CN106487005A - A kind of Electric power network planning method considering T-D tariff - Google Patents

Abstract

The invention discloses a kind of Electric power network planning method considering T-D tariff.The planning systems such as existing Electric Power Network Planning principle, investment decision method, Electric power network planning method are difficult in adapt to the new demand that electroduct restricts the complicated Uncertain environments such as bundle, new-energy grid-connected.The present invention builds the Electric Power Network Planning model considering T-D tariff and the constraints setting up this Electric Power Network Planning model；Verify constraints using the probabilistic loadflow method based on Cumulants method, uncertain probability optimization problem is converted into deterministic optimization problem；Using improved NSGA II algorithm, described Electric Power Network Planning model is solved, try to achieve optimum power distribution network Expansion Planning scheme.The Electric Power Network Planning model realization that the present invention adopts is appraised and decided to electrical network cost and power transmission and distribution income in planning process, reduces power grid enterprises' idle cost, further increases the economic benefit complying with power network planning scheme under electricity marketization construction.

Description

A kind of Electric power network planning method considering T-D tariff

Technical field

The present invention relates to technical field of power systems, particularly a kind of Electric power network planning method considering T-D tariff.

Background technology

With the continuous development of World Economics, energy crisis and environmental pollution receive the extensive concern of all circles.In recent years, Because distributed power generation has cleaning, environmental protection and the advantage such as flexibly, obtain quick development.Renewable energy type is distributed Power supply (DG) outstanding feature is climate impact, and generated output has randomness, and the access of distributed power source is power train System planning brings new problem.Distribution network planning needs take into full account that distributed power source generated output is uncertain, comprehensive Consider the relation of economy and security risk, electrical network uncertain factor is included Electric Power Network Planning model.

Additionally, with the propulsion of China's T-D tariff reform, the profit model of the traditional price difference between purchase and sale of grid company Right-about will occur.Power grid enterprises are appraised and decided with " permit cost+reasonable benefit " principle and permits total income and component voltage grade T-D tariff pattern, strengthens the cost constraint to power grid enterprises, and its power transmission and distribution clothes will improve by reduces cost in power grid enterprises The earning rate of business, thus strengthening the requirement to Electric Power Network Planning economy, need to consider T-D tariff to Electric Power Network Planning model and The impact of planing method.

The planning systems such as existing Electric Power Network Planning principle, investment decision method, Electric power network planning method are difficult in adapt to electroduct The new demand of the complicated Uncertain environments such as restriction bundle, new-energy grid-connected.Need to redefine Electric Power Network Planning model, research adapts to The Electric power network planning method of T-D tariff reform.

The impact to Electric Power Network Planning for the T-D tariff reform is mainly reflected in：

1) need with reference to overall life cycle cost management mode, electrical network cost is effectively appraised and decided；

2) new T-D tariff pattern requires to consider defeated Price Regulation constraint in Electric Power Network Planning model；

3) Electric Power Network Planning no longer too pursues high performance technology index, and economic power system requires to improve；

4) the performance assessment criteria aspect to electrical network, will be defined by the examination by government's index of correlation.

Content of the invention

In order to overcome the shortcomings of above-mentioned prior art presence, the present invention provides a kind of Electric Power Network Planning side considering T-D tariff Method, it equalizes social cost's object function, power transmission and distribution earnings target function and network loss constraints by comprising supply of electric power Chance-Constrained Programming Model, to solve the problems, such as the power network expansion planning under T-D tariff reform background.

The technical solution adopted for the present invention to solve the technical problems is：A kind of Electric Power Network Planning side considering T-D tariff Method, it comprises the following steps：Build the Electric Power Network Planning model considering T-D tariff and the constraint bar setting up this Electric Power Network Planning model Part；Verify constraints using the probabilistic loadflow method based on Cumulants method, uncertain probability optimization problem is converted into Deterministic optimization problem；Using improved NSGA- II algorithm, described Electric Power Network Planning model is solved, try to achieve optimum distribution Net Expansion Planning scheme.

Further, described Electric Power Network Planning model includes electrical network enlarging overall life cycle cost, power transmission and distribution operation income And the object function of load bus voltage deviation.

