CN109829560A - A kind of power distribution network renewable energy power generation cluster access planing method - Google Patents
A kind of power distribution network renewable energy power generation cluster access planing method Download PDFInfo
- Publication number
- CN109829560A CN109829560A CN201811214607.4A CN201811214607A CN109829560A CN 109829560 A CN109829560 A CN 109829560A CN 201811214607 A CN201811214607 A CN 201811214607A CN 109829560 A CN109829560 A CN 109829560A
- Authority
- CN
- China
- Prior art keywords
- power
- load
- formula
- renewable energy
- active
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The present invention relates to a kind of power distribution network renewable energy power generation clusters to access planing method, and using upper layer plan model and lower layer's scheduling model, upper layer plan model is using renewable energy power generation investor Income Maximum as target；The objective function of lower layer's scheduling model includes the active reduction of power-balance degree index, the adjustment cost of power distribution company and renewable energy power generation, regulation measure includes the movement for getting in touch with wiretap, the movement of the tap of on-load regulator transformer, the active reduction and reactive compensation of renewable energy power generation, for the timing dependence between load, Renewable Energy Resources, it is modeled using C-Vine Copula model, the typical planning scene for considering load, resource dependencies is generated in conjunction with Latin Hypercube Sampling method.
Description
Technical field
The present invention relates to the power distribution network renewable energy power generation clusters of a kind of consideration load, resource time sequence correlation to access rule
The method of drawing.
Background technique
Renewable energy power generation accesses power distribution network, has positive effect for energy saving, reduction carbon emission.However, when can
When the generated energy of renewable source of energy generation accounts for the 20%-30% of all kinds of power supply total power generations, the interval of generated output of renewable energy source
Property and the problems such as randomness will lead to system overvoltage, power is sent.Therefore, in order to improve power distribution network to renewable energy power generation
Receiving ability, it is necessary to consider the influence that runs to system of renewable energy power generation in the planning stage.
For the renewable energy power generation planning problem of power distribution network, existing research is mostly that progress can under single substation
The addressing of renewable source of energy generation and constant volume.Such methods think that the power distribution network under different substation is mutually indepedent, do not consider system
In the connection and influence that run between multiple substations, have ignored the complementary support ability of power between multiple substations.Due to
Using identical objective function and constraint condition when this method is planned for each substation, so carrying out renewable energy
The power producing characteristics that may cause multiple substations after power generation planning are similar, if multiple substations of the same area while emergent power
It send and is transmitted to upper level power grid, then will affect the normal operation of high voltage distribution network.Active distribution network can utilize advanced
The new technologies such as automation, communication and power electronics, which are realized, leads the renewable energy power generation and other equipment of access power distribution network
Dynamic management.Power distribution network configures interconnection switch and block switch extensively, and active distribution network may be implemented by cut-offfing for control switch
The dynamic restructuring of network is conducive to reduce network loss, balanced load.Therefore, connecing for renewable energy power generation is carried out in power distribution network
When entering and dissolving Study on Problems, need to consider to access the connection of output power between multiple substations after renewable energy power generation with
And influence of the network reconfiguration to enabling capabilities variation of powering between multi-Substation, carry out power distribution network multi-Substation renewable energy power generation
Cluster access planning, determine the cluster access capacity of renewable energy power generation under each substation.
In the planning stage, intermittent renewable energy power generation access power distribution network improves the uncertainty of power distribution network operation,
Increase the difficulty that scene is chosen during distribution network planning.Load, money are accurately portrayed with a small amount of representative scene
The stochastic behaviour in source can effectively reduce calculation amount and improve the precision of program results.Consider probabilistic renewable energy
Power generation planning method mainly has the planning based on multi-scenario technique, the planning based on chance constraint theory and based on fuzzy theory
Planning, wherein scene analysis method is enumerated the possibility value of uncertain factor by rule, is combined into a series of planning scenes, each
Scene has corresponding probability, to convert certain problem for uncertain problem, reduces the difficulty of modeling and solution.It is negative
Lotus and resource are other than having uncertainty, and there is also certain correlations between variable.However, method can only indicate at present
Linear dependence between variable, it is not accurate enough to the modeling between the variable with nonlinear correlation.Copula function should not
Variable edge distribution having the same is sought, the features such as non-linear, asymmetry, the tail-dependence coefficient between variable can be described.
PairCopula method is a branch in Copula function, can characterize the correlation between multidimensional variable, flexible structure,
The correlation between any two variable can be preferably captured, however this method is less at present applied to Power System Planning
In.
In conclusion the defect and deficiency of art methods can be summarized as following several points:
(1) for the planning problem of renewable energy power generation in power distribution network, existing research is mostly under single substation
Carry out addressing and the constant volume of renewable energy power generation.Such methods think that the power distribution network under different substation is mutually indepedent, do not examine
The connection and influence run between multiple substations in worry system.
(2) in renewable energy power generation planning process, do not fully consider that various regulation measures are outstanding in active distribution network
It is influence of the network reconfiguration between enabling capabilities variation of powering multi-Substation.
(3) art methods can only describe the linear dependence between load, resource, to asymmetric, non-linear
Modeling between the variable of correlation is not accurate enough.
Summary of the invention
In view of the above-mentioned problems, the invention proposes it is a kind of consideration load, Renewable Energy Resources timing dependence can be again
Raw energy power generation cluster accesses bi-level programming method.Technical solution is as follows:
A kind of power distribution network renewable energy power generation cluster access planing method, dispatches mould using upper layer plan model and lower layer
Type, for upper layer plan model using renewable energy power generation investor Income Maximum as target, the planning scene used considers load, money
Non-linear, asymmetrical correlation between source；The objective function of lower layer's scheduling model includes power-balance degree index, distribution public affairs
The adjustment cost of department and the active reduction of renewable energy power generation, regulation measure include the movement for getting in touch with wiretap, have load to adjust
Tap movement, the active reduction and reactive compensation of renewable energy power generation of pressure transformer, for load, Renewable Energy Resources
Between timing dependence, modeled using C-Vine Copula model, in conjunction with Latin Hypercube Sampling method generate consider
The typical planning scene of load, resource dependencies.
