CN107301472A - Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy - Google Patents

Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy Download PDF

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CN107301472A
CN107301472A CN201710423245.9A CN201710423245A CN107301472A CN 107301472 A CN107301472 A CN 107301472A CN 201710423245 A CN201710423245 A CN 201710423245A CN 107301472 A CN107301472 A CN 107301472A
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郭力
王成山
杨书强
赵宗政
赵奇
赵一奇
陈煜�
荣秀婷
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Abstract

The present invention relates to a kind of distributed photovoltaic Method for optimized planning based on scene analysis method and consideration voltage-regulation strategy, including：Step one：Scene analysis is carried out using scene analysis method, the true running status of distributed power source and load is characterized using high dimension vector, the compression of scene is realized by clustering method；Step 2：Power distribution network distributed power source dual-layer optimization planing method is set up, step is as follows:(1) network loss, the income of maximization distributed power source operator of upper strata planning to minimize power distribution network etc. builds multiple target, plans the access capacity of distributed power source.(2) different scheduling strategies are taken in lower floor's scheduling model, not using pressure regulation strategy, using pressure regulation strategy and the pressure regulation strategy with reactive-load compensation is contrasted, distributed power source is planned.

Description

Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy
Technical field
Advised the present invention relates to a kind of optimized based on the distributed photovoltaic for improving scene analysis method and consideration voltage-regulation strategy The method of drawing.
Background technology
The raising of economic and technique level and environmental problem it is increasingly serious, promote distribution type renewable energy especially The exploitation of distributed power source.In addition, with the support of national policy, the permeability of Distributed Generation in Distribution System increases rapidly Long, distributed photovoltaic is greatly developed under the policy of " the accurate poverty alleviation of photovoltaic ".Match somebody with somebody to tackle distributed power source in tradition Extensive access in power network, the new theory such as active distribution network and intelligent grid and technology are suggested in succession, following distribution Distributed power source will play the part of prior role in net, therefore the optimization planning problem of distributed power source is also particularly important.
Distributed power source such as photo-voltaic power supply and blower fan have certain uncertainty, how correctly to weigh this uncertain Property, it is ensured that urgently to be resolved hurrily the problem of the reasonability and economy of program results.There is document to be directed in wind-driven generator optimization planning The problem of uncertainty of regional wind speed and load, the uncertain factor existed is handled using many scene analysis methods, is constructed Certainty plan model, the probability distribution situation of wind speed in 1 year is described using Ruili probability density function, necessarily to walk Wind speed is divided into several grades by length；The IEEE-RTS systems load condition of 1 year is divided into 10 using the method for cluster Grade, therefore the scene constituted with [blower fan active power output, load] characterizes each state of system operation.There is document to consider wind The optimization planning of machine, three kinds of distributed power sources of photovoltaic and biomass, regards exerting oneself for biomass power supply as steady state value, with [blower fan Active power output, photovoltaic active power output] build multiple scenes and analyzed.There is document with blower fan active power output, photovoltaic active power output Three-dimensional state space is constituted as reference axis with load, annual 8760 historic sceneries are navigated in space, closed by setting System is divided into some running status subspaces by the indexing of reason so that annual Run-time scenario quantity is compressed.
The access of high permeability distributed power source brings a series of problems, and especially extensive trend, which is fallen, send caused voltage Out-of-limit problem.There is document to damage income, electricity with the investment spending of intermittent distributed power source and capacitor, sale of electricity income, system drop It is object function to press quality and reducing discharge of waste gases amount comprehensive benefit, sets up comprehensive allocation optimum model.There is document to pass through to distribution The combined optimization of formula power supply and idle capacitor, have studied it to improving the effect of quality of voltage and reduction network loss.There is document to build Dual-layer optimization allocation models has been found, complex optimum, upper strata planning distributed power source and electricity are carried out to distributed power source and capacitor The income of container, lower floor's planning simulation idle work optimization.
The defect and deficiency of art methods：
(1) with the increase of distributed power source number amount and type, scene can be caused with existing many scene analysis method processing Quantity is increased rapidly, while the method for existing scene compression can not reflect the actual running status of power network exactly.Tradition Scene analysis regard each object comprising uncertain factor as independent individual, increase with the object of consideration, cause field The problem of quantity exponentially of scape increases, and ignore the correlation between object so that scene analysis has larger with true operation Deviation.
(2) existing distributed power source plan model is all based on operator either some single interests master of power distribution network Body considers, does not account for the multiple-objection optimization of different interests main body.For different Interest Main Bodies, it should which selection can reflect profit The object function of beneficial main body in itself.
(3) on pressure regulation method, existing pressure regulation method does not account for the distributed power source of diverse location for node electricity The contribution degree of pressure is different, because position of the different installation nodes in power network topology is different, therefore for the tune of voltage Section has different contributions.Original method is relatively simple on pressure regulation strategy, simply simple by idle work optimization or active Cut down to be adjusted, the method idle pressure regulation and active pressure regulation is not combined, and realizes common pressure regulation.
(4) art methods do not make full use of the effect of photovoltaic converter.Current transformer can send active or nothing Work(, voltage can be regulated and controled by the output regulation to current transformer.Daylight voltage can more cut down power reduction voltage, night in limited time Reactive power can be sent when load is big, node voltage is relatively low and raises voltage, existing technology does not account for this problem also.
