CN104217302B

CN104217302B - Towards the active power distribution network two-stage programming method and device that carbon footprint is minimized

Info

Publication number: CN104217302B
Application number: CN201410484292.0A
Authority: CN
Inventors: 曾博; 欧阳邵杰; 张建华
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2014-09-15
Filing date: 2014-09-19
Publication date: 2017-03-15
Anticipated expiration: 2034-09-19
Also published as: CN104217302A

Abstract

The invention discloses a kind of active power distribution network two-stage programming method and device minimized towards carbon footprint.Method includes：Set up resource layer model, control variables includes characterizing second and third vector of renewable energy power generation unit and non-renewable energy resources generating set installation site and configuration number of units in the primary vector of system network topology and feeder line line style, sign system, and its object function is minF=(D_C+O_C)/H, D_CRepresent implicit carbon emission, O_CRepresent that generating carbon emission, H represent electricity demand forecasting value；Set up operation layer model, calling power and call the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources generating set when its control variables includes that sign system is run in the simulation cycle, its object function is min f=O_C/ ε, ε are changed factor；Resource layer model is solved, the optimal solution of first, second and third vector is obtained, and using the optimal solution as system programme.The present invention effectively reduces the carbon footprint of terminal electricity consumption.

Description

Towards the active power distribution network two-stage programming method and device that carbon footprint is minimized

Technical field

The present invention relates to distribution optimisation technique, and in particular to a kind of active power distribution network two-stage minimized towards carbon footprint Method and device for planning.

Background technology

Since entering 21 century, what fossil energy shortage, climate change and environmental pollution became that the whole world faces chooses jointly War.Therefore, the diversity of energy supply is improved, reduces dependence of the terminal electricity consumption to fossil energy, so as to reduce to greatest extent Because of the carbon emission that human activity is produced, it is important topic that current power industrial development faces.

Distributed power supply system (Distributed Generation System, DGS) such as active power distribution network is by having Machine integrated polytype distributed power source (Distributed Generation, DG), distribution network and end loads, can To realize on-site elimination and utilization to regenerative resources such as wind, light, therefore it is the effective way for reaching above-mentioned target.

The DGS of low-carbon (LC) is set up, its prerequisite is by scientific and effective planing method, that is, determine what, where in year The distributed power source or feeder line of which kind of type (or capacity) is invested to build, so as to the confession electric energy required for reaching in planning horizon The target of electricity consumption carbon footprint minimumization is realized on the basis of power.For the DGS towards low-carbon (LC), planing method widely used at present For：First, under conditions of given network structure, according to load prediction obtained by planning level year load maximum, adopt Maximum capacity nargin (being multiplied by a nargin coefficient on the basis of each circuit maximum carrying capacity predicted value) is tackling most serious The service condition of operating mode；Then, on the basis of above-mentioned network structure, using in system all DG power outputs summations maximum as Target, optimizes their best positions and capacity in systems.

But, above-mentioned planing method can not fully realize carbon footprint this elementary object for effectively reducing terminal electricity consumption.

Content of the invention

In view of the above problems, it is proposed that the present invention so as to provide one kind overcome the problems referred to above or at least in part solve on State the active power distribution network two-stage programming method and device minimized towards carbon footprint of problem.

According to an aspect of the invention, there is provided a kind of active power distribution network two-stage programming minimized towards carbon footprint Method, including：

The resource layer model of active power distribution network is set up, its control variables includes characterizing system network topology and feeder line Renewable energy power generation unit and non-renewable energy resources generating set installation site in the primary vector of line style, respectively sign system And the installation site configuration number of units secondary vector and the 3rd vector, its object function be min F=(D_C+O_C)/H, its Middle D_CFeeder line, renewable energy power generation unit and non-renewable energy resources generating set contained by expression system are within the planning horizon Corresponding implicit carbon emission, O_CExpression system non-renewable energy resources generating set is called when running within the planning horizon power with And the generating carbon emission corresponding to electric energy is called from higher level's electrical network, H represents the predicted value of system power consumption within the planning horizon；

Set up the operation layer model of active power distribution network, when its control variables includes that sign system is run in the simulation cycle pair Renewable energy power generation unit and non-renewable energy resources generating set call power and call the of electric energy from higher level's electrical network Four vectors, its object function are min f=O_C/ ε, wherein ε are represented and are entered to planning horizon from the corresponding time scale of simulation cycle The changed factor of row conversion；And

Resource layer model is solved using pre-defined algorithm, obtain the optimal solution of first, second and third vector, and by this most Programme of the excellent solution as active power distribution network, wherein when the target function value of resource layer model is obtained, according to first and second, Programme determined by the value of three vectors solves operation layer model, determines resource according to the solving result to running layer model O in the object function of layer model_CValue.

Alternatively, methods described also includes：

Exerting oneself for uncontrollable power generation energy resource resource in system is divided into several scenes, corresponding one of each scene is exerted oneself Interval；

Respectively using the algebraic mean value in corresponding for each scene interval of exerting oneself as uncontrollable power generation energy resource resource under the scene Expectation exert oneself；

Determine that each scene correspondingly expects the probability of happening that exerts oneself under different periods in simulation cycle；

Expectation according to each scene is exerted oneself and corresponding probability of happening determines uncontrollable power generation energy resource resource in day part Under expectation exert oneself.

Alternatively, the object function of operation layer model is specially：

Wherein PP_EG,tRepresent period t From the power that calls of higher level's electrical network, ρ_EGRepresent that higher level's electrical network often provides the generating carbon emission corresponding to 1kWh electric energy, Δ t represents every The duration of individual period, TH represent the fixed number included by simulation cycle, ρ_NRDG,yRepresent that y kinds non-renewable energy resources are sent out Group of motors generating carbon emission often corresponding to generating 1kWh electric energy, PP_{NRDG,y,g’,t}Represent that period t can not to the upper y kinds of node g ' Renewable source of energy generation unit calls power, Φ_NRDGThe species set of non-renewable energy resources generating set in expression system, Ω_NRDG,yIn expression system all types for y non-renewable energy resources generating set to be selected install node constitute set.

Alternatively, the constraints of the resource layer model includes following one or more：Investment totle drilling cost constraint, each In node, the permeability constraint of power generation energy resource resource and holding network topology structure are radial constraint.

Alternatively, the constraints of the operation layer model includes following one or more：Power-balance constraint, networking Power swing constraint, node voltage amplitude constraint, the constraint of feeder line current-carrying capacity, the constraint of system receiving end characteristic, power generation energy resource resource go out Force constraint and the constraint of power generation energy resource resource power factor.

