CN105739308B - Power optimization control method and system applied to temperature control electric appliance - Google Patents
Power optimization control method and system applied to temperature control electric appliance Download PDFInfo
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Abstract
The invention provides a power optimization control method and a power optimization control system applied to a temperature control electric appliance, wherein the power optimization control method comprises the following steps: acquiring a required temperature parameter, a power price parameter and a working principle model parameter of the temperature control electric appliance which are preset in each preset time period within one day of working time; performing genetic algorithm optimization processing under a constraint condition set by adopting an economic index calculation formula and a satisfaction degree calculation formula according to a required temperature parameter, an electricity price parameter and a working principle model parameter of the temperature control electric appliance to obtain power values of the temperature control electric appliance in each preset time period of one day working time; and planning and setting the working power of the temperature control electric appliance according to the obtained power value. The scheme can dynamically and quantitatively analyze the relationship between the economy and the satisfaction of the temperature control electric appliance, and provides data support for the personalized operation of different users. The scheme has the function of peak clipping and valley filling by controlling the household temperature control electric appliance, and can alleviate the contradiction between supply and demand of electric energy to a certain extent.
Description
Technical field
The present invention relates to technical field of electric device control more particularly to a kind of power optimization controlling parties applied to temperature control electric appliance
Method and system.
Background technique
Universal and demand response mechanism implementation with advanced measuring system in electricity consumption side, in family's electricity system,
The advantage of active load gradually shows, and especially in the photovoltaic home system containing photovoltaic power generation equipment and Household accumulation pond, uses
Electric mode is more flexible.In photovoltaic home system, active load is referred to by carrying out intelligent control to electrical equipment, not
Change or less changes user to energy requirement under conditions of, change the electricity consumption time and power load size to cooperate power operation
The demand response of quotient.In general, active load mainly includes controllable load and can plan load, wherein controllable load is logical
Normal long-play, is influenced big by temperature and climatic factor, and power adjustable can be even interrupted intermittently；It can plan load one
As the inconvenient adjustment of power, but runing time can flexible arrangement in a certain range.
By integrating the history use information of weather information and user, the day of user can be formulated in advance for active load
Preceding electricity consumption plan reduces the electric cost of user to cooperate demand response.It is adjusted a few days ago currently, being applied to household electricity system
The method of degree plan is usually mainly to consider that household electricity spends under the premise of considering tou power price and control target to be main,
And be added the room temperature for characterizing user satisfaction difference or water temperature difference with electricity consumption cost, the target letter of optimal control is obtained with this
Number.But this method is when handling usage economy and the two indexs of user satisfaction only by the temperature difference and electricity cost phase
Add, this processing mode can make effect of optimization have one-sidedness, and for a user, it is sometimes desirable to know and use certain family
Meet certain economic goal when electric, how many satisfaction of having compromised as cost (or vice versa), so as to the diversification for user
Selection provides further data and supports, it is therefore desirable to which the economy and satisfaction that use to each household electrical appliances carry out quantitative, dynamic
Analysis.
Summary of the invention
The present invention provides a kind of power optimization control method and system applied to temperature control electric appliance, for solving the prior art
In cannot quantitatively, dynamically analyze each temperature control electric appliance user satisfaction and electricity consumption expenditure between relationship the problem of.
On the one hand, the present invention provides a kind of power optimization control method applied to temperature control electric appliance, comprising:
Obtaining temperature control electric appliance, preset demand temperature parameter, electricity price are joined in each preset time period within the darg time
Several and temperature control electric appliance working principle model parameter；
Referred to according to the working principle model parameter of the demand temperature parameter, electric price parameter and temperature control electric appliance using economy
Mark calculation formula carries out genetic algorithm optimization processing under the first sets of constraints and obtains intraday minimum economic value；
According to the working principle model parameter and minimum economic value of the demand temperature parameter, electric price parameter, temperature control electric appliance
Genetic algorithm optimization processing is carried out under the second sets of constraints using satisfaction calculation formula and obtains institute under different electricity costs
Corresponding satisfactory value and economic value；
Optimal economic value and best satisfactory value are determined using Rule of judgment according to economic value and satisfactory value is obtained；
Determine temperature control electric appliance in each preset time period of darg time according to optimal economic value and best satisfactory value
Performance number；
Planning setting is carried out according to operating power of the performance number of acquisition to temperature control electric appliance.
