CN110224395A - The power distribution network collaborative planning method of meter and DG correlation and EV demand response - Google Patents
The power distribution network collaborative planning method of meter and DG correlation and EV demand response Download PDFInfo
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Classifications
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- 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
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- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H02J3/383—
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- H02J3/386—
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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- 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]
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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Abstract
The invention discloses meter and the power distribution network collaborative planning methods of DG correlation and EV demand response, honourable independent normal is generated using Monte Carlo method of random sampling and wind speed illumination probability Distribution Model and is distributed sample, the application enhancements Correlation Moment tactical deployment of troops is converted to correlated samples to calculate honourable output power；Establish multiple target two-layer hybrid integral linear programming model, planning layer carries out addressing constant volume to blower, photovoltaic and charging pile, firing floor considers that the costs such as power purchase, network loss, load fluctuation punishment and abandoning electricity punishment are minimum based on the thought of penalty function, and investment, DG power output, trend and EV charge and discharge electricity price etc. are indicated with linear restriction；Subregion is carried out to road network system using Voronoi diagram and obtains the service range of EV charging station to determine the upper limit amount of EV response；The time scale of unified bilayer model is translated into single layer and is solved to obtain the program results to blower, photovoltaic and charging station.
Description
Technical field
The invention belongs to urban distribution network planning technical fields, and in particular to a kind of meter and DG correlation and EV demand response
Power distribution network collaborative planning method.
Background technique
Under the promotion of Green Development, blower, the permeability of photovoltaic and electric car and distribution electricity consumption are growing day by day,
Load peak valley, which increases with it, causes the economy decline of electric system to result in waste of resources.And blower, photovoltaic power output are vulnerable to environment
The influence of factor has very strong randomness, timing, and the blower of different zones, photovoltaic power output have correlation, while electricity
The orderly charge and discharge of electrical automobile have the effects that peak load shifting, promote new energy consumption, therefore in urban distribution network planning meter and
Correlation between blower and photovoltaic node simultaneously considers that the response reply of EV charge-discharge power demand reduces compound peak valley gap and has important meaning
Justice.
Summary of the invention
For above-mentioned deficiency in the prior art, the power distribution network of meter provided by the invention and DG correlation and EV demand response
Collaborative planning method solves the problems, such as that existing urban power distribution network load peak-valley difference is excessive.
In order to achieve the above object of the invention, the technical solution adopted by the present invention are as follows: meter and DG correlation and EV demand response
Power distribution network collaborative planning method, comprising the following steps:
S1, honourable independent normal distribution sample matrix is generated, and honourable output power probabilistic model is calculated according to it；
S2, the relevant parameter of EV charging station is obtained to road network system progress subregion by Voronoi diagram；
Wherein, the relevant parameter of EV charging station includes the service range and its charge and discharge bound of EV charging station；
S3, according to the relevant parameter of honourable output power probabilistic model and EV charging station, it is whole to establish multiple target two-layer hybrid
Number linear programming model；
S4, unification is carried out in time scale to multiple target two-layer hybrid integral linear programming model, is translated into list
Layer model simultaneously solves it, obtains power distribution network collaborative planning result.
Further, the step S1 specifically:
S11, basic data is obtained, and honourable correlation matrix ρ is calculated according to itm；
Wherein, basic data includes that model parameter, each blower and the photovoltaic candidate of wind speed and intensity of illumination install node
Related coefficient；
Honourable correlation matrix ρmIncluding wind speed correlation matrixWith intensity of illumination correlation matrix
S12, pass through square-root method for correlation matrix ρmIt is decomposed into triangular matrix A and ATProduct；
Wherein, correlation matrix ρmThe general formula of decomposition are as follows:
In formula,
ρm1nFor correlation matrix ρmIn the 1st row n-th column element；
ρmn1For correlation matrix ρmThe element that middle line n the 1st arranges；
S13, based on the correlation matrix ρ after decompositionm, honourable independent normal is generated by Monte Carlo method of random sampling
It is distributed sample matrix Y；
Wherein, honourable independent normal distribution sample matrix Y includes the independent normal distribution sample matrix Y of wind speedWTGAnd illumination
The independent normal of intensity is distributed sample matrix YPVG；
S14, it is converted into honourable independent normal distribution sample matrix Y to meet correlation matrix ρmCorrelated samples matrix
M；
Wherein, the calculation formula of correlated samples matrix M are as follows:
M=AY
In formula, A is triangular matrix；
S15, correlated samples matrix M is converted to obey different specified distributions and intensity of illumination samples with correlation and
Wind speed sample；
In formula, Φ (mi) it is miStandard normal cumulative distribution function, miFor sample in correlated samples matrix M；
F(xi) it is xiCumulative distribution function；
xiThe intensity of illumination variable for obeying beta distribution for the n both candidate nodes with correlation (or obeys Weibull point
The wind speed variable of cloth) composition vector X=(x1,x2,…,xn) element；
S16, according to intensity of illumination sample and wind speed sample, determine scene output power probabilistic model；
Wherein, honourable output power probabilistic model includes blower output power probabilistic model and photovoltaic output power probability mould
Type.
