CN110504708A - The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource - Google Patents
The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Abstract
It is a kind of meter and charging station and distributed generation resource power distribution network multiple target collaborative planning method, this method comprises: considering the uncertainty of distributed generation resource, wind light generation power output rule is analyzed, its season and temporal characteristics is based on, establishes more scene distribution formula power supplys and load model；Electric vehicle on road traffic network design is established, magnitude of traffic flow distribution, transportation network layout, the magnitude of traffic flow intercepted and captured based on electric automobile charging station and user's average latency is considered, calculates transportation economy benefit, and distribution network is coupled with transportation network；Comprehensively consider distributed generation resource and electric automobile charging station addressing constant volume, to plan that the lowest cost and network node quality of voltage are optimal for upper layer target, the minimum lower layer's target of power supply resection desired value, establishes multiple target collaborative planning model in a distributed manner.The design not only improves operation of power networks economy and power quality, and avoids traffic congestion caused by charging station construction, improves the stability of electric system.
Description
Technical field
The present invention relates to distribution network planning technical field more particularly to the distribution of a kind of meter and charging station and distributed generation resource
Net multiple target collaborative planning method.
Background technique
The variation of electric power system design and the economy and business environment of operation is so that distribution network must be taken into consideration and distribution
The cooperation of power supply, it is a large amount of to change the complexity so that system at random when a large amount of distributed generation resource appears in programme
Property horribly increases.Sufficient ability does not solve the planning problem comprising distributed generation resource to traditional planing method, this is main
It is that objective reality in planning is difficult to because traditional planing method is all to some extent simplified planning problem
The uncertain factor of quantitative expression lacks preferable processing method.
Electric car can not only regard distributed generation resource (Distributed as a kind of completely new vehicles
Generation, DG), it can also be used as energy-storage travelling wave tube, support extensive renewable energy access power grid, may also participate in isolated island
The frequency of power grid is adjusted, and a large amount of electric car charge-discharge facility access urban power distribution networks certainly will bring all various influences.It closes
The influence of reason ground assessment electric car charge-discharge facility access power grid, can preferably evaluate operation of power networks situation, to take
Effective measures can improve operation of power networks economy, reliability and power quality etc..
Current research is substantially the constant volume for individually considering electric automobile charging station access power distribution network and addressing, has one big
Part thinking is carried out around magnitude of traffic flow quantity of the catch.Electric car travels on road, can be regarded as charge requirement and exists
It is flowed in transportation network, this just needs to carry out modeling analysis to transportation network and the magnitude of traffic flow, and then studies electric car traffic
The Site planning method of network and electric automobile charging station in a network.In fact, the addressing of charging station is not only influenced with constant volume
The stability of power grid has an effect on the convenience of electric car trip, while the planning of electric automobile charging station should also have conducive to dividing
The on-site elimination of cloth power supply.
Summary of the invention
The purpose of the present invention is to provide the power distribution network multiple target collaborative plannings of a kind of meter and charging station and distributed generation resource
Method, while considering the influence of power network and the network of communication lines, has adapted to power network development needs under the new situation, at the same consider economy,
Quality of voltage and traffic satisfaction, had both saved electric grid investment cost, in turn ensured the stability of power grid, and greatly weaken
The influence that the construction of electric automobile charging station generates traffic.
In order to achieve the above object, the technical solution of the invention is as follows: a kind of meter and charging station and distributed generation resource are matched
Power grid multiple target collaborative planning method, method includes the following steps:
A, consider that the uncertainty of distributed generation resource, analysis wind light generation power output rule are based on its season and temporal characteristics,
Establish more scene distribution formula power supplys and load model；
B, electric vehicle on road traffic network design is established, magnitude of traffic flow distribution, transportation network layout are considered, based on electronic
Vehicle charging station intercept and capture the magnitude of traffic flow and user's average latency, calculate transportation economy benefit, and by distribution network with
Transportation network coupling, in this, as the basis of power distribution network collaborative planning；
C, distributed generation resource and electric automobile charging station addressing constant volume are comprehensively considered in planning process, to plan totle drilling cost most
Optimal low and network node quality of voltage is upper layer target, and the minimum lower layer's target of power supply resection desired value, builds in a distributed manner
Vertical multiple target collaborative planning model；
D, it is based on genetic algorithm, obtains the Pareto disaggregation for meeting constraint condition, then multiple target is obtained by TOPSIS method
The optimal solution of function.
The step a specifically includes the following steps:
A1, assume that distributed generation resource power output obeys certain probability Distribution Model, and it is not true by the methods of sampling to show it
It is qualitative；The honourable regularity of distribution is fitted using empirical distribution function；
A11, wind speed obey two parameter Weibull distribution, probability density function are as follows:
In formula, V is the wind speed at wind generator unit wheel hub, and k is form parameter, and c is scale parameter；
Wind driven generator output power PwigIt is indicated with the relationship of wind speed V by following piecewise function:
In formula, VciFor the incision wind speed of wind-driven generator, VrFor the rated wind speed of wind-driven generator, VcoFor wind-driven generator
Cut-out wind speed, Pr2For the rated output power of wind-driven generator；
A12, intensity of illumination are distributed to describe using beta:
In formula, I is intensity of illumination, ImaxFor light intensity maximum value, α and β are form parameter, and Γ () is gamma function；
Photovoltaic generator output power PpvgWith the relationship of intensity of illumination I are as follows:
In formula, Pr1For the rated output power of photovoltaic generator, IrFor specified intensity of illumination；
A2, it chooses a typical day respectively in spring, summer, autumn, four seasons of winter, simulates each season with 24 moment
Distributed generation resource power output, the temporal characteristics of distributed power generation are studied, Various Seasonal wind speed is obtained according to meteorological data
Curve and intensity of illumination curve, and then the timing curve of wind-powered electricity generation, photovoltaic power output is extrapolated, meanwhile, carry out mould respectively with these scenes
Quasi- load power scene, is combined into 96 annual scenes, establishes more scene distribution formula power supplys and load model.
