CN111969658A - Defensive-conventional coordination planning method for power generation and transmission system considering wind power - Google Patents

Defensive-conventional coordination planning method for power generation and transmission system considering wind power Download PDF

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CN111969658A
CN111969658A CN202010871363.8A CN202010871363A CN111969658A CN 111969658 A CN111969658 A CN 111969658A CN 202010871363 A CN202010871363 A CN 202010871363A CN 111969658 A CN111969658 A CN 111969658A
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CN111969658B (en
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谢开贵
伏坚
胡博
牛涛
李春燕
邵常政
黄威
孙悦
李凡
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Chongqing University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention discloses a defensive-conventional coordination planning method for a power generation and transmission system considering wind power, which comprises the following steps: acquiring information of an original power transmission system and candidate power transmission systems; establishing a defensive-conventional coordination planning model of a power generation and transmission system considering wind power; converting a defensive-conventional coordination planning model of a power generation and transmission system into a main problem model and a sub-problem model; and iteratively solving the main problem model and the sub problem model based on the information of the original power transmission system and the candidate power transmission system to obtain an optimal planning scheme. The method provided by the invention has the advantages that the coordination consideration of the conventional planning and the defensive planning is carried out, the uncertainty of the information-physical cooperative attack, the load and the wind power output is effectively represented, the capability of the planning system for coping with the uncertainty is obviously reduced, the capability of the planning system for coping with the cooperative attack is improved, and the planning total cost is reduced.

Description

Defensive-conventional coordination planning method for power generation and transmission system considering wind power
Technical Field
The invention relates to the technical field of power engineering, in particular to a defensive-conventional coordination planning method for a power generation and transmission system considering wind power.
Background
With the large-scale construction and development of electric power systems and internet of things in China, distributed power generation systems such as wind power and solar energy are largely connected, micro-grids are raised, computers and communication technologies are largely applied to the electric power systems, and a new generation of electric power system comprises a large number of sensor devices, primary physical devices, computer devices, complex communication network resources and the like, so that the modern electric power system is developed into an information physical fusion system with real-time sensing, dynamic control and information service.
However, as the power grid is more and more open, the number of times of malicious attacks is increased, which has a non-negligible effect on the reliability of the power system. In recent years, the phenomenon of malicious attacks on power systems has been very serious, and there is a significantly rising trend toward malicious attacks on power systems as a whole.
In addition, modern power systems are developing towards 'clean and efficient', and renewable energy power generation is rapidly developing. Renewable energy sources such as wind power and solar power have the characteristics of uncertainty, volatility, non-scheduling and the like, so that impact can be caused on the safe and reliable operation of a power system. The problem can be alleviated through reasonable coordination planning of the renewable energy generator set and the conventional generator set and the line.
However, in the current planning research, it is very rare to coordinate the planning strategy for dealing with malicious attacks with the conventional planning for considering objects such as loads, renewable energy sources, etc., and both plans are considered separately and the relationship between the two plans is not analyzed, and the effect of the planning scheme solved by each of them when applied to another problem may not be ideal.
Disclosure of Invention
The problems actually solved by the invention are as follows: planning of the power system is performed under the comprehensive consideration of malicious attacks, loads and renewable energy sources.
The invention adopts the following technical scheme:
a defensive-conventional coordination planning method for a power generation and transmission system considering wind power comprises the following steps:
s1, acquiring information of the original power transmission and transmission system and the candidate power transmission and transmission system;
s2, establishing a defensive-conventional coordination planning model of the power generation and transmission system considering wind power;
s3, converting the defensive-conventional coordination planning model of the power transmission system into a main problem model and a sub-problem model;
and S4, iteratively solving the main problem model and the sub problem model based on the information of the original power transmission system and the candidate power transmission system to obtain an optimal planning scheme.
