CN106451424B - The power transmission network stochastic programming method of the electricity generation grid-connecting containing large-scale photovoltaic - Google Patents
The power transmission network stochastic programming method of the electricity generation grid-connecting containing large-scale photovoltaic Download PDFInfo
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Abstract
The present invention relates to a kind of power transmission network stochastic programming methods of electricity generation grid-connecting containing large-scale photovoltaic, according to real-time lighting intensity and ambient temperature data, simulate the power curve in large-sized photovoltaic power station, consider the randomness of photovoltaic power output, fluctuation, it is theoretical based on equivalent energy function method and stochastic programming, construct the mathematical model of the power transmission network random expected value bi-level programming of the electricity generation grid-connecting containing large-scale photovoltaic, reasonable hybrid algorithm is designed according to self-adapted genetic algorithm and primal-dual interior method, plan model is effectively solved, obtain optimum programming scheme, meet the demand of the power grid stochastic programming to generate electricity containing large-scale photovoltaic, it is clear with logical construction, practical reasonable advantage.This method is agreed at this stage and the development trend of the following large-scale photovoltaic can be widely applied in the electric system stochastic programming simulation calculating of large-scale photovoltaic power generation with very strong theoretical property and practicability.
Description
Technical field
The present invention relates to a kind of transmission of electricity network technology, in particular to the power transmission network of a kind of electricity generation grid-connecting containing large-scale photovoltaic is random
Planing method.
Background technique
With rapid economic development, energy and environmental problem has become world today's focus of interest.Coal, petroleum,
The demand of the energy such as natural gas is growing day by day, but these energy are non-renewable, and can cause to environment sternly during utilization
Heavily contaminated, also increasing for influencing caused by social sound development and stabilization, solar energy is a kind of clean renewable
Severe situation and the environmental pollution of energy shortages can be effectively relieved in the energy, the photovoltaic power generation technology converted solar energy into electrical energy
Pressure.
Under the support of national governments' policy, by years of researches, nowadays photovoltaic power generation have become one more at
Ripe new energy power generation technology, photovoltaic electric energy is gradually from the supplement energy to alternative energy source transition, and wherein photovoltaic plant is big
Type scale is even more the developing direction and research emphasis become from now on.With shared by photovoltaic power generation in the power system installed capacity
Than increasing, the extensive concentration exploitation of photovoltaic power generation can generate electric system certain influence.Photovoltaic is contributed not
Certainty also influences whether Electric Power Network Planning.
Using stochastic programming theory power planning research field latest development, to grid type centralized photovoltaic on a large scale
Electricity generation system and power network expansion planning method are studied, and the uncertain factor of analyzing influence photovoltaic power output is established extensive
Centralized photovoltaic power output model simulates the electric power containing photovoltaic plant using suitable method then in conjunction with Operation of Electric Systems feature
System random walk establishes the power grid random expected value plan model to generate electricity containing large-scale photovoltaic in conjunction with stochastic programming theory
Electric Power Network Planning is carried out, is fully considered since the uncertain bring of photovoltaic power output influences, to obtain suitable Electric Power Network Planning side
Case.
Summary of the invention
Influence problem the present invention be directed to Solar use to Electric Power Network Planning from now on proposes a kind of containing large-scale photovoltaic
The power transmission network stochastic programming method of electricity generation grid-connecting considers randomness, the wave of photovoltaic power output by simulating the power output of large-scale photovoltaic
Dynamic property, theoretical based on equivalent energy function method and stochastic programming, the power transmission network of building electricity generation grid-connecting containing large-scale photovoltaic is advised at random
The mathematical model drawn effectively solves plan model by the reasonable hybrid algorithm of modern optimization Theoretical Design.
The technical solution of the present invention is as follows: a kind of power transmission network stochastic programming method of electricity generation grid-connecting containing large-scale photovoltaic, specifically
Include the following steps:
1) according to real-time lighting intensity and ambient temperature data, the timing power curve in large-sized photovoltaic power station is simulated:
2) it on the basis of the timing power curve of the photovoltaic plant obtained by step 1), in conjunction with the supplemental characteristic of actual electric network, obtains
To the net load curve for removing photovoltaic power output, initial equivalent electric quantity function is formed, is calculated based on equivalent energy function method and determines light
The maximum size of volt power generation in the power system；
3) the upper layer model of power transmission network random expected value bilevel programming model is with the minimum target of total cost desired value, packet
Include newly-increased route, electric generation investment construction cost, operation and maintenance cost, supply interruption cost and cutting load rejection penalty desired value；Lower layer
Model is with the minimum target of cutting load rejection penalty under N safe operating conditions, N-1 safe operating conditions；According to random expectation
It is worth Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraint condition, establishes the power transmission network random expected value bi-level programming containing large-scale photovoltaic
Model；
4) upper layer carries out global optimizing using self-adapted genetic algorithm, generates optimization rack, obtains construction cost, then pass through
Dry run obtain operation and maintenance cost, environmental costs, short of electricity amount expense desired value, lower layer utilize primal-dual interior method
Cutting load rejection penalty is calculated, upper layer is fed back to and obtains the total cost of optimization aim, optimal rack knot is obtained by iteration convergence
Structure.
