CN106451424A - Random planning method for power distribution network containing large-size photovoltaic power generation and gird connection - Google Patents
Random planning method for power distribution network containing large-size photovoltaic power generation and gird connection Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G—PHYSICS
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/10—Photovoltaic [PV]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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Abstract
The invention relates to a random planning method for a power distribution network containing large-size photovoltaic power generation and gird connection. The random planning method comprises the steps of simulating an output curve of a large-size photovoltaic power station according to real-time illumination intensity and ambient temperature data, considering randomness and fluctuation of photovoltaic output, building a random expectation value two-layer planning mathematical model of the power distribution network containing large-size photovoltaic power generation and gird connection according to an equivalent energy function method and a random planning theory, designing a reasonable hybrid algorithm according to a self-adaptive genetic algorithm and a primal-dual interior point method, and performing effective solving on the planning model to obtain an optimal planning scheme. By the random planning method, the random planning demand of a power grid containing large-size photovoltaic power generation and gird connection is satisfied, and the random planning method has the advantages of clear logic structure, practicability and reasonableness. The method conforms to the large-size photovoltaic development trend at the present stage and in future, has high theoretical property and practicability, and can be widely applied to random planning simulation calculation of a power system for large-size photovoltaic power generation.
Description
Technical field
The present invention relates to a kind of transmission of electricity network technology, random particularly to a kind of power transmission network containing large-scale photovoltaic electricity generation grid-connecting
Planing method.
Background technology
With rapid economic development, the energy and environmental problem have become world today's focus of interest.Coal, oil,
The demand of natural gas equal energy source grows with each passing day, but these energy are non-renewable, and using during environment can be caused sternly
Heavily contaminated, the impact causing for social sound development and stably is also increasing, and solar energy is a kind of the renewable of cleaning
The energy, the photovoltaic power generation technology converting solar energy into electrical energy can effectively alleviate the severe situation of energy scarcity and environmental pollution
Pressure.
Under the support of national governments' policy, through years of researches, nowadays photovoltaic generation has become as one and more becomes
Ripe new energy power generation technology, gradually from supplementing the energy to alternative energy source transition, wherein photovoltaic plant is big for photovoltaic electric energy
Type scale even more becomes developing direction and research emphasis from now on.With shared by photovoltaic generation installed capacity in power system
Ratio is increasing, and the extensive concentration exploitation of photovoltaic generation can produce certain impact for power system.Photovoltaic is exerted oneself not
Definitiveness also influences whether Electric Power Network Planning.
Using the theoretical latest development in power planning research field of stochastic programming, to grid type centralized photovoltaic on a large scale
Electricity generation system and power network expansion planning method are studied, the uncertain factor that analyzing influence photovoltaic is exerted oneself, and set up extensive
Centralized photovoltaic is exerted oneself model, then in conjunction with Operation of Electric Systems feature using the method simulation being suitable for the electric power containing photovoltaic plant
System random walk, theoretical in conjunction with stochastic programming, set up the electrical network random expected value plan model generating electricity containing large-scale photovoltaic
Carry out Electric Power Network Planning, take into full account and exerted oneself the impact that brings of uncertainty due to photovoltaic, thus drawing suitable Electric Power Network Planning side
Case.
Content of the invention
The present invention be directed to the impact problem to Electric Power Network Planning from now on for the Solar use is it is proposed that a kind of contain large-scale photovoltaic
The power transmission network stochastic programming method of electricity generation grid-connecting, by simulate large-scale photovoltaic exert oneself it is considered to photovoltaic exert oneself randomness, ripple
Dynamic property, theoretical based on equivalent energy function method and stochastic programming, build the power transmission network containing large-scale photovoltaic electricity generation grid-connecting and advise at random
The mathematical model drawn, by the rational hybrid algorithm of modern optimization Design Theory, is effectively solved to plan model.