Preferably, the defensive-conventional coordination planning model considering wind power of the power generation and transmission system comprises an upper layer model based on the angle of a planner of the power generation and transmission system, a middle layer model based on the angle of an attacker of the power generation and transmission system and a lower layer model based on the angle of an operator of the power generation and transmission system; the decision variables of the upper layer model are the building judgment values of the candidate power transmission line, the candidate conventional unit and the candidate wind turbine, the decision variables of the middle layer model are a cooperative attack scheme, a load scene and a wind power output scene, and the decision variables of the lower layer model are the output of the conventional unit, the wind turbine air curtailment and the node load cutting.
Preferably, the objective function of the upper model includes:
C＝min(Cinv+Cope+Cperf)
wherein C represents an objective function with the lowest total cost in the planning period, Cinv、CopeAnd CperfRespectively representing investment cost, rated operation cost and system performance loss cost;
Figure BDA0002651238360000021
Figure BDA0002651238360000022
Figure BDA0002651238360000023
in the formula, xL-l、xG-gAnd xW-gRespectively representing the construction judgment values of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g, wherein the construction judgment value is 1 to represent construction, the construction judgment value is 0 to represent non-construction, and CL-l、CG-gAnd CW-gRespectively representing year values of construction investment costs and the like, omega ', of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g'L、Ω'GAnd omega'WRespectively representing a set of candidate power transmission lines, a set of candidate conventional units and a set of candidate wind generation units; t represents the annual running time, ΩGAnd ΩDRespectively representing the set of the original conventional unit and the set of the original load node,
Figure BDA0002651238360000024
and
Figure BDA0002651238360000025
respectively representing the active power output of the conventional unit g and the load shedding amount of the load node d under the normal operation condition, CP-gAnd CS-dRespectively representing the annual value of the unit cost of g output of the conventional unit and the unit cost of d load shedding of the load node; gamma represents the coefficient of converting the power failure capacity into the economic cost under the worst operation scene obtained according to the optimization model, SdRepresenting the load shedding amount of the load node d in the worst operation scene;
the constraints of the upper layer model include:
Figure BDA0002651238360000031
Figure BDA0002651238360000032
Figure BDA0002651238360000033
Figure BDA0002651238360000034
Figure BDA0002651238360000035
Figure BDA0002651238360000036
Figure BDA0002651238360000037
Figure BDA0002651238360000038
Figure BDA0002651238360000039
Figure BDA00026512383600000310
Figure BDA00026512383600000311
Figure BDA00026512383600000312
Figure BDA00026512383600000313
Figure BDA00026512383600000314
Figure BDA00026512383600000315
Figure BDA00026512383600000316
Fl＝Bl(θS(l)-θE(l))vL-l l∈ΩL
Fl＝Bl(θS(l)-θE(l))xL-l l∈Ω'L
Figure BDA0002651238360000041
θr＝0
Figure BDA0002651238360000042
-Fl max≤Fl≤Fl max l∈ΩL∪Ω'L
in the formula, CtotalRepresents the maximum investment cost; v represents the wind speed corresponding to the hub of the fan, vin、vrateAnd voutRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, PwindAnd PrateRespectively representing the output and rated power of a single wind turbine; omegaWThe set of the original wind turbine generator is shown,
Figure BDA0002651238360000043
represents the maximum capacity of the conventional unit g,
Figure BDA0002651238360000044
the capacity of the wind turbine g is represented,
Figure BDA0002651238360000045
representing the peak load of the load node d, and eta represents the lowest standby rate allowed by the system; rgThe peak-load capacity ratio of the conventional unit g is represented, the peak-load capacity ratio is the ratio of the peak-load capacity of the conventional unit to the rated installed capacity of the conventional unit, beta represents the peak-valley difference rate of the load demand, and the peak-valley difference rate is the ratio of the peak-valley difference of the load to the peak load;
Figure BDA0002651238360000046
representing the lowest wind power permeability allowed by the system; fl 0Representing the power flow of the transmission line i in normal operation, BlThe susceptance of the transmission line/is represented,
Figure BDA0002651238360000047
represents the corresponding phase angle of S (l) in normal operation,
Figure BDA0002651238360000048
representing a corresponding phase angle E (l) in normal operation, and respectively representing a starting node and a terminating node of the transmission line l by S (l) and E (l);
