CN104217302A - Method and device for carbon footprint minimization-orientated two-stage planning of active power distribution network - Google Patents

Abstract

The invention discloses a method and a device for the carbon footprint minimization-orientated two-stage planning of an active power distribution network. The method comprises the following steps of: establishing a resource layer model, wherein control variables comprise a first vector which is used for representing the network topology of a system and the line type of a feeder line, and a second vector and a third vector which are used for representing the installation positions of a renewable energy power generator set and a non-renewable energy power generator set in the system, and a configuration number, the objective function thereof is minF=(DC +OC)/H, DC represents embodied carbon emissions, OC represents generation carbon emissions, and H represents an electricity consumption predicted value; establishing a runtime layer model, wherein the control variables comprise a fourth vector which is used for representing the calling powers of the system for the renewable energy power generator set and the non-renewable energy power generator set when the system runs in a simulation period, and calling electric energy from an upper-level power grid, and the objective function thereof is min f=OC/epsilon, and epsilon is a change factor; solving the resource layer model to obtain the optimal solutions of the first vector, the second vector and the third vector, and taking the optimal solutions as the planning scheme of the system. According to the method and the device disclosed by the invention, the carbon footprint of electricity utilization of a terminal is effectively reduced.

Description

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

Technical field

The present invention relates to distribution optimisation technique, be specifically related to a kind of towards carbon footprint minimized active power distribution network two-stage programming method and device.

Background technology

Since entering 21 century, fossil energy shortage, climate change and environmental pollution become the common challenge that the whole world faces.Therefore, improving the diversity of energy supply, reduce terminal electricity consumption to the dependence of fossil energy, thus reduce the carbon emission because human activity produces to greatest extent, is the important topic that current power industrial development faces.

Distributed power supply system (Distributed Generation System, DGS) such as active power distribution network is by the polytype distributed power source of organic integration (Distributed Generation, DG), distribution network and end loads, can realize the on-site elimination to the regenerative resource such as wind, light and utilization, be therefore the effective way reaching above-mentioned target.

Set up the DGS of low carbonization, its condition precedent is by scientific and effective planing method, namely determine what, the distributed power source where investing to build which kind of type (or capacity) or feeder line in year, thus on the basis reaching power supply capacity required in planning horizon the target of realization electrical carbon footprint minimumization.For the DGS towards low-carbon (LC), the planing method extensively adopted at present is: first, under the condition of given network structure, according to the planning level year load maximal value that load prediction obtains, max cap. nargin (being namely multiplied by a nargin coefficient on the basis of each circuit maximum carrying capacity predicted value) is adopted to deal with the service condition of the most serious operating mode; Then, on the basis of above-mentioned network structure, in system, all DG output power summations are maximum as target, optimize their best positions in systems in which and capacity.

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

Summary of the invention

In view of the above problems, propose the present invention in case provide a kind of overcome the problems referred to above or solve the problem at least in part towards carbon footprint minimized active power distribution network two-stage programming method and device.

According to an aspect of the present invention, provide a kind of towards carbon footprint minimized active power distribution network two-stage programming method, comprising:

Set up the resource layer model of active power distribution network, its control variable comprises secondary vector and the 3rd vector of renewable energy power generation unit and non-renewable energy resources genset installation site and the configuration number of units in this installation site in primary vector, the respectively characterization system of characterization system network topology structure and feeder line line style, and its objective function is min F=(D _c+ O _c)/H, wherein D _cthe implicit carbon emission that feeder line, renewable energy power generation unit and non-renewable energy resources genset contained by expression system are corresponding within planning horizon, O _ccall power to non-renewable energy resources genset when expression system is run within planning horizon and call the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within planning horizon;

Set up the firing floor model of active power distribution network, its control variable comprises calling power and calling the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources genset when characterization system runs in simulation cycle, and its objective function is min f=O _c/ ε, wherein ε represents and carries out by the time scale that simulation cycle is corresponding the changed factor changed to planning horizon; And

Pre-defined algorithm is adopted to solve resource layer model, obtain the optimum solution of first, second and third vector, and using the programme of this optimum solution as active power distribution network, wherein when the target function value of Gains resources layer model, firing floor model is solved, according to the O in the objective function of the solving result determination resource layer model to firing floor model according to the determined programme of the value of first, second and third vector _cvalue.

Alternatively, described method also comprises:

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

Respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

Determine that in simulation cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

To exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

Alternatively, the objective function of firing floor model is specially:

$\min f=\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t},$ Wherein PP _{eG, t}represent that period t calls power, ρ from higher level's electrical network _eGrepresent that higher level's electrical network often provides the carbon emission of the generating corresponding to 1kWh electric energy, Δ t represents the duration of each period, and TH represents the fixed number that simulation cycle comprises, ρ _{nRDG, y}represent the generating carbon emission that y kind non-renewable energy resources genset often generates electricity corresponding to 1kWh electric energy, PP _{nRDG, y, g ', t}represent that period t calls power, Φ to node g ' upper y kind non-renewable energy resources genset _nRDGthe kind set of non-renewable energy resources genset in expression system, Ω _{nRDG, y}in expression system, all types is the set of the non-renewable energy resources genset installation node formation to be selected of y.

Alternatively, the constraint condition of described resource layer model comprises following one or more: in the constraint of investment total cost, each node power generation energy resource resource permeability constraint and keep network topology structure to be radial constraint.

Alternatively, the constraint condition of described firing floor model comprises 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, the constraint of system receiving end restrain condition, power generation energy resource resource units limits and power generation energy resource resource power factor.

Alternatively, described pre-defined algorithm is genetic algorithm, adopts the target function value of resource layer model and the fitness function value of the product of-1 as genetic algorithm.

Alternatively, interior point method, particle cluster algorithm or genetic algorithm is adopted to solve firing floor model.

According to a further aspect in the invention, provide a kind of towards carbon footprint minimized active power distribution network two-stage programming device, comprising:

Unit set up by resource layer model, be suitable for the resource layer model setting up active power distribution network, its control variable comprises secondary vector and the 3rd vector of renewable energy power generation unit and non-renewable energy resources genset installation site and the configuration number of units in this installation site in primary vector, the respectively characterization system of characterization system network topology structure and feeder line line style, and its objective function is min F=(D _c+ O _c)/H, wherein D _cthe implicit carbon emission that feeder line, renewable energy power generation unit and non-renewable energy resources genset contained by expression system are corresponding within planning horizon, O _ccall power to non-renewable energy resources genset when expression system is run within planning horizon and call the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within planning horizon;

Unit set up by firing floor model, be suitable for the firing floor model setting up active power distribution network, its control variable comprises calling power and calling the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources genset when characterization system runs in simulation cycle, and its objective function is min f=O _c/ ε, wherein ε represents and carries out by the time scale that simulation cycle is corresponding the changed factor changed to planning horizon; And

Model solution unit, be suitable for adopting pre-defined algorithm to solve resource layer model, obtain the optimum solution of first, second and third vector, and using the programme of this optimum solution as active power distribution network, wherein when the target function value of Gains resources layer model, firing floor model is solved, according to the O in the objective function of the solving result determination resource layer model to firing floor model according to the determined programme of the value of first, second and third vector _cvalue.