Further, target power transmission and distribution operation income P_TDMaximum, its computing formula is as follows：

In formula, Ψ_VLFor the set of each electric pressure of T-D tariff,For the distributed power source collection for VL-k for the electric pressure Close,For the Bulk Supply Substation set for VL-k for the electric pressure；p_TCFor power transmission and distribution transformer capacity electricity price, p_MDFor power transmission and distribution Big demand capacity electricity price,For the power transmission and distribution electric degree electricity price for VL-k for the electric pressure,For the change for VL-k for the electric pressure The active power of power station j,For the active power of the distributed power source i for VL-k for the electric pressure, P_subActive for transformer station Power, P_DGFor the active power of distributed power source, p_{DG_ad}For the additional electricity price of regenerative resource electricity price, p_OGFor rate for incorporation into the power network, r is Grid company synthesis line loss per unit is with reference to ratio, P_iFor the burden with power of load bus i, P_lossFor system active power loss, Ψ_loadFor Load bus set.

Further, target grid enlarging overall life cycle cost is minimum, and its computing formula is as follows：

Electrical network extends overall life cycle cost C_LCCIncluding：Electric grid investment cost C_I, operating cost C_O, maintenance cost C_M, therefore Barrier cost C_F, scrap cost C_D；

The computational methods of above-mentioned each cost are as follows：

In formula, discount rate on the basis of r, T is the full electric network life-span time limit；Ψ_AFor increasing line set, Ψ newly_IFor newly-built transformer station Node set, Ψ_EFor the set of enlarging transformer station；L_{line_i}For the length of circuit i to be selected, c_lineFor newly-built circuit unit price, c_sub,iFor the investment cost of newly-built transformer station i, c_{sub_e,i}For original transformer station dilatation expense；x_line,iTreat for controlling whether to choose The 0-1 variable on route selection road；x_sub,iFor controlling whether the 0-1 variable of newly-built transformer station to be selected, x_{sub_e,i}For controlling whether that enlarging treats Select the 0-1 variable of transformer station；P_lossFor system active power loss, c_priceFor electricity price, T_lossFor system year maximum loss hourage, δ_i Year fault rate for circuit i, c_miUnit fault maintenance cost for circuit i, Ψ_lineFor all line set of electrical network, f is fault Indirect cost during generation and the ratio of direct cost, c_FoutageFor direct fault punishment cost, E (R) is cutting load expectation, T_LOLDLack power-on time, c for system year_dFor equipment retired process unit cost, r_rFor salvage value rate, L_ijFor node i to node j it Between line length.

Further, the constraints of described Electric Power Network Planning model include system load flow equality constraint under polar coordinate, Node voltage random constraints condition, branch road through-put power random constraints condition, network loss constraints and distribution network structure structure are about Bundle condition.

Further, the computing formula of described network loss constraints is as follows：

In formula, r is grid company synthesis line loss per unit with reference to ratio, P_lossFor system active loss, P_iActive for node i Load, Ψ_loadLoad bus set.

Further, included using the process that improved NSGA- II algorithm is solved to described Electric Power Network Planning model： Produce the initial population meeting grid structure constraint with root node fusion method；Based on the Load flow calculation of Cumulants method, calculate mesh Scalar functions, verification constraints；Non-dominated ranking and crowding distance calculate；The genetic manipulation selecting, intersecting, make a variation；Topology Constraint checking, rejects and is unsatisfactory for individuality；Probabilistic loadflow calculates, and obtains ideal adaptation angle value；Population merges；Interim population non-dominant Level and crowding distance-taxis；Break-in operation, produces new contemporary initial population；When reaching maximum evolutionary generation, generate The optimum collection of Pareto, screens optimal case.

