CN115682357A

CN115682357A - Approximation degree-centered cooling water optimization method and independent control system

Info

Publication number: CN115682357A
Application number: CN202211345034.5A
Authority: CN
Inventors: 夏金瑞; 阳红军; 袁玉玲; 林光; 黄维
Original assignee: Guangzhou Sjest Energy Saving Technology Co ltd
Current assignee: Guangzhou Sjest Energy Saving Technology Co ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-02-03
Anticipated expiration: 2042-10-31
Also published as: CN115682357B

Abstract

The application relates to the technical field of cooling water system energy conservation, and discloses a cooling water optimization method taking approximation degree as a center and an independent control system, wherein the cooling water optimization method taking the approximation degree as the center comprises the following steps: acquiring load demand data, system equipment parameters and historical meteorological data of a target project to generate project information; establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model; acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimization model, matching an optimal control scheme, generating a year-round control strategy based on the optimal control scheme, and sending the year-round control strategy to a control system; inputting the current load demand value into a control system, matching the current optimal cooling approximation degree to correct the target value of the cooling inlet water temperature, and executing PID variable frequency regulation; this application has the effect of optimizing cooling water system energy-saving control.

Description

Cooling water optimization method taking approximation degree as center and independent control system

Technical Field

The application relates to the technical field of cooling water system energy conservation, in particular to a cooling water optimization method taking approximation degree as a center and an independent control system.

Background

The cooling water system is a water supply system which takes water as a cooling medium, exchanges heat with the cooling water, cools the water and recycles the water after cooling, and mainly comprises cooling equipment, a water pump and a pipeline; taking a central air-conditioning cold station as an example, the optimization control of the cold station comprising a freezing water system and a cooling water system of a cooling water system plays a very important role in the overall energy conservation of the cold station, and the equipment comprises a cooling water pump, a cooling tower and accessory equipment thereof;

in order to improve the energy-saving effect of the cooling water system, on one hand, how to match loads and high efficiency, such as a frequency conversion system matched with equipment, is required to be considered during system design, and on the other hand, how to operate and control efficiently is also required to be considered after the system is built; at present, a cooling water system has a large lifting space in the aspect of system control; the existing cooling water system control strategy of the central air-conditioning cold station has two modes: one is a traditional control mode that when the host machine executes the number adjustment, the number of the cooling towers and the water pumps changes along with the change, and the frequency conversion adjustment is carried out by manually setting corresponding target values; one is a complex algorithm control method which adopts a reciprocating miscellaneous algorithm to move in a direction and adopts a fuzzy algorithm, a neural algorithm, a genetic algorithm, a bee colony algorithm, an exhaustion method and the like to carry out various complex calculations to obtain a fine calculation result; in practical application, the traditional control method only realizes automatic control from the functional perspective, the equipment is basically independently controlled, the overall energy consumption is not comprehensively considered, and the energy-saving potential of the system is exploited; although the complex algorithm control method can be well realized on a theoretical level, in practical control application, the requirements on data monitoring precision, particularly refrigerating capacity data are high, and the problems of high control stability difficulty, high hardware cost, high maintenance difficulty and the like exist.

In view of the above-mentioned related art, the inventor believes that the existing energy-saving control strategy for the cooling water system has the problem of poor effect.

Disclosure of Invention

In order to optimize the energy-saving control effect of the cooling water system, the application provides a cooling water optimizing method and an independent control system taking the approximation degree as the center.

The first purpose of the invention of the application is realized by adopting the following technical scheme:

a method for approximation-centric cooling water optimization, comprising:

acquiring load demand data, system equipment parameters and historical meteorological data of a target project to generate project information;

establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model;

acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimization model, matching an optimal control scheme, generating a year-round control strategy based on the optimal control scheme, and sending the year-round control strategy to a control device;

and inputting the current load demand value into a control device, matching the current optimal cooling approximation degree to correct the target value of the cooling inlet water temperature, and executing PID frequency conversion regulation.

By adopting the technical scheme, the load demand data, the system equipment parameters and the historical meteorological data of the target project are obtained to form project information, so that the equipment condition and the meteorological condition of the cooling water system of the target project and the use demand of the target project on the cooling water system can be conveniently obtained; respectively creating an approximation degree model, a cooling tower model and a cooling pump model based on parameters of each device in a cooling water system, so as to summarize and generate a system optimization model, and then calculating corresponding power consumption values based on different operation parameters; acquiring annual outdoor wet bulb temperature values according to historical meteorological data, and inputting each outdoor wet bulb temperature value and load demand data into a system optimization model so as to match a corresponding optimal control scheme from the system optimization model based on the environmental temperature and the load demand and improve the energy efficiency of a cooling water system; generating a year-round control strategy based on each optimal control scheme and sending the year-round control strategy to a control device so as to plan year-round operation control of the cooling water system in advance; when the control device is used for regulating and controlling the cooling water system, the current load demand value is input into the control device and is matched with the current optimal cooling approximation degree, so that the target values of the cooling inlet water temperature and the cooling tower temperature difference are further corrected, the cooling tower and the cooling pump are controlled to execute PID variable frequency regulation, and the energy-saving control effect of the cooling water system is optimized.

In a preferred example of the present application: the method comprises the following steps of establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model:

generating a plurality of wet bulb temperature intervals based on historical meteorological data of a target area, setting an initial value of approximation degree for each wet bulb temperature interval based on equipment parameters of a cooling tower, and creating an approximation degree model;

evaluating the water outlet temperature and the power consumption value of the cooling tower under each water inlet temperature, wet bulb temperature, fan frequency and cooling flow based on the equipment parameters of the cooling tower, and creating a cooling tower model;

and evaluating the power consumption values of the cooling pump at different flow rates based on the equipment parameters of the cooling pump, and creating a cooling pump model.