Wherein, lower layer's scheduling model includes:
(1) objective function 1: the operating cost of power distribution company
f1=Closs+Creg
In formula, ClossAnd CregRespectively the Web-based exercise and adjustment cost of power distribution company, adjustment cost include on-load voltage regulation
Transformer tapping adjustment cost, interconnection switch act cost, clTo have power electricity price, Pij、QijTo flow to node from upstream node i
The active and reactive power of j, ViFor the voltage value of node i, i → j indicates that node i is connected with node j, RijFor node i and node j
Between line resistance value, N be power distribution network node set；ctapFor the cost for adjusting tap every time,WithFor moment t and moment
The tap gear of t-1, cswiFor get in touch with wiretap single motion cost,WithFor the interconnection of moment t and moment t-1
Switch state；
(2) objective function 2: block power-balance degree index
With a high pressure/middle buckling power station and its following connected network for a block, propose that active balance degree refers to
Mark and reactive balance degree index, are defined as follows:
In formula, in formula, NblockFor cluster sum, Pblock,iFor the active demand or active output of i-th of block, Qblock,i
For the reactive requirement or idle output of i-th of cluster；
Active balance degree index or reactive balance degree index are smaller, illustrate the active power or idle that block is exchanged with the external world
Power is smaller, and active or idle in block more balances, and by the operation of interconnection switch, carries out to power-balance degree index excellent
Change, reduces the power for flowing through higher level substation as caused by unbalanced power, improve the power-balance degree of system；
Power-balance degree target goals function are as follows:
f2=ω1fP_Bal+ω2fQ_Bal
In formula, ω1And ω2Divide the weight than being active balance degree index and reactive balance degree index, it can be according to index weight
The difference for the property wanted is determined, and needs to meet ω1+ω2=1；
(3) objective function 3: renewable energy reduction
Using renewable energy reduction as one of lower layer's regulation goal:
In formula,WithRespectively i-thPVA photovoltaic or i-thWTGThe active reduction of a blower；
Standardization processing is carried out to 3 objective functions:
In formula,For standardization after objective function,fiminFor the minimum value of i-th of objective function, fimax
For the maximum value of i-th of objective function；
The catalogue scalar functions of lower layer's scheduling model are as follows:
In formula, λ1、λ2、λ3Objective function respectively after planningizationWeight coefficient, can be according to dispatching
The combined factors such as the importance degree of each target of journey and practical operation situation determine, and need to meet λ1+λ2+λ3=1；
Lower layer's scheduling model constraint condition includes:
(1) power flow equation constrains
Wherein, Pj=PLj-Ptotal,PV,j-Ptotal,WTG,j+Pcut,PV,j+Pcut,WTG,j, Qj=QLj-QPV,j-QWTG,j；
In formula, Rij、XijRespectively indicate the resistance value and reactance value of route between node i and node j, PjAnd QjIt is net for node j
The active and reactive power of load, PLjAnd QLjFor the active and reactive power of node j load, Ptotal,PV,jAnd Pcut,PV,jRespectively
For the active power and active reduction of node j photovoltaic, Ptotal,WTG,jAnd Pcut,WTG,jRespectively the active power of node j blower and
Active reduction；
(2) system security constraint
In formula,WithVoltage bound at respectively node j；
(3) distributed photovoltaic operation constraint
QPV, j=(Ptotal,PV,j-Pcut,PV,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of photovoltaic output powerminLimitation；
(4) fan operation constrains
QWTG, j=(Ptotal,WTG,j-Pcut,WTG,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of blower output powerminLimitation；
(5) on-load regulator transformer constrains
Ui=kij,tUj
kij,t=1+Kij,tΔkij
In formula, UiAnd UjRespectively high voltage side of transformer, low-pressure side voltage,Kij WithRespectively transformer tapping gear
Upper and lower limit, Kij,tFor the tap gear of transformer t moment, Δ kijNo-load voltage ratio, k are adjusted for transformer adjacent taps gearij,tFor t
Moment transformer high-low-voltage side voltage change ratio；
(6) interconnection switch constraint
Interconnection switch state should make the load on interconnection continuously power and not operation with closed ring, therefore, for containing N number of
The interconnection of network switch, answers only one interconnection switch to disconnect operation
In formula,It is 1 if closure for the folding condition of the contact wiretap on t moment i-j route, if disconnected
It is then 0；O is the set of fingers along the line for constituting a looped network；
(7) 220kV substation power constraint
To guarantee system safety operation, avoid sending power transmission to power transmission network, it is desirable that 220kV substation power does not fall
It send:
0≤Psub,220kV≤Prated,220kV
G) 220kV following level distribution substation power constraint
Power distribution company has the right to cut down the active output of renewable energy power generation, send power to be less than or equal to substation with limitation
The 60% of rated capacity:
-0.6×PRated, < 220kV≤Psub≤PRated, < 220kV。
Upper layer plan model includes:
Determine the cluster access capacity of photovoltaic and blower under each 35kV substation:
maxFupper=max (Ccell-Cinv-Cmain)
According to photovoltaic and blower sell that electricity acquires renewable energy power generation user sell electric income:
In formula, r is discount rate, Ny, NPV、NWTGIt is the planning time limit, the quantity of photovoltaic, the quantity of blower, c respectivelysell,PVWith
csell,WTGThe respectively rate for incorporation into the power network of photovoltaic and blower,WithIn respectively y, s-th of scene
iPVA photovoltaic or i-thWTGThe practical electricity volume of a blower；
Construction cost is acquired according to the installed capacity of blower and photovoltaic:
In formula, cins,PVAnd cins,WTGThe respectively construction cost of photovoltaic and blower unit capacity,WithRespectively
For the installed capacity of photovoltaic and blower
The installed capacity of photovoltaic and the total power generation of blower are thoroughly done away with, the O&M cost of renewable energy power generation is acquired:
In formula, com,PVFor the year operation and maintenance cost of photovoltaic unit installed capacity, com,WTGFor the fortune of blower unit generated energy
Row maintenance cost,It is in y, s-th of scene i-thWTGThe actual power generation of a blower；
Renewable energy power generation planning is limited by installation ground geographic factor, overall cost of ownership factor, needs to meet installation
Capacity limit:
With
In formula,WithRespectively indicate i-thPVA photovoltaic installation point or i-thWTGThe installed capacity of a assembling point
The upper limit.
