CN104200263B - Power distribution network route planning method based on tabu differential evolution and GIS (Geographic Information System) - Google Patents

Abstract

The invention discloses a power distribution network route planning method based on tabu differential evolution and a GIS (Geographic Information System). Equal allotment capital recovery calculation is performed under the consideration of the time value of capital, a model is established specific to the aims of minimal annul investment and minimal running maintenance cost, a tabu differential evolution algorithm is put forward on the basis of the model, an obtained optimal solution is decoded, and a planned route is drawn on an electric power GIS platform according to the route number obtained by decoding. According to the method, a non-optimal solution is allowed to be accepted by using tobu search, the problem the differential evolution algorithm tends to falling into local optimum easily is solved by improving the capability of jump-out local optimum, and the planning process of a power distribution network line has the advantages of interactivity, more intuitive planning result and higher flexibility in adjustment of a planning scheme by using the electric power GIS system.

Description

It is a kind of based on taboo differential evolution and GIS distribution network layout of roads method

Technical field

The present invention relates to a kind of geographic information data process, system for distribution network of power winding thread circuit planning, more particularly to, It is a kind of based on taboo differential evolution and GIS distribution network layout of roads method.

Background technology

Distribution network layout of roads is referred on the basis of electrical network As-Is analysis and future load forecast of distribution, in transformer station After the work of addressing constant volume is completed, seek an optimum cabling scenario that can meet future customer capacity and the quality of power supply.

In recent years, using Meta-heurist algorithm (such as genetic algorithm, simulated annealing, TABU search, ant group algorithm Deng) solve the problems, such as to achieve the achievement of some protrusions in the global optimizing of distribution network layout of roads.As Wang Jinfeng et al. is proposed A kind of improved adaptive GA-IAGA, in Optimized model consider track investment the time value, and using the spanning tree of alternative network as Initial solution, it is to avoid the drawbacks of solving speed is slower when randomly generating initial feasible solution, the thought design for using for reference branch exchange is miscellaneous Hand over operator and mutation operator, it is to avoid radioactivity checking process；Gao Weixin et al. is proposed load point as food, calculated using ant colony Method carries out layout of roads.Additionally, continuous with GIS (Geographic Information System, GIS-Geographic Information System) Development and progress, also bring new approaches to distribution network layout of roads.Such as soup is red defend et al. propose it is a kind of based on GIS's Distribution system planning, using the spatial analysis capacity of GIS, can effective avoiding obstacles.But current most of distribution networks The heuritic approach that layout of roads is adopted is easily trapped into locally optimal solution seldom in combination with GIS, and arithmetic result is not straight enough See and be also easy to produce invalid branch road, when large-scale distribution network layout of roads is solved, the problems such as efficiency of algorithm is not ideal enough.Cause This, is how effectively by global optimizing algorithm in combination with optimizing algorithm, how effectively by optimizing algorithm in combination with GIS, It is major issue that distribution network layout of roads faces.

The content of the invention

In order to overcome existing be easily trapped into local optimum, algorithm for existing distribution network layout of roads method to be also easy to produce The deficiency of invalid branch road, the present invention propose it is a kind of be prevented effectively from be absorbed in local optimum, reliability it is good based on taboo difference enter Change the distribution network layout of roads method with GIS.

The technical solution adopted for the present invention to solve the technical problems is：

It is a kind of based on taboo differential evolution and GIS distribution network layout of roads method, comprise the following steps：

A1, according to substation locating and sizing and the result of future load forecast of distribution, create load on electric power GIS platform Point, transformer station and branch road to be selected, built branch road element information, and set up the topology rule in corresponding point-point, point-line, line-face Then；

A2, the element information to creating carry out pretreatment：All load points and transformer substation sequence are numbered, all to be selected Road and built branch road serial number, and start of record peripheral node numbering；Relief area is generated for geographic barrier, enters row buffer Analysis, finds out the line corridor for being not suitable for the construction of line；

A3, consideration time value on assets, pay separately capital recovery and calculate by single-candidate, with year investment and operation and maintenance cost most It is little for target, object function such as formula (1)：

In formula：S₁、S₂, S be respectively newly-built sets of lines, built sets of lines and total line collection；ω be year equivalence recovery coefficient, λ For the percentage ratio that maintenance, depreciation account for investment cost；l_iIt is the length of circuit i, f (D_i) it is that line footpath is D_iCircuit unit length Cost, X_iFor the decision variable on i-th line road, the circuit is selected as feeder line branch road, then X_iTake 1, otherwise take 0；g(D_i) it is line Footpath is D_iCircuit resistivity；P_iIt is circuit i by power, U_NFor rated voltage；τ_maxMaximum loss time in year, d is single Position electricity price；

In differential evolution algorithm, dimension D is branch road quantity N to be selected_line, population quantity NP=10*D, mutation operator F be with The string of binary characters that machine is produced, crossover operator CR takes 0.1, and end condition is the optimum for obtaining after tabu search algorithm It is individual identical with the optimum individual fitness before tabu search algorithm is switched into；Switching condition into tabu search algorithm is Iteration exceedes some generations and optimum individual continuous some generations do not change；The condition for exiting tabu search algorithm exceedes for step-length In some steps and optimum individual continuous some generations, do not change；

A4, reading element information, generating algorithm initial population, and initial population is evaluated；

A5, judge whether to reach end condition, if so, then evolving terminates, using optimized individual as solution output, go to step A13；If it is not, then continue algorithm, into step A6；

A6, enter row variation and crossover operation, carry out constraints verification, replacement is unsatisfactory for the individuality for constraining, and obtains interim Population, evaluates interim population；

A7, selection operation is carried out, obtain new population, if so, evaluation algorithm switching condition then enters step A8, be switched into Enter Tabu (TABU search) algorithm；If it is not, then going to step A5；

A8, using optimized individual as initial solution incoming Tabu (TABU search) algorithm, put taboo list for sky；

Whether A9, evaluation algorithm switching condition meet, and if so, then go to step A5, exit Tabu (TABU search) algorithm；If It is no, then into step A10；

A10, the field function generation all spectra solution for utilizing current solution, therefrom take some solutions as candidate solution；

A11, judge candidate solution whether aspiration criterion meets, if so, then replaced with the optimum state y for meeting aspiration criterion X becomes new current solution, and is replaced earliest into the taboo object of taboo list with the corresponding taboo objects of y, while being replaced most with y Good solution, then goes to step A9；If it is not, then entering step A12；

A12, the taboo attribute for judging the corresponding each object of candidate solution, select candidate solution to concentrate non-taboo object corresponding most Good state is new current solution, while replaced earliest into the taboo object elements of taboo list with corresponding taboo object, Then A9 is gone to step；

A13, the optimal solution that algorithm is obtained is decoded, the circuit number obtained according to decoding is on electric power GIS platform Draw out planning circuit.

Wherein, DE (Differential Evolution, differential evolution) algorithm adopts binary coding, i.e. character string every One upper " 1 " represents that the branch road of corresponding numbering is selected, and " 0 " represents that the branch road of corresponding numbering is not chosen.

Further, in step A4, read load point positional information and payload, the positional information of transformer station, Branch road to be selected, built branch road.The random length that generates is N_lineString of binary characters, and guarantee the quantity of " 1 " in character string For N_node(number of nodes), the function for applying mechanically (1) formula calculates each individual target function value.

Further, in step A6, mutation operation is to each target individual X_i,G, i=1,2 ..., NP, according to formula (2) variation is produced individual.Wherein, randomly selected sequence number r₁, r₂, r₃It is different, and r₁, r₂, r₃With target individual sequence number i It is different.Arithmetic operator is logical operator, i.e. X in formula_{R2, G}And X_r3,_GFirst logic is carried out or, acquired results and mutation operator F Carry out logic XOR, finally again with X_{R1, G}Logic and operation is carried out, variation is obtained individual.

Then carry out crossover operation according to formula (3), (4), (5), wherein randb (j) be between [0,1] it is random generate the J estimated value, rnbr (i) is a randomly selected sequence.

U_i,G+1=(U_1i,G+1,U_2i,G+1,…,U_Di,G+1) (3)

(i=1,2 ..., NP, j=1,2 ..., D) (5)

Connectedness, radioactivity, node voltage constraint, capacity-constrained verification are carried out to individuality, if constraint is unsatisfactory for again The individuality for randomly generating meet the constraint is substituted.

Further, in step A7, the individual mesh with relevant position in current population will be tested according to greedy criterion Mark individuality is compared, and takes fitness less individual as of future generation individual.Judge the switching condition into Tabu algorithms, if Satisfaction then carries out optimizing using current optimized individual as the incoming Tabu algorithms of initial individuals, to avoid being absorbed in locally optimal solution.

In step A10, the encoded radio on any two position in initial solution sequence is exchanged at random, produce N_lineIndividual time Choosing solution.

Beneficial effects of the present invention are mainly manifested in：The present invention solves single calculation when distribution network layout of roads is carried out Method is easily absorbed in the problem of local optimum, and the analysis of network with reference to GIS, buffer finish blasting optimized algorithm process, makes arithmetic result more Intuitively.

Description of the drawings

Fig. 1 is the flow chart of the distribution network layout of roads method based on taboo differential evolution and GIS.

Fig. 2 is power transformation station location and load point distribution.

Fig. 3 is initial distribution network.

Fig. 4 is carried out the optimization distribution line obtained after algorithm.

Specific embodiment

Below in conjunction with the accompanying drawings the invention will be further described.

It is a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS with reference to Fig. 1～Fig. 4, such as Fig. 1 institutes Show, comprise the following steps：

A1, according to substation locating and sizing and future load forecast of distribution result, create relevant factor in electric power GIS platform Information；

A2, the element information to creating carry out pretreatment；

A3, determine object function, the corresponding control parameter of algorithm and the specific strategy for being adopted are set；

A4, reading element information, generating algorithm initial population, and initial population is evaluated；

A5, judge whether to reach end condition, if so, then evolving terminates, using optimized individual as solution output, go to step A13；If it is not, then continue algorithm, into step A6；

A6, enter row variation and crossover operation, carry out constraints verification, replacement is unsatisfactory for the individuality for constraining, and obtains interim Population, evaluates interim population；

A7, selection operation is carried out, obtain new population, if so, evaluation algorithm switching condition then enters step A8, be switched into Enter Tabu algorithms；If it is not, then going to step A5；

A8, using optimized individual as the incoming Tabu algorithms of initial solution, put taboo list for sky；

Whether A9, evaluation algorithm switching condition meet, and if so, then go to step A5, exit Tabu algorithms；If it is not, then entering Step A10；

A10, the field function generation all spectra solution for utilizing current solution, therefrom take some solutions as candidate solution；

A11, judge candidate solution whether aspiration criterion meets, if so, then replaced with the optimum state y for meeting aspiration criterion X becomes new current solution, and is replaced earliest into the taboo object of taboo list with the corresponding taboo objects of y, while being replaced most with y Good solution, then goes to step A9；If it is not, then entering step A12；

A12, the taboo attribute for judging the corresponding each object of candidate solution, select candidate solution to concentrate non-taboo object corresponding most Good state is new current solution, while replaced earliest into the taboo object elements of taboo list with corresponding taboo object, Then A9 is gone to step；

A13, the optimal solution that algorithm is obtained is decoded, the circuit number obtained according to decoding is on electric power GIS platform Draw out planning circuit.

Wherein, DE algorithms adopt binary coding, i.e. character string on each " 1 " represent that the branch road of corresponding numbering is selected In, " 0 " represents that the branch road of corresponding numbering is not chosen.

Described method, wherein in step A1, according to substation locating and sizing and the result of future load forecast of distribution, Create load point, transformer station and branch road to be selected, built branch road element information on electric power GIS platform, and set up corresponding point- Point, point-line, the topology rule in line-face.

Described method, wherein in step A2, all nodes (including load point and transformer station) serial number owns Branch road (including branch road to be selected and built branch road) serial number, and start of record peripheral node numbering；Generate for geographic barrier Relief area, carries out buffer zone analysis, finds out the line corridor for being not suitable for the construction of line, reduces algorithm dimension space, reduces blindly Search.

Described method, wherein in step A3, Optimized model considers time value on assets, pays separately capital and returns by single-candidate Receive and calculate, with year investment and the minimum target of operation and maintenance cost, object function such as formula (6).

In formula：S₁、S₂, S be respectively newly-built sets of lines, built sets of lines and total line collection；ω be year equivalence recovery coefficient, λ For the percentage ratio that maintenance, depreciation account for investment cost；l_iIt is the length of circuit i, f (D_i) it is that line footpath is D_iCircuit unit length Cost, X_iDecision variable for i-th line road (selects the circuit as feeder line branch road, then X_i1 is taken, otherwise is taken 0)；g(D_i) it is line Footpath is D_iCircuit resistivity；P_iIt is circuit i by power, U_NFor rated voltage；τ_maxMaximum loss time in year, d is single Position electricity price.In DE (Differential Evolution, differential evolution) algorithm, dimension D is branch road quantity N to be selected_line, population Quantity NP=10*D, mutation operator F is the string of binary characters for randomly generating, and crossover operator CR takes 0.1, end condition be through The optimum individual for obtaining after Tabu (TABU search) algorithm is identical with the optimum individual fitness before Tabu algorithms is switched into. DE algorithms are that iteration exceedes some generations and optimum individual continuous some generations do not change into the switching condition of Tabu；Exit The condition of Tabu algorithms is that step-length exceedes some steps and optimum individual continuous some generations do not change；

Described method, wherein in step A4, reading positional information and payload, the position of transformer station of load point Information, branch road to be selected, built branch road.The random length that generates is N_lineString of binary characters, and guarantee " 1 " in character string Quantity is N_node(number of nodes), the function for applying mechanically (1) formula calculates each individual target function value.

Described method, wherein in step A6, mutation operation is to each target individual X_i,G, i=1,2 ..., NP, according to It is individual that formula (7) produces variation.Wherein, randomly selected sequence number r₁, r₂, r₃It is different, and r₁, r₂, r₃With target individual sequence number i Also it is different.Arithmetic operator is logical operator, i.e. X in formula_{R2, G}And X_{R3, G}First logic is carried out or, acquired results and mutation operator F Carry out logic XOR, finally again with X_{R1, G}Logic and operation is carried out, variation is obtained individual.

Then crossover operation is carried out according to formula (8), (9), (10), wherein randb (j) is random between [0,1] generation J-th estimated value, rnbr (i) is a randomly selected sequence.

U_i,G+1=(U_1i,G+1,U_2i,G+1,…,U_Di,G+1) (8)

(i=1,2 ..., NP, j=1,2 ..., D) (10)

Connectedness, radioactivity, node voltage constraint, capacity-constrained verification are carried out to individuality, if constraint is unsatisfactory for again The individuality for randomly generating meet the constraint is substituted.

Described method, wherein in step A7, will test individual with relevant position in current population according to greedy criterion Target individual be compared, take fitness less individual as of future generation individual.Judge the switching bar into Tabu algorithms Part, carries out optimizing, to avoid being absorbed in local optimum if meeting using current optimized individual as the incoming Tabu algorithms of initial individuals Solution.

Described method, wherein in step A10, the coding on any two position in initial solution sequence is exchanged at random Value, produces N_lineIndividual candidate solution.

Example：The present embodiment is one and has 37 nodes, 2 existing branch roads and 57 10kv distribution that can extend branch road The real system of net, electric power GIS platform manages GIS information systeies using electric lines of force, and the system puts on Shandong Yantai and other places On probation, operation reflection is good.

Node data as shown in table 1, capacity 0 for transformer station.DE algorithms are set to into the switching condition of Tabu algorithms More than 100 generations and continuous 20 generation optimal solution does not change；The condition setting for exiting Tabu algorithms is more than 40 steps and continuous 20 Step optimal solution does not change.Circuit maximum capacity is limited and is set to 5000kw.

Node key element such as Fig. 2 is created on electric lines of force management GIS information system platforms, capacity 0 for transformer station in the middle part of figure (hollow dots), remaining is load point (solid dot), creates circuit key element such as Fig. 3, and to extend circuit, dotted line is to build to solid line Circuit.Carry out after computing using algorithm, obtain the distribution line cabling scenario such as Fig. 4, circuit mounting node details are shown in Table 2.

Table 1

Table 2

Can be seen that every loop line road from above-mentioned wiring result has the supply district of clear and non-overlapping copies, per loop line road The load capacity of mounting also meets the restriction of circuit maximum capacity, it is seen that its result is rational, and meets engineering reality.This reality Apply example and show that method of the present invention is more effectively to calculate a feasible wiring side under certain calculation scale Case, and intuitively reflect result on electric power GIS platform, so as to improve planning personnel's work efficiency.

Claims (5)

1. a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS, it is characterised in that：The distribution network Layout of roads method is comprised the following steps：

A1, according to substation locating and sizing and the result of future load forecast of distribution, on electric power GIS platform create load point, Transformer station and branch road to be selected, built branch road element information, and set up corresponding point-point, point-line, the topology rule in line-face；

A2, the element information to creating carry out pretreatment：All load points and transformer substation sequence are numbered, all branch roads to be selected and Built branch road serial number, and start of record peripheral node numbering；Relief area is generated for geographic barrier, enters row buffer point Analysis, finds out the line corridor for being not suitable for the construction of line；

A3, consideration time value on assets, pay separately capital recovery and calculate by single-candidate, minimum with year investment and operation and maintenance cost Target, object function such as formula (1)：

$\min Q=\underset{i\∈S_1}{\Σ}(\mathrm{\ω}+\mathrm{\λ})l_{i}f\left(D_i\right)X_i+\underset{i\∈S_2}{\Σ}{\mathrm{\λl}}_{i}f\left(D_i\right)+\underset{i\∈S_2}{\Σ}{l}_{i}g\left(D_i\right)\frac{P_i^2}{U_N^2}{\mathrm{\τ}}_{\max }d---\left(1\right)$

In formula：S₁、S₂, S be respectively newly-built sets of lines, built sets of lines and total line collection；ω is year equivalence recovery coefficient, and λ is dimension Repair, depreciation accounts for the percentage ratio of investment cost；l_iIt is the length of circuit i, f (D_i) it is that line footpath is D_iCircuit unit length cost, X_iFor the decision variable on i-th line road, the circuit is selected as feeder line branch road, then X_iTake 1, otherwise take 0；g(D_i) it is that line footpath is D_i Circuit resistivity；P_iIt is circuit i by power, U_NFor rated voltage；τ_maxMaximum loss time in year, d is unit electricity price；

In differential evolution algorithm, dimension D is branch road quantity N to be selected_line, population quantity NP=10*D, mutation operator F is random product Raw string of binary characters, crossover operator CR takes 0.1, and end condition is the optimum individual for obtaining after tabu search algorithm It is identical with the optimum individual fitness before tabu search algorithm is switched into；Switching condition into tabu search algorithm is iteration More than some generations and optimum individual continuous some generations do not change；The condition for exiting tabu search algorithm is step-length more than some Walk and optimum individual continuous some generations do not change；

A4, reading element information, generating algorithm initial population, and initial population is evaluated；

A5, judge whether to reach end condition, if so, then evolving terminates, using optimized individual as solution output, go to step A13；If It is no, then continue algorithm, into step A6；

A6, enter row variation and crossover operation, carry out constraints verification, replacement is unsatisfactory for the individuality for constraining, obtains interim population, Interim population is evaluated；

A7, selection operation is carried out, obtain new population, if so, evaluation algorithm switching condition then enters step A8, switch into taboo Avoid searching algorithm；If it is not, then going to step A5；

A8, using optimized individual as the incoming tabu search algorithm of initial solution, put taboo list for sky；

Whether A9, evaluation algorithm switching condition meet, and if so, then go to step A5, exit tabu search algorithm；If it is not, then entering Step A10；

A10, the field function generation all spectra solution for utilizing current solution, therefrom take some solutions as candidate solution；

A11, judge candidate solution whether aspiration criterion meets, if so, then with the optimum state y for meeting aspiration criterion replace x into For new current solution, and replaced earliest into the taboo object of taboo list with the corresponding taboo objects of y, while replacing optimal with y Solution, then goes to step A9；If it is not, then entering step A12；

A12, the taboo attribute for judging the corresponding each object of candidate solution, select candidate solution to concentrate the corresponding optimal shape of non-taboo object State is new current solution, while the taboo object elements for entering taboo list earliest are replaced with corresponding taboo object, then Go to step A9；

A13, the optimal solution to obtaining are decoded, and rule are drawn out on electric power GIS platform according to the circuit number that decoding is obtained Line road.

2. as claimed in claim 1 a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS, its feature It is：In step A4, positional information and payload, the positional information of transformer station, the branch road to be selected, of load point is read Branch road is built, the random length that generates is N_lineString of binary characters, and guarantee that the quantity of " 1 " in character string is number of nodes N_node, the function for applying mechanically (1) formula calculates each individual target function value.

3. as claimed in claim 1 or 2 a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS, its It is characterised by：In step A6, mutation operation is to each target individual X_i,G, i=1,2 ..., NP produce change according to formula (2) Different individuality, wherein, randomly selected sequence number r₁, r₂, r₃It is different, and r₁, r₂, r₃It is also different from target individual sequence number i, in formula Arithmetic operator is logical operator, i.e. X_{R2, G}And X_{R3, G}First carry out logic or, acquired results to carry out logic with mutation operator F different Or computing, finally again with X_{R1, G}Logic and operation is carried out, variation is obtained individual；

$V_{i,G+1}=X_{r_1,G}+F\·(X_{r_2,G}-X_{r_3,G})---\left(2\right)$

Then crossover operation is carried out according to formula (3), (4), (5), wherein randb (j) is random j-th for generating between [0,1] Estimated value, rnbr (i) is a randomly selected sequence；

U_i,G+1=(U_1i,G+1,U_2i,G+1,…,U_Di,G+1) (3)

$U_{ji,G+1}=\left\{\begin{array}{cccc}{V}_{ji,G+1}& if\left(randb\left(j\right)\≤CR\right)& or& j=rnbr\left(i\right)\\ X_{ji,G+1}& if\left(randb\left(j\right)>CR\right)& and& j\≠rnbr\left(i\right)\end{array}\right.---\left(4\right)$

(i=1,2 ..., NP, j=1,2 ..., D) (5)

Connectedness, radioactivity, node voltage constraint, capacity-constrained verification are carried out to individuality, it is again random if constraint is unsatisfactory for The individuality for producing meet the constraint is substituted.

4. as claimed in claim 1 or 2 a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS, its It is characterised by：In step A7, enter the individual target individual with relevant position in current population is tested according to greedy criterion Row compares, and takes the less individuality of fitness as of future generation individual, judges the switching condition for entering Tabu algorithms, will if meeting Current optimized individual carries out optimizing as the incoming Tabu algorithms of initial individuals, to avoid being absorbed in locally optimal solution.

5. as claimed in claim 1 or 2 a kind of based on the distribution network layout of roads method for avoiding differential evolution and GIS, its It is characterised by：In step A10, the encoded radio on any two position in initial solution sequence is exchanged at random, produce N_lineIt is individual Candidate solution.