JP5033710B2 - Plant operation planning device and program thereof - Google Patents

Description

The present invention relates to a plant operation plan planning apparatus for planning an operation plan for a plant such as a manufacturing plant and a program therefor.

In recent years, with the need for energy saving, there is a demand for minimizing operation costs while performing highly accurate production management in manufacturing plants.
For example, in a utility plant that supplies electric power and steam to a manufacturing process, it is necessary to reduce the operating cost while satisfying the energy demand from the manufacturing process. In addition, in a manufacturing plant having an in-house power generation facility, it is necessary to minimize the operating cost while taking into consideration the buying and selling of electric power.

Until now, many manufacturing plants depended on the intuition and experience of operators for plant monitoring and control. In particular, unlike manufactured products, the automation of monitoring of energy supply facilities is said to be delayed compared to the manufacturing process side because demand prediction is difficult and modeling is difficult due to sudden additional demand.
However, with the recent improvement in computer performance, the plant monitoring control and the automation range have been expanded through the introduction of control computers, and energy supply facilities are also targeted.

In an energy supply facility of a manufacturing plant, that is, a utility plant, the start / stop of the equipment is determined in consideration of energy demand. Therefore, if the energy demand is known in advance, an operation plan for the equipment can be made in consideration of the demand data.
Therefore, automation of operation planning is also progressing. For example, in Patent Document 1 and Patent Document 2, application of linear programming such as solving by mixed integer programming is popular.
JP 2004-317049 A JP 2004-171548 A Japanese Patent Laid-Open No. 7-225038 Toshihide Ibaraki, Masao Fukushima: Optimization programming: Iwanami Shoten (1991)

By the way, in the above-mentioned linear programming, the optimization answer is given as a real value including an integer.
In the above-mentioned utility plant, from the viewpoint of energy saving operation, a plan for starting or stopping the facility equipment, for example, a plan for setting it to 0 or 1, is required instead of putting the equipment into a standby state.
Non-Patent Document 1 also describes a mixed integer programming problem, but the calculation process is complicated, and it is considered unsuitable for responding to demands that change from moment to moment.
Patent Document 1 and Patent Document 2 describe a method of solving by a mixed integer programming, but there are problems such as a heavy program and difficult adjustment coefficient setting.

The present invention has been made in view of the above circumstances, and aims to provide an operation plan for a plant such as a production plant, that is, a plant operation plan planning apparatus and a program thereof that can determine the start / stop state of plant equipment at a low cost. To do.

To achieve the above object, the plant operation planning apparatus according to a first aspect of the present invention is a plant operation planning device for determining the driving or stopping of the equipment for each timekeeping the plant, the energy per time Demand amount or predicted amount bi (i: natural number indicating each energy) data is stored, and data on operating cost cj (j: natural number indicating each facility) for each equipment is stored. An operating cost storage unit, an equipment performance storage unit for storing data of the supplied energy amount aij for each equipment device, data of a demand amount or predicted amount bi of each energy, and data of an operating cost cj for each equipment device, And the data of the supply energy amount aij for each equipment is read, and for each energy, c1 / a11> = c2 / a12> = c3 / a13> = c4 / a14> =... When the order of the subscripts j is sorted, the operation of each equipment is represented as 1 and the function representing the stop as 0 is xj, the supply energy amount (Σaij × xj) of the equipment during operation of each energy is Demand amount or predicted amount (bi) of each energy and (supplied energy amount of facility equipment in a stopped state) + (demand amount or predicted amount bi of each energy) << = (supplied energy amount of all equipment devices) ) On the condition that the two sub-items j) are satisfied, the first operation for determining whether to operate or stop each equipment device so that Σcj × xj is maximized in the order of the subscript j, and As a result, the equipment that is determined to be operating with at least one energy is determined to be operating, the other equipment is determined to be stopped, and the result of the second calculation is that the operating state is changed from the operating state to the stopped state. The determined equipment is the normal operating cost. The operating cost cj is updated as the operating cost cj, and the equipment that has been determined to be operating from the stopped state is updated as the operating cost cj by subtracting the fuel saving cost from the normal operating cost. In addition, the equipment that has been determined to continue the operation state includes an optimization calculation unit that executes a third calculation for updating the normal operation cost as the operation cost cj, and the equipment that is the result of the second calculation. And an operation planning means for reading operation or stop data and outputting information on start / stop of the equipment at each time based on the data .