Plan that scene generation step is as follows:
(1) read wind-resources, light resource, industrial load, agriculture load, Commercial Load and resident load historical data
Xori=(x1,ori,x2,ori,x3,ori,x4,ori,x5,ori,x6,ori), every a kind of data are carried out marking change with obtaining:
In formula, xi,ori, i=1 ..., 6 indicate wind-resources, light resource, industrial load, agriculture load, Commercial Load and residence
The initial data of burden on the people lotus, xi,ori,max, i=1 ..., 6 indicate the equipment installation of the peak value of corresponding resource or corresponding types load
Capacity, xi, i=1 ..., 6 be the wind-resources acquired, light resource, industrial load, agriculture load, Commercial Load and resident load
Original mark change data；
(2) cumulative distribution function u is usedi=Fi(xi),ui∈ [0,1], i=1 ..., 6 are converted to initial data X
[0,1] the Uniform-distributed Data U=(u on1,u2,u3,u4,u5,u6)；
(3) it is directed to equally distributed data U=(u1,u2,u3,u4,u5,u6), use maximum likelihood function and Anderson
Darling method carries out parameter Estimation and the test of fitness of fot, finds out the ginseng of C-Vine Copula function corresponding with data U
Several and structure；
(4) variable (w of independent and uniform distribution on [0,1] is generated using Latin Hypercube Sampling method1,w2,w3,w4,w5,
w6), according to the available following condition distribution formula of obtained C-Vine Copula structure:
Z=(z in formula1,z2,z3,z4,z5,z6) it is six class data corresponding scene in uniform domain；
(5) inverse function of initial data marginal distribution function is usedui∈ [0,1], i=1 ..., 6 acquire
Typical mark of the six class data in actual field changes scene；
(6) the typical mark of four obtained type loads is changed into scene and corresponding four class under substation each in planning region
The installed capacity of load equipment is multiplied and sums, it can the typical scene of total load under each substation is obtained, by resource mark
Change scene to be multiplied with resource peak value Ji Wei the typical scene of resource.
The present invention accesses planning side for the power distribution network renewable energy power generation cluster for considering load, resource time sequence correlation
Method has the advantage that compared with prior art
(1) present invention carries out renewable energy power generation planning for the power distribution network comprising multiple substations, it is contemplated that multiple
The influence of complimentary enabling capabilities and network reconfiguration to planning between substation.This method determines the photovoltaic under each substation
Total installed capacity with blower instructs to determine photovoltaic and the specific installation position of blower under each substation in subsequent planning
It sets and capacity.
(2) in order to improve the digestion capability of renewable energy power generation, the power-balance degree of system is improved, the present invention proposes
Power-balance degree index.Active balance degree index can be optimized by network reconfiguration, be conducive to reduce and send power to higher level
The influence of power grid increases the installed capacity of renewable energy power generation.
(3) the present invention is based on C-Vine Copula methods models the correlation between load, resource, Neng Gouzhun
Really non-linear, asymmetrical correlation between characterization variable.Then it is generated using Latin Hypercube Sampling method small number of
Typical scene is used for the planning of renewable energy power generation, improves calculating speed while guaranteeing computational accuracy.
Detailed description of the invention
Fig. 1 is Bi-level Programming Models flow chart
Fig. 2 is the schematic diagram of C-Vine Copula structure
Fig. 3 is planning implementation example electric hookup
Fig. 4 is the C-Vine Copula result in embodiment between load, resource
Specific embodiment
The present invention will be described with subordinate list with reference to the accompanying drawing.
It includes upper layer plan model and lower layer's scheduling model that renewable energy power generation cluster, which accesses planing method,.Upper layer planning
Model determines the cluster of photovoltaic and blower under each 35kV substation using renewable energy power generation investor Income Maximum as target
Access capacity:
maxFupper=max (Ccell-Cinv-Cmain)
According to photovoltaic and blower sell that electricity acquires renewable energy power generation user sell electric income:
In formula, r is discount rate, Ny, NPV、NWTGIt is the planning time limit, the quantity of photovoltaic, the quantity of blower, c respectivelysell,PVWith
csell,WTGThe respectively rate for incorporation into the power network of photovoltaic and blower,WithIn respectively y, s-th of scene
iPVA photovoltaic or i-thWTGThe practical electricity volume of a blower.
Construction cost is acquired according to the installed capacity of blower and photovoltaic:
In formula, cins,PVAnd cins,WTGThe respectively construction cost of photovoltaic and blower unit capacity,WithRespectively
For the installed capacity of photovoltaic and blower
The installed capacity of photovoltaic and the total power generation of blower are thoroughly done away with, the O&M cost of renewable energy power generation is acquired:
In formula, com,PVFor the year operation and maintenance cost of photovoltaic unit installed capacity, com,WTGFor the fortune of blower unit generated energy
Row maintenance cost,It is in y, s-th of scene i-thWTGThe actual power generation of a blower.
Renewable energy power generation planning is limited by factors such as installation ground geographic factor, overall cost of ownership, needs to meet peace
Fill capacity limit:
With
In formula,WithRespectively indicate i-thPVA photovoltaic installation point or i-thWTGThe installed capacity of a assembling point
The upper limit.
The objective function of lower layer's scheduling model includes the adjustment cost and renewable energy of power-balance degree index, power distribution company
The active reduction of source power generation.Regulation measure include get in touch with the movement of wiretap, the tap movement of on-load regulator transformer, can be again
The active reduction and reactive compensation of raw energy power generation.
(1) objective function 1: the operating cost of power distribution company
f1=Closs+Creg
In formula, ClossAnd CregRespectively the Web-based exercise and adjustment cost of power distribution company, adjustment cost include on-load voltage regulation
Transformer tapping adjustment cost, interconnection switch act cost.clFor active power electricity price, Pij、QijIt is saved to be flowed to from upstream node i
The active and reactive power of point j, ViFor the voltage value of node i, i → j indicates that node i is connected with node j, RijFor node i and section
Line resistance value between point j, N are power distribution network node set.ctapFor the cost for adjusting tap every time,WithFor moment t and
The tap gear of moment t-1, cswiFor get in touch with wiretap single motion cost,WithFor the connection of moment t and moment t-1
Winding thread switch state.
(2) objective function 2: block power-balance degree index
The present invention is with a high pressure/middle buckling power station and its following connected network for a block.High permeability point
Cloth renewable energy power generation access power distribution network can generate power and send, and increase system losses, reduce the use of substation
Service life.In order to improve block to the digestion capability of renewable energy power generation, reduce power and send, in raising cluster distribution network it
Between complimentary, the invention proposes active balance degree indexs and reactive balance degree index, be defined as follows:
In formula, in formula, NblockFor cluster sum, Pblock,iFor the active demand or active output of i-th of block, Qblock,i
For the reactive requirement or idle output of i-th of cluster.
Active balance degree index or reactive balance degree index are smaller, illustrate the active power or idle that block is exchanged with the external world
Power is smaller, and active or idle in block more balances.By the operation of interconnection switch, power-balance degree index can be carried out
Optimization, is reduced the power for being flowed through higher level substation as caused by unbalanced power, improves the power-balance degree of system.
