CN113761807B - Hybrid optimization-based quick air-conditioning air-water system optimization method - Google Patents

Hybrid optimization-based quick air-conditioning air-water system optimization method Download PDF

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CN113761807B
CN113761807B CN202111145227.1A CN202111145227A CN113761807B CN 113761807 B CN113761807 B CN 113761807B CN 202111145227 A CN202111145227 A CN 202111145227A CN 113761807 B CN113761807 B CN 113761807B
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air
conditioning
chilled water
objective function
water system
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CN113761807A (en
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李连宏
吴金顺
王新如
李阳
李鹏
马鑫
张金乾
魏袆璇
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Keruite Air Conditioning Group Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

Abstract

The invention relates to the field of air conditioner control, and discloses a quick air conditioner air-water system optimization method based on hybrid optimization, which comprises the following steps: establishing a centralized air-conditioning air-water system model; determining a target function based on the wind-water system model by taking the minimum energy consumption as a target; optimizing an objective function by using an L-BFGS algorithm to determine a local optimal solution; and (4) performing global optimization of the objective function by using the genetic algorithm and taking the local optimal solution as an initial point, and outputting air-conditioning control parameters of the wind-water system. The method comprises the steps of designing an air-conditioning air-water system optimization model, determining a target function by taking the minimum energy consumption of the model as a target, and quickly realizing the optimization of air-conditioning system parameters in the target function by utilizing a hybrid optimization algorithm combining L-BFGS and a genetic algorithm, thereby realizing the quick response of an air-conditioning system.

Description

Hybrid optimization-based quick air-conditioning air-water system optimization method
Technical Field
The invention relates to the field of air conditioner control, in particular to a quick air conditioner air-water system optimization method based on hybrid optimization.
Background
The centralized air-conditioning system has the characteristics of high energy efficiency and good thermal comfort, and the popularization of the centralized air-conditioning system has important significance for energy conservation of building air conditioners and improvement of indoor environment; reasonable operation parameters are important guarantee for the operation efficiency of the central air conditioner, and have two main values: firstly, the system is integrally coordinated by the optimal operation parameter combination, so that the total energy consumption of the system for processing the same load is the lowest; secondly, a large-scale centralized air-conditioning system simultaneously provides cooling capacity for tens of hundreds of air-conditioning areas, and real-time optimization of operation parameters is carried out according to load difference of each area, so that the system can be ensured to have good load response capacity for all the areas.
Most of existing air conditioner optimization control schemes focus on well-designed air conditioner control models or system structures, for example, patent document CN201410657442.3 adopts an air conditioner optimization control method based on a neural network, and the neural network is used to establish a nonlinear association between input parameters and response control; the patent document CN201910130637.5 uses the calculated marginal efficiency as the basis for air conditioner control; patent documents cn201410161603.X and CN201410048885.2 purchase a design hardware system structure to achieve the purpose of controlling air conditioning energy saving.
Although the above scheme plays a certain role in optimizing the energy-saving control of the air conditioner, the following defects exist:
1. the centralized air-conditioning system is a complex system, and the efficient energy-saving control of the air conditioner cannot be realized by simple hardware design, condition judgment and other methods;
2. the methods such as the neural network and the like are used for the energy-saving control of the air conditioner and lack an effective and reliable training set, and the quality of the training set determines the effectiveness of the neural network parameters, so that the methods are difficult to apply to a real scene;
3. the existing centralized air-conditioning system often needs to provide cooling capacity for a plurality of areas at the same time, for example, each floor in a high-rise building needs to be controlled by the centralized air-conditioning system, which puts a higher requirement on the response time of the centralized air-conditioning system, and how to quickly realize the parameter optimization of the centralized air-conditioning system, so that the problem that the quick response of the air-conditioning system is needed to be solved urgently is solved.
The patent emphasizes on analyzing the problem of quick optimization of the air-conditioning air-water system, and provides a set of air-conditioning air-water system optimization control scheme based on hybrid intelligence.
Disclosure of Invention
The invention provides a quick air-conditioning air-water system optimization method based on hybrid optimization, which aims to (1) quickly realize the parameter optimization of a centralized air-conditioning system so as to realize the quick response of the air-conditioning system; and (2) designing an air-conditioning air-water system optimization model.
The invention provides a quick air-conditioning air-water system optimization method based on hybrid optimization, which comprises the following steps:
s1: establishing a centralized air-conditioning wind-water system model;
s2: determining a target function based on a wind-water system model by taking minimum energy consumption as a target;
s3: optimizing an objective function by using an L-BFGS algorithm to determine a local optimal solution;
s4: and (4) performing global optimization on the objective function by using the genetic algorithm and taking the local optimal solution as an initial point, and outputting the air conditioning control parameters of the wind and water system.