Figure BDA0002651238360000049
representing the air abandoning amount of the wind turbine generator g when the system is in normal operation,
Figure BDA00026512383600000410
representing a typical value of the output of the wind turbine g,
Figure BDA00026512383600000411
a typical value of the load of the node d is represented,
Figure BDA00026512383600000412
representing the active power output of the unit g in normal operation,
Figure BDA00026512383600000413
the air abandon quantity of the unit g in normal operation is shown,
Figure BDA00026512383600000414
and
Figure BDA00026512383600000415
respectively representing the set of original wind turbines connected at the node b and the set of candidate wind turbines,
Figure BDA00026512383600000416
representing the set of units, Ω, connected to node bBRepresenting an original system node set;
Figure BDA00026512383600000417
representing the phase angle of a reference node when the planning system operates normally; fl maxRepresenting the maximum allowable active power flow of the transmission line l;
Figure BDA00026512383600000418
and
Figure BDA00026512383600000419
respectively representing the phase angle, the maximum allowed phase angle and the minimum allowed phase angle of the node b in normal operation;W-grepresenting the maximum wind abandon rate of the wind turbine generator g allowed by the planning system; flRepresenting the power flow, theta, of the transmission line lS(l)Denotes the phase angle, θ, corresponding to S (l)E(l)Denotes the corresponding phase angle of E (l), SW-gRepresents the wind abandon amount of the wind turbine generator g when the system is in normal operation, PW-gRepresenting the active power output, L, of the wind turbine gdRepresenting the load value, P, of node dgRepresenting the active output of the unit g, SgRepresenting the air curtailment quantity of the unit g, K representing a node-load incidence matrix, and K (b, d) representing the elements of the row b and the column d of the node-load incidence matrix; thetarShowing a referencePhase angle of the node; thetabRepresenting the phase angle at node b.
Preferably, the objective function of the middle layer model includes:
Figure BDA0002651238360000051
the constraints of the middle layer model include:
Figure BDA0002651238360000052
-τ·Ld≤ΔLd≤τ·Ldd∈ΩD
Figure BDA0002651238360000053
-(NB-1)·vL-l≤fl≤(NB-1)·vL-l l∈ΩL
Figure BDA0002651238360000054
L＝L0+sLDLeL
Figure BDA0002651238360000055
Figure BDA0002651238360000057
Figure BDA0002651238360000056
in the formula,. DELTA.LdThe load measurement value of the load node d after LR attack is shown as the tampering quantity, the load measurement value is increased to be positive, and is reduced to be negative(ii) a Tau represents the ratio upper limit of the load data tampering amount relative to the original load value; v. ofL-l、vG-gAnd vW-gRespectively representing whether the original transmission line l, the original conventional unit g and the original wind turbine unit g are selected as binary variables of physical attack objects, wherein the value 0 represents attacked, the value 1 represents not attacked, and r representsL-l、rG-gAnd rW-gRespectively representing physical attack resources, R, consumed by attacking the original transmission line l, the original conventional unit g and the original wind turbine unit gmaxRepresenting the upper limit of physical attack resources; f. oflRepresenting the virtual SC flow of the first power transmission line, and being used for judging whether the system has an island or not, NBRepresenting the number of system nodes; a and (b, l) respectively represent the b row and l column elements of the node-line incidence matrix and the node-line incidence matrix of the power system; l represents the load level, L0Representing the load level in normal operation, sLIndicating the magnitude of the load deviation, DLRepresents the lower triangular matrix of the load covariance matrix after Cholesky decomposition, eLAn error vector representing the load; xiLRepresenting a conservative coefficient describing the uncertainty of the load, NLRepresents the total number of loads; pWAnd
Figure BDA0002651238360000061
respectively representing the output of the wind turbine generator in the worst scene and the output of the wind turbine generator in the rated operation scene, wherein the element sequence is that the original wind turbine generator is firstly replaced by a candidate wind turbine generator, H represents a conversion matrix, and sWIndicating deviation amplitude of wind power output, DWRepresenting a lower triangular matrix, e, of the wind turbine generator covariance matrix after Cholesky decompositionWError vector, ξ, representing the wind turbine outputWConservative parameters representing uncertainty of fan output; n is a radical ofWAnd N'WAnd respectively representing the total number of the original wind turbines and the total number of the candidate wind turbines.
