CN110260470A - Central air-conditioning parallel connection cold load optimal distribution method based on colony intelligence framework - Google Patents
Central air-conditioning parallel connection cold load optimal distribution method based on colony intelligence framework Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
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- F24F11/56—Remote control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
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Abstract
The central air-conditioning parallel connection cold load optimal distribution method based on colony intelligence framework that the invention discloses a kind of, a controller is all arranged in each cold in cold system in parallel, and all controllers carry out interconnection by the actual physics topological connection relation of cold and form colony intelligence network system in cold system in parallel；When a certain controller initiates adjusting task, controller is initiated in the cooperation of remaining controller and respective neighbours' controller carries out information exchange, the system operations of response are completed for the task of adjusting, the autonomous synergic adjustment operation for realizing cold system in parallel carries out optimizing to objective function using distributed chaos distribution estimation method and completes optimization distribution task.The present invention improves working efficiency, reduces system energy consumption, energy saving purpose.
Description
Technical field
The invention belongs to technical field of air conditioner refrigeration, and in particular to a kind of central air-conditioning parallel connection based on colony intelligence framework is cold
Machine load optimal distribution method.
Background technique
Central air conditioner system is always the big power consumer in building, therefore carries out energy saving optimizing research to central air conditioner system
It has very important significance.And currently, the operation of the cold in parallel in central air conditioner system is often operation maintenance personnel according to day
Come what is regulated and controled, automatization level is lower for gas and experience, and will cause more energy waste.And have been used for reality
The cold in parallel operation control strategy of system is also all that adding machine strategy is realized according to pre-set threshold value, is adapted to this
The variation of end load.But these methods all cannot provide corresponding refrigerating capacity according to the real-time change of end load, thus
Lead to refrigerating capacity or be higher than end load, or is lower than end load, and then affect the comfort of personnel in room.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that providing a kind of based on gunz
The central air-conditioning parallel connection cold load optimal distribution method of energy framework is guaranteeing the case where safe and reliable and refrigerating capacity requires
Under, rationally control start and stop and the sharing of load of cold in real time according to the variation of end load demand.
The invention adopts the following technical scheme:
Central air-conditioning parallel connection cold load optimal distribution method based on colony intelligence framework, it is every in cold system in parallel
A controller is all arranged in one cold, and all controllers are carried out mutual by actual physics topological connection relation in cold system in parallel
Connection forms colony intelligence network system；When a certain controller initiate adjusting task when, remaining controller cooperation initiate controller with
Respective neighbours' controller carries out information exchange, and the system operations of response are completed for the task of adjusting, realizes cold system in parallel
Autonomous synergic adjustment operation；It is calculated according to cold load factor-power-performance parameter of curve and rated cooling capacity parameter different negative
Power consumption and refrigerating capacity under load rate；Then each controller is obtained in parallel cold under the combination of different loads rate by information exchange
The overall refrigerating effect and total power consumption of machine system；Fitness value is calculated according to fitness function and carries out chaotic mutation operation；It washes in a pan
The sample average and variance for obtaining Gaussian Profile after the individual of partial fitness difference using statistical analysis method as parent population are eliminated,
And new population is generated with this Gaussian distribution model；Fitness is recalculated using new population and parent population as a population
Value becomes progeny population after eliminating the individual of partial fitness difference in population；So far an evolutionary process, progeny population are completed
The smallest direction evolution generation of energy consumption, constantly evolution are until system in the case where meeting end load demand according to cold system in parallel
After the sample variance that meter analysis obtains is less than required precision or reaches maximum evolutionary generation, most by fitness in last generation population
Big load factor combination as optimizing regulation as a result, and each cold completion adjustment process is controlled by controller, completion is excellent
Change distribution.
