CN116697567A - Energy-saving optimal control method and device for central air conditioner water system - Google Patents
Energy-saving optimal control method and device for central air conditioner water system Download PDFInfo
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Abstract
The invention relates to the field of energy conservation of central air-conditioning water systems, in particular to an energy conservation optimal control method and device for a central air-conditioning water system. According to the invention, through collecting the operation data of the central air-conditioning water system, fitting the energy consumption models of the water chilling unit, the chilled water pump, the cooling water pump and the cooling tower, and then determining the optimal control model and the constraint condition of the central air-conditioning water system, an intelligent optimization algorithm which has high convergence rate and strong global searching capability and can jump out local optimal is provided, and the operation parameters of the central air-conditioning water system are optimized, so that the purpose of saving energy is achieved.
Description
Technical Field
The invention relates to the field of energy conservation of central air-conditioning water systems, in particular to an energy conservation optimal control method and device for a central air-conditioning water system.
Background
The central air-conditioning water system is a complex system, mainly comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower, has nonlinear, strong coupling and multivariable complex characteristics, and has certain technical difficulty in optimizing and controlling the water chilling unit, the chilled water pump, the cooling water pump and the cooling tower.
At present, various optimization algorithms are applied to the optimization control problem of a central air-conditioning water system, but the problems of low convergence speed, insufficient optimizing precision and easy sinking into local optimization generally exist. Furthermore, most studies have ignored the relevance and coupling between components for optimal control of only a single device in a central air conditioning water system, in fact the water system is an integrated system, where each component affects the operation of the unit.
Therefore, a brand new energy-saving optimization control method and device for the central air-conditioning water system are needed at present, so that the defects that the existing system is low in optimization convergence speed, insufficient in optimization precision and easy to sink into local optimization are overcome.
Disclosure of Invention
The invention aims to overcome the defects of low optimization convergence speed, insufficient optimization precision and easy sinking into local optimum in the prior art, and provides an energy-saving optimization control method and device for a central air conditioner water system.
In order to achieve the above object, the present invention provides the following technical solutions:
an energy-saving optimization control method for a central air-conditioning water system comprises the following steps:
s1: acquiring operation parameters of each component in the central air-conditioning water system; each assembly comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower;
s2: establishing an energy consumption model of each component in the central air-conditioning water system; the energy consumption model is used for representing the relation between the energy consumption and the operation parameters of each component;
s3: establishing an objective function of the central air-conditioning water system optimization control and constraint conditions of optimized operation parameters according to the energy consumption models of the components;
s4: and determining the optimized operation parameters of the components in the central air-conditioning water system according to the objective function and the constraint condition, and operating the central air-conditioning water system according to the optimized operation parameters.
As a preferred embodiment of the present invention, in S1, the operation parameters include:
cold load of water chilling unitP co Inlet temperature of chilled waterT fi Water outlet temperature of chilled waterT fo Inlet temperature of cooling waterT ci Number of water chilling units startedN c ；
Chilled water flow rate of chilled water pumpQ f Number of chilled water pumps onN fp ；
Cooling water flow of cooling water pumpQ c Number of cooling water pump onN cp ；
Cooling tower fan operating frequency of cooling towerf t Number of cooling towers onN t 。
As a preferable mode of the present invention, in S2, the energy consumption model of each component of the central air-conditioning water system is represented as follows:
the energy consumption model of the water chilling unit is as follows:
，
in the formula ,P chiller the power of the water chilling unit;a i （i=0, 1,..5) is the coefficient to be identified;
the energy consumption model of the chilled water pump is as follows:
，
in the formula ,the actual power of the chilled water pump; />Rated power of the chilled water pump; />Rated flow for the chilled water pump;b i （i=0, 1, 2) is the coefficient to be identified;
the energy consumption model of the cooling water pump is as follows:
，
in the formula ,the actual power of the cooling water pump; />Rated power of the cooling water pump; />Rated flow of the cooling water pump;c i （i=0, 1, 2) is the coefficient to be identified;
the energy consumption model of the cooling tower is as follows:
，
in the formula ,is the actual power of the cooling tower; />Rated power for the cooling tower; />The actual frequency of the cooling tower fan; />Rated frequency for a cooling tower fan;d i （i=0, 1, 2) is the coefficient to be identified;
and the coefficients to be identified in the energy consumption model of each component are identified and fit through a parameter identification algorithm.
