CN103855739B - Charging electric vehicle facility proportional arrangement system and method - Google Patents
Charging electric vehicle facility proportional arrangement system and method Download PDFInfo
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Abstract
The invention discloses a kind of charging electric vehicle facility proportional arrangement method and system.This system comprises local measurement and statistic unit, integrated system; Described local measurement and statistic unit comprise power-measuring circuit, transport module, mileage gathers radio network gateway, ARM9 adds up and output circuit; Described integrated system is for calculating the electrically-charging equipment best configuration ratio in each region of following distribution, the present invention is target according to the overall target optimum of distribution total load, proportional arrangement is carried out to the different charging modes of electric automobile, combined charge facility capacity data can obtain the best configuration ratio of the corresponding electrically-charging equipment of different charging modes, this configuration-system is more accurate, efficiency is better, and can carry out proportional arrangement according to the actual conditions of each different regions.
Description
Technical field
The present invention relates to a kind of based on charging electric vehicle technical field, particularly relate to a kind of charging electric vehicle facility proportional arrangement system and method.
Background technology
Along with the enhancing of social environment consciousness and the rise of gas price, the development of new-energy automobile receives the concern of people gradually, and electric automobile has become the Main way of contemporary automotive development; Charging electric vehicle communal facility also will increase year by year simultaneously, and the coming years, electric automobile field will obtain tremendous development.Electric automobile charging station provides energy supplement for electric automobile runs, and be the necessary important foundation auxiliary facility of Development of EV, therefore the construction of charging station communal facility is the prerequisite that electric automobile is popularized on a large scale.
The factor that electric automobile charging station layout impacts can be comprised: the total demand of charging electric vehicle amount, the charging modes of electric automobile operational mode and electric automobile.Only have the charge power of electric automobile to reach certain scale, charging station just may realize layouting on a large scale economically.And the power demand of electric automobile and its recoverable amount and daily travel, unit mileage energy consumption level etc. closely related.Meanwhile, under different operational mode, electric automobile requires also different to self continual mileage and charging interval, thus affects the way choice of charging, the consumption of electric energy, charging station construction mode and power demand.Therefore, according to the difference of dissimilar charging electric vehicle behavior, the hierarchical classification of research electric automobile is very necessary.In addition, the charging behavior of the electric automobile of different charging modes not only can affect the load curve of distribution, also can impact the construction distribution of corresponding electrically-charging equipment simultaneously.
Ubiquitous charging modes comprises normal charge and quick charge two kinds of patterns now: normal charge adopts constant voltage or the constant current charge of small area analysis, charger and installation cost lower, but the charging interval is longer, electric power low-valley interval can be made full use of and carry out charging and the useful life of improving charge efficiency and prolongation battery; Quick charge is with larger current at short notice for electric automobile provides charging service, and the charging interval is short, but charge efficiency is lower, and corresponding work and installation cost higher.The electric automobile proportional arrangement of the different charging modes of visible research is the key of research charging electric vehicle facility proportional arrangement.
But the configuration of current charging electric vehicle facility ratio, generally obtain according to distribution network planning, owing to not considering the impact of different charging modes on charging electric vehicle power, the configuration mode of existing charging electric vehicle facility ratio, its accuracy and allocative efficiency are all lower; Therefore a kind of system can carrying out configuration automatically to charging electric vehicle facility ratio is provided, becomes those skilled in the art's technical issues that need to address.
Summary of the invention
In view of this, embodiments provide that a kind of accuracy is higher, efficiency better charging electric vehicle facility proportional arrangement method and system.
For achieving the above object, the embodiment of the present invention provides following scheme:
A kind of charging electric vehicle facility proportional arrangement system, comprising: local measurement and statistic unit, described local measurement and statistic unit comprise:
Gather local Vehicular charging power information, and by power-measuring circuit that this power information exports;
Be connected with described power-measuring circuit, receive described power information, and export the transport module of described power information;
Gather daily travel information, calculate daily travel expectation and variance, and the mileage exporting the daily travel expectation and variance information calculated gathers radio network gateway;
Gather radio network gateway with described transport module and described mileage to be respectively connected, receive described power information, and described daily travel expectation and variance information, by described charge power information and described daily travel expectation and variance information are added up, obtain local charging station to start to charge the expectation and variance information in moment, and export ARM9 statistics and the output circuit of the expectation and variance information in the described moment that starts to charge;
To add up with described ARM9 and output circuit is connected, receive the expectation and variance information in the described moment that starts to charge, start to charge according to multiple local charging station the expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile increase the charge power demand information that scale data calculates distribution region corresponding to described multiple local charging station, the following Distribution Network Load Data curve in this distribution region is determined according to the charge power demand information in this distribution region, calculate the indices of the following Distribution Network Load Data curve in this region, according to the ratio of the different charging modes of indices configuration electric automobile, the integrated system of the electrically-charging equipment best configuration ratio in each region of following distribution is calculated according to this ratio and existing electrically-charging equipment capacity data.