The content of the invention
In view of the above-mentioned problems, the purpose of the present invention is to overcome the deficiencies in the prior art, deposited in processing distributed power source planning Uncertainty and consider distributed power source operation in pressure regulation the problem of there is provided one kind reduce amount of calculation while ensure rule The reliability of check off fruit and the distributed photovoltaic Method for optimized planning of validity.Technical scheme is as follows：
Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy, comprises the following steps：
Step one：Scene analysis is carried out using scene analysis method, distributed power source and load are characterized using high dimension vector True running status, the compression of scene is realized by clustering method, step is as follows：
(1) resource scene is generated
The fluctuation of intensity of illumination is utilized into Beta probability density functions fb(s) describe；
Intensity of illumination is classified and represented, for grade k, grade k probability P { G is calculatedkAnd the grade under illumination it is strong Mean μ { the G of degreek}；
(2) load scenarios are generated
By k-means clustering algorithms, to it is multiple when discontinuity surface under the whole network load vector clusters, obtain typical load allusion quotation The whole network load condition under type scene collection, certain moment t sections, uses multi-C vector ltRepresent：
lt={ P1,Q1,P2,Q2,...,Pn,Qn}
Wherein, Pn,QnNode n burden with power power and load or burden without work power is represented respectively；
For the load vector clusters at multiple moment are obtained into typical load scene collection, it is necessary to the distance between definition vector, Contribution degree to active power and reactive power in distance definition sets different weights respectively, and the weight of reactive power is less than The weight of active power, calculates the distance between vector using the Euclidean distance after this weighting, passes through k-means clustering algorithms Iteration obtains typical load scenarios collection, and typical load scene concentrates the probability calculation of each typical scene：
In formula, nkExpression belongs to the number of cluster k original loads scene, and N represents the number of all original loads scenes.
(3) Run-time scenario is generated
The true running status of power distribution network containing distributed power source is by outside regenerative resource and the close phase of load of network Close, the Run-time scenario Y of the whole network is collectively formed by resource scene G and load scenarios C：
Y={ G, C }
NY=NG*NC
In formula, NYThe total quantity of Run-time scenario is represented, is the quantity N of resource sceneGWith typical load scene quantity NCMultiply Product；Run-time scenario YαBy resource scene GβWith load scenarios CγConstitute, its probability of happening is also by GβAnd CγProbability product determine It is fixed.
Step 2：Power distribution network distributed power source dual-layer optimization planing method is set up, step is as follows:
(1) network loss, the income of maximization distributed power source operator of upper strata planning to minimize power distribution network etc. builds many Target, plans the access capacity of distributed power source.
(2) different scheduling strategies are taken in lower floor's scheduling model, not using pressure regulation strategy, using pressure regulation strategy and Pressure regulation strategy with reactive-load compensation is contrasted, and distributed power source is planned.
The present invention characterizes the true running status of distributed power source and load using high dimension vector, suitable poly- by choosing Class method realizes the compression of scene, and computational accuracy is ensured while amount of calculation is reduced.Propose the power distribution network based on scene analysis Distributed power source dual-layer optimization planing method.The upper strata of dual layer resist is planned to be distributed with minimizing the network loss of power distribution network, maximizing Income of formula power grid operator etc. builds multiple target, plans the access capacity of distributed power source；Lower floor's planning considers distributed electrical The cluster voltage adjustment in source, optimizes the quality of voltage of power distribution network.Using bilayer model as big frame, consider to plan using scene analysis In uncertain factor, the acquisition of typical scene is realized by improving clustering algorithm, reduce amount of calculation while ensure rule The reliability and validity of check off fruit.
Brief description of the drawings
Fig. 1 is the intensity of illumination probability density function curve fitted
Fig. 2 is the schematic diagram of multi-C vector
Fig. 3 is the regulating process figure based on sensitivity factor
Fig. 4 is the general procedure block diagram that distributed power source is distributed rationally
Fig. 5 is 10kV circuits height 03 line of dealer
Fig. 6 is the photovoltaic power curve after resource scene analysis
Fig. 7 is the load curve after load scenarios analysis
Each node voltage when Fig. 8 is First Year 12 project period in the case of certain
Fig. 9 is the relation that network loss installs total amount with photovoltaic
Figure 10 is case2 and case3 Pareto optimal solutions contrast
Figure 11 is that the case2 photovoltaics obtained with genetic algorithm install total capacity
Figure 12 is that the case3 photovoltaics obtained with genetic algorithm install total capacity
Figure 13 is the contrast of case2 and case3 photovoltaic capacity configuration
Figure 14 is case2 and case3 operator's accumulated net income present worth contrast
Figure 15 is case2 and case3 operator's annual earnings present worth contrast
Figure 16 is case3 and case4 Pareto optimal solutions contrast
Figure 17 is that the case4 photovoltaics obtained with genetic algorithm install total capacity
Figure 18 is the contrast of case3 and case4 photovoltaic capacity configuration
Figure 19 is case3 and case4 operator's accumulated net income present worth contrast
Figure 20 is case3 and case4 operator's annual earnings present worth contrast
Figure 21 is the power permeability contrast of case2 and case3 distributed power sources
Figure 22 is the power permeability contrast of case3 and case4 distributed power sources
Table 1 is the tactful object function contrasts of case2 and two kinds of case3
Table 2 is the tactful object function contrasts of case3 and two kinds of case4
Embodiment
The present invention will be described with reference to the accompanying drawings and examples.
The present invention is illustrated based on Fig. 1 and Fig. 2 to scene analysis.
Distribution type renewable energy is exerted oneself with randomness and fluctuation, in order to run in view of distributed power source The a variety of situations being likely to occur in journey, it is considered to a variety of possibility of regenerative resource and load condition, herein using improved scene Analytic approach handles the several scenes being likely to occur in distributed power source running.
(1) resource scene is generated
Intensity of illumination has an obvious date periodicity, but on the same period in the same quarter, may be due to day The many factors such as the reason for gas cause intensity of illumination to have certain fluctuation.This fluctuation of intensity of illumination can be utilized Beta probability density functions are described：
In above formula, s represents intensity of illumination, unit kW/m2；fb(s) intensity of illumination s Beta probability density distribution letters are represented Number；α and β are the parameters of probability density function, it is possible to use the average value (μ) and standard deviation (σ) and equation below meter of variable Calculate：
If Fig. 1 is the probability density function curve that fits, intensity of illumination can be divided into some grade G with representing, and And calculate grade k probability P { GkAnd the grade under intensity of illumination mean μ { GkBe respectively：
(2) load scenarios are generated
The generation of the scene of load, is fitted and divides if each node is equally respectively adopted probability density function The method of level, it may appear that larger difficulty.On the one hand, due to the scene enormous amount produced node number, and with node more The increase of number is exponentially increased；On the other hand, each node load scenarios generation after, the load between node have compared with Strong correlation to the probability after each node scene composition using correlation, it is necessary to be corrected, method complexity is relatively difficult to achieve.
For produced problem, the method for proposing typical load scene collection.For the supply load of low-voltage network, due to The periodicity of production activity, payload also has obvious periodicity.Therefore, the whole network of the same quarter similarly hereinafter a period of time discontinuity surface Load has several typical power load distributing sections, herein by improve k-means clustering algorithms, to it is multiple when discontinuity surface under The whole network load vector clusters, obtain typical load typical scene collection.
It is the whole network load condition under the schematic diagram of multi-C vector, certain moment t sections such as Fig. 2, is represented with multi-C vector：
lt={ P1,Q1,P2,Q2,...,Pn,Qn}
Wherein, Pn,QnNode n burden with power power and load or burden without work power is represented respectively；ltRepresent t the whole network fortune The 2n dimensional vectors of row state.
In order to by the load vector clusters at multiple moment obtain typical load scene collection, it is necessary between definition vector away from From traditional k-means is such generally using Euclidean distance or absolute value distance as the definition of distance between two vectors Distance definition, active power and reactive power are treated on an equal basis, but typically more pay attention to wattful power in the planning of power distribution network The calculating of rate, and active power loss assessment, therefore can be to active power and the contribution degree of reactive power in distance definition Different weights are set respectively；On the other hand, empirical tests learn the typical load scene obtained using traditional Euclidean distance cluster Collection reduces compared to true load scenarios, the loss of active power, and with the reduction of typical scene quantity, loss deviation is got over Come smaller, it is therefore desirable to improve the definition of original Euclidean distance.
Original loads scene M and typical load scene collection C are represented with matrix respectively：
Load row vector in M is redefined using weighted euclidean distance, the distance between typical load row vector d into C For:
Wherein ε is directional operator, and τ adjusts the distance the weighted operator of contribution degree to weigh active power and reactive power：
The size for adjusting μ adjusts the position of cluster centre, reduces the error that cluster is brought；Changing v size can be adjusted Active power and reactive power obtain typical load to the contribution degree of distance between vector eventually through k-means iteration Jing Ji.Typical load scene concentrates the probability calculation of each typical scene：
In above formula, nkExpression belongs to the number of cluster k original loads scene, and N represents of all original loads scenes Number.
(3) Run-time scenario is generated
The true running status of power distribution network containing distributed power source is by outside regenerative resource and the close phase of load of network Close, therefore the Run-time scenario Y of the whole network is collectively formed by resource scene G and load scenarios C：
Y={ G, C }
NY=NG*NC
In above formula, NYThe total quantity of Run-time scenario is represented, is the quantity of resource scene and multiplying for typical load scene quantity Product；Run-time scenario YαBy GβAnd CγConstitute, its probability of happening is also by GβAnd CγProbability product determine.
The present invention is illustrated based on Fig. 3 and Fig. 4 to distributed power source Bi-level Programming Models.
Upper strata plan by building multi-objective Model, consider different interests main body and its respective target, below from Object function, three aspects of optimized variable and constraints introduce Multiobjective programming models.
Consider the object function under different interests main body, it is proposed that the network loss based on power distribution network and the operation based on operator Business's net profit is used as object of planning function.
