JP2001273007A - Plant optimal operation control system - Google Patents

Abstract

(57) [Summary] An object of the present invention is to provide a plant optimum operation control system capable of performing plant operation control according to a highly accurate operation plan and outputting a highly reliable operation plan. is there. The present invention provides a plant power / heat load predicted based on past power / heat load data and weather data based on plant operation data from an energy plant,
Plant control data for controlling the number of operating plants and controlling the load on each plant component so that the predetermined evaluation function value becomes maximum or minimum based on the characteristic coefficient of the plant component that has been processed at a predetermined cycle. Is calculated and sent to the energy plant 11.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a plant optimum operation control system for supporting and controlling efficient operation in an energy plant that supplies cold and hot heat and electric power.

[0002]

2. Description of the Related Art FIG. 11 is an explanatory diagram showing the configuration of a conventional plant optimal operation control system. That is, the past heat load data of the energy plant 11 is stored in the heat load data storage unit 13 based on the plant operation data input from the energy plant 11 that supplies the cooling / heating heat and the electric power via the plant operation data input unit 12. Is done.
In the heat load prediction means 16, the heat load data storage means 1
3 and the weather data input unit 15 from the human interface 14
The plant heat load of the current day or the next day is predicted based on the weather data input via the. The value representing the input / output performance of the plant component equipment stored in the plant component equipment characteristic coefficient storage unit 17 (hereinafter, referred to as a characteristic coefficient) and the predicted plant heat load value predicted by the thermal load prediction unit 16 are optimal operation. It is input to the planning means 18. The optimal operation planning means 18 is based on these input information,
The plant control data for performing the control of the number of operating plants and the load control of each plant component obtained by the optimization method is calculated, and the plant control data is output to the energy plant 1 via the plant control data output unit 19.
Send to 1. The energy plant 11 controls the number of operating plants and the load according to the plant control data sent from the optimal operation planning means 18. The characteristic coefficients of the plant components are
Input from the interface 14.

[0003]

The conventional plant optimal operation control system has the following problems. That is,
Since the creation and updating of the characteristic coefficients of the plant components are performed irregularly or empirically, there is a possibility that the characteristic coefficients may be inappropriate values that lack reliability and rationality. In addition, when the device characteristics change due to aging deterioration or changes in weather conditions, the user does not notice, and operation based on values far from actual device characteristics due to continuous use of previous coefficients There was a risk that the plan would be output. Further, there is a problem that the fluctuation of the refrigeration function due to the cold water return temperature is not reflected in the plan.

The present invention has been made in view of the above circumstances, and constantly manages characteristic coefficients of plant components constituting an energy plant on the basis of plant operation data, and implements plant components on the basis of prediction of return water temperature. It is an object of the present invention to provide a plant optimal operation control system capable of performing plant operation control according to a highly accurate operation plan by correcting and using the capability of the plant. In addition, by providing a means for monitoring the change rate of the characteristic coefficient of the plant component equipment and notifying the user when the change rate deviates from the allowable value and inquiring whether or not to update the characteristic coefficient, highly reliable operation is provided. An object is to provide a plant optimal operation control system that outputs a plan.

[0005]

In order to achieve the above object, a plant optimum operation control system according to the present invention is based on plant operation data input from an energy plant that supplies cooling / heating heat and electric power. Power and heat load data storage means for storing heat load data; and past power and power read from the power and heat load data storage means.
Based on heat load data and weather data,
Power / heat load prediction means for predicting a heat load, plant component characteristic coefficient storage means for storing characteristic coefficients of plant components constituting the energy plant, and plant operation data input from the energy plant. A plant operation data storage unit, and a predetermined period of time for determining whether to update the characteristic coefficient of the plant component stored in the plant component characteristic coefficient storage based on the plant operation data read from the plant operation data storage. And a plant component / equipment characteristic coefficient updating means for updating a characteristic coefficient when a predetermined update condition is satisfied, a plant power / heat load predicted by the power / heat load prediction means, and a plant component / equipment characteristic. Based on the characteristic coefficient of the plant component read from the coefficient storage means, Optimum operation planning means for calculating plant control data for controlling the number of operating plants and controlling the load of each plant component so that the constant evaluation function value is maximum or minimum, and sending it to the energy plant. It is characterized by doing.

Further, in the plant optimum operation control system according to the present invention, the optimum operation planning means may determine a capacity of the plant component equipment based on a predicted return temperature of return water from the load side to the energy plant component device. It is characterized by comprising a means for correcting.

