Optimal calculation method of energy use conditions in ironworks, optimal calculation device of energy use conditions in ironworks, and operation method of ironworks

Abstract

In the optimal calculation method of energy use conditions in an ironworks, actual and predicted values of the amount of energy service produced and consumed by each plant constituting the ironworks are used to take the total energy use cost of the ironworks over a predetermined time from the current time as an evaluation function, and the use conditions of the energy equipment in the ironworks that make the value of the evaluation function smaller at each predetermined time over the predetermined time are calculated as decision variables. In the optimal calculation method of energy use conditions in the ironworks, the decision variables related to power generation equipment included in the energy equipment are calculated with an equality constraint applied in such a way that the decision variables become constant over a predetermined aggregation time.

Description

Technical Field

[0001] This invention relates to an optimal calculation method for energy utilization conditions in an ironmaking plant, an optimal calculation device for energy utilization conditions in an ironmaking plant, and an operating method for an ironmaking plant. Background Technology

[0002] Typically, an ironmaking plant consists of many factories and multiple power generation units, from the previous process (blast furnace, coke oven, steelmaking process, etc.) to the next process (rolling process, surface treatment process, etc.), utilizing energy (gas gas / steam / electricity) as shown below.

[0003] That is, by-product gases such as B-gas (B produced in blast furnaces), C-gas (C produced in coke ovens), and LD-gas (LD-converter gas) produced in converters, as well as M-gas (mixed gas) which is a mixture of these by-product gases and heat-adjusted, are used in factories and power generation facilities. Here, when the gas supply is insufficient relative to the factory's demand (e.g., the demand in the heating furnace of a rolling mill), city gas is used to supplement the factory's needs. Additionally, when the gas supply to power generation facilities is insufficient relative to a predetermined amount, heavy oil is used as a supplement. These supplementary fuels incur costs based on usage. On the other hand, when the gas supply is surplus relative to the factory's demand, the gases are rendered harmless through combustion and then diffused into the atmosphere; however, this causes energy loss and carbon dioxide emissions, and therefore should be minimized.

[0004] To reduce costs and suppress diffusion, it is necessary to appropriately adjust the utilization of storage tanks and the gas distribution of by-product gases. For example, in storage tanks, when the supply of by-product gases exceeds demand, diffusion is suppressed by prioritizing the storage of by-product gases in the tanks over diffusion, thereby increasing the storage capacity (storage tank level). Conversely, when the demand for by-product gases exceeds the supply, the demand is met by discharging stored gases from the tanks, reducing the amount of supplemental fuel used. Furthermore, when the demand for by-product gases further exceeds the supply, the output of power generation equipment decreases. In cases where this cannot be resolved, the operating level of the plant may sometimes be reduced.

[0005] Steam is supplied via LD gas, waste heat recovery boilers from the sintering furnace, boilers in CDQ (Coke Drying Quenching) equipment, and extraction from the turbine section of the power generation unit (the operation of obtaining steam from the turbine section reduces power generation). It is used in the plant (pickling tank insulation and vacuum degassing equipment in the cold rolling mill). Any shortfall in steam demand is purchased externally. The plant's electricity demand is met through CDQ, TRT (Top-pressure Recovery Turbine), power generation from the power generation unit, and purchases from the power company. Purchased electricity must be managed to a contracted amount not exceeding approximately one hour (the upper limit for purchased electricity). Furthermore, the unit price of purchased electricity varies by time of day. Therefore, during periods with high unit prices and a surplus of by-product gas supply, the output of the power generation unit is set higher to reduce purchased electricity. The lowest-cost operating conditions vary depending on the time of day and supply and demand.

[0006] Against this background, a technology for minimizing the energy utilization cost of ironmaking plants is proposed (see Patent Documents 1 and 2, and Non-Patent Document 1).

[0007] Patent Document 1: Japanese Patent No. 5862839

[0008] Patent Document 2: Japanese Patent Application Publication No. 2004-171548

[0009] Non-patent literature

[0010] Non-patent document 1: Yujiao Zeng,

[0011] The techniques described in Patent Documents 1 and 2, and Non-Patent Document 1, all aim to solve a mixed-integer programming problem suitable for appropriately describing the operating conditions and rules of a power plant in order to determine the optimal energy utilization conditions for an ironworks or power plant. This mixed-integer programming problem includes both continuous and integer variables in a determination variable vector (a variable vector obtained through a search). Here, the elements of the determination variable vector are variables representing energy utilization conditions such as gas distribution and steam distribution. The branch and bound method, often used as a rigorous solution, is frequently employed to solve this problem. However, the branch and bound method efficiently searches for the optimal solution by ensuring that no candidate solution with expected cost improvement is found, even after searching. Furthermore, the search ends when the cost difference between an executable solution satisfying all constraints and a continuously mitigated solution (the solution that mitigates a portion of the integer variables to continuous variables) falls below a certain threshold during the search process.