By adopting the technical scheme, the cooling water system comprises the cooling tower and the cooling pump, equipment parameters of the cooling tower and the cooling pump can be obtained from specifications provided by a manufacturer, the change range, the time distribution condition and the change trend of the historical wet bulb temperature of the area are calculated and obtained based on the historical meteorological data of the area where the cooling water system needing optimizing control is located, the historical wet bulb temperature change range is divided into a plurality of wet bulb temperature intervals, and corresponding initial value of approximation degree is set for each wet bulb temperature interval based on the equipment parameters of the cooling tower, so that an approximation degree model is created, and the initial value of approximation degree adjustment can be rapidly determined based on the wet bulb temperature values in the following process; evaluating the water outlet temperature and the power consumption value of the cooling tower under different water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates based on the equipment parameters of the cooling tower, thereby creating a cooling tower model and facilitating the subsequent calculation of the power consumption of the cooling tower in various running states when optimizing control is carried out on a cold station; evaluating the power consumption values of the cooling pump under different flow rates based on the equipment parameters of the cooling pump, thereby creating a cooling pump model and facilitating the subsequent evaluation of the energy consumption condition of the cooling pump; and establishing a system optimization model based on the approximation degree model, the cooling tower model and the cooling pump model.

In a preferred example of the present application: the method comprises the following steps of obtaining outdoor wet bulb temperature values and load demand data, inputting the outdoor wet bulb temperature values and the load demand data into a system optimization model, and matching an optimal control scheme, wherein the steps comprise:

acquiring an outdoor wet bulb temperature value, matching a corresponding approximation degree initial value, calculating a cooling inlet water temperature demand value and inputting the cooling inlet water temperature demand value to a cooling tower model;

calculating the heat dissipating capacity of the system according to a preset calculation coefficient based on load demand data, acquiring a control temperature difference value, calculating a cooling water flow rate and a cooling water outlet temperature demand value, and inputting the values into a cooling tower model;

and matching the optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature.

By adopting the technical scheme, the outdoor wet bulb temperature value is obtained and input into the approximation degree model, the corresponding approximation degree initial value is matched, and then the corresponding cooling inlet water temperature is calculated and input into the cooling tower model; based on the load demand data, calculating the heat dissipating capacity of the system according to a preset calculation coefficient, so that the accuracy of calculating the heat dissipating capacity of the system is improved, a control temperature difference is obtained, and then the cooling water flow and the cooling water outlet temperature are calculated according to the control temperature difference and the heat dissipating capacity of the system; and inputting the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature into the cooling tower model and the cooling pump model, and further matching an optimal operation scheme from the cooling tower model and the cooling pump model according to each approximation degree initial value corresponding to the annual outdoor wet bulb temperature value.

In a preferred example of the present application: based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature, after the step of matching the optimal operation scheme of the cooling tower and the cooling pump corresponding to the initial value of the approximation degree, the method further comprises the following steps of:

defining an optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree as a reference scheme, and acquiring a corresponding power consumption value;

respectively setting a positive optimizing approximation degree value and a negative optimizing approximation degree value in the positive direction and the negative direction of the initial approximation degree value based on a preset optimizing resolution, and determining a positive optimizing scheme, a negative optimizing scheme and a corresponding power consumption value based on the positive optimizing approximation degree value and the negative optimizing approximation degree value;

and defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive direction optimizing scheme and the negative direction optimizing scheme as a new approximation degree initial value.

By adopting the technical scheme, based on the initial value of the approximation degree, the optimal operation scheme of the cooling tower and the cooling pump under the current initial value of the approximation degree is matched from the cooling tower model and the cooling pump model to be used as a reference scheme, so that the power consumption value corresponding to the optimal operation scheme is obtained, and the subsequent comparison between the power consumption value of the reference scheme and the power consumption value of the optimization scheme is facilitated; determining a positive optimizing approximation degree value and a negative optimizing approximation degree value based on a preset optimizing resolution and an initial approximation degree value, respectively inputting the positive optimizing approximation degree value and the negative optimizing approximation degree value into a cooling tower model and a cooling pump model, generating an optimal operation scheme based on the positive optimizing approximation degree value as a positive optimizing scheme, generating an optimal operation scheme based on the negative optimizing approximation degree value as a negative optimizing scheme, and calculating power consumption values corresponding to the positive optimizing scheme and the negative optimizing scheme; and comparing the power consumption values of the reference scheme, the positive optimizing scheme and the negative optimizing scheme, and defining the approximation degree value corresponding to the scheme with the lowest power consumption value as a new approximation degree initial value so as to be convenient for executing a further approximation degree optimizing program subsequently.

In a preferred example of the present application: after the step of defining the approximation degree value of the lowest electricity consumption scheme among the reference scheme, the positive optimization scheme and the negative optimization scheme as the new approximation degree initial value, the method further comprises the following steps:

if the power consumption value of the reference scheme is the lowest, generating an optimal control scheme based on the reference scheme;

and if the power consumption value of the reference scheme is not the lowest, executing a new approximation degree optimizing program based on the new approximation degree initial value.

By adopting the technical scheme, the approximation degree optimizing program is executed to generate an approximation degree optimizing result, if the power consumption value of the reference scheme is lower than the power consumption values corresponding to the positive optimizing scheme and the negative optimizing scheme, the energy efficiency of the reference scheme is considered to be higher than that of the positive optimizing scheme and the negative optimizing scheme, and an optimal control scheme is generated based on the reference scheme, so that the working of the cooling water system is conveniently controlled according to the optimal control scheme in the follow-up process; and if the power consumption value of the reference scheme is higher than the power consumption value corresponding to the positive-direction optimizing scheme or the negative-direction optimizing scheme, considering that the energy efficiency of the reference scheme is lower than that of the positive-direction optimizing scheme or the negative-direction optimizing scheme, and executing the next round of approximation degree optimizing program based on the new approximation degree initial value in the approximation degree optimizing result until the optimal control scheme is determined.

In a preferred example of the present application: further comprising:

recording the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demand, the outdoor meteorological parameters and the heat dissipation data in real time to generate historical data;

and correcting parameters of the system optimization model and the annual control strategy according to the historical data periodically.