Preferably, the objective function of the underlying model comprises:
Figure BDA0002651238360000062
the constraint functions of the underlying model include:
Figure BDA0002651238360000063
Figure BDA0002651238360000064
Figure BDA0002651238360000065
Figure BDA0002651238360000066
Figure BDA0002651238360000067
Figure BDA0002651238360000068
Figure BDA0002651238360000069
0≤Sd≤Ld+ΔLdd∈ΩD
Figure BDA00026512383600000610
0≤SW-g≤W-gPW-g g∈ΩW
0≤SW-g≤xW-g W-gPW-g g∈Ω'W
in the formula (I), the compound is shown in the specification,
Figure BDA00026512383600000611
representing a spurious power flow of the transmission line/,
Figure BDA00026512383600000612
represents the corresponding spurious phase angle of s (l),
Figure BDA00026512383600000613
represents the corresponding spurious phase angle e (l),
Figure BDA00026512383600000614
and
Figure BDA00026512383600000615
respectively representing the node phase angle and the reference node phase angle determined by the operator after the system is attacked by LR and analyzed based on the measured false load data.
Preferably, the objective function of the master problem model comprises:
Figure BDA0002651238360000071
the constraints of the main problem model include:
Figure BDA0002651238360000072
Figure BDA0002651238360000073
Figure BDA0002651238360000074
Figure BDA0002651238360000075
Figure BDA0002651238360000076
Figure BDA0002651238360000077
Figure BDA0002651238360000078
Figure BDA0002651238360000079
Figure BDA00026512383600000710
Figure BDA00026512383600000711
Figure BDA00026512383600000712
Figure BDA00026512383600000713
Figure BDA00026512383600000714
Figure BDA00026512383600000715
Figure BDA00026512383600000716
Figure BDA00026512383600000717
Figure BDA00026512383600000718
Figure BDA0002651238360000081
Figure BDA0002651238360000082
-Fl max≤Fl (m)≤Fl max l∈ΩL∪Ω'L
Figure BDA0002651238360000083
Figure BDA0002651238360000084
Figure BDA0002651238360000085
Figure BDA0002651238360000086
Figure BDA0002651238360000087
Figure BDA0002651238360000088
Figure BDA0002651238360000089
Figure BDA00026512383600000810
Figure BDA00026512383600000811
Figure BDA00026512383600000812
Figure BDA00026512383600000813
Figure BDA00026512383600000814
m＝1,…,k
k is the iteration number, and the superscript (m) is the mth iteration of the corresponding parameter;
the objective function of the sub-problem model includes:
Figure BDA00026512383600000815
the constraints of the sub-problem model include:
and (4) upper layer constraint:
Figure BDA0002651238360000091
and (3) lower layer constraint:
Figure BDA0002651238360000092
preferably, step S4 includes:
s401, setting an upper bound UB as a preset value and setting a lower bound LB as 0; initializing convergence precision; let the iteration number k equal to 1, initialize the optimization decision vector:
Figure BDA0002651238360000101
Figure BDA0002651238360000102
Figure BDA0002651238360000103
order to
Figure BDA0002651238360000104
And is
Figure BDA0002651238360000105
N'LAnd N'GRespectively representing the quantity of the candidate power transmission lines and the quantity of the candidate conventional units, and the superscript (k) represents a variable value corresponding to the kth iteration;
s402, mixing
Figure BDA0002651238360000106
And
Figure BDA0002651238360000107
solving the sub-problem to obtain the optimal combination state:
Figure BDA0002651238360000108
Figure BDA0002651238360000109
Figure BDA00026512383600001010
Figure BDA00026512383600001011
Figure BDA00026512383600001012
Figure BDA00026512383600001013
order to
Figure BDA00026512383600001014
S403, judging whether UB-LB is not more than or equal to the established value, if so, executing step S406, otherwise, executing step S404;
s404, mixing
Figure BDA00026512383600001015
ΔL(k)、
Figure BDA00026512383600001016
L(k)And PW(k)Solving the main problem by substituting it as a known quantity
Figure BDA00026512383600001017
And
Figure BDA00026512383600001018
let LB equal to η;
s405, let k equal to k +1, and execute step S402;
and S406, outputting a coordination planning result.
