CN118605327A - Control system and control method - Google Patents

Abstract

The invention provides a control system and a control method, which can obtain the optimal operation of machine equipment without solving the optimization problem in actual operation. A control system according to one embodiment includes a control device that controls a supply and demand system, the control device including: an allocation determination unit that determines allocation of the supply amounts supplied by the one or more supply devices based on the value associated with the given load, using a table in which a value associated with the load of the load device included in the supply and demand system is associated with a value associated with the supply amount of the resource of the one or more supply devices included in the supply and demand system; and an output unit that outputs a control command for realizing the distribution of the supply amount determined by the distribution determination unit to the supply and demand system.

Description

Control system and control method

This application is a divisional application of an application with a filing date of February 7, 2020, application number 202010082502.9, and invention name “Control system, control device, and control method”.

Technical Field

The invention relates to a control system, a control device and a control method.

Background Art

Conventionally, there is a known technology for predicting various loads of a plant (e.g., power load, heat load, air load, etc.) or for finding the optimal operation of the plant (e.g., operation that minimizes operating costs, exhaust gas, etc.) (e.g., Patent Documents 1 and 2).

Prior art literature

Patent Literature

Patent Document 1: Japanese Patent Application Publication No. 2003-84805

Patent Document 2: Japanese Patent No. 4059014

Summary of the invention

Problem that the invention aims to solve

In order to find the optimal operation of the equipment, it is necessary to solve optimization problems such as mixed integer programming problems and combinatorial optimization problems. However, in general, the amount of calculation required for optimization problems is large, so a large amount of computing resources is required to solve the optimization problems in a realistic time in actual operation. Therefore, for example, it is difficult to find the optimal operation of the equipment in actual operation by using machines or devices with relatively small computing resources such as controllers that control various devices in the equipment.

One embodiment of the present invention is completed in view of the above content, and its purpose is to achieve optimal operation of machinery and equipment without solving the optimization problem in actual operation.

Solutions for solving problems

In order to achieve the above-mentioned purpose, a control system involved in one embodiment is a control system including a control device for controlling a supply and demand system, the control device comprising: an allocation determination unit, which uses a table that corresponds values related to the load of a load device included in the supply and demand system with values related to the supply quantity of each resource of one or more supply devices included in the supply and demand system, and determines the allocation of the supply quantity supplied by each of the one or more supply devices according to the values related to the assigned load; and an output unit, which outputs a control instruction to the supply and demand system, the control instruction being used to implement the allocation of the supply quantity determined by the allocation determination unit.

Effects of the Invention

It is possible to achieve the best use of machinery and equipment without having to solve optimization problems in actual use.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing an example of the overall configuration of a control system according to the present embodiment.

FIG. 2 is a diagram showing an example of a supply and demand system model.

FIG. 3 is a diagram showing an example of the hardware configuration of the analysis device according to the present embodiment.

FIG. 4 is a diagram showing an example of a hardware configuration of a control device according to the present embodiment.

FIG. 5 is a diagram showing an example of a functional configuration of a control system according to the present embodiment.

FIG. 6 is a flowchart showing an example of the off-line processing according to the present embodiment.

FIG. 7 is a diagram showing an example of a control table.

FIG. 8 is a flowchart showing an example of online processing according to the present embodiment.

Description of Reference Numerals

1: control system; 10: analysis device; 20: control device; 30: supply and demand system; 101: model generation unit; 102: optimization unit; 103: table generation unit; 201: allocation determination unit; 202: output unit; 1000: control table.

DETAILED DESCRIPTION

Next, an embodiment of the present invention is described. In this embodiment, a control system 1 is described, which solves the optimization problem in an offline state and tabulates the results, thereby achieving optimal operation of the equipment without solving the optimization problem in an online state (that is, in actual operation). In addition, offline refers to when the control object of the equipment is not being controlled, and online refers to when the control object of the equipment is being controlled (that is, in actual operation).

＜Overall structure＞

First, the overall configuration of a control system 1 according to the present embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the overall configuration of a control system 1 according to the present embodiment.

As shown in Fig. 1, a control system 1 according to the present embodiment includes an analysis device 10, a control device 20, and a supply and demand system 30. The control device 20 and the supply and demand system 30 are connected to each other so as to be communicable via a control network or the like, for example.