The step b specifically includes the following steps:
B1, set of minimal paths connected two-by-two is found out using Floyd algorithm, and calculated by gravity-space interactive model
The magnitude of traffic flow that total system fast charge station is intercepted and captured every year
In formula, ΩodThe set of minimal paths being connected two-by-two for all nodes of system；It is shortest path k in period t
One-way traffic flow demand per unit value；woAnd wdThe respectively weight of the starting point O of shortest path k and terminal d, to indicate
The busy extent of each transport node；dkFor the per unit value of shortest path k length；StIt is automobile user in the trip ratio of period t
Example, shIt is automobile user in the trip proportion of traffic peak period；
B2, the average arrival rate that vehicle to be charged is calculated according to the magnitude of traffic flow that fast charge station is intercepted and captured, specific formula is as follows:
In formula,For the magnitude of traffic flow that fast charge station is intercepted and captured in period t at node i；It is shortest path k in the period
The per unit value of the one-way traffic flow demand of t；Whether pass through the two-valued variable of node i for shortest path k；For node
Whether the two-valued variable at fast charge station is built at i；λI, tAnd λI, hFast charge station is in period t and traffic peak period respectively at node i
The inverse of the average arrival rate of vehicle to be charged, i.e. automobile user arrival average time interval；CqcFor quickly filling for system
Electric total frequency demand；
B3, electric automobile charging station constant volume model is established, according to the maximum allowable waiting time of traffic peak period user
WmaxTo configure the charging equipment quantity at each fast charge station, and arrived in conjunction with M/M/s waiting line system model simulation fast charge station vehicle to be charged
Up to process and services duration, most economical fast charge station equipment quantity is set in the case where being no more than the maximum allowable waiting time；
B31, constraint condition are as follows:
s.t. WI, h< Wmax, λI, h≠ 0, i ∈ Ω
In formula, WI, hReceive the average latency of charging service, W for the traffic peak periodmaxWhen for maximum allowable waiting
Between；
B32, the fast charge number of devices that each fast charge station should be arranged is calculated:
In formula,For the number of devices at fast charge station at node i, ρI, hIt is fast charge station at node i in the traffic peak period
Equipment average service rate, ρI, hIt is fast charge station at node i in the average arrival rate of traffic peak period vehicle to be charged, P0iFor section
The whole idle probability of the equipment at fast charge station at point i, μ is the average service rate of the fast fill device of separate unit, i.e. quick charge is averaged
The inverse of time；
B33, introducing magnitude of traffic flow discount factor rationally weaken magnitude of traffic flow economic benefit value:
In formula,For the magnitude of traffic flow discount factor of shortest path k, a is the busy discount of transport node, and m is shortest path
It include the busy section of m item, F in diameter kqcFor magnitude of traffic flow economic benefit value, ωfBy the intercepting and capturing magnitude of traffic flow economic benefit conversion
Coefficient,For the two-valued variable that can the flow on shortest path k be intercepted and captured by fast charge station, if shortest path k passes through at least one
A fast charge station, thenIt is 1, otherwiseIt is 0,It is shortest path k in the one-way traffic flow demand of period t
Per unit value；
B34, fast charge station Installed capital cost Cceq:
In formula, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
B35, fast charge station charge power:
In formula,It is fast charge station at node i in the charge power of period t, λI, tIt is waited for for fast charge station at node i in period t
The average arrival rate of charging vehicle, pqcFor the charge power of the fast fill device of separate unit, μ is the average service rate of the fast fill device of separate unit,
That is the inverse of the average time of quick charge.
The step c specifically includes the following steps:
C1, best using economy and node voltage optimal quality is upper layer target, objective function are as follows:
In formula, f1Indicate economy objectives, ClineIndicate investment and the operation and maintenance cost of route, CDGIndicate distributed electrical
The investment in source and operation and maintenance cost, CenIndicate the purchases strategies to major network, ClossIndicate via net loss cost, CceqExpression is filled
Power station investment cost, CbIndicate government subsidy, FqcIndicate magnitude of traffic flow economic benefit value, f2Indicate power distribution network node voltage quality
Target, UlevelIndicate power distribution network node voltage quality evaluation function values；
C11, economy objectives
C111, the investment of route and operation and maintenance cost Cline:
Cline=CIline+COMline
In formula, CIlineFor the equal years value of route fixed investment, COM1ineFor route year operation and maintenance cost, ΩL1It is newly-built
The set of route, ΩL2For the set for upgrading route, ΩLFor the set of all routes,For new route unit length
Required cost,For upgrade route unit length required cost,For the required cost of route unit length, l is
Line length, α are to wait years value coefficient, and r is discount rate, nlineFor route fixed investment payoff period, γ is route operation and maintenance expense
With rate；
C112, the investment of distributed generation resource and operation and maintenance cost CDG:
CDG=CIDG+COMDG
In formula, CIDGFor the equal years value of renewable energy DG fixed investment, COMDGFor the year operation and maintenance of renewable energy DG
Expense, Cf1For the installation cost of photovoltaic generator unit capacity, Cf2For the installation cost of wind-driven generator unit capacity, Ω1For
The set of newly-built photovoltaic generator, Ω2For the set for creating wind-driven generator, P1jFor photovoltaic generator installed capacity, P2kFor wind
Power generator installed capacity, nDGFor distributed generation resource fixed investment payoff period, COM1It is tieed up for the operation of photovoltaic generator unit quantity of electricity
Shield expense, COM2For the operation and maintenance cost of wind-driven generator unit quantity of electricity, ΩzFor the set of scene, τzFor distribution year under scene z