Wherein, described integrated system comprises:
The first treatment facility that scale calculates the charge power demand information in distribution region corresponding to described multiple local charging station is increased according to multiple local charging station start the to charge expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile;
Be connected with described first treatment facility, determine the second treatment facility of the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in described distribution region;
Be connected with described second treatment facility, calculate the 3rd treatment facility of the indices of the following Distribution Network Load Data curve in this distribution region;
Be connected with described 3rd treatment facility, according to the 4th treatment facility of the ratio of the different charging modes of indices configuration electric automobile;
Be connected with described 4th treatment facility, calculate the 5th treatment facility of the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data.
Wherein, described integrated system comprises integrated treatment facility, and described integrated treatment facility comprises:
The power demand predicting unit that scale calculates the charge power demand information in distribution region corresponding to described multiple local charging station is increased according to multiple local charging station start the to charge expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile;
The Distribution Network Load Data curve prediction unit of the following Distribution Network Load Data curve in this distribution region is determined according to the charge power demand information in described distribution region;
Calculate the index budget unit of the indices of the following Distribution Network Load Data curve in this distribution region;
According to the charging modes proportional arrangement unit of the ratio of the different charging modes of indices configuration electric automobile;
The electrically-charging equipment proportional arrangement unit of the electrically-charging equipment best configuration ratio in each region of following distribution is calculated according to this ratio and existing electrically-charging equipment capacity data.
Wherein, described transport module unit comprises: double port memory RAM transport module and complex programmable logic device (CPLD)/Field Programmable Gate Array FPGA translation interface.
Wherein, described index budget unit comprises:
Measure the first computing unit of peak load increment index;
Measure the second computing unit of duration of peaking time;
Measure the 3rd computing unit of peak load smoothing factor;
Measure the 4th computing unit of average load smoothing factor.
Wherein, described ARM9 statistics and output unit adopt S3C2440A chip.
Wherein, Ethernet transmission technology is adopted to communicate between described power-measuring circuit, transport module, mileage collection radio network gateway and ARM9 statistics and output circuit.
Wherein, it is characterized in that, the treatment facility included by described integrated system is IPC-810 industrial computer host-processor.
The embodiment of the present invention also provides a kind of charging electric vehicle facility proportional arrangement method, comprising:
Local measurement and statistic unit gather local Vehicular charging power information, and daily travel information;
Described charge power information and described daily travel expectation and variance information are added up by local measurement and statistic unit, obtain local charging station and to start to charge the expectation and variance information in moment;
The integrated system be connected with described local measurement and statistic unit, increases according to multiple local charging station start the to charge expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile the charge power demand information that scale data calculates the distribution region at multiple local charging station place;
Described integrated system determines the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in this distribution region, calculates the indices of the following Distribution Network Load Data curve in this region;
Described integrated system, according to the ratio of the different charging modes of indices configuration electric automobile, calculates the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data.