The supply and demand system 30 is various equipment and devices, and includes a plurality of supply equipment that consumes costs to produce and supply resources, and load equipment that consumes resources to achieve prescribed functions.

For example, in the case where the supply and demand system 30 is a food manufacturing machine, the cost is electricity, the supply equipment is a refrigerator, and the load equipment is a manufacturing equipment, etc. In this case, for example, the supply equipment consumes electricity to produce and supply cold water (or cold and hot), and the load equipment needs cold water to manufacture food.

In addition, for example, when the supply and demand system 30 is a steelmaking machine, the cost is fuel, the supply equipment is a power generation equipment, and the load equipment is a conveyor belt, etc. In this case, for example, the supply equipment consumes fuel to produce and supply electricity, and the load equipment requires electricity to drive the conveyor belt.

In addition, for example, when the supply and demand system 30 is a BTG (Boiler, Turbine, Generator) equipment, the cost is fuel, the supply equipment is a boiler, and the load equipment is a turbine generator, etc. In this case, for example, the supply equipment consumes fuel to produce and supply steam, and the load equipment requires steam to produce electricity.

In addition, the supply and demand system 30 described above is an example, and the present embodiment can be applied to any supply and demand system 30 including a plurality of supply facilities and one or more load facilities.

The analysis device 10 is a general computer or computer system (for example, a PC (personal computer), a general server, etc.), and analyzes the model of the supply and demand system 30 (hereinafter referred to as "supply and demand system model 300") in an offline state to generate a control table 1000. The control table 1000 is data for the control device 20 to obtain the optimal operation of the supply and demand system 30 in an online state, for example, a table that corresponds the required amount of resources required by the load equipment and the optimal allocation of the supply amount of resources of each supply equipment. A specific example of the control table 1000 will be described later.

Here, the optimal allocation (hereinafter referred to as "optimal allocation") is the allocation of the supply amount of each supply device that minimizes the cost of the supply and demand system 30 with respect to the required amount of resources required by the load device. The optimal operation of the supply and demand system 30 means operating the supply and demand system 30 with the optimal allocation (that is, controlling the supply amount of each supply device so as to achieve the optimal allocation).

The control device 20 is a controller of various types (e.g., a PLC (Programmable Logic Controller) etc.), and uses the control table 1000 in an online state to obtain the optimal operation of the supply and demand system 30 (that is, the optimal distribution of the supply amount of each supply device), and controls the supply and demand system 30 so that it is optimally operated. In addition, generally speaking, the control device 20 has limited computing resources (e.g., the processing performance of the processor is low) in most cases compared to the analysis device 10. In particular, for example, a controller with more limited computing resources than a general PLC can be used as the control device 20.

In addition, the overall structure of the control system 1 shown in FIG1 is an example, and other structures are also possible. For example, the control system 1 may include a plurality of analysis devices 10, and may include a plurality of control devices 20.

＜Supply and demand system model 300＞

Here, as an example, a supply and demand system model 300 in a case where the supply and demand system 30 is a food production facility will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the supply and demand system model 300.

The supply and demand system model 300 shown in Fig. 2 includes N refrigerators 301 ₁ to 301 _N , a pipe 302, and a manufacturing facility 303. In the supply and demand system model 300 shown in Fig. 2 , each refrigerator 301 ₁ to 301 _N consumes power to produce cold water, and supplies the cold water to the manufacturing facility 303 via the pipe 302. In the example shown in Fig. 2 , each refrigerator 301 ₁ to 301 _N is a supply facility 1 to a supply facility N, and the manufacturing facility 303 is a load facility.

In the present embodiment, the supply and demand system 30 will be described below using the supply and demand system model 300 shown in FIG. 2 as an example.

＜Hardware Structure＞

Next, the hardware configurations of the analysis device 10 and the control device 20 included in the control system 1 according to the present embodiment will be described.

《Analysis Device 10》

Fig. 3 is a diagram showing an example of the hardware configuration of the analysis device 10 according to the present embodiment. As shown in Fig. 3, the analysis device 10 according to the present embodiment includes an input device 11, a display device 12, an external I/F 13, a communication I/F 14, a processor 15, and a memory device 16. These hardware components are connected via a bus 17 so as to be able to communicate with each other.