Alternatively, the pre-defined algorithm is genetic algorithm, is made with -1 product using the target function value of resource layer model Fitness function value for genetic algorithm.

Alternatively, operation layer model is solved using interior point method, particle cluster algorithm or genetic algorithm.

According to a further aspect in the invention, there is provided a kind of active power distribution network two-stage programming minimized towards carbon footprint Device, including：

Resource layer model sets up unit, is adapted to set up the resource layer model of active power distribution network, and its control variables includes characterizing Renewable energy power generation unit and can not in the primary vector of system network topology and feeder line line style, respectively sign system Renewable source of energy generation units' installation position and the installation site configuration number of units secondary vector and the 3rd vector, its target Function is min F=(D_C+O_C)/H, wherein D_CFeeder line contained by expression system, renewable energy power generation unit and non-renewable Energy generator group corresponding implicit carbon emission, O within planning horizon_CTo can not be again when expression system be run within the planning horizon Raw energy generator group is called power and calls the generating carbon emission corresponding to electric energy, H to represent system on rule from higher level's electrical network The predicted value of power consumption in the cycle of drawing；

Operation layer model sets up unit, is adapted to set up the operation layer model of active power distribution network, and its control variables includes characterizing When system is run in the simulation cycle to renewable energy power generation unit and non-renewable energy resources generating set call power with And from higher level's electrical network call electric energy the 4th vector, its object function be minf=O_C/ ε, wherein ε represent corresponding by simulation cycle The changed factor changed to planning horizon of time scale；And

Model solution unit, is suitable for use with pre-defined algorithm and resource layer model is solved, and obtains first, second and third vector Optimal solution, and using the optimal solution as active power distribution network programme, wherein obtain resource layer model object function During value, programme according to determined by the value of first, second and third vector solves operation layer model, according to running layer model Solving result determine the O in the object function of resource layer model_CValue.

Alternatively, the device for planning also includes envisioning scene collection construction unit, is suitable to：

Alternatively, the object function of operation layer model is specially：

Alternatively, the pre-defined algorithm be genetic algorithm, the model solution unit using resource layer model target letter Numerical value with -1 product as genetic algorithm fitness function value.

Alternatively, the model solution unit solves firing floor using interior point method, particle cluster algorithm or genetic algorithm Model.

According to the present invention towards carbon footprint minimize the active power distribution network two-stage programme, while consider because Resource is used, operation generatings, the carbon emission produced by the different physics links such as equipment scrapping, by coordination optimization grid structure, Feeder line line style and DG configurations, ensure that the system that is finally cooked up before the restriction of investment amount and safe operation is met Put, realize the reduction of truly terminal electricity consumption overall process carbon footprint, and meet on the time of optimization and solving precision The demand of engineer applied.

Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention, And can be practiced according to the content of specification, and in order to allow the above and other objects of the present invention, feature and advantage can Become apparent, below especially exemplified by the specific embodiment of the present invention.

Description of the drawings

By reading the detailed description of hereafter preferred embodiment, various other advantages and benefit are common for this area Technical staff will be clear from understanding.Accompanying drawing is only used for the purpose for illustrating preferred embodiment, and is not considered as to the present invention Restriction.And in whole accompanying drawing, it is denoted by the same reference numerals identical part.In the accompanying drawings：

Fig. 1 shows the active power distribution network two-stage programming minimized towards carbon footprint according to an embodiment of the invention Method flow diagram；

Fig. 2 shows the active power distribution network two-stage programming minimized towards carbon footprint according to an embodiment of the invention Structure drawing of device；

Schematic diagram programme encoded when Fig. 3 shows genetic algorithm used in the embodiment of the present invention；

Fig. 4 shows embodiment of the present invention interior joint and plans to build circuit associated diagram；

Fig. 5 shows the system simulation result figure of the embodiment of the present invention；And

Fig. 6 shows impact schematic diagram of the implicit carbon emission amount to actual total carbon emissions and gross investment.

Specific embodiment

The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure and should not be by embodiments set forth here Limited.On the contrary, there is provided these embodiments are able to be best understood from the disclosure, and can be by the scope of the present disclosure Complete conveys to those skilled in the art.

Present inventor is analyzed to the planing method of existing distributed power supply system (such as active power distribution network) Afterwards, find to cause which as follows the reason for can not fully realizing this elementary object of carbon footprint for effectively reducing terminal electricity consumption：

1) have ignored and building, install, making using caused implicit carbon emission, i.e. equipment because of system equipment (feeder line, DG) With and retired during caused by indirect carbon emission, choose (feeder line line style, DG capacity) and produce with which due to failing to characterize resource Indirect carbon emission cost between relation, there is very big one-sidedness in final program results, it is impossible to ensure " terminal electricity consumption carbon footprint Minimum " target.

2) independent optimization is carried out to space truss project and DG addressings constant volume, has artificially isolated natural inner link between the two And have ignored mutual reciprocal effect.Under the power grid architecture for determining, the feas ible space that DG allocation plans are formulated will be limited.

Therefore, the embodiment of the present invention provides a set of more science and advanced optimization planning scheme, with careful consideration because of end The carbon emission factor of caused each process link of system of end need for electricity (processing including construction, operation and equipment scrapping), leads to Complex optimum grid structure, the installation site of feeder line line style and DG and capacity is crossed, raising renewable energy utilization can be played Efficiency, the network loss that reduces, the purpose for delaying resource use demand, provide one kind for Power System Planning manager and can realize terminal The viable capital programme that electricity consumption overall process carbon footprint reduces.

For ease of more understanding and accurately understanding embodiments of the invention, now the part term to being directed to is carried out Explain.

The active power distribution network (Active distribution network, ADN) of the embodiment of the present invention includes various Electric energy resource (generating set), which can be classified in different ways.Under a kind of mode classification, which can divide For controllable power generation energy resource resource and uncontrollable power generation energy resource resource.Controllable power generation energy resource resource refers to that real output can be with root According to the class power output device that actual demand carries out manual control, including gas turbine, diesel-driven generator, coal unit, life Material generating set, geothermal power generation unit and nuclear power generating sets etc.；Uncontrollable power generation energy resource resource refers to that real output is relied on A class power output device of manual control cannot be carried out according to the actual requirements in power generation energy resource feature, including photovoltaic generation, Wind-power electricity generation, tidal power generation and various energy storage devices etc..