Preferably, the economic index calculation formula are as follows:Wherein, CehFor economy
Value, eb (h) are the electric price parameter of each preset time period, PehIt (h) is the operating power of each preset time period；
Preferably, first sets of constraints includes:
0≤Peh(h)≤Peh.max, wherein PehIt (h) is the operating power of each preset time period, Peh.maxFor temperature control electric appliance
Default maximum service rating；
Wherein,For demand temperature parameter in preset time period,For the permitted maximum temperature values of temperature control electric appliance, TehIt (h) is actual temperature parameter in preset time period；
And the working principle model parameter of the temperature control electric appliance.
Preferably, the satisfaction calculation formula are as follows:Wherein, SehFor satisfactory value,For demand temperature parameter, T in preset time periodehIt (h) is actual temperature in preset time period,
For the quadratic function of actual temperature and demand temperature difference；
Preferably, second sets of constraints includes:
0≤Peh(h)≤Peh.max, wherein PehIt (h) is the operating power of each preset time period, Peh.maxFor temperature control electric appliance
Default maximum service rating；
Wherein,For demand temperature parameter in preset time period,For the permitted maximum temperature values of temperature control electric appliance, TehIt (h) is actual temperature parameter in preset time period；
Wherein, eb (h) is the electric price parameter of each preset time period, Peh(h)
For the operating power of each preset time period, Ceh.minFor minimum economic value, α is the constraint factor relaxed to economic index, α=
(α1,α2…,αn)；
And the working principle model parameter of the temperature control electric appliance.
Preferably, the Rule of judgment are as follows: if meeting max (Ceh-Ceh.max)(Seh-Seh.min), then current economic value and work as
Preceding satisfactory value is optimal economic value and best satisfactory value.
Preferably,
On the other hand, the present invention provides a kind of power optimization control system applied to temperature control electric appliance, comprising:
Parameter acquisition module, for obtaining temperature control electric appliance preset demand in each preset time period within the darg time
The working principle model parameter of temperature parameter, electric price parameter and temperature control electric appliance；
First optimization module, the working principle model for temperature parameter according to demand, electric price parameter and temperature control electric appliance are joined
It is intraday most that number uses economic index calculation formula to carry out genetic algorithm optimization processing acquisition under the first sets of constraints
Small economy value；
Second optimization module, the working principle model parameter for temperature parameter according to demand, electric price parameter, temperature control electric appliance
Satisfaction calculation formula is used to carry out genetic algorithm optimization processing acquisition under the second sets of constraints with minimum economic value different
Corresponding satisfactory value and economic value under electricity price；
Judgment module, for determining optimal economic value using Rule of judgment according to acquisition economic value and satisfactory value and most preferably expiring
Meaning value；
Power acquisition module, for determining temperature control electric appliance in the darg time according to optimal economic value and best satisfactory value
Each preset time period performance number；
Regulate and control module, for carrying out planning setting to the operating power of temperature control electric appliance according to the performance number of acquisition.
As shown from the above technical solution, this programme dynamically, quantitatively analyzes the economy and satisfaction of temperature control electric appliance
Between relationship, provide data for the personalized operation of different user and support.For tou power price, the program can be adjusted effectively
The working hour of section temperature control electric appliance shifts to low rate period, for electric heater, in night hot water temperature and hot water amount
The peak period of demand, suitably reduces power, sacrifices a part of user satisfaction, to reach the target for reducing electricity cost.
This programme has the function of peak load shifting, can mitigate electric energy to a certain extent by controlling family's temperature control electric appliance
Imbalance between supply and demand.