By adopting the technical scheme, the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demand, the outdoor meteorological parameters and the heat dissipation data are recorded in real time to generate historical data, so that the abnormal reason can be conveniently and rapidly judged when the cooling water system is abnormal in operation; and parameters of the system optimization model and the annual control strategy are corrected according to the historical data periodically, so that the deviation between the optimal control scheme and the actual optimal control parameters is reduced, and the optimization effect of the optimal control scheme on the energy efficiency of the cooling water system is further improved.

The second invention of the present application is realized by the following technical scheme:

an approximation-centric cooling water optimization independent control system comprising:

the project information acquisition module is used for acquiring load demand data, system equipment parameters and historical meteorological data of a target project and generating project information;

the system optimizing model establishing module is used for establishing a system optimizing model based on system equipment parameters, and the system optimizing model comprises an approximation degree model, a cooling tower model and a cooling pump model;

the optimal control scheme generation module is used for acquiring the outdoor wet bulb temperature value and the load demand data, inputting the outdoor wet bulb temperature value and the load demand data into the system optimization model, matching the optimal control scheme, generating a year-round control strategy based on the optimal control scheme and sending the year-round control strategy to the control device;

and the cooling water system control module is used for inputting the current load demand value to the control device, matching the current optimal cooling approximation degree, correcting the target value of the cooling inlet water temperature and executing PID variable frequency regulation.

By adopting the technical scheme, the load demand data, the system equipment parameters and the historical meteorological data of the target project are acquired to form project information, so that the equipment condition and the meteorological condition of the cooling water system of the target project and the use demand of the target project on the cooling water system are conveniently acquired; respectively creating an approximation degree model, a cooling tower model and a cooling pump model based on parameters of each device in a cooling water system, so as to summarize and generate a system optimization model, and then calculating corresponding power consumption values based on different operation parameters; acquiring annual outdoor wet bulb temperature values according to historical meteorological data, and inputting each outdoor wet bulb temperature value and load demand data into a system optimization model so as to match a corresponding optimal control scheme from the system optimization model based on the environmental temperature and the load demand and improve the energy efficiency of a cooling water system; generating a year-round control strategy based on each optimal control scheme and sending the year-round control strategy to a control device so as to plan the year-round operation control of the cooling water system in advance; when the control device is used for regulating and controlling the cooling water system, the current load demand value is input into the control device and is matched with the current optimal cooling approximation degree, so that the target values of the cooling inlet water temperature and the cooling tower temperature difference are further corrected, the cooling tower and the cooling pump are controlled to execute PID variable frequency regulation, and the energy-saving control effect of the cooling water system is optimized.

In a preferred example of the present application: the system optimizing model creating module comprises:

the approximation degree model creating submodule is used for generating a plurality of wet bulb temperature intervals based on historical meteorological data of a target area, setting an approximation degree initial value for each wet bulb temperature interval based on equipment parameters of the cooling tower, and creating an approximation degree model;

the cooling tower model creating submodule is used for evaluating the outlet water temperature and the power consumption value of the cooling tower under each water inlet temperature, wet bulb temperature, fan frequency and cooling flow based on the equipment parameters of the cooling tower, and creating a cooling tower model;

and the cooling pump model creating submodule is used for evaluating the power consumption values of the cooling pump at different flow rates based on the cooling pump equipment parameters and creating a cooling pump model.

By adopting the technical scheme, the cooling water system comprises the cooling tower and the cooling pump, equipment parameters of the cooling tower and the cooling pump can be obtained from a specification provided by a manufacturer, the change range, the time distribution condition and the change trend of the historical wet bulb temperature of the area are obtained by calculation based on the historical meteorological data of the area where the cooling water system needing optimization control is located, the change range of the historical wet bulb temperature is divided into a plurality of wet bulb temperature intervals, and corresponding initial values of the approximation degree are set for the wet bulb temperature intervals based on the equipment parameters of the cooling tower, so that an approximation degree model is created, and the initial values of the approximation degree adjustment can be conveniently and rapidly determined based on the wet bulb temperature values in the following process; evaluating the water outlet temperature and the power consumption value of the cooling tower under different water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates based on the equipment parameters of the cooling tower, so as to create a cooling tower model, and facilitate the subsequent calculation of the power consumption of the cooling tower in various running states when optimizing control is carried out on a cold station; evaluating the power consumption values of the cooling pump under different flow rates based on the equipment parameters of the cooling pump, thereby creating a cooling pump model and facilitating the subsequent evaluation of the energy consumption condition of the cooling pump; and establishing a system optimization model based on the approximation degree model, the cooling tower model and the cooling pump model.

The third purpose of the invention is realized by adopting the following technical scheme:

a computer apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above approximation-centric cooling water optimization method when executing the computer program.

The fourth purpose of the invention of the application is realized by adopting the following technical scheme:

a computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the above described approximation-centric cooling water optimization method.

In summary, the present application includes at least one of the following beneficial technical effects:

1. acquiring load demand data, system equipment parameters and historical meteorological data of a target project to form project information, so that the equipment condition and the meteorological condition of a cooling water system of the target project and the use demand of the target project on the cooling water system can be conveniently obtained; respectively creating an approximation degree model, a cooling tower model and a cooling pump model based on parameters of each device in a cooling water system, so as to summarize and generate a system optimization model, and then calculating corresponding power consumption values based on different operation parameters; acquiring annual outdoor wet bulb temperature values according to historical meteorological data, and inputting each outdoor wet bulb temperature value and load demand data into a system optimization model so as to match a corresponding optimal control scheme from the system optimization model based on the environmental temperature and the load demand and improve the energy efficiency of a cooling water system; generating a year-round control strategy based on each optimal control scheme and sending the year-round control strategy to a control device so as to plan the year-round operation control of the cooling water system in advance; when the control device is used for regulating and controlling the cooling water system, the current load demand value is input into the control device, and the current optimal cooling approximation degree is matched, so that the target values of the cooling inlet water temperature and the cooling tower temperature difference are further corrected, the cooling tower and the cooling pump are controlled to execute PID variable frequency regulation, and the effect of energy-saving control of the cooling water system is optimized.