Under another mode classification, which can be divided into renewable energy power generation unit and non-renewable energy resources generator Group.Also, renewable energy power generation unit can include controllable power generation energy resource resource and uncontrollable power generation energy resource resource, can not be again Raw energy generator group can also include controllable power generation energy resource resource and uncontrollable power generation energy resource resource.

In the part description of the embodiment of the present invention, controllable power generation energy resource resource is (i.e. controllable with controllable distributed unit DG, as a example by), uncontrollable power generation energy resource resource is illustrated by taking uncontrollable distributed unit (i.e. uncontrollable DG) as an example.

Fig. 1 shows the active power distribution network two-stage programming minimized towards carbon footprint according to an embodiment of the invention Method flow diagram, the planing method can be by various computing devices.With reference to Fig. 1, the planing method starts from step S102, In step S102, the anticipation scene collection of description active power distribution network running status is built, wherein it is possible to going through according to DG power generation energy resources History statistics, sets the anticipation scene collection of DGS running statuses.In one implementation, all DG during plan DGS Type is divided into controllable DG and uncontrollable DG, using the power output of uncontrollable DG as the construction basis of anticipation scene collection, and executes Following steps：

(1) by the division of exerting oneself of uncontrollable power generation energy resource resource in system (active power distribution network) (being uncontrollable DG in this example) For several scenes, the corresponding interval of exerting oneself of each scene；

The division can be carried out according to the experience of designer or subjective judgement, for example, if the unit of certain uncontrollable DG Capacity is 5MW, then actually exerting oneself for the uncontrollable DG can be divided into following 5 scenes using " 1MW " as interval step-length：I.e. [0,1MW], (1MW, 2MW], (2MW, 3MW], (3MW, 4MW], (4MW, 5MW].The selection of interval step-length can be according to designer Experience or subjective judgement and determine.In general, interval step-length is less, the scene quantity of generation is more, will cause emulation essence Degree is higher, but will also bring bigger amount of calculation simultaneously；Conversely, interval step-length is less, overall calculation amount will be reduced, but emulate essence Degree will also be decreased.

(2) respectively using the algebraic mean value in corresponding for each scene interval of exerting oneself as uncontrollable power generation energy resource money under the scene The expectation in source is exerted oneself；

For example, for 5 scenes that is set up in step (1), corresponding expectation is exerted oneself respectively 0.5MW, 1.5MW, 2.5MW, 3.5MW, 4.5MW.

(3) determine that each scene correspondingly expects the probability of happening that exerts oneself under different periods in operation simulation cycle；

Fortune can be determined according to uncontrollable DG (such as Renewable Energy Resources) in the statistics of history year day part The corresponding probability of happening for expecting to exert oneself of each scene under row simulation cycle different periods.By taking wind-resources as an example, if having to planning The wind speed information (wind speed is exerted oneself with the expectation of Wind turbines and is associated) of the regional annual day part of 5 years in history, that is, gather around There is 5*8760=43800 data point.First, daily 0 point to 1 point in these data points of data point is extracted (altogether 5*365=1825 data point), the period (i.e. 0 point to 1 point) regenerative resource DG is calculated according to following formula and is exerted oneself scenic spot on the scene Between [0,1MW] probability：

Wherein, N ([0,1MW]) is that DG exerts oneself in the number of interval [0,1MW] in data point；N_totalAdopted by the period The number at the total strong point of collection.For this example, that is, there is N_total=43800 ÷ 24=1825.

According to upper type, it may be determined that each regenerative resource DG exert oneself scene interval the period appearance (generation) general Rate.

(4) exerted oneself according to the expectation of each scene and corresponding probability of happening determines uncontrollable power generation energy resource resource when each Expectation under section is exerted oneself.

Specifically, for each period, respectively the expectation of each scene can be exerted oneself and is multiplied with corresponding probability of happening, so Expectation of the uncontrollable power generation energy resource resource under the period be obtained exert oneself cumulative for all of multiplied result afterwards.

For example, the expectation of daily 0 point to 1 point uncontrollable power generation energy resource resource is exerted oneself and can be expressed as：

Wherein, S represents the corresponding total number of scenes of uncontrollable power generation energy resource resource, η_σRepresent the starting appearance of σ scene Amount, η_σ+1Represent the termination capacity of σ scene, η_σ+1-η_σIt is then the step-length of a scene of delimitation.For example, for scene 1： [0,1MW], η_σ=0MW, η_σ+1=1MW, step-length are 1MW.

After the anticipation scene collection for completing to describe active power distribution network running status is built, planing method enters step S104, In step S104, the resource layer model of active power distribution network is set up.Resource layer model to set up process specific as follows：

First, determine the control variables of resource layer model, as follows：

(1) the vectorial B of system network topology and feeder line line style is characterized；Wherein, B={ λ_ij,a|ij∈Ω_FD,a∈ Ω_A, Ω_FDFor all set for building line channel composition, Ω in system_ARepresent the set of available feeder line line style (type). λ_ij,aFor (0,1) variable, if λ_ij,a=1, represent the feeder line of the line channel Setup Type a between node i j；If λ_ij,a=0, then Represent, in the passage, feeder line is not installed.

(2) renewable energy power generation unit (RDG) and non-renewable energy resources generating set (NRDG) in sign system respectively Installation site and the vectorial L of the configuration number of units in the installation site_RDGAnd L_NRDG；Wherein, L_RDG={ n_RDG,g,x|g∈Ω_RDG,x, x∈Φ_RDG, Ω_RDG,xFor all types in system for x RDG units to be selected install node constitute set, Φ_RDGExpression system Middle RDG species set, n_RDG,g,xFor integer variable, in expression system, type is configured in installation node g to be selected by the RDG of x Unit number of units；L_NRDG={ n_NRDG,g’,y|g’∈Ω_NRDG,y,y∈Φ_NRDG, Ω_NRDG,yFor the NRDG machines that all types in system are y Group is to be selected to install the set that node is constituted, Φ_NRDGThe set of NRDG species, n in expression system_NRDG,g’,yFor integer variable, system is represented The unit number of units that type is configured in installation node g ' to be selected by the NRDG of y in system.

