CN102593901A - Load forecasting system of electromobile charging facility and forecasting method - Google Patents

Abstract

The invention discloses a load forecasting system of an electromobile charging facility and a load forecasting method in the technical field of electromobile planning and design. The load forecasting system comprises an input module, a processing module and an output module; the input module is used for inputting data of factors influencing load forecasting of the charging facility and automobile data; the processing module is used for computing the maximum load of area charging facilities and the maximum load of concentrated charging facilities; and the output module is used for displaying and outputting the maximum load of the area charging facilities and the maximum load of the concentrated charging facilities. The load forecasting method provided by the invention comprises the steps: inputting the data of the factors influencing the load forecasting of the charging facility and the automobile data; computing the maximum load of the area charging facilities and the maximum load of the concentrated charging facilities according to the input data; outputting the maximum load of the area charging facilities and the maximum load of the concentrated charging facilities. In the load forecasting system and the load forecasting method provided by the invention, the charging load of area electromobiles can be forecasted accurately.

Description

Charging electric vehicle load of utility prognoses system and Forecasting Methodology

Technical field

The invention belongs to electric automobile planning and designing technical field, relate in particular to a kind of charging electric vehicle load of utility prognoses system and Forecasting Methodology.

Background technology

Along with the development of electric automobile, the construction of charging electric vehicle infrastructure must pick up the pace, even needs planning in advance, in order to make planning rationally, accurately, avoids blindness, need predicting the charging electric vehicle load.

Summary of the invention

The objective of the invention is to, propose a kind of charging electric vehicle load of utility prognoses system and Forecasting Methodology.

For realizing above-mentioned purpose; Technical scheme provided by the invention is; A kind of charging electric vehicle load of utility prognoses system is characterized in that said prognoses system comprises input module, processing module and output module, and said processing module links to each other with output module with input module respectively;

Said input module is used to import the data and the car data of the factor that influences the electrically-charging equipment load prediction, and the data of input are sent to processing module;

Said processing module is used for the data according to input module input, zoning electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, and regional electrically-charging equipment peak load and concentrated electrically-charging equipment peak load sent to output module;

Said output module is used for viewing area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, and is used for printout zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

The said data that influence the factor of electrically-charging equipment load prediction comprise the factor that influences the electrically-charging equipment load prediction reference data, influence the electrically-charging equipment load prediction achievement data and the zone of factor to the distance of concentrating electrically-charging equipment; Wherein, the reference data that influences the factor of electrically-charging equipment load prediction comprises maximum use hourage in benchmark permeability, benchmark dispersion rate, reference area maximum use hourage and the benchmark set; The achievement data that influences the factor of electrically-charging equipment load prediction comprises permeability desired value, permeability weights, dispersion rate desired value, dispersion rate weights, regional maximum use hourage desired value, regional maximum use hourage weights, concentrates maximum use hourage desired value and concentrated maximum use hourage weights.

Car data comprises the average annual distance travelled of regional automobile total quantity, electric automobile, the weight of electric automobile and per ton hundred kilometers power consumptions of electric automobile.

Said processing module comprises regional maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit, concentration ratio computing unit, concentrates charge volume apportionment ratio computing unit, concentrated maximum use hourage computing unit, regional electrically-charging equipment peak load to calculate unit and concentrated electrically-charging equipment peak load calculating unit;

Zone maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit calculate the unit with regional electrically-charging equipment peak load respectively and link to each other;

Permeability computing unit, concentration ratio computing unit, concentrated charge volume apportionment ratio computing unit calculate the unit with concentrated electrically-charging equipment peak load respectively with concentrated maximum use hourage computing unit and link to each other;

Said regional maximum use hourage computing unit is used for according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage, and regional maximum use hourage is sent to regional electrically-charging equipment peak load calculating unit;

Said dispersion rate computing unit is used for calculating dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and dispersion rate is sent to regional electrically-charging equipment peak load calculating unit;