As a preferred embodiment of the present invention, the parameter identification algorithm includes a least square method and/or a maximum likelihood estimation method and/or a bayesian estimation method.
As a preferred embodiment of the present invention, in S3, the expression of the objective function is as follows:
，
in the formula ,Jis the target power;
the constraints are as follows:
，
in the formula ,s.t.is a set of constraint conditions;is thatQ * Maximum value of>Is thatQ * Is the minimum of (2); />Is thatT * Maximum value of>Is thatT * Is the minimum of (2); />Is thatf c.tower Maximum value of>Is thatf c.tower Is the minimum of (2);is thatN * Maximum value of>Is thatN * Is the minimum of (2);
wherein ,Q f 、Q c 、T fo 、T fi 、T ci 、f c.tower 、N chiller 、N c.pump 、N f.pump andN c.tower the operating parameters are to be optimized.
As a preferred embodiment of the present invention, the step S4 includes the steps of:
s41: initializing the operation parameters, and outputting a parameter set and a fitness value corresponding to each parameter set;
s42: sequencing the fitness value of each parameter group according to the sequence from small to large, wherein the smaller the fitness value is, the more front the sequencing is, and the smaller the energy consumption of the parameter group is represented;
s43: updating the parameter set by adopting a two-segment updating strategy; the segmentation proportion of the parameter set is a preset parameter;
s44: calculating the updated parameter set and the fitness value thereof;
s45: acquiring a current optimal solution according to the parameter set and the fitness value thereof, and performing disturbance processing to generate an optimized operation parameter and an optimized fitness;
s46: updating and saving the optimization parameters and the optimization fitness.
S47: judging whether the set iteration times are reached, if so, ending the optimizing and outputting optimized operation parameters; if not, return to step S42.
As a preferred embodiment of the present invention, the step S41 includes the steps of:
s411: and initializing the operation parameters, wherein the expression of the initialization is as follows:
，
in the formula ,x i is the first to inputiA plurality of operating parameters;
s412: combining the operation parameters into parameter sets, and storing different operation states of the central air-conditioning water system into parameter set sets; the expression of the parameter set is as follows:
Xone parameter set for each behavior of the parameter set,X mn is the firstmGroup IIInThe values of the individual operating parameters are used,mfor the number of parameter sets,nthe number of operating parameters within the parameter set;
s413: generating the fitness value of the parameter set, wherein the fitness value of the parameter set is as follows:
，
wherein ,F(x) To adapt toA set of metric values;
the fitness value of each parameter group is used;
wherein ,P chiller the power of the water chilling unit;P f.pump the actual power of the chilled water pump;P c.pump the actual power of the cooling water pump;P c.tower is the actual power of the cooling tower.
As a preferred embodiment of the present invention, the step S43 includes the steps of:
s431: the first 70% of the parameter sets are updated by:
，
，
，
wherein ,i=1,2,3,…,m，j=1,2,3,…,n，trepresenting the current iteration number;to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />To at the iteration numbertA parameter group with the optimal lower fitness value,αis of the type (0, 1)]Random numbers in between;x 1 andx 2 according to golden section coefficientsτObtained by the coefficient of the,τtaking 0.618; />A random number for determining the parameter value change in the next iteration; />A random number for determining the next change direction;
s432: the last 30% of the parameter set is updated by:
，
wherein ,is at the iteration numbertThe parameter group with the optimal lower fitness value; />Is at the iteration numbertThe parameter group with the optimal lower fitness value;ωis (0, 1)]Random numbers in between.
As a preferable scheme of the invention, in the S45, the current optimal solution is disturbed by introducing a Laplace probability distribution function, and the expression is as follows:
，
wherein ,to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />Is the best parameter set of the current stage; />The expression of the Laplace distribution probability distribution function is as follows:
，
wherein μAs a function of the position parameter(s),λis a scale parameter.
An energy-saving optimization control device of a central air-conditioning water system comprises at least one processor and a memory which is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the preceding claims.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides an energy-saving optimization control method and device for a central air-conditioning water system, which are characterized in that the operation data of the central air-conditioning water system are collected, energy consumption models of a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower are fitted, and then an optimization control model and constraint conditions of the central air-conditioning water system are determined.
