CN109827310A - A kind of residual air-conditioning load group polymerization model method for building up - Google Patents
A kind of residual air-conditioning load group polymerization model method for building up Download PDFInfo
- Publication number
- CN109827310A CN109827310A CN201910098673.8A CN201910098673A CN109827310A CN 109827310 A CN109827310 A CN 109827310A CN 201910098673 A CN201910098673 A CN 201910098673A CN 109827310 A CN109827310 A CN 109827310A
- Authority
- CN
- China
- Prior art keywords
- residual air
- conditioning load
- polymerization model
- air
- conditioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a kind of residual air-conditioning load group polymerization model method for building up, under the support of non-intruding technology, by obtaining residual air-conditioning load history data and outdoor temperature, correlation properties are run in conjunction with air conditioner load, establish residual air-conditioning load group's polymerization model, and air conditioner load polymerization model parameter is obtained with parameter identification method, calculation amount is greatly reduced, while not being related to the privacy of user.
Description
Technical field
The present invention relates to a kind of residual air-conditioning load group polymerization model method for building up, belong to Power System Intelligent electricity consumption neck
Domain.
Background technique
Energy technology and information technology have been merged in energy internet and smart grid development, can support that indirect is uncertain
Distributed energy accesses on a large scale, and to power grid operation, more stringent requirements are proposed；On the other hand, economic fast development and
The continuous improvement of Living consumption keeps peak load constantly soaring, causes temporary electricity in short supply, to electric system safety,
Economical operation affects greatly.Access the power system stability of initiation on a large scale for load rapid growth and the fluctuation energy
Problem, demand response technology gradually develop.
Building belonging to residual air-conditioning load has thermmal storage ability, while quantity is increasing in recent years, becomes important
Demand response resource.However single air conditioner load rated power is small, power grid can not be produced by controlling single air conditioner load
It comes into force fruit, it is therefore desirable to which a large amount of residual air-conditioning loads are polymerize and are uniformly controlled.Current a large amount of research is occupied based on separate unit
People's air conditioner load model is polymerize to obtain air conditioner load group's polymerization model, and this method needs to obtain building belonging to each air-conditioning
Attribute, or even invade the privacy of user, and calculation amount is huge.
Summary of the invention
Goal of the invention: the present invention proposes that a kind of residual air-conditioning load group polymerization model is established and parameter identification method, is reduced
Calculation amount simultaneously protects privacy of user.
Technical solution: the technical solution adopted by the present invention is a kind of residual air-conditioning load group polymerization model method for building up, packet
Include following steps:
1) residual air-conditioning load group's polymerization model is established；
2) outdoor temperature of residual air-conditioning load history run power data and corresponding moment is obtained；
3) it according to residual air-conditioning load group's polymerization model, using historical power data and outdoor temperature data, seeks average
Value is as final residual air-conditioning load polymerization model Adiabatic Parameters；
4) according to residual air-conditioning load group's polymerization model, Average indoor temperature variation curve is assessed, multiple groups are screened out from it
Historical data corresponding with Average indoor temperature change；
5) according to the room temperature historical data and outdoor temperature of step 4), air conditioner load mould under different historical datas is solved
The corresponding heat accumulation parameter of type heat accumulation characteristic, and averaged is as final residual air-conditioning load polymerization model thermal capacitance parameter.
It further include residual air-conditioning load group's polymerization model that step 6) is obtained according to identification, with air conditioner load history number
According to choosing multi-group data and modify verifying to residual air-conditioning load polymerization model.