Further, in the step S11:
Wind speed correlation matrixWith intensity of illumination correlation matrixThe universal calculation equation of middle related coefficient
Are as follows:
In formula, ρxijFor xiAnd xjRelated coefficient, xi,xj∈ X, X=(x1,x2,...,xn0), X is the n with correlation0
A both candidate nodes obey the intensity of illumination variable of Beta distribution or the wind speed variable of Follow Weibull Distribution；
For the joint density function of standardized normal distribution stochastic variable；
σi,σjRespectively vector XiAnd XjStandard deviation；
F-1() cumulative distribution function F () under the distribution function respectively obeyed by intensity of illumination, wind speed and load
Inverse function；
Φ () is standard normal cumulative distribution function；
μi,μjRespectively vector XiAnd XjMean value；
mi,mjIt is standardized normal distribution random vector M=(m1,m2,…,mn) element；
Further, in the step S16:
The blower output power probabilistic model f (PWTG) are as follows::
In formula, exp () is exponential function；
PWTGFor the output power of blower；
vrateFor the rated wind speed of blower；
vinFor the incision wind speed of blower；
PrateFor the rated power of blower fan group；
C is that Weibull is distributed scale parameter；
K is Weibull profile shape parameter；
The photovoltaic output power probabilistic model are as follows:
In formula, Γ () is Gamma function；
α and β is the form parameter of Beta distribution；
PPVGFor the output power of photovoltaic；
For the corresponding peak power output of maximum intensity of illumination.
Further, which is characterized in that the step S2 specifically:
S21, according to the n in road network system1A EV charging station both candidate nodes position divides corresponding EV by Voronoi diagram
The service range of charging station；
Remember LjFor the position coordinates of EV charging station both candidate nodes, point LjVoronoi diagram service range divide calculation formula
Are as follows:
In formula,
Vn(Lj) it is EV charging station both candidate nodes LjVoronoi diagram service range；
L is the location variable of EV charging station both candidate nodes；
d(L,Lj) it is L to LjEuclidean distance；
d(L,Lj') it is L to Lj'Euclidean distance；
For the both candidate nodes set of EV charging station；
S22, it is carried out according to EV vehicle flowrate and EV trip characteristics in each EV charging station institute service range and charging capacity
Monte Carlo is randomly selected, and the EV charge and discharge bound for participating in demand response is obtained.
Further, which is characterized in that the multiple target two-layer hybrid integral linear programming model in the step S3 includes
The investment decision layer on upper layer and the dry run layer of lower layer；
Wherein, investment decision layer is used to carry out addressing constant volume to blower, photovoltaic and EV charging station；
Dry run layer determines that blower, the position of photovoltaic and EV charging station and quantity post-simulation simulate it in investment decision layer
Operation in power distribution network, and the major network purchases strategies to distribution, line loss cost, V2G cost, DG abandon electricity punishment and load fluctuation
Punishment is calculated.
Further, which is characterized in that the multiple target two-layer hybrid integral linear programming model are as follows:
In formula, F () is investment layer objective function；
F () is firing floor objective function；
xinvFor upper layer investment decision variable；
xopeFor lower layer's dry run variable；
CINVFor the comprehensive method of investment cost of upper layer investment decision layer；
COPEFor the integrated operation cost of lower layer's dry run layer；
Wherein, the comprehensive method of investment cost C of upper layer investment decision layerINVAre as follows:
In formula, κ is year investment equivalent coefficient；
ξ ∈ { WTG, PVG } is the device type of blower and photovoltaic；
cξFor the DG specific investment cost price of equipment ξ in power grid；
For the DG quantity of equipment ξ in power grid interior joint j；
GξAnd GCSIt respectively indicates DG and charging station candidate invests node set；
cCPFor the specific investment cost price of charging station；
For the charging station quantity of power grid interior joint j；
κξFor the year investment equivalent coefficient of the equipment of blower in power grid and photovoltaic；
κCSFor the annual investment coefficient of charging station in power grid；
The integrated operation cost C of lower layer's dry run layerOPEAre as follows:
COpe=CPur+CLoss+CV2G+CDG+CLoadGap
In formula, CPurBased on net purchases strategies；
CLossFor line loss cost；
CV2GFor V2G cost；
CDGElectricity punishment is abandoned for DG；
CLoadGapFor load fluctuation punishment.
