CN114254545A - Parallel connection refrigerator system load control optimization method, system, equipment and medium - Google Patents
Parallel connection refrigerator system load control optimization method, system, equipment and medium Download PDFInfo
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Abstract
The invention discloses a load control optimization method, a system, equipment and a medium for a parallel connection refrigerator system, wherein the method comprises the following steps: determining a control variable to be optimized of the parallel connection refrigerator system through a power consumption model of the parallel connection refrigerator system according to an optimization target; optimizing the control variable to be optimized of the parallel cooler system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooler system, namely the load control optimization result of the parallel cooler system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator; the invention improves the searching capability of the population, exchanges the information between the two populations, breaks the internal balance of the population, enhances the diversity of the population and enables the population to evolve to a higher level; the optimization process has better convergence, low calculation complexity, good robustness and better energy-saving effect.
Description
Technical Field
The invention belongs to the technical field of heating ventilation air conditioning system control, and particularly relates to a parallel-connection cold machine system load control optimization method, system, equipment and medium.
Background
In recent years, heating, ventilation and air conditioning systems are widely applied to modern large buildings, and a water chilling unit is used as main energy consumption equipment of the heating, ventilation and air conditioning systems, wherein the energy consumption of the water chilling unit accounts for about 40% of the total energy consumption of the heating, ventilation and air conditioning systems; in most practical applications, several chillers are typically connected in parallel to form a parallel chiller system, taking into account spare capacity, operational flexibility, and very important operating efficiency at part load.
The energy consumption of the parallel connection cold machine system is not only dependent on the characteristics of the cold water machine sets, but also related to load distribution strategies among the cold water machine sets under different load requirements; therefore, under different load requirements, how to make an optimal load distribution control strategy to improve the energy efficiency of the parallel cooling machine system is the key of energy conservation of the central air-conditioning system.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a load control optimization method, a system, equipment and a medium for a parallel cooling machine system, which aim to solve the technical problem that the prior parallel cooling machine system is low in energy efficiency and realize an optimal load distribution control strategy under different load requirements.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a load control optimization method of a parallel connection refrigerator system, which comprises the following steps:
determining a control variable to be optimized of the parallel connection refrigerator system through a power consumption model of the parallel connection refrigerator system according to an optimization target;
optimizing the control variable to be optimized of the parallel cooler system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooler system, namely the load control optimization result of the parallel cooler system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
Further, the optimization target is to minimize the total power consumption of the parallel cold machine system under the condition of meeting the cold load requirement at the tail end of the air conditioner; and the control variable to be optimized of the parallel connection cold machine system is the partial load rate of each cold machine.
Further, the power consumption model of the parallel cooling machine system is as follows:
wherein, P is the total power consumption of the parallel connection refrigerator system; n is the total number of the cold machines in the parallel cold machine system; pchiller,iThe energy consumption of the ith platform cooler; a isi,bi,ciAnd diRespectively are performance parameters of the ith platform cooler; PLRiIs the partial load rate of the ith chiller.
Further, an improved parallel particle swarm algorithm is adopted to optimize the control variable to be optimized of the parallel cooling machine system, so that the optimized control variable of the parallel cooling machine system is obtained, namely the process of the load control optimization result of the parallel cooling machine system is specifically as follows:
in a feasible solution space of a control variable to be optimized of a parallel cooling machine system, an initial population 1 is constructed in a random mode, and an initial population 2 is constructed in a chaotic mode;
independently optimizing the initial population 1 for k times by adopting an improved first particle swarm algorithm to obtain a progeny population 1; introducing a first nonlinear degressive inertial weight into the improved first particle swarm optimization;
independently optimizing the initial population 2 for k times by adopting an improved second particle swarm algorithm to obtain a progeny population 2; introducing a second nonlinear degressive inertia weight into the improved second particle swarm optimization;
after the offspring population 1 and the offspring population 2 are respectively subjected to iterative optimization k times, judging whether preset iterative optimization times are met; if so, carrying out individual exchange operation; if not, continuously applying the first nonlinear inertia weight or the second nonlinear inertia weight to carry out iterative updating;
carrying out individual exchange between the child population 1 and the child population 2 by using a immigration operator to generate a child population 3 and a child population 4;
an improved first particle swarm algorithm is adopted to independently optimize the offspring population 3; an improved second particle swarm algorithm is adopted to independently optimize the offspring population 4;
after the filial generation population 3 and the filial generation population 4 are respectively and independently optimized for p times, judging whether the preset ending requirement is met or not, if so, ending the optimization, and outputting an optimal individual, namely an optimal control variable of the parallel cooling machine system; if not, the immigration operator is used again for carrying out inter-population individual exchange, and independent optimization is continued.