It is as follows according to thermodynamic argument room temperature dynamic change equation in the step 1):
Wherein, i is room ordinal number；TaFor outdoor temperature, unit DEG C；, TiFor room i room temperature, unit DEG C；CiFor room
Between i thermal capacitance, kWh/ DEG C of unit；PiIt (t) is t moment room i operation of air conditioner power, unit kW；η is air-conditioning Energy Efficiency Ratio；GiIt is exhausted for i
Thermal parameter, kW/ DEG C of unit, when operating normally, room temperature maintains set temperature TsetNear, i.e. [Tset-δ/2,Tset+δ/
2]；
Based on single air conditioner load operation characteristic, it is as follows to establish residual air-conditioning load group's polymerization model:
Wherein, TaFor outdoor temperature, unit DEG C, T (t) is t moment Average indoor temperature, and unit DEG C, C is that resident's air-conditioning is born
Lotus polymerization thermal capacitance parameter, kWh/ DEG C of unit；P (t) is t moment resident air-conditioning aggregate power, and unit kW, η are air-conditioning Energy Efficiency Ratio, G
For residual air-conditioning load polymerizing insulation parameter, kW/ DEG C of unit；
By formula (2) both sides simultaneously divided by η, and enable Then formula (2) converts are as follows:
Multiple groups air conditioner load historical power data are obtained based on noninvasive technique identification in the step 2), and pass through day
Destiny corresponds to the outdoor temperature data of time according to system acquisition.
Multiple groups are calculated by linear fit in the step 3) and describe insulating characteristics in air conditioner load polymerization model
Adiabatic ParametersLast averaged is as final residual air-conditioning load polymerization model Adiabatic Parameters
Wherein, l indicates load data group serial number, and L indicates that data always organize number.
The residual air-conditioning load polymerization model Adiabatic Parameters that formula (4) in step 3) is final are utilized in the step 4)'s
Assessment result calculates estimation room temperature T by formula (5)ev,
Formula (5) is depicted as curve, the room temperature curve ripple emotionally condition is analyzed, filters out room temperature and change
Corresponding historical data.
Curve is assessed according to the room temperature that step 4) assessment obtains in the step 5), identifies room temperature before variation
T1, room temperature T after variation2With indoor temperature change generated in case transition duration ttrans；
It is calculated separately further according to following formula and T1、T2And ttransCorresponding residual air-conditioning load polymerize thermal capacitance parameter
Last averaged is as final residual air-conditioning load polymerization model thermal capacitance parameter
Wherein, i indicates load data group serial number, and K indicates the changed data group quantity of Average indoor temperature.
The utility model has the advantages that the present invention recognizes to obtain resident's sky using residual air-conditioning load group history data and outdoor data
Load group polymerization model is adjusted, the foundation to single air conditioner load model is avoided, reduces the calculating of traditional air conditioner polymerization model
Amount.The present invention accurately describes the dynamic relationship of outdoor temperature, indoor temperature and air conditioner load group's aggregate power simultaneously, uses
In the prediction and analysis air conditioner load response characteristic of air conditioner load aggregate power, and establish air conditioner load demand response control plan
Slightly.
Detailed description of the invention
Fig. 1 is single resident's air-conditioning heat TRANSFER MODEL figure；
Fig. 2 is residual air-conditioning load group's heat transfer illustraton of model；
Fig. 3 is room temperature assessment curve；
Fig. 4 is Adiabatic ParametersIdentification result；
Fig. 5 is heat accumulation parameterIdentification result；
Fig. 6 is residual air-conditioning load group's polymerization model check results.
Specific embodiment
In the following with reference to the drawings and specific embodiments, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate
It the present invention rather than limits the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention each
The modification of kind equivalent form falls within the application range as defined in the appended claims.
Such as
1) single residual air-conditioning load operation characteristic is analyzed according to thermodynamic argument, obtains air conditioner load group's polymerization thermodynamics
Characteristic establishes residual air-conditioning load group's polymerization model.
Single residual air-conditioning load operation characteristic is analyzed according to thermodynamic argument, air conditioner load has heat accumulation and heat dissipation special
Property, as shown in Figure 1, when air conditioner refrigerating amount is more than that outdoor is passed to indoor heat, room temperature decline, it is on the contrary then in room temperature
It rises, room temperature dynamic change equation is as follows:
Wherein, i is room ordinal number；TaFor outdoor temperature, unit DEG C；, TiFor room i room temperature, unit DEG C；CiFor room
Between i thermal capacitance, kWh/ DEG C of unit；PiIt (t) is t moment room i operation of air conditioner power, unit kW；η is air-conditioning Energy Efficiency Ratio；GiIt is exhausted for i
Thermal parameter, kW/ DEG C of unit.When operating normally, room temperature maintains set temperature TsetNear, i.e. [Tset-δ/2,Tset+δ/
2]。
It based on single air conditioner load operation characteristic, obtains air conditioner load clustering and closes thermodynamic behaviour, as shown in Fig. 2, establishing
Residual air-conditioning load group's polymerization model is as follows:
Wherein, TaFor outdoor temperature, unit DEG C, T (t) is t moment Average indoor temperature, and unit DEG C, C is that resident's air-conditioning is born
Lotus polymerization thermal capacitance parameter, kWh/ DEG C of unit；P (t) is t moment resident air-conditioning aggregate power, and unit kW, η are air-conditioning Energy Efficiency Ratio, G
For residual air-conditioning load polymerizing insulation parameter, kW/ DEG C of unit.
By formula (2) both sides simultaneously divided by η, and enable Then formula (2) converts are as follows:
2) outdoor temperature of residual air-conditioning load history run power data and corresponding moment is obtained.
It is recognized based on noninvasive technique and obtains multiple groups air conditioner load historical power data, and adopted by weather data system
Collect the outdoor temperature data of corresponding time.
3) according to residual air-conditioning load group's polymerization model, multiple groups air conditioner load historical power data and outdoor temp degree are utilized
According to the Adiabatic Parameters that multiple groups describe insulating characteristics in air conditioner load polymerization model are calculated by linear fitFinally ask
It is averaged as final residual air-conditioning load polymerization model Adiabatic ParametersIt is as shown in Figure 4:
Wherein, l indicates load data group serial number, and L indicates that data always organize number.
4) according to residual air-conditioning load group's polymerization model, Average indoor temperature variation curve is assessed, multiple groups are selected
Historical data corresponding with Average indoor temperature change.
Utilize the residual air-conditioning load polymerization model Adiabatic Parameters that formula in step 3) (4) is finalAssessment result, lead to
It crosses formula (5) and calculates estimation room temperature Tev,
Formula (5) is depicted as curve, the room temperature curve ripple emotionally condition is analyzed, filters out room temperature and change
Corresponding historical data.
5) the Average indoor temperature curve in step 4) is combined, is calculated and is solved not using historical power data and outdoor temperature
With the corresponding heat accumulation parameter of air conditioner load model heat accumulation characteristic under historical data, last averaged is empty as final resident
Adjust Load aggregation model thermal capacitance parameter
Curve is assessed according to the room temperature that assessment obtains, it is assumed that the corresponding room temperature assessment of i-th group of historical data is bent
Line is as shown in Figure 3.Two sections of horizontal dotted lines respectively indicate room temperature T before variation in Fig. 31, room temperature T after variation2.And the two
Between be exactly indoor temperature change generated in case transition duration ttrans.In conjunction with different historical datas, calculate separately and T1、T2And ttransIt is corresponding
Residual air-conditioning load polymerize thermal capacitance parameterFormula is as follows:
Last averaged is as final residual air-conditioning load polymerization model thermal capacitance parameter
Wherein, i indicates load data group serial number, and K indicates the changed data group quantity of Average indoor temperature, the data
Group is from step 4).As shown in Figure 5.
Multiple groups number is chosen with air conditioner load historical data according to residual air-conditioning load group's polymerization model that identification obtains
It modifies verifying according to residual air-conditioning load polymerization model.
Example
With the 120000 resident's air-conditionings possessed in certain region.Assuming that single residual air-conditioning load parameter value range is such as
Under (every air conditioner load parameter randomly selects in value range):
Air conditioner load historical data outdoor temperature is obtained with Monte Carlo method first.
Residual air-conditioning load group's polymerization model is established, then recognizes to obtain air conditioner load model using the method in the present invention
Parameter, concrete outcome are as follows
The polymerization model assessment air conditioner load power finally obtained with the present invention, and the sky obtained with monte carlo method
Load power is adjusted to compare, as shown in Figure 6.
Claims (7)
1. a kind of residual air-conditioning load group polymerization model method for building up, which comprises the following steps:
1) residual air-conditioning load group's polymerization model is established；
2) outdoor temperature of residual air-conditioning load history run power data and corresponding moment is obtained；
3) according to residual air-conditioning load group's polymerization model, using historical power data and outdoor temperature data, averaged is made
For final residual air-conditioning load polymerization model Adiabatic Parameters；
4) according to residual air-conditioning load group's polymerization model, Average indoor temperature variation curve is assessed, be screened out from it multiple groups and is put down
The corresponding historical data of equal indoor temperature change generated in case；
5) it according to the room temperature historical data and outdoor temperature of step 4), solves air conditioner load model under different historical datas and stores up
The corresponding heat accumulation parameter of thermal characteristics, and averaged is as final residual air-conditioning load polymerization model thermal capacitance parameter.
2. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that further include step
6) the residual air-conditioning load group's polymerization model obtained according to identification chooses multi-group data to residence with air conditioner load historical data
People's air conditioner load polymerization model is modified verifying.
3. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that the step 1)
It is middle as follows according to thermodynamic argument room temperature dynamic change equation:
Wherein, i is room ordinal number；TaFor outdoor temperature, unit DEG C；, TiFor room i room temperature, unit DEG C；CiFor room i heat
Hold, kWh/ DEG C of unit；PiIt (t) is t moment room i operation of air conditioner power, unit kW；η is air-conditioning Energy Efficiency Ratio；GiIt is insulated and joins for i
Number, kW/ DEG C of unit, when operating normally, room temperature maintains set temperature TsetNear, i.e. [Tset-δ/2,Tset+δ/2]；
Based on single air conditioner load operation characteristic, it is as follows to establish residual air-conditioning load group's polymerization model:
Wherein, TaFor outdoor temperature, unit DEG C, T (t) is t moment Average indoor temperature, unit DEG C, and C is poly- for residual air-conditioning load
Conjunction thermal capacitance parameter, kWh/ DEG C of unit；P (t) is t moment resident air-conditioning aggregate power, and unit kW, η are air-conditioning Energy Efficiency Ratio, and G is to occupy
People's air conditioner load polymerizing insulation parameter, kW/ DEG C of unit；
By formula (2) both sides simultaneously divided by η, and enableThen formula (2) converts are as follows:
4. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that the step 2)
In multiple groups air conditioner load historical power data are obtained based on noninvasive technique identification, and it is corresponding by weather data system acquisition
The outdoor temperature data of time.
5. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that the step 3)
In the Adiabatic Parameters that multiple groups describe insulating characteristics in air conditioner load polymerization model are calculated by linear fitFinally ask
It is averaged as final residual air-conditioning load polymerization model Adiabatic Parameters
Wherein, l indicates load data group serial number, and L indicates that data always organize number.
6. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that the step 4)
The middle residual air-conditioning load polymerization model Adiabatic Parameters final using formula (4) in step 3)Assessment result, pass through formula (5)
Calculate estimation room temperature Tev,
Formula (5) is depicted as curve, the room temperature curve ripple emotionally condition is analyzed, it is changed right to filter out room temperature
The historical data answered.
7. residual air-conditioning load group polymerization model method for building up according to claim 1, which is characterized in that the step 5)
The middle room temperature obtained according to step 4) assessment assesses curve, identifies room temperature T before variation1, room temperature T after variation2
With indoor temperature change generated in case transition duration ttrans；
It is calculated separately further according to following formula and T1、T2And ttransCorresponding residual air-conditioning load polymerize thermal capacitance parameter
Last averaged is as final residual air-conditioning load polymerization model thermal capacitance parameter
Wherein, i indicates load data group serial number, and K indicates the changed data group quantity of Average indoor temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098673.8A CN109827310B (en) | 2019-01-31 | 2019-01-31 | Resident air conditioner load cluster die type establishing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910098673.8A CN109827310B (en) | 2019-01-31 | 2019-01-31 | Resident air conditioner load cluster die type establishing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109827310A true CN109827310A (en) | 2019-05-31 |
| CN109827310B CN109827310B (en) | 2020-12-11 |
Family
ID=66863211
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910098673.8A Active CN109827310B (en) | 2019-01-31 | 2019-01-31 | Resident air conditioner load cluster die type establishing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109827310B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111896831A (en) * | 2020-08-04 | 2020-11-06 | 山东大学 | Non-invasive comprehensive energy load monitoring method and system |
| CN112290538A (en) * | 2020-09-30 | 2021-01-29 | 天津大学 | Load model parameter online correction method based on aggregation-identification double-layer framework |
| CN112747413A (en) * | 2019-10-31 | 2021-05-04 | 北京国双科技有限公司 | Air conditioning system load prediction method and device |
| CN113435031A (en) * | 2021-06-24 | 2021-09-24 | 华中科技大学 | Parameter identification method and system for first-order ETP model of house to which air conditioner belongs |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105605733A (en) * | 2015-12-25 | 2016-05-25 | 深圳达实智能股份有限公司 | Power grid responding method and device of air conditioner refrigerator |
| CN106485068A (en) * | 2016-10-08 | 2017-03-08 | 东南大学 | A kind of polymerization modeling method of air conditioner load |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
| WO2017217131A1 (en) * | 2016-06-15 | 2017-12-21 | 日本電気株式会社 | Building thermal model generation apparatus, building thermal model generation method, and building thermal model generation program |
| CN108036468A (en) * | 2017-11-22 | 2018-05-15 | 深圳供电局有限公司 | Aggregation control method for air conditioning system |
| CN108224692A (en) * | 2018-01-09 | 2018-06-29 | 合肥工业大学 | A kind of air-conditioning flexible control responding ability Forecasting Methodology for considering outside air temperature prediction error |
| CN108306284A (en) * | 2018-01-05 | 2018-07-20 | 东南大学 | A kind of online load modeling method measured based on local intelligence |
| CN108426351A (en) * | 2018-03-09 | 2018-08-21 | 广西大学 | The real-time optimal scheduling method that air conditioning system with variable responsive electricity grid frequency is adjusted |
| CN109243547A (en) * | 2018-07-09 | 2019-01-18 | 河海大学 | A kind of air conditioner load group demand response potentiality quantitative evaluating method |
-
2019
- 2019-01-31 CN CN201910098673.8A patent/CN109827310B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105605733A (en) * | 2015-12-25 | 2016-05-25 | 深圳达实智能股份有限公司 | Power grid responding method and device of air conditioner refrigerator |
| WO2017217131A1 (en) * | 2016-06-15 | 2017-12-21 | 日本電気株式会社 | Building thermal model generation apparatus, building thermal model generation method, and building thermal model generation program |
| CN106485068A (en) * | 2016-10-08 | 2017-03-08 | 东南大学 | A kind of polymerization modeling method of air conditioner load |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
| CN108036468A (en) * | 2017-11-22 | 2018-05-15 | 深圳供电局有限公司 | Aggregation control method for air conditioning system |
| CN108306284A (en) * | 2018-01-05 | 2018-07-20 | 东南大学 | A kind of online load modeling method measured based on local intelligence |
| CN108224692A (en) * | 2018-01-09 | 2018-06-29 | 合肥工业大学 | A kind of air-conditioning flexible control responding ability Forecasting Methodology for considering outside air temperature prediction error |
| CN108426351A (en) * | 2018-03-09 | 2018-08-21 | 广西大学 | The real-time optimal scheduling method that air conditioning system with variable responsive electricity grid frequency is adjusted |
| CN109243547A (en) * | 2018-07-09 | 2019-01-18 | 河海大学 | A kind of air conditioner load group demand response potentiality quantitative evaluating method |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112747413A (en) * | 2019-10-31 | 2021-05-04 | 北京国双科技有限公司 | Air conditioning system load prediction method and device |
| CN112747413B (en) * | 2019-10-31 | 2022-06-21 | 北京国双科技有限公司 | Air conditioning system load prediction method and device |
| CN111896831A (en) * | 2020-08-04 | 2020-11-06 | 山东大学 | Non-invasive comprehensive energy load monitoring method and system |
| CN111896831B (en) * | 2020-08-04 | 2021-07-23 | 山东大学 | Non-invasive comprehensive energy load monitoring method and system |
| CN112290538A (en) * | 2020-09-30 | 2021-01-29 | 天津大学 | Load model parameter online correction method based on aggregation-identification double-layer framework |
| CN113435031A (en) * | 2021-06-24 | 2021-09-24 | 华中科技大学 | Parameter identification method and system for first-order ETP model of house to which air conditioner belongs |
| CN113435031B (en) * | 2021-06-24 | 2024-05-14 | 华中科技大学 | Parameter identification method and system of first-order ETP model of house to which air conditioner belongs |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109827310B (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109827310A (en) | A kind of residual air-conditioning load group polymerization model method for building up | |
| CN109799851B (en) | User side load response method based on load cluster temperature regulation and control | |
| CN104214912B (en) | Aggregation air conditioning load scheduling method based on temperature set value adjustment | |
| CN103257571B (en) | Air conditioning load control strategy making method based on direct load control | |
| CN103064285B (en) | A kind of heat pump heating multiobjective optimization control method based on model | |
| CN105042800A (en) | Variable-frequency air conditioner load modeling and operation controlling method based on demand responses | |
| CN107221965B (en) | Day-ahead plan calculation method based on distributed design | |
| CN110111003A (en) | A kind of new energy typical scene construction method based on improvement FCM clustering algorithm | |
| CN111555274B (en) | Dynamic assessment method for air conditioner load demand response capability | |
| Masuta et al. | System frequency control by heat pump water heaters (HPWHs) on customer side based on statistical HPWH model in power system with a large penetration of renewable energy sources | |
| CN109190799B (en) | Collaborative optimization control method and device for industrial and commercial temperature control load | |
| CN109523081A (en) | A kind of ground-source heat pump system running temperature adjusting optimization method | |
| CN110729726A (en) | Intelligent community energy optimization scheduling method and system | |
| CN106849132A (en) | Method and system are stabilized in micro-capacitance sensor dominant eigenvalues fluctuation based on team control heat pump | |
| CN113991655A (en) | Method, device and medium for evaluating load aggregation demand response potential of fixed-frequency air conditioner | |
| CN111598478A (en) | Comprehensive energy demand response quantity calculation method | |
| CN113610152A (en) | Air conditioning system flexible operation strategy making method based on load mode | |
| CN109812946A (en) | A kind of control method suitable for extensive residual air-conditioning load group demand response | |
| CN114285086B (en) | Electrochemical energy storage power station energy scheduling method and system based on improved ant colony algorithm | |
| CN113566401B (en) | Demand side load control method | |
| CN111832593A (en) | Response potential evaluation method and system for cluster variable frequency air conditioner | |
| CN109343461A (en) | A kind of energy-saving control method of building automatic control system | |
| CN112215472A (en) | Clustering-based electric heating load response control method and device | |
| Michailidis et al. | Optimization-based active techniques for energy efficient building control part ii: Real-life experimental results | |
| Wishart et al. | Multi objective decision making method for demand side management of LV residential distribution networks with plug-in electric vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |