CN108155649A - A kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG - Google Patents
A kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG Download PDFInfo
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
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Abstract
The invention discloses a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG, include the following steps：According to the uncertainty of distributed generation resource, a kind of distributed generation resource output model and two class distributed generation resource output models are built；For the two classes distributed generation resource output model, using the constraint of distribution network topological structure, node voltage is not out-of-limit, permeability constrains, system power Constraints of Equilibrium is constraints, establishes the distribution network structure object of planning function containing distributed generation resource based on fuzzy expected value；For the fuzzy expected value, calculated and obtained using Fuzzy Power Flow；It is solved using genetic algorithm, obtains the distribution network structure fuzzy programming of distributed generation resource.The present invention realizes the fuzzy programming of rack containing distributed power distribution network according to the uncertainty of distributed generation resource, highly practical convenient for using genetic algorithm progress optimizing using Average Annual Cost fuzzy expected value as target.
Description
Technical field
The present invention relates to distribution network structure planing method, more particularly, to a kind of consideration probabilistic distribution network structures of DG
Fuzzy Programming.
Background technology
At present, fossil energy is gradually substituted with regenerative resource, promotes share of the clean energy resource in primary energy supply,
Energy transition is pushed, construction cleaning low-carbon, safe and efficient modern energy system are the main targets of China's energy revolution.《Energy
Source production and consumption revolutionary strategy (2016-2030)》It proposes to the year two thousand thirty, non-fossil energy generated energy accounts for the ratio of whole generated energy
Gravity, which is striven, reaches 50%.The important component that distributed power generation generates electricity as clean energy resource, institute's accounting in electric system
It will be significantly increased again, and a large amount of distributed generation resource access will bring severe challenge to power distribution network development.
Containing distributed generation resource (referred to as：DG distribution network structure planning) is a multiple target combinatorial optimization problem, is being planned
It needs to handle various uncertain factors in the process, just distribution network structure plan model can be made to be more nearly reality.Fuzzy mathematics
With the ability of its powerful processing uncertain information, the pro-gaze of expert and scholar are received, is achieved perhaps in terms of theoretical research
More substantive progress, and be applied widely in field of power.In terms of Load flow calculation, described using fuzzy set
A series of uncertainty of load and distributed generation resource, it is proposed that Fuzzy Power Flow New calculating methods.Fuzzy tide in Electric Power Network Planning
Stream calculation [J] (Zhang Yan, Chen Zhang tide Automation of Electric Systems, 1998,22 (3)：Load 20-22.) is represented using Triangular Fuzzy Number
And the output of generator, it is proposed that suitable for the fuzzy DC power flow algorithm of power transmission network.Power distribution network fuzzy optimization planning (I) ---
Model and method [J] (brings up flat, Li Jing rosy clouds Automation of Electric Systems, 2002,26 (14)：45-48.), matching containing distributed generation resource
Power grid fuzzy optimization planning [J] (the Automation of Electric Systems such as Yang Yi, Wei Gang, all ice, 2010,34 (13)：19-23.) etc. texts
It offers, in terms of distribution network planning, it is proposed that the planing method of many processing fuzzy messages.
A kind of planing method (CN101764407A) of power distribution network based on fuzzy expected value model is invested with power distribution network year
The fuzzy expected value of operating cost, the i.e. fuzzy expected value of year investment cost and cost of losses are optimization aim, with power distribution network year
Investment cost fuzzy expected value and year reimbursement for expenses fuzzy expected value optimize benefit to evaluate power distribution network.According to known power distribution network
Projecting parameter and planning region original net rack data and various alternative item datas, wherein representing load using fuzzy variable
Predicted value represents various alternative options using 0-1 variables, establishes the Optimal Planning for Distribution model based on fuzzy expected value, and
It is solved using genetic algorithm.The present invention can largely reduce subsequent compensation investment, reduce loss and waste.It meanwhile should
Method considers the various factors of distribution network planning, includes whether newly-built substation, if increase existing substation capacity and
Whether replace with new route etc..
Distribution network structure planing method [J] (electric system such as Yan Jiong, Wan Tao, Li Haosong of meter and uncertain factor
Protection and control, 2017,45 (18)), in order to distribution network architecture planning fall into a trap and distributed generation resource contribute with load not
Deterministic influence, it is proposed that fuzzy programming method states the uncertainty of distributed generation resource output, and profit by Triangular Fuzzy Number
With confidence level the Theory Construction distribution network structure plan model, it is proposed that a kind of fast-growth tree algorithm forms the first of system to be planned
The beginning network architecture recycles genetic algorithm to be adjusted optimizing to initial rack.The planing method does not go out for distributed generation resource
The uncertainty of power is classified, and targetedly establishes distribution network structure plan model, and the conclusion obtained is inaccurate, with reality
Border situation has certain gap.
But the planning of distributed generation resource is all depended on distribution network planning by most of planing methods, and distributed generation resource is planned
The planning that is uniformly coordinated with distribution network structure planning considers to be not enough.When being planned using uncertain method, need
Certain choice is done at comprehensive two aspects that the complexity and problem of model consider, how not to be known distributed generation resource
Property, which is applied in extensive practical problem, to be needed to further investigate.
According to the uncertainty of distributed generation resource, distributed generation resource is divided into two classes：A kind of distributed generation resource, mainly includes
Wind-power electricity generation and photovoltaic generation etc., the position of access, capacity have carried out planning of science activities, contribute prediction and control strategy compared with into
Ripe, generated output has certain certainty；Two class distributed generation resources, including wind-power electricity generation, photovoltaic generation and biomass power generation
Distributed generation resource built in the range of capacity of limitation according to itself wish and demand Deng, user, capacity is built and distribution has
Have an apparent uncertainty, generated output it is difficult to predict.
Invention content
In view of this, consider that DG is probabilistic matches in view of the deficiencies of the prior art, it is an object of the present invention to provide a kind of
Net Frame of Electric Network Fuzzy Programming establishes two kinds of distributed generation resource output models, the structure on the basis of fuzzy expected value theory
Distribution network structure Fuzzy Programming Model is built, is solved using genetic algorithm, realizes and considers that distributed power distribution network rack obscures rule
It draws.
In order to achieve the above objectives, the present invention uses following technical scheme：A kind of consideration probabilistic distribution network structures of DG
Fuzzy Programming includes the following steps：
(1) according to the uncertainty of distributed generation resource, a kind of distributed generation resource output model and two class distributed electricals are built
Source output model：One kind distributed generation resource output model is using deterministic models, the two classes distributed generation resource output mould
Type is modeled using fuzzy mathematics method；
(2) for two class distributed generation resource output model described in step (1), with the constraint of distribution network topological structure, section
Point voltage is not out-of-limit, permeability constrains, system power Constraints of Equilibrium is constraints, establishes and contains distribution based on fuzzy expected value
The distribution network structure object of planning function of formula power supply；
(3) it for the fuzzy expected value described in step (2), is calculated and obtained using Fuzzy Power Flow；
(4) it is solved using genetic algorithm, obtains the distribution network structure fuzzy programming of distributed generation resource.
Further, in the step (1), one kind distributed generation resource output model is：
Pi,grid=nPb
PI, grid≤Pimax,grid
Wherein, Pi,gridFor the capacity of distributed generation resource, PbFor minimum basic capacity, n is the integer more than or equal to 0,
Plim,gridFor the permeability of a kind of distributed generation resource, PL,iLoad for node i.
Further, in the step (1), the two classes distributed generation resource output model is：
Pj,cus=Plim,cus×PL,j
Plow=0
Wherein, Phig, PlowRespectively node j punishes the highest and minimum of cloth power supply capacity, Plim,cusFor two classes point
The permeability of cloth power supply, PL,jFor node j loads.