Accumulated running time, Pz-1jFor the active power output of j-th of photovoltaic generator under scene z, Pz-2kFor k-th of wind-power electricity generation under scene z
The active power output of machine, β are to wait years value coefficient；
C113, other costs, subsidy:
In formula, CenFor to the purchases strategies of major network, CeFor the energy cost of unit electricity, n is Distribution Network Load Data node total number,
Pz-LiFor the burden with power power of distribution node i under scene z, ClossFor via net loss cost, Δ Pz-i′For route i ' under scene z
Active power loss, CbFor government subsidy, Δ Pz-DGiFor the active power output of i-th of distributed generation resource under scene z, CceqIt is fast
Fill the construction cost at station, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
The node voltage quality objective of c12, power distribution network
f2Middle UlevelCalculation formula it is as follows:
In formula, Ulevel.iIndicate the corresponding assessed value of node voltage of i-th of node in network, VminIndicate node voltage
Permission lower limit value, VmaxIndicate the allowable upper limit value of node voltage, ViIndicate node voltage amplitude, N indicates node number；
C13, constraint condition
C131, trend constraint
In formula, PiFor the active injection power of node i, QiFor the idle injecting power of node i, j ∈ i is all and node i
The node set being connected directly, UiFor the voltage magnitude of node i, UjFor the voltage magnitude of node j, GijFor node admittance matrix
Real part, BijFor the imaginary part of node admittance matrix, θijFor the phase difference of voltage of node i and node j；
C132, node voltage bound probability constraints
P{Umin≤U≤Umax}=k1/N≥β1
In formula, UminFor the lower limit of node voltage, UmaxFor the upper limit of node voltage, k1To meet on voltage in all scenes
The scene number of lower limit constraint, β1For the confidence level of node voltage constraint；
C133, branch power probability constraints
P{P2≤P2max}=k2/N≥β2
In formula, P2For branch power, P2maxFor the upper limit of the power that branch allows, k2To meet branch power in all scenes
The scene number of constraint, β2For the confidence level of branch power constraint；
C134, forbid that power probability is sent to constrain
P{P∑DG≤P∑L}=kB/N≥βB
In formula, P∑DGFor distributed generation resource gross capability, P∑LFor the active aggregate demand of load, kBForbid to meet in all scenes
Send the scene number of power constraint, βBFor the confidence level for forbidding sending power constraint；
C135, the constraint of distributed generation resource installed capacity
In formula, P∑1For the total installed capacity of photovoltaic generator, P∑2For the total installed capacity of wind-driven generator, σ is renewable energy
DG maximum permeability, P∑LmaxFor the summation of distribution maximum burden with power, P1i maxFor grid node i to be selected photovoltaic generator most
Big installed capacity, P2i maxFor the wind-driven generator maximum installed capacity of grid node i to be selected, P1iFor the light of grid node i to be selected
The installed capacity of overhead generator, P2iFor the installed capacity of the wind-driven generator of grid node i to be selected；
The desired value minimum of c2, in a distributed manner power supply year resection is as lower layer's target, objective function are as follows:
In formula, ΩzFor the set of scene, Ω1For the set for creating photovoltaic generator, Ω2For the collection for creating wind-driven generator
It closes, Δ tzFor accumulated running time in distribution year under scene z,For the active reduction of j-th of photovoltaic generator under scene z
Amount,For the active reduction of j-th of wind-driven generator under scene z；
C21, distributed generation resource power output resection constraint
In formula,For the minimum reduction of photovoltaic generator,For the maximum reduction of photovoltaic generator,For the minimum reduction of wind-driven generator,For the maximum reduction of wind-driven generator；
C22, the constraint of transformer tapping adjustable range
Tk min≤Tk≤Tk max
In formula, TkFor transformer tapping current location, Tk minFor the lower limit of transformer tapping adjustable extent, Tk maxFor transformation
The upper limit of device tap adjustable extent.
Compared with prior art, the invention has the benefit that
The power distribution network multiple target collaborative planning method of a kind of meter of the present invention and charging station and distributed generation resource considers distributed
The stochastic uncertainty of power supply, electric car and the network of communication lines are taken into account, and are reasonably assessed electric car charge-discharge facility and are connect
Enter the influence of power grid, so that taking effective measures improves operation of power networks economy and power quality etc.；According to distributed generation resource
Stochastic uncertainty, using the different scenes of timing method simulation distribution formula power supply and load power, in electric automobile charging station
Addressing constant volume on, consider the magnitude of traffic flow distribution and transportation network layout, should sufficiently meet electric car car owner's quick charge
Convenience, do not increase the burden of electric load and transportation network again；Electric car travels on road, and can regard charging as needs
It asking and is flowed in transportation network, the design couples power network with the network of communication lines, using economy and quality of voltage as target, one
Determine influence of the randomness to power grid that distributed generation resource is alleviated in degree, using electric automobile charging station to distributed generation resource into
Row on-site elimination, not only avoided electric automobile charging station construction may will caused by traffic congestion, but also improve electric system
Stability.
Detailed description of the invention
Fig. 1 is dual layer resist relational graph.
Fig. 2 is model solution flow chart.
Fig. 3 is 33 node diagram of power distribution network and the network of communication lines.
Fig. 4 is to be not optimised fast charge station addressing result figure.
Fig. 5 is addressing result figure in fast charge station after optimization.
Fig. 6 is to consider economy, traffic satisfaction, the Pareto result of quality of voltage.
Specific embodiment
Below in conjunction with Detailed description of the invention and specific embodiment, the present invention is described in further detail.