Further, a process of constructing the initial population 2 by using the chaotic mode specifically includes:
randomly generating an initial solution in a feasible solution space of a control variable to be optimized of the parallel cooling machine system, and uniformly distributing the randomly generated initial solution in the feasible solution space by using a chaos mechanism to obtain an initial population 2;
wherein, the chaos mechanism is as follows:
wherein, Xi+1Position, X, of the i +1 th particle calculated for the chaotic sequenceiIs the position of the ith particle that is randomly generated.
Further, the first non-linear decreasing inertial weight is:
wherein, w1initalIs an initial value of inertial weight, w, of the initial population 11(t) is the inertial weight of the offspring population 1 and the offspring population 3; t is the current iteration frequency, and T is the total iteration frequency;
the second non-linearly decreasing inertial weight is:
w2(t)＝w2inital-sin(πt/2T)
wherein, w2initalIs an initial value of inertial weight, w, of the initial population 22And (t) is the inertial weight of the child population 2 and the child population 4.
Further, the preset ending requirement is that the current iteration optimizing times meet a preset maximum iteration time.
The invention also provides a parallel connection refrigerating machine system load control optimization system, which comprises:
the variable determining module is used for determining a control variable to be optimized of the parallel connection refrigerator system through the power consumption model of the parallel connection refrigerator system according to the optimization target;
the optimization module is used for optimizing the control variable to be optimized of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the optimal load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
The invention also provides a parallel connection refrigerator system load control optimization device, which comprises:
a memory for storing a computer program;
and the processor is used for realizing the load control optimization method of the parallel connection cold machine system when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the parallel chiller system load control optimization method.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a load control optimization method of a parallel cooler system, which adopts an improved parallel particle swarm algorithm to optimize a control variable to be optimized of the parallel cooler system; two different population initialization modes are introduced to improve strategies, so that the two initial populations have different characteristics in an initial stage, and the diversity of the populations is enhanced; by introducing different improved strategies of nonlinear decreasing inertial weight, the searching capability of the population is improved; by introducing a new immigration operator, information between the two groups is exchanged, so that the internal balance of the population is broken, the diversity of the population is enhanced, and the population is evolved to a higher level; the optimizing process has better convergence, low calculation complexity and good robustness, can be better used for energy-saving optimization of an actual parallel connection cold machine system, and has better energy-saving effect. .
Furthermore, the minimum total power consumption of the parallel connection refrigerator system is taken as an optimization target, the partial load rate of each refrigerator is taken as a control variable to be optimized, the refrigerators with different capacities and different characteristics can be regulated and controlled according to different load requirements of users, the flexibility of the system is improved, and the running power consumption of the system is reduced.
Further, initializing individual populations by respectively adopting a random mode and a chaotic mode; the two initial populations have different characteristics in the initial stage, so that the diversity of the populations is enhanced; and the immigration operators are used for exchanging individuals of the two populations, so that information exchange between the populations is realized, the internal balance of the populations is broken, and the diversity of the populations is enhanced.
Furthermore, by providing different nonlinear decreasing inertia weights for the population generated by the random mode and the population generated by the chaotic mode, the global search capability of the population generated by the random mode and the local search capability of the population generated by the chaotic mode are effectively enhanced.
Furthermore, the preset ending requirement is set to the current iteration optimization times meeting the preset maximum iteration times, the ending condition is simple, the simplicity and the clarity of the optimization algorithm are ensured, the optimization process time is short, and the cost is low.