Then, corresponding with the unit used electricity amount of load in planning horizon (example below is planning level year) active power distribution network Life cycle management carbon emission (the corresponding generating carbon of implicit carbon and electricity consumption including feeder line in system and each power generation energy resource resource Discharge) minimum as target, the object function of structure resource layer model, expression formula are as follows：

MinF=(D_C+O_C)/H(3)

Wherein, H represents the predicted value of planning level year system loading power consumption；D_CIn expression system contained feeder line and The corresponding implicit carbon year value of each power generation energy resource resource, its expression formula are as follows：

Wherein, β_FDFor the corresponding life-span time limit of feeder line；For the corresponding life-span time limit of xth kind RDG；For The corresponding life-span time limit of y kinds NRDG；Φ_RDGAnd Φ_NRDGIn DGS RDG species set and NRDG species set is represented respectively；D_FDFor The implicit carbon of the corresponding life cycle management of feeder line in DGS, expression are as follows：

Wherein, d_fd,aFor the corresponding implicit carbon of type a feeder line unit km, unit is kg CO₂/ km, l_ijRepresent node i j it Between line channel length, unit is km.

In formula (4),Carbon is implied for the corresponding life cycle management of contained xth kind RDG in DGS, expression is as follows:

Wherein, d_xFor corresponding implicit carbon (the kg CO of separate unit xth kind RDG in DGS₂/ platform).

In formula (4),For the implicit carbon of the corresponding life cycle management of contained y kinds NRDG in DGS, expression is such as Under:

Wherein, d_yFor corresponding implicit carbon (the kg CO of separate unit y kinds NRDG in DGS₂/ platform).

For parameter d in formula (5)～(7)_fd,a, d_xAnd d_yCan count respectively in the manufacture process of feeder line, RDG and NRDG Raw materials used and quality, and Fossil fuel consumption during installing, using and be retired, carry out calculating acquisition.

In formula (3), O_CRepresent planning level year because in DGS power consumption institute caused by generating carbon emission (including system pair Non-renewable energy resources generating set calls power and calls the generating carbon emission corresponding to electric energy from higher level's electrical network), concrete table As follows up to formula：

Wherein, ρ_EGRepresent that higher level's electrical network often provides 1kWh electric energy the corresponding generating carbon emission for producing, unit is kg CO₂/ kWh；PP_EG,tRepresent that period t calls power from higher level's electrical network；The duration that Δ t represents each period, (for example, 1 is little When)；TH represents the fixed number included by simulation cycle (for example, 1 day)；ρ_NRDG,yRepresent that y kinds non-renewable energy resources generate electricity The generating carbon emission of unit (being NRDG in this example) the often corresponding generation of generating 1kWh electric energy, unit is kg CO₂/kWh； PP_{NRDG,y,g’,t}It is that period t calls power to the upper y kinds non-renewable energy resources generating sets of node g ' (in this example be NRDG)；ε The transformation factor that is changed in year from the corresponding time scale of simulation cycle, in one implementation, its table is represented to planning As follows up to formula：

ε=8760/ (TH Δ t) (9)

Wherein, 8760 is the hourage for including for a year.

It should be noted that ε can also adopt other algorithms to obtain, such as integral projection method etc..

Finally, the constraints of resource layer model is set, can be specifically included：

A () investment totle drilling cost constraint, no more than master budget, expression formula is as follows for active power distribution network overall cost of ownership：

0≤C_FD+C_RDG+C_NRDG≤C₀(10)

Wherein, C_FDFor feeder line overall cost of ownership, its concrete expansion is as follows：

Wherein, c_fd,aIt is the cost of investment of every km length feeder line line style a.

In formula (10), C_RDGFor the overall cost of ownership of RDG in system, its concrete expansion is as follows：

Wherein, c_xSingle unit cost for xth kind RDG.

In formula (10), C_NRDGFor the overall cost of ownership of NRDG in system, its concrete expansion is as follows：

Wherein, c_ySingle unit cost for y kinds NRDG.

In formula (10), C₀For building the gross investment budget of active power distribution network.

B () node DG permeabilities are constrained, restricted by floor space, and DG has certain restriction in the access capacity of node, Expression is as follows：

Wherein, nl_iThe DG quantity (including RDG and NRDG) of the maximum that can be accessed for node i；Ω represents system node collection Close.

C () is additionally, it is radial that should keep network topology structure.

After the resource layer model for completing active power distribution network is set up, planing method enters step S106.In step s 106, Set up the operation layer model of active power distribution network.Operation layer model to set up process specific as follows：

First, determine the control variables of operation layer model.One operation simulation cycle (for example, 1 day) is divided into TH Period, the duration of each period is Δ t (hour), and for example, 1 hour, then the control variables of operation layer model is as follows：

Vector PP of all types of power generation energy resource resources in day part optimal power control amount in sign system；Wherein PP= {(PP_RDG,g,t, PPN_RDG,g’,t, PP_EG,t)|g∈Ω_RDG,x, x ∈ Φ_RDG；g’∈Ω_NRDG,y, y ∈ Φ_NRDG；T=1,2 ..., TH }； PP_RDG,g,tRepresent period t and power, PP are actually called to RDG on node g_NRDG,g’,tRepresent period t NRDG's upper to node g ' Power, PP are actually called_EG,tRepresent period t and actually call power from higher level's electrical network.

Then, minimum as target using system operation stage in planning level year expection generating carbon emission, build firing floor mould The object function of type, expression formula are as follows：

Finally, the constraints of operation layer model is set, is specifically included：

(a) power-balance constraint：

Wherein：P_i,tAnd Q_i,tThe active and idle injecting power of respectively period t node is；J ∈ i represent all and node i The node set being joined directly together；V_i,tAnd V_j,tThe voltage magnitude of period t node i and node j is represented respectively；G_ijWith B_ijDifference table Show real part and the imaginary part of bus admittance matrix；θ_ij,tRepresent the phase difference of voltage between period t node i and j.

B () networking power swing is constrained：

Consider that the distributed unit energy supply of bulk power grid energy supply ratio is more favourable from the security and stability of operation of power networks.But big electricity Net not exclusively possesses fast track and the responding ability for system power fluctuation, when the fluctuation of DGS systems internal power is excessive, big electricity Net cannot meet all demands in system at short notice, and which should be controlled in certain limit to the rate of change of DGS injecting powers Interior, it is shown below：

Wherein, ψ_maxRepresent maximum networking power swing rate (MW/h).

C () node voltage amplitude is constrained：

Wherein, V_i,tRepresent the magnitude of voltage of period t system node i；V_maxRepresent the maximum allowed by system node voltage； V_minRepresent the minimum of a value allowed by system node voltage.

D () feeder line current-carrying capacity is constrained：

Wherein, I_ij,tRepresent period t node i, the current value that circuit flows through between j；I_a,maxRepresent that feeder line line style a is allowed Maximum carrying capacity.