Based on technique scheme, the charging electric vehicle facility proportional arrangement system that the embodiment of the present invention provides is by the synergy of local measurement and statistic unit and integrated system, achieve the automatic configuration of charging electric vehicle facility ratio, and the different charge power demands that the embodiment of the present invention causes according to the different charging modes of electric automobile are to the Different Effects of Distribution Network Load Data curve, four load curve indexs are proposed for assessment of the impact of different charging modes electric automobile on Distribution Network Load Data curve under different proportion configuration.Then combined charge facility capacity data carry out proportional arrangement to the construction of following electrically-charging equipment.This configuration-system is more accurate, efficiency is better, and can carry out proportional arrangement according to the actual conditions of each different regions.Can displaying data in real-time have memory function, facilitate data query and management; According to the moon statistics, year statistics, can the concrete configuration of statistical research charging electric vehicle facility ratio further.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The structured flowchart of the charging electric vehicle facility proportional arrangement system that Fig. 1 provides for the embodiment of the present invention;
The structured flowchart of the integrated system that Fig. 2 provides for the embodiment of the present invention;
Another structured flowchart of the integrated system that Fig. 3 provides for the embodiment of the present invention;
The structured flowchart of the electric quantity acquisition unit that Fig. 4 provides for the embodiment of the present invention;
The ARM9 statistics that Fig. 5 provides for the embodiment of the present invention and the structured flowchart of output circuit;
The flow chart of a kind of charging electric vehicle facility proportional arrangement method that Fig. 6 provides for the embodiment of the present invention;
The flow chart of the electric automobile power demand prediction that Fig. 7 provides for the embodiment of the present invention, Distribution Network Load Data curve prediction and index calculating method.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
The structured flowchart of the charging electric vehicle facility proportional arrangement system that Fig. 1 provides for the embodiment of the present invention, as shown in Figure 1, charging electric vehicle facility proportional arrangement system comprises: local measurement and statistic unit 1 and integrated system 2.Local measurement and statistic unit 1 comprise: power-measuring circuit 01, transport module 02, mileage gather radio network gateway 03 and ARM9 adds up and output circuit 04.
Wherein, this charge power information, according to the charge power information of the local vehicle of the information gatherings such as three-phase and neutral point current, three-phase and neutral point voltage, is sent to transport module 02 by power-measuring circuit 1;
Power-measuring circuit 1 is connected with transport module 02, optionally, transport module 02 can comprise: dual port RAM (randomaccessmemory, random asccess memory) transport module, and CPLD(ComplexProgrammableLogicDevice), CPLD)/FPGA(Field-ProgrammableGateArray, field programmable gate array) translation interface.Optionally, transport module 02 is by dual port RAM transport module, the charge power information that CPLD/FPGA translation interface received power measuring circuit 1 transmits, after transport module 02 receives charge power information, can by this charge power information transmission to ARM9 statistics and output circuit 04;
Mileage gathers radio network gateway 03 and gathers daily travel information, is calculated the expectation and variance of daily travel by the daily travel information gathered, and calculated daily travel expectation and variance information is exported to ARM9 statistics and output circuit 04;
ARM9 statistics and output circuit 04 gather radio network gateway 03 with transport module 02 and mileage respectively and are connected, receive the power information that transport module 02 transmits, the daily travel expectation and variance information of radio network gateway 03 transmission is gathered with mileage, by described charge power information and described daily travel expectation and variance information are added up, obtain local charging station to start to charge the expectation and variance information in moment, the expectation and variance information transmission in the moment that started by the local charging station obtained to charge is to integrated system 2.
Integrated system 2 adds up with the ARM9 in local measurement and statistic unit 1 and output circuit 04 is connected, receive local charging station that ARM9 statistics and output circuit 04 transmit to start to charge the expectation and variance information in moment, start to charge according to multiple local charging station the expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile increase the charge power demand information that scale data calculates distribution region corresponding to described multiple local charging station, the following Distribution Network Load Data curve in this distribution region is determined according to the charge power demand information in this distribution region, calculate the indices of the following Distribution Network Load Data curve in this region, according to the ratio of the different charging modes of indices configuration electric automobile, the electrically-charging equipment best configuration ratio in each region of following distribution is calculated according to this ratio and existing electrically-charging equipment capacity data.
The charging electric vehicle facility proportional arrangement system that the embodiment of the present invention provides, by the synergy of local measurement and statistic unit and integrated system, achieves the automatic configuration of charging electric vehicle facility ratio.
The structured flowchart of the integrated system that Fig. 2 provides for the embodiment of the present invention, as shown in Figure 2, integrated system is connected to form successively by five treatment facilities, be respectively the first treatment facility 201, second treatment facility the 202, three treatment facility the 203, four treatment facility 204 and the 5th treatment facility 205;
Wherein, the first treatment facility 201 increases according to multiple local charging station start the to charge expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile the charge power demand information that scale calculates distribution region corresponding to described multiple local charging station;
Second treatment facility 202 is connected with described first treatment facility 201, determines the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in described distribution region;
3rd treatment facility 203 is connected with described second treatment facility 202, calculates the 3rd treatment facility of the indices of the following Distribution Network Load Data curve in this distribution region;
Optionally, the following Distribution Network Load Data curve in distribution region indices can be: peak load increment index, duration of peaking time, peak load smoothing factor and average load smoothing factor.