The input device 11 is, for example, various buttons, a touch panel, a keyboard, a mouse, etc. The display device 12 is, for example, a display, etc.

The external I/F 13 is an interface with an external device. As an external device, there is a recording medium 13a, etc. The analysis device 10 can read and write to the recording medium 13a via the external I/F 13. As the recording medium 13a, there is, for example, an SD memory card (SD memory card), a USB memory, etc.

The communication I/F 14 is an interface for connecting the analysis device 10 to a communication network. The processor 15 is, for example, a computing device such as a CPU (Central Processing Unit). The memory device 16 is, for example, a storage device such as a RAM (Random Access Memory), a ROM (Read Only Memory), a HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory.

The analysis device 10 involved in this embodiment can realize the offline processing described later by having the hardware structure shown in Figure 3. In addition, the hardware structure shown in Figure 3 is an example, and the analysis device 10 can also have other hardware structures. For example, the analysis device 10 can have multiple processors 15 and multiple memory devices 16.

《Control Device 20》

Fig. 4 is a diagram showing an example of the hardware configuration of the control device 20 according to the present embodiment. As shown in Fig. 4, the control device 20 according to the present embodiment includes an input device 21, a display device 22, an external I/F 23, a communication I/F 24, a processor 25, and a memory device 26. These hardware components are connected via a bus 27 so as to be able to communicate with each other.

The input device 21 is, for example, various buttons, etc. The display device 22 is, for example, various display panels, etc. The external I/F 23 is an interface with an external device. As an external device, there is a recording medium 23a, etc. The control device 20 can read and write to the recording medium 23a via the external I/F 23. As the recording medium 23a, there is, for example, an SD memory card, a USB memory, etc.

The communication I/F 24 is an interface for connecting the control device 20 to a communication network. The processor 25 is a computing device such as a microprocessor. The memory device 26 is a storage device such as a flash memory. The memory device 26 may be an SD memory card or the like.

The control device 20 involved in this embodiment can realize the online processing described later by having the hardware structure shown in Figure 4. In addition, the hardware structure shown in Figure 4 is an example, and the control device 20 may also have other hardware structures. For example, the control device 20 may have multiple processors 25 and multiple memory devices 26.

＜Functional structure＞

Next, the functional configuration of the control system 1 according to the present embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the functional configuration of the control system 1 according to the present embodiment.

《Analysis Device 10》

5 , the analysis device 10 according to the present embodiment includes a model generation unit 101, an optimization unit 102, and a table generation unit 103. Each of the above units is implemented by, for example, causing the processor 15 to execute one or more programs installed in the analysis device 10.

The model generation unit 101 generates a supply and demand system model 300. The supply and demand system model 300 is composed of models of supply equipment and load equipment included in the supply and demand system 30 (hereinafter referred to as "equipment models") and the like.

Here, the facility model of the supply facilities 1 to N can be expressed by the following equation (1), for example, for n=1, ..., N.

[Formula 1]

eff＝f(s；r)(1)

Here, s represents the supply amount, eff represents the efficiency, and r represents the condition. The efficiency eff is represented by supply amount/cost. If the cost is set to c, then eff = s/c. In addition, the condition r is, for example, various conditions for the supply amount of the equipment, conditions set by the user, environmental conditions (external conditions), etc. In addition, in this embodiment, the equipment model is used to represent the relationship between efficiency and cost, but more generally, the equipment model can be expressed as a model that represents the relationship between supply amount and cost.

For example, when the supply device is a refrigerator, the condition r includes the temperature of the cold water supplied to the load device, etc. Also, when the supply device is a boiler, the condition r includes the temperature of the water supplied to the boiler, etc.

The optimization unit 102 uses information representing a set of possible values of the condition r (hereinafter referred to as "condition set information") and the supply and demand system model 300 generated by the model generation unit 101 to solve the optimization problem for each combination of a pattern of supply quantities that the supply equipment can supply and the possible values of the condition r, thereby calculating the optimal allocation for that combination.

For example, if the maximum value of the supply amount that supply device n can supply is set to _Sn , the maximum value of the supply amount that all supply devices can supply is expressed as _Smax = _S1 + _S2 + ... + _SN . Therefore, for example, if _σ1 = 0.1 × _Smax , _σ2 = 0.2 × _Smax , ..., _σ10 = 1.0 × _Smax , { _σ1 , _σ2 , ..., _σ10 } is a pattern of the supply amounts that the supply devices can supply.