2. The cooling water system comprises a cooling tower and a cooling pump, equipment parameters of the cooling tower and the cooling pump can be obtained from specifications provided by a manufacturer, based on historical meteorological data of an area where the cooling water system needing optimization control is located, a historical wet bulb temperature change range, a time distribution condition and a change trend of the area are obtained through calculation, the historical wet bulb temperature change range is divided into a plurality of wet bulb temperature intervals, and corresponding initial value of approximation degree is set for each wet bulb temperature interval based on the equipment parameters of the cooling tower, so that an approximation degree model is created, and the initial value of approximation degree adjustment can be rapidly determined based on the wet bulb temperature values in the following process; evaluating the water outlet temperature and the power consumption value of the cooling tower under different water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates based on the equipment parameters of the cooling tower, thereby creating a cooling tower model and facilitating the subsequent calculation of the power consumption of the cooling tower in various running states when optimizing control is carried out on a cold station; evaluating power consumption values of the cooling pump under different flow rates based on cooling pump equipment parameters so as to create a cooling pump model, and facilitating subsequent evaluation of the energy consumption condition of the cooling pump; and establishing a system optimization model based on the approximation degree model, the cooling tower model and the cooling pump model.

3. Acquiring an outdoor wet bulb temperature value, inputting the outdoor wet bulb temperature value into the approximation degree model, matching a corresponding approximation degree initial value, further calculating a corresponding cooling inlet water temperature, and inputting the corresponding cooling inlet water temperature into the cooling tower model; based on the load demand data, calculating the heat dissipating capacity of the system according to a preset calculation coefficient, so that the accuracy of calculating the heat dissipating capacity of the system is improved, a control temperature difference is obtained, and then the cooling water flow and the cooling water outlet temperature are calculated according to the control temperature difference and the heat dissipating capacity of the system; and inputting the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature into the cooling tower model and the cooling pump model, and further matching an optimal operation scheme from the cooling tower model and the cooling pump model according to each approximation degree initial value corresponding to the annual outdoor wet bulb temperature value.

Drawings

Fig. 1 is a schematic diagram of the information transmission flow of the cooling water system control in the present application.

FIG. 2 is a flow chart of a method for approximation-centric cooling water optimization according to an embodiment of the present invention.

FIG. 3 is a flowchart of step S20 of the method for cooling water optimization centered on the approximation degree.

FIG. 4 is a flowchart of step S30 of the method for optimizing cooling water centered around the approximation degree.

FIG. 5 is another flowchart of step S30 of the approach-centric cooling water optimization method of the present application.

FIG. 6 is another flowchart of step S30 of the approach-centric cooling water optimization method of the present application.

FIG. 7 is another flow chart of the approach-centered cooling water optimization method according to the second embodiment of the present application.

FIG. 8 is a schematic block diagram of an approximation-centric cooling water optimization independent control system according to a third embodiment of the present application.

Fig. 9 is a schematic diagram of an apparatus in the fourth embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to figures 1 to 9.

The application discloses a cooling water optimizing method taking approximation degree as a center, which can be used for controlling the working states of a cooling tower and a cooling pump in a cooling water system, wherein the cooling water system refers to a cooling water system taking the cooling tower as a cooling mode in the application, and related application scenes comprise but are not limited to a central air-conditioning cold station cooling water system, an industrial air compressor cooling water system, a data center cooling water system and other process cooling water systems; in this embodiment, a central air-conditioning cold station is taken as an example, the cold station is divided into two side water systems, a chilled water system refers to a water circulation that the cold station provides cold water to a terminal air conditioner, and a cooling water system refers to a water circulation that performs corresponding heat dissipation treatment in order to meet refrigeration requirements.

The cooling water optimizing method taking the approximation degree as the center combines the advantages of the traditional control mode and the complex algorithm control mode, is in accordance with the process characteristics, provides the intelligent automatic optimizing method of the automatic control system under the condition of certain deterministic load requirement, has the advantages of strong practicability, clear control logic and high stability, and overcomes the defects of the current complex algorithm technology; the cooling water optimizing strategy control target is to realize the lowest energy consumption target of cooling water equipment under various working conditions, the core of the method is system energy conservation, and the method is intelligent optimizing; as shown in fig. 1, the main process of the cooling water optimization strategy is to take the approximation degree as the center, execute a series of matching and comparison calculations, and finally output the scheme with the lowest energy consumption, which relates to the control of cooling tower number adjustment, cooling tower frequency conversion adjustment, cooling pump number adjustment, cooling pump frequency adjustment and the like.

In the application, the approximation degree refers to the difference value between the cooling inlet water temperature and the wet bulb temperature, and the lower the approximation degree is, the closer the cooling inlet water temperature is to the wet bulb temperature is, the higher the cooling efficiency is; cooling water energy efficiency = cooling water system heat dissipation capacity/cooling side power consumption, and the cooling side power consumption is generally the sum of cooling tower power and cooling pump power; load demand refers to the amount of refrigeration required at the end (e.g., cold station system) or the amount of direct heat rejection required at the end (e.g., air compression system).

Example one

As shown in fig. 2, the method specifically includes the following steps:

s10: and acquiring load demand data, system equipment parameters and historical meteorological data of the target project to generate project information.

In this embodiment, the target item refers to an item that needs to be generated by a year-round cooling water system control strategy, and may specifically correspond to a building, a power consumption unit, or a cooling water system usage unit; the load demand data refers to the annual load demand data of the target project; the system equipment parameters refer to the model, performance parameters and the like of each equipment in a cooling water system used by a target project; the historical meteorological data are data formed by historical meteorological information of an area where a target project is located, and specifically comprise temperature data and humidity data; the project information is information formed by summarizing load demand data, system equipment parameters and historical meteorological data corresponding to a target project.