In the plant optimum operation control system according to the present invention, the plant component characteristic coefficient updating means includes a division analysis storing rules for performing a division analysis of the operation data for each equipment read from the plant operation data storage means. A rule storage unit is provided, and one or more characteristic coefficients are calculated for each device based on the division analysis rules stored in the division analysis rule storage unit.

Further, the present invention is characterized in that in the above-mentioned plant optimal operation control system, weather data is obtained from a communication interface such as the Internet.

The present invention also provides a plant optimum operation control system, wherein the rate of change of the characteristic is monitored by using the characteristic coefficient of the plant component before and after the updating by the characteristic coefficient of the plant component, and the rate of change is equal to or more than the set value. A characteristic change notifying means for notifying the user when the value is equal to or less than the set value is provided.

Further, in the plant optimum operation control system according to the present invention, the characteristic change notification means receives a permission / prohibition of updating of the plant component characteristic coefficient from a user, and outputs the update permission / prohibition to the plant component characteristic coefficient updating means. It is characterized by the following.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below in detail with reference to the drawings.

FIG. 1 is a structural explanatory view showing an embodiment of the present invention. That is, from the energy plant 11 that supplies cooling / heating heat and electric power, the plant operation data input unit 1
2 based on the plant operation data input via
Past power and heat load data of the energy plant 11 is stored in the power and heat load data storage unit 21. Electric power
In the heat load prediction means 23, the past power / heat load data stored in the power / heat load data storage means 21,
Based on the weather data input via the weather data input unit 22 and the weather data, the plant power / heat load on the current day or the next day is predicted and output to the optimal operation planning means 26. The plant operation data input from the energy plant 11 via the plant operation data input unit 12 is stored in a plant operation data storage unit 24, and the plant operation data read from the plant operation data storage unit 24 is a plant component It is input to the characteristic coefficient updating means 25. The plant component equipment characteristic coefficient updating means 25 periodically analyzes the latest data on each plant component equipment in the plant operation data by applying a statistical regression method, and based on the result, the plant component equipment characteristic coefficient A calculation process of whether to create and update the plant component characteristic coefficients stored in the storage unit 17 is performed. In the arithmetic processing,
If the update is set to be performed automatically, the updated plant component characteristic coefficient is stored in the plant component characteristic coefficient storage unit 17 as it is, and if the update condition is specified in advance, the update condition is set. Only when satisfied, the updated plant component characteristic coefficient is stored in the plant component characteristic coefficient storage unit 17. The characteristic coefficients of the plant components may be input from the human interface 14. The plant component characteristic coefficient read from the plant component characteristic coefficient storage unit 17 is output to the optimal operation planning unit 26. The optimal operation planning means 26 is a plant component characteristic coefficient input from the plant component characteristic coefficient storage means 17,
And correcting the heat source functional capacity based on the return water temperature prediction based on the plant power / heat load prediction value predicted by the power / heat load prediction means 23, and the mathematical operation implemented in the optimal operation planning means 26. Calculate plant control data for controlling the number of operating plants and load control of each plant component so that the plant operation cost is the lowest by an optimization method such as a planning method, and convert the plant control data into plant control data. The output is sent to the energy plant 11 via the output unit 19. The energy plant 11 controls the number of operating plants and the load according to the plant control data sent from the optimal operation planning means 26.

As the optimization evaluation criterion used by the optimum operation planning means 26, for example, the energy consumption of the plant and the carbon dioxide emission may be set in addition to the above-mentioned plant operation cost. Multi-objective optimization may be performed by combining criteria.

FIG. 2 is a structural explanatory view showing another embodiment of the present invention. In the drawings, the same portions are denoted by the same reference numerals and description thereof will be omitted. That is, the weather data input unit 22 is connected to a communication interface 27 such as the Internet,
The detailed and latest weather data is acquired from the communication interface 27 such as the Internet. In addition, a characteristic change notification unit 28 is provided between the plant component characteristic coefficient updating unit 25 and the human interface 14, and the rate of change of the performance characteristic is monitored using the plant component characteristic coefficient (slope / intercept) before and after the update. However, if the rate of change exceeds the set value or falls below the set value and deviates from the allowable value, the user is notified, and after the user permits the update, the updated plant component equipment characteristic coefficient is used for the operation plan. . If the user does not permit the operation, the plant component equipment characteristic coefficient before update is continuously used for the operation plan.