(1) network loss
Power distribution network concerns the operating index of power network, and we are used as optimization aim using network loss here.The meter of year network loss Calculate formula as follows：
In above formula, ElossFor the year loss of electricity of distribution network, NlineFor the bar number of circuit in network, during per Δ t mono- Section, NtFor the total time hop count of 1 year, Plosst,lFor the active power loss on circuit l in the Δ t periods.
(2) operators net profit
What distributed power source operator considered is the income of oneself, and we use the net benefit in present value conduct of operator here Evaluation index, calculation formula is as follows：
Sdg=αtSbenefit-Sinv-αt(Soper-Bre)
In above formula, SdgFor distributed power source annual net income present worth, Sbenefit、Sinv、SoperRespectively distributed power source operation The annual earnings present worth of business, annual investment cost, year operation and maintenance cost, αtIt is discount factor, BreIt is residual value, results from planning The one's last year in cycle, is 0 during remaining time.Its specific formula for calculation is as follows：
Sinv=λdginvPdginstall
Soper=λdgoperPdginstall
In above formula, Pdg,i,tThe active power sent for i-th of distributed power source within the t periods；λdgAccessed for operator Agreement electricity price；λgovFor government subsidy electricity price；λdginvFor the initial outlay expense of the distributed power source of unit capacity；λdgoperFor The operation and maintenance cost of the distributed power source of unit capacity；PdginstallFor the active installed capacity of distributed power source；S, which is represented, to be become Flow the installed capacity of device.Relation between the two can be expressed as：
Q=β × Pdginstall
In above formula, when β is represented distributed power source and run with installed capacity, the nargin and distributed electrical of current transformer reactive power The ratio of the active installed capacity in source, referred to as reactive capability coefficient.
In addition, if it is considered that night load is big, power distribution network node voltage reduction, photovoltaic sends reactive power, does idle benefit Repay, now distributed power source operator can obtain extra profit, calculation formula is as follows：
In above formula, Nt1It is the night photovoltaic idle period exerted oneself, Q in 1 yeardg,i,tIt is i-th of Node distribution formula power supply The reactive power sent in t-th of period.
The active installed capacity of power supply is described as follows as optimized variable in a distributed manner：
X={ Pinstall,1,Pinstall,2...Pinstall,N}
In above formula, N represents that distributed power source installs the total number of node, Pinstall,iRepresent that i-th of distributed power source is active Installed capacity.
Different constraintss are set to plan model：
(1) the distributed power sources installed capacity upper limit is constrained：
Pi≤Pimax i∈Cdg
In above formula, PiRepresent the active installed capacity of i-th of distributed power source, PimaxRepresent i-th of distributed power source most Big installed capacity；CdgRepresent the set of distributed power source.
(2) the discreteness constraint of distributed power sources installed capacity：
Pi=λ Pmin i∈Cdg
In above formula, PminThe minimum active installed capacity of distributed power source is represented, λ is positive integer.
(3) trends equality constraint：
In above formula, PisAnd QisThe active power and reactive power of node i injection are represented respectively；UiFor the voltage amplitude of node i Value；J ∈ i represent all nodes being joined directly together with node i；GijRepresent the real part of bus admittance matrix；BijRepresent node admittance The imaginary part of matrix；θijRepresent the phase angle difference between node i and j.
(4) line transmissions capacity-constrained：
Sj≤Sj,max j∈T
In above formula, SjRepresent circuit actual power；Sj,maxRepresent the maximum allowable capacity of circuit；T is line set.
(5) node voltages are constrained：
Ui,min≤Ui≤Ui,max i∈Nb
In above formula, Ui,maxRepresent the upper limit of node i voltage；Ui,minRepresent the lower limit of node i voltage；NbRepresent set of node Close.
(6) power factors are constrained：
In above formula,The power factor of i-th of distributed power source output is represented,Represent given power factor A reference value.
In order to solve in distributed power source running, fallen due to trend and send asking for caused part of nodes voltage out-of-limit Topic, it is proposed that lower floor's voltage cortrol strategy of distributed power source.
Newton power flow algorithm is the classical calculation of typical power system, and the trend update equation of polar form can be with It is expressed as：
Near stable flow solution, Jacobi's matrix in block form HNML can react the variable quantity pair of active power and reactive power The influence of voltage magnitude and voltage phase angle, only changing active power or reactive power respectively can obtain：
Δ V=(N-HM-1L)-1Δ P=Ap·ΔP
Δ V=(L-MH-1N)-1Δ Q=Ap·ΔQ
In above formula, APAnd AQActive voltage sensitivity and reactive voltage sensitivity matrix are represented respectively.
Numerical value is of different sizes in sensitivity matrix, represents diverse location node to the voltage influence of same node not Together, the size of this influence can be weighed in certain error range with sensitivity factor.
If Fig. 3 is the regulating process figure based on sensitivity factor, for the voltage-regulation of more preferable Optimum distribution formula power supply Effect, reduces the reduction of power, it is necessary to according to electricity of each node to each node of the regulation contribution rate reasonable distribution of voltage Regulated quantity is pressed, therefore, it can set the voltage-regulation amount of each node to be proportional to sensitivity factor, for out-of-limit node m, is set Each the power adjusting amount of node is：
Therefore, for known voltage deviation:
Thus, it is possible to try to achieve proportionality coefficient k：
In above formula, ρiIt is a Boolean quantity, if distributed power source is installed in node i, and distributed power source has Regulating power, then ρi=1, it is otherwise 0.
Fig. 4 is the general procedure block diagram that distributed power source is distributed rationally, and the non-dominated ranking heredity containing elitism strategy is calculated Method (NSGA-II) is a kind of effective ways for solving Multiobjective Optimization Problem.The core of multi-objective genetic algorithm is to coordinate each Relation between individual object function, finds out the optimal solution set [20] for making each object function compromise.Since Pareto optimal solution Concept is used to after calculating individual adaptation degree, and this method that solution is carried out divided rank according to the degree dominated is just wide General application.Non-dominated sorted genetic algorithm with elitism strategy has three main parts, as follows：
(1) quick non-dominated ranking
This is a kind of algorithm being ranked up according to non-dominant to whole population P, and individual is determined by object function Between non-dominant relation, be multiple level Rank by population dividing, the individual in each level has equivalent non-dominant.
(2) crowding is calculated
In order to understand each by the degree of crowding of all solutions on dominance hierarchy, it is necessary to calculate adjacent target function between Distance is used as the crowding distance Distance between individual, and the otherness of the bigger explanation individual of crowding distance is bigger, fitness Better.
(3) comparison operation
By (1) (2), each individual in population P is owned by two parameters of Rank and Distance, comparison operation according to The size of the two parameter decision individual adaptation degrees：Individual non-dominant smaller Rank is stronger, and fitness is big；Same level Two individuals, Distance is bigger, and individual adaptation degree is bigger.
The present invention is illustrated based on Fig. 5 to Figure 20 and table 1- tables 2 to embodiment.
The present invention is using the circuit in Fig. 5 as example, and the circuit is that Jinzhai County 35kV iron punching in Anhui Province's becomes next 10kV Circuit --- height 03 line of dealer, using this situational contrastive, analyzes the shadow to distributed photovoltaic installed capacity under different strategies Ring.Height dealer's a total of 28 nodes of 03 line, 27 circuits, its interior joint 1 is the low-pressure side bus node of transformer, rated voltage For 10.5kV.According to local load level, and soil, Roof Resources, we intend selection line node 3,7,9,13, 16th, 18,21,24,25 this 9 points install photovoltaic.
Fig. 6 and Fig. 7 are resource scene analysis result and load scenarios analysis result respectively.Resource scene refers to light in Fig. 6 The power factor of volt, the maximum active power that its expression photovoltaic can be sent accounts for the ratio of photovoltaic installed capacity, each hour There is the power factor of a photovoltaic.The power factor of each photovoltaic in each hour is regarded as identical.Here it is each when The resource scene for carving (1h) is compressed into 2 classes.Load scenarios analysis result in Fig. 7, here each moment (1h) load scenarios It is compressed into 3 classes.Run-time scenario is combined by resource scene and load scenarios, and thus the Run-time scenario of each moment (1h) has 6 classes, resource scene and load scenarios are separate, so the probability of each scene is by constituting the probability of its resource scene and bearing The probability multiplication of lotus scene is obtained.The Run-time scenario at each moment (1h) is 6 kinds, 24 hours one day, so one day a total of 144 The situation of kind, abscissa is 144.In calculating target function, the object function of each moment (1h) 6 kinds of scenes is obtained respectively, so Weighted average is carried out afterwards and asks expectation, obtains the object function at this moment.
Case1 does not install photovoltaic
The scene of photovoltaic is not installed first, the circuit now calculated year network loss is 686.1784MWh in consideration circuit.Figure Each node voltage distribution under 8 certain scene when being First Year 12 project period.Such as Fig. 8, when no access distributed photovoltaic, this When line node voltage according to trend distribution be gradually reduced, first node is fixed as perunit value 1.05, further away from the voltage of first node It is smaller, therefore the minimum point of voltage is exactly the point farthest from first node on circuit.No. 1 node, that is, first node voltage mark One value is 1.05, because load is heavier on basic routing line, and 16 and 28 nodes are in the end of circuit, and their magnitude of voltage is minimum.
In order to reasonably carry out the planning of distributed power source, it is necessary to substantially know that network loss and photovoltaic install the pass between total amount System.In order to obtain this conclusion, the photovoltaic installed capacity of each photovoltaic mount point is gradually increased from 0, in the case of calculating is various Network loss, so as to find the relation that network loss and photovoltaic install total amount.As shown in figure 9, network loss installs the increase of total amount with photovoltaic The trend for first reducing and increasing afterwards is showed, curve is a parabola, when photovoltaic installs total amount in 4000kW or so, network loss reaches To minimum.When being fitted without photovoltaic, network loss is higher, is become larger with photovoltaic capacity is installed, network loss is gradually reduced, because Photovoltaic is installed equivalent to load is reduced, the voltage of node has been raised, by square being obtained in inverse ratio for network loss and voltage, net Damage is gradually reduced, but after declining to a certain extent, network loss can be raised again, because the photovoltaic capacity installed is higher than line , there is trend and falls situation about sending, trend, which is fallen, send caused network loss at a relatively high in the capacity that road can dissolve, so circuit network loss increases Greatly.Therefore when carrying out photovoltaic capacity configuration, should try one's best selection value near parabola minimum point.
case2：Without pressure regulation strategy and case3：The contrast of pressure regulation strategy
As shown in Figure 10, the Bi-objective in Pareto optimal solutions --- network loss and operator's net profit are conflicts, are matched somebody with somebody The reduction of grid net loss means the reduction of operator's net profit, and the increase of operator's net profit means the increasing of distribution network loss Greatly, because operator and network loss need compromise chosen.Figure 10 is the contrast of case2 and case3 Pareto optimal solutions, The Pareto curved surfaces of case3 optimal solution composition are moved upward compared with case2.When with identical operator net profit When, network loss of the case3 than case2 is low；When with identical network loss, operator net profit of the case3 than case2 is high.Phase With operator's net profit when, network loss of the case3 than case2 is low, because operator's net profit is identical, photovoltaic installed capacity Identical with exerting oneself, the problem of now solving voltage out-of-limit using the case3 of pressure regulation strategy reduces voltage, reduces network loss；It is identical Network loss when, net profit of the case3 than case2 is high, because during identical network loss, installed capacitys of the case3 than case2 Greatly, photovoltaic is exerted oneself greatly.Without using the case2 of pressure regulation strategy, installed capacity is less than normal, without rational estimating system load for light The digestion capability of volt, so being unfavorable for the development of distributed power source.As is illustrated by figs. 11 and 12, case3 optimal solution is corresponding The scope of planned capacity is between 5800kW-6200kW, and the scope of the corresponding planned capacity of case2 optimal solutions is in 4000kW- 4400kW。
In order to be preferably compared to case2 and case3, two groups of individuals of selection network loss identical, two kinds of tactful light Lie prostrate installed capacity configuration as shown in figure 13.Case2 total photovoltaic installed capacity is 4120kW, case3 total photovoltaic installed capacity For 5860kW, case2 photovoltaic installs total capacity and is far smaller than case3.As shown in Table 1, when network loss is identical, pressure regulation strategy institute The operator's net benefit in present value brought is bigger than operator's net benefit in present value of not pressure regulation, and this explanation pressure regulation strategy is than not pressure regulation plan Slightly advantageously.
Shown in Figure 21, the photovoltaic power permeability at night is all 0.Because case3 photovoltaic installed capacity is significantly larger than Case2, so in the case where daylight voltage is not out-of-limit, the active power of case3 photovoltaic output is greater than case2, system Load is constant, so case3 photovoltaic power permeability is more than case2；In the case of voltage out-of-limit, case3 uses pressure regulation Strategy carries out pressure regulation, and the active power of now case3 outputs should be roughly the same with case2, this part case3 photovoltaic infiltration Rate is similar with case2.
Figure 14 is operator's accumulated net income present worth contrast in the case of two kinds of case2, case3.Because case3 photovoltaic is pacified Dressing amount is bigger than case2, and initial outlay cost is higher, year running cost it is bigger, so investment early stage accumulated net income show Value is smaller.But as shown in Figure 15, because case3 photovoltaic installed capacity is bigger, more, the operator year receipts so photovoltaic is exerted oneself Beneficial present worth is more, this be also case3 in last project period operator's net benefit in present value it is higher than case2 the reason for.Can by Figure 14 Know, case2 is that, in the 11st year project period of cost-recovering, case3 is in the 12nd year project period of cost-recovering, at the 14th year Case3 operator's accumulated net income present worth has exceeded case2.
case4:With reactive-load compensation pressure regulation strategy and case3:Simple pressure regulation strategy contrast
Figure 16 is the contrast situation of case3 and case4 Pareto optimal solutions, it is known that case4 optimal solution composition Pareto curved surfaces compared with case3, be moved upward.When with identical operator net profit, case4 is than case3's Network loss is low；When with identical network loss, operator net profit of the case4 than case3 is high.Because case3 during daytime Identical pressure regulation strategy is taken with case4, and network loss is identical with operator net profit.When night, load weight, node voltage compared with Low, current transformer does reactive-load compensation, sends idle, has suitably raised node voltage, and loss and voltage square are in inverse ratio, now net Damage and reduce, being simultaneously emitted by reactive power can be subsidized, so operator's income can increase.So not considering that interconnection falls to send When, it is better using pressure regulation strategy than simple using the pressure regulation strategy with reactive power support.As shown in Figure 17 and Figure 12, case4 rule Total capacity is drawn between 5600kW-6100kW, this is suitable with case3 planning total capacity 5800kW-6200kW quantity.
In order to preferably compare case3 and case4 power grid operation parameter and operator's income, case3 and case4 is selected Two groups of total capacity identical is planned, two kinds of tactful photovoltaic installed capacity configurations are as shown in figure 18.Although specific to each installation The installed capacity of point photovoltaic may be different, but case3 and case4 installation total capacity is all 5860kW.As shown in Table 2, when When installation total capacity is identical, case4 network loss is smaller than case3, case4 operation of operator's net benefit in present value than case3 Business's net benefit in present value is big, when this explanation does not consider that interconnection falls to send, using the pressure regulation strategy with reactive power support than simple use Pressure regulation strategy is good.
As shown in figure 22, photovoltaic is not exerted oneself during night, and the power permeability of two kinds of situations is all 0.On daytime, two kinds of situations Power permeability curve can also regard approximately the same as because two kinds of situations all employ identical pressure regulation side on daytime Formula, at noon 12 when near, power permeability, which is both greater than 1, case3 and has been up to 1.8, case4, has been up to 1.6.This Illustrate that system loading is not dissolved the active power of photovoltaic output, now occurs that dominant eigenvalues fall situation about sending.
As shown in Figure 19 and Figure 20, since project period First Year, case4 accumulated net income present worth is just more than case3. Because case3 is identical with case4 installed capacity, initial outlay cost is just identical, but case4 nights current transformer is carried out Reactive-load compensation, sends reactive power, can be subsidized, the reason for this is also more than operator's annual earnings of the case4 than case3. Case3 and case4 be all in the 12nd year cost-recovering, and accumulated net income present worth reached on the occasion of.
Plan that conclusion is as follows：
(1) using pressure regulation strategy compared with not using pressure regulation strategy, the installed capacity increase of distributed power source, this is conducive to The flexible dispatching of distributed power source and control.Object function is optimized, the increase of operator's income, identical fortune during identical network loss Network loss is reduced when seeking business's income, the lifting of power permeability level.
(2) using the pressure regulation strategy with reactive-load compensation compared with using pressure regulation strategy merely, distributed power source installation is held Amount level is identical, and power permeability is identical, and distribution network loss reduces, the income increase of distributed power source operator, realizes doulbe-sides' victory Situation.
To sum up,
The present invention is directed to based on the distributed power source Method for optimized planning for improving scene analysis method and voltage sensibility pressure regulation, There is advantages below compared with prior art：
(1) present invention considers resource and load respectively, is handled respectively using different methods：For the mould of load section The characteristics of formula is stronger, obtains typical load scene using clustering, on the one hand ensure that the correlation between load, another Aspect greatly reduces scene quantity, improves computational efficiency and precision.For resource scene, it is fitted using probability density function And the method for divided rank.
(2) consider that different Interest Main Bodies set up different object functions：As power market reform dynamics constantly increases, Sales market is constantly being decontroled, and distributed power source investment is also being continuously increased, distributed power source operator and power distribution network company Constitute the Interest Main Body in power distribution network.The operator of distributed power source mainly considers self benefits, it is desirable to access to greatest extent Distributed energy；And power distribution network company is more concerned about the operating index of power network, whether the main network loss including power distribution network reduces, and saves In the reasonable scope whether, whether the power supply reliability of user improves point voltage.The planning of distributed power source must stand in both sides Angle on weigh multiple targets.With the increase of distributed power source permeability, it is out-of-limit that trend falls to send to be easily caused node voltage, The regulating power of reasonable consideration distributed power source, is conducive to improving the energy permeability of distributed power source, improves the operation of power network Level.
(3) consider trend fall to send caused by part of nodes voltage out-of-limit the problem of, it is proposed that distributed power source based on spirit The voltage cortrol strategy of sensitivity：Because position of the different installation nodes in power network topology is different, therefore for the tune of voltage Section has a different contributions, therefore needs in voltage out-of-limit according to different each distributed power source of reasonable disposition of contribution degree The regulated quantity of reactive power and active power so that voltage is maintained at rational range of operation, while ensureing distributed electrical as far as possible The income of source operator.
Table 1
Strategy Case2 Case3
Year network loss (MWh) 426.69 426.73
Operator's net benefit in present value (member) 3900w 4800w
Table 2
Strategy Case3 Case4
Year network loss (MWh) 426.73 391.97
Operator's net benefit in present value (member) 4800w 5200w

Claims (1)

1. a kind of distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy, comprises the following steps：
Step one：Scene analysis is carried out using scene analysis method, the true of distributed power source and load is characterized using high dimension vector Running status, the compression of scene is realized by clustering method, and step is as follows：
(1) resource scene is generated
The fluctuation of intensity of illumination is utilized into Beta probability density functions fb(s) describe；
Intensity of illumination is classified and represented, for grade k, grade k probability P { G is calculatedkAnd the grade under intensity of illumination Mean μ { Gk}；
(2) load scenarios are generated
By k-means clustering algorithms, to it is multiple when discontinuity surface under the whole network load vector clusters, obtain typical load typical field The whole network load condition under Jing Ji, certain moment t sections, uses multi-C vector ltRepresent：
lt={ P1,Q1,P2,Q2,...,Pn,Qn}
Wherein, Pn,QnNode n burden with power power and load or burden without work power is represented respectively；
For the load vector clusters at multiple moment are obtained into typical load scene collection, it is necessary to the distance between definition vector, away from From different weights are set respectively to the contribution degree of active power and reactive power in definition, the weight of reactive power is less than active The weight of power, calculates the distance between vector using the Euclidean distance after this weighting, passes through k-means clustering algorithm iteration Typical load scenarios collection is obtained, typical load scene concentrates the probability calculation of each typical scene：
<mrow> <mi>P</mi> <mo>{</mo> <msub> <mi>C</mi> <mi>k</mi> </msub> <mo>}</mo> <mo>=</mo> <mfrac> <msub> <mi>n</mi> <mi>k</mi> </msub> <mi>N</mi> </mfrac> </mrow>
In formula, nkExpression belongs to the number of cluster k original loads scene, and N represents the number of all original loads scenes.
(3) Run-time scenario is generated
The true running status of power distribution network containing distributed power source is closely related by the load of outside regenerative resource and network, entirely The Run-time scenario Y of net is collectively formed by resource scene G and load scenarios C：
Y={ G, C }
NY=NG*NC
<mrow> <msub> <mi>P</mi> <msub> <mi>Y</mi> <mi>&amp;alpha;</mi> </msub> </msub> <mo>=</mo> <msub> <mi>P</mi> <msub> <mi>G</mi> <mi>&amp;beta;</mi> </msub> </msub> <mo>*</mo> <msub> <mi>P</mi> <msub> <mi>C</mi> <mi>&amp;gamma;</mi> </msub> </msub> <mo>;</mo> <msub> <mi>Y</mi> <mi>&amp;alpha;</mi> </msub> <mo>=</mo> <mo>{</mo> <msub> <mi>G</mi> <mi>&amp;beta;</mi> </msub> <mo>,</mo> <msub> <mi>C</mi> <mi>&amp;gamma;</mi> </msub> <mo>}</mo> </mrow>
In formula, NYThe total quantity of Run-time scenario is represented, is the quantity N of resource sceneGWith typical load scene quantity NCProduct； Run-time scenario YαBy resource scene GβWith load scenarios CγConstitute, its probability of happening is also by GβAnd CγProbability product determine.
Step 2：Power distribution network distributed power source dual-layer optimization planing method is set up, step is as follows:
(1) network loss, the income of maximization distributed power source operator of upper strata planning to minimize power distribution network etc. builds multiple target, Plan the access capacity of distributed power source.
(2) different scheduling strategies are taken in lower floor's scheduling model, not using pressure regulation strategy, using pressure regulation strategy and carried The pressure regulation strategy of reactive-load compensation is contrasted, and distributed power source is planned.
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Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108197837A (en) * 2018-02-07 2018-06-22 沈阳工业大学 Photovoltaic generation Forecasting Methodology based on KMeans clusters
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CN111563691A (en) * 2020-05-18 2020-08-21 国网山西省电力公司经济技术研究院 Performance evaluation method for AC/DC hybrid power distribution network accessed with new energy
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN104376410A (en) * 2014-11-06 2015-02-25 国家电网公司 Planning method for distributed power source in power distribution network
CN104600713A (en) * 2014-12-25 2015-05-06 国家电网公司 Device and method for generating day-ahead reactive power dispatch of power distribution network containing wind/photovoltaic power generation
CN106230026A (en) * 2016-08-30 2016-12-14 华北电力大学（保定） The power distribution network bilayer coordinated planning method containing distributed power source analyzed based on temporal characteristics
CN106253335A (en) * 2016-06-16 2016-12-21 上海交通大学 A kind of distributed power source capacity and on-position uncertain distribution network planning method
US20170083608A1 (en) * 2012-11-19 2017-03-23 The Penn State Research Foundation Accelerated discrete distribution clustering under wasserstein distance

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170083608A1 (en) * 2012-11-19 2017-03-23 The Penn State Research Foundation Accelerated discrete distribution clustering under wasserstein distance
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
CN104376410A (en) * 2014-11-06 2015-02-25 国家电网公司 Planning method for distributed power source in power distribution network
CN104600713A (en) * 2014-12-25 2015-05-06 国家电网公司 Device and method for generating day-ahead reactive power dispatch of power distribution network containing wind/photovoltaic power generation
CN106253335A (en) * 2016-06-16 2016-12-21 上海交通大学 A kind of distributed power source capacity and on-position uncertain distribution network planning method
CN106230026A (en) * 2016-08-30 2016-12-14 华北电力大学（保定） The power distribution network bilayer coordinated planning method containing distributed power source analyzed based on temporal characteristics

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
AMR A.MUNSHIAB等: "Photovoltaic power pattern clustering based on conventional and swarm clustering methods", 《SOLAR ENERGY》 *
袁简: "有源配电网中分布式电源及储能的配置方法硏究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

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CN111563691A (en) * 2020-05-18 2020-08-21 国网山西省电力公司经济技术研究院 Performance evaluation method for AC/DC hybrid power distribution network accessed with new energy
CN111563691B (en) * 2020-05-18 2023-08-04 国网山西省电力公司经济技术研究院 Performance evaluation method of AC/DC hybrid power distribution network connected with new energy
CN112418715A (en) * 2020-12-11 2021-02-26 广西电网有限责任公司 Method and device for generating wind power sequence scene set and storage medium
CN112736905A (en) * 2020-12-25 2021-04-30 浙江华云电力工程设计咨询有限公司 Regional new energy capacity optimal configuration method and system
CN113991742A (en) * 2021-11-19 2022-01-28 国网重庆市电力公司 Distributed photovoltaic double-layer collaborative optimization investment decision method for power distribution network
CN113991742B (en) * 2021-11-19 2024-04-16 国网重庆市电力公司 Distributed photovoltaic double-layer collaborative optimization investment decision-making method for power distribution network

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