CN115455731A - Micro-site selection and cable layout combined optimization design method for offshore wind power plant wind turbine generator - Google Patents

Micro-site selection and cable layout combined optimization design method for offshore wind power plant wind turbine generator Download PDF

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CN115455731A
CN115455731A CN202211210900.XA CN202211210900A CN115455731A CN 115455731 A CN115455731 A CN 115455731A CN 202211210900 A CN202211210900 A CN 202211210900A CN 115455731 A CN115455731 A CN 115455731A
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陶思钰
刘悦
许天赐
周洁敏
郑罡
张潮海
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a micro site selection and cable layout combined optimization design method for an offshore wind power plant wind turbine generator, and belongs to the field of power system planning. The invention synchronously solves the optimal position of an offshore wind farm wind turbine generator and the topological structure of a power collection system, and the specific method comprises the following steps: the outer model is solved by adopting a non-dominated sorting genetic algorithm II (NSGA-II) with the aim of maximizing the profit margin, the average capacity factor and the power quality of the wind power plant; the inner layer model is used for solving by adopting a Binary Particle Swarm Optimization (BPSO) algorithm and a Quadratic Programming (QP) method aiming at determining a topological structure of a power system and a power generation plan of a wind generation unit. The invention adopts a double-layer multi-target combined optimization method, which is beneficial to searching the optimal installation capacity of the offshore wind farm, and determining the position of the wind turbine generator and the planning scheme of the cable system, so that the offshore wind farm realizes the optimal performance.

Description

Micro-site selection and cable layout combined optimization design method for offshore wind power plant wind turbine generator
Technical Field
The invention belongs to the field of power system planning, and particularly relates to a micro-site selection and cable layout combined optimization design method for an offshore wind power plant wind turbine generator.
Background
Wind energy has become one of the key points of renewable energy development as a green clean energy source. The wind energy resources of offshore wind farms are stable and abundant, so that the construction scale of offshore wind farms is rapidly increased in recent years. In order to fully utilize offshore wind energy resources and save investment cost, the layout of many wind power plants is optimally designed, but the research on the combined optimization of the site selection of wind power generation sets and the cable layout is less. Furthermore, research typically sets the output power of a wind farm to a maximum target without considering its power quality, and when calculating wind farm revenue, the price of electricity generated by wind power is typically set to a constant without considering the correlation between wind energy and price.
Therefore, a new technical solution is needed to solve the above technical problems.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a micro-site selection and cable layout combined optimization design method for an offshore wind power plant wind turbine generator, so as to balance the installation capacity of the wind power plant, determine the site selection of the wind turbine generator and the planning scheme of an electrical system, and enable the wind power plant to exert better performance.
The technical scheme is as follows: the invention relates to a micro-site selection and cable layout combined optimization design method for an offshore wind power plant wind turbine generator, which comprises the following steps:
(1) Obtaining numerical values of various parameters required by site selection and wiring planning simulation of a plurality of wind generation sets, and establishing an output power model and a profit model of the wind power plant to be used as a target function of double-layer multi-target optimization in the step (2);
(2) Establishing an optimization model with a two-layer structure, wherein the optimization model comprises an outer layer model and an inner layer model, and selecting one wind turbine generator in a wind power plant as an optimal wind turbine generator; the outer layer model is used for determining the capacity of the wind power plant and the position of an optimal wind turbine generator, the inner layer model is provided with a first submodel and a second submodel, the first submodel is used for designing the type and layout of cables, and the second submodel is used for determining the generated energy of other wind turbine generators except the optimal wind turbine generator in the wind power plant according to the result obtained from the outer layer model;
(3) And solving a double-layer multi-objective optimization model for the combined optimization of the micro site selection and the cable layout of the wind turbine generator of the wind power plant.