Power-balance degree target goals function are as follows:
f2=ω1fP_Bal+ω2fQ_Bal
In formula, ω1And ω2Divide the weight than being active balance degree index and reactive balance degree index, it can be according to index weight
The difference for the property wanted is determined, and needs to meet ω1+ω2=1.
(3) objective function 3: renewable energy reduction
In order to which the consumption for improving distributed generation resource is horizontal, reduces abandonment and abandon light quantity, promote renewable energy utilization efficiency, this
Invention is using renewable energy reduction as one of lower layer's regulation goal.
In formula,WithRespectively i-thPVA photovoltaic or i-thWTGThe active reduction of a blower.
Since three objective function dimensions are different, so needing to carry out standardization processing to it.
In formula,For standardization after objective function,fiminFor the minimum value of i-th of objective function, fimaxFor
The maximum value of i-th of objective function.
The objective function of lower layer's scheduling model are as follows:
In formula, λ1、λ2、λ3Objective function respectively after planningizationWeight coefficient, can be according to dispatching
The combined factors such as the importance degree of each target of journey and practical operation situation determine, and need to meet λ1+λ2+λ3=1.
Lower layer's scheduling model objective function includes:
(1) power flow equation constrains
Wherein, Pj=PLj-Ptotal,PV,j-Ptotal,WTG,j+Pcut,PV,j+Pcut,WTG,j, Qj=QLj-QPV,j-QWTG,j。
In formula, Rij、XijRespectively indicate the resistance value and reactance value of route between node i and node j, PjAnd QjIt is net for node j
The active and reactive power of load, PLjAnd QLjFor the active and reactive power of node j load, Ptotal,PV,jAnd Pcut,PV,jRespectively
For the active power and active reduction of node j photovoltaic, Ptotal,WTG,jAnd Pcut,WTG,jRespectively the active power of node j blower and
Active reduction.
(2) system security constraint
In formula,WithVoltage bound at respectively node j.
(3) distributed photovoltaic operation constraint
QPV, j=(Ptotal,PV,j-Pcut,PV,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of photovoltaic output powerminLimitation.
(4) fan operation constrains
QWTG, j=(Ptotal,WTG,j-Pcut,WTG,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of blower output powerminLimitation.
(5) on-load regulator transformer constrains
Ui=kij,tUj
kij,t=1+Kij,tΔkij
In formula, UiAnd UjRespectively high voltage side of transformer, low-pressure side voltage,Kij WithRespectively transformer tapping gear
Upper and lower limit, Kij,tFor the tap gear of transformer t moment, Δ kijNo-load voltage ratio, k are adjusted for transformer adjacent taps gearij,tFor t
Moment transformer high-low-voltage side voltage change ratio.
(6) interconnection switch constraint
Interconnection switch state should make the load on interconnection continuously power and not operation with closed ring, therefore, for containing N number of
The interconnection of network switch, answers only one interconnection switch to disconnect operation
In formula,It is 1 if closure for the folding condition of the contact wiretap on t moment i-j route, if disconnected
It is then 0；O is the set of fingers along the line for constituting a looped network.
(7) 220kV substation power constraint
To guarantee system safety operation, avoid sending power transmission to power transmission network, it is desirable that 220kV substation power does not fall
It send:
0≤Psub,220kV≤Prated,220kV
G) 220kV following level distribution substation power constraint
Due to the access of power distribution network medium to high permeable rate distribution type renewable energy, power is sent to will lead to network loss increase, line
Stream is passed by, therefore power distribution company has the right to cut down the active output of renewable energy power generation, send power to be less than or equal to become with limitation
The 60% of power station rated capacity.
-0.6×PRated, < 220kV≤Psub≤PRated, < 220kV
Upper layer plan model is solved using genetic algorithm, and obtained blower, photovoltaic installed capacity are passed to lower layer
Scheduling model.Lower layer's scheduling model is a mixed integer nonlinear programming problem, by linearizing and boring relaxation, by original NP
Difficult non-convex nonlinear problem is converted into MIXED INTEGER Second-order cone programming model, and in order to guarantee the accuracy of cone relaxation, addition is cut
Constraint is solved, until boring relaxation error is reduced to preset range, scheduling result is finally passed to upper layer plan model.On
Underlying model alternating iteration solves, until triggering calculates termination condition, output REG program results.The algorithm of Bi-level Programming Models
Process is as shown in Figure 1.
The historical data obtained out of planning region is divided into wind speed, illumination, industrial load, agriculture load, quotient by the present invention
Industry load and resident load data, and according to the installation of corresponding with above-mentioned six classes data resource peak value and all kinds of load equipments
Capacity carries out it to mark change processing, then changes data to above-mentioned six category using C-Vine Copula method and carries out correlation
Property modeling, the considerations of obtaining between variable non-linear, asymmetric correlation C-Vine Copula structure.Then, fixed using drawing
The hypercube method of sampling generates the sample of independent and uniform distribution, considers in conjunction with obtained C-Vine Copula structural generation changeable
The typical mark for measuring correlation changes scene.Finally, it is corresponding under each substation that the mark of four obtained type loads is changed scene
The installed capacities of four type load equipment be multiplied and sum, it can obtain the typical scene of total load under each substation, will
Resource mark changes scene and is multiplied with resource peak value Ji Wei the typical scene of resource.
Copula function is a kind of powerful of correlation between research stochastic variable, it is by the joint of multiple random variable
Distribution and the distribution of each unitary limit connect, and can describe the spies such as non-linear, asymmetry, the tail-dependence coefficient between variable
Sign.According to Sklar theorem, enabling F be one has marginal distribution function F1(x1),...,Fn(xn) n member joint probability distribution letter
Then there is a n Victoria C opula function C, so that right in numberHave
F(x1,x2,···,xn)=C (F1(x1),F2(x2),···,Fn(xn))
If F1(x1),...,Fn(xn) it is continuous distribution function, then C is F uniquely corresponding Copula function.
Enable ui=Fi(xi),ui∈ [0,1], i=1 ..., n obedience are uniformly distributed, then
C(u1,...,un)=P (U1≤u1,...,Un≤un)=F (F1 -1(u1),...,Fn -1(un))
In formula, Fi -1(ui) be marginal distribution function inverse function.
The probability density function of Copula function is defined as follows:
In formula, fi(xi) it is probability density function, f (x1,x2,···,xn) it is joint probability density function, c (F1
(x1),F2(x2),···,Fn(xn)) it is Copula probability density function.
Polynary Copula function decomposition is multipair binary Copula function by Pair Copula structure, and flexible structure can
Preferably capture the dependence relation between any two variable.
