claim 6, characterized in that the optimization unit finds the optimal solution for the pumping amount by performing multi-objective optimization in which the objective functions during the second period are the power consumption of the sewage treatment system, the water level of the sewage before treatment stored in the sewage treatment system, and the water quality of the treated water discharged from the sewage treatment system.

8. The optimization device according to claim 6 or 7, characterized in that the optimization unit finds the optimal solution for the pumping rate by performing single-objective optimization that minimizes power consumption of the sewage treatment system during the first period.

9. 2. The optimization device according to claim 1, wherein the optimization unit determines whether or not to limit the number of objective functions based on the disturbance information, and when it determines not to limit the number of objective functions, uses the same number of objective functions in the first period as in the second period.

10. 2. The optimization apparatus according to claim 1, wherein the disturbance information acquisition unit acquires a predicted value of the magnitude of the disturbance as the disturbance information.

11. 2 . The optimization device according to claim 1 , wherein the optimization unit sets a length of a target period of the optimization process in the first period to be longer than a length of the target period in the second period.

12. 2. The optimization device according to claim 1, wherein the optimization unit performs a multi-objective optimization process in the first period using two or more objective functions, the number of which is less than the number of objective functions used in the second period.

13. 13. The optimization device according to claim 12, wherein the optimization unit uses the objective function indicating the energy consumption of each of a plurality of types of energy used by the controlled object during the first period.

14. An optimization method for determining values ​​of control parameters used in controlling a controlled object, comprising the steps of: acquiring disturbance information indicating a magnitude of a disturbance to the controlled object; a step of performing an optimization process for optimizing variables that change in value according to the value of the control parameter and are set as an objective function based on the disturbance information to obtain an optimal solution for the control parameter; Including, An optimization method characterized in that, in the step of finding the optimal solution, the number of objective functions used is limited in a first period, which is a period up to the peak of the disturbance indicated by the disturbance information, compared to a second period, which is a period after the peak of the disturbance.

15. In the computer system, acquiring disturbance information indicating a magnitude of a disturbance to a control target; a step of performing an optimization process for optimizing variables, which change in value according to values ​​of control parameters used in the control of the controlled object and are set as an objective function, based on the disturbance information to obtain an optimal solution for the control parameters; Run the command, An optimization program characterized in that, in the step of finding the optimal solution, the number of objective functions used is limited in a first period, which is a period up to the peak of the disturbance indicated by the disturbance information, compared to a second period, which is a period after the peak of the disturbance.

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