As a further improvement of the method of the invention:
in the step S1, establishing the centralized air-conditioning air-water system model includes establishing a fan energy consumption system model, which includes:
the blower in the centralized air-conditioning air-water system model comprises a blower and an exhaust fan, wherein the exhaust fan is a small-sized blower and operates independently, and the blower sends outdoor fresh air entering the air pipeline into a room through the air outlet pipeline after heat exchange treatment;
the fan energy consumption system model of the air feeder in the centralized air-conditioning air-water system model is as follows:
Figure BDA0003285218930000021
wherein:
W f (G f ) Shows the amount of air blown by the blower is G f The air conditioner input power of (1);
G f representing the volumetric flow of air in the blower;
Δ p represents a pressure increase from an air inlet duct to an air outlet duct of the blower;
α p (G f ) Indicating that the blower has an air volume flow of G f Full pressure efficiency of the time;
α m (n) represents the frequency converter efficiency when the blower rotation speed is n.
In the step S1, establishing the concentrated air-conditioning air-water system model includes establishing a chilled water pump set energy consumption model, which includes:
two groups of chilled water pump sets are arranged in a centralized air conditioner, each group of chilled water pump sets is formed by connecting 4 chilled water pumps of the same type in parallel, the flow obtained by each chilled water pump in each chilled water pump set is the same, and an energy consumption model of the chilled water pump sets in the centralized air conditioner air-water system model is as follows:
Figure BDA0003285218930000022
wherein:
p represents the density of water;
g represents the gravitational acceleration;
β m (u) the frequency converter efficiency when the rotating speed of the refrigerating water pump is u;
W l (G w ) The total flow of the freezing water in the freezing water pump set is represented as G w Input power of time;
G i indicating the flow rate of the chilled water supplied by the chilled water pump i, 4G i ＝G w 。
In the step S1, establishing the centralized air-conditioning air-water system model includes establishing a heat exchange coil model, which includes:
temperature and humidity sensor is arranged between the heat exchange coil of the blower and the air filterA device for measuring the temperature T' and humidity of air entering from the air inlet pipeline of the blower
Figure BDA0003285218930000023
A temperature and humidity sensor and an air speed sensor are arranged in an air outlet pipeline of the air feeder to measure the temperature T' and the humidity of outlet air in the air outlet pipeline
Figure BDA0003285218930000024
Respectively calculating the specific enthalpy h of the air at the air inlet pipeline 1 And specific enthalpy h of air at air outlet pipeline 2 ：
Figure BDA0003285218930000025
Figure BDA0003285218930000026
Wherein:
c p represents the constant pressure specific heat of dry air;
c q represents the specific heat at constant pressure of the humid air;
p represents the saturation pressure of water vapor, and is set to 2300Pa;
b represents atmospheric pressure;
the heat exchange coil heat exchange model in the built centralized air-conditioning air-water system model is as follows:
W h (G f )＝G f (h 2 -h 1 )
wherein:
W h (G f ) Indicating the amount of air blown by the blower as G f The heat exchange power of the heat exchange coil.
The step S1 of establishing a centralized air-conditioning air-water system model comprises the following steps:
the centralized air-conditioning wind-water system model comprises 30 combined air-conditioning boxes, wherein each combined air-conditioning box is provided with a blower and two chilled water pump sets, and each chilled water pump set is formed by connecting 4 chilled water pumps with the same type in parallel; when the air conditioner has a cold demand, the chilled water in the cold storage pool is conveyed to the air feeder of the corresponding combined air conditioning box by the chilled water pump set through the heat exchange coil, the combined air conditioning box utilizes the air feeder to extract fresh air from the outside, and utilizes the chilled water in the heat exchange coil to carry out heat exchange on the fresh air, so that the air is cooled and then is input to an air conditioning area.
In the step S2, the objective function is determined by taking the minimum energy consumption in the centralized air-conditioning wind-water system model as a target, and the method comprises the following steps:
taking the chilled water flow and the air supply quantity in the air-water system model as unknown parameters to be optimized, and determining an objective function by taking the minimum energy consumption in the centralized air-conditioning air-water system model as a target, wherein the objective function is in the form of:
Figure BDA0003285218930000031
wherein:
P w the total energy consumption of the centralized air-conditioning air-water system in unit time is represented;
z represents the number of modular air conditioning units in the system, and has a value of 30;
W f (G f，i ) The air volume of the blower in the ith combined air-conditioning box is represented as G f，i The energy consumption of the air feeder is reduced;
W l (G w，i ) Indicating that the total flow of chilled water in the ith combined air-conditioning box is G w，i The energy consumption of the chilled water pump set is reduced;
W h (G f，i ) Indicating that the total flow of chilled water in the ith combined air-conditioning box is G f，i The heat exchange coil pipe has heat exchange energy consumption;
in the step S2, determining a constraint condition of the objective function, where determining the constraint condition of the objective function includes:
Figure BDA0003285218930000032
Figure BDA0003285218930000033
wherein:
G f，i showing the air volume of a blower in the ith combined air-conditioning box;
G w，i the total flow of the chilled water in the ith combined air-conditioning box is represented;
G f，min represents the minimum air supply quantity of the air feeder;
G f，max represents the maximum air supply quantity of the blower;
G w，min the minimum total flow of the chilled water pump set is represented;
G w，max representing the maximum total chilled water flow of the chilled water pump unit.