Stochastic variable X=(X1,...,Xn) joint probability density function can be decomposed into
f(x1,x2,···,xn)=fn(xn)·f(xn-1|xn)·f(xn-2|xn-1,xn)...f(x1|x2,...,xn)
Above formula can be broken down into the product of suitable Pair Copula function and conditional probability density function:
In formula, v is a d dimensional vector, vjIt is the either element in v, v-jV is removed in representativejVector v afterwards.Therefore, polynary
Probability density function can be by multiple Pair Copula function representations.
Pair copula structure is related to the rim condition distribution function F (x | v) of variable:
In formula, Ci,j|kFor binary Copula distribution function.When v only includes unitary variant,
Pair Copula structure mainly has two kinds of forms of D-vine and Canonical Vine (C-Vine), and the present invention makes
With C-Vine form.The structure of C-Vine can be expressed as
In formula, j represents the number of plies of C-Vine, and i represents each layer of side, in jth layer, always there is a node and n-j
Side is connected.N Victoria C-VineCopula structure is as shown in Fig. 2.
Pair Copula parameter is estimated using maximum-likelihood method.Assuming that there is n to tie up variable, each variable has T sight
Measured value, then each variable can be indicated with following formula:
xi=(xi,1,...,xi,T)
For a binary Copula density function cj,j+i|1,...,j-1, by asking logarithm maximum likelihood function to obtain C-
The parameter Θ of Vine:
Because there are many correlation that the binary Copula function of type can be used to be fitted between initial data,
Select most suitable Copula function particularly significant in C-Vine Copula function structure.Therefore, it is necessary to use the goodness of fit to survey
Can examination to examine selected Copula type function that can accurately portray the correlation between variable and select most suitable
Copula function.Goodness is fitted using Anderson Darling method to examine.
X and Y is enabled to respectively represent two stochastic variables, respective marginal distribution function is U=FX(x)=P (X≤x) and V
=FY(y)=P (Y≤y), joint distribution function FX,Y(x, y)=P (X≤x, Y≤y),Assuming that FXAnd FYAll it is
Continuous function, then the Copula function C:[0 of existence anduniquess, 1]2→ [0,1]:
FX,Y(x, y)=C (FX(x),FY(y))=C (u, v)=P (U≤u, V≤v)
Conditional distribution function as U=u, between U and V are as follows:
In formula, D1Indicate C (u, v) for the partial derivative of u.
Stochastic variable Z1=U=FX(x) and Z2=C (V | U)=C (FY(y)|FX(x)) independent and uniform point on [0,1]
Cloth.Therefore, stochastic variable S (X, Y)=[Φ-1(FX(X))]2+[Φ-1C(FY(Y)|FX(X))]2It is the χ that freedom degree is 22Distribution.
If (X1,Y1) ..., (Xn,Yn) it is the random sample from overall (X, Y), then S (X1,Y1) ..., S (Xn,Yn) it is from certainly
By spending the χ for 22The random sample of distribution.Therefore, null hypothesis is
H0: (X, Y) there are Copula function C (u, v)
Wherein, marginal distribution function FXAnd FYIt is known.By calculating S (X1,Y1) ..., S (Xn,Yn) can be by null hypothesis
H0It is changed into inspection auxiliary assumptions:
It isDistribution
IfIt sets up, then H0It sets up, if refusalThen refuse H0。
Because Anderson Darling, which is examined, all has relatively good characteristic, this hair for a large amount of different situations
It is bright to use Anderson Darling method pairAssuming that testing.The test statistics of Anderson Darling method
Are as follows:
In formula, Sj=S (Xj,Yj), j=1 ..., n, and S(1)≤…≤S(n)。F0=obey the χ that freedom degree is 22Distribution.
However, F in practical applicationXAnd FYMarginal distribution function be generally unknown, therefore use experience distribution function generation
For marginal distribution function:
With
It usesInstead of S (Xj,Yj):
In addition,
If should be noted that the marginal distribution function of variable is unknown, use experience distribution function carries out the meeting of replacing shadow
Ring the critical value of the test of fitness of fot.The present invention determines that confidence level is the critical value of 1- α, step using Bootstrap method
It is as follows:
(1) by initial observation value (x1,y1) ..., (xn,yn) estimation Copula function C (u, v；The estimation of parameter θ θ)
Value
(2) from Copula functionGenerate n independent observations
(3) byI=1 ..., n estimates Copula function C (u, v；The estimated value of parameter θ θ)ByWithIt calculatesAnderson Darling, which is calculated, using above-mentioned calculated value examines system
The value AD of metering*；
(4) step (2) and step (3) n times are repeated, the value AD of test statistics is obtained*(1),...,AD*(N), 1- α quantile
The value of corresponding test statistics is desired critical value.
If from initial observation value (x1,y1) ..., (xn,yn) value of test statistics that is calculated is greater than and acquires
Critical value then refuses null hypothesis, it is believed that Copula function is not suitable for description observation and obtains Dependence Structure, otherwise receives null hypothesis.
Based on the considerations of the tool of the scene generating method of timing dependence between C-Vine Copula method load, resource
Steps are as follows for body:
(1) read wind-resources, light resource, industrial load, agriculture load, Commercial Load and resident load historical data
Xori=(x1,ori,x2,ori,x3,ori,x4,ori,x5,ori,x6,ori), every a kind of data are carried out marking change with obtaining:
In formula, xi,ori, i=1 ..., 6 indicate wind-resources, light resource, industrial load, agriculture load, Commercial Load and residence
The initial data of burden on the people lotus, xi,ori,max, i=1 ..., 6 indicate the equipment installation of the peak value of corresponding resource or corresponding types load
Capacity, xi, i=1 ..., 6 be the wind-resources acquired, light resource, industrial load, agriculture load, Commercial Load and resident load
Original mark change data.
(2) cumulative distribution function u is usedi=Fi(xi),ui∈ [0,1], i=1 ..., 6 are converted to initial data X
[0,1] the Uniform-distributed Data U=(u on1,u2,u3,u4,u5,u6)。
(3) it is directed to equally distributed data U=(u1,u2,u3,u4,u5,u6), use maximum likelihood function and Anderson
Darling method carries out parameter Estimation and the test of fitness of fot, finds out the ginseng of C-Vine Copula function corresponding with data U
Several and structure.
(4) variable (w of independent and uniform distribution on [0,1] is generated using Latin Hypercube Sampling method1,w2,w3,w4,w5,
w6).According to the available following condition distribution formula of obtained C-Vine Copula structure:
Z=(z in formula1,z2,z3,z4,z5,z6) it is six class data corresponding scene in uniform domain.