Detailed description of the invention
Fig. 1 is the flow diagram for the power optimization control method that the embodiment of the present invention 1 provides；
Fig. 2 is the structural schematic diagram for the power optimization control system that the embodiment of the present invention 2 provides.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
Fig. 1 shows a kind of power optimization control method applied to temperature control electric appliance of the offer of the embodiment of the present invention 1, packet
It includes:
S1, temperature control electric appliance preset demand temperature parameter, electricity price in each preset time period within the darg time are obtained
The working principle model parameter of parameter and temperature control electric appliance.In this step, the demand temperature parameter, which can be, is stored in advance
Course of work historical data.The electric price parameter is the marketing data according to scale fee system.The work of the temperature control electric appliance
Making principle model parameter is the parameter establishing its moving model according to the working principle of temperature control electric appliance and obtaining, and is known ginseng
Number.No longer the foundation of its parameter is described in detail herein.The present embodiment is by taking electric heater as an example, according to the work of electric heater
Its moving model is established as principle, as follows:
cρV0T (h+1)=(1- βw){ψPeh(h)Δh+cρ[(V0-Feh(h))T(h)+Feh(h)Tr]} (1)
Wherein, c is the specific heat capacity for indicating water；The density of ρ expression water；V0Indicate the volume of water tank；T (h) indicates h period water
Water temperature value in case；Ts.ehIt (h) is user to the set water temperature in period h；βwIndicate water tank energy dissipation coefficient；ψ indicates electricity
Transformation coefficient between energy (kilowatt hour) and thermal energy (joule), being worth is 3.6 × 106；Peh(h) indicate that the power of heater water tank is (right
In the electric heater of operating power constant, which is regarded as the mean power in one hour)；TrFor the tap water for injecting water tank
Temperature；FehIt (h) is demand water consumption of the user in period h；Fa.ehIt (h) is actual used water amount of the user in period h.
It is the Spot Price parameter in the time shown in table 1:
Table 2 is the relevant parameter of electric heater:
| Power bracket | 0~3kW |
| Set water temperature DEG C | Ts.eh=[0 00000 30 30 000 30 30 30 000000 50 50 50 0] |
| Set water consumption L | Feh=[0 00000 20 20 000 25 10 15 000000 50 60 20 0] |
| Other parameters | V0=80L, Tmax=75 DEG C, Tr=15 DEG C, βw=5% |
As can be seen from Table 1 and Table 2, the present embodiment within the time using each hour as a period into
Row setting, i.e. 24 periods.
The working principle model parameter of S2, according to demand temperature parameter, electric price parameter and temperature control electric appliance is referred to using economy
Mark calculation formula optimizes processing under the first sets of constraints and obtains intraday minimum economic value.In this step, first
First processing individually is optimized by optimization aim of economic index.The economic index calculation formula are as follows:
Wherein, CehFor economic value, eb (h) is the electric price parameter of each preset time period, PehIt (h) is each preset time period
Operating power.In optimization process, need to optimize processing under the first sets of constraints using genetic algorithm.
First sets of constraints includes:
0≤Peh(h)≤Peh.max (4)
Wherein, PehIt (h) is the operating power of each preset time period, Peh.maxFor the default maximum service rating of temperature control electric appliance.
Wherein,For demand temperature parameter in preset time period,For the permitted maximum temperature of temperature control electric appliance
Angle value, TehIt (h) is actual temperature parameter in preset time period.
Meanwhile first sets of constraints further includes the working principle model parameter of the temperature control electric appliance.Due to electric appliance
For temperature control electric appliance, temperature parameter can be related in working principle model parameter, therefore the working principle model parameter of temperature control electric appliance is made
Constraint condition can be played to temperature parameter for constraint condition.
It should be noted that it is above-mentioned with formula (3) be objective function, with formula (4), formula (5) and surveyed temperature control electric appliance
Working principle model be constraint condition, economic value index is optimized using genetic algorithm, it is final to obtain minimum economic value.
In the optimization process of this step, the difference of economic value index calculation formula and constraint condition is from existing different place.
And be a mature treatment process for those skilled in the art for how to optimize process by genetic algorithm, herein
It repeats no more.