4th treatment facility 204 is connected with described 3rd treatment facility 203, according to the 4th treatment facility of the ratio of the different charging modes of indices configuration electric automobile;
5th treatment facility 205 is connected with described 4th treatment facility 204, calculates the 5th treatment facility of the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data.
Optionally, above-mentioned treatment facility all can be IPC-810 industrial control host, obviously also can be other the treatment facility that can carry out computing, as single-chip microcomputer etc.
Another structured flowchart of the integrated system that Fig. 3 provides for the embodiment of the present invention, as shown in Figure 3, integrated system 2 can comprise an integrated treatment facility 21, integrated treatment facility 21 comprises power demand predicting unit 211, Distribution Network Load Data curve prediction unit 212, index budget unit 213, charging modes proportional arrangement unit 214 and electrically-charging equipment proportional arrangement unit 215, wherein, index budget unit 213 can comprise again: the first computation subunit 2131, second computation subunit 2132, the 3rd computation subunit 2133 and the 4th computation subunit 2134;
Wherein, described power demand predicting unit 211 obtains electrically-charging equipment areally-distributed data by Ethernet transmission technology, electric automobile increases scale data, each local charging station starts to charge the expectation and variance value in moment in distribution region, then draws the charge power demand in whole distribution region after treatment;
The charge power demand information that described distribution network load curve prediction unit 212 calculates according to power demand predicting unit 211, determines the Distribution Network Load Data curve of the following Distribution Network Load Data curve in this distribution region;
The following Distribution Network Load Data curve in distribution region that index budget unit 213 is determined according to distribution network load curve prediction unit 212, calculates the indices of the following Distribution Network Load Data curve in this distribution region.
Wherein, first computation subunit 2131 can calculate peak load increment index, second computation subunit 2132 can calculate duration of peaking time, and the 3rd computation subunit 2133 can calculate peak load smoothing factor, and the 4th computation subunit 2134 can calculate average load smoothing factor.
Described charging modes proportional arrangement unit 214 can according to the ratio of the different charging modes of indices configuration electric automobile of load curve;
The ratio of the different charging modes of the electric automobile that electrically-charging equipment proportional arrangement unit 215 can calculate according to charging modes proportional arrangement unit 214, and existing electrically-charging equipment capacity data calculates the electrically-charging equipment best configuration ratio in each region of following distribution.
Integrated system shown in Fig. 2 and Fig. 3, the different charge power demands caused according to the different charging modes of electric automobile, to the Different Effects of Distribution Network Load Data curve, propose four load curve indexs for assessment of the impact of different charging modes electric automobile on Distribution Network Load Data curve under different proportion configuration.Then combined charge facility capacity data carry out proportional arrangement to the construction of following electrically-charging equipment.This configuration-system is more accurate, efficiency is better, and can carry out proportional arrangement according to the actual conditions of each different regions.Can displaying data in real-time have memory function, facilitate data query and management; According to the moon statistics, year statistics, can the concrete configuration of statistical research charging electric vehicle facility ratio further.
Optionally, ARM9 statistics and output circuit adopt S3C2440A chip.
Optionally, power demand predicting unit 211 receives the parameter of local charging station by wireless data transmission technology, thus obtains the charge power demand in whole distribution region.
Optionally, distribution network load curve prediction unit 212 obtains electrically-charging equipment areally-distributed data by Ethernet transmission technology and electric automobile increases scale data, thus obtains required following Distribution Network Load Data curve.
Charging electric vehicle facility proportional arrangement method and system compared with prior art, also has the following advantages:
1, voltage and current measurement module employing ATT7022C chip is the exemplary voltages electric current connected mode of the current/voltage three-phase power supply system of core, and its operational capability is powerful, certainty of measurement is high;
2, system adopts full-embedded type method for designing, wherein assess software and adopt the Linux embedded real-time operating system of opening source code, measurement platform adopts ARM9 and DSP to form multi-CPU system, greatly improve the reliability of measure-controlling unit, there is stronger reliability, network communications capability and expandability.