Alternatively, described device for planning also comprises anticipation scene collection construction unit, is suitable for:

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

Respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

Determine that in simulation cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

To exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

Alternatively, the objective function of firing floor model is specially:

$\min f=\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t},$ Wherein PP _{eG, t}represent that period t calls power, ρ from higher level's electrical network _eGrepresent that higher level's electrical network often provides the carbon emission of the generating corresponding to 1kWh electric energy, Δ t represents the duration of each period, and TH represents the fixed number that simulation cycle comprises, ρ _{nRDG, y}represent the generating carbon emission that y kind non-renewable energy resources genset often generates electricity corresponding to 1kWh electric energy, PP _{nRDG, y, g ', t}represent that period t calls power, Φ to node g ' upper y kind non-renewable energy resources genset _nRDGthe kind set of non-renewable energy resources genset in expression system, Ω _{nRDG, y}in expression system, all types is the set of the non-renewable energy resources genset installation node formation to be selected of y.

Alternatively, the constraint condition of described resource layer model comprises following one or more: in the constraint of investment total cost, each node power generation energy resource resource permeability constraint and keep network topology structure to be radial constraint.

Alternatively, the constraint condition of described firing floor model comprises 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, the constraint of system receiving end restrain condition, power generation energy resource resource units limits and power generation energy resource resource power factor.

Alternatively, described pre-defined algorithm is genetic algorithm, and described model solution unit adopts the target function value of resource layer model and the fitness function value of the product of-1 as genetic algorithm.

Alternatively, described model solution unit adopts interior point method, particle cluster algorithm or genetic algorithm to solve firing floor model.

According to the programme towards the carbon footprint minimized active power distribution network two-stage of the present invention, consider the carbon emission because the different physics links such as resource use, operation generating, equipment scrapping produce simultaneously, configured by coordination optimization grid structure, feeder line line style and DG, the final system cooked up can be ensured in satisfied investment amount restriction with under the prerequisite of safe operation, realize the reduction of truly terminal electricity consumption overall process carbon footprint, and on optimization time and solving precision, meet the demand of engineer applied.

Above-mentioned explanation is only the general introduction of technical solution of the present invention, in order to technological means of the present invention can be better understood, and can be implemented according to the content of instructions, and can become apparent, below especially exemplified by the specific embodiment of the present invention to allow above and other objects of the present invention, feature and advantage.

Accompanying drawing explanation

By reading hereafter detailed description of the preferred embodiment, various other advantage and benefit will become cheer and bright for those of ordinary skill in the art.Accompanying drawing only for illustrating the object of preferred implementation, and does not think limitation of the present invention.And in whole accompanying drawing, represent identical parts by identical reference symbol.In the accompanying drawings:

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

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

Fig. 3 shows in the embodiment of the present invention when using genetic algorithm the schematic diagram that programme is encoded;

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 implicit carbon emission amount affects schematic diagram to actual total carbon emissions and gross investment.

Embodiment

Below with reference to accompanying drawings exemplary embodiment of the present disclosure is described in more detail.Although show exemplary embodiment of the present disclosure in accompanying drawing, however should be appreciated that can realize the disclosure in a variety of manners and not should limit by the embodiment set forth here.On the contrary, provide these embodiments to be in order to more thoroughly the disclosure can be understood, and complete for the scope of the present disclosure can be conveyed to those skilled in the art.

After the planing method of present inventor to existing distributed power supply system (such as active power distribution network) is analyzed, find that the reason causing it fully can not realize effectively reducing this elementary object of carbon footprint of terminal electricity consumption is as follows:

1) have ignored because system equipment (feeder line, DG) uses the implicit carbon emission caused, namely equipment is in the indirect carbon emission of building, install, use and cause in retired process, relation between (feeder line line style, DG capacity) and the indirect carbon emission cost of its generation is chosen owing to failing to characterize resource, there is very large one-sidedness in final program results, can not ensure the target of " terminal electrical carbon footprint is minimum ".

2) carry out independent optimization to space truss project and DG addressing constant volume, people is the natural inner link of having isolated between the two and the reciprocal effect that have ignored each other.Under the power grid architecture determined, by the feas ible space that restriction DG allocation plan is formulated.

Therefore, the embodiment of the present invention provides a set of more science and advanced optimization planning scheme, with careful consideration because each process link of terminal system that need for electricity causes (comprises construction, run and equipment scrapping process) carbon emission factor, by complex optimum grid structure, the installation site of feeder line line style and DG and capacity, can play and improve renewable energy utilization efficiency, reduce network loss, delay the object of resource user demand, for Power System Planning supvr provides a kind of viable capital programme that can realize terminal electricity consumption overall process carbon footprint and reduce.

For ease of clearly and accurately understanding embodiments of the invention, now the part term wherein related to is made an explanation.

The active power distribution network (Active distribution network, ADN) of the embodiment of the present invention comprises various power generation energy resource resource (genset), and it can be classified in different ways.Under a kind of mode classification, it can be divided into controlled power generation energy resource resource and uncontrollable power generation energy resource resource.Controlled power generation energy resource resource refers to that real output can carry out a class power output device of manual control according to the actual requirements, comprises gas turbine, diesel-driven generator, coal unit, biomass fermentation group of motors, geothermal power generation unit and nuclear power generating sets etc.; Uncontrollable power generation energy resource resource refers to that real output depends on power generation energy resource feature and cannot carry out a class power output device of manual control according to the actual requirements, comprises photovoltaic generation, wind-power electricity generation, tidal power generation and various energy storage devices etc.

Under another mode classification, it can be divided into renewable energy power generation unit and non-renewable energy resources genset.Further, renewable energy power generation unit can comprise controlled power generation energy resource resource and uncontrollable power generation energy resource resource, and non-renewable energy resources genset also can comprise controlled power generation energy resource resource and uncontrollable power generation energy resource resource.

In the part of the embodiment of the present invention describes, controlled power generation energy resource resource is for controlled distributed unit (i.e. controlled DG), and uncontrollable power generation energy resource resource is described for uncontrollable distributed unit (i.e. uncontrollable DG).

Fig. 1 shows according to an embodiment of the invention towards carbon footprint minimized active power distribution network two-stage programming method flow diagram, and this planing method can be performed by various computing equipment.With reference to Fig. 1, this planing method starts from step S102, in step s 102, builds the anticipation scene collection describing active power distribution network running status, wherein, according to the historical statistical data of DG power generation energy resource, can set the anticipation scene collection of DGS running status.In one implementation, all DG types in being planned by DGS are divided into controlled DG and uncontrollable DG, the construction basis using the output power of uncontrollable DG as anticipation scene collection, and perform following steps:

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

Described division can be carried out, such as, if the single-machine capacity of certain uncontrollable DG is 5MW according to the experience of designer or subjective judgement, then can " 1MW " as interval step-length, actual the exerting oneself of this uncontrollable DG is divided into following 5 scenes: i.e. [0,1MW], (1MW, 2MW], (2MW, 3MW], (3MW, 4MW], (4MW, 5MW].The selection of interval step-length can be determined according to the experience of designer or subjective judgement.Generally speaking, interval step-length is less, and the scene quantity of generation is more, and simulation accuracy will be made higher, but also will bring larger calculated amount simultaneously; Otherwise interval step-length is less, overall calculation amount will reduce, but simulation accuracy also will decrease.