S3, according to demand temperature parameter, electric price parameter, the working principle model parameter of temperature control electric appliance and minimum economic value are adopted
Processing is optimized under the second sets of constraints with satisfaction calculation formula and obtains satisfaction corresponding under different electricity costs
Value and economic value.In this step, using the user satisfaction of temperature control electric appliance as optimization aim, at the same it is minimum by being obtained in step S2
Economic value and other parameters are gradually relaxed the constraint to economy by certain step-length, can be obtained using genetic algorithm as constraint
To different electricity consumptions spend under each preset time period of temperature control electric appliance in user satisfaction and corresponding economic value.Optimizing
Cheng Zhong needs satisfaction calculation formula and the second sets of constraints, as follows:
The satisfaction calculation formula are as follows:
Wherein, SehFor satisfactory value,For demand temperature parameter, T in preset time periodeh(h) in preset time period
Actual temperature parameter,For the quadratic function of actual temperature and demand temperature difference.Work as actual water temperature
When reaching (or being more than) demand water temperature, function reaches maximum value, when actual water temperature is lower than the minimum water temperature requirement at the momentWhen, function reaches minimum value 0, for example, the function may be configured as following formula in actually calculating:
As actual water temperature (i.e. Δ T equal with set water temperatureeh=0) (situation when or actual water temperature is greater than set water temperature
Under, leaving water temperature can be adjusted by increasing cold water), Satisfaction index reaches maximum value 10；When actual water temperature is less than setting
Water temperature, satisfaction will be less than 10, and with the reduction of water temperature at this time, and satisfaction will be reduced by the form of quadratic function, work as Δ
TehWhen=10, satisfaction reaches minimum value 0.
Second sets of constraints includes:
0≤Peh(h)≤Peh.max (8)
Wherein, PehIt (h) is the operating power of each preset time period, Peh.maxFor the default maximum service rating of temperature control electric appliance.
Wherein,For demand temperature parameter in preset time period,For temperature control electric appliance predetermined maximum temperature
Value.
Wherein, eb (h) is the electric price parameter of each preset time period, PehIt (h) is the operating power of each preset time period,
Ceh.minFor minimum economic value, α is the constraint factor relaxed to economic index, α=(α1,α2…,αn).It should be noted that
For the number n of α, can specifically it determine as the case may be.Because of the value of α of every variation, according to S=maxSehIt can obtain
A maximum satisfactory value is obtained, all maximum satisfactory values of acquisition can go to judge best satisfaction by the Rule of judgment of later step
Value.Theoretically, the number of α is more, and result obtained in subsequent step is more accurate, but asks in practical calculating in view of to reduce
The required precision for solving time used and optimization problem is not very high, then chooses suitable number of α value according to required problem.
Meanwhile first sets of constraints further includes the working principle model parameter of the temperature control electric appliance.Due to electric appliance
For temperature control electric appliance, temperature parameter can be related in working principle model parameter, therefore the working principle model parameter of temperature control electric appliance is made
Constraint condition can be played to temperature parameter for constraint condition.
It should be noted that it is above-mentioned with formula (6) be objective function, with formula (7), formula (8), formula (9), formula
(10) and the working principle model of surveyed temperature control electric appliance is constraint condition, is optimized using genetic algorithm to economic value index,
The final user satisfaction obtained in each preset time period of temperature control electric appliance and corresponding economic value.In the optimization process of this step
In, the difference of economic value index calculation formula and constraint condition is from existing different place.And for how to pass through heredity
It is a mature treatment process for those skilled in the art that algorithm, which optimizes process, and details are not described herein.
S4, optimal economic value and best satisfactory value are determined using Rule of judgment according to the economic value and satisfactory value of acquisition.?
In this step, need to utilize max (C according to step S3 economic value obtained and satisfactory valueeh-Ceh.max)(Seh-Seh.min) carry out
Judge to determine optimal economic value and best satisfactory value.
S5, determine temperature control electric appliance in each preset time period of darg time according to optimal economic value and best satisfactory value
Performance number.It should be noted that in the optimization process in step S3, before obtaining economic value and satisfactory value really
The performance number of each preset time period corresponding to one group of economic value and satisfactory value is determined.That is a pair of of economic value and satisfaction
Value is to calculate to obtain by the performance number after determination.
S6, planning setting is carried out to the operating power of temperature control electric appliance according to the performance number of acquisition.In this step, to temperature control
Electric appliance is configured according to the performance number of acquisition, and temperature control electric appliance is made to work according to the performance number on each period.
It should be noted that in above-mentioned all steps, the actual temperature parameter being related to is in step implementation procedure
In, it is obtained according to calculation formula, is not related to that temperature is gone to acquire in real time.
Fig. 2 shows a kind of power optimization control system applied to temperature control electric appliance that the embodiment of the present invention 2 provides, packets
It includes:
Parameter acquisition module, for obtaining temperature control electric appliance preset demand in each preset time period within the darg time
The working principle model parameter of temperature parameter, electric price parameter and temperature control electric appliance.