The technical scheme is that：A kind of power transmission network stochastic programming method containing large-scale photovoltaic electricity generation grid-connecting, specifically
Comprise the steps：
1) according to real-time lighting intensity and ambient temperature data, the sequential power curve in simulation large-sized photovoltaic power station：
2) in step 1) on the basis of the sequential power curve of gained photovoltaic plant, in conjunction with the supplemental characteristic of actual electric network, obtain
To removing the net load curve that photovoltaic is exerted oneself, form initial equivalent electric quantity function, calculated based on equivalent energy function method and determine light
Maximum size in power system for the volt generating；
3) the upper layer model of power transmission network random expected value bilevel programming model is with the minimum target of total cost expected value, wraps
Include newly-increased circuit, electric generation investment construction cost, operation and maintenance cost, supply interruption cost and cutting load rejection penalty expected value；Lower floor
Model is with the minimum target of cutting load rejection penalty under N safe operating conditions, N-1 safe operating conditions；Expected according to random
Value Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraints, set up the power transmission network random expected value bi-level programming containing large-scale photovoltaic
Model；
4) upper strata carries out global optimizing using self-adapted genetic algorithm, generates and optimizes rack, obtains construction cost, then pass through
Dry run obtains operation and maintenance cost, environmental costses, the expected value of short of electricity amount expense, and lower floor utilizes primal-dual interior method
Calculate cutting load rejection penalty, feed back to the total cost that upper strata obtains optimization aim, optimum rack knot is obtained by iteration convergence
Structure.
Described step 1) according to real-time lighting intensity and ambient temperature data, the sequential simulating large-sized photovoltaic power station goes out
The model of exerting oneself of force curve, wherein large-sized photovoltaic power station is：
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxFor photovoltaic plant under MPPT maximum power point tracking control
Big output, η is the efficiency of photovoltaic module, ηmpptFor the efficiency of MPPT control assembly, ηinvFor the efficiency of inverter, ηrefFor
Photovoltaic module efficiency under reference temperature, ε is photovoltaic module temperature coefficient, is taken as 0.003～0.005, TaFor real time environment temperature
Degree, TrefFor reference temperature, take 25 DEG C.
Described step 2) comprise the following steps that：
A, the sequential power curve of the photovoltaic generation of gained is added as negative load with sequential load curve, by photovoltaic
Sequential power curve from sequential load curve separate, obtain net load curve；
B, according to net load curve formed with loading as transverse axis, load duration be the longitudinal axis equivalent load curve,
Step-length is chosen according to conventional power unit capacity, according to cost of electricity-generating, conventional power unit is ranked up, by equivalent load curve and combination
Conventional power unit capacity and corresponding forced outage rate combine, and form initial equivalent electric quantity function, arrange each unit operation successively,
Calculate its generated energy；
C, according to equivalent energy function method, form new equivalent load curve in the case of having arranged previous unit,
Capacity according to remaining conventional power unit and corresponding forced outage rate correction equivalent electric quantity function, and check whether all units arrange
Finishing, if it did not, going to step B, if whole arrange to finish, according to arranged order, calculating overall running cost；
D, regulation photovoltaic capacity proportion in systems, the system production cost in the case of calculating is multiple and each index,
Obtain the theoretical upper limit of photovoltaic capacity accounting.
The beneficial effects of the present invention is：The present invention contains the power transmission network stochastic programming method of large-scale photovoltaic electricity generation grid-connecting,
Consider randomness, undulatory property that photovoltaic exerts oneself, theoretical based on equivalent energy function method and stochastic programming, build and contain large-scale photovoltaic
The mathematical model of the power transmission network random expected value bi-level programming of electricity generation grid-connecting, according in self-adapted genetic algorithm and original-antithesis
Point method hybrid algorithm reasonable in design, is effectively solved to plan model, draws optimum programming scheme, meets and contains extensive light
The demand of the electrical network stochastic programming that volt generates electricity, has that logical structure is clear, practical rational advantage.
Brief description
Fig. 1 contains the power transmission network stochastic programming method flow diagram of large-scale photovoltaic electricity generation grid-connecting for the present invention；
Fig. 2 is the electric diagram of the embodiment of the present invention；
Fig. 3 is production graph of simulation results one figure of the embodiment of the present invention；
Fig. 4 is production graph of simulation results two figure of the embodiment of the present invention；
Fig. 5 contains the flow process of hybrid algorithm in the power transmission network stochastic programming method of large-scale photovoltaic electricity generation grid-connecting for the present invention
Figure；
Fig. 6 is the optimum programming result electric diagram of the embodiment of the present invention.