Specifically, historical meteorological data of the area where the target project is located and an annual load demand value of the target project are obtained, future annual meteorological parameter prediction data are estimated based on the historical meteorological data of the area where the target project is located, wherein the load demand data can be obtained by calculation according to the annual meteorological parameter prediction data and an annual temperature control target, and the load demand data and the annual meteorological parameter prediction data need to be expanded to data points of 8760 hours all year round; the performance parameters of a cooling tower, a cooling pump and other equipment are inquired through the model of cooling water system equipment used by a target project and manufacturer information to form system equipment parameters, project information is generated and stored in a database based on load demand data, system equipment parameters, historical meteorological data, annual meteorological parameter prediction data and annual temperature control targets, and required information is conveniently called from the database in a subsequent cooling water system control strategy generation link.

S20: and establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model.

In this embodiment, the system optimization model refers to a model created according to system equipment parameters, and is used for matching an optimal control scheme of a cooling water system in various scenarios, and the system optimization model includes an approximation degree model, a cooling tower model and a cooling pump model.

Specifically, system equipment parameters are obtained from the project information, wherein the system equipment parameters comprise cooling tower equipment parameters and cooling pump equipment parameters, and a closeness model is created based on the cooling tower equipment parameters and historical meteorological data, so that the corresponding closeness numerical values can be matched conveniently according to real-time meteorological data; a cooling tower model is established based on the cooling tower equipment parameters, so that the power consumption value of the cooling tower can be conveniently evaluated according to the running state of the cooling tower; a cooling pump model is established based on the cooling pump equipment parameters, so that the power consumption value of the cooling pump can be conveniently evaluated according to the running state of the cooling pump; the system optimization model is established based on the approximation degree model, the cooling tower model and the cooling pump model, so that the sharing and the association of data among the approximation degree model, the cooling tower model and the cooling pump model are convenient to realize, and the generation efficiency of the optimal control scheme of the cooling water system is improved.

Referring to fig. 3, step S20 includes:

s21: and generating a plurality of wet bulb temperature intervals based on historical meteorological data of the target area, setting an initial value of the approximation degree for each wet bulb temperature interval based on the equipment parameters of the cooling tower, and creating an approximation degree model.

Specifically, a temperature value change range and a humidity value change range of the target area, and the change and distribution rules of the temperature value and the humidity value with the seasons and the months are respectively judged based on historical meteorological data of the target area; calculating a corresponding wet bulb temperature change range based on the temperature value and the corresponding humidity value, dividing the wet bulb temperature change range into a plurality of wet bulb temperature intervals, preferably, the temperature value span of each wet bulb temperature interval is 1 ℃, setting a corresponding initial approximation degree value based on the cooling tower equipment parameters and the midpoint value of the corresponding wet bulb temperature value in each wet bulb temperature interval, creating an approximation degree model based on the initial approximation degree value corresponding to each wet bulb temperature interval, and facilitating automatic matching of the corresponding initial approximation degree value after the wet bulb temperature value is subsequently input to the approximation degree model.

S22: and evaluating the outlet water temperature and the power consumption value of the cooling tower under each inlet water temperature, wet bulb temperature, fan frequency and cooling flow based on the equipment parameters of the cooling tower, and creating a cooling tower model.

Specifically, based on cooling tower equipment parameters, corresponding cooling water outlet temperature and power consumption values of the cooling tower under various cooling tower water inlet temperatures, wet bulb temperatures, fan frequencies and cooling tower flow are evaluated, so that a cooling tower model is created; the corresponding relation among the cooling tower water inlet temperature, the wet bulb temperature, the fan frequency, the cooling tower flow, the cooling water outlet temperature and the power consumption value can be obtained from historical operating parameters of cooling tower equipment, regression algorithm analysis is carried out based on the historical operating parameters of the cooling tower, and therefore the corresponding relation among the cooling tower water inlet temperature, the wet bulb temperature, the fan frequency, the cooling tower flow, the cooling water outlet temperature and the power consumption value is obtained, the reliability of a cooling tower model is convenient to improve, and the reliability of the cooling tower model can be gradually optimized along with the accumulation of the historical operating parameters.

S23: and evaluating the power consumption values of the cooling pump at different flow rates based on the equipment parameters of the cooling pump, and creating a cooling pump model.

Specifically, based on the cooling pump equipment parameters, the power consumption values of the cooling pump equipment at different flow rates are evaluated, so that a cooling pump model is created, wherein the corresponding relation between the cooling water flow rate and the power consumption values of the cooling pump equipment can be obtained from historical operating parameters of the cooling pump equipment, regression algorithm analysis is performed based on the historical operating parameters of the cooling pump, so that the corresponding relation between the cooling water flow rate and the power consumption values of the cooling pump equipment is obtained, the reliability of the cooling pump model is convenient to improve, and the reliability of the cooling pump model can be gradually optimized along with the accumulation of the historical operating parameters.

S30: and acquiring the outdoor wet bulb temperature value and the load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimization model, matching an optimal control scheme, generating a year-round control strategy based on the optimal control scheme, and sending the year-round control strategy to a control device.

In the present embodiment, the control device means a device for automatically controlling the cooling water system based on a year-round control strategy.

Specifically, the predicted value and the load demand data of the outdoor wet bulb temperature value of each time period all the year are obtained, the outdoor wet bulb temperature value and the load demand data of the corresponding time period are input into the system optimization model one by one, so that the corresponding approximation degree value, the cooling tower operation scheme and the cooling pump operation scheme are matched, the optimal control scheme of each time period is determined according to the control target with the lowest overall energy consumption of the cooling water system, the annual control strategy is generated based on the optimal control scheme of each time period and is sent to the control device, and the operation state of the cooling water system is automatically controlled through the control device in the follow-up process.

In this embodiment, the annual working condition may be split into 8760 time periods (i.e., 8760 hours, where the working condition of the time period includes the load demand and the meteorological parameters), the single-point optimization process is respectively executed on the working condition of each time period, and after 8760 times of calculation, the annual control strategy is finally formed; in the actual operation of the subsequent cooling water system, the system can be matched to the closest working condition, so as to output a control approximation degree value.

Further, after sending the year-round control strategy generated based on the target project to the control device, the control device executes the cooling water system control as follows: at a certain moment, the system acquires the current load demand and acquires the current outdoor dry bulb temperature and relative humidity through an outdoor temperature and humidity sensor; the system acquires an approximation degree value of an optimal control scheme under the current working condition according to a year-round control strategy; correcting a cooling inlet water temperature control target of cooling tower equipment according to the approximation value, and controlling a cooling tower fan to execute PID variable frequency regulation; the cooling pump equipment executes PID variable frequency regulation according to the temperature difference target value; after the cooling tower equipment and the cooling pump equipment are adjusted in place, acquiring the energy consumption value of the cooling water system in the current time period; and acquiring and recording data such as cooling tower operation parameters, cooling pump operation parameters, actual load requirements, outdoor meteorological parameters, system heat dissipation data and the like in real time.

Referring to fig. 4, step S30 includes:

s31: and acquiring an outdoor wet bulb temperature value, matching the corresponding initial value of the approximation degree, calculating a cooling inlet water temperature demand value and inputting the cooling inlet water temperature demand value into the cooling tower model.

Specifically, outdoor meteorological data of a target time period are obtained, and a current outdoor wet bulb temperature value is calculated according to the obtained outdoor dry bulb temperature and the obtained outdoor relative humidity; selecting a corresponding approximation degree initial value under the current outdoor wet bulb temperature value according to the approximation degree model; and calculating a cooling return water temperature requirement value according to the initial value of the approximation degree, and inputting the cooling return water temperature requirement value into the cooling tower model.

S32: based on the load demand data, calculating the heat dissipating capacity of the system according to a preset calculation coefficient, acquiring a control temperature difference value, calculating a cooling water flow rate and a cooling water outlet temperature demand value, and inputting the values into a cooling tower model.

Specifically, based on the load demand data required at the end of the target time period, the heat dissipation capacity of the system is calculated according to a preset calculation coefficient, which is, in this embodiment, the calculation coefficient is; in the air pressure embodiment of the application, if the cooling water system is an air compressor cooling water system, the heat dissipation capacity of the system can also be directly obtained; based on the heat dissipation capacity of the system, obtaining a control temperature difference, calculating a cooling water flow demand value and inputting the cooling water flow demand value into a cooling tower model and a cooling pump model; and calculating a cooling water outlet temperature demand value based on the system heat dissipation capacity and the control temperature difference, and inputting the cooling water outlet temperature demand value into the cooling tower model.

S33: and matching the optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature.

Specifically, based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature which are input into the cooling tower model, the number of the cooling tower devices and the cooling tower frequency which correspond to the lowest power consumption of the cooling tower equipment are matched to serve as an optimal cooling tower operation scheme, and a power consumption value corresponding to the optimal cooling tower operation scheme is output; matching the number of cooling pumps and the frequency of the cooling pumps corresponding to the lowest power consumption of the cooling pump equipment as an optimal cooling pump operation scheme based on the flow of cooling water input into the cooling pump model, and outputting a power consumption value corresponding to the optimal cooling pump operation scheme; and generating an optimal operation scheme of the cooling water system based on the optimal operation scheme of the cooling tower and the optimal operation scheme of the cooling pump.

Further, in this embodiment, the core objective of the cooling water optimization strategy control is that the power consumption of the cooling water system is the lowest, so if the operation states of the cooling water system corresponding to the optimal cooling tower operation scheme and the optimal cooling pump operation scheme are not the operation states with the lowest energy consumption of the cooling water system, the parameters are adjusted based on the optimal cooling tower operation scheme and the optimal cooling pump operation scheme, so as to determine the operation state with the lowest energy consumption of the cooling water system and generate the optimal operation scheme of the cooling water system.

With reference to fig. 5, after step S33, the method further includes:

s34: and defining an optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree as a reference scheme, and acquiring a corresponding power consumption value.

Specifically, in this embodiment, the core objective of the cooling water optimization strategy control is that the power consumption of the cooling water system is the lowest, so the operation states of the cooling water system corresponding to the optimal cooling tower operation scheme and the optimal cooling pump operation scheme may not be the most energy-saving operation scheme of the cooling water system; and after generating an optimal operation scheme of the cooling water system based on the initial value of the approximation degree, defining the optimal operation scheme as a reference scheme, and calculating the total power consumption value of the cooling water system corresponding to the reference scheme, so that the total power consumption value of each optimization scheme can be compared with the total power consumption value of the reference scheme in the follow-up process, and the more energy-saving operation scheme of the cooling water system can be determined.

S35: and respectively setting a positive direction optimizing approximation degree value and a negative direction optimizing approximation degree value in the positive and negative directions of the initial approximation degree value based on a preset optimizing resolution, and determining a positive direction optimizing scheme, a negative direction optimizing scheme and corresponding power consumption values based on the positive direction optimizing approximation degree value and the negative direction optimizing approximation degree value.

In this embodiment, the optimization resolution refers to a difference value between a new optimization approximation value and an initial approximation value when the approximation optimization procedure is executed based on the reference scheme.

Specifically, in this embodiment, the optimization resolution is 0.2 ℃, and a positive optimization approximation value and a negative optimization approximation value are respectively set in the positive and negative directions based on the initial value of the approximation and the preset optimization resolution, for example, if the initial value of the approximation is 3 ℃, the positive optimization approximation value is 3.2 ℃, and the negative optimization approximation value is 2.8 ℃; and determining a positive direction optimizing scheme and a negative direction optimizing scheme based on the positive direction optimizing approximation degree value and the negative direction optimizing approximation degree value.