Drawings
FIG. 1 is a block diagram of a parallel chiller system in an embodiment;
FIG. 2 is a flow chart of the improved parallel particle swarm algorithm in the embodiment;
FIG. 3 is a graph of the results of an iteration of two different non-linearly decreasing inertial weights in an embodiment;
FIG. 4 is a diagram showing the process of exchanging individuals among populations in the example.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects of the present invention more apparent, the following embodiments further describe the present invention in detail. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a load control optimization method of a parallel connection refrigerator system, which comprises the following steps:
In the invention, the optimization target is to minimize the total power consumption of the parallel cold machine system under the condition of meeting the cold load requirement at the tail end of the air conditioner; and the control variable to be optimized of the parallel connection cold machine system is the partial load rate of each cold machine.
The power consumption model of the parallel connection refrigerator system is as follows:
wherein, P is the total power consumption of the parallel connection refrigerator system; n is the total number of the cold machines in the parallel cold machine system; pchiller,iThe energy consumption of the ith platform cooler; a isi,bi,ciAnd diRespectively are performance parameters of the ith platform cooler; PLRiIs the partial load rate of the ith chiller.
Step 2, optimizing the to-be-optimized control variable of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies and two different nonlinear degressive inertial weight improvement strategies; the specific process is as follows:
and step 21, in a feasible solution space of the to-be-optimized control variable of the parallel cooling machine system, constructing an initial population 1 by adopting a random mode, and constructing an initial population 2 by adopting a chaotic mode.
Randomly generating initial solutions in a feasible solution space of a control variable to be optimized of a parallel cooling machine system, and uniformly distributing the randomly generated initial solutions in the feasible solution space by using a chaos mechanism to obtain an initial population 2;
wherein, the chaos mechanism is as follows:
wherein, Xi+1Position, X, of the i +1 th particle calculated for the chaotic sequenceiIs the position of the ith particle that is randomly generated.
Step 22, performing independent optimization k times on the initial population 1 by adopting an improved first particle swarm algorithm to obtain a progeny population 1; introducing a first nonlinear degressive inertial weight into the improved first particle swarm optimization; wherein the first non-linear decreasing inertial weight is:
wherein, w1initalIs an initial value of inertial weight, w, of the initial population 11(t) is the inertial weight of the offspring population 1 and the offspring population 3; t is the current iteration frequency, and T is the total iteration frequency;
step 23, performing independent optimization k times on the initial population 2 by adopting an improved second particle swarm algorithm to obtain a progeny population 2; introducing a second nonlinear degressive inertia weight into the improved second particle swarm optimization; wherein the second non-linear decreasing inertial weight is:
w2(t)＝w2inital-sin(πt/2T)
wherein, w2initalIs an initial value of inertial weight, w, of the initial population 22And (t) is the inertial weight of the child population 2 and the child population 4.
Step 24, after the child population 1 and the child population 2 are respectively and independently iterated and optimized for k times, judging whether preset iteration optimization times are met; if yes, go to step 25; if not, continuously applying respective nonlinear inertia weight to carry out iterative updating.
And 25, performing individual exchange between the child population 1 and the child population 2 by using the immigration operator to generate a child population 3 and a child population 4.
26, independently optimizing the offspring population 3 by adopting an improved first particle swarm algorithm; and adopting a second improved particle swarm optimization algorithm to independently optimize the offspring population 4.
26, after the filial generation population 3 and the filial generation population 4 are independently optimized for p times respectively, judging whether a preset ending requirement is met, if so, ending optimization, and outputting an optimal individual, namely an optimal control variable of the parallel connection refrigerator system; if not, the immigration operator is used again for carrying out inter-population individual exchange, and independent optimization is continued; preferably, the preset ending requirement is that the current iteration optimizing times meet a preset maximum iteration time.
The invention also provides a parallel connection refrigerating machine system load control optimization system, which comprises a variable determination module and an optimization module; the variable determining module is used for determining a control variable to be optimized of the parallel connection refrigerator system through the power consumption model of the parallel connection refrigerator system according to the optimization target; the optimization module is used for optimizing the control variable to be optimized of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the optimal load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
The invention also provides a parallel connection refrigerator system load control optimization device, which comprises: a memory for storing a computer program; and the processor is used for realizing the steps of the load control optimization method of the parallel connection cold machine system when executing the computer program.