(2) respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

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

(3) determine that in the working train family cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

Can according to the statistics of uncontrollable DG (such as Renewable Energy Resources) at history year day part, the corresponding probability of happening expecting to exert oneself of each scene under determining working train family cycle Different periods.For wind-resources, if having the planning area wind speed information (wind speed exert oneself be associated) with the expectation of Wind turbines of annual day part of 5 years in history, namely have 5*8760=43800 data point.First, the data point of 0 o'clock to 1 o'clock every day in these data points is extracted (amounting to 5*365=1825 data point), calculate this period (namely) regenerative resource DG at 0 o'clock to 1 o'clock according to following formula to exert oneself and drop on the probability of scene interval [0,1MW]:

$P_s\left(\right[\mathrm{0,1}\mathrm{MW}\left]\right)=\frac{N\left(\right[\mathrm{0,1}\mathrm{MW}\left]\right)}{N_{\mathrm{total}}}---\left(1\right)$

Wherein, N ([0,1MW]) drops on the number of interval [0,1MW] for DG in data point exerts oneself; N _totalfor the number at the total strong point that this period gathers.For this example, namely there is N _total=43800 ÷ 24=1825.

According to upper type, can determine that each regenerative resource DG exerts oneself appearance (generation) probability of scene interval in this period.

(4) to exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

Particularly, for each period, can the expectation of each scene be exerted oneself respectively is multiplied with corresponding probability of happening, then all multiplied result is added up can obtain the expectation of uncontrollable power generation energy resource resource under this period and exert oneself.

Such as, every day 0 o'clock to 1 o'clock uncontrollable power generation energy resource resource expectation exert oneself and can be expressed as:

$E{\left(P_s\right)}_{0\→1}=\underset{\mathrm{\σ}=1}{\overset{S}{\mathrm{\Σ}}}{P}_s\left(\right[{\mathrm{\η}}_{\mathrm{\σ}},{\mathrm{\η}}_{\mathrm{\σ}+1}\left]\right)\·\frac{{\mathrm{\η}}_{\mathrm{\σ}}+{\mathrm{\η}}_{\mathrm{\σ}+1}}{2}---\left(2\right)$

Wherein, S represents total number of scenes that uncontrollable power generation energy resource resource is corresponding, η _σrepresent the initial capacity of σ scene, η _σ+1represent the termination capacity of σ scene, η _σ+1-η _σit is then the step-length of a scene of delimitation.Such as, for scene 1:[0,1MW], η _σ=0MW, η _σ+1=1MW, step-length is 1MW.

After having built the anticipation scene collection describing active power distribution network running status, planing method has entered step S104, in step S104, sets up the resource layer model of active power distribution network.The process of establishing of resource layer model is specific as follows:

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

(1) the vectorial B of characterization system network topology structure and feeder line line style; Wherein, B={ λ _{ij, a}| ij ∈ Ω _fD, a ∈ Ω _a, Ω _fDfor set of building line channel and forming all in system, Ω _arepresent the set of available feeder line line style (type).λ _{ij, a}for (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 and feeder line is not installed at this passage.

(2) the vectorial L of renewable energy power generation unit (RDG) and non-renewable energy resources genset (NRDG) installation site and the configuration number of units in this installation site in characterization system is distinguished _rDGand L _nRDG; Wherein, L _rDG={ n _{rDG, g, x}| g ∈ Ω _{rDG, x}, x ∈ Φ _rDG, Ω _{rDG, x}be the set of the RDG unit installation node formation to be selected of x for all types in system, Φ _rDGrDG kind set in expression system, n _{rDG, g, x}for integer variable, the unit number of units that the RDG that in expression system, type is x configures at installation node g to be selected; L _nRDG={ n _{nRDG, g ', y}| g ' ∈ Ω _{nRDG, y}, y ∈ Φ _nRDG, Ω _{nRDG, y}be the set of the NRDG unit installation node formation to be selected of y for all types in system, Φ _nRDGnRDG kind set in expression system, n _{nRDG, g ', y}for integer variable, the unit number of units that the NRDG that in expression system, type is y configures at installation node g ' to be selected.

Then, the unit used electricity amount of load is corresponding in planning horizon (following example is planning level year) active power distribution network life cycle management carbon emission (comprising the implicit carbon of feeder line and each power generation energy resource resource in system and generating carbon emission corresponding to electricity consumption) is minimum as target, build the objective function of resource layer model, expression formula is as follows:

minF＝(D _C+O _C)/H (3)

Wherein, H represents the predicted value of planning level year system loading power consumption; D _ccontained feeder line and corresponding being worth in implicit carbon year of each power generation energy resource resource in expression system, its expression formula is as follows:

$D_C=(D_{\mathrm{FD}}/{\mathrm{\β}}_{\mathrm{FD}})+\underset{x\∈{\mathrm{\Φ}}_{\mathrm{RDG}}}{\mathrm{\Σ}}{D}_x^{\mathrm{RDG}}/{\mathrm{\β}}_x^{\mathrm{RDG}}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}{D}_y^{\mathrm{NRDG}}/{\mathrm{\β}}_y^{\mathrm{NRDG}}---\left(4\right)$

Wherein, β _fDfor the life-span time limit that feeder line is corresponding; for the life-span time limit that xth kind RDG is corresponding; it is the life-span time limit that y kind NRDG is corresponding; Φ _rDGand Φ _nRDGrepresent the set of RDG kind and the set of NRDG kind in DGS respectively; D _fDthe life cycle management corresponding for feeder line in DGS implies carbon, and expression is as follows:

$D_{\mathrm{FD}}=\underset{\mathrm{ij}\∈{\mathrm{\Ω}}_{\mathrm{FD}}}{\mathrm{\Σ}}\underset{a\∈{\mathrm{\Ω}}_A}{\mathrm{\Σ}}{d}_{\mathrm{fd},a}{l}_{\mathrm{ij}}{\mathrm{\λ}}_{\mathrm{ij},a}---\left(5\right)$

Wherein, d _{fd, a}for the implicit carbon that type a feeder line unit km is corresponding, unit is kg CO ₂/ km, l _ijrepresent the length of the line channel between node i j, unit is km.