Specifically, a corresponding cooling inlet water temperature demand value is calculated based on a forward optimization approximation degree value and is input to a cooling tower model; calculating the heat dissipating capacity of the system according to a preset calculation coefficient based on load demand data, acquiring a control temperature difference value, calculating corresponding cooling water flow and cooling water outlet temperature demand values and inputting the corresponding cooling water flow and cooling water outlet temperature demand values into a cooling tower model; based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature, matching an optimal operation scheme of the cooling tower and the cooling pump in a corresponding forward optimization approximation degree value as a forward optimization scheme; and calculating a corresponding power consumption value based on the forward optimization scheme.

Specifically, a corresponding cooling inlet water temperature demand value is calculated based on a negative direction optimization approximation degree numerical value and is input into a cooling tower model; calculating the heat dissipating capacity of the system according to a preset calculation coefficient based on load demand data to obtain a control temperature difference value, calculating corresponding cooling water flow and a cooling water outlet temperature demand value, and inputting the corresponding cooling water flow and cooling water outlet temperature demand value into a cooling tower model; based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature, matching an optimal operation scheme of the cooling tower and the cooling pump in a corresponding negative direction optimizing approximation degree value to serve as a negative direction optimizing scheme; and calculating a corresponding power consumption value based on a negative optimizing scheme.

S36: and defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as a new approximation degree initial value.

Specifically, the electricity consumption values of the reference scheme, the positive direction optimizing scheme and the negative direction optimizing scheme are compared, and the approximation degree value corresponding to the scheme with the lowest electricity consumption value is defined as a new approximation degree initial value, so that the next approximation degree optimizing program can be executed conveniently, and the energy consumption of the cooling water system control scheme can be further optimized.

With reference to fig. 6, after step S36, the method further includes:

s37: and if the power consumption value of the reference scheme is the lowest, generating an optimal control scheme based on the reference scheme.

In this embodiment, the optimal control scheme refers to a scheme finally used for controlling the cooling water system, which is obtained after multiple cycles of the approximation degree optimizing program.

Specifically, after an approximation degree optimizing program is executed and an approximation degree optimizing result is generated, power consumption values corresponding to a reference scheme, a positive optimizing scheme and a negative optimizing scheme in the approximation degree optimizing result are compared, if the power consumption value of the reference scheme is lower than the power consumption values corresponding to the positive optimizing scheme and the negative optimizing scheme, the energy efficiency of the reference scheme is considered to be higher than that of the positive optimizing scheme and that of the negative optimizing scheme, an optimal control scheme is generated based on the reference scheme, and the operation of the cooling water system is conveniently controlled according to the optimal control scheme in the follow-up process.

S38: and if the power consumption value of the reference scheme is not the lowest value, executing a new approximation degree optimizing program based on the new approximation degree initial value.

Specifically, after an approximation degree optimizing program is executed and an approximation degree optimizing result is generated, power consumption values corresponding to a reference scheme, a positive optimizing scheme and a negative optimizing scheme in the approximation degree optimizing result are compared, if the power consumption value of the reference scheme is higher than that corresponding to the positive optimizing scheme, the energy efficiency of the reference scheme is considered to be lower than that of the positive optimizing scheme, and if the power consumption value of the reference scheme is higher than that corresponding to the negative optimizing scheme, the energy efficiency of the reference scheme is considered to be lower than that of the negative optimizing scheme; therefore, the next round of approximation degree optimizing program needs to be executed based on the new approximation degree initial value in the approximation degree optimizing result until the optimal control scheme is determined, so that the operation of the cooling water system is conveniently controlled according to the optimal control scheme in the follow-up process.

S40: and inputting the current load demand value into a control device, matching the current optimal cooling approximation degree to correct the target value of the cooling inlet water temperature, and executing PID frequency conversion regulation.

Specifically, when the control device executes automatic control for the cooling water system, the current load demand value and the wet bulb temperature are input to the control device, the approximation degree value of the optimal control scheme under the current working condition is obtained according to the annual control strategy so as to correct the cooling inlet water temperature control target of the cooling tower equipment, the cooling tower fan is controlled to execute PID variable frequency regulation, and the cooling pump equipment is controlled to execute PID variable frequency regulation according to the temperature difference target value, so that the effect of optimizing the energy efficiency of the cooling water system is achieved.

Example two

On the basis of the first embodiment, referring to fig. 7, the approach-centered cooling water optimization method further includes:

s50: and recording the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load requirement, the outdoor meteorological parameters and the heat dissipation data in real time to generate historical data.

Specifically, in the daily operation process of the cooling water system equipment, data such as cooling tower operation parameters, cooling pump operation parameters, actual load requirements, outdoor meteorological parameters, system heat dissipation capacity data and the like are obtained in real time and recorded, and historical data are generated to facilitate quick judgment of abnormal reasons when the cooling water system is abnormal in operation in the follow-up process.

S60: and correcting parameters of the system optimization model and the annual control strategy according to historical data periodically.

Specifically, parameters of a system optimization model and parameters of a year-round control strategy are corrected according to historical data periodically, so that the deviation between an optimal control scheme and actual optimal control parameters is reduced, the actual running condition of the cooling water system is matched better, and the optimization effect of the optimal control scheme on the energy efficiency of the cooling water system is further improved.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the steps, and should not constitute any limitation to the implementation process of the embodiments of the present application.

EXAMPLE III

As shown in fig. 8, the present application discloses an approximation degree-centered cooling water optimization independent control system for executing the steps of the approximation degree-centered cooling water optimization method, which corresponds to the approximation degree-centered cooling water optimization method in the above embodiment.