FIG. 3 is an explanatory diagram showing the relationship between the optimum operation control system, the energy plant, and the customer according to the present invention.
That is, plant operation data is input from the energy plant 11 to the optimum operation control system 31 as a measurement signal, and plant control data is input from the optimum operation control system 31 to the energy plant 11 as a control instruction. The energy plant 11 is an energy system that supplies electric power and heat required by consumers by consuming electric power, gas, heavy oil, water and sewage, and the like. Energy plant 11 is a cogeneration system (CGS)
32, a boiler 33, a refrigerator 34, a heat exchanger 35, transfer equipment, auxiliary equipment, piping systems, etc., and an optimal operation control system 3
The power generated from the cogeneration system (CGS) 32, which is instructed from 1 to start / stop and the amount of power generation, is supplied to the customer as power, and the hot water and steam generated from the cogeneration system (CGS) 32 are optimally controlled. It is supplied to the refrigerator 34 that has received the instruction of the start / stop / production heat quantity from the system 31, and the refrigerator 34 uses this as a drive source to supply cold heat to the customer. In addition, steam or hot water generated from the boiler 33 which has been instructed by the optimal operation control system 31 for starting / stopping / manufacturing calorie is supplied to the customer as steam or hot water via the heat exchanger 35, and a part of the steam or hot water is supplied. It is used as a drive source of the refrigerator 34. Of the steam and hot water generated from the cogeneration system (CGS) 32 and the boiler 33, those not used as supply energy to consumers are discarded or stored as surplus steam and surplus hot water, respectively.

FIG. 4 is a view for explaining a procedure for automatically grasping characteristic coefficients of constituent devices and reflecting the same in a plan according to the present invention.

The characteristic coefficients of the components of a plant greatly change due to weather conditions such as the outside air temperature and deterioration due to aging. In the conventional optimal operation system / operation planning system, a fixed characteristic coefficient was used, or how to use it was not clear.

According to the present invention, since the change of the characteristic coefficient is periodically calculated based on the actual operation data of each component, it is possible to always reflect the latest characteristic coefficient of the plant component in the operation plan.

As shown in FIG. 4, the procedure for automatically grasping the characteristic coefficient and reflecting it in the plan is as follows: First, the cogeneration system (CGS) 3 constituting the energy plant 11 is used.
2. Measure the operation information of the boiler 33, the refrigerator 34, and the heat exchanger 35, and collect and accumulate the measurement data in the plant operation data storage unit via the plant operation data input unit. In the plant component equipment characteristic coefficient updating means, after performing data trimming of the measurement data for the purpose of eliminating abnormal data and missing data, a distribution state of input / output data is created for each equipment, and a distribution state is created as necessary. Request the user to confirm. The user confirms the distribution state, and inputs and sets an instruction regarding a policy for creating the input / output correlation through the human interface. The plant component characteristic coefficient updating means creates a correlation in accordance with the creation policy and / or automatically performs processing at regular intervals according to settings to create characteristic coefficients. When there is a change larger than the set value between the characteristic coefficient up to the previous time and the characteristic coefficient obtained this time, the user is inquired via the characteristic change notification means as to whether or not the update is possible. The above-described processing is performed every day at a preset time at midnight. Then, for example, the user determines whether or not updating is possible in the next morning, and when an update permission is instructed, the updated characteristic coefficient is reflected in the optimal operation plan. The updated characteristic coefficients are developed in the optimal operation planning means in the form of an optimal calculation constraint model (hereinafter referred to as a constraint expression).

For example, a constraint formula relating to a refrigerator is expressed by the following formula.

Output chilled water heat quantity = prepared device characteristic coefficient × device input energy amount Data trimming is performed according to the following criteria. Excluding a certain range, based on the average value of the data in the Y value direction and X value direction during the designated period (the effective range is determined by statistical processing such as the average value ± 3σ). Data not used within 30 minutes immediately after startup is not used (data immediately after startup is unstable, so it is not used for regression processing for obtaining characteristics). Also, characteristic regression is performed only from the effective data after trimming, and the characteristic coefficient for each device is obtained. Can be selected for each device whether it is a regression line passing through the origin or a regression line not passing through. Note that the characteristics are usually linearly regressed, and a statistical method such as the least squares method is generally used. If the degree of coincidence with the actual data is low in the linear approximation, a characteristic coefficient based on a multi-stage linear regression described later can be configured.