According to real-time lighting intensity and ambient temperature data in the step 1), the timing for simulating large-sized photovoltaic power station goes out
Force curve, wherein the power output model in large-sized photovoltaic power station are as follows:
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxFor photovoltaic plant MPPT maximum power point tracking control under most
Big output power, η are 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 are photovoltaic module temperature coefficient, are taken as 0.003~0.005, TaFor real time environment temperature
Degree, TrefFor reference temperature, 25 DEG C are taken.
Specific step is as follows for the step 2):
A, it is added the timing power curve of resulting photovoltaic power generation as negative load with timing load curve, by photovoltaic
Timing power curve separated from timing load curve, obtain net load curve；
B, it being formed according to net load curve using load as horizontal axis, load duration is the equivalent load curve of the longitudinal axis,
Step-length is chosen according to conventional power unit capacity, is ranked up conventional power unit according to cost of electricity-generating, by equivalent load curve and combination
Conventional power unit capacity and corresponding forced outage rate combine, and form initial equivalent electric quantity function, and each unit is successively arranged to run,
Calculate its generated energy；
C, according to equivalent energy function method, new equivalent load curve is formed in the case where having arranged previous unit,
Equivalent electric quantity function is corrected according to the capacity of remaining conventional power unit and corresponding forced outage rate, and checks whether all units arrange
It finishes, if not provided, going to step B, if all arranging to finish, according to the sequence arranged properly, calculates overall running cost；
D, the specific gravity of photovoltaic capacity in systems is adjusted, the system production cost and each index in a variety of situations are calculated,
Obtain the theoretical upper limit of photovoltaic capacity accounting.
The beneficial effects of the present invention are: the power transmission network stochastic programming method of present invention electricity generation grid-connecting containing large-scale photovoltaic,
Randomness, the fluctuation for considering photovoltaic power output, theoretical based on equivalent energy function method and stochastic programming, building contains 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 designs reasonable hybrid algorithm, is effectively solved to plan model, obtains optimum programming scheme, meets and contains extensive light
The demand for lying prostrate the power grid stochastic programming of power generation, has the advantages that logical construction is clear, practical reasonable.
Detailed description of the invention
Fig. 1 is the power transmission network stochastic programming method flow diagram of the electricity generation grid-connecting of the invention containing large-scale photovoltaic；
Fig. 2 is the electric diagram of the embodiment of the present invention；
Fig. 3 is one figure of production graph of simulation results of the embodiment of the present invention；
Fig. 4 is two figure of production graph of simulation results of the embodiment of the present invention；
Fig. 5 is the process of hybrid algorithm in the power transmission network stochastic programming method of the electricity generation grid-connecting of the invention containing large-scale photovoltaic
Figure；
Fig. 6 is the optimum programming result electric diagram of the embodiment of the present invention.
Specific embodiment
The power transmission network stochastic programming method flow diagram of the electricity generation grid-connecting containing large-scale photovoltaic as shown in Figure 1, specifically includes following
Step:
S1 simulates the power curve in large-sized photovoltaic power station according to real-time lighting intensity and ambient temperature data；
The supplemental characteristic of S2 combination actual electric network is calculated based on equivalent energy function method and determines photovoltaic power generation in electric system
In maximum size；
S3 is established according to random expected value Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraint condition containing the defeated of large-scale photovoltaic
Power grid random expected value bilevel programming model；
S4 is obtained according to model feature using the hybrid algorithm solving model of improved adaptive GA-IAGA and primal-dual interior method
Optimum programming scheme.
According to real-time lighting intensity and ambient temperature data in step S1, the timing power output for simulating large-sized photovoltaic power station is bent
Line, wherein the power output model in large-sized photovoltaic power station are as follows:
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxIt is controlled for photovoltaic plant in MPPT (MPPT maximum power point tracking)
Under peak power output, η be photovoltaic module efficiency, ηmpptFor the efficiency of MPPT control assembly, ηinvFor the effect of inverter
Rate, ηrefFor the photovoltaic module efficiency under reference temperature, ε is photovoltaic module temperature coefficient, is generally taken as 0.003~0.005, Ta
For real time environment temperature, TrefFor reference temperature, 25 DEG C are taken.