2. The invention relates to energy consumption of a water chilling unit, energy consumption of a chilled water pump, energy consumption of a cooling water pump and energy consumption of a cooling tower. The energy consumption model is built through the expression with the coefficient to be identified, the coefficient to be identified can be fitted through the parameter identification algorithm according to the historical operation data of different equipment, and the energy consumption model can be better suitable for different equipment.
3. According to the invention, the running state of the central air-conditioning water system can be regulated and controlled more comprehensively by setting a plurality of optimized parameters (including the chilled water flow, the cooling water flow, the chilled water outlet temperature, the chilled water inlet temperature, the cooling tower running frequency, the running number of the water chilling units, the running number of the freezing pumps, the running number of the cooling pumps and the running number of the cooling towers), so that the purpose of energy conservation is realized. In addition, the central air-conditioning water system comprises a plurality of devices, and the running number of different devices is optimized, so that the large-scale air-conditioning water system is convenient to adjust.
4. The invention provides a new population intelligent algorithm, and overcomes the defects of low optimization convergence speed, insufficient optimization precision and easy sinking into local optimization in the prior art.
Drawings
Fig. 1 is a schematic flow chart of a method for optimizing and controlling energy conservation of a central air-conditioning water system according to embodiment 1 of the present invention;
FIG. 2 is a schematic flow chart of determining optimized operation parameters in an energy-saving optimization control method for a water system of a central air conditioner according to embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of a central air-conditioning water system energy-saving optimization control device using the central air-conditioning water system energy-saving optimization control method described in embodiment 1 according to embodiment 3 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should not be construed that the scope of the above subject matter of the present invention is limited to the following embodiments, and all techniques realized based on the present invention are within the scope of the present invention.
Example 1
As shown in fig. 1, the energy-saving optimization control method for the central air-conditioning water system comprises the following steps:
s1: acquiring operation parameters of each component in the central air-conditioning water system; each assembly comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower. The operating parameters include:
cold load of water chilling unitP co Inlet temperature of chilled waterT fi Water outlet temperature of chilled waterT fo Inlet temperature of cooling waterT ci Number of water chilling units startedN c ；
Chilled water flow rate of chilled water pumpQ f Number of chilled water pumps onN fp ；
Cooling water flow of cooling water pumpQ c Number of cooling water pump onN cp ；
Cooling tower fan operating frequency of cooling towerf t Number of cooling towers onN t 。
S2: establishing an energy consumption model of each component in the central air-conditioning water system; the energy consumption model is used for representing the relation between the energy consumption and the operation parameters of each component.
S3: and establishing an objective function of the optimal control of the central air-conditioning water system and constraint conditions of optimal operation parameters according to the energy consumption models of the components.
S4: and determining the optimized operation parameters of the components in the central air-conditioning water system according to the objective function and the constraint condition, and operating the central air-conditioning water system according to the optimized operation parameters.
Example 2
This example is a specific implementation of the method described in example 1, and includes the following steps:
s1: and acquiring the operation parameters of each component in the central air-conditioning water system.
S2: establishing an energy consumption model of each component in the central air-conditioning water system; the energy consumption model is used for representing the relation between the energy consumption and the operation parameters of each component.
The energy consumption model of each component of the central air-conditioning water system is expressed as follows:
the energy consumption model of the water chilling unit is as follows:
，
in the formula ,P chiller -chiller power, kW;
P co -chiller unit cooling load, kW;
T ci cooling water inlet temperature, DEG C;
T fo -chilled water outlet temperature, DEG C;
a i （i=0, 1,..5) -coefficients to be identified.
The energy consumption model of the chilled water pump is as follows:
，
in the formula ,-the actual power of the chilled water pump, kW;
rated power of the chilled water pump, kW;
Q f -actual flow of chilled water pump, m 3 /h；
Rated flow of chilled water pump, m 3 /h；
b i （i=0, 1, 2) -coefficients to be identified.
The energy consumption model of the cooling water pump is as follows:
，
in the formula ,-cooling water pump actual power, kW;
-rated power of the cooling water pump, kW;
Q c -actual flow of cooling water pump, m 3 /h；
Rated flow of cooling water pump, m 3 /h；
c i （i=0, 1, 2) -coefficients to be identified.
The energy consumption model of the cooling tower is as follows:
，
in the formula ,-cooling tower actual power, kW;
cooling tower rated power, kW;
-cooling tower fan actual frequency, hz;
-cooling tower fan nominal frequency, hz;
d i （i=0, 1, 2) -coefficients to be identified.