Specific embodiment
Contain the power transmission network stochastic programming method flow diagram of large-scale photovoltaic electricity generation grid-connecting as shown in Figure 1, specifically include following
Step：
S1, according to real-time lighting intensity and ambient temperature data, simulates the power curve in large-sized photovoltaic power station；
S2 combines the supplemental characteristic of actual electric network, is calculated based on equivalent energy function method and determines photovoltaic generation in power system
In maximum size；
S3, according to random expected value Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraints, sets up defeated containing large-scale photovoltaic
Electrical network random expected value bilevel programming model；
S4 is obtained using the hybrid algorithm solving model of improved adaptive GA-IAGA and primal-dual interior method according to model feature
Optimum programming scheme.
According to real-time lighting intensity and ambient temperature data in step S1, the sequential in simulation large-sized photovoltaic power station goes out Massa Medicata Fermentata
The model of exerting oneself of line, wherein large-sized photovoltaic power station is：
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxControl in MPPT (MPPT maximum power point tracking) for photovoltaic plant
Under peak power output, η be photovoltaic module efficiency, ηmpptFor the efficiency of MPPT control assembly, ηinvEffect for inverter
Rate, ηrefFor the photovoltaic module efficiency under reference temperature, ε is photovoltaic module temperature coefficient, is typically taken as 0.003～0.005, Ta
For real time environment temperature, TrefFor reference temperature, take 25 DEG C.
PmaxValue relevant with the U-I characteristic of photovoltaic, its mathematical model is shown below：
In formula, I, U are respectively output current and the voltage of single photovoltaic cell；C1、C2For intermediate variable, need according to light
Change according to intensity and ambient temperature is constantly modified；IscFor short circuit current, UocFor open-circuit voltage.Because solar irradiation is strong
Degree and ambient temperature are continually changing, therefore C in U-I mathematical model1、C2Update equation be shown below：
In formula, ImFor maximum power point electric current, UmFor maximum power point voltage.Isc、Uoc、Im、UmFor photovoltaic cell technology ginseng
Numerical value, relevant with illumination variation and variation of ambient temperature.The present embodiment, with reference to the technical parameter of monocrystalline Silicon photrouics, takes respectively
Optimum operating voltage Um=17.1V, open-circuit voltage Uoc=22V, recommended current Im=3.5A, open-circuit current Isc=3.8V.
In the present embodiment, the update equation of photovoltaic cell technical parameter is shown below：
X=R/60 × 697.33
K=X/Xref
Tc(t)=Ta(t)+gR
Δ T=Tc(t)-Tref
I'm=Imk(1+aΔT+bX)
I′sc=Isck(1+aΔT+bX)
U'm=Um(k+c)(1-dΔT-eX)
U'oc=Uoc(k+c)(1-dΔT-eX)
In formula, R is any intensity of solar radiation, and unit is mWcm2, XrefFor standard intensity of illumination 1000W/m2, X is monthly
The real-time lighting intensity of typical day, k is the ratio of real-time lighting intensity and standard intensity of illumination；TrefFor reference temperature, it is taken as 25
DEG C, TcFor the temperature of photovoltaic module, TaFor the on-site ambient temperature of photovoltaic module, Tmax、TminTemperature maximum for typical day
And minima, tpThe moment occurring for one day highest temperature is it is considered that be 14:00, g is illumination temperature coefficient, takes 0.03 DEG C
m2/W；Isc’、Uoc’、Im’、Um' it is Isc、Uoc、Im、UmDifferent illumination intensity and at a temperature of correction value, a, b, c, d, e are normal
Number, representative value is a=0.0025/ DEG C, b=7.5e-5m2/ W, c=0.5, d=0.0028/ DEG C, e=8.4e-5m2/W.