The S3 step comprises the step of solving the objective function by using an L-BFGS algorithm to obtain a local optimal solution of the objective function, and the specific steps are as follows:
1) Randomly taking a set of variable values R = { R = 1 ，r 2 ，...，r i ，...，r Z }＝{(x 1 ，y 1 )，(x 2 ，y 2 )，...，(x i ，y i )，...，(x Z ，y Z ) }; wherein r is i A set of variable values, r, representing the ith combined air-conditioning box i ＝(x i ，y i )，x i The value y of the air volume of the blower in the ith combined air-conditioning box is shown i The value of the total flow of the chilled water in the ith combined air-conditioning box is represented;
2) For the objective function P w (r k ) Solving the stationary point r by using a Newton iteration method k+1 The initial value of k is 0:
Figure BDA0003285218930000034
k =1, 2.., Z, representing Z combined air conditioning boxes in a centralized air conditioner;
r k indicating delivery of kth combined air-conditioning boxVariable values of air quantity and total flow of chilled water;
g k is the derivative of the objective function;
Figure BDA0003285218930000037
is the reciprocal of the second derivative of the objective function;
3) Is calculated to obtain s k And y k ：
s k ＝r k+1 -r k
y k ＝g k+1 -g k
4) By means of iteration, obtain
Figure BDA0003285218930000035
Approximation D of k+1 ：
Figure BDA0003285218930000036
Wherein:
i is an identity matrix;
g k is the derivative of the objective function;
when k =0,d 0 Is an identity matrix;
5) If k < Z, k = k +1, and returning to step 2); otherwise, outputting a set of feasible solutions R '= { (x' 1 ，y′ 1 )，(x′ 2 ，y′ 2 )，...，(x′ i ，y′ i )…，(x′ Z ，y′ Z ) And taking the feasible solution R' output by iteration as a group of local optimal solutions for optimizing the air-conditioning air-water system.
In step S4, performing global optimization of the objective function using the genetic algorithm with the local optimal solution as an initial point, including:
1) Mixing the current locally optimal solution R '= { (x' 1 ，y′ 1 )，(x′ 2 ，y′ 2 )，...，(x′ i ，y′ i )…，(x′ Z ，y′ Z ) As the initial parent of the genetic algorithmGenerating individuals, and calculating an objective function value of the current local optimal solution as fB; setting the maximum iteration number of the genetic algorithm as Max;
2) Randomly generating a plurality of groups of variable values, wherein each group of variable values comprises Z values of the air supply volume of the combined air conditioning box and the total flow of chilled water, adding each group of randomly generated variable values as an individual into the solving process of a genetic algorithm, and calculating the objective function values of all the individuals;
3) Selecting whether to perform replacement recombination on partial structures of the parent individuals according to the replacement recombination probability of each iteration so as to generate new variant individuals, wherein the partial structures of the parent individuals are the air supply volume and the total chilled water flow volume of different combined air-conditioning boxes, and the iteration times are increased by one when each replacement recombination is performed; selecting whether to mutate the current local optimal solution according to the following probability calculation mode:
Figure BDA0003285218930000041
wherein:
k 1 ，k 2 represents [0,1 ]]A constant between;
f max representing a maximum objective function value in the individual;
f avg representing an average objective function value of the individual;
f v an objective function value representing a parent individual;
4) Calculating objective function values of all individuals, and taking the individual with the minimum objective function value as a parent individual;
5) Judging whether the current iteration times reach the maximum iteration times Max, if not, returning to the step 3), if so, taking the solution corresponding to the individual with the minimum current objective function value as the solution of the objective function, wherein the solution of the objective function comprises
Figure BDA0003285218930000042
Wherein
Figure BDA0003285218930000043
Showing an optimization solution of the ith combined type air conditioning box,
Figure BDA0003285218930000044
the solution value of the air supply amount of the blower in the ith combined air-conditioning box is shown,
Figure BDA0003285218930000045
the method comprises the following steps of (1) representing a solving value of total flow of chilled water in a chilled water pump set in an ith combined air-conditioning box, wherein Z represents the number of the combined air-conditioning boxes; and the centralized air-conditioning air-water system controls the operation of different combined air-conditioning boxes by taking the air supply quantity value and the chilled water total flow value as control parameters of the combined air-conditioning boxes according to the air supply quantity value and the chilled water total flow value of the different combined air-conditioning boxes.