Further, the constraint condition of the multiple target two-layer hybrid integral linear programming model includes investment and recovery, wind
The units limits of machine and photovoltaic, trend constraint, power system security constraints, the constraint of EV charging station and guidance electricity tariff constraint；
Wherein, the investment and recovery are as follows:
In formula,
GξNode set is invested for DG candidate；
GCPNode set is invested for charging pile candidate；Quantity is invested for DG；
The upper limit of the number is invested for DG；
Quantity is invested for EV charging station；
The upper limit of the number is invested for EV charging station；The units limits of the blower and photovoltaic are as follows:
In formula,
T is the set of all periods；
For the practical power generating value of blower and photovoltaic；
For the prediction power generating value of blower and photovoltaic；Trend constraint includes are as follows:
In formula,
E is the set of all branches in network system；K is the node of power grid；
J is the start node of certain branch in power grid, and Ω (j) is using j as the set where the corresponding terminal of start node；
When using j as the terminal of branch, Λ (j) is using j as the set where the corresponding start node of terminal；
Pjk,tFor the corresponding active power of branch being made of node i and node j；
For square of the electric current for the branch being made of node i and node j；
rijFor the branch resistance being made of node i and node j；
GEVFor EV charging station discharge capacity；
For EV charging station discharge capacity；
For the practical power output of DG；
For node j the voltage of t moment square；
For node i the voltage of t moment square；
Pij,tFor branch ij t moment active power；
Qij,tFor the corresponding reactive power of branch being made of node i and node j；
xijReactance for the branch being made of node i and node j；
At the time of t is operation of power networks；
T is the set of all periods；
GLoadFor load node set；
||·||2For 2 norm matrixes；
The power system security constraints are as follows:
In formula, GEFor the set of nodes all in network system；
For node voltage amplitude lower limit；
For the node voltage amplitude upper limit；
Uj,tFor node voltage amplitude；
Iij,tFor node current amplitude；
The node current amplitude upper limit；
The EV charging station constraint are as follows:
In formula,WithThe respectively 0-1 variable of EV charging station charging and discharging；To participate in demand response
The charge volume of EV charging station；
For the discharge capacity of the EV charging station of participation demand response；
For the chargeable total capacity upper limit of EV charging station for participating in demand response；
It can discharge the total capacity upper limit to participate in the EV charging station of demand response；
For the quantity of the charging station of EV charging station j；
PCPFor the specified charge-discharge electric power of EV charging station；
ηchaFor the charge efficiency of EV charging station；
ηdisFor the discharging efficiency of EV charging station；
The guidance electricity tariff constraint are as follows:
In formula,And σEVFor the bound ratio of charge and discharge Electricity price fluctuation；
For the charge and discharge basis electricity price of EV charging station；
For the charging guidance price of EV charging station；
For V2G dynamic electricity price；
For power load demand:
PAveFor the load mean value of system.
Further, in the step S4, multiple target two-layer hybrid integral linear programming model carries out in time scale
System calculation formula for the moment are as follows:
Min F=CINV+ω·COPE
In formula, ω is transforming factor.
The beneficial effects of the present invention are: in the present invention, based on Monte Carlo method of random sampling and wind speed illumination probability point
Cloth model generates honourable independent normal and is distributed sample, and the application enhancements Correlation Moment tactical deployment of troops is converted to correlated samples to calculate scene
Power output, more meets the actual conditions of each distributed new power plant in reality；Road network system is divided using Voronoi diagram
Area obtains the service range of EV charging station to determine the upper limit amount of EV response, and carrying out subregion to the service range of charging station can be more
Accurately estimation electric car response quautity；In the multiple target two-layer hybrid integral linear programming model of foundation consider power purchase, network loss,
The costs such as electricity punishment are punished and are abandoned in load fluctuation, and higher level's power grid purchase of electricity, reduction network electricity can be effectively reduced in planning
It can be lost, limit load fluctuation, honourable resource is made full use of to generate electricity；Constraint condition will investment, DG power output, trend and EV charge and discharge
Valence etc. is indicated with linear restriction, and the time scale of unified bilayer model is translated into single layer, reduces solution planning knot
The time of fruit.
Detailed description of the invention
Fig. 1 falls into a trap for the present invention and the power distribution network collaborative planning method implementation flow chart of DG correlation and EV demand response.
Fig. 2 is power distribution network schematic diagram of a scenario in embodiment provided by the invention.
Fig. 3 is distribution network planning result schematic diagram in embodiment provided by the invention.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
As shown in Figure 1, the power distribution network collaborative planning method of meter and DG correlation and EV demand response, comprising the following steps:
S1, honourable independent normal distribution sample matrix is generated, and honourable output power probabilistic model is calculated according to it；
S2, the relevant parameter of EV charging station is obtained to road network system progress subregion by Voronoi diagram；
Wherein, the relevant parameter of EV charging station includes the service range and its charge and discharge bound of EV charging station；
S3, according to the relevant parameter of honourable output power probabilistic model and EV charging station, it is whole to establish multiple target two-layer hybrid
Number linear programming model；
S4, unification is carried out in time scale to multiple target two-layer hybrid integral linear programming model, is translated into list
Layer model simultaneously solves it, obtains power distribution network collaborative planning result.
