CN107726533A - A kind of air conditioner load oscillation of power suppresses control method - Google Patents
A kind of air conditioner load oscillation of power suppresses control method Download PDFInfo
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- CN107726533A CN107726533A CN201710985076.8A CN201710985076A CN107726533A CN 107726533 A CN107726533 A CN 107726533A CN 201710985076 A CN201710985076 A CN 201710985076A CN 107726533 A CN107726533 A CN 107726533A
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- air conditioner
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- conditioner load
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Abstract
The invention discloses a kind of air conditioner load oscillation of power to suppress control method, and step includes：The air conditioner load of all control to be suppressed is divided into M groups, every group of air conditioner load for including identical quantity, and M group air conditioner loads are made into priority ranking；Selected according to priority orders and close the one of which of M group air conditioner loads, h performs a secondary control at regular intervals, until oscillation of power scope is in the range of the stable threshold of setting.The control method of the oscillation of power can significantly improve control effect, improve the robustness of control.
Description
Technical field
Vibrated the invention provides one kind and suppress control method, especially a kind of air conditioner load oscillation of power suppresses controlling party
Method.
Background technology
When implementing to control to a large amount of air conditioner loads, the diversity of air conditioner load is easily destroyed, can be drawn after control terminates
The aggregate power vibration for sending out air conditioner load a large amount of, in order to avoid the vibration of air conditioner load aggregate power as far as possible, it is necessary to terminate in control
Rational oscillation of power is taken air conditioner load to stabilize control strategy afterwards.
The content of the invention
The technical problem to be solved in the present invention is when implementing to control to a large amount of air conditioner loads, is easily destroyed air conditioner load
Diversity, after control terminates the aggregate power of a large amount of air conditioner loads can be triggered to vibrate.
In order to solve the above-mentioned technical problem, the invention provides a kind of air conditioner load oscillation of power to suppress control method, step
Suddenly include：
Step 1, the air conditioner load of all controls to be suppressed is divided into M groups, every group of air conditioner load for including identical quantity, and
M group air conditioner loads are made into priority ranking；
Step 2, selected according to priority orders and close the one of which of M group air conditioner loads, h is performed at regular intervals
One secondary control, until oscillation of power scope is in the range of the stable threshold of setting.
As the further limits scheme of the present invention, the number M and time interval h of packet, which are utilized, is based on genetic algorithm optimization
Obtain.
As the further limits scheme of the present invention, using number M and the time that packet is obtained based on genetic algorithm optimization
Interval h's concretely comprises the following steps：
Step a, an initial population is randomly generated, the individual encoded by M and h of certain amount is included in population；
Step b, the parameter M and h corresponding to each individual are substituted into object function, calculated according to object function every in population
Individual fitness；
Step c, the survival of the fittest, the higher individual of fitness is selected, eliminate the relatively low individual of fitness；
Step d, crossover operation is performed, randomly choose two individuals, its gene is swapped by the crossover probability of setting；
Step e, mutation operation is performed, randomly choose an individual, row variation is entered to its gene by the mutation probability of setting；
Step f, repeat step b~step e, until reaching maximum iteration.
As the further limits scheme of the present invention, using number M and the time that packet is obtained based on genetic algorithm optimization
Object function when being spaced h is defined as the form of the integrated square error of hunting power：
In formula, Δ P is the difference of hunting power and steady state power, and T is the time of simulation run.
The beneficial effects of the present invention are：(1) selected by priority orders and close wherein the one of M group air conditioner loads
Group, it is substantially achieved air conditioner load oscillation of power and stabilizes；(2) control effect can be significantly improved, improves the robustness of control.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the simulation result figure of the present invention.
Embodiment
As shown in figure 1, the invention provides a kind of air conditioner load oscillation of power to suppress control method, the control of oscillation of power
What strategy was taken is a kind of central controlled framework, it is necessary to which controller is had time by the two-way communication with air conditioner load, grasp
The state of temperature information of load is adjusted, specific vibration, which suppresses control method step, to be included：
Step 1, the air conditioner load of all controls to be suppressed is divided into M groups, every group of air conditioner load for including identical quantity, and
M group air conditioner loads are made into priority ranking；
Step 2, selected according to priority orders and close the one of which of M group air conditioner loads, h is performed at regular intervals
One secondary control, until oscillation of power scope is in the range of the stable threshold of setting.
In above control strategy, the number M and time interval h of packet value are extremely important to control result.Packet
Number M and time interval h using being obtained based on genetic algorithm optimization, pass through and calculate, in optimization process, object function definition
For the integrated square error (Integrated Square Error, abbreviation ISE) of hunting power, by performing, selecting, making a variation
Deng operation, until obtaining optimal M and h.The number M and time interval h of packet utilize the tool obtained based on genetic algorithm optimization
Body step is：
Step a, an initial population is randomly generated, the individual encoded by M and h of certain amount is included in population；
Step b, the parameter M and h corresponding to each individual are substituted into object function, calculated according to object function every in population
Individual fitness；
Step c, the survival of the fittest, the higher individual of fitness is selected, eliminate the relatively low individual of fitness；
Step d, crossover operation is performed, randomly choose two individuals, its gene is swapped by the crossover probability of setting；
Step e, mutation operation is performed, randomly choose an individual, row variation is entered to its gene by the mutation probability of setting；
Step f, repeat step b~step e, until reaching maximum iteration.
Object function when using number M and time interval h that packet is obtained based on genetic algorithm optimization is defined as shaking
Swing the form of the integrated square error (ISE) of power：
In formula, Δ P is the difference of hunting power and steady state power, and T is the time of simulation run.