FIG. 5 is a diagram for explaining a method of creating characteristic coefficients and constraint equations for plant components according to the present invention. In order to formulate an appropriate operation plan reflecting the actual performance of the equipment, it is necessary to appropriately reflect the current characteristics of the equipment in the constraint expression. It is premised that the constraint equation is represented by a linear equation as exemplified above. However, in the linear equation, there are devices that deviate from the actual change in characteristics, and the constraint equation may not be accurately expressed.

As an example in which this is improved, see JP-A-8-24.
As disclosed in Japanese Unexamined Patent Publication No. 9005, a method of approximating the characteristics of a device by a plurality of linear equations using a piecewise linear equation with respect to an input has been proposed. However, the procedure for dividing the input / output range is not specified. Depending on how to categorize the devices, the characteristics of the equipment may change significantly, and a categorization method without rationality and uniformity may completely change the overall planning tendency.

In the present invention, the procedure common to the following devices
A section can be set by a method, and a characteristic coefficient can be calculated based on the section and reflected in an operation plan. In addition, information on the division setting of the operation data for each device is stored in a classification analysis rule storage unit (not shown) implemented in the plant component characteristic coefficient updating unit.

1) As shown in the input / output value distribution of the device 1,
The stored input / output operation data for each device is plotted in an XY diagram at regular intervals. When the number of sections is one, that is, when the constraint equation is represented by a single linear equation, the input / output characteristics of the device 1 can be variably set depending on the presence or absence of the intercept.

2) When the number of divisions is two or more, the division is set by any of the following after trimming invalid data based on the distribution: i) Division designation input: From the structure / setting of the device, etc. If the input value / output value serving as a change point is known, the user or the like inputs the point and classifies the point by the corresponding input value or output value.

Ii) Classification by period: If the data used for plotting is long-term, such as several months, the data itself is classified for each period, such as one month, in which a change in the outside air environment appears. For example, summer data and winter data.

Iii) Classification by statistical processing: After performing a polynomial approximation process in which the degree is specified and obtaining a polynomial approximation formula,
Find the rate of change by differentiation. For example, in the case of the quadratic approximation, the point at which the first derivative value exceeds a certain value (the slope a ′ is −α ≦
a ′ ≦ α) or the like, and in the case of a third-order or higher approximation, a maximum / minimum point can be used.

In selecting a division method, it is preferable to apply the priority in the order of i) → iii) in order to create a rational division. In either case, the characteristic straight lines usually intersect at the section point (or in the vicinity thereof).

3) Regression processing is performed on the data for each section by the least square method or the like to calculate the characteristic coefficient for each section.

4) The characteristic coefficient is stored in the characteristic coefficient storage means of the plant component equipment, and can be used in a plan thereafter if there is permission to update the characteristic coefficient up to the previous time.

According to the above procedure, it is possible to use characteristic coefficients and constraint expressions that reflect the actual performance of each plant component.

FIG. 6 is a diagram for explaining a function of notifying a user of a change in a characteristic coefficient according to the present invention.

Updating of the characteristic coefficients of the plant components may cause a significant change in the tendency of the overall plan of the plant operation control, and should be performed particularly carefully. It is also necessary not to use non-stationary data such as at the time of equipment adjustment or at the time of failure to create characteristic coefficients that should represent the intrinsic characteristics of the equipment.

According to the present invention, 1) the device characteristic coefficient is recreated at regular intervals. After passing through the process of creating the characteristic coefficient, it is compared with the previous characteristic coefficient. The following discriminant is used as an example of the change rate judgment index (if this discriminant is satisfied, the effect on the overall plan is considered to be minimal, and the step of obtaining permission is skipped and the characteristics are automatically updated). .

Discriminant: Whichever is greater (first rate of change, second rate of change) <permission rate However, first rate of change: | (a1−a2) / a1 | second rate of change: | ( b1-b2) / b1 | Constraint formula before update: Y = a1X + b1 Constraint formula after update: Y = a2X + b2.

The permission rate is set in advance to a value such as 0.1 (= 10%) for each device. If the characteristic has changed, it is determined whether or not the value of the discriminant equation is equal to or higher than the set permission rate. If the value is equal to or lower than the set permission rate, the characteristic coefficient is automatically updated without notifying the user.

2) If there is a change exceeding the preset permission rate, the user is asked for permission / non-permission as to whether the corresponding characteristic coefficient may be updated. Examples of this means include: i) display of messages and marks on the human interface screen, ii) automatic printing of printed materials at night, iii) notification of changes for each device to the user through notification by pager, e-mail, etc. And so on.