Above-mentioned steps S1 specifically:
S11, basic data is obtained, and honourable correlation matrix ρ is calculated according to itm；
Wherein, basic data includes that model parameter, each blower and the photovoltaic candidate of wind speed and intensity of illumination install node
Related coefficient；
Honourable correlation matrix ρmIncluding wind speed correlation matrixWith intensity of illumination correlation matrix
The universal calculation equation of related coefficient therein are as follows:
In formula, ρxijFor xiAnd xjRelated coefficient, xi,xj∈ X, X=(x1,x2,...,xn0), X is the n with correlation0
A both candidate nodes obey the intensity of illumination variable of Beta distribution or the wind speed variable of Follow Weibull Distribution；
For the joint density function of standardized normal distribution stochastic variable；
σi,σjRespectively vector XiAnd XjStandard deviation；
F-1() cumulative distribution function F () under the distribution function respectively obeyed by intensity of illumination, wind speed and load
Inverse function；
Φ () is standard normal cumulative distribution function；
μi,μjRespectively vector XiAnd XjMean value；
mi,mjIt is standardized normal distribution random vector M=(m1,m2,…,mn) element；
Wherein, the joint density function of standardized normal distribution stochastic variableAre as follows:
S12, pass through square-root method for correlation matrix ρmIt is decomposed into triangular matrix A and ATProduct；
Wherein, correlation matrix ρmThe general formula of decomposition are as follows:
In formula,
ρm1nFor correlation matrix ρmIn the 1st row n-th column element；
ρmn1For correlation matrix ρmThe element that middle line n the 1st arranges；
S13, based on the correlation matrix ρ after decompositionm, honourable independent normal is generated by Monte Carlo method of random sampling
It is distributed sample matrix Y；
Wherein, honourable independent normal distribution sample matrix Y includes the independent normal distribution sample matrix Y of wind speedWTGAnd illumination
The independent normal of intensity is distributed sample matrix YPVG；
S14, it is converted into honourable independent normal distribution sample matrix Y to meet correlation matrix ρmCorrelated samples matrix
M；
Wherein, the calculation formula of correlated samples matrix M are as follows:
M=AY (4)
In formula, A is triangular matrix；
S15, correlated samples matrix M is converted to obey different specified distributions and intensity of illumination samples with correlation and
Wind speed sample；
In formula, Φ (mi) it is miStandard normal cumulative distribution function, miFor sample in correlated samples matrix M；
F(xi) it is xiCumulative distribution function；
xiThe intensity of illumination variable for obeying beta distribution for the n both candidate nodes with correlation (or obeys Weibull point
The wind speed variable of cloth) composition vector X=(x1,x2,…,xn) element；
S16, according to intensity of illumination sample and wind speed sample, determine scene output power probabilistic model；
Wherein, honourable output power probabilistic model includes blower output power probabilistic model and photovoltaic output power probability mould
Type.
Above-mentioned blower output power probabilistic model f (PWTG) are as follows:
In formula, exp () is exponential function；
PWTGFor the output power of blower；
vrateFor the rated wind speed of blower；
vinFor the incision wind speed of blower；
PrateFor the rated power of blower fan group；
C is that Weibull is distributed scale parameter；
K is Weibull profile shape parameter；
Wherein, the output power P of blowerWTGAre as follows:
In formula, voutFor the cut-out wind speed of blower；
Above-mentioned photovoltaic output power probabilistic model are as follows:
In formula, Γ () is Gamma function；
α and β is the form parameter of Beta distribution；
PPVGFor the output power of photovoltaic；
For the corresponding peak power output of maximum intensity of illumination.
Wherein, the output power P of photovoltaicPVGAre as follows:
Above-mentioned steps S2 specifically:
S21, according to the n in road network system1A EV charging station both candidate nodes position divides corresponding EV by Voronoi diagram
The service range of charging station；
Remember LjFor the position coordinates of EV charging station both candidate nodes, point LjVoronoi diagram service range divide calculation formula
Are as follows:
In formula,
Vn(Lj) it is EV charging station both candidate nodes LjVoronoi diagram service range；
L is the location variable of EV charging station both candidate nodes；
d(L,Lj) it is L to LjEuclidean distance；
d(L,Lj') it is L to Lj'Euclidean distance；
For the both candidate nodes set of EV charging station；
S22, it is carried out according to EV vehicle flowrate and EV trip characteristics in each EV charging station institute service range and charging capacity
Monte Carlo is randomly selected, and the EV charge and discharge bound for participating in demand response is obtained.