Said permeability computing unit is used for calculating permeability according to benchmark permeability, permeability desired value and permeability weights; According to regional automobile total quantity and permeability zoning electric automobile quantity; According to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile, again permeability and regional charging electric vehicle demand are sent to regional electrically-charging equipment peak load calculating unit and concentrated electrically-charging equipment peak load calculating unit;

Said concentration ratio computing unit is used for calculating concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and concentration ratio is sent to concentrated electrically-charging equipment peak load calculating unit;

Said concentrated charge volume apportionment ratio computing unit is used for arriving each zone of distance calculation of concentrated electrically-charging equipment apportionment ratio to each concentrated electrically-charging equipment according to the zone, and each regional apportionment ratio to each concentrated electrically-charging equipment is sent to concentrated electrically-charging equipment peak load calculating unit;

Said concentrated maximum use hourage computing unit is used for according to benchmark set maximum use hourage, concentrates maximum use hourage in maximum use hourage desired value and the concentrated maximum use hourage weights calculated set, and will concentrate the maximum use hourage to send to and concentrate the electrically-charging equipment peak load to calculate the unit;

Said regional electrically-charging equipment peak load is calculated the unit and is used for according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load, and regional electrically-charging equipment peak load is sent to output module;

Said concentrated electrically-charging equipment peak load is calculated the unit and is used for concentrating charge volume according to concentration ratio and regional charging electric vehicle demand zoning; Again according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each concentrated electrically-charging equipment; Basis is concentrated electrically-charging equipment peak load in electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set then, and will concentrate the electrically-charging equipment peak load to send to output module.

A kind of charging electric vehicle load of utility Forecasting Methodology is characterized in that said Forecasting Methodology comprises:

Step 1: input influences the data and the car data of the factor of electrically-charging equipment load prediction;

Step 2: according to the data computation zone electrically-charging equipment peak load and the concentrated electrically-charging equipment peak load of input;

Step 3: output area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

The said data that influence the factor of electrically-charging equipment load prediction comprise the factor that influences the electrically-charging equipment load prediction reference data, influence the electrically-charging equipment load prediction achievement data and the zone of factor to the distance of concentrating electrically-charging equipment; Wherein, the reference data that influences the factor of electrically-charging equipment load prediction comprises maximum use hourage in benchmark permeability, benchmark dispersion rate, reference area maximum use hourage and the benchmark set; The achievement data that influences the factor of electrically-charging equipment load prediction comprises permeability desired value, permeability weights, dispersion rate desired value, dispersion rate weights, regional maximum use hourage desired value, regional maximum use hourage weights, concentrates maximum use hourage desired value and concentrated maximum use hourage weights.

Said car data comprises the average annual distance travelled of regional automobile total quantity, electric automobile, the weight of electric automobile and per ton hundred kilometers power consumptions of electric automobile.

Said step 2 specifically comprises:

Step 201: according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage;

Calculate dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights;

Calculate permeability according to benchmark permeability, permeability desired value and permeability weights;

Calculate concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights;

According to the apportionment ratio of zone to each zone of the distance calculation of concentrating electrically-charging equipment to each concentrated electrically-charging equipment;

According to maximum use hourage in maximum use hourage, concentrated maximum use hourage desired value and the concentrated maximum use hourage weights calculated set in the benchmark set;

Step 202: according to regional automobile total quantity and permeability zoning electric automobile quantity;

Step 203: according to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile, while execution in step 204 and step 205;

Step 204: according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load; Execution in step 208;

Step 205: concentrate charge volume according to concentration ratio and regional charging electric vehicle demand zoning;

Step 206: according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each concentrated electrically-charging equipment;

Step 207: according to electrically-charging equipment peak load in concentrated electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set;

Step 208: sending zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

The present invention is estimation range charging electric vehicle load exactly, for the early stage planning of charging electric vehicle facility provides foundation.

Description of drawings

Fig. 1 is a charging electric vehicle load of utility prognoses system structure chart;

Fig. 2 is a charging electric vehicle load of utility Forecasting Methodology flow chart;

Fig. 3 is the weight table of achievement data that influences the factor of electrically-charging equipment load prediction;

Fig. 4 is near the concentrated electrically-charging equipment sketch map the concentrated charge capacity in a certain zone is assigned to.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit scope of the present invention and application thereof.

Fig. 1 is a charging electric vehicle load of utility prognoses system structure chart.Among Fig. 1, charging electric vehicle load of utility prognoses system provided by the invention comprises input module, processing module and output module.Wherein, processing module links to each other with output module with input module respectively.

Input module is used to import the data and the car data of the factor that influences the electrically-charging equipment load prediction, and the data of input are sent to output module.

In the present embodiment, choose the factor that influences the electrically-charging equipment load prediction and comprise economy/income level, area type, scale and position; The electric automobile price; Go/continual mileage vehicle laws of use/orderly management, charging price; The electrically-charging equipment system, quality and totally 9 kinds of maintenance and other policy factors.

To the factor that influences the electrically-charging equipment load prediction, confirm reference data, desired value and weights.

The data that influence the factor of electrically-charging equipment load prediction comprise the factor that influences the electrically-charging equipment load prediction reference data, influence the electrically-charging equipment load prediction achievement data and the zone of factor to the distance of concentrating electrically-charging equipment.

Influence maximum use hourage in reference data benchmark permeability, benchmark dispersion rate, reference area maximum use hourage and the benchmark set of factor of electrically-charging equipment load prediction.

The achievement data that influences the factor of electrically-charging equipment load prediction comprises permeability desired value, permeability weights, dispersion rate desired value, dispersion rate weights, regional maximum use hourage desired value, regional maximum use hourage weights, concentrates maximum use hourage desired value and concentrated maximum use hourage weights.

Car data comprises the average annual distance travelled of regional automobile total quantity, electric automobile, the weight of electric automobile and per ton hundred kilometers power consumptions of electric automobile.

Processing module is used for the data according to input module input, zoning electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, and regional electrically-charging equipment peak load and concentrated electrically-charging equipment peak load sent to output module.

Processing module comprises regional maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit, concentration ratio computing unit, concentrates charge volume apportionment ratio computing unit, concentrated maximum use hourage computing unit, regional electrically-charging equipment peak load to calculate unit and concentrated electrically-charging equipment peak load calculating unit.

Zone maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit calculate the unit with regional electrically-charging equipment peak load respectively and link to each other.

Permeability computing unit, concentration ratio computing unit, concentrated charge volume apportionment ratio computing unit calculate the unit with concentrated electrically-charging equipment peak load respectively with concentrated maximum use hourage computing unit and link to each other.

Zone maximum use hourage computing unit is used for according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage, and regional maximum use hourage is sent to regional electrically-charging equipment peak load calculating unit.

The dispersion rate computing unit is used for calculating dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and dispersion rate is sent to regional electrically-charging equipment peak load calculating unit.

The permeability computing unit is used for calculating permeability according to benchmark permeability, permeability desired value and permeability weights; According to regional automobile total quantity and permeability zoning electric automobile quantity; According to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile, again permeability and regional charging electric vehicle demand are sent to regional electrically-charging equipment peak load calculating unit and concentrated electrically-charging equipment peak load calculating unit.

The concentration ratio computing unit is used for calculating concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and concentration ratio is sent to concentrated electrically-charging equipment peak load calculating unit.

Concentrated charge volume apportionment ratio computing unit is used for arriving each zone of distance calculation of concentrated electrically-charging equipment apportionment ratio to each electrically-charging equipment according to the zone, and each regional apportionment ratio to each electrically-charging equipment is sent to concentrated electrically-charging equipment peak load calculating unit.

Concentrate maximum use hourage computing unit to be used for according to benchmark set maximum use hourage, to concentrate maximum use hourage in maximum use hourage desired value and the concentrated maximum use hourage weights calculated set, and will concentrate the maximum use hourage to send to and concentrate the electrically-charging equipment peak load to calculate the unit.

Zone electrically-charging equipment peak load is calculated the unit and is used for according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load, and regional electrically-charging equipment peak load is sent to output module.

Concentrating the electrically-charging equipment peak load to calculate the unit is used for concentrating charge volume according to concentration ratio and regional charging electric vehicle demand zoning; Again according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each electrically-charging equipment; Basis is concentrated electrically-charging equipment peak load in electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set then, and will concentrate the electrically-charging equipment peak load to send to output module.

Output module is used for viewing area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.Also be used for demonstrating all types of zones (shopping centre, office building, residential area) and charging station with the form of element; Click certain element and can eject a display window, show that computing module is to the result of calculation of this element and the partial data of the original input of element (sign, coordinate, type, scale).Through dragging each element, can put their relative position.

Output module also is used for printout zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.And can print the plane graph that comprises each element, also can generate the electronic document that contains each component information (sign, coordinate, type, scale, peak load etc.).

Fig. 2 is a charging electric vehicle load of utility Forecasting Methodology flow chart, and among Fig. 2, charging electric vehicle load of utility Forecasting Methodology comprises:

Step 1: input influences the data and the car data of the factor of electrically-charging equipment load prediction.

In the present embodiment, choose the factor that influences the electrically-charging equipment load prediction and comprise economy/income level, area type, scale and position; The electric automobile price; Go/continual mileage vehicle laws of use/orderly management, charging price; The electrically-charging equipment system, quality and totally 9 kinds of maintenance and other policy factors.

To the factor that influences the electrically-charging equipment load prediction, confirm reference data, desired value and weights.

Reference data comprises benchmark permeability α _D0, benchmark dispersion rate β _D0, reference area maximum use hourage σ _D0With maximum use hourage σ in the benchmark set _C0

Fig. 3 is the weight table of achievement data that influences the factor of electrically-charging equipment load prediction.Provided the concrete numerical value of permeability weights, dispersion rate weights, regional maximum use hourage weights and concentrated maximum use hourage weights among Fig. 3.And the permeability desired value in the present embodiment, dispersion rate desired value, regional maximum use hourage desired value and concentrated maximum use hourage desired value (are the x among Fig. 3 ₁-x ₉) can rule of thumb draw or utilize least square fitting to draw.

J regional distance to i concentrated electrically-charging equipment, promptly the zone is r to the distance of concentrating electrically-charging equipment _DC(ji).

Car data comprises regional automobile total quantity N _EVD(j) (quantity of electric automobile in j zone), the average annual distance travelled S (getting the S=15000 kilometer in the present embodiment) of electric automobile, the weight H (getting the H=2 ton in the present embodiment) of electric automobile and per ton hundred kilometers power consumption k (getting k=10 kilowatt-hour/ton hundred kilometers in the present embodiment) of electric automobile.

Step 2: data computation zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load according to input comprise:

Step 201: according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage.Its computing formula does

${\mathrm{\σ}}_D\left(j\right)={\mathrm{\σ}}_{D0}\underset{i=1}{\overset{9}{\mathrm{\Σ}}}{g}_i{x}_{\mathrm{ji}}---\left(1\right)$

Wherein, σ _D(j) be the regional maximum use hourage in j zone, σ _D0Be reference area maximum use hourage, g _i(i=1,2 ..., 9) be that under the regional maximum use hourage each influences the weights of the factor of electrically-charging equipment load prediction, x _JiBe j the zone each influence the desired value of the factor of electrically-charging equipment load prediction.

Calculate dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights.Its computing formula does

${\mathrm{\β}}_D\left(j\right)={\mathrm{\β}}_{D0}\underset{i=1}{\overset{9}{\mathrm{\Σ}}}{f}_i{x}_{\mathrm{ji}}---\left(2\right)$

Wherein, β _D(j) be the dispersion rate in j zone, β _D0Be benchmark dispersion rate, f _i(i=1,2 ..., 9) influence the weights of the factor of electrically-charging equipment load prediction, x under the dispersion rate each _JiBe j the zone each influence the desired value of the factor of electrically-charging equipment load prediction.

Calculate permeability according to benchmark permeability, permeability desired value and permeability weights.Its computing formula does

${\mathrm{\α}}_D\left(j\right)={\mathrm{\α}}_{D0}\underset{i=1}{\overset{9}{\mathrm{\Σ}}}{e}_j{x}_{\mathrm{ji}}---\left(3\right)$

Wherein, α _D(j) be the permeability in j zone, α _D0Be benchmark permeability, e _i(i=1,2 ..., 9) be that under the permeability each influences the weights of the factor of electrically-charging equipment load prediction, x _JiBe j the zone each influence the desired value of the factor of electrically-charging equipment load prediction.

Calculate concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights.Its computing formula does

${\mathrm{\γ}}_D\left(j\right)=1-{\mathrm{\β}}_D\left(j\right)=1-{\mathrm{\β}}_{D0}\underset{i=1}{\overset{9}{\mathrm{\Σ}}}{f}_i{x}_{\mathrm{ji}}---\left(4\right)$

Wherein, β _D(j) be the dispersion rate in j zone, β _D0Be benchmark dispersion rate, f _i(i=1,2 ..., 9) influence the weights of the factor of electrically-charging equipment load prediction, x under the dispersion rate each _JiBe j the zone each influence the desired value of the factor of electrically-charging equipment load prediction.

According to the apportionment ratio of zone to each zone of the distance calculation of concentrating electrically-charging equipment to each electrically-charging equipment.Because near the electrically-charging equipment need the concentrated charge capacity of regional j being assigned to; The electrically-charging equipment that exceeds setpoint distance then will not distribute charge capacity; Shown in accompanying drawing 4, with interior electrically-charging equipment, adopt here and wait load apart from apportion design for this distance; Be the electric weight that is assigned to of each electrically-charging equipment therewith electrically-charging equipment equate that to the product of the distance in zone the apportionment ratio of establishing i the electrically-charging equipment of j region allocation in I the charging station is λ _DC(ji), then j regional apportionment ratio for all electrically-charging equipments need meet the following conditions:

$\left\{\begin{array}{c}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{\mathrm{\λ}}_{\mathrm{DC}}\left(\mathrm{ji}\right)=1\\ {\mathrm{\λ}}_{\mathrm{DC}}\left(\mathrm{ji}\right)=0,r_{\mathrm{DC}}\left(\mathrm{ji}\right)>r_{D\max }\\ r_{\mathrm{DC}}\left({\mathrm{ji}}_1\right)\×{\mathrm{\λ}}_{\mathrm{DC}}\left({\mathrm{ji}}_1\right)=r_{\mathrm{DC}}\left({\mathrm{ji}}_2\right)\×{\mathrm{\λ}}_{\mathrm{DC}}\left({\mathrm{ji}}_2\right)=...=r_{\mathrm{DC}}\left({\mathrm{ji}}_m\right)\×{\mathrm{\λ}}_{\mathrm{DC}}\left({\mathrm{ji}}_m\right),r_{\mathrm{DC}}\left({\mathrm{ji}}_p\right)\≤r_{D\max }\end{array}\right.---\left(5\right)$

In the following formula, λ _DC(ji) be the apportionment ratio of j region allocation to i electrically-charging equipment; r _DC(ji) be the distance of j zone to i electrically-charging equipment; r _DmaxBe the maximum service radius of electrically-charging equipment, present embodiment is got r _Dmax=3 kilometers; Subscript 1,2 ..., m representes total m the r that satisfies condition _DC(ji _p)≤r _DmaxThe site.

Therefore, can obtain apportionment ratio λ by formula (5) from j region allocation to i electrically-charging equipment _DC(j) be:

${\mathrm{\λ}}_{\mathrm{DC}}\left(\mathrm{ji}\right)=\left\{\begin{array}{c}\frac{1/r_{\mathrm{DC}}\left(\mathrm{ji}\right)}{\underset{l=1}{\overset{I}{\mathrm{\Σ}}}1/r_{\mathrm{DC}}\left(\mathrm{jl}\right)},r_{\mathrm{DC}}\left(\mathrm{ji}\right)\≤r_{D\max },r_{\mathrm{DC}}\left(\mathrm{jl}\right)\≤r_{D\max }\\ 0,r_{\mathrm{DC}}\left(\mathrm{ji}\right)>r_{D\max }\end{array}\right.---\left(6\right)$

According to maximum use hourage in maximum use hourage, concentrated maximum use hourage desired value and the concentrated maximum use hourage weights calculated set in the benchmark set.Its computing formula does

${\mathrm{\σ}}_C\left(j\right)={\mathrm{\σ}}_{C0}\underset{i=1}{\overset{9}{\mathrm{\Σ}}}{h}_i{x}_{\mathrm{ji}}---\left(7\right)$

Wherein, σ _C(j) be the concentrated maximum use hourage of j electrically-charging equipment, σ _C0Be maximum use hourage in the benchmark set, h _i(i=1,2 ..., 9) influence the weights of the factor of electrically-charging equipment load prediction, x for concentrating under the maximum use hourage each _JiBe j electrically-charging equipment each influence the desired value of the factor of electrically-charging equipment load prediction.

Step 202: according to regional automobile total quantity and permeability zoning electric automobile quantity.Its computing formula does

N _EVD(j)＝α _D(j)·N _VD(j) (8)

Wherein, N _EVD(j) be the quantity of electric automobile in j the zone, α _D(j) be the permeability in j zone, N _VD(j) be j regional automobile total quantity.

Step 203: according to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile.Its computing formula does

E _D(j)＝kHSN _EVD(j) (9)

Wherein, E _D(j) be j regional charging electric vehicle demand, k is per ton hundred kilometers power consumptions of electric automobile, and H is the weight of electric automobile, and S is the average annual distance travelled of electric automobile, N _EVD(j) be the quantity of electric automobile in j the zone.Simultaneously execution in step 204 and step 205 then.

Step 204: according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load.Its computing formula does

$P_{D\max }\left(j\right)=\frac{{\mathrm{\β}}_D\left(j\right)E_D\left(j\right)}{{\mathrm{\σ}}_D\left(j\right)}---\left(10\right)$

Wherein, P _Dmax(j) be j regional electrically-charging equipment peak load, β _D(j) be the dispersion rate in j zone, E _D(j) be j regional charging electric vehicle demand, σ _D(j) be j regional maximum use hourage.Afterwards, skip to step 208.

Step 205: concentrate charge volume according to concentration ratio and regional charging electric vehicle demand zoning.Its computing formula does

Q _D(j)＝γ _D(j)E _D(j) (11)

Wherein, Q _D(j) be j regional centralized charge volume, γ _D(j) be j Area Concentration, E _D(j) be j regional charging electric vehicle demand.

Step 206: according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each concentrated electrically-charging equipment.Its computing formula does

$E_C\left(i\right)=\underset{j=1}{\overset{J}{\mathrm{\Σ}}}{\mathrm{\λ}}_{\mathrm{DC}}\left(\mathrm{ji}\right)Q_D\left(j\right)=\underset{j=1}{\overset{J}{\mathrm{\Σ}}}{\mathrm{\λ}}_{\mathrm{DC}}\left(\mathrm{ji}\right){\mathrm{\γ}}_D\left(j\right)E_D\left(j\right)---\left(12\right)$

Wherein, E _C(i) be i concentrated electrically-charging equipment charging demand, Q _D(j) be j regional centralized charge volume, λ _DC(ji) being j region allocation is γ to the apportionment ratio of i concentrated electrically-charging equipment _D(j) be j Area Concentration, E _D(j) be j regional charging electric vehicle demand, J is the zone sum.

Step 207: according to electrically-charging equipment peak load in concentrated electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set.Its computing formula does

P _Cmax(i)＝E _C(i)/σ _C(i) (13)

Wherein, P _Cmax(i) be i concentrated electrically-charging equipment peak load, E _C(i) be i concentrated electrically-charging equipment charging demand, σ _C(i) be the concentrated maximum use hourage of i concentrated electrically-charging equipment.

Step 208: regional electrically-charging equipment peak load and concentrated electrically-charging equipment peak load are sent to the module that is used to export.

Step 3: after the module that is used to export is received regional electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, output area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technical staff who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (8)

1. a charging electric vehicle load of utility prognoses system is characterized in that said prognoses system comprises input module, processing module and output module, and said processing module links to each other with output module with input module respectively;

Said input module is used to import the data and the car data of the factor that influences the electrically-charging equipment load prediction, and the data of input are sent to processing module;

Said processing module is used for the data according to input module input, zoning electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, and regional electrically-charging equipment peak load and concentrated electrically-charging equipment peak load sent to output module;

Said output module is used for viewing area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load, and is used for printout zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

2. charging electric vehicle load of utility prognoses system according to claim 1 is characterized in that the said data that influence the factor of electrically-charging equipment load prediction comprise the reference data of the factor that influences the electrically-charging equipment load prediction, achievement data and the zone of factor that influence the electrically-charging equipment load prediction be to the distance of concentrating electrically-charging equipment; Wherein, the reference data that influences the factor of electrically-charging equipment load prediction comprises maximum use hourage in benchmark permeability, benchmark dispersion rate, reference area maximum use hourage and the benchmark set; The achievement data that influences the factor of electrically-charging equipment load prediction comprises permeability desired value, permeability weights, dispersion rate desired value, dispersion rate weights, regional maximum use hourage desired value, regional maximum use hourage weights, concentrates maximum use hourage desired value and concentrated maximum use hourage weights.

3. charging electric vehicle load of utility prognoses system according to claim 2 is characterized in that car data comprises the average annual distance travelled of regional automobile total quantity, electric automobile, the weight of electric automobile and per ton hundred kilometers power consumptions of electric automobile.

4. charging electric vehicle load of utility prognoses system according to claim 3 is characterized in that said processing module comprises regional maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit, concentration ratio computing unit, concentrates charge volume apportionment ratio computing unit, concentrated maximum use hourage computing unit, regional electrically-charging equipment peak load to calculate unit and concentrated electrically-charging equipment peak load calculating unit;

Zone maximum use hourage computing unit, dispersion rate computing unit, permeability computing unit calculate the unit with regional electrically-charging equipment peak load respectively and link to each other;

Permeability computing unit, concentration ratio computing unit, concentrated charge volume apportionment ratio computing unit calculate the unit with concentrated electrically-charging equipment peak load respectively with concentrated maximum use hourage computing unit and link to each other;

Said regional maximum use hourage computing unit is used for according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage, and regional maximum use hourage is sent to regional electrically-charging equipment peak load calculating unit;

Said dispersion rate computing unit is used for calculating dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and dispersion rate is sent to regional electrically-charging equipment peak load calculating unit;

Said permeability computing unit is used for calculating permeability according to benchmark permeability, permeability desired value and permeability weights; According to regional automobile total quantity and permeability zoning electric automobile quantity; According to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile, again permeability and regional charging electric vehicle demand are sent to regional electrically-charging equipment peak load calculating unit and concentrated electrically-charging equipment peak load calculating unit;

Said concentration ratio computing unit is used for calculating concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights, and concentration ratio is sent to concentrated electrically-charging equipment peak load calculating unit;

Said concentrated charge volume apportionment ratio computing unit is used for arriving each zone of distance calculation of concentrated electrically-charging equipment apportionment ratio to each concentrated electrically-charging equipment according to the zone, and each regional apportionment ratio to each concentrated electrically-charging equipment is sent to concentrated electrically-charging equipment peak load calculating unit;

Said concentrated maximum use hourage computing unit is used for according to benchmark set maximum use hourage, concentrates maximum use hourage in maximum use hourage desired value and the concentrated maximum use hourage weights calculated set, and will concentrate the maximum use hourage to send to and concentrate the electrically-charging equipment peak load to calculate the unit;

Said regional electrically-charging equipment peak load is calculated the unit and is used for according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load, and regional electrically-charging equipment peak load is sent to output module;

Said concentrated electrically-charging equipment peak load is calculated the unit and is used for concentrating charge volume according to concentration ratio and regional charging electric vehicle demand zoning; Again according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each concentrated electrically-charging equipment; Basis is concentrated electrically-charging equipment peak load in electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set then, and will concentrate the electrically-charging equipment peak load to send to output module.

5. charging electric vehicle load of utility Forecasting Methodology is characterized in that said Forecasting Methodology comprises:

Step 1: input influences the data and the car data of the factor of electrically-charging equipment load prediction;

Step 2: according to the data computation zone electrically-charging equipment peak load and the concentrated electrically-charging equipment peak load of input;

Step 3: output area electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

6. charging electric vehicle load of utility Forecasting Methodology according to claim 5 is characterized in that the said data that influence the factor of electrically-charging equipment load prediction comprise the reference data of the factor that influences the electrically-charging equipment load prediction, achievement data and the zone of factor that influence the electrically-charging equipment load prediction be to the distance of concentrating electrically-charging equipment; Wherein, the reference data that influences the factor of electrically-charging equipment load prediction comprises maximum use hourage in benchmark permeability, benchmark dispersion rate, reference area maximum use hourage and the benchmark set; The achievement data that influences the factor of electrically-charging equipment load prediction comprises permeability desired value, permeability weights, dispersion rate desired value, dispersion rate weights, regional maximum use hourage desired value, regional maximum use hourage weights, concentrates maximum use hourage desired value and concentrated maximum use hourage weights.

7. charging electric vehicle load of utility Forecasting Methodology according to claim 6 is characterized in that said car data comprises the average annual distance travelled of regional automobile total quantity, electric automobile, the weight of electric automobile and per ton hundred kilometers power consumptions of electric automobile.

8. charging electric vehicle load of utility Forecasting Methodology according to claim 7 is characterized in that said step 2 specifically comprises:

Step 201: according to reference area maximum use hourage, regional maximum use hourage desired value and regional maximum use hourage weights zoning maximum use hourage;

Calculate dispersion rate according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights;

Calculate permeability according to benchmark permeability, permeability desired value and permeability weights;

Calculate concentration ratio according to benchmark dispersion rate, dispersion rate desired value and dispersion rate weights;

According to the apportionment ratio of zone to each zone of the distance calculation of concentrating electrically-charging equipment to each concentrated electrically-charging equipment;

According to maximum use hourage in maximum use hourage, concentrated maximum use hourage desired value and the concentrated maximum use hourage weights calculated set in the benchmark set;

Step 202: according to regional automobile total quantity and permeability zoning electric automobile quantity;

Step 203: according to the average annual distance travelled of electric automobile, the weight of electric automobile, the per ton hundred kilometers power consumptions and the regional electric automobile quantity zoning charging electric vehicle demand of electric automobile, while execution in step 204 and step 205;

Step 204: according to dispersion rate, regional maximum use hourage and regional charging electric vehicle demand zoning electrically-charging equipment peak load; Execution in step 208;

Step 205: concentrate charge volume according to concentration ratio and regional charging electric vehicle demand zoning;

Step 206: according to regional centralized charge volume and each zone electrically-charging equipment charging demand in the apportionment ratio calculated set of each concentrated electrically-charging equipment;

Step 207: according to electrically-charging equipment peak load in concentrated electrically-charging equipment charging demand and the concentrated maximum use hourage calculated set;

Step 208: sending zone electrically-charging equipment peak load and concentrated electrically-charging equipment peak load.

Images