The coefficients to be identified in the energy consumption model of each component are identified and fit through a parameter identification algorithm; the parameter identification algorithm comprises a least square method and/or a maximum likelihood estimation method and/or a Bayesian estimation method and the like.
S3: and establishing an objective function of the optimal control of the central air-conditioning water system and constraint conditions of optimal operation parameters according to the energy consumption models of the components.
According to the energy consumption models of the water chilling unit, the chilled water pump, the cooling water pump and the cooling tower, an objective function is established by taking the minimum sum of the energy consumption of the water chilling unit, the chilled water pump, the cooling water pump and the cooling tower as an optimization target, and the expression of the objective function is as follows:
，
in the formula ,Jis the target power;
the central air-conditioning water system is a complex system consisting of a plurality of devices, has strong coupling, and has the total energy consumption equal to the sum of the energy consumption of each device, and the change of any parameter can cause the change of the total energy consumption of the central air-conditioning water system. Meanwhile, parameters of different devices have corresponding working ranges. In the optimization, various restrictions need to be considered according to the characteristics of different devices, and in addition, different devices are mutually connected in operation. Therefore, the constraint conditions set according to actual conditions are as follows:
，
in the formula ,s.t.is a set of constraint conditions;is thatQ * Maximum value of>Is thatQ * Is the minimum of (2); />Is thatT * Maximum value of>Is thatT * Is the minimum of (2); />Is thatf c.tower Maximum value of>Is thatf c.tower Is the minimum of (2); />Is thatN * Maximum value of>Is thatN * Is the minimum of (2);
wherein ,Q f 、Q c 、T fo 、T fi 、T ci 、f c.tower 、N chiller 、N c.pump 、N f.pump andN c.tower the operating parameters are to be optimized.
S4: as shown in fig. 2, according to the objective function and the constraint condition, an optimized operation parameter of each component in the central air-conditioning water system is determined, and the central air-conditioning water system is operated according to the optimized operation parameter.
S41: initializing the operation parameters, and outputting a parameter set and a fitness value corresponding to each parameter set;
s411: and initializing the operation parameters of the central air-conditioning water system by adopting Circle chaotic mapping, wherein the initialized operation parameters are distributed more uniformly, the diversity of the parameters is increased, the searching range is enlarged, and the global searching capability of an algorithm is enhanced. The expression of the initialization process is as follows:
，
in the formula ,x i is the first to inputiA plurality of operating parameters;
s412: combining the operation parameters into parameter sets, and storing different operation states of the central air-conditioning water system into parameter set sets; in the method, the operation parameters to be optimized in each device of the central air-conditioning water system are combined into one parameter set, and each parameter set represents different operation states of the central air-conditioning water system. The expression of the parameter set is as follows:
Xone parameter set for each behavior of the parameter set,X mn is the firstmGroup IIInThe values of the individual operating parameters are used,mfor the number of parameter sets,nthe number of operating parameters within the parameter set;
s413: generating the fitness value of the parameter set, wherein the fitness value of the parameter set is as follows:
，
wherein ,F(x) Is a fitness value set;
the fitness value of each parameter group is used;
wherein ,P chiller the power of the water chilling unit;P f.pump the actual power of the chilled water pump;P c.pump the actual power of the cooling water pump;P c.tower is the actual power of the cooling tower.
S42: according to the size of the fitness value of each parameter group, the fitness value of each parameter group is ordered according to the order from small to large, the smaller the fitness value is, the more front the ordering is, and the smaller the energy consumption of the parameter group is represented;
s43: updating the parameter set; the invention provides a two-section updating strategy, wherein the first section contains 70% of parameter groups, and the second section contains 30% of parameter groups.
S431: the first 70% of the parameter sets are updated by:
，
，
，
wherein ,i=1,2,3,…,m，j=1,2,3,…,n，trepresenting the current iteration number;to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />To at the iteration numbertA parameter group with the optimal lower fitness value,αis of the type (0, 1)]Random numbers in between;x 1 andx 2 according to golden section coefficientsτObtained by the coefficient of the,τtaking 0.618; />A random number for determining the parameter value change in the next iteration; />Is a random number that determines the direction of the next change.