First optimization module, the working principle model for temperature parameter according to demand, electric price parameter and temperature control electric appliance are joined
It is intraday most that number uses economic index calculation formula to carry out genetic algorithm optimization processing acquisition under the first sets of constraints
Small economy value.
Second optimization module, the working principle model parameter for temperature parameter according to demand, electric price parameter, temperature control electric appliance
Satisfaction calculation formula is used to carry out genetic algorithm optimization processing acquisition under the second sets of constraints with minimum economic value different
Corresponding satisfactory value and economic value under electricity price.
Judgment module, for determining optimal economic value using Rule of judgment according to acquisition economic value and satisfactory value and most preferably expiring
Meaning value.
Power acquisition module, for determining temperature control electric appliance in the darg time according to optimal economic value and best satisfactory value
Each preset time period performance number.
Regulate and control module, for carrying out planning setting to the operating power of temperature control electric appliance according to the performance number of acquisition.
Due to above system be based on the basis of the control method, this system is in working principle and above-mentioned control
The principle of method is identical, and details are not described herein.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments mean it is of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
Meaning one of can in any combination mode come using.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
Those of ordinary skill in the art will appreciate that: the above embodiments are only used to illustrate the technical solution of the present invention., and
It is non-that it is limited；Although present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art
It is understood that it is still possible to modify the technical solutions described in the foregoing embodiments, either to part of or
All technical features are equivalently replaced；And these are modified or replaceed, it does not separate the essence of the corresponding technical solution this hair
Bright claim limited range.
Claims (7)
1. a kind of power optimization control method applied to temperature control electric appliance characterized by comprising
Temperature control electric appliance preset demand temperature parameter, electric price parameter in each preset time period within the darg time are obtained, with
And the working principle model parameter of temperature control electric appliance；
Economic index meter is used according to the working principle model parameter of the demand temperature parameter, electric price parameter and temperature control electric appliance
It calculates formula and carries out the intraday minimum economic value of genetic algorithm optimization processing acquisition under the first sets of constraints；
It is used according to the demand temperature parameter, electric price parameter, the working principle model parameter of temperature control electric appliance and minimum economic value
Satisfaction calculation formula carries out corresponding under the different electricity costs of genetic algorithm optimization processing acquisition under the second sets of constraints
Satisfactory value and economic value；
Optimal economic value and best satisfactory value are determined using Rule of judgment according to economic value and satisfactory value is obtained；
Determine temperature control electric appliance in the power of each preset time period of darg time according to optimal economic value and best satisfactory value
Value；
Planning setting is carried out according to operating power of the performance number of acquisition to temperature control electric appliance；
Second sets of constraints includes:
0≤Peh(h)≤Peh.max, wherein PehIt (h) is the operating power of each preset time period, Peh.maxFor the default of temperature control electric appliance
Maximum service rating；
Wherein,For demand temperature parameter in preset time period,For
Temperature control electric appliance predetermined maximum temperature value, TehIt (h) is actual temperature parameter in preset time period；
Wherein, eb (h) is the electric price parameter of each preset time period, PehIt (h) is each
The operating power of preset time period, Ceh.minFor minimum economic value, α is the constraint factor relaxed to economic index, α=(α1,
α2…,αn)；
And the working principle model parameter of the temperature control electric appliance.
2. power optimization control method according to claim 1, which is characterized in that the economic index calculation formula
Are as follows:Wherein, CehFor economic value, eb (h) is the electric price parameter of each preset time period, Peh(h)
For the operating power of each preset time period.
3. power optimization control method according to claim 1, which is characterized in that first sets of constraints includes:
0≤Peh(h)≤Peh.max, wherein PehIt (h) is the operating power of each preset time period, Peh.maxFor the default of temperature control electric appliance
Maximum service rating；
Wherein,For demand temperature parameter in preset time period,
For the permitted maximum temperature values of temperature control electric appliance, TehIt (h) is actual temperature parameter in preset time period；
And the working principle model parameter of the temperature control electric appliance.
4. power optimization control method according to claim 1, which is characterized in that the satisfaction calculation formula are as follows:
Wherein, SehFor satisfactory value,For demand temperature in preset time period
Parameter, TehIt (h) is actual temperature parameter in preset time period,For about actual temperature and demand temperature
The quadratic function of the difference of degree.