Further, the concrete step using improved NSGA- II algorithm, described Electric Power Network Planning model being solved Suddenly as follows：

1) individual UVR exposure：Electric Power Network Planning model is treated route selection road and is selected, and decision vector x adopts by-line binary system to compile Code, indicates whether to build corresponding circuit to be selected with 0-1 variable；

2) initialize initial population G：Using root node fusion method, randomly generate and meet network connectivty and radioactivity requirement Individuality, generate population quantity be Np initial population G, initial population is carried out non-dominated ranking and crowding distance calculate；

3) genetic manipulation：Based on crowding distance-taxis, parent individuality is selected using binary system tournament method, by 2 points Intersect, exchange sequence number variation mode is intersected and mutation operator；In conjunction with distribution network source and load bus quantity and circuitry number The relation of amount, in crossover operation it is ensured that the individual exchange bases of parent two because of section in the branch road number of individuals that selects identical, exclusion A large amount of infeasible solutions；In mutation operation, the gene position randomly choosing genic value respectively 0 and 1 in chromosome is interchangeable Sequence number makes a variation, it is to avoid invalid mutation operation；Progeny population Q is produced by genetic manipulation；

4) calculating target function：Using the tidal current computing method based on Cumulants method, solve the mesh of population Different Individual Scalar functions, and constraints is processed using penalty function method；

5) population merges：Merge initial population G and progeny population Q, and reject the individuality being unsatisfactory for network topology constraint, raw The interim population Q ' of Cheng Xin；

6) non-dominated ranking and crowding calculate：Interim population Q ' is carried out with quick non-dominated ranking and crowding calculates；

7) generate new contemporary initial population：According to non-dominated ranking level and crowding distance, interim population Q ' is arranged Sequence, Np individual, new contemporary initial population G of formation before selection；

8) g=g+1, as evolutionary generation g<g_maxWhen, return operation 3) enter next circulate operation；

9) after iteration terminates, using TOPSIS, non-domination solution is ranked up, output optimum power distribution network extension rule The scheme of drawing.

Compared with prior art, the invention has the advantages that：The present invention considers me during Electric Power Network Planning The impact to Electric power network planning method for state's T-D tariff reform, carries out new transformation and adjusts with optimizing to Electric Power Network Planning model and method Whole.In plan model, carry out supply of electric power equalization social cost and adjust, count and power grid enterprises' power transmission and distribution profit maximization mesh Mark and government's performance assessment criteria network loss constraint, using Chance-Constrained Programming Model, set up the Electric Power Network Planning model considering T-D tariff, And using the Load flow calculation based on Cumulants method and improved NSGA- II Algorithm for Solving multiple target Chance-Constrained Programming Model.

The Electric Power Network Planning model realization that the present invention adopts core to electrical network cost and power transmission and distribution income in planning process Fixed, reduce power grid enterprises' idle cost, further increase the economic effect complying with power network planning scheme under electricity marketization construction Benefit.

Brief description

Fig. 1 is the schematic diagram of the present invention.

Fig. 2 is the flow chart of the improved NSGA- of the present invention II algorithm.

Fig. 3 is IEEE54 node power distribution net original network topology figure.

Fig. 4 implements the power network expansion planning optimal case topological diagram after the inventive method for Fig. 3.

Specific embodiment

1) consider the Multi-Objective Electric Power Network Planning model of T-D tariff

Traditional Electric Power Network Planning more focuses on the requirement of electric network reliability, under new power transmission and distribution mechanism, the investment of electrical network and Income receives the supervision of government, and electric grid investment benefit and return efficiency etc. are required to improve, and Electric Power Network Planning work has to economy Higher requirement.Development with electrical network and newly electricity change the propulsion of work, and a large amount of distributed power sources access power distribution networks, combine and divide Cloth power supply is exerted oneself the feature of randomness, using considering probabilistic Chance-Constrained Programming Model it is ensured that necessarily may be used meeting On the premise of property level, improve the economy of Electric Power Network Planning.

Build the Electric Power Network Planning model of the consideration T-D tariff being made up of object function and constraints.

1.1) object function

(1) electrical network enlarging overall life cycle cost

In Electric Power Network Planning, the requirement of " economy " is not only short-term investment or cost minimization, but is conceived to long term time Include project construction, operation etc. in section and make an investment in interior all cost minimizations, need to introduce new cost after T-D tariff reform Computational methods, weigh " economy " of investment.Strengthen asset life cycle management, improve the requirement of project study depth, carry The comprehensive benefit that high Electric Power Network Planning is built.

Power grid construction overall life cycle cost C_LCCIncluding：Electric grid investment cost C_I, operating cost C_O, maintenance cost C_M, therefore Barrier cost C_F, scrap cost C_D.Target grid enlarging overall life cycle cost is minimum.

Computational methods are as follows；

In formula, discount rate on the basis of r, T is the full electric network life-span time limit；Ψ_AFor increasing line set, Ψ newly_IFor newly-built transformer station Node set, Ψ_EFor the set of enlarging transformer station；L_{line_i}For the length of circuit i to be selected, c_lineFor newly-built circuit unit price, c_{sub_i}For the investment cost of newly-built transformer station i, c_{sub_e}For original transformer station dilatation expense；x_line,iTo be selected for controlling whether to choose The 0-1 variable of circuit；x_sub,iFor controlling whether the 0-1 variable of newly-built transformer station to be selected, x_{sub_e,i}To be selected for controlling whether to extend The 0-1 variable of transformer station；P_lossFor system active power loss, c_priceFor electricity price, T_lossFor system year maximum loss hourage, δ_iFor The year fault rate of circuit i, c_miUnit fault maintenance cost for circuit i, Ψ_lineFor all line set of electrical network, f is that fault is sent out Indirect cost when raw and the ratio of direct cost, c_FoutageFor direct fault punishment cost, E (R) is cutting load expectation, T_LOLD Lack power-on time, c for system year_dFor equipment retired process unit cost, r_rFor salvage value rate, L_ijBetween node i to node j Line length.

(2) power transmission and distribution operation income

Different power distribution network planning schemes also can produce T-D tariff operation income.Adjust whole distribution network component voltage etc. The power transmission and distribution operation income P of level_TD, including electricity price, capacity price of electricity, additional electricity price and operation of power networks line loss profit four parts. P is taken in target power transmission and distribution operation_TDMaximum.

Different power distribution network planning schemes also can produce T-D tariff operation income.Adjust whole distribution network component voltage etc. The power transmission and distribution operation income P of level_TD, including electricity price, capacity price of electricity, additional electricity price and operation of power networks line loss profit four parts. P is taken in target power transmission and distribution operation_TDMaximum.

In formula, Ψ_VLFor the set of each electric pressure of T-D tariff,For the distributed power source collection for VL-k for the electric pressure Close,For the Bulk Supply Substation set for VL-k for the electric pressure；p_TCFor power transmission and distribution transformer capacity electricity price, p_MDFor power transmission and distribution Big demand capacity electricity price,For the power transmission and distribution electric degree electricity price for VL-k for the electric pressure,For the change for VL-k for the electric pressure The active power of power station j,For the active power of the distributed power source i for VL-k for the electric pressure, P_subActive for transformer station Power, P_DGFor the active power of distributed power source, p_{DG_ad}For the additional electricity price of regenerative resource electricity price, p_OGFor rate for incorporation into the power network, r is Grid company synthesis line loss per unit is with reference to ratio, P_iFor the burden with power of load bus i, P_lossFor system active power loss, Ψ_loadFor Load bus set.

(3) load bus voltage deviation is minimum

In formula:Δ U is load bus i voltage deviation；Ui is node i virtual voltage；For desired voltage values； For maximum permissible voltage deviation；γ_i=P_i/P_lodeFor weight factor, P_iFor node i original loads, P_lodeAlways bear for distribution system Lotus.

1.2) constraints

(1) system load flow equality constraint under polar coordinate：

In formula：P_giAnd Q_giIt is respectively bus active and idle injecting power vector；P_LiAnd Q_LiIt is respectively bus active and no Workload vector；P(V,θ)_iWith Q (V, θ)_iIt is respectively that each bus nodes load is active and reactive power.

(2) node voltage random constraints：

In formula：P_rFor probability event, V_iFor each load bus magnitude of voltage,WithBe respectively each node voltage lower limit and Higher limit, β_VFor the confidence level of voltage constraint, Φ is system node set.

(3) branch road through-put power random constraints：

In formula, S_jCircuit j apparent energy capacity,Allow maximum capacity, β for feeder line_LConfidence for Branch Power Flow constraint Level, Ω is grid branch set.

(4) network loss constraint：

After T-D tariff reform, each provincial electric power company synthesis line loss per unit is calculated in each self-reference ratio, actual motion center line The risk that loss rate is brought above or below this ratio or income are undertaken by power grid enterprises.Network loss should be less than with reference to line loss in principle Rate, network loss is constrained to：

In formula, r is grid company synthesis line loss per unit with reference to ratio, P_lossFor system active loss.P_iActive for node i Load, Ψ_loadFor load bus set.

(5) distribution network structure structural constraint

Grid structure is the important component part of power distribution network, and in order to ensure the feasibility of program results, programme is necessary Strictly meet distribution network structure connectivity and radial constraint.

2) consider the Electric Power Network Planning model solution method of T-D tariff

2.1) tidal current computing method based on Cumulants method

The present invention carries out school using the probabilistic loadflow based on Cumulants method to the random constraints condition of Electric Power Network Planning model Test.In the power distribution network probabilistic loadflow calculating process containing distributed power source, by population at individual decision vector determine network structure and Its parameter, predicts the outcome in conjunction with each node load and distributed power source uncertainty models, using cumulant and newton-pressgang The probabilistic loadflow computational methods that inferior Load flow calculation combines, check Electric Power Network Planning model constraints condition of opportunity.Concrete calculation procedure As follows.

Carry out the Load flow calculation under normal operating condition, obtain the node voltage vector X under benchmark running status₀, branch road Through-put power vector Z₀, Jacobian matrix J₀.The present invention only considers that distributed power source is exerted oneself the uncertain stochastic variable Δ producing W_DG, calculate each rank cumulant Δ W that distributed power source installs node power^(f).

Power flow equation is in benchmark operating point linearisation：

Wherein：W is node injecting power.

Tried to achieve each rank cumulant of each node voltage states variable Δ X and Branch Power Flow Δ Z by formula, utilize Edgeworth series expansion obtains the probability density function of Δ X and Δ Z.Calculate node voltage and Branch Power Flow are in confidence interval Interior distribution probability, verifies constraints condition of opportunity, uncertain probabilistic programming problem is converted into definitiveness multiple-objection optimization and asks Topic.

2.2) the improved non-dominated sorted genetic algorithm with elitism strategy (NSGA- II)

Non-dominated sorted genetic algorithm with elitism strategy (NSGA- II) is widely used in solving multiple target, non-linear mixed Close integer optimization problem.NSGA- II algorithm is based on traditional genetic algorithm (GA), by introducing crowding operator and quick non-dominant Sequence, algorithm possesses elitism strategy and relatively low computation complexity, has preferably overall optimizing ability.

In conjunction with the constraint of real power distribution network network topology structure, to the initialization of population of NSGA- II algorithm, intersection, mutation operation It is correspondingly improved, search space is cut down by the infeasible solution that exclusion is unsatisfactory for network topology in a large number, accelerates Evolution of Population Speed.The key step of the NSGA- II Algorithm for Solving distribution network planning model of application enhancements is as follows：

(1) individual UVR exposure.Plan model is treated route selection road and is selected, and decision vector x adopts by-line binary coding, uses 0-1 variable indicates whether to build corresponding circuit to be selected.

(2) initialize initial population G.Using root node fusion method, randomly generate meet network connectivty and radioactivity will The individuality asked, generates initial population G that population quantity is Np.Initial population is carried out with non-dominated ranking and crowding distance meter Calculate.

(3) genetic manipulation.Based on crowding distance-taxis, parent individuality is selected using binary system tournament method.By two Point intersects, exchange sequence number variation mode is intersected and mutation operator.In conjunction with distribution network source and load bus quantity and branch road The relation of quantity, in crossover operation it is ensured that the individual exchange bases of parent two because of section in the branch road number of individuals that selects identical, can Exclude a large amount of infeasible solutions.In mutation operation, the gene position randomly choosing genic value respectively 0 and 1 in chromosome is carried out Exchange sequence number variation, invalid mutation operation can be avoided.Progeny population Q is produced by genetic manipulation.

(4) calculating target function.Using the tidal current computing method based on Cumulants method, solve the mesh of population Different Individual Scalar functions, and constraints is processed using penalty function method.

(5) population merges.Merge contemporary population G and progeny population Q, and reject the individuality being unsatisfactory for network topology constraint, Generate new interim population Q '.

(6) non-dominated ranking and crowding calculate.Interim population Q ' is carried out with quick non-dominated ranking and crowding calculates.

(7) generate new contemporary initial population.According to non-dominated ranking level and crowding distance, interim population Q ' is arranged Sequence, Np individual, new contemporary initial population G of formation before selection.

(8) g=g+1, as evolutionary generation g<During gmax, return operation (3) and enter next circulate operation.

After iteration terminates, in the population obtaining, all non-dominant levels are multi-objective optimization question for the individuality of ground floor Pareto forward position, using TOPSIS, non-domination solution is ranked up, export optimum programming scheme.

The improved non-dominated sorted genetic algorithm algorithm basic flow sheet with elitism strategy is as shown in Figure 2.

3) example and analysis

Using IEEE 54 node example, its original network topology figure is as shown in Figure 3.Wherein S1, S2 are built power transformation Stand, S3, S4 are newly-built transformer station, solid line is built circuit, dotted line is to consider newly-built circuit, and node 10,33,49 connects for wind-powered electricity generation Access point, its capacity is as shown in topological diagram.Node 17 to 50 is newly-increased load bus, and load prediction situation is shown in Table 1.The present invention uses The Electric power network planning method of the consideration T-D tariff proposing, the example in the case of distributed power source is determined implements multiple target power distribution network Expansion Planning.

Table 1 54 Node power distribution system load prediction situation

If line resistance r=0.1208 is Ω/km, reactance x=0.1442 Ω/km, capacity of trunk is 10MVA, node voltage It is limited to 0.95p.u.-1.05p.u., newly-built 220,000 yuan/km of circuit cost, electrical network service life is 15 years, and discount rate is 10%, electrical network value-added tax is 17%, and salvage value of fixed assets rate is 5%.Using improved NSGA- II algorithm, population scale Pop= 200, iterationses G=500, crossover probability P1=0.9.

Based on the distribution network planning model considering T-D tariff, when setting constraints condition of opportunity confidence level as 0.9, in conjunction with Load flow calculation based on cumulant and improved NSGA- II algorithm, obtain individual 42 of Pareto forward position.Using approaching ideal To non-dominated sorting, ' virtual optimal solution ' object function is solution：【6924,24059,0.008】, ' virtual inferior solution ' target Functional vector is：【7184,21725,0.044】, filter out optimum programming scheme, electrical network enlarging overall life cycle cost is 7031 Wan Yuan, 237,240,000 yuan of electrical network year operation income, load bus voltage deviation is 0.8%.The electricity being obtained by the present invention Net Expansion Planning optimal case network topological diagram is as shown in Figure 4.The method of the present invention is before meeting certain reliability level Put, improve the economy of Electric Power Network Planning.

Claims (8)

1. a kind of Electric power network planning method considering T-D tariff, it comprises the following steps：Build the electrical network rule considering T-D tariff Draw model and the constraints setting up this Electric Power Network Planning model；Using the probabilistic loadflow method verification constraint based on Cumulants method Condition, uncertain probability optimization problem is converted into deterministic optimization problem；Using improved NSGA- II algorithm to described Electric Power Network Planning model is solved, and tries to achieve optimum power distribution network Expansion Planning scheme.

2. Electric power network planning method according to claim 1 is it is characterised in that described Electric Power Network Planning model includes electrical network expansion Build the object function of overall life cycle cost, power transmission and distribution operation income and load bus voltage deviation.

3. Electric power network planning method according to claim 2 is it is characterised in that P is taken in target power transmission and distribution operation_TDMaximum, its Computing formula is as follows：

$\begin{array}{c}\max P_{TD}=\underset{k\&Element;{\mathrm{\&Psi;}}_{VL}}{\mathrm{\&Sigma;}}(\underset{j\&Element;{\mathrm{\&Psi;}}_{sub}^{VL-k}}{\mathrm{\&Sigma;}}{P}_{sub-j}^{VL-k}{p}_{TD}^{VL-k}+\underset{i\&Element;{\mathrm{\&Psi;}}_{DG}^{VL-k}}{\mathrm{\&Sigma;}}{P}_{DG-i}^{VL-k}{p}_{TD}^{VL-k})+(p_{TC}+p_{MD}){\mathrm{\&Sigma;P}}_{sub}+{\mathrm{\&Sigma;P}}_{DG}{p}_{DG\_ad}\\ +(\frac{r}{1-r}\underset{i\&Element;{\mathrm{\&Psi;}}_{load}}{\mathrm{\&Sigma;}}{P}_i-P_{loss})\&CenterDot;(p_{OG}+p_{TD})\end{array}$

In formula, Ψ_VLFor the set of each electric pressure of T-D tariff,For the distributed power source set for VL-k for the electric pressure,For the Bulk Supply Substation set for VL-k for the electric pressure；p_TCFor power transmission and distribution transformer capacity electricity price, p_MDNeed for power transmission and distribution are maximum Seek capacity price of electricity,For the power transmission and distribution electric degree electricity price for VL-k for the electric pressure,For the transformer station for VL-k for the electric pressure The active power of j,For the active power of the distributed power source i for VL-k for the electric pressure, P_subWattful power for transformer station Rate, P_DGFor the active power of distributed power source, p_{DG_ad}For the additional electricity price of regenerative resource electricity price, p_OGFor rate for incorporation into the power network, r is electricity Net company synthesis line loss per unit is with reference to ratio, P_iFor the burden with power of load bus i, P_lossFor system active power loss, Ψ_loadIt is negative Lotus node set.

4. Electric power network planning method according to claim 2 is it is characterised in that target grid extends overall life cycle cost Little, its computing formula is as follows：

$\min C_{LCC}=C_I+(C_O+C_M+C_F)\&CenterDot;\frac{{(1+r)}^T-1}{r{(1+r)}^T}+C_D\&CenterDot;\frac{1}{{(1+r)}^T},$

Electrical network extends overall life cycle cost C_LCCIncluding：Electric grid investment cost C_I, operating cost C_O, maintenance cost C_M, fault one-tenth This C_F, scrap cost C_D；

The computational methods of above-mentioned each cost are as follows：

$\left\{\begin{array}{c}{C}_I=C_{line\_add}^{INV}+C_{sub\_ins}^{INV}+C_{sub\_ex}^{INV}\\ =\underset{i\&Element;{\mathrm{\&Psi;}}_A}{\mathrm{\&Sigma;}}{c}_{line}{L}_{line\_i}{x}_{line,i}+\underset{i\&Element;{\mathrm{\&Psi;}}_I}{\mathrm{\&Sigma;}}{c}_{sub,i}{x}_{sub,i}+\underset{i\&Element;{\mathrm{\&Psi;}}_E}{\mathrm{\&Sigma;}}{c}_{sub,e,i}{x}_{sub,e,i}\\ C_O=P_{loss}{c}_{price}{T}_{loss}\\ C_M=\underset{i\&Element;{\mathrm{\&Psi;}}_{line}}{\mathrm{\&Sigma;}}{\mathrm{\&delta;}}_i{c}_{mi}\\ C_F=(1+f)C_{Foutage}=(1+f)E\left(R\right)T_{LOTD}{c}_{price}\\ C_D=c_d{\mathrm{\&Sigma;L}}_{ij}-r_r{C}_I\end{array}\right.$

In formula, discount rate on the basis of r, T is the full electric network life-span time limit；Ψ_AFor increasing line set, Ψ newly_IFor newly-built power transformation tiny node Set, Ψ_EFor the set of enlarging transformer station；L_{line_i}For the length of circuit i to be selected, c_lineFor newly-built circuit unit price, c_sub,iFor The investment cost of newly-built transformer station i, c_{sub_e,i}For original transformer station dilatation expense；x_line,iFor controlling whether to choose circuit to be selected 0-1 variable；x_{Sub, i}For controlling whether the 0-1 variable of newly-built transformer station to be selected, x_{sub_e,i}For controlling whether to extend power transformation to be selected The 0-1 variable stood；P_lossFor system active power loss, c_priceFor electricity price, T_lossFor system year maximum loss hourage, δ_iFor circuit i Year fault rate, c_miUnit fault maintenance cost for circuit i, Ψ_lineFor all line set of electrical network, when f is that fault occurs Indirect cost and direct cost ratio, c_FoutageFor direct fault punishment cost, E (R) is cutting load expectation, T_LOLDFor being System year lacks power-on time, c_dFor equipment retired process unit cost, r_rFor salvage value rate, L_ijFor the circuit between node i to node j Length.

5. Electric power network planning method according to claim 1 is it is characterised in that the constraints bag of described Electric Power Network Planning model Include system load flow equality constraint under polar coordinate, node voltage random constraints condition, branch road through-put power random constraints condition, Network loss constraints and distribution network structure structure constraint.

6. Electric power network planning method according to claim 5 it is characterised in that the computing formula of described network loss constraints such as Under：

$P_{loss}\&le;\frac{r}{1-r}\underset{i\&Element;{\mathrm{\&Psi;}}_{load}}{\mathrm{\&Sigma;}}{P}_i$

In formula, r is grid company synthesis line loss per unit with reference to ratio, P_lossFor system active loss, P_iFor the burden with power of node i, Ψ_loadFor load bus set.

7. Electric power network planning method according to claim 1 is it is characterised in that use improved NSGA- II algorithm to described The process that solved of Electric Power Network Planning model include：Produce the initial kind meeting grid structure constraint with root node fusion method Group；Based on the Load flow calculation of Cumulants method, calculating target function, verification constraints；Non-dominated ranking and crowding distance Calculate；The genetic manipulation selecting, intersecting, make a variation；Topological constraints verify, and reject and are unsatisfactory for individuality；Probabilistic loadflow calculates, and obtains individual Body fitness value；Population merges；Interim population non-dominant level and crowding distance-taxis；Break-in operation, produces the new present age Initial population；When reaching maximum evolutionary generation, generate the optimum collection of Pareto, screen optimal case.

8. Electric power network planning method according to claim 7 is it is characterised in that use improved NSGA- II algorithm to described Comprising the following steps that of being solved of Electric Power Network Planning model：

1) individual UVR exposure：Electric Power Network Planning model is treated route selection road and is selected, and decision vector x adopts by-line binary coding, uses 0-1 variable indicates whether to build corresponding circuit to be selected；

2) initialize initial population G：Using root node fusion method, randomly generate meet network connectivty and radioactivity requirement Body, generates initial population G that population quantity is Np, initial population is carried out with non-dominated ranking and crowding distance calculates；

3) genetic manipulation：Based on crowding distance-taxis, parent individuality is selected using binary system tournament method, handed over by 2 points Fork, exchange sequence number variation mode are intersected and mutation operator；In conjunction with distribution network source and load bus quantity and branch road quantity Relation, in crossover operation it is ensured that the individual exchange bases of parent two because of section in the branch road number of individuals that selects identical, exclusion is big Amount infeasible solution；In mutation operation, the gene position randomly choosing genic value respectively 0 and 1 in chromosome is interchangeable sequence Number variation, it is to avoid invalid mutation operation；Progeny population Q is produced by genetic manipulation；

4) calculating target function：Using the tidal current computing method based on Cumulants method, solve the target letter of population Different Individual Number, and constraints is processed using penalty function method；

5) population merges：Merge initial population G and progeny population Q, and reject the individuality being unsatisfactory for network topology constraint, generate new Interim population Q '；

6) non-dominated ranking and crowding calculate：Interim population Q ' is carried out with quick non-dominated ranking and crowding calculates；

7) generate new contemporary initial population：According to non-dominated ranking level and crowding distance, interim population Q ' is sorted, choosing Select first Np individual, new contemporary initial population G of formation；

8) g=g+1, as evolutionary generation g<g_maxWhen, return operation 3) enter next circulate operation；

9) after iteration terminates, using TOPSIS, non-domination solution is ranked up, the optimum power distribution network Expansion Planning side of output Case.