When the processor executes the computer program, the steps of the load control optimization method for the parallel chiller system are implemented, for example: determining a control variable to be optimized of the parallel connection refrigerator system through a power consumption model of the parallel connection refrigerator system according to an optimization target; optimizing the control variable to be optimized of the parallel cooler system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooler system, namely the load control optimization result of the parallel cooler system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
Alternatively, the processor implements the functions of the modules in the system when executing the computer program, for example: the variable determining module is used for determining a control variable to be optimized of the parallel connection refrigerator system through the power consumption model of the parallel connection refrigerator system according to the optimization target; the optimization module is used for optimizing the control variable to be optimized of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the optimal load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units can be a series of instruction segments of a computer program capable of performing specific functions, and the instruction segments are used for describing the execution process of the computer program in the parallel cooling machine system load control optimization device. For example, the computer program may be partitioned into a variable determination module and an optimization module; the specific functions of each module are as follows: the variable determining module is used for determining a control variable to be optimized of the parallel connection refrigerator system through the power consumption model of the parallel connection refrigerator system according to the optimization target; the optimization module is used for optimizing the control variable to be optimized of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the optimal load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
The load control optimization equipment of the parallel connection refrigerator system can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The parallel cooling machine system load control optimization device can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the foregoing is merely an example of the parallel chiller system load control optimization device, and does not constitute a limitation of the parallel chiller system load control optimization device, and may include more components, or combine some components, or different components, for example, the parallel chiller system load control optimization device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can also be any conventional processor and the like, the processor is a control center of the parallel connection refrigerator system load control optimization device, and various interfaces and lines are utilized to connect all parts of the whole parallel connection refrigerator system load control optimization device.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the parallel connection cold machine system load control optimization device by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid state storage device.
The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the parallel chiller system load control optimization method.
If the module/unit integrated with the parallel cooling machine system load control optimization device is realized in the form of a software functional unit and sold or used as an independent product, the module/unit can be stored in a computer readable storage medium.
Based on such understanding, all or part of the processes in the method can be implemented by the present invention, and can also be implemented by a computer program for instructing relevant hardware, where 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 parallel chiller system load control optimization method can be implemented. Wherein the computer program comprises computer program code, which may be in source code form, object code form, executable file or preset intermediate form, etc.
The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-only memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.
It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The load control optimization method, the system, the equipment and the medium of the parallel connection refrigerator system adopt an improved parallel particle swarm algorithm to optimize the control variable to be optimized of the parallel connection refrigerator system; two different population initialization modes are introduced to improve strategies, so that the two initial populations have different characteristics in an initial stage, and the diversity of the populations is enhanced; by introducing different improved strategies of nonlinear decreasing inertial weight, the searching capability of the population is improved; the optimizing process has better convergence, low calculation complexity and good robustness, can be better used for energy-saving optimization of an actual parallel connection cold machine system, and has better energy-saving effect.
Examples
Taking the load control optimization process of a parallel cold machine system of a certain central air-conditioning system as an example; as shown in the attached figure 1, the parallel-connection cold machine system comprises four cold machines, wherein the four cold machines are connected to a public distribution system through parallel-connection pipelines to control the flow of water supply and return; the cold load needed in the system is distributed to four mutually parallel cold machines; because the rated capacity and the performance curve of each cold machine are different, the load of each cold machine can be optimally distributed under the condition of meeting the cold load requirement, so that each cold machine can operate at the optimal working point.
The embodiment provides a load control optimization method for a parallel connection refrigerator system, which specifically comprises the following steps:
In the embodiment, the problem of cold machine load distribution of the central air-conditioning system is set to be that on the premise of meeting the air-conditioning load requirement, the energy consumption of the system is reduced as low as possible by optimizing the load distribution strategy among cold machines; the distributed load of the chiller is expressed in terms of the fractional load rate PLR, i.e., the ratio of the chiller's cooling load to the design capacity.
In this embodiment, the power consumption model of the chiller is:
wherein, Pchiller,iThe energy consumption of the ith platform cooler; a isi,bi,ciAnd diRespectively are performance parameters of the ith platform cooler; PLRiIs the partial load rate of the ith chiller.
The power consumption model of the parallel cold machine system is as follows:
wherein, P is the total power consumption of the parallel connection refrigerator system; and N is the total number of the coolers in the parallel cooler system.