The structure chart of the electric quantity acquisition unit that Fig. 4 provides for the embodiment of the present invention, in the embodiment of the present invention, electric quantity acquisition unit is the combination of power-measuring circuit and transport module, as shown in Figure 4: collecting unit has preposition Signal-regulated kinase, A/D acquisition module DSP processing module to form successively.Electric quantity acquisition unit mainly completes the collection of voltage to collection point, current signal information, and to the frequency of voltage, electric current and the hard ware measure of phase place.The input of electric quantity acquisition unit is directly from voltage, the electric current input variable of instrument transformer secondary side, by Signal-regulated kinase such as bandpass filterings (45-55HZ), signal is being input to the frequency acquisition dedicated input mouth of A/D acquisition module and DSP processing module, the last computing being carried out data by DSP processing module, and data are sent to dual port RAM transport module, be sent to ARM9 statistics and output unit by dual port RAM transport module.In the present invention, this device needs sampling 4 road voltage signal and 4 road current signals, samples to meet 8 roads simultaneously, adopts 8 tunnels or more passage A/D acquisition module at a high speed, coordinates 8 road sampling holders to carry out electric current and voltage sampling.
Fig. 5 is the structure chart of ARM9 statistics and output circuit, as shown in the figure: form statistics and output unit by ARM9 processor, ARM9 and DSP processing module directly utilizes dual port RAM transport module to carry out the transmission of data.It utilizes dual port RAM and DSP to carry out transmission, the reading also unified management of data, data communication facility is completed by Ethernet, the data measured, CRC check code and temporal information are packaged into Frame, through Ethernet, data are sent to host computer and carry out web displaying.ARM9 statistics and output unit also comprise 2, RS485/232 interface, Ethernet interface 1, usb 1, CF card or one, electronic hard disc interface, one, LCD interface, and this plate carries the functional circuits such as RTC.
The flow chart of a kind of charging electric vehicle facility proportional arrangement method that Fig. 6 provides for the embodiment of the present invention, as shown in Figure 6, the method can comprise step:
Step 601, local measurement and statistic unit gather local Vehicular charging power information, and daily travel information;
Described charge power information and described daily travel expectation and variance information are added up by step 602, local measurement and statistic unit, obtain local charging station and to start to charge the expectation and variance information in moment;
Step 603, the integrated system be connected with described local measurement and statistic unit, increase according to multiple local charging station start the to charge expectation and variance value in moment, electrically-charging equipment areally-distributed data and electric automobile the charge power demand information that scale data calculates distribution region corresponding to described multiple local charging station;
Step 604, described integrated system determine the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in this distribution region, calculate the indices of the following Distribution Network Load Data curve in this region;
Step 605, described integrated system, according to the ratio of the different charging modes of indices configuration electric automobile, calculate the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data.
Fig. 7 is the flow chart of the prediction of electric automobile power demand, Distribution Network Load Data curve prediction and index calculating method, and as shown in the figure, flow chart is as follows:
1. the selected input data that can reflect electric power demand characteristics, comprise the purposes of electric automobile, the charging modes of electric automobile, the electric automobile recoverable amount in distribution region, electric automobile growth scale, electrically-charging equipment areally-distributed data, charging electric vehicle power data and probabilistic model.
2. first judge the function type of electric automobile, thus obtain corresponding distance travelled probabilistic model characteristic parameter; Then by SOC calculating formula, also charging interval length is determined accordingly to the initial SOC sampling of storage battery; To obtain starting charging moment probabilistic model to its sampling in conjunction with concrete vehicle again; Finally according to the charge power of the sample value determination separate unit electric automobile of the charging interval length of the moment that starts to charge, initial SOC and correspondence thereof.The daily travel d of electric automobile meets logarithm normal distribution,
Wherein, μ and σ 2 is respectively logarithmic average and variance.Suppose that electric automobile all just starts to travel after storage battery is full of, then the initial state-of-charge SOC of storage battery is such as formula (2), and its probability density function fSOC (x) is relevant to stochastic variable d simultaneously, charging interval length Tc then under different initial SOC needed for charge in batteries to Full Charge Capacity is as follows
Wherein, α is the interval number of days of adjacent twice charging; D is electric automobile daily travel; DR is the maximum range of electric automobile; Tcmax is that SOC is from 0% to 100% required time; P (t) is charging electric vehicle power.
3. the moment of charging of starting of electric automobile depends on himself function type, is uniformly distributed, then can extracts according to its probability-distribution function if it meets.