Referring to Fig. 1, Fig. 2, it is a kind of meter and charging station and distributed generation resource power distribution network multiple target collaborative planning method, the party
Method the following steps are included:
A, consider that the uncertainty of distributed generation resource, analysis wind light generation power output rule are based on its season and temporal characteristics,
Establish more scene distribution formula power supplys and load model；
A1, assume that distributed generation resource power output obeys certain probability Distribution Model, and it is not true by the methods of sampling to show it
It is qualitative；The power output of wind-driven generator changes with wind speed and is changed, and the power output of photovoltaic generator depends primarily on intensity of illumination, although
Wind speed and intensity of illumination have randomness and fluctuation, but its distribution still has certain rule, using empirical distribution function
It is fitted the honourable regularity of distribution；
A11, wind speed obey two parameter Weibull distribution, probability density function are as follows:
In formula, V is the wind speed at wind generator unit wheel hub, and k is form parameter, and c is scale parameter；
Wind driven generator output power PwtgIt is indicated with the relationship of wind speed V by following piecewise function:
In formula, VciFor the incision wind speed of wind-driven generator, VrFor the rated wind speed of wind-driven generator, VcoFor wind-driven generator
Cut-out wind speed, Pr2For the rated output power of wind-driven generator；
A12, intensity of illumination are distributed to describe using beta:
In formula, I is intensity of illumination, ImaxFor light intensity maximum value, α and β are form parameter, and Γ () is gamma function；
Photovoltaic generator output power PpvgWith the relationship of intensity of illumination I are as follows:
In formula, Pr1For the rated output power of photovoltaic generator, IrFor specified intensity of illumination；
A2, further wind light generation power output rule is analyzed, chooses one respectively in spring, summer, autumn, four seasons of winter
The distributed generation resource power output in each season is simulated with 24 moment, the temporal characteristics of distributed power generation are carried out a typical case's day
Research, Various Seasonal wind speed curve and intensity of illumination curve are obtained according to meteorological data, and then extrapolate wind-powered electricity generation, photovoltaic power output
Timing curve, meanwhile, load power scene is simulated with these scenes respectively, 96 annual scenes is combined into, establishes more
Scape distributed generation resource and load model；
B, electric vehicle on road traffic network design is established, magnitude of traffic flow distribution, transportation network layout are considered, based on electronic
Vehicle charging station intercept and capture the magnitude of traffic flow and user's average latency, calculate transportation economy benefit, and by distribution network with
Transportation network coupling, in this, as the basis of power distribution network collaborative planning；
B1, set of minimal paths connected two-by-two is found out using Floyd algorithm, and calculated by gravity-space interactive model
The magnitude of traffic flow that total system fast charge station is intercepted and captured every year
In formula, ΩodThe set of minimal paths being connected two-by-two for all nodes of system；It is shortest path k in period t
One-way traffic flow demand per unit value；woAnd wdThe respectively weight of the starting point o of shortest path k and terminal d, to indicate
The busy extent of each transport node；dkFor the per unit value of shortest path k length；stIt is automobile user in the trip ratio of period t
Example, shIt is automobile user in the trip proportion of traffic peak period；
B2, the average arrival rate that vehicle to be charged is calculated according to the magnitude of traffic flow that fast charge station is intercepted and captured, if shortest path k passes through
Node i, and node i has fast charge station, then and node i can intercept and capture the flow of shortest path k, the average arrival rate of vehicle to be charged
The proportional assignment of time and node are directed to for the total frequency demand of fast charge, specific formula is as follows:
In formula,For the magnitude of traffic flow that fast charge station is intercepted and captured in period t at node i；It is shortest path k in the period
The per unit value of the one-way traffic flow demand of t；Whether pass through the two-valued variable of node i for shortest path k；For section
Whether the two-valued variable at fast charge station is built at point i；λI, tAnd λI, hFast charge station is when period t and traffic peak respectively at node i
The inverse that the average arrival rate of section vehicle to be charged, i.e. automobile user reach average time interval；CqcFor the quick of system
Charge total frequency demand；
B3, electric automobile charging station constant volume model is established, according to the maximum allowable waiting time of traffic peak period user
WmaxTo configure the charging equipment quantity at each fast charge station, and arrived in conjunction with M/M/s waiting line system model simulation fast charge station vehicle to be charged
Up to process and services duration, most economical fast charge station equipment quantity is set in the case where being no more than the maximum allowable waiting time；
B31, constraint condition are as follows:
s.t. WI, h< Wmax, λI, h≠ 0, i ∈ Ω
In formula, WI, hReceive the average latency of charging service, W for the traffic peak periodmaxWhen for maximum allowable waiting
Between；
B32, the fast charge number of devices that each fast charge station should be arranged is calculated:
In formula,For the number of devices at fast charge station at node i, ρI, hIt is fast charge station at node i in the traffic peak period
Equipment average service rate, λI, hIt is fast charge station at node i in the average arrival rate of traffic peak period vehicle to be charged, P0iFor section
The whole idle probability of the equipment at fast charge station at point i, μ is the average service rate of the fast fill device of separate unit, i.e. quick charge is averaged
The inverse of time；
B33, to enable the addressing at electric car fast charge station not fall in heavy traffic section densely excessively, introduce traffic
Flow discount factor rationally weakens magnitude of traffic flow economic benefit value:
In formula,For the magnitude of traffic flow discount factor of shortest path k, a is the busy discount of transport node, and m is most short
In the k of path comprising the busy section of m item (busy section show the way a section first and last node weights be in all-network node weight it is maximum
Preceding k node), FqcFor magnitude of traffic flow economic benefit value, ωfBy the intercepting and capturing magnitude of traffic flow economic benefit conversion factor,