In the embodiment, the optimization target is to minimize the total power consumption of the parallel cold machine system under the condition of meeting the cold load requirement at the tail end of the air conditioner; the control variable to be optimized of the parallel connection cold machine system is the partial load rate of each cold machine; wherein, the objective function of the optimal cold machine load problem is to minimize the total energy consumption of the parallel cold machine system; the cold load requirement at the tail end of the air conditioner is used as a constraint condition; generally, when the PLR of the refrigerator is lower than 0.3, it indicates that it is in a low-efficiency state; thus, the PLR of the chiller should not be less than 0.3.
Therefore, the objective function for minimizing the total power consumption of the parallel chiller system is:
the constraint conditions of the partial load rate of each cooler are as follows:
0.3≤PLRi≤1,or,PLRi＝0
wherein Q isneedThe requirement of the cold load at the tail end of the air conditioner is met;
the design load capacity for the ith chiller.
Step 2, optimizing the to-be-optimized control variable of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the load control optimization result of the parallel cooling machine system; as shown in fig. 2; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies and two different nonlinear degressive inertial weight improvement strategies; as shown in fig. 2, the method specifically comprises the following steps:
and step 21, in a feasible solution space of a control variable to be optimized of the parallel connection refrigerator system, namely in a feasible solution space of a partial load rate of the refrigerator, constructing an initial population 1 by adopting a random model, and constructing an initial population 2 by adopting a chaotic mode.
In step 21, randomly generating an initial solution in a feasible solution space as an initial population 1 under the condition of meeting the constraint condition of the partial load rate of the refrigerator in a random mode; the chaotic mode means that an initial population is generated through a random mode, and individual populations are uniformly distributed in a feasible solution space by utilizing a chaotic mechanism; in this embodiment, in the process of constructing the initial population 2 by using the chaotic mode, the following is specifically performed:
randomly generating an initial solution in a feasible solution space of a control variable to be optimized of the parallel cooling machine system, and uniformly distributing the randomly generated initial solution in the feasible solution space by using a chaos mechanism to obtain an initial population 2;
wherein, the chaos mechanism is as follows:
wherein, Xi+1Position, X, of the i +1 th particle calculated for the chaotic sequenceiIs the position of the ith particle that is randomly generated.
Step 22, performing independent optimization k times on the initial population 1 by adopting an improved first particle swarm algorithm to obtain a progeny population 1; introducing a first nonlinear degressive inertial weight into the improved first particle swarm optimization; wherein the first non-linear decreasing inertial weight is:
wherein, w1initalIs an initial value of inertial weight, w, of the initial population 11(t) is the inertial weight of the offspring population 1 and the offspring population 3; t is the current iteration number, and T is the total iteration number.
In this embodiment, an improved first example population algorithm is adopted to perform independent optimization k times on the initial population 1 to obtain a child population 1, which is specifically as follows:
step 221, taking the power consumption model of the refrigerator as a fitness function, and respectively calculating the fitness value of each individual in the initial population 1;
step 222, in the fitness value of each individual in the initial population 1, taking the minimum value of the fitness value as the initial value of a population extreme value, and taking the fitness value of each particle as the initial value of an individual extreme value;
step 223, iteratively updating the position and the velocity of the particle according to the first nonlinear decreasing inertia weight;
224, calculating the fitness value of the updated particle position, and updating the individual extreme value and the population extreme value;
and step 225, independently operating for k times according to the operations of the step 223 to the step 224 to obtain the child population 1.
Step 23, performing independent optimization k times on the initial population 2 by adopting an improved second particle swarm algorithm to obtain a progeny population 2; introducing a second nonlinear degressive inertia weight into the improved second particle swarm optimization; wherein the second non-linear decreasing inertial weight is:
w2(t)＝w2inital-sin(πt/2T)
wherein, w2initalIs an initial value of inertial weight, w, of the initial population 22And (t) is the inertial weight of the child population 2 and the child population 4.
In this embodiment, an improved second particle swarm algorithm is adopted to perform independent optimization on the initial population 2 for k times to obtain a progeny population 2, which is the same as the independent optimization operation principle on the initial population 2, but differs in that when iterative update is performed on the position and the velocity of a particle, iterative update is performed according to a first nonlinear degressive inertia weight, and a specific iterative process is not repeated here.