Multiple target two-layer hybrid integral linear programming model in above-mentioned steps S3 includes the investment decision layer on upper layer under
The dry run layer of layer；
Wherein, investment decision layer is used to carry out addressing constant volume to blower, photovoltaic and EV charging station；
Dry run layer determines that blower, the position of photovoltaic and EV charging station and quantity post-simulation simulate it in investment decision layer
Operation in power distribution network, and the major network purchases strategies to distribution, line loss cost, V2G cost, DG abandon electricity punishment and load fluctuation
Punishment is calculated.Dry run layer considers power purchase, network loss, load fluctuation punishment and abandons the costs such as electricity punishment, and determines corresponding
Constraint condition；
The multiple target two-layer hybrid integral linear programming model are as follows:
In formula, F () is investment layer objective function；
F () is firing floor objective function；
xinvFor upper layer investment decision variable；
xopeFor lower layer's dry run variable；
CINVFor the comprehensive method of investment cost of upper layer investment decision layer；
COPEFor the integrated operation cost of lower layer's dry run layer；
Wherein, the comprehensive method of investment cost C of upper layer investment decision layerINVAre as follows:
In formula, κ is year investment equivalent coefficient；
ξ ∈ { WTG, PVG } is the device type of blower and photovoltaic；
cξFor the DG specific investment cost price of equipment ξ in power grid；
For the DG quantity of equipment ξ in power grid interior joint j；
GξAnd GCSIt respectively indicates DG and charging station candidate invests node set；
cCPFor the specific investment cost price of charging station；
For the charging station quantity of power grid interior joint j；
κξFor the year investment equivalent coefficient of the equipment of blower in power grid and photovoltaic；
κCSFor the annual investment coefficient of charging station in power grid；
The integrated operation cost C of lower layer's dry run layerOPEAre as follows:
COpe=CPur+CLoss+CV2G+CDG+CLoadGap (13)
In formula, CPurBased on net purchases strategies；
CLossFor line loss cost；
CV2GFor V2G cost；
CDGElectricity punishment is abandoned for DG；
CLoadGapFor load fluctuation punishment.
Wherein,
In formula, cpurThe tou power price of major network；
clossFor main net wire loss electricity price；
cv2gFor major network V2G dynamic electricity price；
cξElectric penalty price is abandoned for major network DG；
cloadgapFor major network load fluctuation penalty price；
Square for the branch current being made of node i and node j；
rijThe corresponding resistance of branch being made of node i and node j；
For EV charging station discharge capacity；
It predicts to contribute for DG；
For the practical power output of DG；
For power load demand；
PAveFor the load mean value of system；
The constraint condition of multiple target two-layer hybrid integral linear programming model in the present invention include investment and recovery, blower and
Units limits, trend constraint, power system security constraints, the constraint of EV charging station and the guidance electricity tariff constraint of photovoltaic；
(1) restriction by factors such as place and funds, there are the upper limits for each installable number of devices of node, therefore throw
Money constraint are as follows:
In formula,
GξNode set is invested for DG candidate；
GCPNode set is invested for charging pile candidate；
Quantity is invested for DG；
The upper limit of the number is invested for DG；
Quantity is invested for EV charging station；
The upper limit of the number is invested for EV charging station；
The units limits of blower and photovoltaic are as follows:
In formula,
T is the set of all periods；
For the practical power generating value of blower and photovoltaic；
For the prediction power generating value of blower and photovoltaic；
(2) constraint of power distribution network Branch Power Flow uses distflow Branch Power Flow form, and will with second order cone relaxing techniques
Master mould linearisation, therefore trend constraint includes are as follows:
In formula,
E is the set of all branches in network system；
K is the node of power grid；
J is the start node of certain branch in power grid, and Ω (j) is using j as the set where the corresponding terminal of start node；
When using j as the terminal of branch, Λ (j) is using j as the set where the corresponding start node of terminal；
Pjk,tFor the corresponding active power of branch being made of node i and node j；
For square of the electric current for the branch being made of node i and node j；
rijFor the branch resistance being made of node i and node j；
GEVFor EV charging station discharge capacity；
For EV charging station discharge capacity；
For the practical power output of DG；
For node j the voltage of t moment square；
For node i the voltage of t moment square；
Pij,tFor branch ij t moment active power；
Qij,tFor the corresponding reactive power of branch being made of node i and node j；
xijReactance for the branch being made of node i and node j；
At the time of t is operation of power networks；
T is the set of all periods；
GLoadFor load node set；
||·||2For 2 norm matrixes；
(4) power system security constraints are as follows:
In formula, GEFor the set of nodes all in network system；
For node voltage amplitude lower limit；
For the node voltage amplitude upper limit；
Uj,tFor node voltage amplitude；
Iij,tFor node current amplitude；
The node current amplitude upper limit；
(5) EV charging station constrains are as follows:
In formula,WithThe respectively 0-1 variable of EV charging station charging and discharging；
For the charge volume of the EV charging station of participation demand response；
For the discharge capacity of the EV charging station of participation demand response；
For the chargeable total capacity upper limit of EV charging station for participating in demand response；
It can discharge the total capacity upper limit to participate in the EV charging station of demand response；
For the quantity of the charging station of EV charging station j；
PCPFor the specified charge-discharge electric power of EV charging station；
ηchaFor the charge efficiency of EV charging station；
ηdisFor the discharging efficiency of EV charging station；
(6) the guidance flexible charge and discharge of EV of corresponding EV charge and discharge electricity price are formulated according to load variations it can inhibit its fluctuation and reach and cut
The purpose of peak load mitigates power grid pressure, therefore guides electricity tariff constraint are as follows:
In formula,And σEVFor the bound ratio of charge and discharge Electricity price fluctuation；
For the charge and discharge basis electricity price of EV charging station；
For the charging guidance price of EV charging station；
For V2G dynamic electricity price；
For power load demand:
PAveFor the load mean value of system.