Further, in the step (2), the rule of the distribution network structure containing distributed generation resource based on fuzzy expected value
Draw object function：
Wherein, E is fuzzy expected value operator；γ is fixed investment Average Annual Cost coefficient；I is earning rate；N is planning week
Phase/year；L1To create the set of feeder line；AjFixed investment expense for circuit j；L2Distributed generation resource may be built for power grid
The set of node；njPbCapacity for distributed generation resource of the power grid at node j；cjFor unit capacity distributed generation resource capital cost
With；PbFor minimum basic capacity；xj, njFor decision variable, xj0/1,1 expression is taken to build this circuit, 0 represents not build this
Circuit, njThe integer more than or equal to 0 is taken, represents the size of the distributed generation resource capacity of power grid construction；
Wherein, P is unit electricity price；τiHourage is lost for branch i annual peak loads；PilossFuzzy network loss for branch i；
N is branch sum in network；
The permeability is constrained to：
The node voltage constraint：
Umin≤Uj≤Umax(j=1,2 ..., m)；
The distribution network topological structure constraint：The rack of planning should be radial structure；
Wherein, Plim,gridFor power grid construction distributed generation resource permeability；PL,jFor the load of node j, m is that node j is corresponding
Number of network node.
Further, in the step (3), the Fuzzy Power Flow algorithm is calculated for the power distribution network Fuzzy Power Flow based on boundary value
Method, the specific steps are：
1) the left margin S of load obscurity number is takenLLiAnd the right margin S of distributed generation resource output fuzzy numberGLiTrend is calculated, is obtained
To the left boundary value P of Line FlowLi, network loss left boundary value Δ PLi, node voltage right boundary value URi；
2) the central value S of load obscurity number is takenLDiAnd the central value S of distributed generation resource output fuzzy numberGDiTrend is calculated, is obtained
To the central value P of Line FlowDi, network loss central value Δ PDi, node voltage central value UDi；
3) the right margin S of load obscurity number is takenLRiAnd the left margin S of distributed generation resource output fuzzy numberGRiTrend is calculated, is obtained
To the right boundary value P of Line FlowRi, network loss right boundary value Δ PRi, node voltage left boundary value ULi；
4) after the central value of Fuzzy Power Flow distribution and left and right boundary value is obtained, it is assumed that Fuzzy Power Flow result is approximately three
Angle fuzzy number, load Triangular Fuzzy Number are (SLLi,SLDi,SLRi), distributed generation resource output is expressed as (S with Triangular Fuzzy NumberGLi,
SGDi,SGRi), the desired value of Triangular Fuzzy Number is calculated, and then obtain the fuzzy expected value of object function.
Further, in the step 4, the genetic algorithm includes：
Fitness function：Fitness of the Average Annual Cost desired value as genetic algorithm is subtracted using a larger positive number
Function, the fitness function are：
Wherein, Z is a positive number for being more than total cost desired value；
Coding：Using genetic algorithm integer coding form, integer coding string is divided into two parts：Space truss project coded portion
Coded portion is planned with a kind of distributed generation resource；
Genetic operator：Fitness highest individual is directly reserved to the next generation in selection operation, the selection of rest part is adopted
With roulette strategy.
Further, the permeability of a kind of distributed generation resource is：
The output factor of one kind distributed generation resource is 1.
Further, the permeability of the two classes distributed generation resource is：
For some node in planning region, the variation range of the total capacity of the two classes distributed generation resource is 0 to the node
Maximum capacity.
Further, the arrangement of interconnection is not considered in the cataloged procedure.
Further, Pb=100kW.
Further, use the above method carry out the distribution network structure fuzzy programming flow containing distributed generation resource for：
(a) it determines the maximum size of distributed generation resource, carries out distributed generation resource modeling；
(b) initial rack is obtained using Kruskal algorithms, determines the coding structure；
(c) initial population is formed；
(d) the application genetic algorithm obtains distribution network structure optimization planning.