Further, the step (1) comprises the following steps:
(11) Calculating the total useful work output power P of the wind power plant according to the basic parameters, the wind resource distribution function and the wake flow model of the wind power plant and the wind turbine generator wf ：
Figure BDA0003875191440000021
In the formula, N wt Is the total number of wind turbines, L w For dispersing wind directionTotal number of spaces to the same width, f k (theta) is the frequency of occurrence in the k-th wind direction, v in For wind turbines cut-in wind speed, v out Is the cut-off wind speed, P, of the wind turbine wt (v i ) Is the power equation, pdf, of a wind turbine k (v, α) is the uncertainty value of the wind speed described using the Weibull probability distribution function, dv is the differential operator of the wind speed;
(12) Calculating the profit margin p of the wind power plant according to the output power and income related parameters of the wind power plant wf ：
Figure BDA0003875191440000022
In the formula, R wf For total income of wind farm, C wt For the total cost of all wind turbines, C c For the total cost of the wind farm cables, C t For the total cost of the wind farm transformers, C g For the cost of electricity generation of all wind turbines, C e The lost cost of pollutant emission for all wind turbines.
Further, the step (2) comprises the following steps:
(21) Establishing an outer layer model: the outer layer model is a multi-objective optimization problem and respectively maximizes the daily profit margin p of the wind power plant wf Capacity factor mu wf And power quality ε wf The method comprises the following steps:
Obj1：max f 1 (X)＝p wf (3)
Obj2：max f 2 (X)＝μ wf (4)
Obj3：min f 3 (X)＝ε wf (5)
Figure BDA0003875191440000031
wherein X = [ C ] wf ，x i ，y i ]i＝1，2，…，N wt For capacity of wind farm and coordinates of wind turbine，r r Is the rotor radius, P, of the wind turbine wt，r Is the rated power of the wind power plant,
Figure BDA0003875191440000032
lower and upper limits of wind farm capacity, x, respectively min ，x max ，y min ，y max The upper and lower bounds of the boundary of the wind farm in the x and y directions,
Figure BDA0003875191440000033
and (c) and (d),
Figure BDA0003875191440000034
respectively representing useful work power and useless work power of a wind farm,
Figure BDA0003875191440000035
and
Figure BDA0003875191440000036
respectively useful power and useless power on the mth bus, G mn ，B mn The conductance and susceptance values of the nth line respectively,
Figure BDA0003875191440000037
for the useful work power generated by the wind turbine on the m-th bus,
Figure BDA0003875191440000038
and
Figure BDA0003875191440000039
respectively the minimum value and the maximum value of the useful work power on the mth bus,
Figure BDA00038751914400000310
for the useless power generated by the wind turbine generator on the mth bus,
Figure BDA00038751914400000311
and
Figure BDA00038751914400000312
respectively as the minimum value and the maximum value of the useless power on the mth bus,
Figure BDA00038751914400000313
is the voltage of the m-th bus bar,
Figure BDA00038751914400000314
and
Figure BDA00038751914400000315
respectively the minimum value and the maximum value of the voltage on the mth bus,
Figure BDA00038751914400000316
is the voltage of the n-th bus bar,
Figure BDA00038751914400000317
is the phase angle of the m-th bus bar,
Figure BDA00038751914400000318
and
Figure BDA00038751914400000319
respectively the minimum value and the maximum value of the phase angle on the mth bus bar,
Figure BDA00038751914400000320
represents the phase angle of the nth bus bar connected to the mth bus bar,
Figure BDA00038751914400000321
and
Figure BDA00038751914400000322
respectively, useful work and useless work power flows on the t hour line, and the maximum value and the minimum value are respectively
Figure BDA00038751914400000323
And
Figure BDA00038751914400000324
t m is a binary parameter, if the wind farm is integrated to the mth bus, then t m =1, otherwise t m ＝0，N b The number of buses in the wind power plant;
(22) Establishing a first sub-model of the inner layer model: decision variable Z = [ Z ] i，j，l ]Representing the connection state and the cable type between the two wind turbines, and obtaining the total cable cost C in the objective function of the outer layer model from the first sub-model cb The second constraint conditions make each connection use only one type of cable, the third and fourth constraint conditions guarantee power flow balance and topological connectivity of the cables, and the fifth constraint conditions specify the capacity limit of each cable, which is expressed in detail as:
Obj：min g(Z)＝C cb (7)
Figure BDA0003875191440000041
L＝{1，2，…，L cb }W＝{1，2，…，N wt +1} (9)
in the formula, f i，j Power, P, of a bus between two wind turbines c，r Is the rated power of the bus.
Establishing a second submodel of the inner layer model: decision variables
Figure BDA0003875191440000042
The output power of the wind turbine generator in the t hour is shown, wherein i =1,2, \8230;, N g T =1,2, \8230, 24, the first constraint condition indicates the power balance of the system, the second constraint condition indicates the limitation of the rotating reserve capacity, and the third constraint condition indicates the limitation of the output power of other wind power units; the generating cost C of all the wind turbine generators in the outer layer model objective function can be obtained from the model g And cost of wind farm pollutant discharge C e Specifically, it is represented as:
Obj：min h(Y)＝C g +C e (10)
Figure BDA0003875191440000043
in the formula, N g Representing the total number of other generators, N ld Indicating the total number of cables, R, connecting other wind turbines s Indicating the minimum value of the spinning reserve capacity,
Figure BDA0003875191440000044
representing the total power of the wind farm,
Figure BDA0003875191440000045
represents the power of the ith wind turbine generator set,
Figure BDA0003875191440000046
indicating the power on the jth bus bar,
Figure BDA0003875191440000047
represents the minimum output power of the ith wind turbine generator set,
Figure BDA0003875191440000048
the maximum output power of the ith wind turbine generator set is represented.
Further, the step (3) comprises the following steps:
(31) Initial population of NSGA-II was randomly generated: finding out a candidate combination set X = { χ = (X) = meeting constraint conditions of the outer layer model 1 ，χ 2 ，χ 3 8230, each χ m E X contains wind farm capacity C wf And coordinates (x, y) of the selected wind turbine;
(32) Calculating an inner layer model: solving the first sub-model through BPSO to obtain the cable layout and the total cost of the outer layer model, solving the second sub-model through QP to determine a power generation plan, and obtaining the pollutant emission penalty and the power generation cost of the outer layer model, which are respectively expressed as Y m And Z m ；
(33) Calculating the objective function value in the outer model: calculating each χ m Model { p in outer layer wf μ wf ε wf The target value in (1);
(34) Generating a pareto optimal solution set: according to { p wf μ wf ε wf Mapping X to pareto frontier, and generating a pareto optimal solution set omega = { omega = omega by a second-generation non-dominant sorting genetic algorithm 1 ，ω 2 ，ω 3 ，…}。
Further, in step (34), a pareto optimal solution is generated by using a non-dominated sorting technique in the following manner:
(41) For each solution { χ m ，Y m ，Z m Assign value to n m ＝0；
(42) Will { χ m ，Y m ，Z m And all other solutions { χ } n ，Y n ，Z n F, m, n =1,2,3, \ 8230 ≠ m, if { χ ≠ m compares m ，Y m ，Z m Is composed of { χ } n ，Y n ，Z n Dominant, then n m ＝n m +1；
(43) Find n is m All solutions of =0, i.e. constituting the pareto optimal set Ω.
Has the advantages that: compared with the prior art, the invention provides a double-layer optimization framework. Firstly, optimizing the capacity of a wind power plant, the position of a wind turbine generator and the type and layout of cables at the same time instead of independently optimizing; secondly, the three targets of the outer model comprise the maximization of the output power of the wind power plant, the power quality, the total profit and the correlation between the power and the price; furthermore, the capacity of the wind farm is not a predetermined value, taking into account the interaction with the grid.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a wind rose diagram of an offshore wind farm;
FIG. 3 is a diagram of an IEEE-30 bus system test system;
FIG. 4 is a result diagram of a design method for optimizing the micro-site selection of the wind turbine generator of the offshore wind farm and the cable layout in a combined manner.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in figure 1, the invention provides a method for optimizing and designing the wind turbine unit micro site selection and cable layout combination of an offshore wind farm, which adopts the real wind characteristic of the offshore wind farm, the wind direction rose diagram of the offshore wind farm is shown in figure 2, and the offshore wind farm is integrated into an IEEE-30 bus system as shown in figure 3.
Step 1: obtaining various parameter values required by site selection and wiring planning simulation of wind power generation sets of a wind power plant, and establishing an output power model and a profit model of the wind power plant according to basic parameters of the wind power generation sets, wind resource distribution parameters and a wake model.
Basic parameters of the wind turbine generator: rated power P of wind turbine generator and cut-in wind speed v of wind turbine generator in Rated wind speed v r Cut-out wind speed v out Height h of hub 0 And the diameter D of the wind wheel of the wind turbine generator system.
TABLE 1 basic parameters of wind turbines
Figure BDA0003875191440000061
Relevant parameters of the wind farm: lower and upper limits of wind farm capacity
Figure BDA0003875191440000062
Service life M of wind turbine generator wt (yr), surface roughness Length z 0 Angle of power factor
Figure BDA0003875191440000063
Service life M of transformer t (yr), service life M of cable c (yr)。
TABLE 2 basic parameters of offshore wind farms
Figure BDA0003875191440000064
Parameters of wind resource distribution: the shape parameters and scale parameters of the Weibull distribution function, and the wind rose plot, are obtained according to FIG. 2.
And then, establishing an output power model and a profit model of the wind power plant. The wind farm output power and profit margin are calculated as follows:
total useful work output power P of wind farm wf ：
Figure BDA0003875191440000065
In the formula, N wt Is the total number of wind turbines, L w Total number of intervals, f, discretized into the same width for wind direction k (θ) is the frequency of occurrence in the k-th wind direction, v in For wind turbines cut-in wind speed, v out Is the cut-off wind speed, P, of the wind turbine wt (v i ) Is the power equation, pdf, of a wind turbine k (v, α) is the uncertainty value of the wind speed described using the Weibull probability distribution function.
Profit margin p of wind farm wf ：
Figure BDA0003875191440000071
In the formula, R wf For total income of wind farm, C wt For the total cost of wind farm units, C c For the total cost of the cables of the wind farm, C t For the total cost of the wind farm transformers, C g For the cost of wind-driven generators in wind farms, C e The loss cost of pollutants discharged by the wind turbine generator is reduced.
Step 2: and establishing a joint optimization double-layer multi-objective model.
Outer layer model: selecting one wind turbine in a wind power plant as an optimal wind turbine;
the optimization target is respectively the maximum daily profit rate p of the wind power plant wf Capacity factor mu wf And power quality ε wf ：
Obj1：max f 1 (X)＝p wf (14)
Obj2：max f 2 (X)＝μ wf (15)
Obj3：min f 3 (X)＝ε wf (16)
Figure BDA0003875191440000072
Wherein, X is the capacity of the wind power plant and the coordinate of the wind power generation set, and can be expressed as X = [ C = [ (=) wf ，x i ，y i ]i＝1，2，…，N wt Form (a) of r Is the radius of the rotor of the wind turbine,
Figure BDA0003875191440000073
lower and upper limits, x, respectively, of the wind farm capacity min ，x max ，y min ，y max The upper and lower bounds of the boundary of the wind farm in the x and y directions,
Figure BDA0003875191440000074
and
Figure BDA0003875191440000075
respectively the useful work and the useless power generated by the wind turbine generator on the mth bus,
Figure BDA0003875191440000076
and
Figure BDA0003875191440000077
respectively the voltage and phase angle of the m (n) th bus,
Figure BDA0003875191440000078
and
Figure BDA0003875191440000079
respectively, useful and useless power flow on the tth hour line, t m Is a binary parameter, if the wind farm is integrated to the mth bus, then t m =1, otherwise t m ＝0，N b Is the total number in the wind farm.
A first submodel of the inner model: decision variable Z = [ Z ] i，j，l ]Indicating the connection status and the cable type between the two wind turbines. C in the outer layer model objective function can be obtained from the model c A second constraint ensuring that only one type of cable is selected for each link, a third and a fourth constraint ensuring power flow balancing and topological connectivity of the cables, and a fifth constraint specifying a capacity limit for each cable, expressed in particular as:
Obj：min g(Z)＝C c (18)
Figure BDA0003875191440000081
L＝{1，2，…，L c }W＝{1，2，…，N wt +1} (20)
second submodel of the inner model: decision variables
Figure BDA0003875191440000082
Representing the output power of other wind turbines in the t hour, wherein i =1,2, \8230;, N g T =1,2, \ 8230;, 24, the first constraint indicating the power balance of the system, the second constraint representing the spinning reserve capacity limit, and the third constraint representing the output power limit of the wind turbine. From the model, C in the outer layer model objective function 1 can be obtained g And C e 。
Obj：min h(Y)＝C g +C e (21)
Figure BDA0003875191440000083
And 3, step 3: and solving a double-layer multi-target planning model.
Randomly generating an NSGA-II initial population, and finding out a candidate combination set X = { χ ] meeting the constraint condition of an outer model 1 ，χ 2 ，χ 3 8230j, each chi m E X contains wind farm capacity C wf Coordinates (x, y) of the wind turbine; calculate the inner model, for each χ m The first sub-model is solved through BPSO, parameters of the BPSO algorithm are set as shown in table 3, the cable layout and the total cost of the outer layer model are obtained, the second sub-model is solved through QP, the power generation plan is determined, the pollutant emission penalty and the power generation cost of the outer layer model are quantified and are respectively represented as Y m And Z m (ii) a Evaluating the objective function in the outer layer model, and calculating each chi m e.X in the outer layer model { p wf μ wf ε wf The target value in (1); generation of pareto optimal solution set omega = { omega ] by non-dominated sorting method in NSGA-II 1 ，ω 2 ，ω 3 \ 8230;, NSGA-II parameter settings are shown in Table 3, giving each solution { χ;) m ，Y m ，Z m The initialized assignment n m =0, will { χ } m ，Y m ，Z m And other solutions { χ } n ，Y n ，Z n H, m, n =1,2,3, \8230 ≠ m, if { χ ≠ m compares m ，Y m ，Z m Composed of { χ } n ，Y n ，Z n Is dominant, then n m ＝n m +1, all n m The solution of =0 constitutes Ω.
TABLE 3 parameter settings for NSGA-II and BPSO algorithms
Figure BDA0003875191440000091
The result of the optimized planning of the offshore wind farm by adopting the double-layer multi-objective optimization model is shown in fig. 4. It can be seen that: the combined optimization of the position and the wiring of the wind turbine generator is superior to the single optimization, the operation cost of a power system is considered during the planning of a wind power plant, and in addition, not only wind power resources but also the topological structure of a local power grid are considered during the planning and design.
The invention is not limited to the specific details of the above-described embodiments. Any person skilled in the art can easily think of changes or substitutions in the technical scope of the present disclosure, which shall be covered by the protection scope of the present disclosure.

Claims (5)

1. A micro-site selection and cable layout combined optimization design method for an offshore wind power plant wind turbine generator is characterized by comprising the following steps:
(1) Obtaining numerical values of various parameters required by site selection and wiring planning simulation of a plurality of wind generation sets, and establishing an output power model and a profit model of the wind power plant to be used as a target function of double-layer multi-target optimization in the step (2);
(2) Establishing an optimization model with a two-layer structure, wherein the optimization model comprises an outer layer model and an inner layer model, and selecting one wind turbine generator in a wind power plant as an optimal wind turbine generator; the outer layer model is used for determining the capacity of the wind power plant and the position of an optimal wind turbine, the inner layer model is provided with a first sub model and a second sub model, the first sub model is used for designing the type and the layout of a cable, and the second sub model is used for determining the generated energy of other wind turbines in the wind power plant except the optimal wind turbine according to the result obtained from the outer layer model;
(3) And solving a double-layer multi-objective optimization model for the combined optimization of the micro site selection and the cable layout of the wind turbine generator of the wind power plant.
2. The method for the combined optimization design of the micro-siting and the cable layout of the wind turbine generator of the offshore wind farm according to claim 1, wherein the step (1) comprises the following steps:
(11) Calculating the total useful work output power P of the wind power plant according to the basic parameters, the wind resource distribution function and the wake flow model of the wind power plant and the wind turbine generator wf ：
Figure FDA0003875191430000011
In the formula, N wt Is the total number of wind turbines, L w Total number of intervals, f, discrete to the same width in the direction of the wind k (theta) is the frequency of occurrence in the k-th wind direction, v in For wind motorsGroup cut-in wind speed, v out Is the cut-off wind speed, P, of the wind turbine wt (v i ) Is the power equation, pdf, of a wind turbine k (v, α) is the uncertainty value of the wind speed described using the Weibull probability distribution function, dv is the differential operator of the wind speed;
(12) Calculating the profit margin p of the wind power plant according to the output power and income related parameters of the wind power plant wf ：
Figure FDA0003875191430000012
In the formula, R wf For total revenue of wind farm, C wt For the total cost of all wind turbines, C c For the total cost of the cables of the wind farm, C t For the total cost of the wind farm transformers, C g For the cost of electricity generation of all wind turbines, C e The loss cost of pollutants discharged by all wind turbines is reduced.
3. The method for the micro-site selection and the cable layout joint optimization design of the wind turbine generator of the offshore wind farm according to claim 2, wherein the step (2) comprises the following steps:
(21) Establishing an outer layer model: the outer layer model is a multi-objective optimization problem and respectively maximizes the daily profit margin p of the wind power plant wf Capacity factor mu wf And power quality ε wf Aiming at the following steps:
Obj1：max f 1 (X)＝p wf (3)
Obj2：max f 2 (X)＝μ wf (4)
Obj3：min f 3 (X)＝ε wf (5)
Figure FDA0003875191430000021
wherein X = [ C ] wf ，x i ，y i ]i＝1，2，…，N wt For capacity of wind farms and wind generatorsCoordinates of the group, r r Is the rotor radius, P, of the wind turbine wt，r Is the rated power of the wind power plant,
Figure FDA0003875191430000022
lower and upper limits of wind farm capacity, x, respectively min ，x max ，y min ，y max The upper and lower bounds of the boundary of the wind farm in the x and y directions,
Figure FDA0003875191430000023
and the combination of (a) and (b),
Figure FDA0003875191430000024
respectively representing useful work power and useless work power of a wind farm,
Figure FDA0003875191430000025
and
Figure FDA0003875191430000026
respectively useful power and useless power on the mth bus, G mn ，B mn The conductance and susceptance values of the nth line respectively,
Figure FDA0003875191430000027
for the useful work power generated by the wind turbine on the m-th bus,
Figure FDA0003875191430000028
and
Figure FDA0003875191430000029
respectively the minimum value and the maximum value of the useful work power on the mth bus,
Figure FDA00038751914300000210
for the useless power generated by the wind turbine generator on the mth bus,
Figure FDA00038751914300000211
and
Figure FDA00038751914300000212
respectively is the minimum value and the maximum value of the useless power on the mth bus,
Figure FDA00038751914300000213
is the voltage of the m-th bus bar,
Figure FDA00038751914300000214
and
Figure FDA00038751914300000215
respectively the minimum value and the maximum value of the voltage on the mth bus,
Figure FDA00038751914300000216
is the voltage of the n-th bus bar,
Figure FDA00038751914300000217
is the phase angle of the m-th bus bar,
Figure FDA00038751914300000218
and
Figure FDA00038751914300000219
respectively the minimum value and the maximum value of the phase angle on the mth bus bar,
Figure FDA00038751914300000220
represents the phase angle of the nth bus bar connected to the mth bus bar,
Figure FDA0003875191430000031
and
Figure FDA0003875191430000032
respectively of interest on the t hour lineWork and idle power flows, the maximum and minimum of which are
Figure FDA0003875191430000033
And
Figure FDA0003875191430000034
t m is a binary parameter, if the wind farm is integrated to the mth bus, then t m =1, otherwise t m ＝0，N b The number of buses in the wind power plant;
(22) Establishing a first sub-model of the inner layer model: decision variable Z = [ Z ] i，j，l ]Representing the connection state and the cable type between the two wind turbines, and obtaining the total cable cost C in the objective function of the outer layer model from the first sub-model cb A second constraint that each link uses only one type of cable, a third and a fourth constraint that guarantee power flow balancing and topological connectivity of the cables, and a fifth constraint that specifies a capacity limit for each cable, expressed in particular as:
Obj：min g(Z)＝C cb (7)
Figure FDA0003875191430000035
L＝{1，2，…，L cb }W＝{1，2，…，N wt +1} (9)
in the formula (f) i，j Is the power of a bus between two wind turbines, P c，r Is the rated power of the bus;
establishing a second sub-model of the inner layer model: decision variables
Figure FDA0003875191430000036
The method represents the output power of the wind turbine generator at the t hour, wherein i =1,2, \8230, N g T =1,2, \ 8230;, 24, the first constraint indicating the power balance of the system, the second constraint representing the spinning reserve capacity limit, the third constraint representing the other wind turbinesThe output power limit of (d); the power generation cost C of all the wind turbine generators in the objective function of the outer layer model can be obtained from the model g And cost of wind farm pollutant discharge C e Specifically, it is represented as:
Obj：min h(Y)＝C g +C e (10)
Figure FDA0003875191430000037
in the formula, N g Representing the total number of other generators, N ld Indicating the total number of cables, R, connecting other wind turbines s Indicating the minimum value of the spinning reserve capacity,
Figure FDA0003875191430000038
representing the total power of the wind farm,
Figure FDA0003875191430000039
represents the power of the ith wind turbine generator set,
Figure FDA0003875191430000041
indicating the power on the jth bus bar,
Figure FDA0003875191430000042
represents the minimum output power of the ith wind turbine generator set,
Figure FDA0003875191430000043
the maximum output power of the ith wind turbine generator set is represented.
4. The method for the combined optimization design of the micro-siting and the cable layout of the wind turbine generator of the offshore wind farm according to claim 3, wherein the step (3) comprises the following steps:
(31) Random generation of initial population of NSGA-II: finding out candidate combination set X = { χ ] satisfying outer layer model constraint condition 1 ，χ 2 ，χ 3 8230, each χ m The epsilon X contains the wind power plant capacity C wf And the coordinates (x, y) of the selected wind turbine;
(32) Calculating an inner layer model: solving the first sub-model through BPSO to obtain the cable layout and the total cost of the outer layer model, solving the second sub-model through QP to determine a power generation plan, and obtaining the pollutant emission penalty and the power generation cost of the outer layer model, which are respectively expressed as Y m And Z m ；
(33) Calculating the objective function value in the outer model: calculating each χ m Outer model { p } wf μ wf ε wf The target value in (1);
(34) Generating a pareto optimal solution set: according to { p wf μ wf ε wf Mapping X to a pareto frontier, and generating a pareto optimal solution set omega by a second-generation non-dominated sorting genetic algorithm, = { omega = 1 ，ω 2 ，ω 3 ，…}。
5. The method for jointly optimizing design of micro-site selection and cable layout for wind turbines of offshore wind farms according to claim 4, wherein in step (34), a pareto optimal solution is generated by using a non-dominated sorting technique in the following manner:
(41) For each solution { χ m ，Y m ，Z m Assign a value to n m ＝0；
(42) Will { x } m ，Y m ，Z m And all other solutions { χ } n ，Y n ，Z n H, m, n =1,2,3, \8230 ≠ m, if { χ ≠ m compares m ，Y m ，Z m Composed of { χ } n ，Y n ，Z n Dominant, then n m ＝n m +1；
(43) Find n m All solutions of =0, i.e. constituting the pareto optimal set Ω.
CN202211210900.XA 2022-09-30 2022-09-30 Micro-site selection and cable layout combined optimization design method for offshore wind power plant wind turbine generator Pending CN115455731A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117371151A (en) * 2023-10-10 2024-01-09 电子科技大学中山学院 Cable connection optimization method for power supply of floating wind power plant and oil-gas platform
CN117371151B (en) * 2023-10-10 2024-03-22 电子科技大学中山学院 Cable connection optimization method for power supply of floating wind power plant and oil-gas platform

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