Combine the supplemental characteristic of actual electric network in step S2, calculated based on equivalent energy function method and determine photovoltaic generation in electricity
Maximum size in Force system, concretely comprises the following steps：
1st, using the sequential power curve of the photovoltaic generation of gained in step S1 as negative load and sequential load curve phase
Plus, the sequential power curve of photovoltaic is separated from sequential load curve, obtains net load curve；Sequential load curve is same
Loads all in power system are added the time dependent curve of sign load thus generating by one time point；
2nd, formed with loading as transverse axis according to net load curve, load duration is the equivalent load curve of the longitudinal axis,
Suitable step-length is chosen according to conventional power unit capacity, according to cost of electricity-generating, conventional power unit is ranked up, by equivalent load curve
Combine with reference to conventional power unit capacity and corresponding forced outage rate, form initial equivalent electric quantity function, arrange each machine successively
Group is run, and calculates its generated energy；
3rd, according to equivalent energy function method (equivalent energy function method can be revised according to calculation procedure in the calculation automatically, this
Individual be equivalent energy function method core methed), form new equivalent load curve in the case of having arranged previous unit,
Capacity according to remaining conventional power unit and corresponding forced outage rate correction equivalent electric quantity function, and check whether all units arrange
Finishing, if it did not, going to step 2, if whole arrange to finish, according to arranged order, calculating overall running cost；
4th, photovoltaic capacity proportion in systems, the system production cost in the case of calculating is multiple and each index are adjusted,
Obtain the theoretical upper limit of photovoltaic capacity accounting.
Fig. 2 is the electric diagram of the present embodiment, and in figure G represents conventional power unit, and Bus represents node bus, Synch.Cond.
(synchronous condenser) represents phase modifier, total producing cost C of this power systemtotalIncluding fuel cost Cfuel、
Operation and maintenance cost CO&M, average short of electricity making up price CUEC, environmental costses Cenvi, that is,：
Ctotal=Cfuel+CO&M+CUEC+Cenvi
In formula：Cfuel,iFuel cost for i-th unit unit generated energy；CO＆M, iFor i-th unit unit generated energy
Operation and maintenance cost；EENS is system loss of energy expectation；Cenvi,iFor the environmental costses of i-th unit unit generated energy, it is
Altogether there is n platform unit generation.For photovoltaic plant, it does not consume fuel, does not discharge waste gas, therefore its fuel cost, ring
Border expense is all 0.
In an embodiment, in system, original installed capacity is constant, and the ratio that photovoltaic capacity accounts for system total installation of generating capacity is divided
Wei 0,5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%.Analog result as shown in Figure 3,4, is schemed
3 represent the situation of change with increase this two indices of EENS and LOLP of photovoltaic plant capacity in system, and wherein, LOLP represents
The loss of load probability (power system proper noun) of system, Fig. 4 represents the increase with photovoltaic plant capacity in system, system
In abandon the relation of light rate and total cost, comprehensive Fig. 3 and Fig. 4, for photovoltaic generation of dissolving as far as possible, reduce and abandon light rate, and protect
The operational reliability of card system, in system, photovoltaic capacity should be less than the 50% of total capacity.
According to random expected value Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraints in step S3, set up and contain extensive light
The power transmission network random expected value bilevel programming model of volt, wherein, power transmission network random expected value bilevel programming model in the present embodiment
Upper layer model be with the minimum target of total cost expected value, including newly-increased circuit, electric generation investment construction cost, operation maintenance expense
With, supply interruption cost and cutting load rejection penalty expected value；Underlying model is with N safe operating conditions, N-1 safe operating conditions
The minimum target of cutting load rejection penalty.
Upper layer model is：
Min E [S]=E [Sinv]+E[Soper]
In formula, S is system total cost, SinvFor increasing circuit, electric generation investment construction cost, S newlyoperThe fortune returning for lower floor
Row, maintenance and load-shedding cost；ClFor transmission line of electricity specific investment cost expense, take 1,300,000 yuan/km；Nl-ijPlanning for node i to j
The number of lines,N l-ij 、It is respectively its maximin, Pl-ijFor the rated capacity of circuit, PijFor circuit Real-time Power Flow；Cpv
For the specific investment cost expense of photovoltaic plant, take 6,500,000 yuan/MW, PN.pviPlanned capacity value for node i photovoltaic plant；OCGiFor
The unit operating cost of node i thermal power generation unit, PGiFor exerting oneself of node i thermal power generation unit,P Gi Send out for node i firepower
The lower limit of exerting oneself of group of motors,The upper limit of exerting oneself for node i thermal power generation unit；OCpviUnit fortune for node i photovoltaic plant
Row expense, PpviFor exerting oneself of node i photovoltaic plant,P PVi For the lower limit of exerting oneself of node i photovoltaic plant,For node i photovoltaic electric
The upper limit of exerting oneself stood；F is lower floor's cutting load rejection penalty,For cutting load expected value, PCiFor the cutting load amount in node i, PDi
For the load in node i, B is the admittance matrix of system rack, and θ is node voltage phase angle matrix, UOC (Unit Outage
Cost it is) unit outage cost cost, take 19.5 yuan/kW h, EENSiInterior each node expected loss of energy during for research,For its upper limit.