In the current step, each parameter set exchanges information with the optimal parameter set every time an iteration is performed, so that the information of the current optimal solution is fully utilized. Meanwhile, according to the relation between the sine and cosine functions and the unit circle, the algorithm can traverse all values on the sine and cosine functions, namely all points on the unit circle, and has stronger global searching capability. On the other hand, golden section coefficients progressively reduce the search space and passφAndδthe distance and the direction of parameter searching are controlled, so that the algorithm fully explores the region capable of generating the high-quality solution, the optimal position is reached faster, and the optimizing capability and the iteration speed of the algorithm are further improved.
S432: the last 30% of the parameter set is updated by:
，
wherein ,is at the iteration numbertThe parameter group with the optimal lower fitness value; />Is at the iteration numbertThe parameter group with the optimal lower fitness value;ωis (0, 1)]Random numbers in between.
S44: calculating the updated parameter set and the fitness value thereof;
s45: acquiring a current optimal solution according to the parameter set and the fitness value thereof, and performing disturbance processing to generate an optimized operation parameter and an optimized fitness;
in order to avoid the algorithm being trapped in the local optimum, the invention perturbs the current optimum solution by introducing the Laplace probability distribution function, expands the search range near the current optimum solution, and then jumps out of the local optimum. The expression is as follows:
，
wherein ,to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />Is the best parameter set of the current stage; />The expression of the Laplace distribution probability distribution function is as follows:
，
wherein μAs a function of the position parameter(s),λis a scale parameter.
S46: updating and saving the optimization parameters and the optimization fitness.
S47: judging whether the set iteration times are reached, if so, ending the optimizing and outputting optimized operation parameters; if not, return to step S42.
Example 3
As shown in fig. 3, the energy-saving optimization control device of the central air-conditioning water system comprises at least one processor, a memory in communication connection with the at least one processor, and at least one input/output interface in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a central air-conditioning water system energy-saving optimization control method according to the foregoing embodiment. The input/output interface may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data.
Those skilled in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
The above-described integrated units of the invention, when implemented in the form of software functional units and sold or used as stand-alone products, may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The energy-saving optimal control method for the central air-conditioning water system is characterized by comprising the following steps of:
s1: acquiring operation parameters of each component in the central air-conditioning water system; each assembly comprises a water chilling unit, a chilled water pump, a cooling water pump and a cooling tower;
s2: establishing an energy consumption model of each component in the central air-conditioning water system; the energy consumption model is used for representing the relation between the energy consumption and the operation parameters of each component;
s3: establishing an objective function of the central air-conditioning water system optimization control and constraint conditions of optimized operation parameters according to the energy consumption models of the components;
s4: and determining the optimized operation parameters of the components in the central air-conditioning water system according to the objective function and the constraint condition, and operating the central air-conditioning water system according to the optimized operation parameters.
2. The energy-saving optimization control method for a central air-conditioning water system according to claim 1, wherein in S1, the operation parameters include:
cold load of water chilling unitP co Inlet temperature of chilled waterT fi Water outlet temperature of chilled waterT fo Inlet temperature of cooling waterT ci Number of water chilling units startedN c ；
Chilled water flow rate of chilled water pumpQ f Number of chilled water pumps onN fp ；
Cooling water flow of cooling water pumpQ c Number of cooling water pump onN cp ；
Cooling tower fan operating frequency of cooling towerf t Number of cooling towers onN t 。
3. The energy-saving optimization control method of a central air-conditioning water system according to claim 2, wherein in S2, an energy consumption model of each component of the central air-conditioning water system is represented as follows:
the energy consumption model of the water chilling unit is as follows:
，
in the formula ,P chiller the power of the water chilling unit;a i （i =0, 1,..5) is the coefficient to be identified;
the energy consumption model of the chilled water pump is as follows:
，
in the formula ,the actual power of the chilled water pump; />Rated power of the chilled water pump; />Rated flow for the chilled water pump;b i （i =0, 1, 2) is the coefficient to be identified;
the energy consumption model of the cooling water pump is as follows:
，
in the formula ,the actual power of the cooling water pump; />Rated power of the cooling water pump; />Rated flow of the cooling water pump;c i （i =0, 1, 2) is the coefficient to be identified;
the energy consumption model of the cooling tower is as follows:
，
in the formula ,is the actual power of the cooling tower; />Rated power for the cooling tower; />The actual frequency of the cooling tower fan; />Rated frequency for a cooling tower fan;d i （i=0, 1, 2) is the coefficient to be identified;
and the coefficients to be identified in the energy consumption model of each component are identified and fit through a parameter identification algorithm.