In above-mentioned steps S4, meter of the multiple target two-layer hybrid integral linear programming model when carrying out unified in time scale
Calculate formula are as follows:
MinF=CINV+ω·COPE (19)
In formula, ω is transforming factor.
In one embodiment of the invention, the example for carrying out power distribution network collaborative planning by the method for the invention is provided:
As shown in Fig. 2, planning that the time limit takes 5 years in this example, investment operation transforming factor takes 2000, and blower both candidate nodes are
[3,17,21], photovoltaic both candidate nodes are [10,24,31], and electric automobile charging station both candidate nodes are [5,16,25,33], simultaneously
Blower, photovoltaic and charging pile are divided with green solid lines to the Voronoi diagram of charging station coverage and use market average price, electronic vapour
Vehicle charging pile uses 69kW direct current charge, and load is divided into residential electricity consumption load by part throttle characteristics by load bus, commercial power is born
Lotus and commercial power load, grey grid representation road network system solid black lines indicate network system.
In embodiments of the present invention, through improving at the Correlation Moment tactical deployment of troops after being sampled by taking the medium degree of correlation as an example by Monte Carlo
Reason obtains that wind at each node, the associated output power of optical electric field are as shown in table 1, extract participate in the EV of demand response can be in charge and discharge
Limit is as shown in table 2.
The associated output power of wind at each node of table 1, optical electric field
2 electric car demand response of table can the charge and discharge upper limit
In embodiments of the present invention, minimum according to the upper layer investment decision cost of double-layer satellite network model, considering to throw
Money constraint lower determining blower, photovoltaic and charging pile quantity substitute into lower layer's dry run layer；According to the decision variable of input and
DG units limits, electric network swim constraint, power system security constraints, charging station constraint and charge and discharge price constraints, lower layer's dry run
Layer abandons electric punishment cost to power grid purchases strategies, via net loss cost, V2G cost, DG and load fluctuation punishment cost is counted
It calculates and its overall cost is fed back into objective function sum up the costs；The result calculated every time is recorded and is compared final obtain
To the blower of an optimal meter and DG correlation and EV demand response, photovoltaic and EV charging station collaborative planning result.
As shown in figure 3, will consider that the medium related and EV charge and discharge of DG both candidate nodes need in 33 node electric wiring systems
Ask the blower, photovoltaic and EV charging station collaborative planning result of response.
The beneficial effects of the present invention are: in the present invention, based on Monte Carlo method of random sampling and wind speed illumination probability point
Cloth model generates honourable independent normal and is distributed sample, and the application enhancements Correlation Moment tactical deployment of troops is converted to correlated samples to calculate scene
Power output, more meets the actual conditions of each distributed new power plant in reality；Road network system is divided using Voronoi diagram
Area obtains the service range of EV charging station to determine the upper limit amount of EV response, and carrying out subregion to the service range of charging station can be more
Accurately estimation electric car response quautity；The multiple target two-layer hybrid integral linear programming model kind of foundation consider power purchase, network loss,
The costs such as electricity punishment are punished and are abandoned in load fluctuation, and higher level's power grid purchase of electricity, reduction network electricity can be effectively reduced in planning
It can be lost, limit load fluctuation, honourable resource is made full use of to generate electricity；Constraint condition will investment, DG power output, trend and EV charge and discharge
Valence etc. is indicated with linear restriction, and the time scale of unified bilayer model is translated into single layer, reduces solution planning knot
The time of fruit.
Claims (9)
1. the power distribution network collaborative planning method of meter and DG correlation and EV demand response, which comprises the following steps:
S1, honourable independent normal distribution sample matrix is generated, and honourable output power probabilistic model is calculated according to it；
S2, the relevant parameter of EV charging station is obtained to road network system progress subregion by Voronoi diagram；
Wherein, the relevant parameter of EV charging station includes the service range and its charge and discharge bound of EV charging station；
S3, according to the relevant parameter of honourable output power probabilistic model and EV charging station, establish multiple target two-layer hybrid integer line
Property plan model；
S4, unification is carried out in time scale to multiple target two-layer hybrid integral linear programming model, is translated into single layer mould
Type simultaneously solves it, obtains power distribution network collaborative planning result.