4. when electric automobile permeability is fixed, by regulating the ratio beta adopting quick charge mode electric automobile, the charging behavior of electric automobile and Distribution Network Load Data curve are matched, and what play charging load more fully fills out paddy effect, reduces the load impact to distribution simultaneously.For the impact of charging electric vehicle access on distribution peak load under quantitative analysis different proportion, increment index η LM and duration of peaking time index TLM is as follows for definition peak load,
In formula, P0Lmax is original loads peak value, PL (t) and PnLmax is respectively the sequential load after electric automobile access distribution and new load peak, μ is the load level parameter (μ=85%) of specifying, and T is load curve cycle (T=24h).Consider the impact that electric automobile behavior of charging at random is fluctuated on Distribution Network Load Data, peak load smoothing factor η LSmax and average load smoothing factor η LSavg is as follows in definition,
In formula, PLavg is the average load in time 0 ~ Δ T, and Δ T is the time interval (Δ T=2h), n=1,2 ..., N and N=T/ Δ T.
Because η LM, TLM, η LSmax and η LSavg is more little more excellent type index, four index linear weighted functions can be obtained overall target η LC=[ω M, ω TM, ω Smax, ω Savg] [η LM, TLM, η LSmax, η LSavg] T, and select the ratio beta of Quick charging electric automobile so that η LC is minimum for target.Then according to the electrically-charging equipment of the capacity data geometric ratio configuration same ratio of the proportional arrangement combined charge facility of charging modes.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (9)
1. a charging electric vehicle facility proportional arrangement system, is characterized in that, this system comprises:
(1) local measurement and statistic unit, described local measurement and statistic unit comprise:
Gather local Vehicular charging power information, and by the power-measuring circuit of this charge power information output;
Be connected with described power-measuring circuit, receive described charge power information, and export the transport module of described charge power information;
Gather daily travel information, calculate daily travel expectation and variance, and the mileage exporting the daily travel expectation and variance information calculated gathers radio network gateway;
Gather radio network gateway with described transport module and described mileage to be respectively connected, receive described charge power information, and described daily travel expectation and variance information, by described charge power information and described daily travel expectation and variance information are added up, obtain local charging station to start to charge the expectation and variance information in moment, and export ARM9 statistics and the output circuit of the expectation and variance information in the described moment that starts to charge;
(2) integrated system, described integrated system adds up with described ARM9 and output circuit is connected, receive the expectation and variance information in the described moment that starts to charge, start to charge according to multiple local charging station the expectation and variance information in moment, electrically-charging equipment areally-distributed data and electric automobile increase the charge power demand information that scale data calculates distribution region corresponding to described multiple local charging station, the following Distribution Network Load Data curve in this distribution region is determined according to the charge power demand information in this distribution region, calculate the indices of the following Distribution Network Load Data curve in this region, according to the ratio of the different charging modes of indices configuration electric automobile, the electrically-charging equipment best configuration ratio in each region of following distribution is calculated according to this ratio and existing electrically-charging equipment capacity data,
Described local measurement and statistic unit and described integrated system synergy, realize the automatic configuration of charging electric vehicle facility ratio.
2. system according to claim 1, is characterized in that, described integrated system comprises:
The first treatment facility that scale data calculates the charge power demand information in distribution region corresponding to described multiple local charging station is increased according to multiple local charging station start the to charge expectation and variance information in moment, electrically-charging equipment areally-distributed data and electric automobile;
Be connected with described first treatment facility, determine the second treatment facility of the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in described distribution region;
Be connected with described second treatment facility, calculate the 3rd treatment facility of the indices of the following Distribution Network Load Data curve in this distribution region;
Be connected with described 3rd treatment facility, according to the 4th treatment facility of the ratio of the different charging modes of indices configuration electric automobile;
Be connected with described 4th treatment facility, calculate the 5th treatment facility of the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data.
3. system according to claim 1, is characterized in that, described integrated system comprises integrated treatment facility, and described integrated treatment facility comprises:
The power demand predicting unit that scale data calculates the charge power demand information in distribution region corresponding to described multiple local charging station is increased according to multiple local charging station start the to charge expectation and variance information in moment, electrically-charging equipment areally-distributed data and electric automobile;
The Distribution Network Load Data curve prediction unit of the following Distribution Network Load Data curve in this distribution region is determined according to the charge power demand information in described distribution region;
Calculate the index budget unit of the indices of the following Distribution Network Load Data curve in this distribution region;
According to the charging modes proportional arrangement unit of the ratio of the different charging modes of indices configuration electric automobile;
The electrically-charging equipment proportional arrangement unit of the electrically-charging equipment best configuration ratio in each region of following distribution is calculated according to this ratio and existing electrically-charging equipment capacity data.