E () DGS receiving ends characteristic is constrained：

PP_EG,t>=0, t=1 ... TH (20)

(f) DG units limits：

Wherein, e_x,tAnd e_y,tXth kind RDG and y kind NRDG unit EIAJ in period t is represented respectively.Concrete next Say, for NRDG, e_y,t=Cap_y,rated, Cap_y,ratedRepresent the rated capacity of NRDG；For RDG, then e_x,t=min (Cap_x,rated,Poten_x,t), Cap_x,ratedRepresent the rated capacity of RDG, Poten_x,tRepresent that type exists for the separate unit RDG units of x The maximum possible of period t is exerted oneself, and is determined by primary energy supply situation,.

G () DG power factors are constrained：

Wherein, σ_RDG,g,tRepresent the power factor of RDG operations in period t node g；σ_NRDG,g’,tRepresent in period t node g ' The power factor of NRDG operations.The embodiment of the present invention sets all DG (including RDG and NRDG) in DGS systems and only produces wattful power Rate, therefore, Const 1=Const 2=1.

Constraints above condition conducts a research by taking controllable DG and uncontrollable DG as an example and builds, if can with other types Control and uncontrollable power generation energy resource resource conduct a research, then can be commonly used with the field according to concrete power generation energy resource resource type and be closed The constraints of reason is replaced and builds again.

After the operation layer model for completing active power distribution network is set up, planing method enters step S108.In step S108, Resource layer model is solved using pre-defined algorithm, obtain control variables (vectorial B, the vector L of resource layer model_RDGAnd vector L_NRDG) optimal solution, and using the optimal solution as active power distribution network programme.In embodiments of the present invention, resource layer mould Type and operation layer model are Nonlinear programming Model, therefore, the solution of resource layer model and operation layer model can be adopted The various algorithms for solving nonlinear programming problem, such as interior point method, particle cluster algorithm and genetic algorithm etc., the embodiment of the present invention pair Specific algorithm is not limited, and those skilled in the art can reasonable selection as needed.

The solution procedure of resource layer model is illustrated by taking genetic algorithm as an example below, wherein, using resource layer model Target function value with -1 product as genetic algorithm fitness function value, and obtain resource layer model target letter During numerical value, according to vectorial B, vector L_RDGAnd vector L_NRDGValue determined by programme solve operation layer model, according to right The solving result of operation layer model determines the O in the object function of resource layer model_CValue.It should be noted that solving operation During layer model, the capacity (expectation is exerted oneself) of renewable energy power generation unit can adopt the calculating in step S102 As a result.

Following steps are specifically included to the solution procedure of resource layer model：

(N is the integer more than 1, and such as N values (determine system for 50) individual feasible programme for A, at random generation N Network topology structure, the dominant vector B of feeder line line style, and renewable energy power generation unit and non-renewable energy resources are sent out in system Group of motors installation site and the dominant vector L of configuration number of units_RDGAnd L_NRDG), " feasible " refers to the constraints for meeting resource layer model (i.e. formula (10)～(14) and the radial constraint of network topology structure).

B, using real coding strategy, each programme of step A gained is encoded.

Schematic diagram programme encoded when Fig. 3 shows genetic algorithm used in the embodiment of the present invention.Reference Fig. 3, for feeder line region, each one represented in system can build line channel.“n^fd=0 " represent that the position institute is right Answer^fd=1 " position corresponding to line channel on erection feeder line Class1 is represented, with This analogizes.

For RDG regions, each represents the RDG installation nodes to be selected in system.“n_R=0 " the position institute is represented Corresponding RDG is to be selected to install the RDG, " n for not configuring any types x on node_R=1 " represent that the position is corresponding to install on node The RDG units of 1 type x of configuration, but machine set type is depending on optimum results, by that analogy.

For NRDG regions, each NRDG power supply represented in system installation node " n to be selected_NR=0 " representing should NRDG power supply that any types be y, " n are not configured on NRDG installation nodes to be selected corresponding to position_NR=1 " the position institute is represented Correspondingly install and on node, configure the NRDG power supplys of 1 type for y, but machine set type is also depending on optimum results, by that analogy.

C, for step A gained each programme, solve fortune using interior point method, particle cluster algorithm or genetic algorithm Row layer model, determines that system (determines that all types of power generation energy resource resources are optimum in day part in the optimized operation strategy of day part The vector PP of power control quantity).

D, according to step C obtained by system day part optimized operation strategy, by formula (15) computing system operation Generating carbon emission O in simulation cycle_C；

E, by step D obtained by O_CValue substitutes into formula (3) as known parameters, right to calculate acquisition programme institute The complete target function value (formula (3)) that answers.

F, by the product of resource layer model objective function and " -1 "As fitness function Fit (), Calculate the corresponding fitness function value of each programme, record simultaneously descending is ranked up.

G, a generation (quantity the is N) programme for utilizing genetic operation generation new, specifically include：

G1, work as in former generation programme, selection operation is carried out based on direct ratio selection strategy, for work as former generation programme In any individual i, its selected probability be Pr_i, its calculating formula is as follows：

In above formula, Fit (x_i) represent the corresponding fitness function values of individual i that acquisition is calculated through step F.

On the basis of the above, selection operation is realized using spinning roller method, is made

PP₀=0

Corotation wheel n times, during each runner, produce random number ξ, ξ for (0,1) between be uniformly distributed, work as PP_i-1≤ξ≤ PP_iWhen, then select individuality i.The above-mentioned N number of programme that selects collectively forms mating pond.

Individuality in G2, the mating pond generated for step G1, with crossover probability J_c(span is for example set to 0.4 ～crossover operation 0.99) is carried out, in crossover operation, two point of contacts (optional 2 positions i.e. in code pattern) are randomly selected, handed over The substring changed between corresponding two point of contacts of two programmes.

G3, after crossover operation, with mutation probability J_m(span is for example set to 0.0001～0.1) is to scheme Body carries out mutation operation, and (the individual sequence in referring to colony selects one or more point of contacts at random, and changes its position at random Value).For the adopted real coding mode of the embodiment of the present invention, the expression formula of its mutation operation is as follows：

For circuit feeder zone：

Wherein,For the corresponding new place value in corresponding point of contact；INT () is the bracket function based on " rounding up " principle；Number is generated for random, being uniformly distributed (total quantity that wherein, NA is optional feeder line line style) between [0, NA] is met, and is required Make

For RDG regions：

Wherein, n_RDG,newFor the corresponding new place value in corresponding point of contact；INT () is to round letter based on " rounding up " principle Number；Number is generated for random, [0, Nl is met_RDG] between be uniformly distributed (wherein, Nl_RDGCan for related RDG installation nodes to be selected The maximum RDG number of power sources of installation), and require to make n_RDG,new≠n_RDG.