3) When the user gives permission to update, the characteristic is reflected in the planning function and the characteristic coefficient of the corresponding device is updated. If update permission is not granted, the creation result is discarded.

FIG. 10 is a flowchart showing the process of updating the device characteristic coefficient according to the present invention.

FIG. 7 is a diagram for explaining the prediction of the chilled water return temperature (returned water temperature) according to the present invention and the function of reflecting the effect on the plan. That is, the refrigerating machine has a cycle of [production of chilled water (normally 5 ° C.) → pump to the air-conditioning load side → cools back the returned water (normally 12 ° C.) which has been consumed and returned as cold water. repeat.

At this time, when the amount of cold consumed on the customer air conditioning load side is small, a phenomenon occurs in which the temperature difference in the return of the chilled water becomes small (the return temperature is reduced to 12 ° C. or less for the delivery temperature of 5 ° C. Become). Originally, a design temperature difference (ΔT = 7 ° C.) is ensured by reducing the flow rate of delivery to the load side.
However, in order to secure the water supply pressure at the end of the piping system and to absorb sudden fluctuations on the load side, the flow rate is somewhat increased. The excess cold water returns to the return water side without consuming cold water heat through the bypass pipe.

Accordingly, the temperature of the return chilled water is inevitably reduced during a period in which the chilled water load calorific value decreases, particularly during a low load period (winter to mid-night / nighttime).

In the refrigerator, the cold water sending temperature (=
5 ° C). For this reason, a temperature difference cannot be created in a state where the cold water return temperature is lowered, and the production capacity is apparently suppressed. It is necessary to appropriately reflect this “capacity reduction phenomenon” in the operation plan, but it has not been addressed in the past.

According to the present invention, 1) cold water return temperature is measured for each device. The cold water return temperature of the refrigerator has a high correlation with the heat load of the target system. Using this, a correlation equation between [load heat quantity and cold water return temperature for each device] is created from actual data. (Regression is made from annual data in advance, or it is created from actual data accumulated while running). Usually, it can be expressed by the following linear expression. That is, a correlation equation is created in the form of chilled water return temperature = heat load of the entire plant × coefficient a + intercept b. Only one relational expression may be provided for each device, or a plurality of relational expressions may be provided for each device for each month and season.

2) This relational expression is also valid in the future, and if the predicted chilled water load calorie is substituted for the portion corresponding to the whole plant load calorie, the chilled water return temperature of each refrigerator at the time of the prediction can be predicted. become. If this predicted chilled water return temperature is used as a parameter for the change in refrigeration function, the decrease in refrigeration function can be easily reflected in the operation plan. For example, a decrease in the refrigerating function can be expressed by the following equation.

Upper limit capacity of refrigerator = (Predicted refrigerator cold water return temperature−delivery temperature) / rated temperature difference × rated capacity FIG. 9 is a flowchart showing processing of the cold water return temperature prediction function according to the present invention.

In addition, since the estimated temperature difference (predicted return temperature-delivery temperature) can be obtained, the required fluctuation of the flow rate can be predicted, and a constraint using this can be described. The above concept can be applied not only to refrigerators but also to all plant components. In other words, a highly accurate operation plan can be created by correcting the capacity of the plant component equipment based on the return water temperature prediction value of the return water returning to the plant component equipment.

FIG. 8 is a flowchart showing each processing including the operation planning function according to the present invention. That is, the state data of the plant components is measured and the data is stored. Next, load prediction and correction of the planning capability of each machine are performed by a function of acquiring information from a user and acquiring information from the Internet or the like, perform an operation plan, display the operation plan, and output control data.

[0050]

As described above, according to the present invention, the characteristic coefficients of the plant components constituting the energy plant are constantly managed based on the plant operation data, and the plant component components are controlled based on the return water temperature prediction. By correcting and using the capacity, it is possible to provide a plant optimum operation control system capable of performing plant operation control according to a highly accurate operation plan. In addition, by providing a means for monitoring the change rate of the characteristic coefficient of the plant component equipment and notifying the user when the change rate deviates from the allowable value and inquiring whether or not to update the characteristic coefficient, highly reliable operation is provided. A plant optimal operation control system that outputs a plan can be provided.

[Brief description of the drawings]

FIG. 1 is a configuration explanatory diagram showing an embodiment of the present invention.

FIG. 2 is a configuration explanatory view showing another embodiment of the present invention.