Program plant operation planning device according to a second aspect of the present invention, Ru program der for realizing functions of the first plant operation planning apparatus of the present invention.

  ADVANTAGE OF THE INVENTION According to this invention, the start stop state of the plant equipment can be determined in cost reduction.

Embodiments of the present invention will be described below with reference to the accompanying drawings.
<Overview>
The manufacturing plant operation planning method P according to the embodiment of the present invention is based on the energy demand data, the operating cost of the utility plant, and the equipment performance in facilities such as the manufacturing plant, and the supply capacity of the utility plant exceeds the demand. The start and stop of the equipment is determined so as to achieve the minimum operation cost, and low-cost operation of equipment such as a manufacturing plant, that is, energy-saving operation is realized.

<< Overall structure >>
FIG. 1 is a diagram showing a configuration for realizing a manufacturing plant operation planning method P according to an embodiment of the present invention.
The manufacturing plant operation plan planning method P according to the embodiment includes energy demand data 10, operation cost data 30, equipment performance data 40 that are input data, and optimization calculation means 20 and operation plan planning means that are processing units. 50 and operation plan data 60 that is an output. The hardware configuration for realizing the manufacturing plant operation plan planning method P will be described later.

<Energy demand data 10>
The energy demand data 10 of the input data shown in FIG. 1 describes the requirements for the manufacturing process of the manufacturing plant, and includes the demand on the day or forecast data. Examples of energy include electric power used in factories and the like, steam used to make hot water in a heat storage tank, and the like.
In the energy demand data 10, an energy demand amount per unit time is recorded.
FIG. 2 is a diagram illustrating an example of the energy demand data 10 according to the embodiment.
The time after the second row in the first column in FIG. 2 is a unit time for acquiring a change in demand, for example, a time unit or a day unit.

Figure 2 shows the energy demand items of the manufacturing plant shown in the first line of FIG. 2, ie, power (demand), steam (demand), other A (demand), other B (demand), and other C (demand) of the manufacturing plant. In the time unit shown in the first column of 2, each data is described in chronological order after the second row.
In FIG. 2, the example which described the energy demand data 10 of 1 hour interval is shown.
For example, the power demand at time 3 shown in FIG. 2 is w003 and the steam demand is F003. In addition, the energy demand item of the column item of FIG. 2 is added according to a demand object. The energy demand data 10 may be a predicted value.

<Operating cost data 30>
The operation cost data 30 which is the input data shown in FIG. 1 describes the cost required for the operation of the equipment in the manufacturing plant. For each equipment device, for example, the cost per unit time is recorded as the operating cost. The operating cost is numerical data represented by a monetary amount.
FIG. 3 is a diagram illustrating an example of the operation cost data 30 according to the embodiment.

The cost required for operation for each utility facility device in the manufacturing plant, that is, for each utility facility device of A, B, C,... In the first column shown in FIG. (After the line) is described. When updating the operating cost according to the operating state, for example, updating is performed using data described in other items. For example, when the equipment device ID is A, the operation cost during steady operation indicates p1 per hour, and q1 indicates the activation cost required when A is activated from the stop state. The same applies when the equipment ID is B, C,.
For example, when the equipment B is in a stopped state, the cost required for activation is q2 described elsewhere, and the operating cost of the stopped equipment B is set as p2 + q2.

<Facility performance data 40>
The facility performance data 40, which is input data shown in FIG. 1, records the energy supply capability of the facility equipment in the manufacturing plant. For example, the power generation amount or steam generation amount that can be supplied per unit time is a specification possessed by each equipment.
FIG. 4 is a diagram illustrating an example of the facility performance data 40 according to the embodiment.
The rated quantity that can be supplied is described for each energy item for each utility facility device in the manufacturing plant. For example, the device with the equipment device ID A shown in FIG. 4 can supply power E1 and steam S1 per unit time by itself. The same applies when the equipment ID is B, C,.

