Load distribution control method, system, equipment and medium for parallel chiller system

Abstract

The present invention discloses a load distribution control method, system, equipment and medium for a parallel chiller system. The method comprises: under the condition of meeting the terminal cooling load demand of an air-conditioning system, taking the minimum total energy consumption of the parallel chiller system as an optimization target, and taking the partial load rate of each preset chiller in the parallel chiller system as a variable to be optimized; adopting an improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal value of the variables to be optimized; taking the optimal value of the variables to be optimized as the load distribution control result of the parallel chiller system; wherein the improved sparrow search algorithm is a chaos-based population initialization strategy, a nonlinear decreasing inertia weight strategy and a wolf following strategy in a wolf pack algorithm introduced into a typical sparrow search algorithm; the present invention realizes precise control of the load of the chiller in the parallel chiller, has good convergence, greatly improves the flexibility of the system, and effectively reduces the energy consumption of system operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of air conditioning system control, and in particular relates to a load distribution control method, system, equipment and medium for a parallel refrigeration system. Background Art

[0002] Chillers are one of the main energy-consuming devices in central air-conditioning systems. Due to their low operating efficiency and poor flexibility, chillers in air-conditioning systems consume too much energy. In practical applications, multiple chillers are usually connected in parallel to form a parallel chiller system. The characteristics of the chillers themselves and the different load distribution strategies between each chiller will affect the energy consumption of the parallel chiller system. Therefore, under the conditions of meeting different load demands, how to formulate the optimal load distribution control strategy and accurately operate the parallel chiller system to minimize energy consumption is the key to energy saving in high-voltage AC systems.

[0003] At present, meta-heuristic algorithms are mostly used to control the load of parallel chillers. However, the existing meta-heuristic algorithms are prone to fall into local optimality due to the decrease in population diversity when solving complex multi-dimensional optimization problems, which leads to slow algorithm convergence and low optimization accuracy, which in turn leads to poor system control flexibility and cannot achieve the goal of reducing system operation energy consumption. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a load distribution control method, system, equipment and medium for a parallel chiller system to solve the problems that the existing parallel chiller load control method has slow convergence speed and low optimization accuracy, resulting in poor system control flexibility and failure to achieve the purpose of reducing system operating energy consumption.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The present invention provides a load distribution control method for a parallel chiller system, comprising:

[0007] Under the condition of meeting the cooling load demand of the air-conditioning system terminal, the total energy consumption of the parallel chiller system is minimized as the optimization target, and the partial load rate of each pre-installed chiller in the parallel chiller system is taken as the variable to be optimized.

[0008] An improved sparrow search algorithm is used to optimize the variables to be optimized to obtain the optimal values ​​of the variables to be optimized; the optimal values ​​of the variables to be optimized are used as the load distribution control results of the parallel chiller system;

[0009] The improved sparrow search algorithm introduces a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm.

[0010] Furthermore, the optimization goal is:

[0011]

[0012]

[0013] Among them, Obj is the optimization target; P chiller,i is the energy consumption of the i-th pre-installed chiller; N is the total number of chillers in the parallel chiller system; a i ,b i ,c i and i are the kW-PLR curve coefficients of the i-th pre-installed chiller; i is the partial load rate of the i-th pre-installed chiller.

[0014] Furthermore, the improved sparrow search algorithm is used to optimize the variables to be optimized to obtain the optimal value of the variables to be optimized, which is specifically as follows:

[0015] Under the condition of satisfying the boundary constraints of the variables to be optimized, the initial population is constructed by using the chaotic sequence mechanism;

[0016] Calculate the individual fitness value of each individual in the population separately;

[0017] Among all individual fitness values, the smallest individual fitness value is selected as the initial value of the group extreme value, and the fitness value of each particle is used as the initial value of the individual extreme value;

[0018] Select and retain the smallest individual fitness value, the largest individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population;

[0019] A nonlinear decreasing inertia weight strategy is introduced to update the position of the discoverer by combining the speed in the particle swarm algorithm, the optimal position of the discoverer, and the position of the individual with the best fitness in the population;

[0020] The following strategy of the fierce wolf in the wolf pack algorithm is introduced, and the position of the follower is updated based on the position of the individual with the best fitness in the population and the position of the individual with the worst fitness in the population;

[0021] Update the position of the scout based on the minimum individual fitness value, the maximum individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population;

[0022] Calculate the fitness value of the individual after the position is updated, and update the individual extreme value and the group extreme value;

[0023] Set the population to run independently M times according to the above steps to determine whether the end condition is met. If so, output the optimal individual to obtain the optimal value of the variable to be optimized, that is, the load distribution control result of the parallel chiller system is obtained.

[0024] Furthermore, under the condition that the boundary constraints of the variables to be optimized are met, the process of constructing the initial population using the chaotic sequence mechanism is as follows:

[0025] Under the condition that the boundary constraints of the variables to be optimized are met, the initial individuals are randomly generated;

[0026] The chaotic sequence mechanism is used to make the initial individuals evenly distributed in the feasible solution space of the variables to be optimized, so as to obtain the initial population.

[0027] Furthermore, the chaotic sequence mechanism is:

[0028] x i+1 =mod(x i +4δ 3 -3δ,1)

[0029] Among them, x i+1 is the position vector of the ith individual after the chaotic sequence; x i is the randomly generated position vector of the i-th individual; δ is the chaos factor.

[0030] Further, when the position of the discoverer is updated, it is updated according to the discoverer position update formula;

[0031] The finder location update formula is:

[0032]

[0033] v i+1 =ω1*v i +c1*r1*(X pbest -X i )+c2*r2*(X gbest -X i )

[0034]

[0035] Among them, X i+1 is the updated position of the i-th sparrow; X i is the position of the i-th sparrow; α is a random number, α∈(0,1]; M is the maximum number of independent runs; R2 is the warning value of whether the discoverer finds the presence of a predator; ST is the safety threshold of the discoverer's search; Q is a random number that obeys a normal distribution; L is an all-1 matrix, the number of rows of the all-1 matrix is ​​1, and the number of columns is d; v i+1is the updated position of the i-th sparrow; ω1 is the inertia weight; v i is the position of the i-th sparrow; c1 is the learning factor; r1 is a random number in [0,1]; X pbest is the optimal position of the discoverer; c2 is the learning factor; r2 is a random number in [0,1]; X gbest is the position of the individual with the best fitness in the population; ω start is the maximum value of the inertia weight; ω end is the minimum value of the inertia weight; t is the current number of runs.

[0036] Furthermore, when updating the position of the follower, the follower position update formula is used for updating;

[0037] Wherein, the follower position update formula is:

[0038]

[0039]

[0040] Among them, X worst is the position of the individual with the worst fitness in the population; λ is the step size of change.

[0041] The present invention also provides a load distribution control system for a parallel refrigeration system, comprising:

[0042] A variable module is used to minimize the total energy consumption of the parallel chiller system as the optimization target under the condition of meeting the terminal cooling load demand of the air-conditioning system, and to take the partial load rate of each pre-installed chiller in the parallel chiller system as the variable to be optimized;

[0043] An optimization output module is used to optimize the variables to be optimized by using an improved sparrow search algorithm to obtain the optimal value of the variables to be optimized; and the optimal value of the variables to be optimized is used as the load distribution control result of the parallel chiller system;

[0044] The improved sparrow search algorithm introduces a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm.

[0045] The present invention also provides a load distribution control device for a parallel chiller system, comprising:

[0046] Memory for storing computer programs;

[0047] A processor is used to implement the load distribution control method of the parallel chiller system when executing the computer program.

[0048] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the load distribution control method of the parallel chiller system are implemented.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention provides a load distribution control method and system for a parallel chiller system, wherein the partial load rate of each preset chiller in the parallel chiller system is taken as a variable to be optimized, and an improved sparrow search algorithm is used for optimization; wherein, by introducing a chaotic method to initialize a population strategy, the population diversity is effectively enhanced, ensuring that the initial positions of individuals are evenly distributed in the search space; by introducing a nonlinear decreasing inertia weight strategy, the global and local search capabilities in the initial stage of the search can be effectively balanced; by introducing a following strategy of a fierce wolf in a wolf pack algorithm, the ability of the population to jump out of the local optimum can be effectively improved, thereby improving the search efficiency; the present invention realizes precise control of the load of the chiller in the parallel chiller, has a simple process, has good convergence, greatly improves the flexibility of the system, and effectively reduces the energy consumption of the system operation.

[0051] Furthermore, the concept of "speed" is introduced into the discoverer's position update formula, and a nonlinear decreasing inertia weight is introduced into the concept of "speed", so that the population is affected not only by the global optimal individual position, but also by the individual's historical optimal position, so as to achieve a balance between global and local search capabilities, effectively realize the ability to communicate the position information of sparrow individuals, avoid the algorithm from converging to the optimal solution too early, and improve the population diversity in the early iteration.

[0052] Furthermore, by introducing the following strategy of the fierce wolf in the wolf pack algorithm into the follower's position update formula, the ability of the population to jump out of the local optimum can be effectively improved, thereby accelerating the search efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a structural schematic diagram of a parallel chiller system in an embodiment;

[0054] Figure 2 It is a flow chart of the improved sparrow search algorithm in the embodiment;

[0055] Figure 3 A load distribution control convergence curve comparison diagram of the parallel chiller system in the first central air-conditioning system in the embodiment;

[0056] Figure 4 It is a comparison diagram of the load distribution control convergence curves of the parallel chiller system in the second central air-conditioning system in the embodiment. DETAILED DESCRIPTION

[0057] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail in the following specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] The present invention provides a load distribution control method for a parallel chiller system, comprising the following steps:

[0059] Step 1: Under the condition of meeting the terminal cooling load demand of the air-conditioning system, the total energy consumption of the parallel chiller system is minimized as the optimization goal, and the partial load rate of each pre-installed chiller in the parallel chiller system is taken as the variable to be optimized.

[0060] Wherein, the optimization goal is:

[0061]

[0062]

[0063] Among them, Obj is the optimization target; P chiller,i is the energy consumption of the i-th pre-installed chiller; n is the total number of chillers in the parallel chiller system; a i ,b i ,c i and i are the kW-PLR curve coefficients of the i-th pre-installed chiller; i is the partial load rate of the i-th pre-installed chiller.

[0064] Step 2: Use an improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal values ​​of the variables to be optimized; use the optimal values ​​of the variables to be optimized as the load distribution control results of the parallel chiller system; wherein the improved sparrow search algorithm introduces a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm.

[0065] The process of using an improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal value of the variables to be optimized specifically includes the following steps:

[0066] Step 21: Under the condition that the boundary constraints of the variables to be optimized are met, an initial population is constructed by using a chaotic sequence mechanism; wherein the process of constructing the initial population by using the chaotic sequence mechanism is as follows:

[0067] Under the condition that the boundary constraints of the variables to be optimized are met, the initial individuals are randomly generated;

[0068] The initial individuals are uniformly distributed in the feasible solution space of the variable to be optimized by using a chaotic sequence mechanism to obtain the initial population; wherein the chaotic sequence mechanism is:

[0069] x i+1 =mod(x i +4δ 3 -3δ,1)

[0070] Among them, x i+1 is the position vector of the ith individual after the chaotic sequence; x i is the randomly generated position vector of the i-th individual; δ is the chaos factor.

[0071] Step 22: Calculate the individual fitness value of each individual in the population.

[0072] Step 23: Among all individual fitness values, select the smallest individual fitness value as the initial value of the group extreme value, and use the fitness value of each particle as the initial value of the individual extreme value.

[0073] Step 24: Select and retain the smallest individual fitness value, the largest individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population.

[0074] Step 25, introducing a nonlinear decreasing inertia weight strategy, combining the speed in the particle swarm algorithm, the optimal position of the discoverer and the position of the individual with the best fitness in the population, to update the position of the discoverer; wherein, when updating the position of the discoverer, it is updated according to the discoverer position update formula;

[0075] The finder location update formula is:

[0076]

[0077] v i+1 =ω1*v i +c1*r1*(X pbest -X i )+c2*r2*(X gbest -X i )

[0078]

[0079] Among them, X i+1 is the updated position of the i-th sparrow; X iis the position of the i-th sparrow; α is a random number, α∈(0,1]; M is the maximum number of independent runs; R2 is the warning value of whether the discoverer finds the presence of a predator; ST is the safety threshold of the discoverer's search; Q is a random number that obeys a normal distribution; L is an all-1 matrix, the number of rows of the all-1 matrix is ​​1, and the number of columns is d; v i+1 is the updated position of the i-th sparrow; ω1 is the inertia weight; v i is the position of the i-th sparrow; c1 is the learning factor; r1 is a random number in [0,1]; X pbest is the optimal position of the discoverer; c2 is the learning factor; r2 is a random number in [0,1]; X gbest is the position of the individual with the best fitness in the population; ω start is the maximum value of the inertia weight; ω end is the minimum value of the inertia weight; t is the current number of runs.

[0080] Step 26, introducing the following strategy of the fierce wolf in the wolf pack algorithm, combining the position of the individual with the best fitness in the population and the position of the individual with the worst fitness in the population, and updating the position of the follower; wherein, when updating the position of the follower, the follower position update formula is used for updating;

[0081] Wherein, the follower position update formula is:

[0082]

[0083]

[0084] Among them, X worst is the position of the individual with the worst fitness in the population; λ is the step size of change.

[0085] Step 27: Update the position of the scout by combining the minimum individual fitness value, the maximum individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population.

[0086] Step 28: Calculate the fitness value of the individual after the position is updated, and update the individual extreme value and the group extreme value.

[0087] Step 29, set the population to run independently M times according to the above steps, and determine whether the end condition is met. If so, output the optimal individual to obtain the optimal value of the variable to be optimized, that is, obtain the load distribution control result of the parallel chiller system.

[0088] The load distribution control method for the parallel chiller system described in the present invention takes the minimum total energy consumption of the parallel chiller system as the optimization goal, and takes the partial load rate of each pre-installed chiller in the parallel chiller system as the variable to be optimized. According to the different load requirements of users, the operating conditions of the parallel chiller system can be accurately regulated to improve the system flexibility, while effectively reducing the system operating energy consumption.

[0089] The present invention also provides a load distribution control system for a parallel chiller system, comprising a variable module and an optimization output module; the variable module is used to minimize the total energy consumption of the parallel chiller system as an optimization target while satisfying the terminal cooling load demand of the air-conditioning system, and to use the partial load rate of each pre-installed chiller in the parallel chiller system as a variable to be optimized; the optimization output module is used to use an improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal values ​​of the variables to be optimized; the optimal values ​​of the variables to be optimized are used as the load distribution control result of the parallel chiller system; wherein the improved sparrow search algorithm is a chaos-based population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm introduced into a typical sparrow search algorithm.

[0090] The present invention also provides a load distribution control device for a parallel chiller system, comprising: a memory for storing a computer program; a processor for implementing the steps of the load distribution control method for a parallel chiller system when executing the computer program. The processor implements the steps of the load distribution control method for the parallel chiller system when executing the computer program, for example: under the condition of satisfying the terminal cooling load demand of the air-conditioning system, the total energy consumption of the parallel chiller system is minimized as the optimization target, and the partial load rate of each preset chiller in the parallel chiller system is used as the variable to be optimized; the improved sparrow search algorithm is used to optimize the variable to be optimized to obtain the optimal value of the variable to be optimized; the optimal value of the variable to be optimized is used as the load distribution control result of the parallel chiller system; wherein the improved sparrow search algorithm is to introduce a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm.

[0091] Alternatively, the processor implements the functions of each module in the above system when executing the computer program, for example: a variable module, which is used to minimize the total energy consumption of the parallel chiller system as the optimization goal while meeting the terminal cooling load demand of the air-conditioning system, and to use the partial load rate of each pre-set chiller in the parallel chiller system as the variable to be optimized; an optimization output module, which is used to use an improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal value of the variables to be optimized; and use the optimal value of the variable to be optimized as the load distribution control result of the parallel chiller system; wherein the improved sparrow search algorithm is to introduce a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy, and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm.

[0092] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the load distribution control device of the parallel chiller system. For example, the computer program can be divided into a variable module and an optimization output module; the specific functions of each module are as follows: the variable module is used to minimize the total energy consumption of the parallel chiller system as the optimization target under the condition of meeting the terminal cooling load demand of the air-conditioning system, and the partial load rate of each preset chiller in the parallel chiller system is used as the variable to be optimized; the optimization output module is used to optimize the variables to be optimized by using an improved sparrow search algorithm to obtain the optimal value of the variables to be optimized; the optimal value of the variables to be optimized is used as the load distribution control result of the parallel chiller system; wherein the improved sparrow search algorithm is to introduce a chaotic method to initialize the population strategy, a nonlinear decreasing inertia weight strategy and a wolf following strategy in the wolf pack algorithm in the typical sparrow search algorithm.

[0093] The load distribution control device of the parallel chiller system may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The load distribution control device of the parallel chiller system may include, but is not limited to, a processor and a memory. Those skilled in the art may understand that the above is only an example of a load distribution control device of a parallel chiller system, and does not constitute a limitation on the load distribution control device of the parallel chiller system, and may include more components, or a combination of certain components, or different components. For example, the load distribution control device of the parallel chiller system may also include input and output devices, network access devices, buses, etc.

[0094] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the load distribution control device of the parallel chiller system, and uses various interfaces and lines to connect various parts of the load distribution control device of the entire parallel chiller system.

[0095] The memory can be used to store the computer program and / or module, and the processor implements various functions of the load distribution control device of the parallel chiller system by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0096] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0097] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the load distribution control method for a parallel refrigeration system are implemented.

[0098] If the module / unit integrated in the load distribution control device of the parallel chiller system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0099] Based on such understanding, the present invention implements all or part of the processes in the above method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the load distribution control method of the parallel chiller system can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0100] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0101] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0102] Example

[0103] In this embodiment, the load distribution control process of a parallel chiller system in a central air-conditioning system is taken as an example.

[0104] As attached Figure 1 As shown, the parallel chiller system includes n pre-installed chillers, which are respectively connected to a public distribution system through parallel pipes to control the flow of water supply and return water; the cooling load required in the central air-conditioning system is distributed to a number of chillers working independently of each other; in the central air-conditioning system, the operating conditions of the parallel chiller system composed of chillers of different capacities and different characteristics are adjusted to reduce energy consumption; in this embodiment, under the condition of meeting the end load demand of the system, the load of each chiller is optimized and distributed, so as to achieve the best optimization effect of the parallel chiller system.

[0105] As attached Figure 2 As shown, this embodiment provides a load distribution control method for a parallel chiller system, comprising the following steps:

[0106] Step 1: Establish the energy consumption model of the parallel chiller system

[0107] In the central air-conditioning system, the optimal chiller load distribution problem is to achieve the goal of minimizing the energy consumption of the parallel chiller system by optimizing the load distribution of the chiller components under the premise of meeting the terminal cooling load demand of the air-conditioning system; therefore, under the condition of meeting the terminal cooling load demand of the air-conditioning system, the minimum total energy consumption of the parallel chiller system is taken as the optimization goal, and the partial load rate of each pre-installed chiller in the parallel chiller system is taken as the variable to be optimized; among them, the load distribution of the chiller is expressed by the partial load rate PLR, which is the ratio of the cooling load to the design load capacity.

[0108] Among them, the energy consumption model of the chiller is:

[0109]

[0110] Among them, P chiller,i is the energy consumption of the i-th pre-installed chiller; a i ,b i ,c i and i are the kW-PLR curve coefficients of the i-th pre-installed chiller; i is the partial load rate of the i-th pre-installed chiller.

[0111] Wherein, the optimization goal is:

[0112]

[0113] Among them, Obj is the optimization target; n is the total number of chillers in the parallel chiller system.

[0114] In this embodiment, the objective function of the optimal load problem of the chiller is to minimize the total energy consumption of the parallel chiller system; the load demand at the end of the air conditioning system is used as a constraint condition.

[0115] Among them, the load demand at the end of the air conditioning system is:

[0116]

[0117] Among them, CL is the load demand at the end of the air conditioning system; RT i is the design load capacity of the i-th chiller.

[0118] In actual operation, when the partial load rate PLR ​​of the chiller i When the value is lower than 0.3, it means that the chiller is in a low efficiency state; therefore, the partial load rate PLR ​​of the chiller i The value is not less than 0.3; that is, it satisfies:

[0119] PLR i ∈[0.3,1]orPLR i=0

[0120] Step 2: Under the condition that the boundary constraints of the variables to be optimized are met, the initial population is constructed by using the chaotic sequence mechanism; wherein, the process of constructing the initial population by using the chaotic sequence mechanism is as follows:

[0121] Under the condition that the boundary constraints of the variables to be optimized are met, the initial individuals are randomly generated;

[0122] The initial individuals are uniformly distributed in the feasible solution space of the variable to be optimized by using a chaotic sequence mechanism to obtain the initial population; wherein the chaotic sequence mechanism is:

[0123] x i+1 =mod(x i +4δ 3 -3δ,1)

[0124] Among them, x i+1 is the position vector of the ith individual after the chaotic sequence; x i is the randomly generated position vector of the i-th individual; δ is the chaos factor, δ∈(0.3,1).

[0125] In this embodiment, considering that the variables to be optimized may be subject to small-range boundary constraints, the population is initialized by introducing a chaotic sequence mechanism; the chaotic sequence mechanism is used to improve the uniformity of the distribution of population individuals in the feasible solution space, and has the characteristics of ergodicity, non-repeatability and sensitivity to initial values; therefore, in this embodiment, cubic mapping is used on sparrow individuals to effectively avoid the inhomogeneity of the initial solution.

[0126] Step 3: Calculate the individual fitness value of each individual in the population.

[0127] Step 4: Among all individual fitness values, select the smallest individual fitness value as the initial value of the group extreme value, and use the fitness value of each particle as the initial value of the individual extreme value.

[0128] Step 5: Select and retain the smallest individual fitness value, the largest individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population.

[0129] Step 6: Introduce a nonlinear decreasing inertia weight strategy, combine the speed in the particle swarm algorithm, the optimal position of the discoverer, and the position of the individual with the best fitness in the population, and update the position of the discoverer; when updating the position of the discoverer, it is updated according to the discoverer position update formula;

[0130] The finder location update formula is:

[0131]

[0132] v i+1 =ω1*v i +c1*r1*(X pbest -X i )+c2*r2*(X gbest -X i )

[0133]

[0134] Among them, X i+1 is the updated position of the i-th sparrow; X i is the position of the i-th sparrow; α is a random number, α∈(0,1]; M is the maximum number of independent runs; R2 is the warning value of whether the discoverer finds the presence of a predator; ST is the safety threshold of the discoverer's search; Q is a random number that obeys a normal distribution; L is an all-1 matrix, the number of rows of the all-1 matrix is ​​1, and the number of columns is d; v i+1 is the updated position of the i-th sparrow; ω1 is the inertia weight; v i is the position of the i-th sparrow; c1 is the learning factor; r1 is a random number in [0,1]; X pbest is the optimal position of the discoverer; c2 is the learning factor; r2 is a random number in [0,1]; X gbest is the position of the individual with the best fitness in the population; ω start is the maximum value of the inertia weight; ω end is the minimum value of the inertia weight; t is the current number of runs.

[0135] In this embodiment, during the population iteration process, individuals in the sparrow population will move closer to the position of the global optimal value in the initial optimization stage, resulting in a decrease in population diversity and a problem of low search accuracy; therefore, in this embodiment, it is proposed to introduce the concept of "speed" in the particle swarm algorithm into the finder's position update formula, and combine the nonlinear decreasing inertia weight to balance the global and local search capabilities, effectively realize the position information communication ability of individual sparrows, avoid the algorithm from converging to the optimal solution too early, and improve the population diversity in the initial iteration stage.

[0136] Step 7, introducing the following strategy of the fierce wolf in the wolf pack algorithm, combining the position of the individual with the best fitness in the population and the position of the individual with the worst fitness in the population, and updating the position of the follower; wherein, when updating the position of the follower, the follower position update formula is used for updating;

[0137] Wherein, the follower position update formula is:

[0138]

[0139]

[0140] Among them, X worst is the position of the individual with the worst fitness in the population; λ is the step size of change.

[0141] In this embodiment, since the position of the i-th sparrow individual will be updated according to the position of the i-1-th sparrow individual, it is highly dependent on the previous individual, and the typical sparrow search algorithm is prone to falling into the local optimum and being difficult to jump out when solving complex optimization problems, resulting in a rapid decrease in population diversity in the later stage of iteration, which limits the search speed of the algorithm; therefore, the present invention adopts the following strategy of the fierce wolf in the wolf pack algorithm to the follower's position update formula, which can effectively improve the ability of the population to jump out of the local optimum, thereby speeding up the search efficiency.

[0142] Step 8: Update the position of the scout based on the minimum individual fitness value, the maximum individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population.

[0143] Step 9: Calculate the fitness value of the individual after the position is updated, and update the individual extreme value and the group extreme value.

[0144] Step 10, set the population to run independently M times according to the above steps to determine whether the end condition is met. If so, output the optimal individual to obtain the optimal value of the variable to be optimized, that is, obtain the load distribution control result of the parallel chiller system; in this embodiment, the end condition of the algorithm is set to the maximum number of optimizations, which effectively reduces the complexity of the algorithm.

[0145] The following is a result of load distribution control of parallel chiller systems in two different central air-conditioning systems using different load distribution control methods.

[0146] Test results 1

[0147] The cooling load demand at the end of the air conditioning system in the first central air conditioning system is 960-2160RT, and the parallel chiller system of the first central air conditioning system includes three chillers; when the load distribution control of the parallel chiller system is performed using different optimization algorithms, the energy consumption optimization results of the parallel chiller system are shown in Table 1 below;

[0148] Table 1 Comparison of energy consumption optimization of parallel chiller system in the first central air conditioning system

[0149]

[0150] It can be seen from Table 1 that, compared with the genetic algorithm (GA), the load distribution control method of the parallel chiller system described in this embodiment can save 2.83-149.93 kW of energy under different load demands; compared with the particle swarm algorithm (PSO), it can save 1.26-2.13 kW of energy under different load demands; compared with the evolutionary strategy (ES), it still has an energy-saving effect.

[0151] As attached Figure 3 As shown, attached Figure 3 A load distribution control convergence curve comparison diagram of the parallel chiller system in the first central air conditioning system in the embodiment is given in the figure; Figure 3 It can be seen that the ISSA algorithm can converge to the minimum value before 20 generations and reach the most stable state.

[0152] Test result 2

[0153] The cooling load demand at the end of the air conditioning system in the second central air conditioning system is 5334-6858RT. The parallel chiller system of the second central air conditioning system includes six chillers. When the load distribution control of the parallel chiller system is performed using different optimization algorithms, the energy consumption optimization results of the parallel chiller system are shown in Table 2 below.

[0154] Table 2 Comparison of energy consumption optimization of parallel chiller system in Case 2

[0155]

[0156] It can be seen from Table 2 that compared with the genetic algorithm (GA), the load distribution control method for the parallel chiller system described in this embodiment can save 27.76-159.79 kW of energy under different load demands; compared with the particle swarm algorithm (PSO), it can save 0.96-96.12 kW of energy under different load demands; compared with the evolutionary strategy (ES), it can save 0.19-81.03 kW of energy; therefore, the overall energy-saving effect of the load distribution control method for the parallel chiller system described in this embodiment is relatively better, and the performance of solving the parallel chiller load distribution control optimization problem is better.

[0157] As attached Figure 4 As shown, attached Figure 4 A comparison diagram of load distribution control convergence curves of the parallel chiller system in the second central air conditioning system in the embodiment is given in the figure; Figure 4 It can be seen that the ISSA algorithm can reach the most stable state before 40 generations.

[0158] The description of the relevant parts of the load distribution control system, equipment and medium of the parallel chiller system provided in this embodiment can be found in the detailed description of the corresponding parts of the load distribution control method of a parallel chiller system described in this embodiment, and will not be repeated here.

[0159] The load distribution control method for a parallel chiller system described in the present invention takes the minimum energy consumption of the parallel chiller system as an optimization goal, takes the partial load rate of each chiller as an optimization variable, and uses an improved sparrow search algorithm to optimize and solve the optimization variables, thereby reducing the system operation energy consumption; wherein, in the population initialization stage, a chaotic sequence mechanism is used to initialize the individual population, enhance the diversity of the population, and ensure that the individual initial positions are evenly distributed in the search space; secondly, according to the characteristics of the population, a nonlinear decreasing inertia weight is introduced on the basis of the "speed" of the finder in the improved sparrow search algorithm, so that the population is not only affected by the global optimal individual position, but also by the individual historical optimal position, thereby improving the information exchange ability between the population and effectively balancing the global and local search capabilities in the initial stage of the search; at the same time, combined with the following strategy of the fierce wolf in the wolf pack algorithm, it is introduced into the position update formula of the follower, which can effectively improve the ability of the population to jump out of the local optimum, thereby accelerating the search efficiency.

[0160] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A load distribution control method for a parallel chiller system, characterized in that: include: Under the condition of meeting the cooling load demand of the air-conditioning system terminal, the total energy consumption of the parallel chiller system is minimized as the optimization target, and the partial load rate of each pre-installed chiller in the parallel chiller system is taken as the variable to be optimized. Using an improved sparrow search algorithm to optimize the variables to be optimized, and obtaining the optimal value of the variables to be optimized; Taking the optimal value of the variable to be optimized as the load distribution control result of the parallel chiller system; The improved sparrow search algorithm is to introduce a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm; The optimization goal is: P chiller,i =a i +b i ·PLR i +c i ·PLR i 2 +d i ·PLR i 3 Among them, Obj is the optimization target; P chiller,i is the energy consumption of the i-th pre-installed chiller; N is the total number of chillers in the parallel chiller system; a i ,b i ,c i and i are the kW-PLR curve coefficients of the i-th pre-installed chiller; i is the partial load rate of the i-th pre-installed chiller; The process of using the improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal value of the variables to be optimized is as follows: Under the condition of satisfying the boundary constraints of the variables to be optimized, the initial population is constructed by using the chaotic sequence mechanism; Calculate the individual fitness value of each individual in the population separately; Among all individual fitness values, the smallest individual fitness value is selected as the initial value of the group extreme value, and the fitness value of each particle is used as the initial value of the individual extreme value; Select and retain the smallest individual fitness value, the largest individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population; A nonlinear decreasing inertia weight strategy is introduced to update the position of the discoverer by combining the speed in the particle swarm algorithm, the optimal position of the discoverer, and the position of the individual with the best fitness in the population; The following strategy of the fierce wolf in the wolf pack algorithm is introduced, and the position of the follower is updated based on the position of the individual with the best fitness in the population and the position of the individual with the worst fitness in the population; Update the position of the scout based on the minimum individual fitness value, the maximum individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population; Calculate the fitness value of the individual after the position is updated, and update the individual extreme value and the group extreme value; Set the population to run independently M times according to the above steps to determine whether the end condition is met. If so, output the optimal individual to obtain the optimal value of the variable to be optimized, that is, the load distribution control result of the parallel chiller system is obtained.

2. The load distribution control method of a parallel chiller system according to claim 1, characterized in that: Under the condition of satisfying the boundary constraints of the variables to be optimized, the process of constructing the initial population using the chaotic sequence mechanism is as follows: Under the condition that the boundary constraints of the variables to be optimized are met, the initial individuals are randomly generated; The chaotic sequence mechanism is used to make the initial individuals evenly distributed in the feasible solution space of the variables to be optimized, so as to obtain the initial population.

3. The load distribution control method of a parallel chiller system according to claim 2, characterized in that: The chaotic sequence mechanism is: x i+1 =mod(x i +4δ 3 -3δ,1) Among them, x i+1 is the position vector of the ith individual after the chaotic sequence; x i is the randomly generated position vector of the i-th individual; δ is the chaos factor.

4. The load distribution control method of a parallel chiller system according to claim 1, characterized in that: When updating the discoverer's position, it is updated according to the discoverer's position update formula; The finder location update formula is: v i+1 =ω1*v i +c1*r1*(X pbest -X i )+c2*r2*(X gbest -X i ) Among them, X i+1 is the updated position of the i-th sparrow; X i is the position of the i-th sparrow; α is a random number, α∈(0,1]; M is the maximum number of independent runs; R2 is the warning value of whether the discoverer finds the presence of a predator; ST is the safety threshold of the discoverer's search; Q is a random number that obeys a normal distribution; L is an all-1 matrix, the number of rows of the all-1 matrix is ​​1, and the number of columns is d; v i+1 is the updated position of the i-th sparrow; ω1 is the inertia weight; v i is the position of the i-th sparrow; c1 is the learning factor; r1 is a random number in [0,1]; X pbest is the optimal position of the discoverer; c2 is the learning factor; r2 is a random number in [0,1]; X gbest is the position of the individual with the best fitness in the population; ω start is the maximum value of the inertia weight; ω end is the minimum value of the inertia weight; t is the current number of runs.

5. The load distribution control method of a parallel chiller system according to claim 1, characterized in that: When updating the position of the follower, the follower position update formula is used for updating; Wherein, the follower position update formula is: Among them, X worst is the position of the individual with the worst fitness in the population; λ is the step size of change.

6. A load distribution control system for a parallel chiller system, characterized in that: include: A variable module is used to minimize the total energy consumption of the parallel chiller system as the optimization target under the condition of meeting the terminal cooling load demand of the air-conditioning system, and to take the partial load rate of each pre-installed chiller in the parallel chiller system as the variable to be optimized; An optimization output module is used to optimize the variables to be optimized by using an improved sparrow search algorithm to obtain the optimal value of the variables to be optimized; Taking the optimal value of the variable to be optimized as the load distribution control result of the parallel chiller system; The improved sparrow search algorithm is to introduce a chaotic population initialization strategy, a nonlinear decreasing inertia weight strategy and a wolf following strategy in a wolf pack algorithm into a typical sparrow search algorithm; The optimization goal is: P chiller,i =a i +b i ·PLR i +c i ·PLR i 2 +d i ·PLR i 3 Among them, Obj is the optimization target; P chiller,i is the energy consumption of the i-th pre-installed chiller; N is the total number of chillers in the parallel chiller system; a i ,b i ,c i and i are the kW-PLR curve coefficients of the i-th pre-installed chiller; i is the partial load rate of the i-th pre-installed chiller; The process of using the improved sparrow search algorithm to optimize the variables to be optimized and obtain the optimal value of the variables to be optimized is as follows: Under the condition of satisfying the boundary constraints of the variables to be optimized, the initial population is constructed by using the chaotic sequence mechanism; Calculate the individual fitness value of each individual in the population separately; Among all individual fitness values, the smallest individual fitness value is selected as the initial value of the group extreme value, and the fitness value of each particle is used as the initial value of the individual extreme value; Select and retain the smallest individual fitness value, the largest individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population; A nonlinear decreasing inertia weight strategy is introduced to update the position of the discoverer by combining the speed in the particle swarm algorithm, the optimal position of the discoverer, and the position of the individual with the best fitness in the population; The following strategy of the fierce wolf in the wolf pack algorithm is introduced, and the position of the follower is updated based on the position of the individual with the best fitness in the population and the position of the individual with the worst fitness in the population; Update the position of the scout based on the minimum individual fitness value, the maximum individual fitness value, the position of the individual with the best fitness in the population, and the position of the individual with the worst fitness in the population; Calculate the fitness value of the individual after the position is updated, and update the individual extreme value and the group extreme value; Set the population to run independently M times according to the above steps to determine whether the end condition is met. If so, output the optimal individual to obtain the optimal value of the variable to be optimized, that is, the load distribution control result of the parallel chiller system is obtained.

7. A load distribution control device for a parallel chiller system, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the load distribution control method for a parallel chiller system as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the load distribution control method for a parallel chiller system as claimed in any one of claims 1 to 5 are implemented.

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