Therefore, for example, when the set of possible values of the condition r is {r ₁ , r ₂ , r ₃ }, the optimization unit 102 solves the optimization problem for each combination of σ _i (i=1, ..., 10) and r _j (j=1, 2, 3) to determine the optimal allocation.

More specifically, assuming that the supply amount of supply equipment n is _sn and the cost is c _n , the optimization unit 102 solves the optimization problem represented by the following equation (2) for each combination of i and j.

[Formula 2]

Thus, the optimal allocation s ₁ ^{(i, j)} , ..., s _N ^{(i, j)} is obtained for each combination of i and j. The optimization problem shown in equation (2) can be solved by a known method (for example, the method disclosed in Patent Documents 1 and 2).

The above-mentioned supply amount pattern is an example, and other patterns may be used. As the supply amount pattern, any pattern may be used as long as it is a pattern of supply amount that can be supplied by all supply facilities that supply resources to load facilities in the supply and demand system 30.

The table generation unit 103 generates the control table 1000 using the optimal allocation calculated by the optimization unit 102. The table generation unit 103 generates the control table 1000 by, for example, associating the optimal allocation with each combination of the supply amount and the condition r. The control table 1000 is stored in the memory device 16, for example.

In the present embodiment, the supply and demand system model 300 is generated by the analysis device 10, but the present invention is not limited thereto, and the supply and demand system model 300 generated by another device or apparatus may be provided to the analysis device 10. Therefore, the analysis device 10 does not necessarily have the model generation unit 101.

《Control Device 20》

5 , the control device 20 according to the present embodiment includes an allocation determination unit 201 and an output unit 202. These units are implemented by, for example, causing the processor 25 to execute one or more programs installed in the control device 20.

The allocation determination unit 201 uses information indicating the value of the current condition r (hereinafter referred to as "condition information") and the current demand of the load device included in the supply and demand system 30 (hereinafter referred to as "current demand value") to determine the optimal allocation based on the control table 1000. In addition, the control table 1000 is stored in the memory device 26, for example. The control table 1000 can be stored in the memory device 26 by being read out from the recording medium 23a, etc., or can be stored in the memory device 26 by being received from the analysis device 10 via a communication network, etc.

The output unit 202 outputs (transmits) a control instruction for realizing the optimal allocation determined by the allocation determination unit 201 to the supply and demand system 30 (more precisely, a device or machine that controls the supply amount of the supply equipment). Thus, the optimal operation of the supply and demand system 30 is realized.

<Offline Processing>

Next, the offline process according to the present embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the offline process according to the present embodiment.

Step S101: First, the model generation unit 101 generates a supply and demand system model 300. In addition, in generating the supply and demand system model 300, a known method (for example, the method disclosed in the above-mentioned patent documents 1, 2, etc.) may be used, but for example, the supply and demand system model 300 can be generated by generating a device model based on the catalog specifications of the supply device, etc.

Step S102: Next, the optimization unit 102 uses the condition set information and the supply and demand system model 300 generated by the above step S101 to solve the optimization problem for each combination of the supply quantity pattern that the supply equipment can supply and the value that the condition r can take, thereby calculating the optimal allocation for this combination.

Generally speaking, when the supply amount pattern is set to {σ ₁ , …, σ _I } and the condition set information is set to {r ₁ , …, r _J }, the optimization unit 102 solves the optimization problem shown in the above formula (2) for each combination of i (i=1, …, I) and j (j=1, …, J), thereby calculating the optimal allocation s ₁ ^{(i, j)} , …, s _N ^{(i, j)} for each combination of i and j.

Step S103: Next, the table generation unit 103 generates the control table 1000 using the optimal assignments s ₁ ^{(i , j)} , ..., s _N ^{(i, j)} calculated in the above-mentioned step S102.