PmaxValue it is related with the U-I characteristic of photovoltaic, mathematical model is shown below:
In formula, I, U are respectively the output electric current and voltage of single photovoltaic cell；C1、C2For intermediate variable, need according to light
Variation according to intensity and environment temperature is constantly modified；IscFor short circuit current, UocFor open-circuit voltage.Because solar irradiation is strong
It is continually changing for spending with environment temperature, 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, it is related with illumination variation and variation of ambient temperature.The present embodiment takes respectively referring to the technical parameter of monocrystalline Silicon photrouics
Optimum operating voltage Um=17.1V, open-circuit voltage Uoc=22V, recommended current Im=3.5A, open-circuit current Isc=3.8V.
The update equation of photovoltaic cell technical parameter is shown below in the present embodiment:
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, unit mWcm2, XrefFor standard intensity of illumination 1000W/m2, X is monthly
The real-time lighting intensity of typical day, k are 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 environment temperature in photovoltaic module location, Tmax、TminFor the temperature maximum value of typical day
And minimum value, tpAt the time of for one day, the highest temperature occurred, 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 are a=0.0025/ DEG C, b=7.5e-5m2/ W, c=0.5, d=0.0028/ DEG C, e=8.4e-5m2/W。
The supplemental characteristic that actual electric network is combined in step S2 is calculated based on equivalent energy function method and determines photovoltaic power generation in electricity
Maximum size in Force system, specific steps are as follows:
1, using the timing power curve of photovoltaic power generation obtained in step S1 as negative load and timing load curve phase
Add, the timing power curve of photovoltaic is separated from timing load curve, obtains net load curve；Timing load curve is same
Loads all in electric system are added the curve changed over time to the characterization load generated by one time point；
2, it being formed according to net load curve using load as horizontal axis, load duration is the equivalent load curve of the longitudinal axis,
Suitable step-length is chosen according to conventional power unit capacity, is ranked up conventional power unit according to cost of electricity-generating, by equivalent load curve
It is combined with combination conventional power unit capacity and corresponding forced outage rate, forms initial equivalent electric quantity function, successively arrange each machine
Group operation, calculates its generated energy；
3, according to equivalent energy function method (equivalent energy function method in calculating can according to calculate step correct automatically, this
A is the core methed of equivalent energy function method), new equivalent load curve is formed in the case where having arranged previous unit,
Equivalent electric quantity function is corrected according to the capacity of remaining conventional power unit and corresponding forced outage rate, and checks whether all units arrange
It finishes, if not provided, going to step 2, if all arranging to finish, according to the sequence arranged properly, calculates overall running cost；
4, the specific gravity of photovoltaic capacity in systems is adjusted, the system production cost and each index in a variety of situations are calculated,
Obtain the theoretical upper limit of photovoltaic capacity accounting.
Fig. 2 is the electric diagram of the present embodiment, and G indicates that conventional power unit, Bus indicate node bus, Synch.Cond. in figure
(synchronous condenser) indicates phase modifier, the total producing cost C of the electric systemtotalIncluding fuel cost Cfuel、
Operation and maintenance cost CO&M, average short of electricity making up price CUEC, environmental costs Cenvi, it may be assumed that
Ctotal=Cfuel+CO&M+CUEC+Cenvi
In formula: Cfuel,iFor the fuel cost of 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 costs of i-th unit unit generated energy, it is
Altogether there is n platform unit generation.For photovoltaic plant, fuel is not consumed, does not discharge exhaust gas, therefore its fuel cost, ring
Border expense is all 0.
In embodiment, original installed capacity is constant in system, and photovoltaic capacity accounts for the ratio point of system total installation of generating capacity
It Wei 0,5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%.Analog result is as shown in Figure 3,4, figure
3 indicate the situation of change with increase the two indexs of EENS and LOLP of photovoltaic plant capacity in system, wherein LOLP is indicated
The loss of load probability (electric system proper noun) of system, Fig. 4 indicate the increase with photovoltaic plant capacity in system, system
The middle relationship for abandoning light rate and total cost, complex chart 3 and Fig. 4 are reduced to dissolve photovoltaic power generation as far as possible and are abandoned light rate, and protect
The operational reliability of card system, photovoltaic capacity should be no more than the 50% of total capacity in system.