For the two-valued variable that can the flow on shortest path k be intercepted and captured by fast charge station, if shortest path k passes through at least one fast charge station,
ThenIt is 1, otherwiseIt is 0,It is shortest path k in the per unit value of the one-way traffic flow demand of period t；
B34, model finally need to consider the Installed capital cost C at fast charge stationceq, build a station number and each with fast charge station
The equipment number of a charging station is related:
In formula, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
It b35, is that automobile user is enable to find charging pile and quick charge as far as possible, the fast charge station of day part nearby
Charge power is determined by its charging time ratio:
In formula,It is fast charge station at node i in the charge power of period t, λI, tIt is waited for for fast charge station at node i in period t
The average arrival rate of charging vehicle, pqcFor the charge power of the fast fill device of separate unit, μ is the average service rate of the fast fill device of separate unit,
That is the inverse of the average time of quick charge；
C, distributed generation resource and electric automobile charging station addressing constant volume are comprehensively considered in planning process, to plan totle drilling cost most
Optimal low and network node quality of voltage is upper layer target, meanwhile, it is electric in a distributed manner to make distributed generation resource be utilized effectively
The minimum lower layer's target of source resection desired value, establishes multiple target collaborative planning model；
C1, best using economy and node voltage optimal quality is upper layer target, objective function are as follows:
In formula, f1Indicate economy objectives, ClineIndicate investment and the operation and maintenance cost of route, CDGIndicate distributed electrical
The investment in source and operation and maintenance cost, CenIndicate the purchases strategies to major network, ClossIndicate via net loss cost, CceqExpression is filled
Power station investment cost, CbIndicate government subsidy, FqcIndicate magnitude of traffic flow economic benefit value, f2Indicate power distribution network node voltage quality
Target, UlevelIndicate power distribution network node voltage quality evaluation function values；
C11, economy objectives
C111, the investment of route and operation and maintenance cost Cline:
Cline=CIline+COMline
In formula, CIlineFor the equal years value of route fixed investment, COMlineFor route year operation and maintenance cost, ΩL1It is newly-built
The set of route, ΩL2For the set for upgrading route, ΩLFor the set of all routes,For new route unit length
Required cost,For upgrade route unit length required cost,For the required cost of route unit length, l is
Line length, a are to wait years value coefficient, and r is discount rate, nlineFor route fixed investment payoff period, γ is route operation and maintenance expense
With rate；
C112, the investment of distributed generation resource and operation and maintenance cost CDG:
CDG=CIDG+COMDG
In formula, CIDGFor the equal years value of renewable energy DG fixed investment, COMDGFor the year operation and maintenance of renewable energy DG
Expense, Cf1For the installation cost of photovoltaic generator unit capacity, Cf2For the installation cost of wind-driven generator unit capacity, Ω1For
The set of newly-built photovoltaic generator, Ω2For the set for creating wind-driven generator, P1jFor photovoltaic generator installed capacity, P2kFor wind
Power generator installed capacity, nDGFor distributed generation resource fixed investment payoff period, COM1It is tieed up for the operation of photovoltaic generator unit quantity of electricity
Shield expense, COM2For the operation and maintenance cost of wind-driven generator unit quantity of electricity, ΩzFor the set of scene, τzFor distribution year under scene z
Accumulated running time, Pz-1jFor the active power output of j-th of photovoltaic generator under scene z, Pz-2kFor k-th of wind-power electricity generation under scene z
The active power output of machine, β are to wait years value coefficient；
C113, other costs, subsidy:
In formula, CenFor to the purchases strategies of major network, CeFor the energy cost of unit electricity, n is Distribution Network Load Data node total number,
Pz-LiFor the burden with power power of distribution node i under scene z, ClossFor via net loss cost, Δ Pz-i′For route i ' under scene z
Active power loss, CbFor government subsidy, Δ Pz-DGiFor the active power output of i-th of distributed generation resource under scene z, CceqIt is fast
Fill the construction cost at station, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
The node voltage quality objective of c12, power distribution network
f2Middle UlevelIndicate power distribution network node voltage quality evaluation function values, UlevelSmaller, distribution network voltage quality is better,
Ulevel.iIt indicates the corresponding assessed value of i-th of node voltage in network, first finds out the U of each nodelevel.i, then take all nodes
The average value of assessed value is as Ulevel, calculation formula is as follows:
In formula, Ulevel.iIndicate the corresponding assessed value of node voltage of i-th of node in network, VminIndicate node voltage
Permission lower limit value, VmaxIndicate the allowable upper limit value of node voltage, ViIndicate node voltage amplitude, N indicates node number；This
In all voltage value refer to per unit value, the lower limit value and upper limit value of permission will be according to different distribution network voltage grades come really
It is fixed, " distribution network planning designing technique directive/guide " can be referred to；
C13, consider by after distributed generation resource access system to original electrical spies such as the trend distribution of power distribution network, voltage levels
Property will cause impact, need to constrain it, specific constraint condition are as follows:
C131, trend constraint
In formula, PiFor the active injection power of node i, QiFor the idle injecting power of node i, j ∈ i is all and node i
The node set being connected directly, UiFor the voltage magnitude of node i, UiFor the voltage magnitude of node j, GijFor node admittance matrix
Real part, BijFor the imaginary part of node admittance matrix, θijFor the phase difference of voltage of node i and node j；
C132, node voltage bound probability constraints
P{Umin≤U≤Umax}=k1/N≥β1
In formula, UminFor the lower limit of node voltage, UmaxFor the upper limit of node voltage, k1To meet on voltage in all scenes
The scene number of lower limit constraint, β1For the confidence level of node voltage constraint；
C133, branch power probability constraints
P{P2≤P2max}=k2/N≥β2
In formula, P2For branch power, P2maxFor the upper limit of the power that branch allows, k2To meet branch power in all scenes
The scene number of constraint, β2For the confidence level of branch power constraint；
C134, forbid that power probability is sent to constrain
P{P∑DG≤P∑L}=kB/N≥βB