Compared with the prior art, the invention provides a quick air-conditioning air-water system optimization method based on hybrid optimization, which has the following advantages:
firstly, the scheme establishes a centralized air-conditioning air-water system model, and measures the temperature T' and the humidity of the air entering at the air inlet pipeline of the air feeder in real time by arranging a temperature and humidity sensor between a heat exchange coil of the air feeder and an air filter
Figure BDA0003285218930000048
A temperature and humidity sensor and an air speed sensor are arranged in an air outlet pipeline of the air feeder, and the temperature T' and the humidity of air outlet in the air outlet pipeline are measured in real time
Figure BDA0003285218930000049
Respectively calculating the specific enthalpy h of the air at the air inlet pipeline 1 And specific enthalpy h of air at the air outlet duct 2 ：
Figure BDA0003285218930000046
Figure BDA0003285218930000047
Wherein: c. C p Represents the constant pressure specific heat of dry air; c. C g Represents the specific heat at constant pressure of the humid air; p represents the saturation pressure of water vapor, and is set to 2300Pa; b represents atmospheric pressure; the established heat exchange coil heat exchange model is as follows:
W h (G f )＝G f (h 2 -h 1 )
wherein: w h (G f ) Indicating the amount of air blown by the blower as G f The concentrated air-conditioning air-water system comprises 30 combined air-conditioning boxes, each combined air-conditioning box is provided with an air feeder and two chilled water pump sets, and each chilled water pump set is formed by connecting 4 chilled water pumps of the same type in parallel; when the air conditioner needs cooling, the chilled water in the cold storage pool is conveyed to the air feeder of the corresponding combined air conditioning box by the chilled water pump set through the heat exchange coil, the combined air conditioning box extracts fresh air from the outside by the air feeder, carries out heat exchange on the fresh air by the chilled water in the heat exchange coil, and inputs the air into an air conditioning area after cooling, so that the concentrated air conditioning air-water system is expressed as a system model of the chilled water pump set, the air feeder and the heat exchange coil.
Meanwhile, the scheme takes the chilled water flow and the air supply quantity in the air-water system model as unknown parameters to be optimized, and determines an objective function by taking the minimum energy consumption in the concentrated air-conditioning air-water system model as a target, wherein the objective function has the form:
Figure BDA0003285218930000051
wherein: p is w The total energy consumption of the centralized air-conditioning air-water system in unit time is represented; z represents the number of combined air conditioning boxes in the system, and the value of Z is 30; w f (G f，i ) The air volume of the blower in the ith combined air-conditioning box is represented as G f，i The energy consumption of the air blower is reduced; w l (G w，i ) Indicating the ith combined air-conditioning boxTotal flow rate of chilled water is G w，i The energy consumption of the chilled water pump set is reduced; w h (G f，i ) Shows that the total flow of the chilled water in the ith combined air-conditioning box is G f，i The heat exchange coil has heat exchange energy consumption. The air-conditioning air-water energy consumption model designed by the scheme solves the air supply volume and the total chilled water flow of different combined air-conditioning boxes under the condition that the requirement of meeting equipment constraint conditions is guaranteed, and reduces the energy consumption of a centralized air-conditioning system by taking the minimum energy consumption of a chilled water pump set, an air feeder and a heat exchange coil in the centralized air-conditioning air-water system model as a target.
According to the scheme, the target function is solved by using the L-BFGS algorithm to obtain the local optimal solution of the target function, the obtained local optimal solution of the target function is used as the initial point of the genetic algorithm, the situation that the genetic algorithm needs multiple iterations to obtain the initial point is avoided, the optimization efficiency of the genetic algorithm is improved, the global optimization is carried out on the target function by using the genetic algorithm, the parameter optimization of the centralized air-conditioning system is rapidly realized, and the rapid response of the air-conditioning system is realized.
Drawings
Fig. 1 is a schematic flow chart of a hybrid optimization-based method for optimizing an air-water system of a rapid air conditioner according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a centralized air-conditioning air-water system according to an embodiment of the present invention;
the implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1:
s1: and establishing a centralized air-conditioning air-water system model.
In the step S1, establishing the centralized air-conditioning air-water system model includes establishing a fan energy consumption system model, and establishing the fan energy consumption system model includes:
the blower in the centralized air-conditioning air-water system model comprises a blower and an exhaust blower, wherein the exhaust blower is a small-sized blower and operates independently, and the blower sends outdoor fresh air entering an air pipeline into a room through an air outlet pipeline after heat exchange treatment;
the fan energy consumption system model of the air feeder in the centralized air-conditioning air-water system model is as follows:
Figure BDA0003285218930000052
wherein:
W f (G f ) Indicating the amount of air blown by the blower as G f The air conditioner input power of (1);
G f representing the volumetric flow of air in the blower;
Δ p represents a pressure increase from an air inlet duct to an air outlet duct of the blower;
α p (G f ) Indicating that the blower has an air volume flow of G f Full pressure efficiency of the time;
α m (n) represents the frequency converter efficiency when the blower rotation speed is n.