<Optimization calculation means 20>
The optimization calculation means 20 of the processing unit shown in FIG. 1 has a function of solving a problem that satisfies a predetermined constraint condition and optimizes a predetermined objective function.
The predetermined constraints are that, firstly, supply exceeding the supply capacity is impossible, so that energy is produced within the range of the supply capacity of the manufacturing plant, and second, energy production cannot be negative. This means that energy production is zero or more.
The predetermined objective function is a mathematical expression representing the operating cost of the manufacturing plant, a mathematical expression representing the greenhouse gas emission amount, or the like. The objective is to optimize, ie minimize, operating costs and greenhouse gas emissions.

In addition, since the operation plan related to the start / stop of the equipment is created, for example, the start of the equipment is represented by “1” and the stop of the equipment is represented by “0”, so the optimization calculation means 20 solves the integer programming problem. It has a function and determines the start / stop state of the equipment. Various methods have been proposed for this integer programming problem, which is also described in Non-Patent Document 1.
In the present embodiment, a solution method for the “knapsack problem” described on pages 359 to 394 described in Non-Patent Document 1 is used.

<Operation planning means 50>
The operation plan planning means 50 of the processing unit shown in FIG. 1 has a function of associating the output value of the optimization calculation means 20 as operation plan data. The output result of the optimization calculation means 20 is “1” or “0” data corresponding to the start / stop state of the equipment or ON / OFF data.
Moreover, the said operating cost data 30 may be updated according to the state of the said equipment.

For example, when the equipment is stopped, the operating cost data 30 is corrected to a large value by adding the starting cost required for starting. In addition, since the start-up cost is not incurred when operating, the operation cost is corrected to a small value so as to keep the state. Similarly, when the cost for stopping is required, the operating cost is corrected to be small. The reason why the startup cost is high is that, for example, in the case of a boiler, the hot standby idle state is taken at startup.
With this configuration, it is possible to reduce the cost and save energy by continuing the operation of the operating equipment and stopping the stopped equipment.

<Operation plan data 60>
The operation plan data 60 shown in FIG. 1 is an output value of the operation plan drafting means 50 described above. However, the operation plan data 60 may be a start / stop signal of the manufacturing plant, or both. In the present embodiment, it will be described later as data to be presented to the operator of the start / stop schedule.

<< Hardware configuration >>
Next, a hardware configuration for embodying the production plant operation planning method P of the embodiment will be described with reference to FIG.
FIG. 5 is a conceptual diagram showing hardware for realizing the manufacturing plant operation plan planning method P.
The manufacturing plant operation planning method P of the embodiment is realized by using a computer 1 and a printer 2 that is an output device of the computer 1.
The computer 1 includes a CPU (Central Processing Unit) 3 that is an arithmetic device, a main storage device 4 that is a storage device, and an auxiliary storage device 5 such as an HDD (Hard Disk Drive), and these include a system bus 6. Connected by.

The auxiliary storage device 5 includes an energy demand data file 11 in which energy demand data 10 is stored, an operating cost database 31 in which operating cost data 30 is stored, and an equipment performance database 41 in which equipment performance data 40 is stored. Stored.
The energy demand data file 11 may be created by inputting using the keyboard 1k which is an input device of the computer 1, or may be a text data file created by other spreadsheet software, for example. Of course, the creation mode is not limited.
The auxiliary storage device 5 includes an optimization calculation program 21 for realizing the optimization calculation means 20 shown in FIG. 1 and an operation plan planning program 51 for realizing the operation plan planning means 50 as target programs. An OS (Operating System) for operating these application programs is also stored.

1, the CPU 3 loads the corresponding optimization calculation program 21 and operation plan planning program 51 stored in the auxiliary storage device 5 into the main storage device 4 and executes them. By doing so, it is embodied.
The operation plan data 60, which is the output of the manufacturing plant operation planning method P shown in FIG. 1, may be printed by the printer 2 of the output device shown in FIG. 5, or may be displayed on the display 1d of the output device. Alternatively, an output file may be created and recorded on a magnetic recording medium such as a CD (Compact Disc) or a USB (Universal Serial Bus) memory.
Moreover, it is also possible to transmit the output file of the created operation plan data 60 to a terminal device (not shown) of an operator of the manufacturing plant via a communication network (not shown).

<< Processing of Manufacturing Plant Operation Planning Method P >>
Next, the processing of the manufacturing plant operation planning method P will be described with reference to FIG.
FIG. 6 is a flowchart illustrating a processing flow of the manufacturing plant operation plan planning method P according to the embodiment.
In the manufacturing plant operation planning method P, first, the optimization calculation means 20 (see FIG. 1) reads the energy demand data 10 from the energy demand data file 11 (S10 in FIG. 6).
As shown in FIG. 2, the energy demand data 10 is numerical data (second row and second column in FIG. 2) arranged at predetermined time intervals for each demand item (after the first row and second column in FIG. 2) of each target energy. Refer to the following) and take the form of [Table 1] below.

As described above, the energy demand data 10 (see FIG. 2) may be a predicted value. In this case, the demand prediction data becomes the energy demand data 10, and the future time is described as the corresponding time.
Similarly, the optimization calculation means 20 (see FIG. 1) reads the operating cost data 30 (see FIG. 3) from the operating cost database 31 (S20 in FIG. 6).
As described above, the operation cost data 30 shown in FIG. 3 describes the operation cost for each facility device on a monetary basis. It is mainly determined from fuel costs, labor costs required for operation, etc., and takes the form of [Table 2] below as shown in FIG.

In [Table 2], the operation cost is the cost of steady operation, and the additional cost corresponds to the start-up cost described elsewhere in FIG.
Subsequently, the optimization calculation means 20 (see FIG. 1) reads the equipment performance data 40 (see FIG. 4) from the equipment performance database 41 (S30 in FIG. 6).
The equipment performance data 40 shown in FIG. 4 is data indicating the production capacity and energy supply capacity of each equipment as described above. Data is defined for each facility device. For example, a plurality of performance items are set in consideration of a plurality of facility devices capable of supplying energy. For example, the case where the energy supply capacity and the rated value are written together is shown in [Table 3].

Thus, after reading the energy demand data 10, the operating cost data 30, and the facility performance data 40, the optimization calculation means 20 constructs an integer programming problem and executes the optimization calculation described later (S40 in FIG. 6). ).
The integer programming problem of this embodiment is composed of an objective function and constraint conditions as described in Non-Patent Document 1, page 359.

[Formula 1] Objective function: Σcj × xj (i = 1, ..., m, j = 1, ..., n)
[Expression 2] Constraint: Σaij × xj ≦ bi (i = 1, ..., m, j = 1, ..., n)
Note that the variable j is set for each equipment that is the target of the operation plan, and the variable i is set for each demand item. For example, if there are 10 facilities, j = 1,. When the demand items are electric power and steam, i = 1,2.

Each demand amount of the energy demand data 10 shown in FIG. 2 is a variable bi of [Expression 2], and the operating cost data 30 shown in FIG. 3 is a variable cj of [Expression 1], and the equipment performance data shown in FIG. 40 corresponds to the variable aij in [Expression 2].
For example, as shown in FIG. 4, for the power of the equipment C, the demand item is the first i = 1 and the equipment ID of the equipment C is the third, so j = 3. ,
[Formula 3] a13 = E3
It becomes.

The variable xj is a variable indicating whether the equipment is operating or stopped. That is, an integer value of 0 or 1 corresponds to the operation or stop.
After the optimization calculation in S40 of FIG. 6 (details will be described later), an optimization calculation result reading process for reading this optimization calculation result is executed (S50 of FIG. 6).
In this optimization calculation result reading process, an integer value of 0 or 1 that is the optimization calculation result of S40 of FIG. 6 is associated with the state of the equipment being in operation or stopped. Based on the associated result, the start / stop of the equipment is controlled or an operation plan is made.

Subsequently, the operating cost data 30 is updated based on the optimization calculation result of S40 of FIG. 6 (S60 of FIG. 6).
In S60 of FIG. 6, the operating cost data 30 is corrected depending on whether the equipment is in an operating state or a stopped state. For example, for the equipment device j in a stopped state, the cost required for starting is considered in the corresponding operation cost during normal operation. The update formula is shown below. The variable cj of the operating cost data 30 is

[Formula 4] cj = cj + (startup cost)
For example, as shown in FIG. 3, when the equipment device B is in a stopped state, the cost required for starting is q2 described elsewhere, so the operating cost of the stopped equipment device B as a whole is set as p2 + q2.
According to [Formula 4], since equipment B is the second equipment, j is 2. [Formula 5] c2 = p2 + q2
It becomes.

In [Expression 4], the activation cost is added, but depending on the objective function of [Expression 1], there may be a case where subtraction is performed.
Similarly, the operating cost of the equipment in operation state j may be renewed in consideration of the case where fuel is sufficient during rated operation.
[Formula 6] cj = cj− (equivalent to fuel cost reduced by rated operation)
It becomes.

[Expression 4] and [Expression 6] are reflected in the next optimization calculation execution (S40 in FIG. 6).
Subsequently, in the operation plan creation (S70 in FIG. 6), the operation plan is created from the optimization calculation result reading (S50 in FIG. 6). Specifically, the start / stop pattern of the equipment is output at each time. Output formats include tabular formats and Gantt charts.
Finally, the results obtained up to S70 in FIG. 6 are written and outputted (S80 in FIG. 6). It may be output as a control signal for equipment.
The above calculation is executed every time the energy demand data 10 is input or at a fixed cycle.
The fixed period may be executed according to a time interval described in the energy demand data 10 (see FIG. 2) or may be executed at an integer multiple of the time interval.
For example, when the time data 10 is one hour interval, it is as shown in [Table 4].

計算実行間隔は１時間毎、あるいはその整数倍である１日の２４時間毎となる。
図２に示す（需要項目）の各需要値のエネルギ需要データ１０を、［式２］の制約条件の右辺のbiの導出に用いる。

The calculation execution interval is every hour or every 24 hours of the day which is an integral multiple of the hour.
The energy demand data 10 of each demand value (demand item) shown in FIG. 2 is used to derive bi on the right side of the constraint condition of [Expression 2].

<< Optimization calculation means 20 >>
Next, the calculation in the optimization calculation means 20 will be described in detail.
FIG. 7 is a diagram illustrating the optimization calculation means 20 in the manufacturing plant operation plan planning method P of the embodiment.
In the present embodiment, the knapsack problem described in Non-Patent Document 1 is applied as a solution to the integer programming problem.

First, as shown in FIG. 7, an operation equipment group 200 and a stop equipment group 210 are provided.
This corresponds to the value of the variable xj in [Expression 1] and [Expression 2]. That is, when the variable xj is a value indicating an operation state of 0 or 1, the device j is in the operating equipment group 200 and the variable xj is a value indicating a stop state of 0 or 1 The device j is in the facility device group 210 in the stopped state.

For example, when the operation state is 1 as xj, the stop state is 0. On the other hand, when the operation state is 0, the stop state is 1.
Next, the integer programming problem solution processing means 250 solves the above [Formula 1] and [Formula 2].
[Formula 1] Objective function: Σcj × xj (i = 1, ..., m, j = 1, ..., n)
[Expression 2] Constraint: Σaij × xj <= bi (i = 1, ..., m, j = 1, ..., n)
The optimization of the objective function is maximized or minimized. In the present embodiment, the optimization is performed according to the description on page 360 of Non-Patent Document 1. That is,
[Expression 7] Objective function: Σcj × xj → Maximize (i = 1, ..., m, j = 1, ..., n)
And

Therefore, in the present embodiment, a large operating cost is imposed on the stopped equipment device group 210, that is, the total operating cost of all the equipment including the equipment that is not cost-effective can be minimized, or a combination thereof. Ask for.
Next, with respect to bi (demand amount) on the right side of [Equation 2], the sum of the supply amount of the facility equipment in the stopped state and the demand amount of the energy demand data 10 shown in FIG. Do not exceed. This is because the supply amount of all facility equipment is the sum of the supply amount of operating equipment and the supply amount of facility equipment in a stopped state that covers the demand amount of the energy demand data 10, and the demand amount of the energy demand data 10. This is because the supply amount of the facility equipment in operation that satisfies the above condition is more than the demand amount of the energy demand data 10.

Therefore,
(Device capacity in a stopped state) + (Demand) <= (Maximum capacity of all devices)
And
(Device capacity in a stopped state) <= (Maximum capacity of all devices)-(Demand)
So,
[Formula 8] bi <= (Maximum capacity of all devices)-(Demand i)
It is.

  By solving the problem of maximizing the operating cost of the equipment to be stopped by [Equation 7] and [Equation 8], the minimization of the operating cost of all the equipment in the manufacturing plant that can meet the energy demand is satisfied. By applying the constraint condition as in [Equation 8], a solution satisfying the constraint is derived for the equipment to be stopped so as not to fall below the demand data described in the energy demand data 10 shown in FIG.

<Solution>
From [Formula 2] and [Formula 8],
[Formula 9] Σaij × xj <= bi <= (Maximum capacity of all devices)-(Demand i)
Get.
Using the expression (13.3) on page 360 of Non-Patent Document 1, first, the order of the subscript j is rearranged so that the power (i = 1) is in the relationship of the following [Expression 10].
[Formula 10] c1 / a11> = c2 / a12> = c3 / a13> = c4 / a14> =
As described above, cj is the operating cost (see FIG. 3) of each equipment (j = 1, 2,...), And aij is the equipment of each equipment (j = 1, 2,...). Performance (i = 1 (power)) (see FIG. 4).

In the order of [Expression 10], that is, the operation cost is high, that is, in the order of each equipment device that is not cost-effective (j = 1, 2,...), Σaij × xj of [Expression 8] is calculated. [Equation 9] is satisfied, and [Equation 7] is maximized, that is, the facility equipment group 210 in a stopped state in which the total cost of the stopped equipment is maximized is obtained by the knapsack method.
Subsequently, for the steam (i = 2), the order of the subscript j is rearranged as in [Formula 11].
[Formula 11] c1 / a21> = c2 / a22> = c3 / a23> = c4 / a24> =
As described above, cj is the cost (see FIG. 3) of each equipment (j = 1, 2,...), And aij is the equipment performance of each equipment (j = 1, 2,...). (i = 2 (steam)) (see FIG. 4).

In the order of [Expression 11], that is, in the order of equipment costing operation costs, that is, cost-effective equipment (j = 1, 2,...), Σaij × xj of [Expression 8] is performed. The stopped equipment device group 210 that satisfies the constraint condition of [Equation 9] and maximizes [Equation 7], that is, the total cost of the stopped equipment is maximized by the knapsack method.
As a result of the calculation regarding steam, if there is a facility device that has entered the facility device group 210 that is in the operation state with respect to steam and that is in the facility device group 210 that is in the stop state with respect to power, the operation state is finally To do. This is a solution that satisfies the constraints of equipment.

Although the case where the energy is electric power and steam has been described above, when the energy is 3 or more, the calculation of this energy item is similarly performed for each energy item and the facility equipment that has not been operated in the calculation of the energy item so far. When equipment that should be put into operation appears, it can be solved by a method of putting it in operation.
In addition, the said solution method shows an example, Solution methods other than this may be applied and it is not limited to this solution method.

As described above, when the optimization calculation is performed, as shown in FIG. 7, when the equipment A is stopped, the equipment B is operating, and the equipment C is continuously operated,
Equipment A: Operational state → Stopped state Equipment device B: Stopped state → Started state Equipment device C: When the operation state is continued, the operating cost data is updated as follows.

Since the equipment A changes from the operating state to the stopped state, the operating cost variable c1 is obtained by adding the starting cost q1 to the normal operating cost p1 (see FIG. 3), and the operating cost variable c1 is expressed by the following equation. .
c1 = p1 + q1
Since the equipment B changes from the stopped state to the activated state, the operating cost variable c2 is obtained by subtracting the fuel saving cost r2 from the normal operating cost, and the operating cost variable c2 is expressed by the following equation.
c2 = p2 ― r2
Note that r2 represents the fuel saving cost due to the continued operation of the device B.

Since the equipment C continues to operate, the operating cost variable c3 is a normal operating cost p3, as shown in the following equation.
c3 = p3
In this way, by updating the operating cost variable cj, the operating equipment continues to operate as much as possible, and the stopped equipment continues to stop as much as possible to reduce the startup cost, It is possible to reduce the cost of operation of the manufacturing plant by generating fuel saving costs.

<Calculation result>
FIG. 8 is a diagram illustrating a calculation example when the manufacturing plant operation planning method P of the embodiment is used. FIG. 8A is a diagram showing a calculation example of the power generation amount, with the elapsed time (h) on the horizontal axis and the power generation amount (kWh) on the vertical axis. FIG. 8B is a diagram showing a calculation example of the steam generation amount, with the elapsed time (h) on the horizontal axis and the steam generation amount (kWh) on the vertical axis.
8 (a) and 8 (b) calculate the start / stop of five equipment devices (A, B, C, D, E) based on changes in demand for electric power and steam. In this example, the unit time for the demand change is 1 hour (shown on the horizontal axis in FIG. 8).
From the calculation results shown in FIGS. 8A and 8B, the supply capacity exceeds or is equal to the demand at all times. That is, the supply capacity does not fall below the demand at all times.

Although there is a time zone in which the supply of power generation is excessive in FIG. 8 (a), this is a time zone in which the rise of steam demand is large (see FIG. 8 (b)). The result is a large supply. In this way, since a plurality of constraint conditions are considered at the same time, it can be avoided that specific energy falls below demand.
FIG. 9 is a diagram showing the operation plan of the manufacturing plant obtained as a result of applying this embodiment in a Gantt chart.
By displaying the start / stop schedule of the five equipments, it is possible to present the operation plan of the utility equipment to the operator of the manufacturing plant.

<< Summary >>
In the present embodiment, the energy demand data 10 (see FIG. 2) from the manufacturing plant to the utility plant, the utility plant operating cost data 30 (see FIG. 3), and the equipment performance data 40 (see FIG. 4) are taken into consideration. The start and stop of the equipment is determined so that the supply capacity of the plant exceeds the demand and the minimum operation cost is reached, and an operation plan is made from the obtained results. As the calculation method for determining the start / stop, a method that can be solved at high speed as described above is used based on the update frequency of the energy demand data 10.

<Effect>
According to the operation planning method of the manufacturing plant of this embodiment, the supply capacity of the utility plant exceeds the demand in consideration of the energy demand from the manufacturing plant to the utility plant, the operation cost of the utility plant, and the facility equipment performance (capacity). However, it is determined to start and stop the equipment and the operation plan based on the results obtained so that the minimum operation cost is achieved (the demand does not exceed the supply capacity). The energy can be stably supplied to the manufacturing process.

In addition, since the calculation method for determining the start / stop is based on the method that can be solved at a higher speed than the update frequency of the energy demand data 10, even if the energy demand data 10 is suddenly changed, it is possible to immediately respond to the operation plan change. Become. For example, in the present embodiment, the calculation time that conventionally takes several tens of minutes can be completed within a few minutes.
In FIGS. 8 and 9 of the embodiment, power and steam are illustrated as energy items. However, the present invention is not limited to the manufacturing plant, and can be widely applied to many manufacturing processes.
Further, it can be applied to energy saving monitoring and demand prediction, and for example, it is possible to consider the constraint condition of the amount of carbon dioxide.

It is a figure which shows the structure which implement | achieves the manufacturing plant operation plan planning method of embodiment concerning this invention. It is the figure which showed an example of the energy demand data of embodiment. It is the figure which showed an example of the operating cost data of embodiment. It is the figure which showed an example of the equipment performance data of embodiment. It is the conceptual diagram which showed the hardware which implement | achieves the operation plan planning method of the manufacturing plant of embodiment. It is a flowchart which shows the flow of a process of the manufacturing plant operation plan planning method of embodiment. It is the figure which showed the optimization calculation means in the manufacturing plant operation plan planning method of embodiment. (a) is the figure which showed the example of calculation of the electric power generation amount using the manufacturing plant operation plan planning method of embodiment, (b) is the amount of steam generation using the manufacturing plant operation planning method of embodiment. It is the figure which showed the example of calculation. It is the figure which showed the operation plan of the manufacturing plant of embodiment with the Gantt chart.

Explanation of symbols

1 computer
10 Energy demand data (demand data value)
20 Optimization calculation means 21 Optimization calculation program (program)
30 Operating cost data (operating cost)
40 Equipment performance data (equipment performance)
51 Operation planning program (program)
60 Operation plan data
Cj Operating cost P Manufacturing plant operation planning method (plant operation planning method)
q1 Startup cost
r2 Fuel saving cost (Fuel reduction value)

Claims (2)

A plant operation planning device for determining the driving or stopping of the equipment for each timekeeping the plant,
An energy demand storage unit for storing data of demand amount or predicted amount bi (i: natural number indicating each energy) of each energy at each time;
An operation cost storage unit for storing data of the operation cost cj (j: natural number indicating each facility) for each facility device;
An equipment performance storage unit for storing data of the supplied energy amount aij for each equipment;
Read the demand amount or predicted amount bi of each energy, the data of the operating cost cj for each facility device, and the data of the supplied energy amount aij for each facility device,
For each energy
c1 / a11> = c2 / a12> = c3 / a13> = c4 / a14> = ... in order of subscript j,
When the operation of each facility device is represented as 1 and the function representing the stop as 0 is xj, the amount of energy supplied to the facility device during operation of each energy (Σaij × xj) is the demand amount or prediction of each energy. An amount (bi) or less, and
On condition that the following two conditions are satisfied: (Supply energy amount of equipment in a stopped state) + (Demand amount or predicted amount bi of each energy) <= (Supply energy amount of all equipment devices)
A first operation for determining whether to operate or stop each facility device so that Σcj × xj is maximized in the order of the sorted subscript j;
As a result of the first calculation, a second calculation that determines that the facility device that is determined to be operating with at least one energy is determined to be active and that the other facility devices are determined to be stopped;
As a result of the second operation,
The equipment that has been determined to be stopped from the operating state is updated as the operating cost cj by adding the starting cost to the normal operating cost, and
The facility equipment determined to be in the operation state from the stop state is updated as the operation cost cj by subtracting the fuel saving cost from the normal operation cost, and
The facility device determined to be in the operation state continuation is optimized calculation means for executing a third operation for updating the normal operation cost as the operation cost cj;
An operation planning means for reading data on the operation or stop of the equipment as a result of the second calculation, and outputting start / stop information of the equipment at each time based on the data;
Plant operation planning device according to claim Rukoto provided. An energy demand storage unit storing data of demand amount or predicted amount bi (i: natural number indicating each energy) for each time, and operating cost cj for each facility device (j: natural number indicating each facility) A plant for determining whether to operate or stop the equipment at each time in the plant, comprising an operating cost storage section in which the data of the equipment is stored and an equipment performance storage section in which the data of the supply energy amount aij for each equipment is stored A program for an operation planning device,
On the computer,
The energy demand storage unit, the operation data for the energy demand amount or the predicted amount bi, the data for the operation cost cj for each facility device, and the data for the supply energy amount aij for each facility device, respectively. Read from the cost storage unit and the equipment performance storage unit, for each energy,
a first procedure for sorting the order of subscripts j in order of c1 / a11> = c2 / a12> = c3 / a13> = c4 / a14> =.
When the operation of each facility device is represented as 1 and the function representing the stop as 0 is xj, the amount of energy supplied to the facility device during operation of each energy (Σaij × xj) is the demand amount or prediction of each energy. An amount (bi) or less, and
On condition that the following two conditions are satisfied: (Supply energy amount of equipment in a stopped state) + (Demand amount or predicted amount bi of each energy) <= (Supply energy amount of all equipment devices)
A second procedure for performing a first calculation for determining whether to operate or stop each facility device such that Σcj × xj is maximized in the order of the sorted subscript j;
As a result of the first calculation, a third procedure for performing a second calculation that determines that the facility device that is determined to be operating with at least one energy is determined to be active and that other facility devices are determined to be stopped;
As a result of the second operation,
The equipment that has been determined to be stopped from the operating state is updated as the operating cost cj by adding the starting cost to the normal operating cost, and
The facility equipment determined to be in the operation state from the stop state is updated as the operation cost cj by subtracting the fuel saving cost from the normal operation cost, and
The facility device that is determined to continue the operation state has a fourth procedure for performing a third calculation for updating the normal operation cost as the operation cost cj, and
A fifth procedure for reading data on the operation or stop of the equipment as a result of the second calculation and outputting start / stop information of the equipment at each time based on the data;
A program for a plant operation planning device for execution .

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