According to random expected value Two-Hierarchical Programming Theory and Transmission Expansion Planning in Electric constraint condition in step S3, establishes 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 including newly-increased route, electric generation investment construction cost, operation and maintenance expense with the minimum target of total cost desired value
With, supply interruption cost and cutting load rejection penalty desired value；Underlying model is under N safe operating conditions, N-1 safe operating conditions
The minimum target of cutting load rejection penalty.
Upper layer model are as follows:
Min E [S]=E [Sinv]+E[Soper]
In formula, S is system total cost, SinvTo increase route, electric generation investment construction cost, S newlyoperThe fortune returned for lower layer
Row, maintenance and load-shedding cost；ClFor transmission line of electricity specific investment cost expense, 1,300,000 yuan/km is taken；Nl-ijFor the planning of node i to j
The number of lines,Nl-ij 、Respectively its maximin, Pl-ijFor the rated capacity of route, PijFor route Real-time Power Flow；Cpv
For the specific investment cost expense of photovoltaic plant, 6,500,000 yuan/MW, P are takenN.pviFor the planned capacity value of node i photovoltaic plant；OCGiFor
The unit operating cost of node i thermal power generation unit, PGiFor the power output of node i thermal power generation unit,PGi For node i firepower hair
The power output lower limit of motor group,For the power output upper limit of node i thermal power generation unit；OCpviIt is transported for the unit of node i photovoltaic plant
Row expense, PpviFor the power output of node i photovoltaic plant,PPVi For the power output lower limit of node i photovoltaic plant,For node i photovoltaic electric
The power output upper limit stood；F is lower layer's cutting load rejection penalty,For cutting load desired 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, takes 19.5 yuan/kWh, EENSiEach node expected loss of energy during for research,For its upper limit.
Underlying model are as follows:
Min F=fN+fN-1
s.t.
hN(x, y)=0
hN-1(x, y)=0
In formula, F is lower layer'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 differing under N safe operating conditions
Formula constraint,gN 、For gNThe minimum value and maximum value of (x, y),For under N-1 safe operating conditions
Inequality constraints,gN-1 、For gN-1The minimum value and maximum value of (x, y)；X is state variable, including admittance matrix, node
Injecting power, generator output bound, route rated capacity, y are decision variable, including cutting load amount, node voltage phase angle,
Line Flow.
Mould is solved using the hybrid algorithm of improved adaptive GA-IAGA and primal-dual interior method according to model feature in step S4
Type obtains optimum programming scheme, wherein upper layer carries out global optimizing using improved adaptive GA-IAGA, generates optimization rack, is built
If expense, then by dry run obtain operation and maintenance cost, environmental costs, short of electricity amount expense desired value, lower layer is using former
Beginning-dual interior point calculates cutting load rejection penalty, feeds back to upper layer and obtains the total cost of optimization aim, is obtained by iteration convergence
To optimal grid structure.
The constraint of lower layer's cutting load uses primal-dual interior method in the present embodiment, and upper layer uses improved adaptive GA-IAGA, Fig. 5
For the flow chart of the present embodiment hybrid algorithm, specific steps are as follows:
Step 1: input parameter, generates initial population, which includes M sample, and sample is initial individuals, and individual exists
The Transmission Expansion Planning in Electric scheme generated is specifically indicated in system, each sample carries out under N safe operation, N-1 safe operating conditions
Cutting load amount desired value calculates, line construction expense calculates and production simulation expense calculates, and acquires the total cost of each individual
Desired value；
Step 2: being arranged from big to small according to total cost desired value, m individual before memory, and carries out network connectivty and repair
Just and total cost desired value calculates, and re-starts sequence；
Step 3: carrying out crossover operation to randomly selected 2 Different Individuals, if 2 individuals are identical, individual is carried out inverse
Turn operation；
Step 4: network connectivty amendment and the calculating of total cost desired value are carried out to new individual, if the new individual total cost phase
Prestige value is then replaced former individual, otherwise, is not replaced better than former individual；
Step 5: random picking individual carries out mutation operation；
Step 6: network connectivty amendment and the calculating of total cost desired value are carried out to new individual, if the new individual total cost phase
Prestige value then replaces former individual better than former individual, is otherwise again carried out benefit to the individual and calculates operation；
Step 7: network connectivty amendment and the calculating of total cost desired value are carried out to new individual, if the new individual total cost phase
Prestige value is then replaced former individual, otherwise, is not replaced better than former individual；
Step 8: checking whether that satisfaction terminates the number of iterations condition, if not satisfied, continuing third step operation；If satisfied, eventually
Only iteration exports result sample.
Step 9: being arranged from small to large respectively according to load-shedding cost desired value, line construction expense and total cost desired value
Sequence exports the optimal case of each result.
Wherein, the improved adaptive GA-IAGA of the present embodiment uses self-adapted genetic algorithm, crossover probability and mutation probability energy
It is enough to be changed automatically according to fitness.The algorithm and upper layer models coupling, firstly generate a certain number of initial plan schemes, i.e., and the
Sample described in one step calculates the fitness of these samples then in conjunction with underlying model, then counts compared with average fitness
The crossover probability and mutation probability for intersecting individual and variation individual (i.e. Transmission Expansion Planning in Electric scheme) are calculated, is finally meeting the number of iterations
Optimum individual, i.e. optimum programming scheme are exported after condition.Crossover probability P in self-adapted genetic algorithmcWith mutation probability PmMeter
It is as follows to calculate formula:
In formula, fmaxFor fitness value maximum in group, favgFor the average fitness value of per generation group, f ' is to intersect
Two individuals in biggish fitness value, f is the fitness value for wanting variation individual, k1、k2、k3And k4For constant.Wherein, group
Body refers to all Transmission Expansion Planning in Electric schemes that made a variation and be intersected, and individual 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, is obtained according to above-mentioned random expected value bilevel programming model and hybrid algorithm process
The optimum programming scheme arrived is as shown in Figure 6.
Claims (2)
1. a kind of power transmission network stochastic programming method of electricity generation grid-connecting containing large-scale photovoltaic, specifically comprises the following steps:
1) according to real-time lighting intensity and ambient temperature data, the timing power curve in large-sized photovoltaic power station is simulated:
2) it on the basis of the timing power curve of the photovoltaic plant obtained by step 1), in conjunction with the supplemental characteristic of actual electric network, is removed
The net load curve for going photovoltaic to contribute, forms initial equivalent electric quantity function, is calculated based on equivalent energy function method and determines photovoltaic hair
The maximum size of electricity in the power system；
3) the upper layer model of power transmission network random expected value bilevel programming model be with the minimum target of total cost desired value, including it is new
Increase route, electric generation investment construction cost, operation and maintenance cost, supply interruption cost and cutting load rejection penalty desired 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 constraint condition establish the power transmission network random expected value bi-level programming mould containing large-scale photovoltaic
Type；
4) upper layer carries out global optimizing using self-adapted genetic algorithm, generates optimization rack, obtains construction cost, then pass through simulation
Operation obtain operation and maintenance cost, environmental costs, short of electricity amount expense desired value, lower layer using primal-dual interior method calculating
Cutting load rejection penalty, feeds back to upper layer and obtains the total cost of optimization aim, obtains optimal grid structure by iteration convergence；
It is characterized in that, simulating large-sized photovoltaic power station according to real-time lighting intensity and ambient temperature data in the step 1)
Timing power curve, the wherein power output model in large-sized photovoltaic power station are as follows:
PPV=η Pmaxηmpptηinv
η=ηref[1-ε(Ta-Tref)]
In formula, PPVFor photovoltaic plant real output, PmaxIt is defeated for maximum of the photovoltaic plant under MPPT maximum power point tracking control
Power out, η are the efficiency of photovoltaic module, ηmpptFor the efficiency of MPPT control assembly, ηinvFor the efficiency of inverter, ηrefFor 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, 25 DEG C are taken.
2. the power transmission network stochastic programming method of the electricity generation grid-connecting containing large-scale photovoltaic according to claim 1, which is characterized in that institute
Stating step 2), specific step is as follows:
A, the timing power curve of resulting photovoltaic power generation is subtracted each other with timing load curve, by the timing power curve of photovoltaic from
It is separated in timing load curve, obtains net load curve；
B, it being formed according to net load curve using load as horizontal axis, load duration is the equivalent load curve of the longitudinal axis, according to
Conventional power unit capacity chooses step-length, is ranked up conventional power unit according to cost of electricity-generating, by equivalent load curve and combines conventional
Unit capacity and corresponding forced outage rate combine, and form initial equivalent electric quantity function, and each unit is successively arranged to run, and calculate
Its generated energy；
C, according to equivalent energy function method, new equivalent load curve is formed in the case where having arranged previous unit, according to
The capacity of remaining conventional power unit and corresponding forced outage rate correct equivalent electric quantity function, and check whether all units have arranged
Finish, if not provided, going to step B, if all arranging to finish, according to the sequence arranged properly, calculates overall running cost；
D, the specific gravity of photovoltaic capacity in systems is adjusted, the system production cost and each index in a variety of situations is calculated, obtains
The theoretical upper limit of photovoltaic capacity accounting.
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