In the step S1, establishing the concentrated air-conditioning air-water system model includes establishing a chilled water pump set energy consumption model, and establishing the chilled water pump set energy consumption model includes:
the method is characterized in that two groups of chilled water pump sets are arranged in the central air conditioner, each group of chilled water pump sets is formed by connecting 4 chilled water pumps of the same type in parallel, the flow obtained by each chilled water pump in each chilled water pump set is the same, and an energy consumption model of the chilled water pump sets in the central air conditioner air-water system model is as follows:
Figure BDA0003285218930000061
wherein:
p represents the density of water;
g represents the acceleration of gravity;
β m (u) the frequency converter efficiency when the rotating speed of the refrigerating water pump is u;
W l (G w ) Indicating chilled waterThe total flow of the freezing water in the pump set is G w Input power of time;
G i indicating the flow rate of the chilled water supplied by the chilled water pump i, 4G i ＝G w 。
In the step S1, establishing the concentrated air-conditioning air-water system model includes establishing a heat exchange coil model, and establishing the heat exchange coil model includes:
a temperature and humidity sensor is arranged between the heat exchange coil of the blower and the air filter to measure the temperature T' and the humidity of the air entering at the air inlet pipeline of the blower
Figure BDA0003285218930000062
A temperature and humidity sensor and an air speed sensor are arranged in an air outlet pipeline of the air feeder to measure the temperature T' and the humidity of outlet air in the air outlet pipeline
Figure BDA0003285218930000063
Respectively calculating the specific enthalpy h of the air at the air inlet pipeline 1 And specific enthalpy h of air at the air outlet duct 2 ：
Figure BDA0003285218930000064
Figure BDA0003285218930000065
Wherein:
c p represents the specific heat at constant pressure of dry air;
c q represents the constant pressure specific heat of the humid air;
p represents the saturation pressure of water vapor, and is set to 2300Pa;
b represents atmospheric pressure;
the heat exchange coil heat exchange model in the built centralized air-conditioning air-water system model is as follows:
W h (G f )＝G f (h 2 -h 1 )
wherein:
W h (G f ) Indicating the amount of air blown by the blower as G f The heat exchange power of the heat exchange coil.
S2: and determining an objective function based on the wind-water system model by taking the minimum energy consumption as a target.
In the step S2, the objective function is determined by taking the minimum energy consumption in the centralized air-conditioning wind-water system model as a target, and the method comprises the following steps:
taking the flow rate of chilled water and the air output in an air-water system model as unknown parameters to be optimized, and determining an objective function by taking the minimum energy consumption in the centralized air-conditioning air-water system model as a target, wherein the form of the objective function is as follows:
Figure BDA0003285218930000066
wherein:
P w the total energy consumption of the centralized air-conditioning air-water system in unit time is represented;
z represents the number of combined air conditioning boxes in the system, and the value of Z is 30;
W f (G f，i ) The air volume of an air blower in the ith combined air-conditioning box is represented as G f，i The energy consumption of the air blower is reduced;
W l (G w，i ) Shows that the total flow of the chilled water in the ith combined air-conditioning box is G w，i The energy consumption of the chilled water pump set is reduced;
W h (G f，i ) Indicating that the total flow of chilled water in the ith combined air-conditioning box is G f，i The heat exchange coil has heat exchange energy consumption.
S3: and optimizing the objective function by using an L-BFGS algorithm to determine a local optimal solution.
The S3 step comprises the step of solving the objective function by using an L-BFGS algorithm to obtain a local optimal solution of the objective function, and the specific steps are as follows:
1) Randomly taking a set of variable values R = { R = 1 ，r 2 ，...，r i ，...，r Z }＝{(x 1 ，y 1 )，(x 2 ，y 2 )，...，(x i ，y i )，...，(x Z ，y Z ) }; wherein r is i A set of variable values, r, representing the ith combined air-conditioning box i ＝(x i ，y i )，x i The value y of the air volume of the blower in the ith combined air-conditioning box is shown i The value of the total flow of the chilled water in the ith combined air-conditioning box is represented;
2) For the objective function P w (r k ) Solving the stationary point r by using Newton iteration method k+1 And k has an initial value of 0:
Figure BDA0003285218930000071
k =1, 2., Z, representing Z combined air conditioning boxes in a central air conditioner;
r k variable values representing the air supply quantity and the total flow of the chilled water of the kth combined air conditioning box;
g k is the derivative of the objective function;
Figure BDA0003285218930000074
is the reciprocal of the second derivative of the objective function;
3) Is calculated to obtain s k And y k ：
s k ＝r k+1 -r k
y k ＝g k+1 -g k
4) By means of iteration, obtain
Figure BDA0003285218930000072
Approximation D of k+1 ：
Figure BDA0003285218930000073
Wherein:
i is an identity matrix;
g k is the derivative of the objective function;
when k =0,d 0 Is a unit matrix;
5) If k < Z, k = k +1, and returning to step 2); otherwise, outputting a set of feasible solutions R '= { (x' 1 ，y′ 1 )，(x′ 2 ，y′ 2 )，...，(x′ i ，y′ i )，…，(x′ Z ，y′ Z ) And taking the feasible solution R' output by iteration as a group of local optimal solutions for optimizing the air-conditioning air-water system.
S4: and (4) performing global optimization on the objective function by using the genetic algorithm and taking the local optimal solution as an initial point, and outputting the air conditioning control parameters of the wind and water system.