(5) the inverse function x of initial data marginal distribution function is usedi=Fi -1(zi),ui∈ [0,1], i=1 ..., 6 ask
It obtains typical mark of the six class data in actual field and changes scene.
(6) the typical mark of four obtained type loads is changed into scene and corresponding four class under substation each in planning region
The installed capacity of load equipment is multiplied and sums, it can the typical scene of total load under each substation is obtained, by resource mark
Change scene to be multiplied with resource peak value Ji Wei the typical scene of resource.
The present invention is based on Fig. 3 to Fig. 4 and table 1 to table 5 to be illustrated to embodiment.
Choosing Chinese somewhere part mesohigh power distribution network is embodiment, and Fig. 3 is the electric hookup of the embodiment.The implementation
Example includes 8 substations, including 1 220kV/110kV substation, 2 110kV/35kV substations, 5 35kV/10kV
Substation.In addition, the embodiment further includes 8 routes, wherein 2 are 110kV route, 6 are 35kV route, on 35kV route
It is each equipped with section breaker.
The specific parameter of double--layer grids under 35kV substation is not considered, the load under each 35kV substation is equivalent to its low pressure
Side.Intensity of illumination, wind speed, the demand history data of 1 year 8760 hour of past in planning region are obtained, and load is drawn
It is divided into industrial load, agriculture load, Commercial Load and resident load, peak value and all kinds of demand history data for counting resource are corresponding
Installed capacity.The capacity of each substation and its installed capacity for connecing all kinds of load equipments see attached list 1.The present invention couple
Each 35kV substation carries out photovoltaic and the cluster of blower accesses planning, and 15 years planning horizons, year load growth rate is set as
3%.Blower individual capacity is 2MW.Planning economic parameters and scheduling parameter see attached list 2.
C-Vine Copula structure such as attached drawing between multiple variables that the scene generating method proposed according to the present invention obtains
Shown in 3.Typical planning scene is generated in conjunction with Latin Hypercube Sampling method, and obtains average load and peak under each substation
Duty value sees attached list 3.
4 are seen attached list using the program results that bi-level programming method proposed by the present invention acquires, other results see attached list 5.
Table 1 is the transformer capacity of planning region Nei Ge 35kV substation and the installed capacity of all kinds of load equipments
Table 2 is the parameter of planning implementation example
Table 3 is average load and peak load situation under each 35kV substation
Table 4 is the program results of each substation's blower and photovoltaic
Table 5 is other results that planning calculates
Claims (4)
1. a kind of power distribution network renewable energy power generation cluster accesses planing method, mould is dispatched using upper layer plan model and lower layer
Type, upper layer plan model is using renewable energy power generation investor Income Maximum as target；The objective function packet of lower layer's scheduling model
The active reduction of power-balance degree index, the adjustment cost of power distribution company and renewable energy power generation is included, regulation measure includes
Get in touch with the movement of wiretap, tap movement, the active reduction and reactive compensation of renewable energy power generation of on-load regulator transformer；
For the timing dependence between load, Renewable Energy Resources, modeled using C-Vine Copula model, in conjunction with drawing
The fourth hypercube method of sampling generates the typical planning scene for considering load, resource dependencies.
2. planing method according to claim 1, wherein lower layer's scheduling model includes:
(1) objective function 1: the operating cost of power distribution company
f1=Closs+Creg
In formula, ClossAnd CregThe respectively Web-based exercise and adjustment cost of power distribution company, adjustment cost include on-load voltage regulation transformation
Device tap adjustment cost, interconnection switch act cost, clTo have power electricity price, Pij、QijTo flow to node j's from upstream node i
Active and reactive power, ViFor the voltage value of node i, i → j indicates that node i is connected with node j, RijBetween node i and node j
Line resistance value, N be power distribution network node set；ctapFor the cost for adjusting tap every time,WithFor moment t and moment
The tap gear of t-1, cswiFor get in touch with wiretap single motion cost,WithFor the interconnection of moment t and moment t-1
Switch state；
(2) objective function 2: block power-balance degree index
With a high pressure/middle buckling power station and its following connected network for a block, propose active balance degree index and
Reactive balance degree index, is defined as follows:
In formula, in formula, NblockFor cluster sum, Pblock,iFor the active demand or active output of i-th of block, Qblock,iIt is i-th
The reactive requirement of a cluster or idle output；
Active balance degree index or reactive balance degree index are smaller, illustrate active power or reactive power that block is exchanged with the external world
Smaller, active or idle in block more balances, and by the operation of interconnection switch, optimizes, subtracts to power-balance degree index
Few power that higher level substation is flowed through as caused by unbalanced power, improves the power-balance degree of system；
Power-balance degree target goals function are as follows:
f2=ω1fP_Bal+ω2fQ_Bal
In formula, ω1And ω2Divide the weight than being active balance degree index and reactive balance degree index, it can be according to index importance
Difference be determined, and need to meet ω1+ω2=1；
(3) objective function 3: renewable energy reduction
Using renewable energy reduction as one of lower layer's regulation goal:
In formula,WithRespectively i-thPVA photovoltaic or i-thWTGThe active reduction of a blower；
Standardization processing is carried out to 3 objective functions:
In formula, fi *For the objective function after standardization, fi *∈ [0,1], fiminFor the minimum value of i-th of objective function, fimaxFor
The maximum value of i-th of objective function；
The catalogue scalar functions of lower layer's scheduling model are as follows:
In formula, λ1、λ2、λ3Objective function f respectively after planningization1 *、Weight coefficient, can be each according to scheduling process
The combined factors such as the importance degree of target and practical operation situation determine, and need to meet λ1+λ2+λ3=1；
Lower layer's scheduling model constraint condition includes:
(1) power flow equation constrains
Wherein, Pj=PLj-Ptotal,PV,j-Ptotal,WTG,j+Pcut,PV,j+Pcut,WTG,j, Qj=QLj-QPV,j-QWTG,j；
In formula, Rij、XijRespectively indicate the resistance value and reactance value of route between node i and node j, PjAnd QjFor node j net load
Active and reactive power, PLjAnd QLjFor the active and reactive power of node j load, Ptotal,PV,jAnd Pcut,PV,jRespectively save