The beneficial effects of the invention are as follows：A kind of consideration probabilistic distribution network structure Fuzzy Programming needles of DG of the present invention
To the uncertain size of distributed generation resource, distributed generation resource is divided into a kind of distributed generation resource and two class distributed generation resources, and
Establish two kinds of distributed generation resource output models.Two class distributed generation resource capacity are handled using fuzzy expected value model not knowing to ask
Topic, establishes the distribution network structure plan model containing distributed generation resource based on fuzzy expected value, the phase is obscured with Average Annual Cost
Prestige is worth minimum object function, is constrained with permeability, node voltage, network topology about with trend balance are constraints, uses
The tidal current computing method of power distribution network Fuzzy Power Flow algorithm and fuzzy expected value theory based on boundary value is merged, utilizes genetic algorithm
Integer coding mode is improved to solve.
The feasibility and practicability of the method provided by the present invention are shown by simulation example.Simulation example shows disregarding
Distributed generation resource investment cost, the corresponding Average Annual Cost fuzzy expected value of optimal case provided by the invention is smaller, power distribution network
Investment has apparent reduction with operating cost；The access of distributed generation resource is conducive to alleviate distribution network load pressure, is conducive to save
The lifting of point voltage, has some improvement to system power supply, particularly evident for feeder terminal node.
Planing method proposed by the present invention can fully consider the uncertain problem that user distribution formula power supply is contributed, and realization contains
Distributed power distribution network rack fuzzy programming, using Average Annual Cost fuzzy expected value as target, convenient for utilizing genetic algorithm
Optimizing is carried out, it is highly practical.
Description of the drawings
Fig. 1 is two class distributed generation resource output model membership function curves of the invention；
Fig. 2 is the flow chart of space truss project containing distributed power distribution network of the invention；
Fig. 3 is simulation example area to be planned of the present invention；
Fig. 4 is the initial grid structure of simulation example of the present invention；
Fig. 5 is simulation example distribution network structure containing distribution power program results of the present invention；
Fig. 6 is distributed for node voltage before and after simulation example distributed generation resource of the present invention access.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
According to the degree of uncertainty of distributed generation resource, distributed generation resource is divided into two classes：A kind of distributed generation resource, mainly
Including wind-power electricity generation and photovoltaic generation etc., the position of access, capacity have carried out planning of science activities, contribute prediction and control strategy compared with
Maturation, generated output has certain certainty, using deterministic models；Two class distributed generation resources, including wind-power electricity generation, photovoltaic
Power generation and biomass power generation etc., user builds distributed generation resource according to itself wish and demand in the range of capacity of limitation,
Construction capacity and distribution have apparent uncertainty, and it is difficult to predict modeled generated output using fuzzy mathematics method.
Distributed generation resource access capacity it is bigger, contribute it is bigger, the influence to power distribution network is bigger.Using《Genetic
algorithms in optimal multistage Distribution network planning》(Vladimiro
Miranda, J V Ranito, L M Proenca.Genetic algorithms in optimal multistage
Distribution network planning [J] .IEEE Trans.on Power Systems, 1994,9 (4):895-
900.) permeability of the distributed generation resource defined, such as following formula：
A kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG, include the following steps：
(1) according to the uncertainty of distributed generation resource, a kind of distributed generation resource output model and two class distributed electricals are built
Source output model.
A kind of distributed generation resource output model
For simplifying the analysis, it is assumed that integral multiple of the capacity of single distributed generation resource for 100kW in a network, distributed electrical
The output factor in source is 1.One kind distributed generation resource output model is as follows：
Pi,grid=nPb (2)
PI, grid≤Pimax,grid (3)
Wherein, Pi,gridFor the capacity of distributed generation resource, Pb=100kW is minimum basic capacity, and n is more than or equal to 0
Integer, Plim,gridFor the permeability of a kind of distributed generation resource, PL,iLoad for node i.
Two class distributed generation resource output models
If each user individually builds distributed generation resource, power grid only sets one accordingly to the distributed generation resource of user's access
Permeability value.For some node in planning region, the variation range of the total capacity of the two classes distributed generation resource is arrived for 0
The maximum capacity of the node establishes two class distributed generation resource Triangular Fuzzy Number models.The two classes distribution power model is as follows
It is shown：
Pj,cus=Plim,cus×PL,j (6)
Plow=0 (7)
Wherein, Phig, PlowRespectively node j punishes the highest and minimum of cloth power supply capacity, Plim,cusFor distribution
The permeability of power supply, PL,jFor node j loads.
The two classes distributed generation resource output model membership function curve is as shown in Figure 1.
(2) it for two class distributed generation resource output model described in step (1), establishes and distribution is contained based on fuzzy expected value
The distribution network structure object of planning function of formula power supply.
Fuzzy expected value theory can handle the fuzzy uncertain sex chromosome mosaicism in planning problem well, for sentencing for optimal solution
One good foundation is provided surely.For the uncertainty of the two classes distributed generation resource, cannot function as controlling in optimization process
Variable processed represents the output of the two classes distributed generation resource using fuzzy number.With network topology structure, node voltage it is not out-of-limit,
Network power balance etc. is constraints, establishes the distribution network structure object of planning containing distributed generation resource based on fuzzy expected value
Function：
In formula：E is fuzzy expected value operator；γ is fixed investment Average Annual Cost coefficient；I is earning rate；N is planning week
Phase/year；L1To create the set of feeder line；AjFixed investment expense for circuit j；L2Distributed generation resource may be built for power grid
The set of node；njPbCapacity for distributed generation resource of the power grid at node j；cjFor unit capacity distributed generation resource capital cost
With；Pb=100kW is minimum basic capacity；xj, njFor decision variable, xj0/1,1 expression is taken to build this circuit, 0 represents not build
If this circuit, njThe integer more than or equal to 0 is taken, represents the size of the distributed generation resource capacity of power grid construction.
P is unit electricity price；τiHourage is lost for branch i annual peak loads；PilossFuzzy network loss for branch i；N is net
Branch sum in network.
The constraints is：
Permeability constrains：
Node voltage constrains：
Umin≤Uj≤Umax(j=1,2 ..., m) (13)；
Distribution network topological structure constrains：The rack of planning should be radial structure；
System power Constraints of Equilibrium；
Wherein, Plim,gridFor power grid construction distributed generation resource permeability；PL,jFor the load of node j, m is that node j is corresponding
Number of network node, m are positive integer.
Using《The distribution network structure planing method of meter and uncertain factor》(such as Yan Jiong, Wan Tao, Li Haosong count and not
Distribution network structure planing method [J] electric power system protection and controls of certainty factor, 2017,45 (18)) ambiguity in definition expectation
It is as follows to be worth formula：
If ξ is regular fuzzy variable, the desired value about fuzzy variable ξ is defined as
When ξ is regular Continuous Fuzzy variable, fuzzy expected value definition can be used and directly ask for.Positive triangle fuzzy number ξ,
ξ=(a1,a2,a3), a1>=0, fuzzy expected value calculates as follows：
(3) it for the fuzzy expected value described in step (2), is calculated and obtained using Fuzzy Power Flow.
The radial structure of power distribution network, when substation's leading-out terminal node voltage is constant, system loading is bigger and distributed
Power supply output is smaller, and the trend flowed through in network loss and circuit is bigger, and node voltage reduces more；Conversely, then network loss and circuit
On the trend that flows through with regard to smaller, node voltage reduces fewer.When the output of load and distributed generation resource is fuzzy number, trend
As a result it should be also a fuzzy number, may be irregular fuzzy number under normal circumstances.
The Fuzzy Power Flow algorithm is the power distribution network Fuzzy Power Flow algorithm based on boundary value, the specific steps are：
1) the left margin S of load obscurity number is takenLLiAnd the right margin S of distributed generation resource output fuzzy numberGLiTrend is calculated, is obtained
To the left boundary value P of Line FlowLi, network loss left boundary value Δ PLi, node voltage right boundary value URi；
2) the central value S of load obscurity number is takenLDiAnd the central value S of distributed generation resource output fuzzy numberGDiTrend is calculated, is obtained
To the central value P of Line FlowDi, network loss central value Δ PDi, node voltage central value UDi；
3) the right margin S of load obscurity number is takenLRiAnd the left margin S of distributed generation resource output fuzzy numberGRiTrend is calculated, is obtained
To the right boundary value P of Line FlowRi, network loss right boundary value Δ PRi, node voltage left boundary value ULi；
4) after the left and right boundary value and central value for obtaining Fuzzy Power Flow result, substantially dividing for power flow solutions has been determined that
Cloth；After the central value of Fuzzy Power Flow distribution and left and right boundary value is obtained, it is assumed that Fuzzy Power Flow result is approximately that triangle obscures
Number, load Triangular Fuzzy Number are (SLLi,SLDi,SLRi), distributed generation resource output is expressed as (S with Triangular Fuzzy NumberGLi,SGDi,
SGRi), the desired value of Triangular Fuzzy Number is calculated using formula (15), and then obtain the fuzzy expected value of object function.
(4) it is solved using self-adapted genetic algorithm, obtains the distribution network structure fuzzy programming of distributed generation resource.
The self-adapted genetic algorithm includes：
Fitness function：Average Annual Cost desired value is subtracted as genetic algorithm fitness letter using a larger positive number
Number, mathematic(al) representation are as follows：
Wherein, Z is a positive number for being more than total cost desired value, as long as the positive number that is, more than total cost desired value is
It can.
Coding：Using genetic algorithm integer coding form, integer coding string is divided into two parts：Space truss project coded portion
Coded portion is planned with a kind of distributed generation resource.Wherein, the arrangement of interconnection is not considered in planning process, it is assumed that outside initial rack
Circuitry number for k, the number of a kind of distributed generation resource is m, then chromosome is made of k+m integer.
Genetic operator：Fitness highest individual is directly reserved to the next generation in selection operation, the selection of rest part is adopted
With roulette strategy.
The distribution network structure containing distributed generation resource is carried out using the above method to plan, the distribution network structure planning planning stream
Journey first determines initial grid structure as shown in Fig. 2, when carrying out distribution network structure planning, is matched by genetic algorithm adjustment
Power grid structure；Simultaneously the addressing of distributed generation resource, constant volume are determined using the concurrency feature of the self-adapted genetic algorithm.
The specific steps of distribution network structure planning planning containing distributed generation resource：
(a) it determines the maximum size of distributed generation resource, carries out distributed generation resource modeling；
(b) initial rack is obtained using Kruskal algorithms, determines the coding structure；
(c) initial population is formed；
(d) the application self-adapted genetic algorithm obtains distribution network structure optimization planning.
Simulation example is analyzed
Simulation example uses《Consider the distribution network structure multiple objective programming of reliability》(Nie Minglin, the such as Wang Buoyant, Chen Chun are examined
Consider distribution network structure multiple objective programming [J] Power System and its Automation journals of reliability, 2016,28 (1)：10-16.)
IEEE-54 node systems, planning region is as shown in figure 3, number of the number for load point or crosspoint, S in Fig. 31~S4For power supply
Point, dotted line are possible line corridor.
Parameter setting is as follows：
1. power grid relevant parameter：The unit electricity charge are 0.5 yuan/kWh, and planning horizon is 5 years, annual electricity consumption 6000h, specified
Voltage is 10kV, and the power factor of each load point takes 0.85, it is assumed that the operating expenses of unit length circuit is 0.5 ten thousand yuan/km；
2. emulation experiment environment：Processor host frequency 2.2GHz, memory 2GB, translation and compiling environment are Visual Studio2010；
3. genetic manipulation parameter：Population scale 50, crossing-over rate 0.6, aberration rate 0.05, elite population individual are no more than
1000。
Estimate conducting wire by power when, using distributed generation resource output Triangular Fuzzy Number median SGDi。
Assuming that it is a kind of distributed electrical source node (2,5,16,19,33,37,39) that planning region, which has seven nodes, it is distributed
Power supply unit capacity cost takes 1.2 ten thousand yuan/KVA.The permeability of a kind of distributed generation resource and two class distributed generation resources is set as
10%.
Obtain initial rack first with Kruskal algorithms, as shown in figure 4, in Fig. 4 solid line represent be initial rack knot
Structure, the branch that dotted line represents are the branch outside initial rack.On the basis of initial rack, with reference to the parameter of arrangement above,
It is encoded according to the self-adapted genetic algorithm described in step (4).With the self-adapted genetic algorithm to original net
Frame structure is adjusted, optimizes, final to obtain desired value preferably program results.
Table 1 gives the corresponding coding of programme and cost value of Average Annual Cost fuzzy expected value minimum, planning knot
Fruit is as shown in Figure 5.
The programme of table 1 fuzzy expected value minimum
As shown in Table 1, the joint space truss project containing distributed generation resource comprising 54 nodes, 61 circuits undetermined encodes only
There are 18 integers, code length is shorter, and can space truss project result and distributed generation resource program results be unified into a kind of form,
Combining adaptive genetic operator can improve the whole efficiency of planning in planning process.It is used in Fig. 5The node of expression represents
The node is connected to distributed generation resource.Table 2 gives the capacity of the distributed generation resource of each node access.
The capacity of 2 distributed generation resource of table access and position
Fig. 5 can be seen that the planning of combining with rack for distributed generation resource, and the position of distributed generation resource access is generally
The second half section of feeder line, interior joint 2, node 16, node 33 are end-nodes.Distributed generation resource accesses the end of feeder line, can
Effectively reduce the fixed investment of circuit.
(1) comprehensive Average Annual Cost comparison before and after distributed generation resource access
Disregard distributed generation resource investment cost, the corresponding Average Annual Cost fuzzy expected value of optimal case is 1369.5 ten thousand
Member,《Consider the distribution network structure multiple objective programming of reliability》(Nie Minglin, the such as Wang Buoyant, Chen Chun consider the power distribution network net of reliability
Frame multiple objective programming [J] Power System and its Automation journals, 2016,28 (1)：Distributed generation resource situation is free of in 10-16.)
Lower 1979.75 ten thousand yuan of Average Annual Cost is compared, and power distribution network investment has apparent reduction with operating cost.
(2) node voltage compares before and after distributed generation resource access
Comparisons of the Fig. 6 for each node voltage before and after distributed generation resource access on the basis of initial grid structure shown in Fig. 4
Situation as shown in fig. 6, the access of distributed generation resource is conducive to alleviate distribution network load pressure, is conducive to the lifting of node voltage,
Rational planning and operational management can improve the power quality of system, S in Fig. 4, Fig. 51~S4The number of corresponding node is 51~
54, power supply point voltage perunit value is 1.With the curve of triangle described point it is each node voltage feelings after distributed generation resource accesses in Fig. 6
Condition, i.e., network node voltage shown in corresponding diagram 5；Curve with round described point is each node voltage feelings before distributed generation resource accesses
Condition, i.e., network node voltage shown in corresponding diagram 4.After the comparison of each node voltage can be seen that distributed generation resource access, to being
System power supply has some improvement, particularly evident for feeder terminal node.
By analyzing above, planing method proposed by the present invention can fully consider the uncertainty that user distribution formula power supply is contributed
Problem realizes the fuzzy programming of rack containing distributed power distribution network, shows the feasibility of this method.Phase is obscured with Average Annual Cost
Prestige value, convenient for carrying out optimizing using genetic algorithm, there is preferable practicability as target.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, this field is common
Other modifications or equivalent replacement that technical staff makes technical scheme of the present invention, without departing from technical solution of the present invention
Spirit and scope, be intended to be within the scope of the claims of the invention.
Claims (10)
1. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG, it is characterised in that：Include the following steps：
(1) it according to the uncertainty of distributed generation resource, builds a kind of distributed generation resource output model and two class distributed generation resources goes out
Power model：One kind distributed generation resource output model using deterministic models, adopt by the two classes distributed generation resource output model
It is modeled with fuzzy mathematics method；
(2) for two class distributed generation resource output model described in step (1), with the constraint of distribution network topological structure, node electricity
Pressure is not out-of-limit, permeability constrains, system power Constraints of Equilibrium is constraints, establishes and contains distributed electrical based on fuzzy expected value
The distribution network structure object of planning function in source；
(3) it for the fuzzy expected value of the object function described in step (2), is calculated and obtained using Fuzzy Power Flow；
(4) it is solved using genetic algorithm, obtains the distribution network structure fuzzy programming of distributed generation resource.
2. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 1, feature exist
In：In the step (1), one kind distributed generation resource output model is：
Pi,grid=nPb
PI, grid≤Pimax,grid
Wherein, Pi,gridFor the capacity of distributed generation resource, PbFor minimum basic capacity, n is the integer more than or equal to 0, Plim,grid
For the permeability of a kind of distributed generation resource, PL,iLoad for node i.
3. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 1, feature exist
In：In the step (1), the two classes distributed generation resource output model is：
Pj,cus=Plim,cus×PL,j
Plow=0
Wherein, Phig, PlowRespectively node j punishes the highest and minimum of cloth power supply capacity, Plim,cusFor two class distributed electricals
The permeability in source, PL,jFor node j loads.
4. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 1, feature exist
In：In the step (2), the distribution network structure object of planning function containing distributed generation resource based on fuzzy expected value：
Wherein, E is fuzzy expected value operator；γ is fixed investment Average Annual Cost coefficient；I is earning rate；N for planning horizon/
Year；L1To create the set of feeder line；AjFixed investment expense for circuit j；L2The node of distributed generation resource may be built for power grid
Set；njPbCapacity for distributed generation resource of the power grid at node j；cjFor unit capacity distributed generation resource investment cost；Pb
For minimum basic capacity；xj, njFor decision variable, xj0/1,1 expression is taken to build this circuit, 0 represents not building this circuit,
njThe integer more than or equal to 0 is taken, represents the size of the distributed generation resource capacity of power grid construction；
Wherein, P is unit electricity price；τiHourage is lost for branch i annual peak loads；PilossFuzzy network loss for branch i；N is
Branch sum in network；
The permeability is constrained to：
The node voltage constraint：
Umin≤Uj≤Umax(j=1,2 ..., m)；
The distribution network topological structure constraint：The rack of planning should be radial structure；
Wherein, Plim,gridFor power grid construction distributed generation resource permeability；PL,jFor the load of node j, m is the corresponding networks of node j
Number of nodes.
5. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 1, feature exist
In：In the step (3), the Fuzzy Power Flow algorithm is the power distribution network Fuzzy Power Flow algorithm based on boundary value.
6. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 4, feature exist
In：In the step (4), the genetic algorithm includes：
Fitness function：Fitness letter of the Average Annual Cost desired value as genetic algorithm is subtracted using a larger positive number
Number, the fitness function are：
Wherein, Z is a positive number for being more than total cost desired value；
Coding：Using genetic algorithm integer coding form, integer coding string is divided into two parts：Space truss project coded portion and one
Class distributed generation resource plans coded portion；
Genetic operator：Fitness highest individual is directly reserved to the next generation in selection operation, the selection of rest part is using wheel
Disk gambling strategy.
7. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 2, feature exist
In：It is described one kind distributed generation resource permeability be：
PermeabilityDescribed a kind of point
The output factor of cloth power supply is 1.
8. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 3, feature exist
In：The permeability of the two classes distributed generation resource is：
PermeabilityFor planning region
Some node in domain, the variation range of the total capacity of the two classes distributed generation resource is 0 maximum capacity for arriving the node.
9. a kind of consideration probabilistic distribution network structure Fuzzy Programmings of DG according to claim 6, feature exist
In：The arrangement of interconnection is not considered in the cataloged procedure.
10. the probabilistic distribution network structure Fuzzy Programmings of a kind of consideration DG according to claim 2 or 4, special
Sign is：Pb=100kW.
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