In formula (4), the life cycle management corresponding for xth kind RDG contained in DGS implies carbon, and expression is as follows:

$D_x^{\mathrm{RDG}}=\underset{g\∈{\mathrm{\Ω}}_{\mathrm{RDG},x}}{\mathrm{\Σ}}{d}_x{n}_{\mathrm{RDG},g,x}---\left(6\right)$

Wherein, d _xfor implicit carbon (the kg CO that separate unit xth kind RDG in DGS is corresponding ₂/ platform).

In formula (4), the life cycle management corresponding for y kind NRDG contained in DGS implies carbon, and expression is as follows:

$D_y^{\mathrm{NRDG}}=\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}{d}_y{n}_{\mathrm{NRDG},g^{,},y}---\left(7\right)$

Wherein, d _yfor implicit carbon (the kg CO that separate unit y kind NRDG in DGS is corresponding ₂/ platform).

For the parameter d in formula (5) ~ (7) _{fd, a}, d _xand d _yraw materials used in the manufacture process of feeder line, RDG and NRDG and quality can be added up respectively, and installing, using and Fossil fuel consumption in retired process, carry out calculating and obtain.

In formula (3), O _crepresent the generating carbon emission (comprising system call power to non-renewable energy resources genset and call the generating carbon emission corresponding to electric energy from higher level's electrical network) caused because of power consumption in DGS in planning level year, expression is as follows:

$O_C=\mathrm{\ϵ}(\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t})---\left(8\right)$

Wherein, ρ _eGrepresent that higher level's electrical network often provides 1kWh electric energy the corresponding generating carbon emission produced, unit is kg CO ₂/ kWh; PP _{eG, t}represent that period t calls power from higher level's electrical network; Δ t represents the duration (being such as 1 hour) of each period; TH represents the fixed number that simulation cycle (being such as 1 day) comprises; ρ _{nRDG, y}represent the generating carbon emission of y kind non-renewable energy resources genset (being NRDG in this example) the corresponding generation of 1kWh electric energy that often generates electricity, unit is kg CO ₂/ kWh; PP _{nRDG, y, g ', t}for period t calls power to node g ' upper y kind non-renewable energy resources genset (in this example for NRDG); ε represents and carries out by time scale corresponding to simulation cycle the transformation factor changed year to planning, and in one implementation, its expression formula is as follows:

ε＝8760/(TH·Δt) (9)

Wherein, 8760 is the hourage comprised for a year.

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

Finally, the constraint condition of setting resource layer model, specifically can comprise:

The constraint of (a) investment total cost, active power distribution network overall cost of ownership can not exceed master budget, and expression formula is as follows:

0≤C _FD+C _RDG+C _NRDG≤C ₀ (10)

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

$C_{\mathrm{FD}}=\underset{\mathrm{ij}\∈{\mathrm{\Ω}}_{\mathrm{FD}}}{\mathrm{\Σ}}\underset{a\∈{\mathrm{\Ω}}_A}{\mathrm{\Σ}}{c}_{\mathrm{fd},a}{l}_{\mathrm{ij}}{\mathrm{\λ}}_{\mathrm{ij},a}---\left(11\right)$

Wherein, c _{fd, a}for 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:

$C_{\mathrm{RDG}}=\underset{x\∈{\mathrm{\Φ}}_{\mathrm{RDG}}}{\mathrm{\Σ}}\underset{g\∈{\mathrm{\Ω}}_{\mathrm{RDG},x}}{\mathrm{\Σ}}{c}_x{n}_{\mathrm{RDG},g,x}---\left(12\right)$

Wherein, c _xfor the single unit cost of xth kind RDG.

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

$C_{\mathrm{NRDG}}=\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}{c}_y{n}_{\mathrm{NRDG},g^{,},y}---\left(13\right)$

Wherein, c _yit is the single unit cost of y kind NRDG.

In formula (10), C ₀for building the gross investment budget of active power distribution network.

B () node DG permeability retrains, restrict by floor area, DG has certain restriction at the access capacity of node, and expression is as follows:

$0\≤\underset{x\∈{\mathrm{\Φ}}_{\mathrm{RDG}}}{\mathrm{\Σ}}{n}_{\mathrm{RDG},i,x}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}{n}_{\mathrm{NRDG},i,y}\≤{\mathrm{nl}}_i,\∀i\∈\mathrm{\Ω}---\left(14\right)$

Wherein, nl _ifor the maximum DG quantity (comprising RDG and NRDG) that node i can access; Ω represents system node set.

C () in addition, should keep network topology structure to be radial.

After having set up the resource layer model of active power distribution network, planing method has entered step S106.In step s 106, the firing floor model of active power distribution network is set up.The process of establishing of firing floor model is specific as follows:

First, the control variable of firing floor model is determined.A working train family cycle (being such as 1 day) is divided into TH period, and the duration of each period is Δ t (hour), and be such as 1 hour, then the control variable of firing floor model is as follows:

In characterization system, all types of power generation energy resource resource is in the vector PP of day part optimal power controlled quentity controlled variable; 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, t}represent period t and power is called, PP to the actual of RDG on node g _{nRDG, g ', t}represent period t and power is called, PP to upper the actual of NRDG of node g ' _{eG, t}represent period t and call power from the actual of higher level's electrical network.

Then, minimum as target using system cloud gray model stage in planning level year expection generating carbon emission, build the objective function of firing floor model, expression formula is as follows:

$\min f=\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t}---\left(15\right)$

Finally, the constraint condition of setting firing floor model, specifically comprises:

A () power-balance retrains:

$\left\{\begin{array}{c}{P}_{i,t}=V_{i,t}\underset{j\∈i}{\mathrm{\Σ}}{V}_{j,t}(G_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij},t}+B_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij},t})\\ Q_{i,t}=V_{i,t}\underset{j\∈i}{\mathrm{\Σ}}{V}_{j,t}(G_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij},t}-B_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij},t})\end{array}\right.---\left(16\right)$

Wherein: P _i,tand Q _i,tbe respectively the meritorious of period t node i and idle injecting power; J ∈ i represents all node set be directly connected with node i; V _i,tand V _j,trepresent the voltage magnitude of period t node i and node j respectively; G _ijwith B _ijrepresent real part and the imaginary part of bus admittance matrix respectively; θ _{ij, t}represent the phase difference of voltage between period t node i and j.

B () networking power swing retrains:

Consider from the security and stability of operation of power networks, bulk power grid energy supply is more favourable than distributed unit energy supply.But bulk power grid not exclusively possesses the fast track and responding ability that fluctuate for system power, when the fluctuation of DGS system internal power is excessive, bulk power grid cannot meet all demands in system at short notice, and its rate of change to DGS injecting power should control in certain limit, is shown below:

$|{\mathrm{PP}}_{\mathrm{EG},t}-{\mathrm{PP}}_{\mathrm{EG},t-1}|/\mathrm{\Δt}\≤{\mathrm{\ψ}}_{\max },\∀t=1.\·\·\·\mathrm{TH}---\left(17\right)$

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

C () node voltage amplitude retrains:

$V_{\min }\≤V_{i,t}\≤V_{\max },\∀i\∈\mathrm{\Ω},t=1,\·\·\·\mathrm{TH}---\left(18\right)$

Wherein, V _i,trepresent the magnitude of voltage of period t system node i; V _maxrepresent the maximal value that system node voltage allows; V _minrepresent the minimum value that system node voltage allows.

D () feeder line current-carrying capacity retrains:

$0\≤I_{\mathrm{ij},t}\≤\underset{a\∈{\mathrm{\Ω}}_A}{\mathrm{\Σ}}{I}_{a,\max }{\mathrm{\λ}}_{\mathrm{ij},a},\∀\mathrm{ij}\∈{\mathrm{\Ω}}_{\mathrm{FD}},t=1,\·\·\·\mathrm{TH}---\left(19\right)$

Wherein, I _{ij, t}represent period t node i, the current value that between j, circuit flows through; I _{a, max}represent the maximum carrying capacity that feeder line line style a allows.

(e) DGS receiving end restrain condition:

PP _EG,t≥0,t＝1,…TH (20)

(f) DG units limits:

$\mathrm{RDG}:0\≤{\mathrm{PP}}_{\mathrm{RDG},g,t}\≤\underset{x\∈{\mathrm{\Φ}}_{\mathrm{RDG}}}{\mathrm{\Σ}}{n}_{\mathrm{RDG},g,x}{e}_{x,t},\∀g\∈{\mathrm{\Ω}}_{\mathrm{RDG},x};t=1,\·\·\·\mathrm{TH}---\left(21\right)$

$\mathrm{NRDG}:0\≤{\mathrm{PP}}_{\mathrm{NRDG},g^{,},t}\≤\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}{n}_{\mathrm{NRE},g^{,},y}{e}_{y,t},\∀g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y};t=1,\·\·\·\mathrm{TH}---\left(22\right)$

Wherein, e _x,tand e _y,trepresent xth kind RDG and the y kind NRDG unit maximum output at period t respectively.Specifically, for NRDG, e _y,t=Cap _{y, rated}, Cap _{y, rated}represent the rated capacity of NRDG; For RDG, then e _x,t=min (Cap _{x, rated}, Poten _x,t), Cap _{x, rated}represent the rated capacity of RDG, Poten _x,texpression type is that the separate unit RDG unit of x is exerted oneself in the maximum possible of period t, supplies situation determine by primary energy.

G () DG power factor retrains:

${\mathrm{\σ}}_{\mathrm{RDG},g,t}=\mathrm{Const}1,{\mathrm{\σ}}_{\mathrm{NRDG},g^{,},t}=\mathrm{Const}2,\∀g\∈{\mathrm{\Ω}}_{\mathrm{RDG},x};g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y};t=1,\·\·\·\mathrm{TH}---\left(23\right)$

Wherein, σ _{rDG, g, t}represent the power factor that in period t node g, RDG runs; σ _{nRDG, g ', t}represent the power factor that the interior NRDG of period t node g ' runs.In embodiment of the present invention setting DGS system, all DG (comprising RDG and NRDG) only produce active power, therefore, and Const 1=Const 2=1.

Above constraint condition conducts a research for controlled DG and uncontrollable DG and builds, if controlled and uncontrollable power generation energy resource resource conducts a research with other types, then can according to concrete power generation energy resource resource type with this field conventional and reasonably constraint condition carry out replacement and build again.

After having set up the firing floor model of active power distribution network, planing method has entered step S108.In step S108, adopt pre-defined algorithm to solve resource layer model, obtain control variable (vectorial B, the vectorial L of resource layer model _rDGand vectorial L _nRDG) optimum solution, and using the programme of this optimum solution as active power distribution network.In embodiments of the present invention, resource layer model and firing floor model are Nonlinear programming Model, therefore, the various algorithm solving nonlinear programming problem can be adopted to resource layer model and solving of firing floor model, such as interior point method, particle cluster algorithm and genetic algorithm etc., the embodiment of the present invention does not limit concrete algorithm, and those skilled in the art can choose reasonable as required.

Be described for the solution procedure of genetic algorithm to resource layer model below, wherein, adopt the target function value of resource layer model and the fitness function value of the product of-1 as genetic algorithm, and when the target function value of Gains resources layer model, according to vectorial B, vectorial L _rDGand vectorial L _nRDGthe determined programme of value solve firing floor model, according to the O in the objective function of the solving result determination resource layer model to firing floor model _cvalue.It should be noted that, in the process solving firing floor model, the capacity (expectation is exerted oneself) of renewable energy power generation unit can adopt the result of calculation in step S102.

The solution procedure of resource layer model is specifically comprised the steps:

A, stochastic generation N (N be greater than 1 integer, such as N value is 50) individual feasible programme (i.e. the control vector B of certainty annuity network topology structure, feeder line line style, and the control vector L of renewable energy power generation unit and non-renewable energy resources genset installation site and configuration number of units in system _rDGand L _nRDG), " feasible " refers to the constraint condition (i.e. formula (10) ~ (14) and the radial constraint of network topology structure) meeting resource layer model.

B, use real coding strategy, encode each programme of steps A gained.

Fig. 3 shows in the embodiment of the present invention when using genetic algorithm the schematic diagram that programme is encoded.With reference to Fig. 3, for feeder line region, line channel can be built for one in each representative system." n ^fd=0 " represent and line channel corresponding to this position does not set up any feeder line, " n ^fd=1 " represent and line channel corresponding to this position sets up feeder line Class1, by that analogy.

For RDG region, the RDG installation node to be selected in each representative system." n _r=0 " the RDG RDG installation to be selected node corresponding to this position not configuring any type x is represented, " n _r=1 " represent the corresponding RDG unit installing configuration 1 type x on node in this position, but machine set type is depending on optimum results, by that analogy.

For NRDG region, a NRDG power supply installation node to be selected " n in each representative system _nR=0 " represent and the NRDG installation to be selected node corresponding to this position does not configure the NRDG power supply that any type is y, " n _nR=1 " represent that corresponding the installation on node in this position configures the NRDG power supply that 1 type is y, but machine set type is also depending on optimum results, by that analogy.

C, for each programme of steps A gained, adopt interior point method, particle cluster algorithm or genetic algorithm to solve firing floor model, certainty annuity is at the optimized operation strategy (namely determining the vector PP of all types of power generation energy resource resource in day part optimal power controlled quentity controlled variable) of day part.

D, the system that obtains according to step C at the optimized operation strategy of day part, by the generating carbon emission O of formula (15) computing system within the working train family cycle _c;

E, the O that step D is obtained _cvalue, as known parameters, substitutes into formula (3), to calculate the complete target function value (formula (3)) obtained corresponding to this programme.

F. by the product of resource layer model objective function with "-1 " as fitness function Fit (), calculate the fitness function value that each programme is corresponding, record also descendingly to sort.

G, utilize genetic operation to generate a new generation (quantity is N) programme, specifically comprise:

G1, when in former generation programme, carry out selection operation based on direct ratio selection strategy, for as any individual i in former generation programme, its selected probability is Pr _i, its calculating formula is as follows:

${\mathrm{Pr}}_i=\frac{\mathrm{Fit}\left(x_i\right)}{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\mathrm{Fit}\left(x_i\right)}$

In above formula, Fit (x _i) represent through fitness function value corresponding to the individual i of step F calculating acquisition.

On the basis of the above, spinning roller method is adopted to realize selecting operation, order

PP ₀＝0

${\mathrm{PP}}_i=\underset{j=1}{\overset{i}{\mathrm{\Σ}}}{\mathrm{Pr}}_i$

Corotation wheel N time, during each runner, producing random number ξ, ξ is being uniformly distributed between (0,1), works as PP _i-1≤ ξ≤PP _itime, then select individual i.The above-mentioned N number of programme selected is namely common forms mating pond.

G2, for step G1 generate mating pond in individuality, with crossover probability J _c(span is such as set to 0.4 ~ 0.99) carries out interlace operation, and when interlace operation, random selecting two point of contacts (optional 2 positions namely in code pattern), exchange the substring between two point of contacts corresponding to two programmes.

G3, after interlace operation, with mutation probability J _m(span is such as set to 0.0001 ~ 0.1) carries out mutation operation (namely refer to the one or more point of contact of individual code string random choose in colony, and change its place value at random) to scheme individuality.Adopt for real coding mode for the embodiment of the present invention, the expression formula of its mutation operation is as follows:

For circuit feeder zone:

Wherein, for the new place value that corresponding point of contact is corresponding; INT () is the bracket function based on " rounding up " principle; ζ is stochastic generation number, meets being uniformly distributed (wherein, NA is the total quantity of optional feeder line line style) between [0, NA], and requires to make

For RDG region: n _{rDG, new}=INT (ζ ')

Wherein, n _{rDG, new}for the new place value that corresponding point of contact is corresponding; INT () is the bracket function based on " rounding up " principle; ζ ' is stochastic generation number, meets [0, Nl _rDG] between be uniformly distributed (wherein, Nl _rDGfor the installable maximum RDG number of power sources of relevant RDG installation to be selected node), and require to make n _{rDG, new}≠ n _rDG.

For NRDG region: n _{nRDG, new}=INT (ζ ")

Wherein, n _{nRDG, new}for the new place value that corresponding point of contact is corresponding; INT () is the bracket function based on " rounding up " principle; ζ " is stochastic generation number, meets [0, Nl _nRDG] between be uniformly distributed (wherein, Nl _nRDGfor the installable maximum NRDG number of power sources of relevant NRDG installation to be selected node), and require to make n _{nRDG, new}≠ n _nRDG.

H, the programme of new generation generated for step G, repeat step C to step F.The fitness function value of fitness function value corresponding for each programme of a new generation and each programme of previous generation is compared, sorts from big to small, and retain rank accordingly in the programme of top N, give up residue programme simultaneously.

E. repeated execution of steps G ~ step H, to Iter _maxsecondary (according to practical experience, Iter _maxspan be [500,1500]).Now the optimum results of resource layer model is the optimal programming scheme of realization oriented minimumization electrical carbon footprint, and exports.

It should be noted that, the execution sequence of the embodiment of the present invention to above-mentioned step does not limit, and can exchange order wherein as required, and can accept or reject some steps as required.Such as, can the execution sequence of exchange step S104 and step S106.

Again such as, can not perform step S102, namely step S102 is optional step.If do not perform step S102, in such cases, do not carry out the structure of system cloud gray model scene, but uncontrollable power generation energy resource resource is set according to the experience of those skilled in the art or subjective judgement exerts oneself in the expectation of day part.

Fig. 2 shows according to an embodiment of the invention towards carbon footprint minimized active power distribution network two-stage programming structure drawing of device.With reference to Fig. 2, described device for planning comprises: envision scene collection construction unit 202, unit 204 set up by resource layer model, unit 206 set up by firing floor model and model solution unit 208.

Anticipation scene collection construction unit 202 is suitable for building the anticipation scene collection describing active power distribution network running status, wherein, according to the historical statistical data of DG power generation energy resource, can set the anticipation scene collection of DGS running status.In one implementation, all DG types in being planned by DGS are divided into controlled DG and uncontrollable DG, the construction basis using the output power of uncontrollable DG as anticipation scene collection, and perform following steps:

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

Respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

Determine that in simulation cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

To exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

The actuating logic of anticipation scene collection construction unit 202 is identical with step S102, specifically see the description of step S102, can not repeat here.

Unit 204 set up by resource layer model, be suitable for the resource layer model setting up active power distribution network, its control variable comprises secondary vector and the 3rd vector of renewable energy power generation unit and non-renewable energy resources genset installation site and the configuration number of units in this installation site in primary vector, the respectively characterization system of characterization system network topology structure and feeder line line style, and its objective function is min F=(D _c+ O _c)/H, wherein D _cthe implicit carbon emission that feeder line, renewable energy power generation unit and non-renewable energy resources genset contained by expression system are corresponding within planning horizon, O _ccall power to non-renewable energy resources genset when expression system is run within planning horizon and call the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within planning horizon.The constraint condition of described resource layer model comprises following one or more: in the constraint of investment total cost, each node power generation energy resource resource permeability constraint and keep network topology structure to be radial constraint.

The actuating logic that unit 204 set up by resource layer model is identical with step S104, specifically see the description of step S104, can not repeat here.

Unit 206 set up by firing floor model, be suitable for the firing floor model setting up active power distribution network, its control variable comprises calling power and calling the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources genset when characterization system runs in simulation cycle, and its objective function is min f=O _c/ ε, wherein ε represents and carries out by the time scale that simulation cycle is corresponding the changed factor changed to planning horizon.The constraint condition of described firing floor model comprises 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, the constraint of system receiving end restrain condition, power generation energy resource resource units limits and power generation energy resource resource power factor.

The actuating logic that unit 206 set up by firing floor model is identical with step S106, specifically see the description of step S106, can not repeat here.

Model solution unit 208, be suitable for adopting pre-defined algorithm to solve resource layer model, obtain the optimum solution of first, second and third vector, and using the programme of this optimum solution as active power distribution network, wherein when the target function value of Gains resources layer model, firing floor model is solved, according to the O in the objective function of the solving result determination resource layer model to firing floor model according to the determined programme of the value of first, second and third vector _cvalue.Model solution unit 208 can adopt interior point method, particle cluster algorithm or genetic algorithm to solve resource layer model and firing floor model.When described pre-defined algorithm is genetic algorithm, described model solution unit 208 adopts the target function value of resource layer model and the fitness function value of the product of-1 as genetic algorithm.

The actuating logic of model solution unit 208 is identical with step S108, specifically see the description of step S108, can not repeat here.

The carbon emission problem considering distribution system towards carbon footprint minimized active power distribution network two-stage programming scheme in life cycle management time scale of the embodiment of the present invention.In objective function, the careful implicit carbon emission taking into account system equipment contributes to characterizing resource and chooses relation between the indirect carbon emission cost of its generation, thus conscientiously ensures the realization of " terminal electric energy carbon footprint is minimum " this low-carbon (LC) Electric Power Network Planning basic goal.Complex optimum is carried out to rack and distributed power source, capacity mismatch problem between the two can be avoided to greatest extent, delay electrical network dilatation construction demand, improve renewable energy utilization efficiency simultaneously, for planning personnel provides believable optimal investing strategy, meet system high efficiency to run and the application demand of low carbonization electric power supply, and to the low carbonization transformation of distribution system and development has important practical significance and good promotion prospect.

Below provide an application example of the present invention.

The urban power distribution network system that this application example adopts comprises 24 nodes, 23 optional feeder line branch roads, and amount electric pressure 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 the firing floor time interval is 15 minutes, and system loading rate of growth gets 3%, and discount rate gets 8%.System principal permeability 20%, node voltage allowable fluctuation range is ± 7%, and circuit maximum carrying capacity is set as 1.2 times of rated capacities, and the single node access DG number upper limit is 5.Consider in native system that wind-powered electricity generation and photovoltaic generation are as RDG, gas turbine (GT) is as NRDG.The outer bulk power grid electric energy carbon emission of system is 0.85kg/kWh.Various kinds of equipment specifying information is in table 1.

The each equipment essential information of table 1

Based on above-mentioned input parameter, analog simulation is carried out to the method for the embodiment of the present invention.The validity of extracting method for highlighting, sets 2 groups of contrast sights: (1), when carrying out Electric Power Network Planning, first carries out the optimization of Power grid structure, then carries out the addressing constant volume of DG; (2) only considered carbon emission during operation when Electric Power Network Planning, do not consider the implicit carbon emission of equipment.

Find by contrasting with the program results of sight (1), the planning of the embodiment of the present invention is more reasonable in DG layout.According to Fig. 5, from total amount, the inventive method is more than the DG installation of sight (1), this is because, sight (1) carries out independent optimization to space truss project and DG addressing constant volume, people is the natural inner link of having isolated between the two and the reciprocal effect that have ignored each other, when determining power grid architecture, certainly will limit the feas ible space that DG allocation plan is formulated.From gross investment, the inventive method is less, this is because the layout of DG is more reasonable, and degrees of coordination between electrical network and DG is better, avoids inefficient investment; And sight (1) is although only become originally to see it is minimum from investment economy, take temperature from the carbon emission of life cycle management, method carbon emission of the present invention is minimum, and sight (1) is maximum.This is because in this programme, the installed capacity of RDG is less, needs to buy a large amount of electric energy from bulk power grid.And under the programme obtained in the present invention, the permeability of RDG is high, has desirable operational efficiency simultaneously, therefore overall carbon emission is less.Concrete RDG layout and program results are as shown in table 2 and table 3.

Table 2 each node blower fan, photovoltaic and the new installed capacity of GT

Table 3 program results contrasts

Contrast with the inventive method, from comprehensive benefit, under identical constraint condition, the inventive method investment is less, this is because the layout of DG is more reasonable, and degrees of coordination between electrical network and DG is better, avoids inefficient investment; And sight (2) is not owing to taking into account implicit carbon emission, can not reflect DG unit truly in the realistic case to the true impact that environment produces, anticipation carbon emission is more optimistic than actual conditions.The contrast program results of the inventive method and the result of sight (2) can be found out, the actual carbon emission of sight (2) is 333.94 × 10 ³t, exceeds 18.71% than the inventive method.Visible, the implicit carbon emission of system feeder line, DG is very large on the actual carbon emission impact in system life cycle management, as shown in Figure 6.

Fig. 6 is the impact of implicit carbon emission amount on actual total carbon emissions and gross investment of various equipment in system.Wherein, basic sight is the inventive method acquired results, the implicit carbon sight of nothing is sight (2) analog result, and about basic sight, listed sight is respectively and implies carbon variable quantity (implying carbon increase by 10% compared to basic sight as 10% sight is) compared to basic sight.As can be seen from Figure 6, implicit carbon emission amount and correlation between them, and increase along with it is lasting, the impact of actual total carbon emissions is also being strengthened.

Intrinsic not relevant to any certain computer, virtual system or miscellaneous equipment with display at this algorithm provided.Various general-purpose system also can with use based on together with this teaching.According to description above, the structure constructed required by this type systematic is apparent.In addition, the present invention is not also for any certain programmed language.It should be understood that and various programming language can be utilized to realize content of the present invention described here, and the description done language-specific is above to disclose preferred forms of the present invention.

In instructions provided herein, describe a large amount of detail.But can understand, embodiments of the invention can be put into practice when not having these details.In some instances, be not shown specifically known method, structure and technology, so that not fuzzy understanding of this description.

Similarly, be to be understood that, in order to simplify the disclosure and to help to understand in each inventive aspect one or more, in the description above to exemplary embodiment of the present invention, each feature of the present invention is grouped together in single embodiment, figure or the description to it sometimes.But, the method for the disclosure should be construed to the following intention of reflection: namely the present invention for required protection requires feature more more than the feature clearly recorded in each claim.Or rather, as claims below reflect, all features of disclosed single embodiment before inventive aspect is to be less than.Therefore, the claims following embodiment are incorporated to this embodiment thus clearly, and wherein each claim itself is as independent embodiment of the present invention.

Those skilled in the art are appreciated that and adaptively can change the module in the equipment in embodiment and they are arranged in one or more equipment different from this embodiment.Module in embodiment or unit or assembly can be combined into a module or unit or assembly, and multiple submodule or subelement or sub-component can be put them in addition.Except at least some in such feature and/or process or unit be mutually repel except, any combination can be adopted to combine all processes of all features disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) and so disclosed any method or equipment or unit.Unless expressly stated otherwise, each feature disclosed in this instructions (comprising adjoint claim, summary and accompanying drawing) can by providing identical, alternative features that is equivalent or similar object replaces.

In addition, those skilled in the art can understand, although embodiments more described herein to comprise in other embodiment some included feature instead of further feature, the combination of the feature of different embodiment means and to be within scope of the present invention and to form different embodiments.Such as, in the following claims, the one of any of embodiment required for protection can use with arbitrary array mode.

All parts embodiment of the present invention with hardware implementing, or can realize with the software module run on one or more processor, or realizes with their combination.It will be understood by those of skill in the art that the some or all functions that microprocessor or digital signal processor (DSP) can be used in practice to realize according to the some or all parts in the device for planning of the low carbonization electric power system of the embodiment of the present invention.The present invention can also be embodied as part or all equipment for performing method as described herein or device program (such as, computer program and computer program).Realizing program of the present invention and can store on a computer-readable medium like this, or the form of one or more signal can be had.Such signal can be downloaded from internet website and obtain, or provides on carrier signal, or provides with any other form.

The present invention will be described instead of limit the invention to it should be noted above-described embodiment, and those skilled in the art can design alternative embodiment when not departing from the scope of claims.In the claims, any reference symbol between bracket should be configured to limitations on claims.Word " comprises " not to be got rid of existence and does not arrange element in the claims or step.Word "a" or "an" before being positioned at element is not got rid of and be there is multiple such element.The present invention can by means of including the hardware of some different elements and realizing by means of the computing machine of suitably programming.In the unit claim listing some devices, several in these devices can be carry out imbody by same hardware branch.Word first, second and third-class use do not represent any order.Can be title by these word explanations.

Claims (10)

1., towards a carbon footprint minimized active power distribution network two-stage programming method, comprising:

Set up the resource layer model of active power distribution network, its control variable comprises secondary vector and the 3rd vector of renewable energy power generation unit and non-renewable energy resources genset installation site and the configuration number of units in this installation site in primary vector, the respectively characterization system of characterization system network topology structure and feeder line line style, and its objective function is min F=(D _c+ O _c)/H, wherein D _cthe implicit carbon emission that feeder line, renewable energy power generation unit and non-renewable energy resources genset contained by expression system are corresponding within planning horizon, O _ccall power to non-renewable energy resources genset when expression system is run within planning horizon and call the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within planning horizon;

Set up the firing floor model of active power distribution network, its control variable comprises calling power and calling the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources genset when characterization system runs in simulation cycle, and its objective function is min f=O _c/ ε, wherein ε represents and carries out by the time scale that simulation cycle is corresponding the changed factor changed to planning horizon; And

Pre-defined algorithm is adopted to solve resource layer model, obtain the optimum solution of first, second and third vector, and using the programme of this optimum solution as active power distribution network, wherein when the target function value of Gains resources layer model, firing floor model is solved, according to the O in the objective function of the solving result determination resource layer model to firing floor model according to the determined programme of the value of first, second and third vector _cvalue.

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

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

Respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

Determine that in simulation cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

To exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

3. planing method as claimed in claim 1 or 2, wherein, the objective function of firing floor model is specially: $\min f=\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t},$ Wherein PP _{eG, t}represent that period t calls power, ρ from higher level's electrical network _eGrepresent that higher level's electrical network often provides the carbon emission of the generating corresponding to 1kWh electric energy, Δ t represents the duration of each period, and TH represents the fixed number that simulation cycle comprises, ρ _{nRDG, y}represent the generating carbon emission that y kind non-renewable energy resources genset often generates electricity corresponding to 1kWh electric energy, PP _{nRDG, y, g ', t}represent that period t calls power, Φ to node g ' upper y kind non-renewable energy resources genset _nRDGthe kind set of non-renewable energy resources genset in expression system, Ω _{nRDG, y}in expression system, all types is the set of the non-renewable energy resources genset installation node formation to be selected of y.

4. planing method as claimed in claim 1, wherein, the constraint condition of described resource layer model comprises following one or more: in the constraint of investment total cost, each node power generation energy resource resource permeability constraint and keep network topology structure to be radial constraint.

5. planing method as claimed in claim 1, wherein, the constraint condition of described firing floor model comprises 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, the constraint of system receiving end restrain condition, power generation energy resource resource units limits and power generation energy resource resource power factor.

6. planing method as claimed in claim 1, wherein, described pre-defined algorithm is genetic algorithm, adopts the target function value of resource layer model and the fitness function value of the product of-1 as genetic algorithm.

7. planing method as claimed in claim 1, wherein, adopts interior point method, particle cluster algorithm or genetic algorithm to solve firing floor model.

8., towards a carbon footprint minimized active power distribution network two-stage programming device, comprising:

Unit set up by resource layer model, be suitable for the resource layer model setting up active power distribution network, its control variable comprises secondary vector and the 3rd vector of renewable energy power generation unit and non-renewable energy resources genset installation site and the configuration number of units in this installation site in primary vector, the respectively characterization system of characterization system network topology structure and feeder line line style, and its objective function is min F=(D _c+ O _c)/H, wherein D _cthe implicit carbon emission that feeder line, renewable energy power generation unit and non-renewable energy resources genset contained by expression system are corresponding within planning horizon, O _ccall power to non-renewable energy resources genset when expression system is run within planning horizon and call the generating carbon emission corresponding to electric energy from higher level's electrical network, H represents the predicted value of system power consumption within planning horizon;

Unit set up by firing floor model, be suitable for the firing floor model setting up active power distribution network, its control variable comprises calling power and calling the 4th vector of electric energy from higher level's electrical network to renewable energy power generation unit and non-renewable energy resources genset when characterization system runs in simulation cycle, and its objective function is min f=O _c/ ε, wherein ε represents and carries out by the time scale that simulation cycle is corresponding the changed factor changed to planning horizon; And

Model solution unit, be suitable for adopting pre-defined algorithm to solve resource layer model, obtain the optimum solution of first, second and third vector, and using the programme of this optimum solution as active power distribution network, wherein when the target function value of Gains resources layer model, firing floor model is solved, according to the O in the objective function of the solving result determination resource layer model to firing floor model according to the determined programme of the value of first, second and third vector _cvalue.

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

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

Respectively the expectation of interval algebraic mean value uncontrollable power generation energy resource resource under this scene of exerting oneself corresponding for each scene is exerted oneself;

Determine that in simulation cycle, under Different periods, each scene correspondence expects the probability of happening of exerting oneself;

To exert oneself according to the expectation of each scene and the probability of happening of correspondence determines that the expectation of uncontrollable power generation energy resource resource under day part is exerted oneself.

10. device for planning as claimed in claim 8 or 9, wherein, the objective function of firing floor model is specially: $\min f=\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{EG}}{\mathrm{PP}}_{\mathrm{EG},t}+\underset{y\∈{\mathrm{\Φ}}_{\mathrm{NRDG}}}{\mathrm{\Σ}}\underset{g^{,}\∈{\mathrm{\Ω}}_{\mathrm{NRDG},y}}{\mathrm{\Σ}}\underset{t=1}{\overset{\mathrm{TH}}{\mathrm{\Σ}}}\mathrm{\Δt}{\mathrm{\ρ}}_{\mathrm{NRDG},y}{\mathrm{PP}}_{\mathrm{NRDG},y,g^{,},t},$ Wherein PP _{eG, t}represent that period t calls power, ρ from higher level's electrical network _eGrepresent that higher level's electrical network often provides the carbon emission of the generating corresponding to 1kWh electric energy, Δ t represents the duration of each period, and TH represents the fixed number that simulation cycle comprises, ρ _{nRDG, y}represent the generating carbon emission that y kind non-renewable energy resources genset often generates electricity corresponding to 1kWh electric energy, PP _{nRDG, y, g ', t}represent that period t calls power, Φ to node g ' upper y kind non-renewable energy resources genset _nRDGthe kind set of non-renewable energy resources genset in expression system, Ω _{nRDG, y}in expression system, all types is the set of the non-renewable energy resources genset installation node formation to be selected of y.