The active power and active reduction of point j photovoltaic, Ptotal,WTG,jAnd Pcut,WTG,jThe respectively active power of node j blower and active
It cuts down；
(2) system security constraint
In formula,WithVoltage bound at respectively node j；
(3) distributed photovoltaic operation constraint
QPV, j=(Ptotal,PV,j-Pcut,PV,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of photovoltaic output powerminLimitation；
(4) fan operation constrains
QWTG, j=(Ptotal,WTG,j-Pcut,WTG,j)tanθ
In formula, θ=cos-1PFminIndicate the minimum power factor PF of blower output powerminLimitation；
(5) on-load regulator transformer constrains
Ui=kij,tUj
kij,t=1+Kij,tΔkij
In formula, UiAnd UjRespectively high voltage side of transformer, low-pressure side voltage,Kij WithRespectively under transformer tapping gear,
The upper limit, Kij,tFor the tap gear of transformer t moment, Δ kijNo-load voltage ratio, k are adjusted for transformer adjacent taps gearij,tFor t moment
Transformer high-low-voltage side voltage change ratio；
(6) interconnection switch constraint
The load on interconnection should be made continuously to power for interconnection switch state and therefore operation with closed ring is not opened for containing N number of contact
The interconnection of pass answers only one interconnection switch to disconnect operation
In formula,For the folding condition of the contact wiretap on t moment i-j route, it is 1 if closure, is if disconnecting
0；O is the set of fingers along the line for constituting a looped network；
(7) 220kV substation power constraint
For guarantee system safety operation, avoid sending power transmission to power transmission network, it is desirable that 220kV substation power is not sent:
0≤Psub,220kV≤Prated,220kV
G) 220kV following level distribution substation power constraint
Power distribution company has the right to cut down the active output of renewable energy power generation, send power to be less than or equal to substation with limitation specified
The 60% of capacity:
-0.6×PRated, < 220kV≤Psub≤PRated, < 220kV。
3. planing method according to claim 1, which is characterized in that upper layer plan model includes:
Determine the cluster access capacity of photovoltaic and blower under each 35kV substation:
maxFupper=max (Ccell-Cinv-Cmain)
According to photovoltaic and blower sell that electricity acquires renewable energy power generation user sell electric income:
In formula, r is discount rate, Ny, NPV、NWTGIt is the planning time limit, the quantity of photovoltaic, the quantity of blower, c respectivelysell,PVWith
csell,WTGThe respectively rate for incorporation into the power network of photovoltaic and blower,WithIn respectively y, s-th of scene
iPVA photovoltaic or i-thWTGThe practical electricity volume of a blower；
Construction cost is acquired according to the installed capacity of blower and photovoltaic:
In formula, cins,PVAnd cins,WTGThe respectively construction cost of photovoltaic and blower unit capacity,WithRespectively light
The installed capacity of volt and blower
The installed capacity of photovoltaic and the total power generation of blower are thoroughly done away with, the O&M cost of renewable energy power generation is acquired:
In formula, com,PVFor the year operation and maintenance cost of photovoltaic unit installed capacity, com,WTGIt is tieed up for the operation of blower unit generated energy
Shield expense,It is in y, s-th of scene i-thWTGThe actual power generation of a blower；
Renewable energy power generation planning is limited by installation ground geographic factor, overall cost of ownership factor, needs to meet installed capacity
Limitation:
With
In formula,WithRespectively indicate i-thPVA photovoltaic installation point or i-thWTGThe installed capacity upper limit of a assembling point.
4. planing method according to claim 1, which is characterized in that planning scene generation step is as follows:
(1) read wind-resources, light resource, industrial load, agriculture load, Commercial Load and resident load historical data Xori=
(x1,ori,x2,ori,x3,ori,x4,ori,x5,ori,x6,ori), every a kind of data are carried out marking change with obtaining:
In formula, xi,ori, i=1 ..., 6 indicate that wind-resources, light resource, industrial load, agriculture load, Commercial Load and resident are negative
The initial data of lotus, xi,ori,max, i=1 ..., 6 equipment for indicating the peak value of corresponding resource or corresponding types load install appearance
Amount, xi, i=1 ..., 6 be the wind-resources acquired, light resource, industrial load, agriculture load, Commercial Load and resident load
Original mark changes data；
(2) cumulative distribution function u is usedi=Fi(xi),ui∈ [0,1], i=1 ..., 6 are converted to initial data X on [0,1]
Uniform-distributed Data U=(u1,u2,u3,u4,u5,u6)；
(3) it is directed to equally distributed data U=(u1,u2,u3,u4,u5,u6), use maximum likelihood function and Anderson
Darling method carries out parameter Estimation and the test of fitness of fot, finds out the ginseng of C-Vine Copula function corresponding with data U
Several and structure；
(4) variable (w of independent and uniform distribution on [0,1] is generated using Latin Hypercube Sampling method1,w2,w3,w4,w5,w6),
According to the available following condition distribution formula of obtained C-Vine Copula structure:
Z=(z in formula1,z2,z3,z4,z5,z6) it is six class data corresponding scene in uniform domain；
(5) the inverse function x of initial data marginal distribution function is usedi=Fi -1(zi),ui∈ [0,1], i=1 ..., 6 acquire six
Typical mark of the class data in actual field changes scene；
(6) the typical mark of four obtained type loads is changed into scene and corresponding four type load under substation each in planning region
The installed capacity of equipment is multiplied and sums, it can obtains the typical scene of total load under each substation, resource mark is changed
Scene is multiplied with resource peak value Ji Wei the typical scene of resource.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811214607.4A CN109829560B (en) | 2018-10-18 | 2018-10-18 | Renewable energy power generation cluster access planning method for power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811214607.4A CN109829560B (en) | 2018-10-18 | 2018-10-18 | Renewable energy power generation cluster access planning method for power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109829560A true CN109829560A (en) | 2019-05-31 |
| CN109829560B CN109829560B (en) | 2023-07-18 |
Family
ID=66859516
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811214607.4A Active CN109829560B (en) | 2018-10-18 | 2018-10-18 | Renewable energy power generation cluster access planning method for power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109829560B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111900732A (en) * | 2020-08-04 | 2020-11-06 | 四川大学 | Wind power cluster access mode planning method considering wind power uncertainty and reactive power flow and voltage constraints |
| CN112347694A (en) * | 2020-10-26 | 2021-02-09 | 重庆大学 | Sea island micro-grid power supply planning method containing ocean current-offshore wind power-tidal current power generation |