2. the power distribution network collaborative planning method of meter according to claim 1 and DG correlation and EV demand response, feature
It is, the step S1 specifically:
S11, basic data is obtained, and honourable correlation matrix ρ is calculated according to itm；
Wherein, basic data includes that wind speed is related to the model parameter of intensity of illumination, each blower and photovoltaic candidate installation node
Coefficient；
Honourable correlation matrix ρmIncluding wind speed correlation matrixWith intensity of illumination correlation matrix
S12, pass through square-root method for correlation matrix ρmIt is decomposed into triangular matrix A and ATProduct；
Wherein, correlation matrix ρmThe general formula of decomposition are as follows:
In formula,
ρm1nFor correlation matrix ρmIn the 1st row n-th column element；
ρmn1For correlation matrix ρmThe element that middle line n the 1st arranges；
S13, based on the correlation matrix ρ after decompositionm, honourable independent normal distribution is generated by Monte Carlo method of random sampling
Sample matrix Y；
Wherein, honourable independent normal distribution sample matrix Y includes the independent normal distribution sample matrix Y of wind speedWTGAnd intensity of illumination
Independent normal be distributed sample matrix YPVG；
S14, it is converted into honourable independent normal distribution sample matrix Y to meet correlation matrix ρmCorrelated samples matrix M；
Wherein, the calculation formula of correlated samples matrix M are as follows:
M=AY
In formula, A is triangular matrix；
S15, correlated samples matrix M is converted to and obeys different specified distributions and with the intensity of illumination sample and wind speed of correlation
Sample；
In formula, Φ (mi) it is miStandard normal cumulative distribution function, miFor sample in correlated samples matrix M；
F(xi) it is xiCumulative distribution function；
xiFor with correlation n both candidate nodes obey beta distribution intensity of illumination variable (or obey Weibull distribution wind
Fast variable) composition vector X=(x1,x2,…,xn) element；
S16, according to intensity of illumination sample and wind speed sample, determine scene output power probabilistic model；
Wherein, honourable output power probabilistic model includes blower output power probabilistic model and photovoltaic output power probabilistic model.
3. the power distribution network collaborative planning method of meter according to claim 2 and DG correlation and EV demand response, feature
It is, in the step S11:
Wind speed correlation matrixWith intensity of illumination correlation matrixThe universal calculation equation of middle related coefficient are as follows:
In formula, ρxijFor xiAnd xjRelated coefficient, xi,xj∈ X, X=(x1,x2,...,xn0), X is the n with correlation0A time
Node is selected to obey the intensity of illumination variable of Beta distribution or the wind speed variable of Follow Weibull Distribution；
For the joint density function of standardized normal distribution stochastic variable；
σi,σjRespectively vector XiAnd XjStandard deviation；
F-1() by intensity of illumination, wind speed and load under the distribution function respectively obeyed cumulative distribution function F () it is inverse
Function；
Φ () is standard normal cumulative distribution function；
μi,μjRespectively vector XiAnd XjMean value；
mi,mjIt is standardized normal distribution random vector M=(m1,m2,…,mn) element.
4. the power distribution network collaborative planning method of meter according to claim 2 and DG correlation and EV demand response, feature
It is, in the step S16:
The blower output power probabilistic model f (PWTG) are as follows:
In formula, exp () is exponential function；
PWTGFor the output power of blower；
vrateFor the rated wind speed of blower；
vinFor the incision wind speed of blower；
PrateFor the rated power of blower fan group；
C is that Weibull is distributed scale parameter；
K is Weibull profile shape parameter；
The photovoltaic output power probabilistic model are as follows:
In formula, Γ () is Gamma function；
α and β is the form parameter of Beta distribution；
PPVGFor the output power of photovoltaic；
For the corresponding peak power output of maximum intensity of illumination.
5. the power distribution network collaborative planning method of meter according to claim 2 and DG correlation and EV demand response, feature
It is, the step S2 specifically:
S21, according to the n in road network system1A EV charging station both candidate nodes position divides corresponding EV charging station by Voronoi diagram
Service range；
Remember LjFor the position coordinates of EV charging station both candidate nodes, point LjVoronoi diagram service range divide calculation formula are as follows:
In formula,
Vn(Lj) it is EV charging station both candidate nodes LjVoronoi diagram service range；
L is the location variable of EV charging station both candidate nodes；
d(L,Lj) it is L to LjEuclidean distance；
d(L,Lj') it is L to Lj'Euclidean distance；
For the both candidate nodes set of EV charging station；
S22, it is carried out covering spy according to EV vehicle flowrate and EV trip characteristics in each EV charging station institute service range and charging capacity
Carlow is randomly selected, and the EV charge and discharge bound for participating in demand response is obtained.
6. the power distribution network collaborative planning method of meter according to claim 5 and DG correlation and EV demand response, feature
It is, the multiple target two-layer hybrid integral linear programming model in the step S3 includes investment decision layer and the lower layer on upper layer
Dry run layer；
Wherein, investment decision layer is used to carry out addressing constant volume to blower, photovoltaic and EV charging station；
Dry run layer determines that blower, the position of photovoltaic and EV charging station and quantity post-simulation are simulated it and matched in investment decision layer
Operation in power grid, and the major network purchases strategies to distribution, line loss cost, V2G cost, DG abandon electricity punishment and load fluctuation punishment
It is calculated.