In formula, P∑DGFor distributed generation resource gross capability, P∑LFor the active aggregate demand of load, kBForbid to meet in all scenes
Send the scene number of power constraint, βBFor the confidence level for forbidding sending power constraint；
C135, the constraint of distributed generation resource installed capacity
In formula, P∑1For the total installed capacity of photovoltaic generator, P∑2For the total installed capacity of wind-driven generator, σ is renewable energy
DG maximum permeability, R∑LmaxFor the summation of distribution maximum burden with power, P1i maxFor grid node i to be selected photovoltaic generator most
Big installed capacity, P2i maxFor the wind-driven generator maximum installed capacity of grid node i to be selected, P1iFor the light of grid node i to be selected
The installed capacity of overhead generator, P2iFor the installed capacity of the wind-driven generator of grid node i to be selected；
The desired value minimum of c2, in a distributed manner power supply year resection is as lower layer's target, objective function are as follows:
In formula, ΩzFor the set of scene, Ω1For the set for creating photovoltaic generator, Ω2For the collection for creating wind-driven generator
It closes, Δ tzFor accumulated running time in distribution year under scene z,For the active reduction of j-th of photovoltaic generator under scene z
Amount,For the active reduction of j-th of wind-driven generator under scene z；
C21, distributed generation resource power output resection constraint
In formula,For the minimum reduction of photovoltaic generator,For the maximum reduction of photovoltaic generator,For the minimum reduction of wind-driven generator,For the maximum reduction of wind-driven generator；
C22, the constraint of transformer tapping adjustable range
Tk min≤Tk≤Tk max
In formula, TkFor transformer tapping current location, Tk minFor the lower limit of transformer tapping adjustable extent, Tk maxFor transformation
The upper limit of device tap adjustable extent；
D, it is based on genetic algorithm, obtains the Pareto disaggregation for meeting constraint condition, then multiple target is obtained by TOPSIS method
The optimal solution of function.
It is illustrated below by specific example:
The design analogue system uses 33 node system of IEEE, the topological diagram and transportation network topological diagram of system, the network of communication lines
Network interstitial content and distribution network node number are consistent, as shown in Figure 3；Line parameter circuit value and DG parameter are respectively such as table 1, table 2
It is shown:
1 line parameter circuit value of table
2 DG parameter of table
It chooses a typical day respectively in spring, summer, autumn, 4 seasons of winter, the DG in each season is simulated with 24 moment
Power output, specific data are as shown in table 3:
The typical sunrise force data of 3 wind-powered electricity generation of table, photovoltaic and load
In the example, transformer grade used in electric automobile charging station is 63kVA, transformer efficiency and charging engine efficiency
Respectively 95%, 90%, each electric car single charge amount are 30kWh, and the charge power of separate unit charging equipment is 60kW；It fills
The addressing number in power station is limited to 8；Improved NSGA-II algorithm greatest iteration number is set as 40, Population Size 50, and crossing-over rate is
0.9, aberration rate 0.1, multinomial index of variability is 20；Transport node vehicle flowrate weight and EV day part trip proportion difference
As shown in table 4 and table 5:
4 node vehicle flowrate weight of table
5 electric car day part trip proportion of table
This example calculates the magnitude of traffic flow using gravity-space interactive model, and wherein the magnitude of traffic flow becomes with the apparent time
Change feature；By calculating, the traffic flow magnitude at 24 hours each moment of extraction road 1-2 node, as shown in table 6:
6 moment of table corresponds to flowmeter
The results are shown in Table 7 for the fast charge station addressing of comparison model optimization front and back, optimizes the addressing figure of front and back respectively such as attached drawing
4, shown in attached drawing 5.
The optimization of table 7 front and back fast charge station addressing result
Optimal solution is found through genetic algorithm, comparison optimization front and back addressing result is it is found that addressing node is most before being not optimised
It is in busy node, the selected probability of the bigger point of traffic weight is bigger；But it is gathered around since busy node is easy to appear traffic
Stifled, this has certain repulsive interaction to user's charging wish, therefore should suitably avoid absolutely crowded section；After optimization, fast charge
Addressing of standing avoids busy node substantially, and adjacent with busy node, has taken into account the initial purpose of the more magnitudes of traffic flow of intercepting and capturing
The considerations of with traffic congestion section is avoided, generally achieves good effect.
Electric power networks and transportation network are coupled, Pareto disaggregation is obtained by emulation as shown in fig. 6, taking out all
Scheme and its optimum results carry out list, are handled by TOPSIS method the Pareto disaggregation, it is assumed that the weight of each index
It is worth equal, then multiple target collaborative planning can be obtained, and the results are shown in Table 8, and optimal compromise solution is scheme 10.
8 multiple target collaborative planning result of table
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (4)
1. a kind of power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource, which is characterized in that this method
The following steps are included:
A, consider that the uncertainty of distributed generation resource, analysis wind light generation power output rule are based on its season and temporal characteristics, establish
More scene distribution formula power supplys and load model；
B, electric vehicle on road traffic network design is established, magnitude of traffic flow distribution, transportation network layout is considered, is based on electric car
The magnitude of traffic flow and user's average latency that charging station is intercepted and captured, calculate transportation economy benefit, and by distribution network and traffic
Network coupling, in this, as the basis of power distribution network collaborative planning；
C, distributed generation resource and electric automobile charging station addressing constant volume are comprehensively considered in planning process, with plan the lowest cost with
Optimal network node quality of voltage is upper layer target, and the minimum lower layer's target of power supply resection desired value, is established more in a distributed manner
Target cooperative plan model；
D, it is based on genetic algorithm, obtains the Pareto disaggregation for meeting constraint condition, then multiple objective function is obtained by TOPSIS method
Optimal solution.
2. the power distribution network multiple target collaborative planning side of a kind of meter according to claim 1 and charging station and distributed generation resource
Method, it is characterised in that: the step a specifically includes the following steps:
A1, assume that distributed generation resource power output obeys certain probability Distribution Model, and uncertainty is showed by the methods of sampling；
The honourable regularity of distribution is fitted using empirical distribution function；
A11, wind speed obey two parameter Weibull distribution, probability density function are as follows:
In formula, V is the wind speed at wind generator unit wheel hub, and k is form parameter, and c is scale parameter；
Wind driven generator output power PwtgIt is indicated with the relationship of wind speed V by following piecewise function:
In formula, VciFor the incision wind speed of wind-driven generator, VγFor the rated wind speed of wind-driven generator, VcoFor cutting for wind-driven generator
Wind speed out, Pr2For the rated output power of wind-driven generator；
A12, intensity of illumination are distributed to describe using beta:
In formula, I is intensity of illumination, ImaxFor light intensity maximum value, α and β are form parameter, and Γ () is gamma function；
Photovoltaic generator output power PpvgWith the relationship of intensity of illumination I are as follows:
In formula, Pr1For the rated output power of photovoltaic generator, IγFor specified intensity of illumination；
A2, it chooses a typical day respectively in spring, summer, autumn, four seasons of winter, point in each season is simulated with 24 moment
Cloth power supply power output, studies the temporal characteristics of distributed power generation, obtains Various Seasonal wind speed curve according to meteorological data
With intensity of illumination curve, and then extrapolate wind-powered electricity generation, photovoltaic power output timing curve, meanwhile, it is negative to simulate with these scenes respectively
Lotus power scene is combined into 96 annual scenes, establishes more scene distribution formula power supplys and load model.
3. the power distribution network multiple target collaborative planning side of a kind of meter according to claim 2 and charging station and distributed generation resource
Method, it is characterised in that: the step b specifically includes the following steps:
B1, set of minimal paths connected two-by-two is found out using Floyd algorithm, and complete set is calculated by gravity-space interactive model
The magnitude of traffic flow that system fast charge station is intercepted and captured every year
In formula, ΩodThe set of minimal paths being connected two-by-two for all nodes of system；It is shortest path k in the list of period t
To the per unit value of magnitude of traffic flow demand；woAnd wdThe respectively weight of the starting point o of shortest path k and terminal d, to indicate each friendship
The busy extent of logical node；dkFor the per unit value of shortest path k length；StIt is automobile user in the trip proportion of period t,
ShIt is automobile user in the trip proportion of traffic peak period；
B2, the average arrival rate that vehicle to be charged is calculated according to the magnitude of traffic flow that fast charge station is intercepted and captured, specific formula is as follows:
In formula,For the magnitude of traffic flow that fast charge station is intercepted and captured in period t at node i；It is shortest path k in the list of period t
To the per unit value of magnitude of traffic flow demand；Whether pass through the two-valued variable of node i for shortest path k；At node i
Whether the two-valued variable at fast charge station is built；λI, tAnd λI, hRespectively fast charge station waits filling in period t and traffic peak period at node i
The inverse of the average arrival rate of electric car, i.e. automobile user arrival average time interval；CqcIt is total for the quick charge of system
Frequency demand；
B3, electric automobile charging station constant volume model is established, according to the maximum allowable waiting time W of traffic peak period usermaxCome
The charging equipment quantity at each fast charge station is configured, and M/M/s waiting line system model simulation fast charge station vehicle to be charged is combined to reach
Most economical fast charge station equipment quantity is arranged in the case where being no more than the maximum allowable waiting time in journey and service duration；
B31, constraint condition are as follows:
s.t. WI, h< Wmax, λI, h≠ 0, i ∈ Ω
In formula, WI, hReceive the average latency of charging service, W for the traffic peak periodmaxFor the maximum allowable waiting time；
B32, the fast charge number of devices that each fast charge station should be arranged is calculated:
In formula,For the number of devices at fast charge station at node i, ρI, hIt is fast charge station at node i in the equipment of traffic peak period
Average service rate, λI, hIt is fast charge station at node i in the average arrival rate of traffic peak period vehicle to be charged, P0iAt node i
The whole idle probability of the equipment at fast charge station, μ is the average service rate of the fast fill device of separate unit, i.e. the average time of quick charge
It is reciprocal；
B33, introducing magnitude of traffic flow discount factor rationally weaken magnitude of traffic flow economic benefit value:
In formula,For the magnitude of traffic flow discount factor of shortest path k, a is the busy discount of transport node, and m is shortest path k
In include the busy section of m item, FqcFor magnitude of traffic flow economic benefit value, ωfBy the intercepting and capturing magnitude of traffic flow economic benefit convert system
Number,For the two-valued variable that can the flow on shortest path k be intercepted and captured by fast charge station, if shortest path k passes through at least one
Fast charge station, thenIt is 1, otherwiseIt is 0,It is shortest path k in the mark of the one-way traffic flow demand of period t
Value；
B34, fast charge station Installed capital cost Cceq:
In formula, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
B35, fast charge station charge power:
In formula,It is fast charge station at node i in the charge power of period t, λI, tIt is to be charged in period t for fast charge station at node i
The average arrival rate of vehicle, pqcFor the charge power of the fast fill device of separate unit, μ is the average service rate of the fast fill device of separate unit, i.e., fastly
The inverse of the average time of speed charging.