For NRDG regions：

Wherein, n_NRDG,newFor the corresponding new place value in corresponding point of contact；INT () is to round letter based on " rounding up " principle Number；Number is generated for random, [0, Nl is met_NRDG] between be uniformly distributed (wherein, Nl_NRDGSection is installed for related NRDG is to be selected The installable maximum NRDG number of power sources of point), and require to make n_NRDG,new≠n_NRDG.

H, the programme of new generation generated for step G, repeat step C is to step F.By each programme of new generation Corresponding fitness function value is compared with the fitness function value of each programme of previous generation, is arranged from big to small Sequence, and retain programme of the ranking in top N accordingly, while giving up remaining programme.

I, step G～step H is repeated, to Iter_maxSecondary (according to practical experience, Iter_maxSpan be [500,1500]).Now the optimum results of resource layer model are the optimum programming side of realization oriented minimumization electricity consumption carbon footprint Case, and export.

It should be noted that the embodiment of the present invention is not limited to the execution sequence of above-mentioned step, can be as needed Exchange order therein, it is possible to as needed some steps are accepted or rejected.For example, it is possible to exchange step S104 and step The execution sequence of S106.

Again for example, it is possible to execution step S102, i.e. step S102 are not optional step.If not execution step S102, this In the case of kind, do not carry out the structure of system operation scene, but according to the experience or subjective judgement of those skilled in the art come Arrange uncontrollable power generation energy resource resource to exert oneself in the expectation of day part.

Fig. 2 shows the active power distribution network two-stage programming minimized towards carbon footprint according to an embodiment of the invention Structure drawing of device.With reference to Fig. 2, the device for planning includes：Anticipation scene collection construction unit 202, resource layer model sets up unit 204th, operation layer model sets up unit 206 and model solution unit 208.

Anticipation scene collection construction unit 202 is suitable to the anticipation scene collection for building description active power distribution network running status, wherein, According to the historical statistical data of DG power generation energy resources, the anticipation scene collection of DGS running status can be set.In a kind of implementation In, all DG types during DGS is planned are divided into controllable DG and uncontrollable DG, using the power output of uncontrollable DG as anticipation field The construction basis of Jing Ji, and execute following steps：

The execution logic of anticipation scene collection construction unit 202 is identical with step S102, specifically can be found in retouching for step S102 State, do not repeat here.

Resource layer model sets up unit 204, is adapted to set up the resource layer model of active power distribution network, and its control variables includes table Levy system network topology and the primary vector of feeder line line style, renewable energy power generation unit and not in sign system respectively Renewable energy power generation units' installation position and the installation site configuration number of units secondary vector and the 3rd vector, its mesh Scalar functions are min F=(D_C+O_C)/H, wherein D_CFeeder line contained by expression system, renewable energy power generation unit and can not be again Raw energy generator group corresponding implicit carbon emission, O within planning horizon_CTo can not when expression system be run within the planning horizon Renewable source of energy generation unit calls power and calls the generating carbon emission corresponding to electric energy, H to represent that system exists from higher level's electrical network The predicted value of power consumption in planning horizon.The constraints of the resource layer model includes following one or more：Investment is total In cost constraint, each node, the permeability constraint of power generation energy resource resource and holding network topology structure are radial constraint.

The execution logic that resource layer model sets up unit 204 is identical with step S104, specifically can be found in retouching for step S104 State, do not repeat here.

Operation layer model sets up unit 206, is adapted to set up the operation layer model of active power distribution network, and its control variables includes table Levy when system is run in the simulation cycle and power is called to renewable energy power generation unit and non-renewable energy resources generating set And from higher level's electrical network call electric energy the 4th vector, its object function be min f=O_C/ ε, wherein ε are represented by simulation cycle The changed factor changed to planning horizon by corresponding time scale.The constraints of the operation layer model includes following One or more：Power-balance constraint, the constraint of networking power swing, node voltage amplitude constraint, the constraint of feeder line current-carrying capacity, system The constraint of receiving end characteristic, power generation energy resource resource units limits and the constraint of power generation energy resource resource power factor.

The execution logic that operation layer model sets up unit 206 is identical with step S106, specifically can be found in retouching for step S106 State, do not repeat here.

Model solution unit 208, is suitable for use with pre-defined algorithm and resource layer model is solved, obtain first, second and third to The optimal solution of amount, and using the optimal solution as active power distribution network programme, wherein obtain resource layer model target letter During numerical value, programme according to determined by the value of first, second and third vector solves operation layer model, according to firing floor mould The solving result of type determines the O in the object function of resource layer model_CValue.Model solution unit 208 can adopt interior point method, grain Swarm optimization or genetic algorithm are solving resource layer model and operation layer model.When the pre-defined algorithm is genetic algorithm, The model solution unit 208 adopts the target function value of resource layer model and -1 product as the fitness letter of genetic algorithm Numerical value.

The execution logic of model solution unit 208 is identical with step S108, specifically can be found in the description of step S108, here Do not repeat.

The active power distribution network two-stage programming scheme towards carbon footprint minimum of the embodiment of the present invention is in life cycle management The carbon emission problem of distribution system is considered in time scale.The implicit carbon emission of careful meter and system equipment in object function Contribute to characterizing the relation between the indirect carbon emission cost that resource is chosen and which produces, so as to ensure conscientiously " terminal electric energy carbon Footprint is minimum " realization of this low-carbon (LC) Electric Power Network Planning basic goal.Complex optimum is carried out to rack and distributed power source, can be most Capacity mismatch problem between the two is avoided to limits, delays electrical network dilatation construction demand, while improving regenerative resource profit With efficiency, provide believable optimal investing strategy for planning personnel, meet system high efficiency operation and low-carbon (LC) electric energy supply Application demand, and the low carbonization transformation to distribution system and development have important practical significance and good promotion prospect.

One application example of the present invention given below.

The urban power distribution network system that the application example is adopted includes 24 nodes, 23 optional feeder line branch roads, amount voltage Grade is 12.66kV (as shown in Figure 4), and its interior joint 7-24 can install DG.