FIG. 3 is an explanatory diagram showing a relationship among an optimal operation control system, an energy plant, and a customer according to the present invention.

FIG. 4 is a diagram illustrating a procedure for automatically grasping characteristic coefficients of constituent devices and reflecting the characteristics in a plan according to the present invention.

FIG. 5 is a diagram illustrating a method of creating a characteristic coefficient and a constraint equation of a plant component device according to the present invention.

FIG. 6 is a diagram illustrating a function of notifying a user of a change in a characteristic coefficient according to the present invention.

FIG. 7 is a diagram illustrating a function of predicting a cold water return temperature (return water temperature) according to the present invention and reflecting its influence on a plan.

FIG. 8 is a flowchart showing each processing including an operation planning function according to the present invention.

FIG. 9 is a flowchart showing processing of a chilled water return temperature prediction function according to the present invention.

FIG. 10 is a flowchart showing an apparatus characteristic coefficient update process of the present invention.

FIG. 11 is a configuration explanatory view showing a conventional plant optimal operation control system.

[Explanation of symbols]

 REFERENCE SIGNS LIST 11 energy plant 12 plant operation data input unit 14 human interface 17 plant constituent device characteristic coefficient storage unit 19 plant control data output unit 21 power / heat load data storage unit 22 weather data input unit 23 power / heat load prediction unit 24 plant operation Data storage means 25 Plant constituent equipment characteristic coefficient updating means 26 Optimal operation planning means 27 Communication interface 28 Characteristic change notification means

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Kazuhide Kamata, 390 Kita-Nagai, Miyoshi-cho, Iruma-gun, Saitama Prefecture Inside (72) Inventor Hisashi Saito 390 Kita-Nagai, Miyoshi-cho, Iruma-gun, Saitama prefecture Daidan Co., Ltd. 3L060 CC05 CC10 CC19 DD02 EE22 5H004 GA14 GA15 GB06 HA14 KA16 KC03 KC23 KC26 KC28 LA15 MA04 9A001 HH34 JJ61 LL09

Claims (6)

[Claims] 1. Based on plant operation data input from an energy plant that supplies cooling / heating and electric power,
Power / heat load data storage means for storing past power / heat load data; and plant power / heat load based on past power / heat load data and weather data read from the power / heat load data storage means. Power / heat load prediction means for predicting the power consumption, plant component characteristic coefficient storage means for storing characteristic coefficients of plant components constituting the energy plant, and plant operation for storing plant operation data inputted from the energy plant. A data storage unit, and calculates, at a predetermined cycle, whether or not to update the characteristic coefficient of the plant component stored in the plant component characteristic coefficient storage based on the plant operation data read from the plant operation data storage. A plant component machine that processes and updates characteristic coefficients when a predetermined update condition is satisfied Device characteristic coefficient updating means, a plant power / heat load predicted by the power / heat load prediction means, and a predetermined evaluation based on the characteristic coefficient of the plant component read from the plant component characteristic coefficient storage means. Optimal operation planning means for calculating plant control data for controlling the number of operating plants and controlling the load on each plant component so that the function value is maximum or minimum and sending the calculated control data to the energy plant. Characteristic plant optimal operation control system. 2. The system according to claim 1, wherein the optimal operation planning means includes means for correcting the capacity of the plant component equipment based on a predicted return temperature of the return water returned from the load side to the energy plant component equipment. Item 2. A plant optimal operation control system according to Item 1. 3. A plant component characteristic coefficient updating means,
A classification analysis rule storage unit storing rules for classifying and analyzing the operation data of each device read from the plant operation data storage means, and for each device based on the classification analysis rule stored in the classification analysis rule storage unit. 3. The plant optimal operation control system according to claim 1, wherein one or more characteristic coefficients are calculated. 4. The plant optimal operation control system according to claim 1, wherein the weather data is obtained from a communication interface such as the Internet. 5. A change rate of the characteristic is monitored using the characteristic coefficient of the plant component before and after the updating by the characteristic coefficient of the component component of the plant, and the user is notified when the change rate is equal to or more than the set value or equal to or less than the set value. 5. The plant optimum operation control system according to claim 1, further comprising a characteristic change notification unit. 6. The plant according to claim 5, wherein the characteristic change notifying unit receives from the user whether or not the updating of the plant component characteristic coefficient is permitted, and outputs the update permission / prohibition to the plant constituent device characteristic coefficient updating unit. Optimal operation control system.