4. system according to claim 1, is characterized in that, described transport module unit comprises: Double Port Random Memory RAM, and complex programmable logic device (CPLD)/Field Programmable Gate Array FPGA translation interface.
5. system according to claim 3, is characterized in that, described index budget unit comprises:
Measure the first computation subunit of peak load increment index;
Measure the second computation subunit of duration of peaking time;
Measure the 3rd computation subunit of peak load smoothing factor;
Measure the 4th computation subunit of average load smoothing factor.
6. system according to claim 1, is characterized in that, described ARM9 statistics and output circuit are S3C2440A chip.
7. system according to claim 1, is characterized in that, described power-measuring circuit, transport module, mileage gather radio network gateway and ARM9 adds up and adopt Ethernet transmission technology to communicate between output circuit.
8. the system according to Claims 2 or 3, is characterized in that, the treatment facility included by described integrated system is IPC-810 industrial computer host-processor.
9. a charging electric vehicle facility proportional arrangement method, is characterized in that, comprising:
Local measurement and statistic unit gather local Vehicular charging power information and daily travel information, calculate daily travel expectation and variance information by the daily travel information gathered;
Described charge power information and described daily travel expectation and variance information are added up by local measurement and statistic unit, obtain local charging station and to start to charge the expectation and variance information in moment;
The integrated system be connected with described local measurement and statistic unit, increases according to multiple local charging station start the to charge expectation and variance information in moment, electrically-charging equipment areally-distributed data and electric automobile the charge power demand information that scale data calculates distribution region corresponding to described multiple local charging station;
Described integrated system determines the following Distribution Network Load Data curve in this distribution region according to the charge power demand information in this distribution region, calculates the indices of the following Distribution Network Load Data curve in this region;
Described integrated system, according to the ratio of the different charging modes of indices configuration electric automobile, calculates the electrically-charging equipment best configuration ratio in each region of following distribution according to this ratio and existing electrically-charging equipment capacity data;
By the synergy of described local measurement and statistic unit and described integrated system, realize the automatic configuration of charging electric vehicle facility ratio.
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| CN104283288B (en) * | 2014-10-28 | 2017-02-15 | 国家电网公司 | Charging method and device of charging station |
| CN106532686A (en) * | 2016-11-09 | 2017-03-22 | 国网山东省电力公司济南供电公司 | Area power distribution network load prediction method based on electric automobile charging apparatus |
| CN108090620A (en) * | 2017-12-27 | 2018-05-29 | 国网北京市电力公司 | Electrically-charging equipment market demand forecast method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102290852A (en) * | 2011-06-16 | 2011-12-21 | 丁凯 | Charging control method and system |
| CN102315677A (en) * | 2011-09-08 | 2012-01-11 | 中国电力科学研究院 | Monitoring system for electric automobile battery charging and replacing station |
| CN203014424U (en) * | 2012-11-30 | 2013-06-19 | 余杭供电局 | Electric automobile charging facility proportion configuration system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7956570B2 (en) * | 2008-01-07 | 2011-06-07 | Coulomb Technologies, Inc. | Network-controlled charging system for electric vehicles |
-
2012
- 2012-11-30 CN CN201210509889.7A patent/CN103855739B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102290852A (en) * | 2011-06-16 | 2011-12-21 | 丁凯 | Charging control method and system |
| CN102315677A (en) * | 2011-09-08 | 2012-01-11 | 中国电力科学研究院 | Monitoring system for electric automobile battery charging and replacing station |
| CN203014424U (en) * | 2012-11-30 | 2013-06-19 | 余杭供电局 | Electric automobile charging facility proportion configuration system |
Non-Patent Citations (2)
| Title |
|---|
| 基于层次分析和概率模拟的电动汽车对配网负荷影响研究;徐国钧等;《电力系统保护与控制》;20121116;第40卷(第22期);第38-45页 * |
| 电动汽车充电设施规划方法;吴春阳等;《电力系统自动化》;20101225;第34卷(第24期);第36-39、45页 * |
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