| CN116581815A (en) * | 2023-05-19 | 2023-08-11 | 国网黑龙江省电力有限公司经济技术研究院 | Source network load coordination power distribution control system based on big data |
| CN116632946A (en) * | 2023-07-21 | 2023-08-22 | 湖南大学 | Openable capacity assessment method for power distribution area of industrial park |
| CN116993032A (en) * | 2023-09-28 | 2023-11-03 | 国网山西省电力公司运城供电公司 | Distribution network planning method, distribution network planning device, storage medium and computer equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090313033A1 (en) * | 2008-06-16 | 2009-12-17 | International Business Machines Corporation | Generating Energy Transaction Plans |
| CN102509175A (en) * | 2011-11-07 | 2012-06-20 | 上海电力学院 | Reliability optimization method of distributed power supply system |
| CN103903073A (en) * | 2014-04-23 | 2014-07-02 | 河海大学 | Planning method and system for optimizing micro-grid containing distributed power sources and stored energy |
| CN106815657A (en) * | 2017-01-05 | 2017-06-09 | 国网福建省电力有限公司 | A kind of power distribution network bi-level programming method for considering timing and reliability |
| CN107194523A (en) * | 2017-07-24 | 2017-09-22 | 国网江苏省电力公司无锡供电公司 | Active distribution network planing method based on overall life cycle cost |
| CN107947165A (en) * | 2017-11-30 | 2018-04-20 | 国网浙江省电力公司经济技术研究院 | A kind of power distribution network flexibility evaluation method towards regulatory demand |
-
2018
- 2018-10-18 CN CN201811214607.4A patent/CN109829560B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090313033A1 (en) * | 2008-06-16 | 2009-12-17 | International Business Machines Corporation | Generating Energy Transaction Plans |
| CN102509175A (en) * | 2011-11-07 | 2012-06-20 | 上海电力学院 | Reliability optimization method of distributed power supply system |
| CN103903073A (en) * | 2014-04-23 | 2014-07-02 | 河海大学 | Planning method and system for optimizing micro-grid containing distributed power sources and stored energy |
| CN106815657A (en) * | 2017-01-05 | 2017-06-09 | 国网福建省电力有限公司 | A kind of power distribution network bi-level programming method for considering timing and reliability |
| CN107194523A (en) * | 2017-07-24 | 2017-09-22 | 国网江苏省电力公司无锡供电公司 | Active distribution network planing method based on overall life cycle cost |
| CN107947165A (en) * | 2017-11-30 | 2018-04-20 | 国网浙江省电力公司经济技术研究院 | A kind of power distribution network flexibility evaluation method towards regulatory demand |
Non-Patent Citations (2)
| Title |
|---|
| 王成山等: ""微电网规划设计方法综述"", 《电力建设》 * |
| 贺继锋等: ""考虑储能站投资收益的配电网规划模型"", 《电力建设》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111900732A (en) * | 2020-08-04 | 2020-11-06 | 四川大学 | Wind power cluster access mode planning method considering wind power uncertainty and reactive power flow and voltage constraints |
| CN111900732B (en) * | 2020-08-04 | 2022-08-26 | 四川大学 | Wind power cluster access mode planning method considering wind power uncertainty and reactive power flow and voltage constraints |
| CN112347694A (en) * | 2020-10-26 | 2021-02-09 | 重庆大学 | Sea island micro-grid power supply planning method containing ocean current-offshore wind power-tidal current power generation |
| CN112347694B (en) * | 2020-10-26 | 2023-05-12 | 重庆大学 | Island micro-grid power supply planning method for power generation by ocean current, offshore wind power and tidal current |
| CN116581815A (en) * | 2023-05-19 | 2023-08-11 | 国网黑龙江省电力有限公司经济技术研究院 | Source network load coordination power distribution control system based on big data |
| CN116581815B (en) * | 2023-05-19 | 2023-10-27 | 国网黑龙江省电力有限公司经济技术研究院 | Source network load coordination power distribution control system based on big data |
| CN116632946A (en) * | 2023-07-21 | 2023-08-22 | 湖南大学 | Openable capacity assessment method for power distribution area of industrial park |
| CN116632946B (en) * | 2023-07-21 | 2023-09-19 | 湖南大学 | Openable capacity assessment method for power distribution area of industrial park |
| CN116993032A (en) * | 2023-09-28 | 2023-11-03 | 国网山西省电力公司运城供电公司 | Distribution network planning method, distribution network planning device, storage medium and computer equipment |
| CN116993032B (en) * | 2023-09-28 | 2024-01-19 | 国网山西省电力公司运城供电公司 | Distribution network planning method, distribution network planning device, storage medium and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109829560B (en) | 2023-07-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ehsan et al. | State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review | |
| CN109829560A (en) | A kind of power distribution network renewable energy power generation cluster access planing method | |
| Ding et al. | Multi-stage distributionally robust stochastic dual dynamic programming to multi-period economic dispatch with virtual energy storage | |
| Rejc et al. | Short-term transmission-loss forecast for the slovenian transmission power system based on a fuzzy-logic decision approach | |
| CN102934312B (en) | Energy production system and control thereof | |
| CN107947165A (en) | A kind of power distribution network flexibility evaluation method towards regulatory demand | |
| Dalhues et al. | Research and practice of flexibility in distribution systems: A review | |
| Yi et al. | Coordinated operation strategy for a virtual power plant with multiple DER aggregators | |
| CN108306303A (en) | A kind of consideration load growth and new energy are contributed random voltage stability assessment method | |
| CN107145707A (en) | It is a kind of to count and photovoltaic is exerted oneself the power distribution network transformer planing method of uncertain and overall life cycle cost | |
| CN106529737A (en) | Planning and distribution method for peak load regulation power source on supply side of power distribution network | |
| CN106709626A (en) | Power grid development dynamic comprehensive evaluation method based on Bayesian network | |
| CN115528817A (en) | Intelligent power distribution and utilization system, method, medium, equipment and terminal for user side | |
| CN113269371A (en) | Method and system for evaluating comprehensive performance of power supply of intelligent power distribution network | |
| CN110061496A (en) | The confidence level optimization method of source net lotus Multiobjective programming models | |
| Kaplan et al. | Fault diagnosis of smart grids based on deep learning approach | |
| Huang et al. | Adaptability evaluation of distributed power sources connected to distribution network | |
| Yang et al. | Network-constrained transactive control for multi-microgrids-based distribution networks with soft open points | |
| Mohammadi et al. | Synthetic benchmarks for power systems | |
| CN115796711A (en) | Auxiliary decision-making method and device for loss reduction of distribution line | |
| Langouranis et al. | Fuzzy total power quality index for electric networks | |
| CN113890029A (en) | Multi-objective optimization method for power distribution network with openable capacity improvement | |
| Tan et al. | Estimation method of line loss rate in low voltage area based on mean shift clustering and BP neural network | |
| Yuqing et al. | Investment demand forecasting model of power grid based on different development paths | |
| Cheng et al. | Study on the model of advanced asset management in smart grid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |