CN106208388A - A kind of intelligence charging system and short term basis load prediction implementation method thereof in order - Google Patents
A kind of intelligence charging system and short term basis load prediction implementation method thereof in order Download PDFInfo
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- CN106208388A CN106208388A CN201610793356.4A CN201610793356A CN106208388A CN 106208388 A CN106208388 A CN 106208388A CN 201610793356 A CN201610793356 A CN 201610793356A CN 106208388 A CN106208388 A CN 106208388A
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- distribution transformer
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- electric automobile
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- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 claims abstract description 21
- 239000002245 particle Substances 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 3
- 238000013277 forecasting method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
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- H02J13/0017—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00007—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using the power network as support for the transmission
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
- Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
- Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
- Y04S30/12—Remote or cooperative charging
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of intelligence charging system in order, including the current transformer and the voltage transformer that are arranged on residential quarter distribution transformer low-pressure side, the outfan of the two is connected with the input of distribution transformer monitoring terminal, the orderly charge controller of electric automobile communicates with distribution transformer monitoring terminal in the way of RS485 or RS232, communicates between the orderly charge controller of electric automobile and each charging pile in the way of power line carrier.The invention also discloses the short term basis load prediction implementation method of the orderly charging system of a kind of intelligence.The orderly charge power of electric automobile formulated in orderly for electric automobile charge controller is sent to distribution transformer monitoring terminal by the present invention, and completes the prediction of distribution transformer short term basis load wherein.Compare existing method, it is possible to take into full account the impact of the orderly charge power of electric automobile, meanwhile, be greatly improved the precision of the prediction of distribution transformer short term basis load.
Description
Technical field
The present invention relates to the intelligent power technical field of power system, especially a kind of intelligence charging system in order and short
Phase basic load prediction implementation method.
Background technology
In recent years, along with popularizing rapidly of the electric automobile bigger with tradition load property difference, electric automobile is utilized to join
Get the attention with the orderly charging technique of electric automobile supporting supply side economical operation with demand response, and electric automobile
Orderly charging technique then need set up on distribution transformer short term basis load prediction.
The data of distribution transformer load prediction come from power information acquisition system, it is impossible to effectively that electric automobile is orderly
Charging load and transformer foundation load are distinguish between.Therefore, up to now, there is no meter and the resident that charges in order of electric automobile
Community distribution transformer short term basis load prediction implementation method.
Summary of the invention
The primary and foremost purpose of the present invention is to provide a kind of by obtaining the charging load participating in the electric automobile of charging in order,
So that realizing the intelligence charging system in order that residential quarter distribution transformer short term basis load is predicted.
For achieving the above object, present invention employs techniques below scheme: a kind of intelligence charging system in order, including installing
At current transformer and the voltage transformer of residential quarter distribution transformer low-pressure side, the outfan of the two is supervised with distribution transformer
The input surveying terminal is connected, and the orderly charge controller of electric automobile is in the way of RS485 or RS232 and distributing transformer monitoring
Terminal communicates, and leads between the orderly charge controller of electric automobile and each charging pile in the way of power line carrier
Letter；Described distribution transformer monitoring terminal obtains the voltage and current of residential quarter distribution transformer low-pressure side, calculates in real time
Comprise whole power loads of electric automobile, preserve whole power load at regular intervals；Described electric automobile is orderly
Charge controller formulates the plan of charging in order of each charging pile, assigns the orderly charging plan of each charging pile, monitors each
The charged state of charging pile, stores the load that charges in order of all charging piles at regular intervals；Electric automobile charges control in order
The load of charging in order of all charging piles that device processed is obtained in RS485 or RS232 mode is sent to distributing transformer monitoring
Terminal, asks for whole power loads of corresponding synchronization point and the difference of the load that charges in order in distribution transformer monitoring terminal
Divide, and the basic load that this difference is considered as residential quarter distribution transformer is stored.
Described whole power load isFor kth day, whole power loads of the i-th time point；Described orderly charging
Load isFor kth day, the load that charges in order of all charging piles of the i-th time point；Described basic load isBeing the residential quarter distribution transformer base load of kth day, the i-th time point, its computing formula is:
Described certain time is 15 minutes.
Another object of the present invention is to provide the short term basis load prediction realization side of the orderly charging system of a kind of intelligence
Method, the method includes the step of following order:
(1) order arrangement after temporally being selected by residential quarter distribution transformer basic load, forms original loads sequence, so
Afterwards this sequence is carried out empirical mode decomposition, obtain intrinsic mode function and the remainder of different frequency bands；
(2) being normalized the component after decomposing and date type, festivals or holidays, type was 1, and working day, type was
0；
(3) remainder decomposed by EMD uses linear neural network to be predicted it；Remaining IMF component use support to
It is predicted by amount machine；When building forecast model temperature being included, the maximum temperature on the same day and mean temperature are as model
One group of input；
(4) use particle cluster algorithm that the parameter of load prediction is optimized；
(5) utilize the model after optimizing that residential quarter distribution transformer carries out short-term base load prediction.