As shown in Fig. 2 using 10000 air-conditionings as research object, sample calculation analysis is carried out, is determined first using genetic algorithm empty
The packet number M and time interval h of tune, it is assumed that optimum results M=7, h=5min, in oscillation of power control process is stabilized,
Each state of 10000 air-conditionings is divided into 7 groups, every the 5min times by one of which be in "On" state air-conditioning close,
By taking 10000 air-conditionings the strategy of additional control it can be seen from simulation result Fig. 2, can suppress to cut empty after load
The oscillation of power of tune.
Claims (4)
1. a kind of air conditioner load oscillation of power suppresses control method, it is characterised in that step includes：
Step 1, the air conditioner load of all controls to be suppressed is divided into M groups, every group of air conditioner load for including identical quantity, and by M
Group air conditioner load makees priority ranking；
Step 2, selected according to priority orders and close the one of which of M group air conditioner loads, h is performed once at regular intervals
Control, until oscillation of power scope is in the range of the stable threshold of setting.
2. air conditioner load oscillation of power according to claim 1 suppresses control method, it is characterised in that the number M of packet
Utilize with time interval h and obtained based on genetic algorithm optimization.
3. air conditioner load oscillation of power according to claim 2 suppresses control method, it is characterised in that using based on heredity
Algorithm optimization obtains concretely comprising the following steps for the number M and time interval h of packet：
Step a, an initial population is randomly generated, the individual encoded by M and h of certain amount is included in population；
Step b, the parameter M and h corresponding to each individual are substituted into object function, calculated according to object function in population per each and every one
The fitness of body；
Step c, the survival of the fittest, the higher individual of fitness is selected, eliminate the relatively low individual of fitness；
Step d, crossover operation is performed, randomly choose two individuals, its gene is swapped by the crossover probability of setting；
Step e, mutation operation is performed, randomly choose an individual, row variation is entered to its gene by the mutation probability of setting；
Step f, repeat step b~step e, until reaching maximum iteration.
4. air conditioner load oscillation of power according to claim 3 suppresses control method, it is characterised in that using based on heredity
Object function when algorithm optimization obtains the number M and time interval h of packet is defined as the integrated square error of hunting power
Form：
<mrow>
<mi>M</mi>
<mi>i</mi>
<mi>n</mi>
<mi> </mi>
<mi>I</mi>
<mi>S</mi>
<mi>E</mi>
<mo>=</mo>
<msubsup>
<mo>&Integral;</mo>
<mn>0</mn>
<mi>T</mi>
</msubsup>
<msup>
<mi>&Delta;P</mi>
<mn>2</mn>
</msup>
<mi>d</mi>
<mi>t</mi>
</mrow>
In formula, Δ P is the difference of hunting power and steady state power, and T is the time of simulation run.
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| CN201710985076.8A CN107726533B (en) | 2017-10-20 | 2017-10-20 | Air conditioner load power oscillation suppression control method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109698510A (en) * | 2019-01-25 | 2019-04-30 | 国网江苏省电力有限公司电力科学研究院 | Inhibit the control method of low-frequency oscillation of electric power system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001227794A (en) * | 2000-02-16 | 2001-08-24 | Daikin Ind Ltd | Method of estimating air-conditioning load, and method of controlling regenerative air-conditioning system, and these devices |
| CN104214912A (en) * | 2014-09-24 | 2014-12-17 | 东南大学 | Aggregation air conditioning load scheduling method based on temperature set value adjustment |
| CN104566868A (en) * | 2015-01-27 | 2015-04-29 | 徐建成 | Central air-conditioning control system and control method thereof |
| CN104636987A (en) * | 2015-02-06 | 2015-05-20 | 东南大学 | Dispatching method for power network load with extensive participation of air conditioner loads of institutional buildings |
| CN106849062A (en) * | 2015-05-14 | 2017-06-13 | 南通大学 | Reduce system cost based on electric energy close friend's air conditioner load side active demand strategy |
| CN106907828A (en) * | 2017-02-21 | 2017-06-30 | 国网山东省电力公司电力科学研究院 | A kind of dispersion modulator approach of air conditioner load group response frequency |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
-
2017
- 2017-10-20 CN CN201710985076.8A patent/CN107726533B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001227794A (en) * | 2000-02-16 | 2001-08-24 | Daikin Ind Ltd | Method of estimating air-conditioning load, and method of controlling regenerative air-conditioning system, and these devices |
| CN104214912A (en) * | 2014-09-24 | 2014-12-17 | 东南大学 | Aggregation air conditioning load scheduling method based on temperature set value adjustment |
| CN104566868A (en) * | 2015-01-27 | 2015-04-29 | 徐建成 | Central air-conditioning control system and control method thereof |
| CN104636987A (en) * | 2015-02-06 | 2015-05-20 | 东南大学 | Dispatching method for power network load with extensive participation of air conditioner loads of institutional buildings |
| CN106849062A (en) * | 2015-05-14 | 2017-06-13 | 南通大学 | Reduce system cost based on electric energy close friend's air conditioner load side active demand strategy |
| CN106907828A (en) * | 2017-02-21 | 2017-06-30 | 国网山东省电力公司电力科学研究院 | A kind of dispersion modulator approach of air conditioner load group response frequency |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
Non-Patent Citations (1)
| Title |
|---|
| 高赐威,李倩玉,李扬: "基于DLC的空调负荷双层优化调度和控制策略", 《中国电机工程学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109698510A (en) * | 2019-01-25 | 2019-04-30 | 国网江苏省电力有限公司电力科学研究院 | Inhibit the control method of low-frequency oscillation of electric power system |
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