7. the power distribution network collaborative planning method of meter according to claim 6 and DG correlation and EV demand response, feature
It is, the multiple target two-layer hybrid integral linear programming model are as follows:
In formula, F () is investment layer objective function；
F () is firing floor objective function；
xinvFor upper layer investment decision variable；
xopeFor lower layer's dry run variable；
CINVFor the comprehensive method of investment cost of upper layer investment decision layer；
COPEFor the integrated operation cost of lower layer's dry run layer；
Wherein, the comprehensive method of investment cost C of upper layer investment decision layerINVAre as follows:
In formula, κ is year investment equivalent coefficient；
ξ ∈ { WTG, PVG } is the device type of blower and photovoltaic；
cξFor the DG specific investment cost price of equipment ξ in power grid；
For the DG quantity of equipment ξ in power grid interior joint j；
GξAnd GCSIt respectively indicates DG and charging station candidate invests node set；
cCPFor the specific investment cost price of charging station；
For the charging station quantity of power grid interior joint j；
κξFor the year investment equivalent coefficient of the equipment of blower in power grid and photovoltaic；
κCSFor the annual investment coefficient of charging station in power grid；
The integrated operation cost C of lower layer's dry run layerOPEAre as follows:
COpe=CPur+CLoss+CV2G+CDG+CLoadGap
In formula, CPurBased on net purchases strategies；
CLossFor line loss cost；
CV2GFor V2G cost；
CDGElectricity punishment is abandoned for DG；
CLoadGapFor load fluctuation punishment.
8. the power distribution network collaborative planning method of meter according to claim 7 and DG correlation and EV demand response, feature
It is, the constraint condition of the multiple target two-layer hybrid integral linear programming model includes going out for investment and recovery, blower and photovoltaic
Force constraint, trend constraint, power system security constraints, the constraint of EV charging station and guidance electricity tariff constraint；
Wherein, the investment and recovery are as follows:
In formula,
GξNode set is invested for DG candidate；
GCPNode set is invested for charging pile candidate；
Quantity is invested for DG；
The upper limit of the number is invested for DG；
Quantity is invested for EV charging station；
The upper limit of the number is invested for EV charging station；
The units limits of the blower and photovoltaic are as follows:
In formula,
T is the set of all periods；
For the practical power generating value of blower and photovoltaic；
For the prediction power generating value of blower and photovoltaic；
Trend constraint includes are as follows:
In formula,
E is the set of all branches in network system；
K is the node of power grid；
J is the start node of certain branch in power grid, and Ω (j) is using j as the set where the corresponding terminal of start node；When with
When j is the terminal of branch, Λ (j) is using j as the set where the corresponding start node of terminal；
Pjk,tFor the corresponding active power of branch being made of node i and node j；
For square of the electric current for the branch being made of node i and node j；
rijFor the branch resistance being made of node i and node j；
GEVFor EV charging station discharge capacity；
For EV charging station discharge capacity；
For the practical power output of DG；
For node j the voltage of t moment square；
For node i the voltage of t moment square；
Pij,tFor branch ij t moment active power；
Qij,tFor the corresponding reactive power of branch being made of node i and node j；
xijReactance for the branch being made of node i and node j；
At the time of t is operation of power networks；
T is the set of all periods；
GLoadFor load node set；
||·||2For 2 norm matrixes；
The power system security constraints are as follows:
In formula, GEFor the set of nodes all in network system；
For node voltage amplitude lower limit；
For the node voltage amplitude upper limit；
Uj,tFor node voltage amplitude；
Iij,tFor node current amplitude；
The node current amplitude upper limit；
The EV charging station constraint are as follows:
In formula,WithThe respectively 0-1 variable of EV charging station charging and discharging；
For the charge volume of the EV charging station of participation demand response；
For the discharge capacity of the EV charging station of participation demand response；
For the chargeable total capacity upper limit of EV charging station for participating in demand response；
It can discharge the total capacity upper limit to participate in the EV charging station of demand response；
For the quantity of the charging station of EV charging station j；
PCPFor the specified charge-discharge electric power of EV charging station；
ηchaFor the charge efficiency of EV charging station；
ηdisFor the discharging efficiency of EV charging station；
The guidance electricity tariff constraint are as follows:
In formula,Withσ EVFor the bound ratio of charge and discharge Electricity price fluctuation；
For the charge and discharge basis electricity price of EV charging station；
For the charging guidance price of EV charging station；
For V2G dynamic electricity price；
For power load demand:
PAveFor the load mean value of system.
9. the power distribution network collaborative planning method of meter according to claim 7 and DG correlation and EV demand response, feature
It is, in the step S4, calculating of the multiple target two-layer hybrid integral linear programming model when carrying out unified in time scale
Formula are as follows:
Min F=CINV+ω·COPE
In formula, ω is transforming factor.
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