4. A central air-conditioning water system energy-saving optimization control method according to claim 3, wherein the parameter identification algorithm comprises a least square method and/or a maximum likelihood estimation method and/or a bayesian estimation method.
5. The energy-saving optimization control method for a central air-conditioning water system according to claim 3, wherein in S3, the expression of the objective function is as follows:
，
in the formula ,Jis the target power;
the constraints are as follows:
，
in the formula ,s.t.is a set of constraint conditions;is thatQ * Maximum value of>Is thatQ * Is the minimum of (2); />Is thatT * Maximum value of>Is thatT * Is the minimum of (2); />Is thatf c.tower Maximum value of>Is thatf c.tower Is the minimum of (2); />Is thatN * Maximum value of>Is thatN * Is the minimum of (2);
wherein ,Q f 、Q c 、T fo 、T fi 、T ci 、f c.tower 、N chiller 、N c.pump 、N f.pump andN c.tower the operating parameters are to be optimized.
6. The energy-saving optimization control method for a central air-conditioning water system according to claim 5, wherein S4 comprises the steps of:
s41: initializing the operation parameters, and outputting a parameter set and a fitness value corresponding to each parameter set;
s42: sequencing the fitness value of each parameter group according to the sequence from small to large;
s43: updating the parameter set by adopting a two-segment updating strategy; the segmentation proportion of the parameter set is a preset parameter;
s44: calculating the updated parameter set and the fitness value thereof;
s45: acquiring a current optimal solution according to the parameter set and the fitness value thereof, and performing disturbance processing to generate an optimized operation parameter and an optimized fitness;
s46: updating and saving optimization parameters and optimization fitness;
s47: judging whether the set iteration times are reached, if so, ending the optimizing and outputting optimized operation parameters; if not, return to step S42.
7. The energy-saving optimization control method for a central air-conditioning water system according to claim 6, wherein S41 comprises the steps of:
s411: and initializing the operation parameters, wherein the expression of the initialization is as follows:
，
in the formula ,x i is the first to inputiA plurality of operating parameters;
s412: combining the operation parameters into parameter sets, and storing different operation states of the central air-conditioning water system into parameter set sets; the expression of the parameter set is as follows:
，
Xone parameter set for each behavior of the parameter set,X mn is the firstmGroup IIInThe values of the individual operating parameters are used,mfor the number of parameter sets,nthe number of operating parameters within the parameter set;
s413: generating the fitness value of the parameter set, wherein the fitness value of the parameter set is as follows:
，
wherein ,F(x) Is a fitness value set;
the fitness value of each parameter group is used;
wherein ,P chiller the power of the water chilling unit;P f.pump the actual power of the chilled water pump;P c.pump the actual power of the cooling water pump;P c.tower is the actual power of the cooling tower.
8. The energy-saving optimization control method for a central air-conditioning water system according to claim 7, wherein S43 comprises the steps of:
s431: the first 70% of the parameter sets are updated by:
，
，
，
wherein ,i =1,2,3,…,m，j=1,2,3,…,n，trepresenting the current iteration number;to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />To at the iteration numbertA parameter group with the optimal lower fitness value,αis of the type (0, 1)]Random numbers in between;x 1 andx 2 according to golden section coefficientsτObtained by the coefficient of the,τtaking 0.618; />A random number for determining the parameter value change in the next iteration; />A random number for determining the next change direction;
s432: the last 30% of the parameter set is updated by:
，
wherein ,is at the iteration numbertThe parameter group with the optimal lower fitness value; />Is at the iteration numbertThe parameter group with the optimal lower fitness value;ωis (0, 1)]Random numbers in between.
9. The energy-saving optimization control method of a central air-conditioning water system according to claim 6, wherein the disturbance of the current optimal solution is performed by introducing a laplace probability distribution function in S45, and the expression is as follows:
，
wherein ,to at the iteration numbertLower, the firstiThe first of the parameter setsjA parameter; />Is the best parameter set of the current stage; />The expression of the Laplace distribution probability distribution function is as follows:
，
wherein μAs a function of the position parameter(s),λis a scale parameter.
10. The energy-saving optimizing control device for the central air-conditioning water system is characterized by comprising at least one processor and a memory which is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 9.
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