As shown from the above technical solution, the orderly charge controller of the electric automobile in the present invention and distributing transformer monitoring are eventually
Carry out data transmission between end, the orderly charge power of electric automobile formulated in orderly for electric automobile charge controller is sent to
Distribution transformer monitoring terminal, and complete the prediction of distribution transformer short term basis load wherein.Compare existing method, it is possible to
Take into full account the impact of the orderly charge power of electric automobile, meanwhile, present invention also offers a kind of based on multiple load prediction mould
The distribution transformer short term basis load forecasting method of type, is greatly improved the essence of the prediction of distribution transformer short term basis load
Degree.
Accompanying drawing explanation
Fig. 1 is the system structure schematic diagram of the present invention；
Fig. 2 is the load prediction flow chart in the present invention.
Detailed description of the invention
As it is shown in figure 1, a kind of intelligence charging system in order, including being arranged on residential quarter distribution transformer 1 low-pressure side
Current transformer 2 and be arranged on the voltage transformer 3 at the low bus of residential quarter distribution transformer 1, the outfan of the two with join
The input of piezoelectric transformer monitoring terminal 4 be connected, the orderly charge controller of electric automobile 5 in the way of RS485 or RS232 with join
Piezoelectric transformer monitoring terminal 4 communicates, with power line carrier between the orderly charge controller of electric automobile 5 and each charging pile
Mode communicate；Described distribution transformer monitoring terminal 4 obtains voltage and the electricity of residential quarter distribution transformer 1 low-pressure side
Stream, calculates the whole power loads comprising electric automobile in real time, preserves whole power load at regular intervals；Described
The orderly charge controller of electric automobile 5 formulates the orderly charging plan of each charging pile, assigns the orderly charging of each charging pile
Plan, monitors the charged state of each charging pile, stores the load that charges in order of all charging piles at regular intervals；Electronic vapour
The load of charging in order of all charging piles that the orderly charge controller of car 5 is obtained in RS485 or RS232 mode is sent to join
Piezoelectric transformer monitoring terminal 4, ask in distribution transformer monitoring terminal 4 corresponding synchronization i point whole power loads and
The difference of charging load, and the basic load that this difference is considered as residential quarter distribution transformer 1 in order is stored.Described one
Fix time is 15 minutes.
Described whole power load isFor kth day, whole power loads of the i-th time point；Described orderly charging
Load isFor kth day, the load that charges in order of all charging piles of the i-th time point；Described basic load isBeing residential quarter distribution transformer 1 base load of kth day, the i-th time point, its computing formula is:Basic load in Fig. 1 is to point out the power load outside electric automobile, this basic load namely this
The prediction object of invention.
As in figure 2 it is shown, this method includes the step of following order:
(1) order arrangement after temporally being selected by residential quarter distribution transformer 1 basic load, forms original loads sequence,
Then this sequence is carried out empirical mode decomposition (Empirical Mode Decomposition, EMD), obtains different frequency bands
Intrinsic mode function (Intrinsic Mode Function, IMF) and remainder；
(2) residential quarter distribution transformer 1 short term is relatively big, to the component after decomposing and date class by being affected festivals or holidays
Type is normalized, and festivals or holidays, type was 1, and working day, type was 0；
(3) residential quarter distribution transformer 1 short term is affected by extraneous various factors, the single employing of prediction one to it
The method of kind can not reach preferable effect, for each IMF component different characteristics on frequency domain and time domain, takes different pre-respectively
Survey model.There is obvious linear character in the remainder decomposed due to EMD, EMD the remainder decomposed uses linear neural network pair
It is predicted；Remaining IMF component uses support vector machine (Support Vector Machine, SVM) to be predicted it；
When building forecast model temperature included the one group of input as model of the maximum temperature on the same day and mean temperature；
(4) use particle cluster algorithm that the parameter of load prediction is optimized；: SVM and linear neural network model general
Change learning capacity and select to have much relations with its parameter, use fast convergence rate, realize simple particle cluster algorithm (Particle
Swarm Optimization, PSO) solve the Parametric optimization problem in load prediction problem.
(5) utilize the model after optimizing that residential quarter distribution transformer 1 is carried out short-term base load prediction.
In sum, carry out between the orderly charge controller of the electric automobile of the present invention 5 and distribution transformer monitoring terminal 4
Data are transmitted, and the orderly charge power of electric automobile formulated in orderly for electric automobile charge controller 5 is sent to distribution transformer
Device monitoring terminal 4, and complete the prediction of distribution transformer short term basis load wherein.Compare existing method, it is possible to fully examine
Consider the impact of the orderly charge power of electric automobile, meanwhile, present invention also offers a kind of based on multiple load forecasting model join
Piezoelectric transformer short term basis load forecasting method, is greatly improved the precision of the prediction of distribution transformer short term basis load.
Claims (4)
1. the orderly charging system of intelligence, it is characterised in that: include the electricity being arranged on residential quarter distribution transformer low-pressure side
Current transformer and voltage transformer, the outfan of the two is connected with the input of distribution transformer monitoring terminal, and electric automobile has
Sequence charge controller communicates with distribution transformer monitoring terminal in the way of RS485 or RS232, and electric automobile charges in order
Communicate in the way of power line carrier between controller and each charging pile；Described distribution transformer monitoring terminal obtains and occupies
The voltage and current of people community distribution transformer low-pressure side, calculates the whole power loads comprising electric automobile in real time,
Preserve whole power load at regular intervals；The orderly charge controller of described electric automobile formulates filling in order of each charging pile
Electricity plan, assigns the orderly charging plan of each charging pile, monitors the charged state of each charging pile, store at regular intervals
The load that charges in order of all charging piles；The institute that the orderly charge controller of electric automobile is obtained in RS485 or RS232 mode
The load of charging in order having charging pile is sent to distribution transformer monitoring terminal, asks for correspondence in distribution transformer monitoring terminal
Whole power loads of synchronization point and the difference of the load that charges in order, and this difference is considered as residential quarter distribution transformer
Basic load stored.
The orderly charging system of intelligence the most according to claim 1, it is characterised in that: described whole power loads are
For kth day, whole power loads of the i-th time point；Described orderly charging load isFor kth day, i-th time point
The load that charges in order of all charging piles；Described basic load isIt is kth day, the residential quarter of the i-th time point
Distribution transformer base load, its computing formula is:
The orderly charging system of intelligence the most according to claim 1, it is characterised in that: described certain time is 15 minutes.
The short term basis load prediction realization side of the orderly charging system of intelligence the most according to any one of claim 1 to 3
Method, the method includes the step of following order:
(1) order arrangement after temporally being selected by residential quarter distribution transformer basic load, forms original loads sequence, the most right
This sequence carries out empirical mode decomposition, obtains intrinsic mode function and the remainder of different frequency bands；
(2) being normalized the component after decomposing and date type, festivals or holidays, type was 1, and working day, type was 0；
(3) remainder decomposed by EMD uses linear neural network to be predicted it；Remaining IMF component uses support vector machine
It is predicted；When building forecast model temperature being included, the maximum temperature on the same day and mean temperature are as a group of model
Input；
(4) use particle cluster algorithm that the parameter of load prediction is optimized；
(5) utilize the model after optimizing that residential quarter distribution transformer carries out short-term base load prediction.
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| CN201610793356.4A CN106208388A (en) | 2016-08-31 | 2016-08-31 | A kind of intelligence charging system and short term basis load prediction implementation method thereof in order |
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Cited By (6)
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| CN108075536A (en) * | 2017-11-10 | 2018-05-25 | 深圳供电局有限公司 | The flexible charging regulation and control method and charging pile system of charging pile |
| CN108596242A (en) * | 2018-04-20 | 2018-09-28 | 浙江大学 | Power grid meteorology load forecasting method based on wavelet neural network and support vector machines |
| CN109787218A (en) * | 2018-12-21 | 2019-05-21 | 北京华商三优新能源科技有限公司 | Duty control method, apparatus and system |
| CN112124135A (en) * | 2020-08-19 | 2020-12-25 | 国电南瑞科技股份有限公司 | Electric vehicle shared charging demand analysis method and device |
| CN113298298A (en) * | 2021-05-10 | 2021-08-24 | 国核电力规划设计研究院有限公司 | Charging pile short-term load prediction method and system |
| CN113902183A (en) * | 2021-09-28 | 2022-01-07 | 浙江大学 | BERT-based non-invasive distribution room charging pile state monitoring and electricity price adjusting method |
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| CN113902183B (en) * | 2021-09-28 | 2022-11-08 | 浙江大学 | BERT-based non-invasive transformer area charging pile state monitoring and electricity price adjusting method |
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Application publication date: 20161207 |