Specifically, specific step is as follows for distributed chaos distribution estimation method:
S1, each cold controller press one-dimensional logistic model and generate N-dimensional chaos sequence as PLR value, and by PLR
It is Population Size that < 0.3 value, which is set to 0, N,；
S2, all cold controllers calculate refrigerating capacity and power corresponding to respective N number of PLR value；
S3, all cold controllers combine down cold system in parallel by being communicated to obtain N group PLR with neighbours' controller
Overall refrigerating effect and corresponding system total power；
S4, every cold carry out chaotic mutation operation using N number of PLR value of the chaotic mutation operation to itself；
S5, to complete chaotic mutation operation after PLR population carry out fitness evaluation, and by population by fitness from greatly to
Small to be ranked up, M individual is for statistical analysis before taking and finds out sample average and variance；
S6, all cold controllers judge stopping criterion for iteration, if this sample variance found out reaches precision
It is required that or reach maximum evolutionary generation, then flag signal flag is set 0, is otherwise set to 1；
S7, each cold controller initiate global read group total, iteration ends if being 0 if the summation of flag, and otherwise every
Cold controller according to calculated sample average and variance the PLR value of K*N Gaussian distributed is randomly generated, with M
For the more excellent PLR value combination of parent as new population, K is the ratio of newborn progeny population quantity and parent population quantity；
S8, cold controller and neighbours' controller carry out information exchange, carry out fitness evaluation to K*N+M PLR combination,
And be ranked up K*N+M individual from big to small by fitness, it takes individual as population of new generation, returns to S4.
Further, in S1, one-dimensional logistic model are as follows:
αi+1=β αi(1-αi)
Wherein, αi+1For chaos sequence value caused by one-dimensional logistic model, β=4, α1For the random number in 0 to 1.
Further, S4 specifically:
S401, fitness calculating is carried out to current population, and is ranked up from small to large by fitness, then by Population adaptation
Degree is allocated according to linear method, the linear fitness value fit'(i of i-th of individual in population) as follows:
Fit'(i)=position (i)/N
Wherein, position (i) is the location of i individual after population is sorted from small to large by fitness；
S402, chaotic mutation radius is determined, for linear fitness value fit'(i) small individual, by fitness from it is small to
The forward individual that sorts greatly increases variation radius, linear fitness value fit'(i) big individual sorts from small to large by fitness
Individual rearward reduces variation radius；
S403, chaotic mutation operation is executed to each individual, pop' is operated to the chaotic mutation of i-th of individual in population
(i) are as follows:
Pop'(i)=pop (i)+r (i) * 2 (1-2*A (i))
Wherein, pop (i) is i-th of individual in population, and r (i) is the chaotic mutation radius of i-th of individual in population, A (i)
For chaos sequence generated in step S1；
S404, fitness evaluation is carried out to the individual after execution chaotic mutation, if the fitness value of new individual is a greater than former
Body then replaces original PLR with the PLR after variation, obtains the combination of the big cold PLR of the overall fitness value of N group, i.e. population is pressed
Meet the smallest direction of energy consumption under conditions of end load demand according to cold in parallel to evolve forward a generation.
Further, in S402, the chaotic mutation radius r (i) of i-th of individual in population are as follows:
R (i)=Δ (i) * (1-fit'(i))
Wherein, Δ (i) is variation step-length, is determined according to the real time information of population in evolutionary process, i-th in population
The variation step-length of body pop (i):
When Δ (i) < ΔminWhen, enable Δ (i)=Δmin, pop (j), pop (k) are a certain individual in population.
Further, in S404, fitness evaluation is obtained according to fitness function minf:
Wherein, WtotalFor the total energy consumption of cold in parallel, penal is penalty factor,It is i-th in cold system in parallel
The rated cooling capacity of cold, QneedFor system end workload demand, PLRiFor the load factor of i-th cold in cold system in parallel,
R is total number of units of cold in cold system in parallel.
Further, in S5, the mean μ of its Gaussian distribution model is found out according to more excellent individual specimeniAnd variances sigmai:
Wherein, PLRjFor the value of j-th of individual in more excellent individual specimen, M is the quantity of more excellent individual.
Further, S7 specifically:
S701, a certain cold controller initiate global read group total task to flag value；
S702, spanning tree is formed as root node using the controller for initiating task；
S703, all controllers in spanning tree top end start to calculate, brother of node parallel computation.Each node
Receive data from its child node, wait collect it is complete after execute adduction and calculate and be sent to his father's node, each intra-node executes
Identical operator, that is, Y=∑ Xi+ A, Y are output variable, XiFor child node input variable, A is local node variable；It goes to most
After the latter node, that is, initiate the summation that the flag value of all controllers can be obtained in node；
S704, initiate Node Controller the summation of flag value is judged, if flag summation be 0 if iteration ends,
Otherwise every cold controller according to calculated sample average and variance generate the PLR value of K*N Gaussian distributed, with
The more excellent PLR value combination of M parent is as new population.
Specifically, cold load factor-power performance curve are as follows:
Pchiller=a+b*PLR+c*PLR2+d*PLR3
Pchiller=e+f*PLR+g*PLR2
Wherein, a, b, c, d, e, f, g are fixed and invariable parameter, and PLR indicates the load factor value of cold, PchillerIndicate cold
The operation power of machine.
Specifically, objective function is to make cold system total work in parallel in the case where meeting system end refrigeration duty demand
Rate is minimum, specifically:
min(Wtotal)
s.t.0.3≤PLRi≤ 1 or PLRi=0
Wherein, WtotalFor the total energy consumption of cold in parallel, PLRiFor the load factor of i-th cold in cold system in parallel,
For the rated cooling capacity of i-th cold in cold system in parallel, QneedFor system end workload demand, R is cold system in parallel
Total number of units of middle cold.
Compared with prior art, the present invention at least has the advantages that
The present invention is based on the central air-conditioning parallel connection cold load optimal distribution methods of colony intelligence framework, ensure safety same
Under the premise of according to the demand of terminal temperature difference side, it is minimum with cold system total energy consumption in parallel based on the mathematical model of cold energy consumption
Optimizing is carried out for target, start and stop and the sharing of load of air-conditioning system water cooler, Ji Nengbao are controlled according to optimizing result
Demonstrate,prove the safe and reliable operation of user side, but can reasonable distribution load so that cold system in parallel under specific operating condition with compared with
Low power work, improves the working efficiency of cold, ensure that the total energy consumption of water cooler is down to most under secure conditions
It is low, reach energy-efficient purpose, reduces the operation energy consumption of system.
Further, distributed chaos Estimation of Distribution Algorithm is calculated by the way that chaotic mutation is added in traditional Estimation of Distribution Algorithm
Son increases the multifarious purpose of algorithm population to reach, expands the range that algorithm is searched in solution space, improve algorithm
Performance.
Further, generating chaos sequence as cold PLR value according to one-dimensional logistic model in step S1 is real number
Coding, overcomes the discontinuous disadvantage of binary coding, and consider cold manufacturer and think that cold load factor should be greater than
In 0.3 suggestion.
Further, the chaotic mutation operation of step S4 is overcome by carrying out corresponding mutation operation to individual in population
Common Estimation of Distribution Algorithm population diversity is insufficient, easily fall into local optimum and the shortcomings that Premature Convergence.
Further, step S5 has filtered out the part excellent individual in parent population, and thus establishes Gaussian Profile mould
Type achievees the purpose that population is constantly evolved to optimal value to generate individual of new generation.
Further, step S7, S8 sorts newly generated individual screening with parent excellent individual jointly, is to plant
Retain the information of parent excellent individual in group's evolutionary process.
Further, cold load factor-power-performance curve is the load factor value in order to enable algorithm according to each cold
Power consumption of each cold under different loads rate is calculated, consequently facilitating carrying out fitness value calculation.
Further, objective function is that cold system optimization in parallel adjusts the mathematical expression for operating purpose, and fitness letter
Number is accordingly converted by objective function to be obtained.
In conclusion cooperative cooperating of the present invention by each cold controller, using the thought of distributed computing, using compared with
Few computing resource completes the optimizing regulation task of cold system in parallel.The present invention in Estimation of Distribution Algorithm by being added
Chaotic mutation operation, improves the diversity of algorithm population, to expand search range of the algorithm in solution space, the calculation for being
Method is easier to acquire the optimum combination of each cold load factor under certain end load demand, so that it is excellent to reach performance cold system in parallel
Gesture improves working efficiency, reduces system energy consumption, energy saving purpose.
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Detailed description of the invention
Fig. 1 is cold colony intelligence control system schematic diagram of the invention；
Fig. 2 is sharing of load fitness curve；
Fig. 3 is cold system total energy consumption comparison diagram in parallel；
Fig. 4 is distributed chaos distribution estimation method flow chart of the invention.
Specific embodiment
Referring to Fig. 1, the present invention provides a kind of, the central air-conditioning parallel connection cold load optimal based on colony intelligence framework divides
Each cold in cold system in parallel is all arranged a controller and is attached thereto by method of completing the square, and by all control
Device is interconnected by actual physics topological connection relation to form colony intelligence network system；When a certain controller initiates adjusting task
When, controller is initiated in the cooperation of remaining controller and respective neighbours' controller carries out information exchange, is completed for the task of adjusting
The system operations of response realize the autonomous synergic adjustment operation of cold system in parallel；When system end gives a workload demand, and
When being sent to cold system in parallel, regulating calculation task is triggered, one group of load factor value is randomly generated in cold controller each first
As initial population, and according to built-in cold load factor-power-performance parameter of curve and rated cooling capacity parameter, calculate
Power consumption and refrigerating capacity under different loads rate；Each controller is obtained under the combination of different loads rate by information exchange later
The overall refrigerating effect and total power consumption of cold system in parallel；Fitness value is calculated further according to fitness function and carries out chaotic mutation
Operation；Obtain the sample of Gaussian Profile after the poor individual of superseded partial fitness using statistical analysis technique as parent population
Mean value and variance, and new population is generated with this Gaussian distribution model；Using new population and parent population as a population according to
Preceding method calculates fitness value, becomes progeny population after the poor individual of partial fitness in population is eliminated；So far it completes
Evolutionary process, progeny population according to cold system in parallel meet in the case where end load demand the smallest direction of energy consumption into
A generation is changed, has so constantly evolved until the sample variance that statistical analysis obtains is less than required precision or reaches maximum evolutionary generation
Afterwards, the maximum load factor of fitness in last generation population is combined as optimizing regulation as a result, and passing through controller control
Each cold completes adjustment process, to complete optimization distribution task.
Under normal circumstances, central air conditioner system is made of more water coolers, and specification is also to be not quite similar, and is mainly adopted
It is the control method of chilled water supply water temperature, if water cooler rated cooling capacity all in system is all the same, then respectively
Platform unit uniformly shares cooling capacity；If unit rated cooling capacity is not exactly the same, each unit is accounted for according to its rated cooling capacity
The ratio of operating unit overall refrigerating effect provides cooling capacity.Water cooler schedule model strategy proposed by the present invention, be
Under the basis of refrigerating capacity needed for solving end, according to relationship between cold power and PLR, water cooler total energy consumption target letter is established
Then number solves and obtains the load ratio that each water cooler is undertaken, so that the load to water cooler is allocated.
Cold load factor-power performance curve are as follows:
Pchiller=a+b*PLR+c*PLR2+d*PLR3
Pchiller=e+f*PLR+g*PLR2
Wherein, a, b, c, d, e, f, g are fixed and invariable parameter, and PLR indicates the load factor value of cold, PchillerIndicate cold
The operation power of machine.
The objective function of optimization method is to make cold system in parallel in the case where meeting system end refrigeration duty demand
General power is minimum, mathematical expression such as:
min(Wtotal)
s.t.0.3≤PLRi≤ 1 or PLRi=0
Wherein, constraint condition 0.3≤PLR≤1 allows for the performance of cold and the suggestion of cold manufacturer, each cold
The PLR of water dispenser group cannot be less than 0.3.
The distributed chaos distribution estimation method used optimizes distribution estimation method using chaotic mutation operation, and
The method after optimization is rewritten as distributed method on this basis.
Referring to Fig. 4, the step of distributed chaos distribution estimation method, is as follows:
S1, each cold controller press one-dimensional logistic model and generate N-dimensional chaos sequence as PLR value, and by PLR
< 0.3 value is set to 0；
One-dimensional logistic model are as follows:
αi+1=β αi(1-αi)
Wherein, to make the chaos sequence that there is aperiodicity to expand the traversal range of system to take β=4, α1It is 0 to 1
Interior random number.
S2, all cold controllers are according to respectively built-in cold load factor-power performance curve parameter and specified
Refrigerating capacity parameter calculates refrigerating capacity and power corresponding to respective N number of PLR value；
S3, all cold controllers combine down cold in parallel by being communicated to obtain all N group PLR with neighbours' controller
The overall refrigerating effect of system and corresponding system total power；
S4, every cold carry out chaotic mutation operation using N number of PLR value of the chaotic mutation operation to itself；
S401, fitness calculating is carried out to current population, and is ranked up from small to large by fitness, then by Population adaptation
Degree is allocated according to linear method, and Population Size is the linear fitness value fit'(i of i-th of individual in the population of N) as follows:
Fit'(i)=position (i)/N
Wherein, position (i) is the location of i individual after population is sorted from small to large by fitness, and N is population
Size.
S402, chaotic mutation radius is determined, individual lesser for fitness increases variation radius, and fitness is biggish a
Body reduces variation radius.
The chaotic mutation radius r (i) of i-th of individual in population are as follows:
R (i)=Δ (i) * (1-fit'(i))
Wherein, Δ (i) is variation step-length, and the size of the value influences the search performance of method, at the initial stage of execution, needs one
A biggish variation step-length, to expand search range；And in later stage of evolution, then a lesser variation step-length is needed, with reality
Existing decision variable carries out fining search in a small range of optimization solution, therefore, according to the real time information of population in evolutionary process
Carry out definitive variation step-length, the variation step-length of i-th of individual pop (i) in population:
In order to which ensuring method has certain randomness, when Δ (i) < ΔminWhen, enable Δ (i)=Δmin, with increasing method
Jump out the probability of locally optimal solution.
S403, chaotic mutation operation is executed to each individual, pop' is operated to the chaotic mutation of i-th of individual in population
(i) are as follows:
Pop'(i)=pop (i)+r (i) * 2 (1-2*A (i))
Wherein, A (i) is chaos sequence generated in step S1.
S404, fitness evaluation is carried out to the individual after execution chaotic mutation, if the fitness of new individual is individual greater than former,
Original PLR then is replaced with the PLR after variation, to obtain population of new generation.
Fitness evaluation is obtained according to fitness function:
S5, fitness evaluation is carried out to the PLR population after completion chaotic mutation operation, and from big to small according to fitness value
Sequence, M more excellent individuals are for statistical analysis before choosing, and find out its sample average and variance；
Statistical analysis is the mean μ that its Gaussian distribution model is found out according to more excellent individual specimeniAnd variances sigmai:
Wherein, pop (j) is the value of j-th of individual in more excellent individual specimen.
S6, all cold controllers judge stopping criterion for iteration, if this sample variance found out reaches precision
It is required that or reach maximum evolutionary generation, then flag signal flag is set 0, is otherwise set to 1；
S7, each cold controller initiate global read group total, iteration ends if being 0 if the summation of flag, and otherwise every
Cold controller according to calculated sample average and variance generate the PLR value of K*N Gaussian distributed, with M parent
More excellent PLR value combination is as new population；
S701, a certain cold controller initiate global read group total task to flag value；
S702, spanning tree is formed as root node using the controller for initiating task；
S703, all controllers in spanning tree top end start to calculate, brother of node parallel computation.Each node
From its child node receive data, wait collect it is complete after execute adduction calculate and be sent to his father's node.Each intra-node executes
Identical operator, that is, Y=∑ Xi+ A, Y are output variable, XiFor child node input variable, A is local node variable；It goes to most
After the latter node, that is, initiate the summation that the flag value of all controllers can be obtained in node；
S704, initiate Node Controller the summation of flag value is judged, if flag summation be 0 if iteration ends,
Otherwise every cold controller according to calculated sample average and variance generate the PLR value of K*N Gaussian distributed, with
The more excellent PLR value combination of M parent is as new population.
S8, cold controller and neighbours' controller carry out information exchange, carry out fitness evaluation to K*N+M PLR combination,
N number of more excellent individual is taken to be used as population of new generation, and return step S4.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being described and shown in usually here in attached drawing is real
The component for applying example can be arranged and be designed by a variety of different configurations.Therefore, below to the present invention provided in the accompanying drawings
The detailed description of embodiment be not intended to limit the range of claimed invention, but be merely representative of of the invention selected
Embodiment.Based on the embodiments of the present invention, those of ordinary skill in the art are obtained without creative efforts
The every other embodiment obtained, shall fall within the protection scope of the present invention.
Using Taibei hotel as research object, which is 450RT and two refrigerating capacity by two refrigerating capacitys
It is formed for the cold of 1000RT, each cold specific performance parameter is as shown in table 1:
Each cold performance parameter of table 1
It is now assumed that user side aggregate demand is 60%, i.e. 1740RT of overall refrigerating effect, normally-open two refrigerating capacitys are such as pressed
It can be met the requirements for 1000RT water cooler.Analysis through the invention constructs cold gunz as shown in Figure 1 and can control
System, and more optimal scheme is obtained using DCEDA optimizing, it is specific as follows:
The objective function of this suboptimization are as follows:
min(Wtotal)
s.t.0.3≤PLRi≤ 1 or PLRi=0
Fitness function are as follows:
By the emulation of distributed chaos distribution estimation method, specific step is as follows:
Step 1: four cold controllers generate respective 80 dimension chaos sequence as cold PLR value respectively；
Step 2: all cold controllers calculate refrigerating capacity and power corresponding to respective 80 PLR values；
Step 3: all cold controllers combine down parallel connection by being communicated to obtain all 80 groups of PLR with neighbours' controller
The overall refrigerating effect of cold system and corresponding system total power；
Step 4: every cold carries out chaotic mutation operation using 80 PLR values of the chaotic mutation operation to itself；
Step 5: carrying out fitness evaluation to the PLR population after chaotic mutation operates is completed, and 5 more excellent individuals is taken to carry out
Statistical analysis, finds out its sample average and variance；
Step 6: all cold controllers judge stopping criterion for iteration, if this sample variance σ < for finding out
0.01, or reach maximum evolutionary generation Gmax=50, then flag signal flag is set 0, is otherwise set to 1；
Step 7: each cold controller initiates global read group total, iteration ends if being 0 if the summation of flag, otherwise often
Platform cold controller according to calculated sample average and variance generate the PLR values of 120 Gaussian distributeds, with 5 fathers
In generation, more excellent PLR value was combined as new population；
Step 8: cold controller and neighbours' controller carry out information exchange, carry out fitness to 125 PLR combinations and comment
Valence takes 80 more excellent individuals to be used as population of new generation, and return step 4.
Show that fitness curve is as shown in Figure 2:
As can be seen from the figure distributed chaos distribution estimation method embodies in terms of the extreme value of a function optimizing of belt restraining
Preferable optimizing ability, convergence rate is also very fast, and fairly simple easy to operate.
The distribution such as table 2 of operation method taking-up three groups of data and original scheme four:
Sharing of load table after the optimization of table 2
The sharing of load of first three scheme runs cold system in parallel under low energy consumption as can be seen from the table, and side
The allocation result of case four is not satisfactory, and system total energy consumption is higher.Again by calculating comparison it can be concluded that, this cold system in parallel
Operating scheme relative to initial scheme can energy conservation 25.5% or so, from the point of view of air-conditioning system longtime running, it will right and wrong
The energy of Chang Keguan.
Referring to Fig. 3, scheme 1 is the obtained sharing of load of the method for the present invention as a result, scheme four is empirically
The obtained sharing of load result of method.Histogram is the load factor situation of each cold under different allocation plans, broken line in Fig. 3
Figure is the total energy consumption of system under different allocation plans.Although can be seen that the load factor value of each cold in scheme 1 not
It is identical to the greatest extent, but the total energy consumption of system is close, and respectively less than scheme four, energy-saving effect about 25%, therefore the method for the present invention will
The energy consumption that cold system in parallel can be greatly reduced, obtains considerable economic benefit.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press
According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention
Protection scope within.
Claims (10)
1. the central air-conditioning parallel connection cold load optimal distribution method based on colony intelligence framework, which is characterized in that in cold in parallel
A controller is all arranged in each cold in system, and all controllers press actual physics Topology connection in cold system in parallel
Relationship carries out interconnection and forms colony intelligence network system；When a certain controller initiates adjusting task, remaining controller cooperation hair
It plays controller and respective neighbours' controller carries out information exchange, complete corresponding system operations for the task of adjusting, realize simultaneously
Join the autonomous synergic adjustment operation of cold system；According to cold load factor-power-performance parameter of curve and rated cooling capacity parameter
Calculate the power consumption and refrigerating capacity under different loads rate；Then each controller obtains the combination of different loads rate by information exchange
Under cold system in parallel overall refrigerating effect and total power consumption；Fitness value is calculated according to fitness function and carries out chaos change
ETTHER-OR operation；Obtain the sample standard deviation of Gaussian Profile after the individual of superseded partial fitness difference using statistical analysis method as parent population
Value and variance, and new population is generated with this Gaussian distribution model；New population is counted with parent population as a population again
Fitness value is calculated, becomes progeny population after the individual of partial fitness difference in population is eliminated；So far an evolutionary process is completed,
Progeny population meets the smallest direction evolution generation of energy consumption in the case where end load demand according to cold system in parallel, so not
It is disconnected to evolve after statisticalling analyze obtained sample variance and being less than required precision or reach maximum evolutionary generation, by last generation kind
The maximum load factor combination of fitness is as optimizing regulation as a result, and controlling each cold completion adjusting by controller in group
Process completes optimization distribution.
2. the central air-conditioning parallel connection cold load optimal distribution method according to claim 1 based on colony intelligence framework,
It is characterized in that, specific step is as follows for distributed chaos distribution estimation method:
S1, each cold controller press one-dimensional logistic model and generate N-dimensional chaos sequence as PLR value, and by PLR < 0.3
Value to be set to 0, N be Population Size；
S2, all cold controllers calculate refrigerating capacity and power corresponding to respective N number of PLR value；
S3, all cold controllers combine down the total of cold system in parallel by being communicated to obtain N group PLR with neighbours' controller
Refrigerating capacity and corresponding system total power；
S4, every cold carry out chaotic mutation operation using N number of PLR value of the chaotic mutation operation to itself；
S5, to complete chaotic mutation operation after PLR population carry out fitness evaluation, and by population by fitness from big to small into
Row sequence, M individual is for statistical analysis before taking and finds out sample average and variance；
S6, all cold controllers judge stopping criterion for iteration, if this sample variance found out reaches required precision,
Or reach maximum evolutionary generation, then flag signal flag is set 0, is otherwise set to 1；
S7, each cold controller initiate global read group total, iteration ends if being 0 if the summation of flag, otherwise every cold
Controller according to calculated sample average and variance the PLR value of K*N Gaussian distributed is randomly generated, with M parent
As new population, K is the ratio of newborn progeny population quantity and parent population quantity for more excellent PLR value combination；
S8, cold controller and neighbours' controller carry out information exchange, carry out fitness evaluation to K*N+M PLR combination, and will
K*N+M individual is ranked up from big to small by fitness, is taken top n individual as population of new generation, is returned to S4.
3. the central air-conditioning parallel connection cold load optimal distribution method according to claim 2 based on colony intelligence framework,
It is characterized in that, in S1, one-dimensional logistic model are as follows:
αi+1=β αi(1-αi)
Wherein, αi+1For chaos sequence value caused by one-dimensional logistic model, β=4, α1For the random number in 0 to 1.
4. the central air-conditioning parallel connection cold load optimal distribution method according to claim 2 based on colony intelligence framework,
It is characterized in that, S4 specifically:
S401, fitness calculating is carried out to current population, and is ranked up from small to large by fitness, then population's fitness is pressed
It is allocated according to linear method, the linear fitness value fit'(i of i-th of individual in population) as follows:
Fit'(i)=position (i)/N
Wherein, position (i) is the location of i individual after population is sorted from small to large by fitness；
S402, chaotic mutation radius is determined, for linear fitness value fit'(i) small individual, it is arranged from small to large by fitness
The forward individual of sequence increases variation radius, linear fitness value fit'(i) big individual sorts rearward from small to large by fitness
Individual reduce variation radius；
S403, chaotic mutation operation is executed to each individual, pop'(i is operated to the chaotic mutation of i-th of individual in population) are as follows:
Pop'(i)=pop (i)+r (i) * 2 (1-2*A (i))
Wherein, pop (i) is i-th of individual in population, and r (i) is the chaotic mutation radius of i-th of individual in population, and A (i) is step
Chaos sequence generated in rapid S1；
S404, fitness evaluation is carried out to the individual after execution chaotic mutation, if the fitness value of new individual is individual greater than former,
Original PLR is replaced with the PLR after variation, obtains the combination of the big cold PLR of the overall fitness value of N group, i.e., population is according to simultaneously
Connection cold meets the smallest direction of energy consumption under conditions of end load demand and evolves forward a generation.
5. the central air-conditioning parallel connection cold load optimal distribution method according to claim 4 based on colony intelligence framework,
It is characterized in that, in S402, the chaotic mutation radius r (i) of i-th of individual in population are as follows:
R (i)=Δ (i) * (1-fit'(i))
Wherein, Δ (i) is variation step-length, is determined according to the real time information of population in evolutionary process, i-th of individual in population
The variation step-length of pop (i):
When Δ (i) < ΔminWhen, enable Δ (i)=Δmin, pop (j), pop (k) are a certain individual in population.
6. the central air-conditioning parallel connection cold load optimal distribution method according to claim 4 based on colony intelligence framework,
It is characterized in that, in S404, fitness evaluation is obtained according to fitness function minf:
Wherein, WtotalFor the total energy consumption of cold in parallel, penal is penalty factor,For i-th cold in cold system in parallel
Rated cooling capacity, QneedFor system end workload demand, PLRiFor the load factor of i-th cold in cold system in parallel, R is simultaneously
Join total number of units of cold in cold system.
7. the central air-conditioning parallel connection cold load optimal distribution method according to claim 2 based on colony intelligence framework,
It is characterized in that, in S5, the mean μ of its Gaussian distribution model is found out according to more excellent individual specimeniAnd variances sigmai:
Wherein, PLRjFor the value of j-th of individual in more excellent individual specimen, M is the quantity of more excellent individual.
8. the central air-conditioning parallel connection cold load optimal distribution method according to claim 2 based on colony intelligence framework,
It is characterized in that, S7 specifically:
S701, a certain cold controller initiate global read group total task to flag value；
S702, spanning tree is formed as root node using the controller for initiating task；
S703, all controllers in spanning tree top end start to calculate, brother of node parallel computation, each node from its
Child node receives data, wait collect it is complete after execute adduction and calculate and be sent to his father's node, each intra-node executes identical
Operator, that is, Y=∑ Xi+ A, Y are output variable, XiFor child node input variable, A is local node variable；Go to last
After a node, that is, initiate the summation that the flag value of all controllers can be obtained in node；
S704, initiate Node Controller the summation of flag value is judged, if flag summation be 0 if iteration ends, otherwise
Every cold controller according to calculated sample average and variance generate the PLR value of K*N Gaussian distributed, it is a with M
The more excellent PLR value combination of parent is as new population.
9. the central air-conditioning parallel connection cold load optimal distribution method according to claim 1 based on colony intelligence framework,
It is characterized in that, cold load factor-power performance curve are as follows:
Pchiller=a+b*PLR+c*PLR2+d*PLR3
Pchiller=e+f*PLR+g*PLR2
Wherein, a, b, c, d, e, f, g are fixed and invariable parameter, and PLR indicates the load factor value of cold, PchillerIndicate cold
Run power.
10. the central air-conditioning parallel connection cold load optimal distribution method according to claim 1 based on colony intelligence framework,
It is characterized in that, objective function is to make cold system total power in parallel in the case where meeting system end refrigeration duty demand most
It is small, specifically:
min(Wtotal)
s.t.0.3≤PLRi≤ 1 or PLRi=0
Wherein, WtotalFor the total energy consumption of cold in parallel, PLRiFor the load factor of i-th cold in cold system in parallel,For simultaneously
Join the rated cooling capacity of i-th cold in cold system, QneedFor system end workload demand, R is cold in cold system in parallel
Total number of units of machine.
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