In step S4, performing global optimization of the objective function using the genetic algorithm with the local optimal solution as an initial point, including:
1) Mixing the current locally optimal solution R '= { (x' 1 ，y′ 1 )，(x′ 2 ，y′ 2 )，...，(x′ i ，y′ i )，…，(x′ Z ，y′ Z ) Using the obtained solution as an initial parent individual of a genetic algorithm, and calculating an objective function value f of the current local optimal solution B (ii) a Setting the maximum iteration number of the genetic algorithm as Max;
2) Randomly generating a plurality of groups of variable values, wherein each group of variable values comprises Z values of the air supply volume of the combined air conditioning box and the total flow of chilled water, adding each group of randomly generated variable values as an individual into the solving process of a genetic algorithm, and calculating the objective function values of all the individuals;
3) Selecting whether to perform replacement recombination on partial structures of the parent individuals according to the replacement recombination probability of each iteration so as to generate new variant individuals, wherein the partial structures of the parent individuals are the air supply volume and the total chilled water flow volume of different combined air-conditioning boxes, and the iteration times are increased by one when each replacement recombination is performed; selecting whether to mutate the current local optimal solution according to the following probability calculation mode:
Figure BDA0003285218930000081
wherein:
k 1 ，k 2 represents [0,1 ]]A constant between;
f max representing a maximum objective function value in the individual;
f avg representing an average objective function value of the individual;
f v an objective function value representing the parent individual;
4) Calculating objective function values of all individuals, and taking the individual with the minimum objective function value as a parent individual;
5) Judging whether the current iteration times reach the maximum iteration times Max, if not, returning to the step 3), if so, taking the solution corresponding to the individual with the minimum current objective function value as the solution of the objective function, wherein the solution of the objective function comprises
Figure BDA0003285218930000082
Wherein
Figure BDA0003285218930000083
Showing an optimization solution of the ith combined type air conditioning box,
Figure BDA0003285218930000084
the solution value of the air supply amount of the blower in the ith combined air-conditioning box is shown,
Figure BDA0003285218930000085
the method comprises the following steps of (1) representing a solving value of total flow of chilled water in a chilled water pump set in an ith combined air-conditioning box, wherein Z represents the number of the combined air-conditioning boxes; and the centralized air-conditioning air-water system controls the operation of different combined air-conditioning boxes by taking the air supply quantity value and the chilled water total flow value as control parameters of the combined air-conditioning boxes according to the air supply quantity value and the chilled water total flow value of the different combined air-conditioning boxes.
Example 2:
this example is substantially the same as example 1, except that:
s1: and establishing a centralized air-conditioning air-water system model.
The step S1 of establishing a centralized air-conditioning air-water system model comprises the following steps:
the centralized air-conditioning wind-water system model comprises 30 combined air-conditioning boxes, wherein each combined air-conditioning box is provided with a blower and two chilled water pump sets, and each chilled water pump set is formed by connecting 4 chilled water pumps with the same type in parallel; when the air conditioner needs cold, the chilled water pump set conveys chilled water in the cold storage pool to a corresponding air feeder of the combined air conditioning box through the heat exchange coil, the combined air conditioning box utilizes the air feeder to extract fresh air from the outside, and utilizes the chilled water in the heat exchange coil to carry out heat exchange on the fresh air, so that the air is cooled and then is input to an air conditioning area.
S2: and determining an objective function based on the wind-water system model by taking the minimum energy consumption as a target.
The determining the constraint condition of the objective function in the step S2 includes:
Figure BDA0003285218930000086
Figure BDA0003285218930000087
wherein:
G f，i showing the air volume of a blower in the ith combined air-conditioning box;
G w，i the total flow of the chilled water in the ith combined air-conditioning box is represented;
G f，min represents the minimum air supply quantity of the air feeder;
G f，max represents the maximum air supply quantity of the blower;
G w，min the minimum total flow of the chilled water pump set is represented;
G w，max representing the maximum total chilled water flow of the chilled water pump unit.
Referring to fig. 2, a schematic diagram of a centralized air-conditioning air-water system provided in the present embodiment is shown; the centralized air-conditioning air-water system comprises a plurality of combined air-conditioning boxes, each combined air-conditioning box is provided with an air feeder and two chilled water pump sets, and each chilled water pump set is formed by connecting 4 chilled water pumps of the same type in parallel; when the air conditioner needs cold, the chilled water pump set conveys chilled water in the cold storage pool to a corresponding air feeder of the combined air conditioning box through the heat exchange coil, the combined air conditioning box utilizes the air feeder to extract fresh air from the outside, and utilizes the chilled water in the heat exchange coil to carry out heat exchange on the fresh air, so that the air is cooled and then is input to an air conditioning area.
It should be noted that, the above numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of another identical element in a process, apparatus, article, or method comprising the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A quick air-conditioning air-water system optimization method based on hybrid optimization is characterized by comprising the following steps:
s1: establishing a centralized air-conditioning air-water system model, wherein the establishing of the centralized air-conditioning air-water system model comprises the establishing of a fan energy consumption system model, and the establishing of the fan energy consumption system model comprises the following steps:
the blower in the centralized air-conditioning air-water system model comprises a blower and an exhaust fan, wherein the exhaust fan is a small-sized blower and operates independently, and the blower sends outdoor fresh air entering the air pipeline into a room through the air outlet pipeline after heat exchange treatment;
the fan energy consumption system model of the air feeder in the centralized air-conditioning air-water system model is as follows:
Figure FDA0003856972580000011
wherein:
W f (G f ) Indicating the amount of air blown by the blower as G f The air conditioner input power of (1);
G f representing the volumetric flow of air in the blower;
Δ p represents a pressure increase from an air inlet duct to an air outlet duct of the blower;
α p (G f ) Indicating that the blower has an air volume flow of G f Full pressure efficiency of the time;
α m (n) represents the frequency converter efficiency when the blower speed is n;
the establishment of the concentrated air-conditioning air-water system model comprises the establishment of a chilled water pump set energy consumption model, and the establishment of the chilled water pump set energy consumption model comprises the following steps:
the method is characterized in that two groups of chilled water pump sets are arranged in the central air conditioner, each group of chilled water pump sets is formed by connecting 4 chilled water pumps of the same type in parallel, the flow obtained by each chilled water pump in each chilled water pump set is the same, and an energy consumption model of the chilled water pump sets in the central air conditioner air-water system model is as follows:
Figure FDA0003856972580000012
wherein:
ρ represents the density of water;
g represents the acceleration of gravity;
β m (u) the frequency converter efficiency when the rotating speed of the refrigerating water pump is u;
W l (G w ) The total flow of the freezing water in the freezing water pump set is represented as G w Input power of time;
G i indicating the flow rate of the chilled water supplied by the chilled water pump i, 4G i ＝G w ；
The establishing of the concentrated air-conditioning air-water system model comprises the establishing of a heat exchange coil model, and the establishing of the heat exchange coil model comprises the following steps:
a temperature and humidity sensor is arranged between the heat exchange coil of the blower and the air filter to measure the temperature T' and the humidity of the air entering at the air inlet pipeline of the blower
Figure FDA0003856972580000013
A temperature and humidity sensor and an air speed sensor are arranged in an air outlet pipeline of the air feeder to measure the temperature T' and the humidity of outlet air in the air outlet pipeline
Figure FDA0003856972580000014
Respectively calculating the specific enthalpy h of the air at the air inlet pipeline 1 And specific enthalpy h of air at the air outlet duct 2 ：
Figure FDA0003856972580000015
Figure FDA0003856972580000016
Wherein:
c p represents the constant pressure specific heat of dry air;
c q represents the specific heat at constant pressure of the humid air;
p represents the saturation pressure of water vapor, and is set to 2300Pa;
b represents atmospheric pressure;
the heat exchange coil heat exchange model in the built centralized air-conditioning air-water system model is as follows:
W h (G f )＝G f (h 2 -h 1 )
wherein:
W h (G f ) Indicating the amount of air blown by the blower as G f The heat exchange power of the heat exchange coil;
s2: determining a target function based on the wind-water system model by taking the minimum energy consumption as a target;
s3: optimizing an objective function by using an L-BFGS algorithm to determine a local optimal solution;
s4: and (4) performing global optimization on the objective function by using the genetic algorithm and taking the local optimal solution as an initial point, and outputting the air conditioning control parameters of the wind and water system.
2. The hybrid optimization-based rapid air-conditioning air-water system optimization method as claimed in claim 1, wherein the step S1 of building a centralized air-conditioning air-water system model comprises:
the centralized air-conditioning wind-water system model comprises 30 combined air-conditioning boxes, wherein each combined air-conditioning box is provided with an air feeder and two chilled water pump sets, and each chilled water pump set is formed by connecting 4 chilled water pumps of the same type in parallel; when the air conditioner needs cold, the chilled water pump set conveys chilled water in the cold storage pool to a corresponding air feeder of the combined air conditioning box through the heat exchange coil, the combined air conditioning box utilizes the air feeder to extract fresh air from the outside, and utilizes the chilled water in the heat exchange coil to carry out heat exchange on the fresh air, so that the air is cooled and then is input to an air conditioning area.
3. The hybrid optimization-based rapid air-conditioning air-water system optimization method as claimed in claim 2, wherein the step S2 of determining the objective function with the minimum energy consumption in the centralized air-conditioning air-water system model as a target comprises:
taking the flow rate of chilled water and the air output in an air-water system model as unknown parameters to be optimized, and determining an objective function by taking the minimum energy consumption in the centralized air-conditioning air-water system model as a target, wherein the form of the objective function is as follows:
Figure FDA0003856972580000021
wherein:
P w the total energy consumption of the centralized air-conditioning air-water system in unit time is represented;
z represents the number of combined air conditioning boxes in the system, and the value of Z is 30;
W f (G f,i ) The air volume of the blower in the ith combined air-conditioning box is represented as G f,i The energy consumption of the air blower is reduced;
W l (G w,i ) Indicating that the total flow of chilled water in the ith combined air-conditioning box is G w,i The energy consumption of the chilled water pump set is reduced;
W h (G f,i ) Indicating that the total flow of chilled water in the ith combined air-conditioning box is G f,i The heat exchange coil pipe has heat exchange energy consumption.