The cooling water optimization independent control system with the approximation degree as the center comprises a project information acquisition module, a system optimization model creation module, an optimal control scheme generation module and a cooling water system control module. The detailed description of each functional module is as follows:

The system optimizing model creating module comprises:

the cooling tower model creating submodule is used for evaluating the water outlet temperature and the power consumption value of the cooling tower under various water inlet temperatures, wet bulb temperatures, fan frequencies and cooling flow rates based on the equipment parameters of the cooling tower and creating a cooling tower model;

and the cooling pump model creating submodule is used for evaluating the power consumption values of the cooling pump at different flow rates based on the equipment parameters of the cooling pump and creating a cooling pump model.

The optimal control scheme generation module comprises:

the approximation degree initial value matching sub-module is used for acquiring the outdoor wet bulb temperature value, matching the corresponding approximation degree initial value, calculating the cooling inlet water temperature demand value and inputting the cooling inlet water temperature demand value to the cooling tower model;

the system heat dissipation capacity calculation submodule is used for calculating the heat dissipation capacity of the system according to a preset calculation coefficient based on load demand data, acquiring a control temperature difference value, calculating a cooling water flow rate and a cooling water outlet temperature demand value and inputting the values into a cooling tower model;

the optimal operation scheme matching sub-module is used for matching the optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature;

the reference scheme acquisition sub-module is used for defining the optimal operation scheme of the cooling tower and the cooling pump at the corresponding initial value of the approximation degree as a reference scheme and acquiring a corresponding power consumption value;

the positive and negative optimizing sub-module is used for respectively setting a positive optimizing approximation degree value and a negative optimizing approximation degree value in the positive and negative directions of the initial approximation degree value based on a preset optimizing resolution ratio, and determining a positive optimizing scheme, a negative optimizing scheme and corresponding power consumption values based on the positive optimizing approximation degree value and the negative optimizing approximation degree value;

an approximation degree initial value updating sub-module, configured to define an approximation degree value of a lowest power consumption value scheme among the reference scheme, the positive direction optimization scheme, and the negative direction optimization scheme as a new approximation degree initial value;

the optimal control scheme generation submodule is used for generating an optimal control scheme based on the reference scheme if the power consumption value of the reference scheme is the lowest;

and the approximation degree optimizing program executing submodule is used for executing a new approximation degree optimizing program based on the new approximation degree initial value if the power consumption value of the reference scheme is not the lowest.

The cooling water optimization independent control system taking the approximation degree as the center further comprises:

the historical data acquisition module is used for recording the operation parameters of the cooling tower, the operation parameters of the cooling pump, the actual load demand, the outdoor meteorological parameters and the heat dissipation data in real time to generate historical data;

and the system optimizing model correcting module is used for correcting parameters of the system optimizing model and the annual control strategy according to the historical data periodically.

For the specific limitation of the cooling water optimization independent control system with proximity as the center, reference may be made to the above limitation on the cooling water optimization method with proximity as the center, and details thereof are not repeated herein; all or part of each module in the cooling water optimization independent control system taking the approximation degree as the center can be realized by software, hardware and combination thereof; the modules can be embedded in a hardware form or independent from a processor in the computer device, or can be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

Example four

A computer device, which may be a server, may have an internal structure as shown in FIG. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer equipment is used for storing project information, system optimization models, optimal control schemes, annual control strategies, cooling tower operation parameters, cooling pump operation parameters, actual load requirements, outdoor meteorological parameters, heat dissipation data and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for approximation-centric cooling water optimization.

In one embodiment, there is provided a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:

s10: acquiring load demand data, system equipment parameters and historical meteorological data of a target project to generate project information;

s20: establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model;

s30: acquiring an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimization model, matching an optimal control scheme, generating a year-round control strategy based on the optimal control scheme, and sending the year-round control strategy to a control device;

s40: and inputting the current load demand value into a control device, matching the current optimal cooling approximation degree to correct the cooling inlet water temperature target value, and executing PID variable frequency regulation.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchlink (Synchlink), DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.

It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus may be divided into different functional units or modules to perform all or part of the above described functions.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art; the technical solutions described in the foregoing embodiments may still be modified, or some features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A method for optimizing cooling water centered on an approximation degree, comprising:

2. A proximity-centric cooling water optimization method according to claim 1, characterized by: the method comprises the following steps of establishing a system optimization model based on system equipment parameters, wherein the system optimization model comprises an approximation degree model, a cooling tower model and a cooling pump model:

3. A proximity-centric cooling water optimization method according to claim 1, characterized by: the method comprises the steps of obtaining an outdoor wet bulb temperature value and load demand data, inputting the outdoor wet bulb temperature value and the load demand data into a system optimization model, and matching an optimal control scheme system, wherein the steps comprise:

4. A method of approximation-centric cooling water optimization according to claim 3, characterized by: based on the wet bulb temperature value, the cooling inlet water temperature, the cooling water flow and the cooling outlet water temperature, after the step of matching the optimal operation scheme of the cooling tower and the cooling pump corresponding to the initial value of the approximation degree, the method further comprises the following steps of:

respectively setting a positive direction optimizing approximation degree value and a negative direction optimizing approximation degree value in the positive and negative directions of the initial approximation degree value based on a preset optimizing resolution ratio, and determining a positive direction optimizing scheme, a negative direction optimizing scheme and corresponding power consumption values based on the positive direction optimizing approximation degree value and the negative direction optimizing approximation degree value;

and defining the approximation degree value of the scheme with the lowest power consumption value in the reference scheme, the positive optimizing scheme and the negative optimizing scheme as a new approximation degree initial value.

5. A proximity-centric cooling water optimization method according to claim 4, characterized in that: after the step of defining the approximation degree value of the lowest electricity consumption scheme among the reference scheme, the positive optimizing scheme and the negative optimizing scheme as a new approximation degree initial value, the method further comprises the following steps:

6. A proximity-centric cooling water optimization method according to claim 1, characterized by: further comprising:

7. An approximation-centric cooling water optimization independent control system, comprising:

8. An approximation-centric cooling water optimization independent control system according to claim 7, characterized in that: the system optimizing model creating module comprises:

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the approximation-centric cooling water optimization method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the approximation-centric cooling water optimization method according to any one of claims 1 to 6.