The system general planning phase is set as 15 years, and firing floor time interval is 15 minutes, and system loading growth rate takes 3%, folding Now rate takes 8%.System maximum permeability 20%, node voltage allowable fluctuation range are ± 7%, and circuit maximum carrying capacity is set as 1.2 times of rated capacities, it is 5 that single node accesses the DG numbers upper limit.In the system, consideration wind-powered electricity generation is fired with photovoltaic generation as RDG Gas-turbine (GT) is used as NRDG.The outer bulk power grid electric energy carbon emission of system is 0.85kg/kWh.Various kinds of equipment specifying information is shown in Table 1.

1 each equipment essential information of table

Above-mentioned |input paramete is based on, emulation is simulated to the method for the embodiment of the present invention.In order to highlight, extracting method has Effect property, sets 2 groups of contrast scenes：(1) optimization of Power grid structure when Electric Power Network Planning is carried out, is first carried out, then carries out DG Addressing constant volume；(2) carbon emission when only considered operation in Electric Power Network Planning, does not consider the implicit carbon emission of equipment.

Find that the planning of the embodiment of the present invention is more reasonable in DG layouts by the program results contrast with scene (1).Root According to shown in Fig. 5, from total amount, the inventive method is more than the DG installations of scene (1), this is because, scene (1) is to space truss project Independent optimization is carried out with DG addressings constant volume, natural inner link between the two has artificially been isolated and have ignored mutual interactive shadow Ring, the feas ible space of DG allocation plans formulation in the case where power grid architecture is determined, certainly will be limited.From the point of view of gross investment, this Inventive method is less, this is because the layout of DG is more reasonable, and the degrees of coordination between electrical network and DG is more preferable, it is to avoid poor efficiency Investment；And scene (1) although only from investment economy into seeing originally it is minimum, take temperature from the carbon emission of life cycle management, this The method carbon emission of invention is minimum, and scene (1) is at most.This is because in the programme, the installed capacity of RDG is less, need A large amount of electric energy to be bought from bulk power grid.And under the programme that the present invention is obtained, the permeability of RDG is high, while having ideal Operational efficiency, therefore overall carbon emission is less.Concrete RDG layouts and program results are as shown in table 2 and table 3.

2 each node blower fan of table, photovoltaic and the new installed capacities of GT

3 program results of table is contrasted

Contrasted with the inventive method, from comprehensive benefit, under identical constraints, the inventive method investment compared with Few, this is because the layout of DG is more reasonable, and the degrees of coordination between electrical network and DG is more preferable, it is to avoid inefficient investment；And feelings Scape (2) can not truly reflect DG units in the realistic case to true produced by environment due to not counting and implying carbon emission Real impact, it is contemplated that carbon emission is more optimistic than actual conditions.The result of the program results and scene (2) of contrast the inventive method can be seen Go out, the actual carbon emission of scene (2) is 333.94 × 10³T, is higher by 18.71% than the inventive method.It can be seen that, system feeder line, DG Implicit carbon emission the actual carbon emission in system life cycle management is affected very big, as shown in Figure 6.

Fig. 6 be system in various equipment implicit carbon emission amount to actual total carbon emissions and the impact of gross investment.Wherein, base This scene is the inventive method acquired results, is scene (2) analog result without implicit carbon scene, the listed scene of basic scene or so Respectively compared to the implicit carbon variable quantity of basic scene (increasing by 10% compared to the implicit carbon of basic scene as 10% scene is). From fig. 6 it can be seen that implicit carbon emission amount and correlation between them, and persistently increase with which, to actual total The impact of carbon emission is also strengthening.

Algorithm and display be not inherently related to any certain computer, virtual system or miscellaneous equipment provided herein. Various general-purpose systems can also be used together based on teaching in this.As described above, construct required by this kind of system Structure be obvious.Additionally, the present invention is also not for any certain programmed language.It is understood that, it is possible to use various Programming language realizes the content of invention described herein, and the above description done by language-specific is to disclose this Bright preferred forms.

In specification mentioned herein, a large amount of details are illustrated.It is to be appreciated, however, that the enforcement of the present invention Example can be put into practice in the case where not having these details.In some instances, known method, structure are not been shown in detail And technology, so as not to obscure the understanding of this description.

Similarly, it will be appreciated that in order to simplify the disclosure helping understand one or more in each inventive aspect, Above in the description to the exemplary embodiment of the present invention, each feature of the present invention is grouped together into single enforcement sometimes In example, figure or descriptions thereof.However, should not be construed to reflect following intention by the method for the disclosure：I.e. required guarantor The more features of feature that the application claims ratio of shield is expressly recited in each claim.More precisely, such as following Claims reflected as, inventive aspect is all features less than single embodiment disclosed above.Therefore, Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself All as the separate embodiments of the present invention.

Those skilled in the art be appreciated that can to embodiment in equipment in module carry out adaptively Change and they are arranged in one or more equipment different from the embodiment.Can be the module in embodiment or list Unit or component are combined into a module or unit or component, and can be divided in addition multiple submodule or subelement or Sub-component.In addition at least some in such feature and/or process or unit is excluded each other, can adopt any Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (includes adjoint power Profit is required, summary and accompanying drawing) disclosed in each feature can identical by offers, be equal to or the alternative features of similar purpose carry out generation Replace.

Although additionally, it will be appreciated by those of skill in the art that some embodiments described herein include other embodiments In some included features rather than further feature, but the combination of the feature of different embodiments means in of the invention Within the scope of and form different embodiments.For example, in the following claims, embodiment required for protection appoint One of meaning can in any combination mode using.

The present invention all parts embodiment can be realized with hardware, or with one or more processor operation Software module realize, or with combinations thereof realize.It will be understood by those of skill in the art that can use in practice Microprocessor or digital signal processor (DSP) are filled come the planning for realizing low-carbon (LC) electric power system according to embodiments of the present invention The some or all functions of some or all parts in putting.The present invention is also implemented as described here for executing Method some or all equipment or program of device (for example, computer program and computer program).This The program of the realization present invention of sample can be stored on a computer-readable medium, or can have one or more signal Form.Such signal can be downloaded from internet website and be obtained, or provide on carrier signal, or with any other Form is provided.

It should be noted that above-described embodiment the present invention will be described rather than limits the invention, and ability Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims, Any reference symbol being located between bracket should not be configured to limitations on claims.Word "comprising" is not excluded the presence of not Element listed in the claims or step.Word "a" or "an" before being located at element does not exclude the presence of multiple such Element.The present invention can come real by means of the hardware for including some different elements and by means of properly programmed computer Existing.If in the unit claim for listing equipment for drying, several in these devices can be by same hardware branch To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and be run after fame Claim.

Claims

1. a kind of towards carbon footprint minimize active power distribution network two-stage programming method, including：

The resource layer model of active power distribution network is set up, its control variables includes characterizing system network topology and feeder line line style Primary vector, renewable energy power generation unit (RDG) and non-renewable energy resources generating set (NRDG) in sign system respectively Installation site and the secondary vector L of the configuration number of units in the installation site_RDGWith the 3rd vector L_NRDG, its object function is min F=(D_C+O_C)/H, wherein D_CFeeder line, renewable energy power generation unit and non-renewable energy resources generator contained by expression system Group corresponding implicit carbon emission, O within planning horizon_CNon-renewable energy resources are generated electricity when expression system was run within the planning horizon Unit calls power and calls the generating carbon emission corresponding to electric energy, H to represent that system was used within planning horizon from higher level's electrical network The predicted value of electricity；

Set up the operation layer model of active power distribution network, to can be again when its control variables includes that sign system is run in the simulation cycle Raw energy generator group and non-renewable energy resources generating set call power and call the four-way of electric energy from higher level's electrical network Amount, its object function are min f=O_C/ ε, wherein ε are represented to be carried out turning from the corresponding time scale of simulation cycle to planning horizon The changed factor for changing；And

Resource layer model is solved using pre-defined algorithm, obtain the optimal solution of first, second and third vector, and by the optimal solution As the programme of active power distribution network, wherein when the target function value of resource layer model is obtained, according to first, second and third to Programme determined by the value of amount solves operation layer model, determines resource layer mould according to the solving result to running layer model O in the object function of type_CValue.

2. planing method as claimed in claim 1, wherein, methods described also includes：

Exerting oneself for uncontrollable power generation energy resource resource in system is divided into several scenes, the corresponding area of exerting oneself of each scene Between；

Respectively using corresponding for each scene interval algebraic mean value of exerting oneself as the phase of uncontrollable power generation energy resource resource under the scene Prestige is exerted oneself；

Expectation according to each scene is exerted oneself and corresponding probability of happening determines uncontrollable power generation energy resource resource under day part Expect to exert oneself.

3. planing method as claimed in claim 1 or 2, wherein, the object function for running layer model is specially：

Wherein PP_EG,tRepresent period t from higher level Electrical network calls power, ρ_EGRepresent that higher level's electrical network often provides the generating carbon emission corresponding to 1kWh electric energy, Δ t represents each period Duration, TH represents the fixed number included by simulation cycle, ρ_NRDG,yRepresent y kind non-renewable energy resources generating sets The every generating carbon emission corresponding to generating 1kWh electric energy, PP_{NRDG,y,g’,t}Represent period t to the non-renewable energy of the upper y kinds of node g ' Source generating set calls power, Φ_NRDGThe species set of non-renewable energy resources generating set, Ω in expression system_NRDG,yRepresent In system all types for y non-renewable energy resources generating set to be selected install node constitute set.

4. planing method as claimed in claim 1, wherein, the constraints of the resource layer model include following one or Multiple：In investment totle drilling cost constraint, each node, the permeability constraint of power generation energy resource resource and holding network topology structure are radiation The constraint of shape.

5. planing method as claimed in claim 1, wherein, the constraints of the operation layer model include following one or Multiple：Power-balance constraint, the constraint of networking power swing, node voltage amplitude constraint, the constraint of feeder line current-carrying capacity, system receiving end are special Property constraint, power generation energy resource resource units limits and power generation energy resource resource power factor constraint.

6. planing method as claimed in claim 1, wherein, the pre-defined algorithm is genetic algorithm, using resource layer model Target function value with -1 product as genetic algorithm fitness function value.

7. planing method as claimed in claim 1, wherein, is solved using interior point method, particle cluster algorithm or genetic algorithm Operation layer model.

8. a kind of towards carbon footprint minimize active power distribution network two-stage programming device, including：

Resource layer model sets up unit, is adapted to set up the resource layer model of active power distribution network, and its control variables includes sign system Renewable energy power generation unit (RDG) and not in the primary vector of network topology structure and feeder line line style, respectively sign system Renewable energy power generation unit (NRDG) installation site and the secondary vector L of the configuration number of units in the installation site_RDGWith the 3rd Vectorial L_NRDG, its object function is minF=(D_C+O_C)/H, wherein D_CFeeder line, renewable energy power generation contained by expression system Unit and non-renewable energy resources generating set corresponding implicit carbon emission, O within planning horizon_CExpression system is within the planning horizon Power is called during operation to non-renewable energy resources generating set and calls the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within the planning horizon；

Operation layer model sets up unit, is adapted to set up the operation layer model of active power distribution network, and its control variables includes sign system When running in the simulation cycle to renewable energy power generation unit and non-renewable energy resources generating set call power and from Higher level's electrical network calls the 4th vector of electric energy, and its object function is minf=O_C/ ε, wherein ε represent by simulation cycle corresponding when Between the changed factor changed to planning horizon of yardstick；And

Model solution unit, is suitable for use with pre-defined algorithm and resource layer model is solved, obtain first, second and third vector most Excellent solution, and using the optimal solution as active power distribution network programme, wherein obtain resource layer model target function value when, Programme according to determined by the value of first, second and third vector solves operation layer model, according to the solution to running layer model As a result determine the O in the object function of resource layer model_CValue.

9. device for planning as claimed in claim 8, wherein, also includes envisioning scene collection construction unit, is suitable to：

10. device for planning as claimed in claim 8 or 9, wherein, the object function for running layer model is specially：

11. device for planning as claimed in claim 8, wherein, the constraints of the resource layer model includes following one Or it is multiple：In investment totle drilling cost constraint, each node, the permeability constraint of power generation energy resource resource and holding network topology structure are spoke Penetrate the constraint of shape.

12. device for planning as claimed in claim 8, wherein, the constraints of the operation layer model includes following one Or it is multiple：Power-balance constraint, the constraint of networking power swing, node voltage amplitude constraint, the constraint of feeder line current-carrying capacity, system receiving end Characteristic constraint, power generation energy resource resource units limits and the constraint of power generation energy resource resource power factor.

13. device for planning as claimed in claim 8, wherein, the pre-defined algorithm be genetic algorithm, the model solution unit Using the target function value of resource layer model and -1 product as genetic algorithm fitness function value.

14. device for planning as claimed in claim 8, wherein, the model solution unit using interior point method, particle cluster algorithm or Person's genetic algorithm is solving operation layer model.