5. power optimization control method according to claim 1, which is characterized in that the Rule of judgment are as follows: if meeting max
(Ceh-Ceh.max)(Seh-Seh.min), then current economic value and current satisfactory value are optimal economic value and best satisfactory value.
6. power optimization control method according to claim 4, which is characterized in that
7. a kind of power optimization control system applied to temperature control electric appliance characterized by comprising
Parameter acquisition module, for obtaining temperature control electric appliance preset demand temperature in each preset time period within the darg time
The working principle model parameter of parameter, electric price parameter and temperature control electric appliance；
First optimization module, the working principle model parameter for temperature parameter according to demand, electric price parameter and temperature control electric appliance are adopted
Genetic algorithm optimization processing is carried out under the first sets of constraints with economic index calculation formula and obtains intraday minimum warp
Ji value；
Second optimization module, for temperature parameter according to demand, electric price parameter, the working principle model parameter of temperature control electric appliance and most
Small economy value carries out genetic algorithm optimization processing using satisfaction calculation formula under the second sets of constraints and obtains different electricity consumptions
Corresponding satisfactory value and economic value under expense；
Judgment module, for determining optimal economic value and best satisfaction using Rule of judgment according to acquisition economic value and satisfactory value
Value；
Power acquisition module, for determining that temperature control electric appliance is each in the darg time according to optimal economic value and best satisfactory value
The performance number of preset time period；
Regulate and control module, for carrying out planning setting to the operating power of temperature control electric appliance according to the performance number of acquisition；
Second sets of constraints includes:
0≤Peh(h)≤Peh.max, wherein PehIt (h) is the operating power of each preset time period, Peh.maxFor the default of temperature control electric appliance
Maximum service rating；
Wherein,For demand temperature parameter in preset time period,
For temperature control electric appliance predetermined maximum temperature value, TehIt (h) is actual temperature parameter in preset time period；
Wherein, eb (h) is the electric price parameter of each preset time period, PehIt (h) is each
The operating power of preset time period, Ceh.minFor minimum economic value, α is the constraint factor relaxed to economic index, α=(α1,
α2…,αn)；
And the working principle model parameter of the temperature control electric appliance.
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| CN107965919A (en) * | 2017-11-17 | 2018-04-27 | 昆山伊斯科特电子科技有限公司 | A kind of storage-type electric water heater Time-sharing control method |
| CN113608446B (en) * | 2021-09-10 | 2023-09-05 | 广东电网有限责任公司 | Building group demand response optimization scheduling method, device, terminal equipment and medium |
| CN113872215B (en) * | 2021-09-26 | 2024-01-09 | 国网电力科学研究院武汉能效测评有限公司 | Household appliance load optimization control system and control method based on demand response |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3988853B2 (en) * | 2000-04-18 | 2007-10-10 | 伸哉 小原 | Energy supply system control device and control method thereof |
| CN102193550B (en) * | 2011-05-31 | 2013-02-27 | 北京许继电气有限公司 | Networked intelligent household electricity optimizing control method |
| CN102629340B (en) * | 2012-03-06 | 2015-04-08 | 江苏节安得能源科技有限公司 | Energy efficiency artificial intelligence analysis system |
| JP2014170432A (en) * | 2013-03-05 | 2014-09-18 | Nihon Univ | Home appliances operation optimization system |
| CN103235980B (en) * | 2013-04-08 | 2017-03-22 | 广东电网公司信息中心 | Power consumption management off-peak strategy optimization method based on multi-objective programming |
| US20150378381A1 (en) * | 2014-06-30 | 2015-12-31 | Qualcomm Incorporated | Systems and methods for energy cost optimization |
| CN104200297B (en) * | 2014-07-11 | 2018-04-20 | 浙江大学 | The energy-optimised dispatching method of household mixed power supply system under Spot Price environment |
| CN105184408B (en) * | 2015-09-14 | 2019-08-20 | 上海上塔软件开发有限公司 | Power-economizing method based on electric appliance effectiveness classification and user power utilization Activity recognition |
-
2016
- 2016-02-01 CN CN201610069167.2A patent/CN105739308B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 基于Queen_bee进化的遗传算法在电力系统经济调度中应用;秦梁栋;《电力自动化设备》;20050531;第64-66页 * |
| 面向家庭智能用电的用电任务调度优化;陆青 等;《华东电力》;20140531;第816-821页 * |
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