Here, for example, it is assumed that I=10, J=3, {σ ₁ =0.1×S _max , σ ₂ =0.2×S _max , ..., σ ₁₀ =1.0×S _max }, {r ₁ = "condition 1", r ₂ = "condition 2", r ₃ = "condition 3"}. In addition, the ratio between σ _i and the maximum value S _max of the supply amount that can be supplied by the supply facilities 1 to N under the condition r _j is defined as the load factor d ^{(i, j)} , and the ratio between s _n ^{(i, j)} and the maximum value S _n of the supply amount that can be supplied by the supply facility n under σ _i and the condition r _j is defined as the operation rate _an ^{(i, j)} . At this time, the optimization unit 102 associates the condition r _j , the load factor d ^{(i, j)} , and the operation rate _an ^{(i, j)} of each supply facility n for each combination of i and j, thereby generating a control table 1000. FIG. 7 shows the control table 1000 generated in this way. FIG. 7 is a diagram showing an example of the control table 1000 .

In the control table 1000 shown in Fig. 7, the condition _rj , the load factor d ^{(i,j), and the operation rate an(i,j)} _of ^each supply facility n are associated with each combination of i and j, for n=1, ..., N. For example, when i=1, j=1, the load factor d ^(1,1) =10% is associated with the operation rates _a1 ^(1,1) to _aN ^(1,1) of the supply facilities 1 to N.

In this way, the analysis device 10 according to the present embodiment uses the predetermined supply amount pattern and the values that the condition can take to calculate the optimal distribution of the supply amount of each supply device, and on this basis, generates the control table 1000 based on the calculation result. As described later, the control device 20 according to the present embodiment, when given the condition information and the current value of the required amount of the load device, can calculate the optimal distribution of the supply amount of each supply device using the control table 1000. In other words, the control device 20 according to the present embodiment can obtain the optimal distribution without solving the optimization problem.

<Online processing>

Next, the online processing involved in the present embodiment is described with reference to FIG8. FIG8 is a flowchart showing an example of the online processing involved in the present embodiment. For example, the following steps S101 to S102 are repeatedly executed in each control cycle. In addition, for example, condition information and a current value of the required amount are given to the control device 20 in each control cycle.

Step S201: The distribution determination unit 201 determines the optimal distribution s ₁ , ..., s _N from the control table 1000 using the given condition information and the current value of the demand amount.

For example, when the condition information is r _now and the ratio between the current value of the demand and the maximum value S _max of the supply amount that can be supplied by the supply equipment 1 to N is the current load factor d _now , the allocation decision unit 201 searches the control table 1000 using the condition r _j and the load factor d ^{(i, j)} as keywords to obtain the operation rates a ₁ ^{(i, j)} to a _N ^{(i, j)} corresponding to the condition information r _now and the current load factor d _now . Then, the allocation decision unit 201 determines the optimal allocations s 1 , ..., s N by s _n = a _n ^{(i, j)} × S _n for each n = ₁ , ..., _N. In addition, S _n is the maximum value of the supply amount that can be supplied by the supply equipment n.

Here, when there is no load factor d ^{(i, j)} that matches the current load factor d _now , for example, the minimum load factor d ^{(i, j)} that exceeds the current load factor d _now may be set as the matching load factor d ^{(i, j)} .

Alternatively, when there is no load rate d ^{(i, j)} that _is consistent with the current load rate d _now , for example, the optimal allocation may be obtained by proportionally allocating the operation rates a 1 ^{(i′, j)} to a N (i′, j) and _{a 1} ^{(i″, j)} to a N ^{(i″, j} ) corresponding to the load rate before and the load rate after _{the current} load rate d _now (that is, the maximum load rate d ^{(i′, j)} that satisfies _d ( ^{i′, j)} ≤ d now and the _minimum load rate d ^{(i″, j} ) that satisfies d now ≤ _d ^{(i″, j))} . Specifically, for example, for n = 1, ..., N, the optimal allocation s _n of the supply equipment n may be set to s _n = (1-w) × a _n ^{(i′, j)} + w × a _n ^{(i″, j)} . Here, w is w = (d _now - d ^{(i′, j)} )/(d ^{(i″, j)} - d ^{(i′, j)} ). For example, when a refrigerator can be continuously and finely controlled, such a method of performing proportional allocation (interpolation) is effective.

Step S202: Then, the output unit 202 outputs a control command for realizing the optimal allocation s ₁ , ..., s _N determined in the above step S202 to the supply and demand system 30. Thus, the optimal operation of the supply and demand system 30 is realized.