[0012] However, while the branch and bound method is generally an efficient search method, its computation time varies depending on the problem's structure and scale, and sometimes a satisfactory solution cannot be obtained within the set time, depending on the circumstances. This is particularly problematic in energy utilization systems seeking optimal solutions with a constant period. Patent documents 1 and 2, and non-patent document 1, describe fixed methods for improving solution quality, but no fixed methods for shortening computation time have been studied. Especially in systems like ironworks, which include multiple plants and power generation facilities, the following problem arises: with complex operating rules, the number of determining variables increases, leading to increased computation time. Summary of the Invention

[0013] This invention was made in view of the aforementioned problems, and its object is to provide an optimal calculation method and apparatus for energy utilization conditions in an ironmaking plant that can shorten the time required for optimal calculation of energy utilization conditions in the ironmaking plant. Furthermore, another object of this invention is to provide an operating method for an ironmaking plant that enables operation under optimal energy utilization conditions.

[0014] In the optimal calculation method for energy utilization conditions in an ironworks involved in this invention, the actual and predicted values ​​of the generation and consumption of energy services of each plant constituting the ironworks are used. The total energy utilization cost of the ironworks within a predetermined time period from the current moment is used as an evaluation function. The utilization conditions of the energy equipment in the ironworks are calculated as the determining variables such that the value of the evaluation function decreases at each predetermined moment within the predetermined time period. The optimal calculation method for energy utilization conditions in the ironworks includes the following steps: calculating the determining variables by applying an equation constraint in a way that makes the determining variables related to the power generation equipment contained in the energy equipment constant within a predetermined collection time period.

[0015] The preferred energy services for each of the above plants include gas, steam, and electricity.

[0016] Preferred energy equipment includes mixed gas manufacturing equipment, gas storage tanks, dry quenching equipment, blast furnace top pressure power generation equipment, and power generation equipment using by-product gases, heavy oil, or extracted gas.

[0017] The total cost of the energy use mentioned above preferably includes the costs associated with the use of heavy oil, city gas and steam, and the costs associated with the purchase of electricity.

[0018] In the optimal calculation device for energy utilization conditions in an ironworks according to the present invention, the actual and predicted values ​​of the generation and consumption of energy services of each plant constituting the ironworks are used. The total energy utilization cost of the ironworks within a predetermined time period from the current moment is used as an evaluation function. The utilization conditions of the energy equipment in the ironworks are calculated as the determining variables such that the value of the evaluation function decreases at each predetermined moment within the predetermined time period. The optimal calculation device for energy utilization conditions in the ironworks includes a unit that calculates the determining variables by applying an equation constraint in a way that makes the determining variables related to the power generation equipment included in the energy equipment constant within a predetermined collection time period.

[0019] The operation method of the ironmaking plant involved in this invention includes the following steps: operating the ironmaking plant according to the determining variables calculated by the optimal calculation method of energy utilization conditions in the ironmaking plant involved in this invention.

[0020] The optimal calculation method and apparatus for energy utilization conditions in an ironmaking plant according to the present invention can shorten the time required for optimal calculation of energy utilization conditions in an ironmaking plant. Furthermore, the operating method for an ironmaking plant according to the present invention enables operation of the ironmaking plant under optimal energy utilization conditions. Attached Figure Description

[0021] Figure 1 This is a block diagram illustrating the structure of an optimal calculation device for energy utilization conditions in an ironworks according to one embodiment of the present invention.

[0022] Figure 2 This is a flowchart illustrating the optimal computational processing flow of one embodiment of the present invention.

[0023] Figure 3 It is a diagram used to illustrate the pooling constraints of the determining variables.

[0024] Figure 4 This is a graph showing the cost shift between the present invention example and the comparative example.

[0025] Figure 5 This is a graph showing the shift in power generation between the present invention example and the comparative example. Detailed Implementation

[0026] Hereinafter, with reference to the accompanying drawings, an optimal calculation device for energy utilization conditions in an ironworks according to an embodiment of the present invention will be described.

[0027] 〔structure〕

[0028] First, refer to Figure 1 The structure of an optimal calculation device for energy utilization conditions in an ironworks according to one embodiment of the present invention will be described.

[0029] Figure 1 This is a block diagram illustrating the structure of an optimal calculation device for energy utilization conditions in an ironworks according to one embodiment of the present invention. Figure 1 As shown, the optimal energy utilization condition calculation device in an ironworks according to one embodiment of the present invention is composed of an information processing device 1 such as a workstation. The optimal energy utilization condition calculation device in an ironworks according to one embodiment of the present invention functions as an information acquisition unit 11 and an optimal calculation unit 12 by executing a computer program through a computational processing unit inside the information processing unit 1. The functions of each of the above units will be described later.

[0030] An optimal computing device with such a structure reduces the time required for optimal calculations of energy utilization conditions in a steel plant by performing the optimal calculation process shown below. (Refer to the following...) Figure 2 , Figure 3 The operation of the optimal computing device during optimal computation processing is explained.

[0031] [Optimal computational processing]

[0032] Figure 2 This is a flowchart illustrating the optimal computational processing flow of one embodiment of the present invention. Figure 2 The flowchart shown begins at the point in time when the execution instruction for optimal calculation processing is input to the information processing device 1, and the optimal calculation processing enters step S1.

[0033] In step S1, the information acquisition unit 11 acquires the actual and predicted values ​​of the energy service generation and consumption of the plant within the ironmaking plant at the current time (t=0) as input data for optimal calculation processing. Specifically, as shown in Table 1 below, the information acquisition unit 11 acquires the actual values ​​S of the generation of gas B, gas C, gas LD, and steam. B (0), S C (0), S L (0), S St (0) and predicted value S B (k), S C (k), S L (k), S StThe data (k) (k=1~N) are used as the actual and predicted values ​​of the energy service generation at the current moment. Additionally, as shown in Table 2 below, the information acquisition unit 11 acquires the actual values ​​D of the consumption of gas B, gas C, gas LD, gas M, steam, and electricity. B (0), D C (0), D L (0), D M (0), D St (0), D E (0) and predicted value D B (k), D C (k), D L (k), D M (k), D St (k), D E The data (k) (k = 1 to N) are used as the actual and predicted values ​​of energy service consumption at the current moment. Here, each predicted value is obtained by predicting the generation and consumption of each energy service based on the production plans of each plant within the ironworks. In addition to short-term plans of a few hours or 1-2 days from the current moment, there are also long-term plans of about one week; the shorter the production plan, the higher the accuracy. Therefore, the predicted values ​​obtained using this plan tend to have the same level of accuracy. Thus, step S1 ends, and the optimal calculation process proceeds to step S2.

[0034] [Table 1]

[0035] (Table 1)

[0036]

[0037] Table 2:

[0038] [Table 2]

[0039] (Table 2)

[0040]

[0041] In step S2, as shown in Table 3 below, the information acquisition unit 11 obtains the actual values ​​S of the current CDQ boiler steam quantity and TRT power generation. StCDQ (0), S ETRT (0) and predicted value S StCDQ (k), S ETRT The data (k) (k = 1 to N) are used as the input data for the optimal computation process. Therefore, step S2 ends, and the optimal computation process proceeds to step S3.

[0042] Table 3:

[0043] [Table 3]

[0044] (Table 3)

[0045]

[0046] In step S3, the information acquisition unit 11 acquires data on the actual amount of fuel used in all power generation equipment within the ironworks at the current time. Specifically, in the ironworks, in addition to B gas, C gas, and LD gas, M gas, which has been thermally adjusted by mixing with by-product gases, is typically used as fuel. Furthermore, when the predetermined power generation cannot be determined, heavy oil and exhaust gas from the CDQ turbine are sometimes used. Therefore, as shown in Table 4 below, the information acquisition unit 11 acquires the actual values ​​D of the amounts of B gas, C gas, LD gas, M gas, heavy oil, and exhaust gas used in all power generation equipment within the ironworks at the current time. BxU (0), D CxU (0), D LxU (0), D MxU (0), D OxU (0), S StxU (0) (x represents the identification number of the power generation equipment). Therefore, step S3 ends, and the optimal calculation process proceeds to step S4.

[0047] Table 4:

[0048] [Table 4]

[0049] (Table 4)

[0050]

[0051] In step S4, as shown in Table 5 below, the information acquisition unit 11 acquires the actual values ​​D of the current amounts of gas B, gas C, gas LD, and city gas supplied to the mixed gas manufacturing equipment that supplies gas M to the power generation equipment in the ironmaking plant. BPow (0), D CPow (0), D LPow (0), D TPow (0) data. Additionally, as shown in Table 5 below, the information acquisition unit 11 acquires the actual values ​​D of the current amounts of gas B, gas C, gas LD, and city gas at the mixed gas manufacturing equipment supplying gas M to the plant within the ironmaking plant. BMill (0), D CMill (0), D LMill (0), D TMill (0) data. Therefore, the processing of step S4 ends, and the optimal calculation process proceeds to the processing of step S5.

[0052] Table 5:

[0053] [Table 5]

[0054] (Table 5)

[0055]

[0056] In step S5, as shown in Table 6 below, the information acquisition unit 11 acquires the gas storage volume H of the gas storage tanks for gas B, gas C, and gas LD at the current time. BLevel (0), H CLevel (0), H LLevel (0) and the actual values ​​of inhalation and exhalation H B (0), H C (0), H L (0) data. Therefore, the processing in step S5 ends, and the optimal calculation process proceeds to step S6.

[0057] Table 6:

[0058] [Table 6]

[0059] (Table 6)

[0060]

[0061] In step S6, the information acquisition unit 11 acquires data on the unit price setpoints required to calculate the energy usage costs of the ironworks. Specifically, as shown in Table 7 below, the information acquisition unit 11 acquires the actual unit prices C of electricity, steam, heavy oil, and city gas at the current moment. Ele (0), C St (0), C O (0), C T (0) and the future contract value C Ele (k), C St (k), C O (k), C T The data is (k) (k=1~N). Therefore, the processing in step S6 ends, and the optimal calculation process proceeds to step S7.

[0062] Table 7:

[0063] [Table 7]

[0064] (Table 7)

[0065]

[0066] In step S7, the optimal calculation unit 12 performs an optimal calculation. This optimal calculation uses the data obtained in steps S1 to S6 to determine the conditions that minimize the cost of energy utilization in the ironworks during a predetermined period (k = 1 to N) from the current moment. Specifically, the determining variables in the optimal calculation (the variables searched in a way that minimizes the cost) are the usage amounts of gas B, gas C, gas LD, gas M, heavy oil, and pumping gas in the power generation equipment shown in Table 8 below, and the amounts of gas B, gas C, gas LD, and city gas D in the mixed gas production equipment shown in Table 9 below. BPow (k), D CPow (k), D LPow (k), D TPow (k), D BMill (k), D CMill (k), D LMill (k), D TMill (k) The gas storage capacity H of the gas storage tanks shown in Table 10 below BLevel (k), H CLevel (k), H LLevel (k) and inhalation and exhalation H B (k), H C (k), H L (k) Purchase electricity S EPurchase (k) Pumping volume S from the CDQ turbine StExtCDQ (k) and steam purchase amount S StPurchase (k).

[0067] Table 8:

[0068] [Table 8]

[0069] (Table 8)

[0070]

[0071] Table 9:

[0072] [Table 9]

[0073] (Table 9)

[0074]

[0075] Table 10:

[0076] [Table 10]

[0077] (Table 10)

[0078]

[0079] Furthermore, the cost to be minimized is the sum of heavy oil, city gas, steam, and purchased electricity used as supplementary fuel. Their respective unit prices are shown in Table 7. Using these, the total cost f from the current moment to N cycles later is described by the following mathematical formula (1). Additionally, the following constraints (a) to (l) are set during optimal calculation. Thus, step S7 is completed, and the series of optimal calculation processes is finished.

[0080] [Mathematical Expression 1]

[0081]

[0082] (a) B Gas balance limitation

[0083] [Mathematical Expression 2]

[0084]

[0085] (b) C Gas Balance Limitation

[0086] [Mathematical Expression 3]

[0087]

[0088] (c) LD gas balance limit [Mathematical formula 4]

[0089]

[0090] (d) Gas balance limits in plant M [Mathematical formula 5]

[0091] D M (k)=D BMill (k)+D CMill (k)+D LMill (k)+D TMill (k)…(5)

[0092] (e) Gas balance limitation of power generation equipment M

[0093] [Mathematical Expression 6]

[0094]

[0095] (f) Storage capacity of gas storage tanks

[0096] [Mathematical Expression 7]

[0097] H BLevel (k)=H BLevel (k-1)+H B (k)T…(7-1)

[0098] H CLevel (k)=H CLevel(k-1)+H C (k)T…(7-2)

[0099] H LLevel (k)=H LLevel (k-1)+H L (k)T…(7-3)

[0100] Here, T represents a fixed time increment.