Because the particle distribution randomness in the initial population 1 or the offspring population 1 is strong, the particles are probably dispersed and also probably concentrated, and in order to ensure that a solution with higher quality can be searched, the global searching capability of the particles is enhanced; for the initial population 2, the particles are uniformly distributed in a feasible solution space and can quickly converge to the vicinity of an optimal solution, and the local search capability of the population is emphasized; as shown in fig. 3, fig. 3 is a graph of the results of two different iterations of non-linearly decreasing inertial weights in an embodiment; as can be seen from fig. 3, the decreasing trend of the inertial weight curve of the initial population 1 or the child population 1 is relatively gentle, so that the global search capability is stronger; the decreasing trend of the inertia weight curve of the initial population 2 or the child population 2 is quicker, and the local search capability of the population is stronger.
Step 24, after the child population 1 and the child population 2 are respectively and independently iterated and optimized for k times, judging whether preset iteration optimization times are met; if yes, go to step 25; if not, continuously applying respective nonlinear inertia weight to carry out iterative updating.
calculating the fitness value of each particle in the offspring population 1;
as shown in fig. 4, the particles in the offspring population 1 are divided into a first particle segment and a second particle segment according to the size of the fitness value; the first particle segment comprises a part of particles with larger fitness value in the filial generation population 1, and the second particle segment comprises a part of particles with smaller residual fitness value in the filial generation population 1;
setting individual exchange scale; in this example, the individual exchange scale was set to C%;
calculating the fitness value of each particle in the offspring population 2;
dividing the particles in the offspring population 2 into a third particle segment and a fourth particle segment according to the size of the fitness value; the third particle segment comprises a part of particles with larger fitness value in the filial generation population 2, and the fourth particle segment comprises a part of particles with smaller residual fitness value in the filial generation population 2;
performing individual exchange between the first particle segment and the third particle segment and performing individual exchange between the second particle segment and the fourth particle segment according to the set individual exchange scale;
the first particle section after individual exchange and the second particle section after individual exchange generate a filial generation population 3, and the third particle section after individual exchange and the fourth particle section after individual exchange generate a filial generation population 4.
26, independently optimizing the offspring population 3 by adopting an improved first particle swarm algorithm; and adopting a second improved particle swarm optimization algorithm to independently optimize the offspring population 4.
The operation of independently optimizing the offspring population 3 is the same as the operation of independently optimizing the offspring population 1; the operation of independently optimizing the offspring population 4 is the same as the operation of independently optimizing the offspring population 2; and will not be described in detail herein.
And 27, after the child population 3 and the child population 4 are independently optimized for p times respectively, judging that the current iteration optimization times meet the preset maximum iteration times.
If so, finishing the optimization, and outputting an optimal individual, namely an optimal control variable of the parallel cooling machine system to obtain a load control optimization result of the parallel cooling machine system; if not, returning to the step 25 to perform inter-population individual exchange by using immigration operators, and continuing independent optimization.
In this embodiment, the following gives the results of load control optimization for two different parallel chiller systems, specifically as follows:
as shown in table 1 below, table 1 below shows a comparison table of energy consumption optimization results of the parallel cooling machine system when the parallel cooling machine system is controlled and optimized by using different optimization algorithms.
Table 1 table for comparing energy consumption optimization results of parallel connection refrigerator system
As can be seen in Table 1, the optimization method of this example can save energy by 2.83-149.93kW at different load demands compared with the Genetic Algorithm (GA); compared with a Particle Swarm Optimization (PSO), the optimization method can save energy by 1.24-2.13kW under different load requirements; compared with the improved invasive weed algorithm, the energy-saving effect is the same.
As shown in table 2 below, table 2 below shows a comparison table of energy consumption optimization results of the parallel cooling machine system when the parallel cooling machine system is controlled and optimized by using different optimization algorithms.
Table 2 energy consumption optimization result comparison table of parallel connection refrigerator system
As can be seen from Table 2, the optimization method of this example can save energy of 27.76-159.79kW at different load demands compared with the Genetic Algorithm (GA); compared with a Particle Swarm Optimization (PSO), the optimization method can save energy by 0.96-96.12kW under different load requirements; compared with the improved invasive weed algorithm, the optimization method of the embodiment can save energy by 0.01-0.52 kW.
In summary, the improved parallel particle swarm algorithm in the embodiment has a better energy-saving effect on the overall energy-saving effect.