4. the power distribution network multiple target collaborative planning side of a kind of meter according to claim 3 and charging station and distributed generation resource
Method, it is characterised in that: the step c specifically includes the following steps:
C1, best using economy and node voltage optimal quality is upper layer target, objective function are as follows:
In formula, f1Indicate economy objectives, ClineIndicate investment and the operation and maintenance cost of route, CDGIndicate distributed generation resource
Investment and operation and maintenance cost, CenIndicate the purchases strategies to major network, ClossIndicate via net loss cost, CceqIndicate charging station
Investment cost, CbIndicate government subsidy, FqcIndicate magnitude of traffic flow economic benefit value, f2Indicate power distribution network node voltage quality mesh
Mark, UlevelIndicate power distribution network node voltage quality evaluation function values；
C11, economy objectives
C111, the investment of route and operation and maintenance cost Cline:
Cline=CIline+COMline
In formula, CIlineFor the equal years value of route fixed investment, COMlineFor route year operation and maintenance cost, ΩL1For new route
Set, ΩL2For the set for upgrading route, ΩLFor the set of all routes,By costing for new route unit length
With,For upgrade route unit length required cost,For the required cost of route unit length, l is that route is long
Degree, α are to wait years value coefficient, and r is discount rate, nlineFor route fixed investment payoff period, γ is route operation and maintenance cost rate；
C112, the investment of distributed generation resource and operation and maintenance cost CDG:
CDG=CIDG+COMDG
In formula, CIDGFor the equal years value of renewable energy DG fixed investment, COMDGFor the year operation and maintenance cost of renewable energy DG,
Cf1For the installation cost of photovoltaic generator unit capacity, Cf2For the installation cost of wind-driven generator unit capacity, Ω1To create light
The set of overhead generator, Ω2For the set for creating wind-driven generator, P1jFor photovoltaic generator installed capacity, P2kFor wind-power electricity generation
Machine installed capacity, nDGFor distributed generation resource fixed investment payoff period, COM1For the operation and maintenance expense of photovoltaic generator unit quantity of electricity
With COM2For the operation and maintenance cost of wind-driven generator unit quantity of electricity, ΩzFor the set of scene, τzIt is accumulative for distribution year under scene Z
Runing time, Pz-1jFor the active power output of j-th of photovoltaic generator under scene z, Pz-2kFor k-th wind-driven generator under scene z
Active power output, β are to wait years value coefficient；
C113, other costs, subsidy:
In formula, CenFor to the purchases strategies of major network, CeFor the energy cost of unit electricity, n is Distribution Network Load Data node total number, Pz-Li
For the burden with power power of distribution node i under scene Z, ClossFor via net loss cost, Δ Pz-i′Have for route i ' under scene Z
Function power loss, CbFor government subsidy, Δ Pz-DGiFor the active power output of i-th of distributed generation resource under scene Z, CceqFor fast charge station
Construction cost, mqcFor the construction cost of the fast fill device of separate unit,For the number of devices at fast charge station at node i；
The node voltage quality objective of c12, power distribution network
f2Middle UlevelCalculation formula it is as follows:
In formula, ULevel, iIndicate the corresponding assessed value of node voltage of i-th of node in network, VminIndicate the permission of node voltage
Lower limit value, VmaxIndicate the allowable upper limit value of node voltage, ViIndicate node voltage amplitude, N indicates node number；
C13, constraint condition
C131, trend constraint
In formula, PiFor the active injection power of node i, QiFor the idle injecting power of node i, j ∈ i is all direct with node i
Connected node set, UiFor the voltage magnitude of node i, UjFor the voltage magnitude of node j, GijFor the reality of node admittance matrix
Portion, BijFor the imaginary part of node admittance matrix, θijFor the phase difference of voltage of node i and node j；
C132, node voltage bound probability constraints
P{Umin≤U≤Umax}=k1/N≥β1
In formula, UminFor the lower limit of node voltage, UmaxFor the upper limit of node voltage, k1To meet voltage bound in all scenes
The scene number of constraint, β1For the confidence level of node voltage constraint；
C133, branch power probability constraints
P{P2≤P2max}=k2/N≥β2
In formula, P2For branch power, P2maxFor the upper limit of the power that branch allows, k2To meet branch power constraint in all scenes
Scene number, β2For the confidence level of branch power constraint；
C134, forbid that power probability is sent to constrain
P{P∑DG≤P∑L}=kB/N≥βB
In formula, P∑DGFor distributed generation resource gross capability, P∑LFor the active aggregate demand of load, kBForbid sending to meet in all scenes
The scene number of power constraint, βBFor the confidence level for forbidding sending power constraint；
C135, the constraint of distributed generation resource installed capacity
In formula, P∑1For the total installed capacity of photovoltaic generator, P∑2For the total installed capacity of wind-driven generator, σ be renewable energy DG most
Big permeability, P∑LmaxFor the summation of distribution maximum burden with power, P1imaxPacify for the photovoltaic generator maximum of grid node i to be selected
Dressing amount, P2imaxFor the wind-driven generator maximum installed capacity of grid node i to be selected, P1iIt is sent out for the photovoltaic of grid node i to be selected
The installed capacity of motor, P2iFor the installed capacity of the wind-driven generator of grid node i to be selected；
The desired value minimum of c2, in a distributed manner power supply year resection is as lower layer's target, objective function are as follows:
In formula, ΩzFor the set of scene, Ω1For the set for creating photovoltaic generator, Ω2For create wind-driven generator set,
ΔtzFor accumulated running time in distribution year under scene z,For the active reduction of j-th of photovoltaic generator under scene z,For the active reduction of j-th of wind-driven generator under scene z；
C21, distributed generation resource power output resection constraint
In formula,For the minimum reduction of photovoltaic generator,For the maximum reduction of photovoltaic generator,For the minimum reduction of wind-driven generator,For the maximum reduction of wind-driven generator；
C22, the constraint of transformer tapping adjustable range
Tkmin≤Tk≤Tkmax
In formula, TkFor transformer tapping current location, TkminFor the lower limit of transformer tapping adjustable extent, TkmaxFor transformer pumping
The upper limit of head adjustable extent.
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