[0101] (g) Power generation equipment model

[0102] [Mathematical Expression 8]

[0103]

[0104] Here, f n (n=1,2,…,x) represents a power generation model that uses the input heat and extraction volume of the power generation equipment as parameters.

[0105] [Mathematical Expression 9]

[0106] S ECDQ (k)=f CDQ (S StCDQ (k), S StExtCDQ (k))…(9) Here, f CDQ This represents a power generation model that uses the boiler steam output and extraction rate of CDQ as parameters.

[0107] (h) Power balance restrictions

[0108] [Mathematical Expression 10]

[0109]

[0110] (i) Steam balance restrictions

[0111] The amount of steam generated is equal to the amount of steam consumed during time k (=1~N).

[0112] [Mathematical Expression 11]

[0113]

[0114] (j) Upper and lower limits

[0115] Define inequalities that restrict the upper and lower limits of the determining variable. For example, if the determining variable is V, its upper limit is U. x Set the lower limit value to L x Then, it becomes the inequality restriction shown in the following mathematical expression (12).

[0116] [Mathematical Expression 12]

[0117] Lx ≤V(x)≤U x …(12)

[0118] (k) Pooling constraints for determining the time reduction variables

[0119] This setting allows for the aggregation of decision variables after a specified time, and reduces computation (search) time by using the same value for searching. For example, ... Figure 3 As shown in (a), when the terminal time is from the current time until the end of T×24 cycles, as Figure 3 As shown in (b), the data is aggregated in 3×T period units after a specified time T×13, during which a restriction is set to search using the same value. Specifically, in the process of setting the restriction conditions corresponding to the period N from the current time, the specified time is set to J, the aggregation number is set to n (a natural number), and the aggregation restriction is applied when conditions 1 and 2 shown below are both met. Furthermore, in this embodiment, the aggregation restriction conditions are applied to the determining variables related to the power generation equipment. This is because the supply and demand equipment of the factory is difficult to change due to the fixed supply and demand plan, while the operating conditions of the power generation equipment are easier to change. In addition, the specified time is preferably a time after which the accuracy of the production plan that the user wants to know as reference information is relatively low.

[0120] (Condition 1) After the specified time J and after the final time N. In other words, J+1≤t≤N holds true.

[0121] (Condition 2) The remainder criterion (t-J) mod n = 1 holds. This is based on the condition that the remainder of (t-J) divided by n is 1 (collection constraint) l = t+1, ..., min(t+n-1, N), and the equality constraint V(t) = V(l) is set.

[0122] (l) Rate of change limit

[0123] Given a specified rate of change, if its upper limit is set to U... V Set the lower limit value to L V Then, it becomes the inequality restriction shown in the following mathematical expressions (13) and (13)'.

[0124] [Mathematical Expression 13]

[0125] For k = 1, 2, ..., J, L v ≤(V(k)-V(k-1)) / T≤U v …(13)

[0126] For k = J+1, ..., N, nL v≤(V(k)-V(k-1)) / T≤nU v …(13)'

[0127] Here, mathematical formula (13)' is required to have the same rate of change as the non-aggregate time by increasing the rate of change according to the aggregation number n of the determining variable.

[0128] Example

[0129] In this invention example, the terminal time is completed in 12 cycles (1 cycle is 5 minutes). Regarding the multiple determinants representing fuel consumption to multiple power generation devices, after 7 cycles, the pooling number is set to 6 for optimal calculation. In the conventional example, the terminal time is also completed in 12 cycles, but optimal calculation is performed without pooling. The results of the two methods are compared. Furthermore, the calculation conditions for both methods are the same except for the presence or absence of pooling of determinants (unit cost, supply and demand measurements, etc.). Figure 2 The processing steps S1 to S6 shown are performed using the same computers for both. As a result, the ratio of computation time between the existing example and the present invention example (=the present invention example / existing example) is approximately 0.90, achieving a 10% reduction in computation time. This can be attributed to the effect of consolidating the constraints on the determining variables in the present invention example. At this time, as for costs, such as Figure 4 As shown, up to six cycles, the cost is almost equal to that of the existing example. However, after seven cycles of aggregation, the cost of the present invention is higher than that of the existing example. This is because, through the aggregation of determination variables, for example... Figure 5 The search range is limited by a method that ensures the power generation becomes constant after seven cycles. This method, which performs optimal calculations at constant cycles and uses the calculation results from the time before aggregation as a reference value, has a minimal impact on cost.

[0130] The embodiments of the invention made by the inventors have been described above, but the invention is not limited by the description and drawings that form part of the disclosure of the invention in this embodiment. That is, all other embodiments, examples, and applications of technology made by those skilled in the art based on this embodiment are included in the scope of the invention.

[0131] Industrial availability

[0132] According to the present invention, an optimal calculation method and apparatus for energy utilization conditions in an ironmaking plant can be provided, which can shorten the time required for optimal calculation of energy utilization conditions in the ironmaking plant. Furthermore, according to the present invention, an operation method for an ironmaking plant that enables operation under optimal energy utilization conditions can be provided.

[0133] Explanation of reference numerals in the attached figures

[0134] 1...Information processing device; 11...Information acquisition unit; 12...Optimal calculation unit.

Claims

1. An optimal calculation method of energy use conditions in an ironworks, which uses actual values and predicted values of amounts of production and consumption of energy services of each plant constituting the ironworks, to calculate, as a decision variable, a use condition of an energy device in the ironworks that makes the value of an evaluation function, which is the total cost of energy use of the ironworks for a predetermined time from a current time, smaller in each time corresponding to N times obtained by dividing the predetermined time with a predetermined period T, in the predetermined time from the current time. The optimal calculation method of energy use conditions in an ironworks according to claim 1, characterized in that comprises the steps of: determining a specified time in advance in the predetermined time, and setting a time after the specified time as a collection time in the predetermined time, in the predetermined time, calculating a decision variable related to a power generation device included in the energy device in each of the times from the current time to the specified time, calculating a decision variable in the collection time with an equation restriction that makes the decision variable related to the power generation device included in the energy device constant in the predetermined period, and a change rate restriction that makes change rates of the decision variable in the collection time and in a time without collection equal.

2. The optimal calculation method of energy use conditions in an ironworks according to claim 1, characterized in that the energy service of each plant includes gas, steam, and electric power.

3. The optimal calculation method of energy use conditions in an ironworks according to claim 1, characterized in that the energy device includes a mixed gas manufacturing device, a gas storage tank, a dry quenching device, a blast furnace top pressure power generation device, and a power generation device using by-product gas, heavy oil, or exhaust gas.

4. The optimal calculation method of energy use conditions in an ironworks according to claim 2, characterized in that the energy device includes a mixed gas manufacturing device, a gas storage tank, a dry quenching device, a blast furnace top pressure power generation device, and a power generation device using by-product gas, heavy oil, or exhaust gas.

5. The optimal calculation method of energy use conditions in an ironworks according to any one of claims 1 to 4, characterized in that the total cost of energy use includes a cost associated with use of heavy oil, city gas, and steam, and a cost associated with purchase of electric power.

6. An optimal calculation device of energy use conditions in an ironworks, which uses actual values and predicted values of amounts of production and consumption of energy services of each plant constituting the ironworks, to calculate, as a decision variable, a use condition of an energy device in the ironworks that makes the value of an evaluation function, which is the total cost of energy use of the ironworks for a predetermined time from a current time, smaller in each time corresponding to N times obtained by dividing the predetermined time with a predetermined period T, in the predetermined time from the current time. The optimal calculation device of energy use conditions in an ironworks according to claim 6, characterized in that is provided with: a unit that determines a specified time in advance in the predetermined time, and sets a time after the specified time as a collection time in the predetermined time, within the predetermined time, at each of the times from the current time to the specified time, calculating a decision variable related to a power generation facility included in the energy facility, within the aggregation time, applying an equality constraint in such a manner that the decision variable related to the power generation facility included in the energy facility becomes constant at a predetermined period, and applying a change rate constraint that makes the change rate of the decision variable within the aggregation time and at the time when aggregation is not performed equal, to calculate the decision variable.

7. An operation method of an ironworks, characterized by comprising the step of operating the ironworks based on the decision variable calculated by the optimal calculation method of the energy use conditions in the ironworks according to any one of claims 1 to 5. ​

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