In this way, the control device 20 involved in the present embodiment uses the control table 1000 generated in an offline state to determine the optimal allocation of the supply amount of each supply device. At this time, the control device 20 involved in the present embodiment can determine the optimal allocation only by searching the control table 1000 or calculating the search result. That is, the control device 20 involved in the present embodiment does not need to solve the optimization problem when determining the optimal allocation. Therefore, even the control device 20 with more limited computing resources than a general computer or computer system can determine the optimal allocation without delay in an online state and achieve optimal operation.

The present invention is not limited to the above-described specifically disclosed embodiments, and various modifications, changes, and combinations with prior arts can be made without departing from the scope of the claims.

Claims (6)

1. A control system, comprising a control device for controlling a supply and demand system, The control device has: an allocation determination unit that uses a table that associates conditions related to the supply and demand system, values related to loads of load devices included in the supply and demand system, and values related to supply amounts of resources of each of one or more supply devices included in the supply and demand system, and determines allocation of supply amounts supplied by each of the one or more supply devices based on the assigned values related to the loads; and an output unit that outputs a control instruction to the supply and demand system, wherein the control instruction is used to implement the distribution of the supply amount determined by the distribution determination unit, Wherein, the control system further comprises an analysis device for generating the table, The analysis device includes a generation unit that generates the table using a model that represents a relationship between a supply amount of a resource of each of the one or more supply devices and a cost when the resource is supplied in the supply amount. The generating unit, in an offline state, solves an optimization problem for each combination of the pattern of the total supply amount that can be supplied by the one or more supply devices and the conditions related to the supply and demand system, for each total supply amount among the plurality of total supply amounts included in the pattern of the total supply amount that can be supplied by the one or more supply devices, calculates the optimal allocation under the combination so as to minimize the total cost for supplying the total supply amount, and generates the table, The distribution determination unit determines the distribution of the supply amounts supplied by each of the one or more supply facilities based on a given system condition related to the system and a given load condition related to the load in an online state. 2. The control system according to claim 1, wherein: The value related to the load is the load rate of the load equipment. The value related to the supply amount is the operation rate of the supply device. 3. The control system according to claim 2, wherein: When there is no load factor matching the load condition in the table, the distribution determination unit determines a value obtained by proportionally distributing operation factors corresponding to the load factors before and after the load condition as the optimal distribution. 4. The control system according to any one of claims 1 to 3, wherein: In the case where the supply device is a refrigerator, the condition related to the supply and demand system is the temperature of the cold water supplied to the load device. When the supply device is a boiler, the condition related to the supply and demand system is the temperature of water supplied to the boiler. 5. The control system according to any one of claims 1 to 3, wherein: The model represents the relationship between the supply amount of a resource of each of the one or more supply devices and efficiency, and the efficiency represents the ratio of the supply amount to the cost when the resource is supplied in the supply amount. 6. A control method is a control method used for a control system, the control system comprising a control device for controlling a supply and demand system, In the control method, the control device performs the following steps: an allocation determination step of determining the allocation of the supply amount supplied by each of the one or more supply devices according to the assigned value related to the load, using a table that associates the condition related to the supply and demand system, the value related to the load of the load device included in the supply and demand system, and the value related to the supply amount of each resource of one or more supply devices included in the supply and demand system; and an output step of outputting a control instruction to the supply and demand system, wherein the control instruction is used to implement the distribution of the supply amount determined by the distribution determination step, Wherein, the control system further comprises an analysis device for generating the table, The analysis device includes a generation unit that generates the table using a model that represents a relationship between a supply amount of a resource of each of the one or more supply devices and a cost when the resource is supplied in the supply amount. The generating unit, in an offline state, solves an optimization problem for each combination of the pattern of the total supply amount that can be supplied by the one or more supply devices and the conditions related to the supply and demand system, for each total supply amount among the plurality of total supply amounts included in the pattern of the total supply amount that can be supplied by the one or more supply devices, calculates the optimal allocation under the combination so as to minimize the total cost for supplying the total supply amount, and generates the table, In the distribution determination step, distribution of the supply amounts supplied by each of the one or more supply facilities is determined based on a given system condition related to the system and a given load condition related to the load in an online state.