4. The hybrid optimization-based rapid air-conditioning air-water system optimization method as claimed in claim 3, wherein the step S2 further comprises determining constraints of an objective function, and the determining constraints of the objective function comprises:
Figure FDA0003856972580000022
Figure FDA0003856972580000023
wherein:
G f,i showing the air volume of a blower in the ith combined air-conditioning box;
G w,i indicating the total flow of the chilled water in the ith combined air-conditioning box;
G f,min represents the minimum air supply quantity of the air feeder;
G f,max the maximum air output of the blower is represented;
G w,min the minimum total flow of the chilled water pump set is represented;
G w,max indicating the maximum total flow of chilled water from the chilled water pump set.
5. The hybrid optimization-based quick air-conditioning air-water system optimization method of claim 4, wherein the S3 step comprises solving an objective function by using an L-BFGS algorithm to obtain a local optimal solution of the objective function, and the specific steps are as follows:
1) Randomly taking a set of variable values R = { R = 1 ,r 2 ,…,r i ,…,r Z }＝{(x 1 ,y 1 ),(x 2 ,y 2 ),…,(x i ,y i ),…,(x Z ,y Z ) }; wherein r is i A set of variable values, r, representing the ith combined air-conditioning box i ＝(x i ,y i )，x i The value y of the air volume of the blower in the ith combined air-conditioning box is shown i The value of the total flow of the chilled water in the ith combined air-conditioning box is represented;
2) For the objective function P w (r k ) Solving the stationary point r by using a Newton iteration method k+1 The initial value of k is 0:
Figure FDA0003856972580000024
k =1,2, \ 8230;, Z, indicating a Z combined air conditioning box in a centralized air conditioner;
r k variable values representing the air supply quantity and the total flow of the chilled water of the kth combined air conditioning box;
g k is the derivative of the objective function;
Figure FDA0003856972580000031
is the reciprocal of the second derivative of the objective function;
3) Is calculated to obtain s k And y k ：
s k ＝r k+1 -r k
y k ＝g k+1 -g k
4) By means of iteration, obtain
Figure FDA0003856972580000032
Approximation D of k+1 ：
Figure FDA0003856972580000033
Wherein:
i is an identity matrix;
g k is the derivative of the objective function;
when k =0,D 0 Is an identity matrix;
5) If k is<Z, k = k +1 and returns to step 2); otherwise, outputting a set of feasible solutions R '= { (x' 1 ,y′ 1 ),(x′ 2 ,y′ 2 ),…,(x′ i ,y′ i ),…,(x′ Z ,y′ Z ) And taking the feasible solution R' output by iteration as a group of local optimal solutions for optimizing the air-conditioning air-water system.
6. The hybrid optimization-based rapid air-conditioning air-water system optimization method of claim 5, wherein in the step S4, the global optimization of the objective function is performed by using a genetic algorithm with the local optimal solution as an initial point, and the method comprises the following steps:
1) Mixing the current local optimal solution R '= { (x' 1 ,y′ 1 ),(x′ 2 ,y′ 2 ),…,(x′ i ,y′ i ),…,(x′ Z ,y′ Z ) Using the obtained result as an initial parent individual of the genetic algorithm, and calculating the objective function value of the current local optimal solution as f B (ii) a Setting the maximum iteration times of the genetic algorithm as Max;
2) Randomly generating a plurality of groups of variable values, wherein each group of variable values comprises values of Z combined air conditioning box air supply volume and total chilled water flow, adding each group of randomly generated variable values as individuals into a genetic algorithm solving process, and calculating objective function values of all the individuals;
3) Selecting whether to perform replacement recombination on partial structures of the parent individuals according to the replacement recombination probability of each iteration so as to generate new variant individuals, wherein the partial structures of the parent individuals are the air supply volume and the total chilled water flow volume of different combined air-conditioning boxes, and the iteration times are increased by one when each replacement recombination is performed; selecting whether to mutate the current local optimal solution according to the following probability calculation mode:
Figure FDA0003856972580000034
wherein:
k 1 ,k 2 represents [0,1 ]]A constant between;
f max representing a maximum objective function value in the individual;
f avg representing an average objective function value of the individual;
f v an objective function value representing a parent individual;
4) Calculating objective function values of all individuals, and taking the individual with the minimum objective function value as a parent individual;
5) Judging whether the current iteration times reach the maximum iteration times Max, if not, returning to the step 3), and if so, selecting the individual with the minimum current objective function valueThe corresponding solution is used as the solution of the objective function, wherein the solution of the objective function comprises
Figure FDA0003856972580000035
Wherein
Figure FDA0003856972580000036
Showing an optimized solution of the ith combined type air conditioning box,
Figure FDA0003856972580000037
the solution value of the air supply amount of the blower in the ith combined air-conditioning box is shown,
Figure FDA0003856972580000038
the method comprises the following steps of (1) representing a solving value of total flow of chilled water in a chilled water pump set in an ith combined air-conditioning box, wherein Z represents the number of the combined air-conditioning boxes; and the centralized air-conditioning air-water system controls the operation of different combined air-conditioning boxes by taking the air supply quantity value and the chilled water total flow value as control parameters of the combined air-conditioning boxes according to the air supply quantity value and the chilled water total flow value of the different combined air-conditioning boxes.
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