Underlying model is：
Min F=fN+fN-1
s.t.
hN(x, y)=0
hN-1(x, y)=0
In formula, F is lower floor's cutting load rejection penalty, fNFor the cutting load rejection penalty under N safe operating conditions, fN-1For
Cutting load rejection penalty under N-1 safe operating conditions；hN(x, y)=0 is the equality constraint under N safe operating conditions, hN-1
(x, y)=0 is the equality constraint under N-1 safe operating conditions；For under N safe operating conditions
Formula constrains,g N 、For gNThe minima of (x, y) and maximum,For under N-1 safe operating conditions
Inequality constraints,g N-1 、For gN-1The minima of (x, y) and maximum；X is state variable, including admittance matrix, node
Injecting power, generator output bound, circuit rated capacity, y be decision variable, including cutting load amount, node voltage phase angle,
Line Flow.
Improved adaptive GA-IAGA and the hybrid algorithm of primal-dual interior method is adopted to solve mould according to model feature in step S4
Type obtains optimum programming scheme, and wherein, upper strata carries out global optimizing using improved adaptive GA-IAGA, generates and optimizes rack, is built
If expense, then obtain the expected value of operation and maintenance cost, environmental costses, short of electricity amount expense by dry run, lower floor utilizes former
Beginning-dual interior point calculates cutting load rejection penalty, feeds back to the total cost that upper strata obtains optimization aim, is obtained by iteration convergence
To optimum grid structure.
In the present embodiment, the constraint of lower floor's cutting load adopts primal-dual interior method, and upper strata adopts improved adaptive GA-IAGA, Fig. 5
For the flow chart of the present embodiment hybrid algorithm, concretely comprise the following steps：
The first step：|input paramete, generates initial population, and this population comprises M sample, and sample is initial individuals, and individuality exists
Concrete in system represent the Transmission Expansion Planning in Electric scheme generating, each sample carries out N safe operation, under N-1 safe operating conditions
Cutting load amount expected value calculates, line construction expense calculates and produces simulation expense calculates, and tries to achieve each individual total cost
Expected value；
Second step：Arrange from big to small according to total cost expected value, m individuality before memory, and carry out network connectivty and repair
Just and total cost expected value calculate, re-start sequence；
3rd step：Crossover operation is carried out to 2 Different Individual randomly choosing, if 2 body phases are same, individuality is carried out inverse
Turn operation；
4th step：New individual is carried out with network connectivty correction and total cost expected value calculated, if the new individual total cost phase
Prestige value is better than former individuality, then replace former individuality, otherwise, do not replace；
5th step：Random choose individuality carries out mutation operation；
6th step：New individual is carried out with network connectivty correction and total cost expected value calculated, if the new individual total cost phase
Prestige value is better than former individuality, then replace former individuality, otherwise this individuality is again carried out mending and calculates operation；
7th step：New individual is carried out with network connectivty correction and total cost expected value calculated, if the new individual total cost phase
Prestige value is better than former individuality, then replace former individuality, otherwise, do not replace；
8th step：Checking whether that satisfaction terminates iterationses condition, if being unsatisfactory for, continuing the 3rd step operation；If meeting, eventually
Only iteration, output result sample.
9th step：Arrange from small to large respectively according to load-shedding cost expected value, line construction expense and total cost expected value
Sequence, exports the optimal case of each result.
Wherein, the improved adaptive GA-IAGA of the present embodiment adopts self-adapted genetic algorithm, its crossover probability and mutation probability energy
Enough automatically changed according to fitness.This algorithm and upper strata models coupling, firstly generate a number of initial plan scheme, and that is, the
Sample described in one step, calculates the fitness of these samples then in conjunction with underlying model, then counts compared with average fitness
Calculate the crossover probability intersecting individual and variation individual (i.e. Transmission Expansion Planning in Electric scheme) and mutation probability, finally meet iterationses
Optimum individual, i.e. optimum programming scheme is exported after condition.Crossover probability P in self-adapted genetic algorithmcWith mutation probability PmMeter
Calculate formula as follows：
In formula, fmaxFor maximum fitness value in colony, favgFor the average fitness value of per generation colony, f ' is to intersect
Two individualities in larger fitness value, f is to make a variation the fitness value of individuality, k1、k2、k3And k4For constant.Wherein, group
Body refers to row variation to be entered and all Transmission Expansion Planning in Electric schemes intersected, and individuality refers to single Transmission Expansion Planning in Electric scheme.
In the present embodiment, the newly-increased photovoltaic capacity of node 1,9,13,24 is 40,100,100,60MW, node 9,13,23,
24 load increases to 255,345,50,40MW, obtained according to above-mentioned random expected value bilevel programming model and hybrid algorithm flow process
The optimum programming scheme arriving is as shown in Figure 6.
Claims (3)
1. a kind of power transmission network stochastic programming method containing large-scale photovoltaic electricity generation grid-connecting is it is characterised in that specifically include following step
Suddenly：
1) according to real-time lighting intensity and ambient temperature data, the sequential power curve in simulation large-sized photovoltaic power station：
2) in step 1) on the basis of the sequential power curve of gained photovoltaic plant, in conjunction with the supplemental characteristic of actual electric network, removed
Remove the net load curve that photovoltaic is exerted oneself, form initial equivalent electric quantity function, calculated based on equivalent energy function method and determine that photovoltaic is sent out
Maximum size in power system for the electricity；
3) the upper layer model of power transmission network random expected value bilevel programming model is with the minimum target of total cost expected value, including new
Increase circuit, electric generation investment construction cost, operation and maintenance cost, supply interruption cost and cutting load rejection penalty expected value；Underlying model
It is with the minimum target of cutting load rejection penalty under N safe operating conditions, N-1 safe operating conditions；According to random expected value two
Layer planning theory and Transmission Expansion Planning in Electric constraints, set up the power transmission network random expected value bi-level programming mould containing large-scale photovoltaic
Type；
4) upper strata carries out global optimizing using self-adapted genetic algorithm, generates and optimizes rack, obtains construction cost, then by simulation
Run and obtain operation and maintenance cost, environmental costses, the expected value of short of electricity amount expense, lower floor utilizes primal-dual interior method to calculate
Cutting load rejection penalty, feeds back to the total cost that upper strata obtains optimization aim, obtains optimum grid structure by iteration convergence.
2. contain the power transmission network stochastic programming method of large-scale photovoltaic electricity generation grid-connecting according to claim 1 it is characterised in that institute
The step 1 stated) according to real-time lighting intensity and ambient temperature data, simulate the sequential power curve in large-sized photovoltaic power station, its
The model of exerting oneself of medium-and-large-sized photovoltaic plant is：
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxDefeated for maximum under MPPT maximum power point tracking control for the photovoltaic plant
Go out power, η is the efficiency of photovoltaic module, ηmpptFor the efficiency of MPPT control assembly, ηinvFor the efficiency of inverter, ηrefIt is reference
At a temperature of photovoltaic module efficiency, ε be photovoltaic module temperature coefficient, be taken as 0.003～0.005, TaFor real time environment temperature,
TrefFor reference temperature, take 25 DEG C.
3. contain the power transmission network stochastic programming method of large-scale photovoltaic electricity generation grid-connecting according to claim 1 it is characterised in that institute
State step 2) comprise the following steps that：
A, the sequential power curve of the photovoltaic generation of gained is added as negative load with sequential load curve, by photovoltaic when
Sequence power curve separates from sequential load curve, obtains net load curve；
B, according to net load curve formed with loading as transverse axis, load duration be the longitudinal axis equivalent load curve, according to
Conventional power unit capacity chooses step-length, is ranked up conventional power unit according to cost of electricity-generating, will be conventional to equivalent load curve and combination
Unit capacity and corresponding forced outage rate combine, and form initial equivalent electric quantity function, arrange each unit operation successively, calculate
Its generated energy；
C, according to equivalent energy function method, form new equivalent load curve in the case of having arranged previous unit, according to
The capacity of remaining conventional power unit and corresponding forced outage rate correction equivalent electric quantity function, and check whether all units have arranged
Finishing, if it did not, going to step B, if whole arrange to finish, according to arranged order, calculating overall running cost；
D, regulation photovoltaic capacity proportion in systems, the system production cost in the case of calculating is multiple and each index, obtain
The theoretical upper limit of photovoltaic capacity accounting.
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