For a description of a relevant part in the parallel chiller system load control optimization system, the device, and the medium provided in this embodiment, reference may be made to a detailed description of a corresponding part in the parallel chiller system load control optimization method described in this embodiment, and details are not described herein again.
The invention discloses a load control optimization method, a system, equipment and a medium of a parallel connection refrigerator system, wherein the method comprises the following steps: firstly, establishing a power consumption model of a cold machine in an air-conditioning system, taking the minimum energy consumption of a parallel cold machine system as an optimization target, and taking the partial load rate of each cold machine as a control variable to be optimized; initializing individual populations by adopting a random mode and a chaotic mode, so that the two initial populations have different characteristics in an initial stage, and the diversity and the ergodicity of muscle populations are improved; respectively calculating the fitness value of each individual in the two populations by using the power consumption model of the refrigerator as a fitness function; taking the minimum value as an initial value of a population in all the fitness values, and taking the fitness value of each particle as an initial value of an individual extreme value; the two groups are subjected to iterative updating according to position and speed updating formulas of two different nonlinear degressive inertial weight improvement strategies; calculating the fitness value of the updated position, and then updating the individual extremum and the group extremum; when the two groups independently run for k times, using immigration operators to carry out individual exchange between the two groups; setting independent evolution for p times, and continuing to evolve according to an independent operation mode; and judging whether the end condition is met, if not, returning to continue iterative optimization, and if so, outputting a cold load optimal distribution result.
The invention takes the minimum power consumption of the parallel connection refrigerator system as an optimization target, takes the partial load rate of each refrigerator as an optimization variable, can regulate and control the refrigerators with different capacities and different characteristics according to different load requirements of users, improves the flexibility of the system and reduces the running power of the system; specifically, in a population initialization stage, a random mode and a chaotic mode are adopted to initialize individual populations. The two populations have different characteristics in the initial stage, so that the diversity of the populations is enhanced; according to the characteristics of the two groups, different nonlinear degressive inertia weights are provided; in an initial population generated through a random mode, the global search capability of the population is enhanced; then, reinforcing local searching capability in an initial population generated by a chaotic pattern; in order to more effectively realize information exchange among the populations, a new immigration operator is provided on the basis of the traditional immigration operator, the internal balance of the populations is broken, and the diversity of the populations is enhanced.
The above-described embodiment is only one of the embodiments that can implement the technical solution of the present invention, and the scope of the present invention is not limited by the embodiment, but includes any variations, substitutions and other embodiments that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed.
Claims (10)
1. A load control optimization method for a parallel connection refrigerator system is characterized by comprising the following steps:
determining a control variable to be optimized of the parallel connection refrigerator system through a power consumption model of the parallel connection refrigerator system according to an optimization target;
optimizing the control variable to be optimized of the parallel cooler system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooler system, namely the load control optimization result of the parallel cooler system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
2. The parallel chiller system load control optimization method according to claim 1, wherein the optimization objective is to minimize the total power consumption of the parallel chiller system under the condition of meeting the end cooling load requirement of the air conditioner; and the control variable to be optimized of the parallel connection cold machine system is the partial load rate of each cold machine.
3. The load control optimization method for the parallel chiller system according to claim 1, wherein the power consumption model of the parallel chiller system is as follows:
Pchiller,i＝ai+bi·PLRi+ci·PLRi 2+di·PLRi 3
wherein, P is the total power consumption of the parallel connection refrigerator system; n is the total number of the cold machines in the parallel cold machine system; pchiller,iThe energy consumption of the ith platform cooler; a isi,bi,ciAnd diRespectively are performance parameters of the ith platform cooler; PLRiIs the partial load rate of the ith chiller.
4. The method for optimizing the load control of the parallel chiller system according to claim 1, wherein an improved parallel particle swarm algorithm is adopted to optimize the control variable to be optimized of the parallel chiller system, so as to obtain the optimized control variable of the parallel chiller system, which is the process of the load control optimization result of the parallel chiller system, and the process is as follows:
in a feasible solution space of a control variable to be optimized of a parallel cooling machine system, an initial population 1 is constructed in a random mode, and an initial population 2 is constructed in a chaotic mode;
independently optimizing the initial population 1 for k times by adopting an improved first particle swarm algorithm to obtain a progeny population 1; introducing a first nonlinear degressive inertial weight into the improved first particle swarm optimization;
independently optimizing the initial population 2 for k times by adopting an improved second particle swarm algorithm to obtain a progeny population 2; introducing a second nonlinear degressive inertia weight into the improved second particle swarm optimization;
after the offspring population 1 and the offspring population 2 are respectively subjected to iterative optimization k times, judging whether preset iterative optimization times are met; if so, carrying out individual exchange operation; if not, continuously applying the first nonlinear inertia weight or the second nonlinear inertia weight to carry out iterative updating;
carrying out individual exchange between the child population 1 and the child population 2 by using a immigration operator to generate a child population 3 and a child population 4;
an improved first particle swarm algorithm is adopted to independently optimize the offspring population 3; an improved second group algorithm is adopted to independently optimize the offspring group 4;
after the filial generation population 3 and the filial generation population 4 are respectively and independently optimized for p times, judging whether the preset ending requirement is met or not, if so, ending the optimization, and outputting an optimal individual, namely an optimal control variable of the parallel cooling machine system; if not, the immigration operator is used again for carrying out inter-population individual exchange, and independent optimization is continued.
5. The load control optimization method of the parallel cooling machine system according to claim 4, wherein a chaotic mode is adopted to construct the initial population 2, and the method specifically comprises the following steps:
randomly generating an initial solution in a feasible solution space of a control variable to be optimized of the parallel cooling machine system, and uniformly distributing the randomly generated initial solution in the feasible solution space by using a chaos mechanism to obtain an initial population 2;
wherein, the chaos mechanism is as follows:
wherein, Xi+1Position, X, of the i +1 th particle calculated for the chaotic sequenceiIs the position of the ith particle that is randomly generated.
6. The parallel chiller system load control optimization method of claim 4, wherein the first non-linear decreasing inertial weight is:
wherein, w1initalIs an initial value of inertial weight, w, of the initial population 11(t) is the inertial weight of the offspring population 1 and the offspring population 3; t is the current iteration frequency, and T is the total iteration frequency;
the second non-linearly decreasing inertial weight is:
w2(t)＝w2inital-sin(πt/2T)
wherein, w2initalIs an initial value of inertial weight, w, of the initial population 22And (t) is the inertial weight of the child population 2 and the child population 4.
7. The parallel cooler system load control optimization method according to claim 4, wherein the preset end requirement is that the current iteration optimization number satisfies a preset maximum iteration number.
8. A parallel connection refrigerator system load control optimization system is characterized by comprising:
the variable determining module is used for determining a control variable to be optimized of the parallel connection refrigerator system through the power consumption model of the parallel connection refrigerator system according to the optimization target;
the optimization module is used for optimizing the control variable to be optimized of the parallel cooling machine system by adopting an improved parallel particle swarm algorithm to obtain the optimal control variable of the parallel cooling machine system, namely the optimal load control optimization result of the parallel cooling machine system; the improved parallel particle swarm optimization introduces two different population initialization mode improvement strategies, two different nonlinear degressive inertial weight improvement strategies and a new immigration operator.
9. A parallel chiller system load control optimization device, comprising:
a memory for storing a computer program;
a processor for implementing the parallel chiller system load control optimization method of any one of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the parallel chiller system load control optimization method of any one of claims 1-7.
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| CN115562034A (en) * | 2022-10-20 | 2023-01-03 | 西安建筑科技大学 | Load distribution control method, system, equipment and medium of parallel connection refrigerator system |
| CN116822709A (en) * | 2023-05-22 | 2023-09-29 | 深圳市中电电力技术股份有限公司 | Parallel water chilling unit load distribution optimization method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115562034A (en) * | 2022-10-20 | 2023-01-03 | 西安建筑科技大学 | Load distribution control method, system, equipment and medium of parallel connection refrigerator system |
| CN116822709A (en) * | 2023-05-22 | 2023-09-29 | 深圳市中电电力技术股份有限公司 | Parallel water chilling unit load distribution optimization method, system and storage medium |
| CN116822709B (en) * | 2023-05-22 | 2024-03-22 | 深圳市中电电力技术股份有限公司 | Parallel water chilling unit load distribution optimization method, system and storage medium |
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