Compared with the prior art, the method has the following technical effects: the method provided by the invention has the advantages that the coordination consideration of the conventional planning and the defensive planning is carried out, the uncertainty of the information-physical cooperative attack, the load and the wind power output is effectively represented, the capability of the planning system for coping with the uncertainty is obviously reduced, the capability of the planning system for coping with the cooperative attack is improved, and the planning total cost is reduced.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
fig. 1 is a flowchart of a defensive-conventional coordination planning method for a power generation and transmission system considering wind power according to an embodiment of the present invention;
FIG. 2 is a flow chart of the algorithm solution according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an improved IEEE RTS79 system according to an embodiment of the present invention;
FIG. 4 is a load shedding diagram of the four planning strategies provided by the embodiment of the present invention in the most severe scenario;
fig. 5 shows cost results of four planning strategies provided in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a defensive-conventional coordination planning method for a power generation and transmission system considering wind power, which comprises the following steps:
s1, acquiring information of the original power transmission and transmission system and the candidate power transmission and transmission system;
s2, establishing a defensive-conventional coordination planning model of the power generation and transmission system considering wind power;
s3, converting the defensive-conventional coordination planning model of the power transmission system into a main problem model and a sub-problem model;
and S4, iteratively solving the main problem model and the sub problem model based on the information of the original power transmission system and the candidate power transmission system to obtain an optimal planning scheme.
Taking an IEEE RTS-79 system as an example, the original transmission and power system information includes initial IEEE RTS-79 system load data, system topology, electrical parameters, economic parameters, and the like, and the candidate transmission and power system information includes electrical parameters and economic parameters of candidate lines, candidate conventional units, and candidate wind power units. Specifically, the results are shown in tables 1 and 2.
TABLE 1
Figure BDA0002651238360000111
TABLE 2
Figure BDA0002651238360000121
The remaining parameters are set as follows: the maximum air abandon rate is 0.01, and the minimum permeability
Figure BDA0002651238360000122
The conversion coefficient gamma of the power failure capacity caused by malicious attack corresponding to the economic cost is 1.0, the minimum spare rate eta is 20%, the peak-to-peak capacity ratio of the conventional unit is uniformly set to be 50%, and the load demand peak-to-valley difference rate beta is 30%. The maximum investment cost is 600M $, and the equal year value is 77.70M $. The cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan are respectively 3m/s, 10m/s and 22 m/s. Uncertain budget xiLAnd xiWIs 1. Under the rated operation condition, the load cutting cost is 1000 $/MWh. The upper limit tau of the ratio of the LR attack load data change quantity relative to the original load value is 0.5. C&The convergence factor in the CG algorithm is set to 10-6. Setting a reference node as a node 1, setting the physical attack resource quantity as 1 and setting the reference capacity as 100 MVA; upper and lower limits theta of phase anglemaxAnd thetaminRespectively taking + pi/2 and-pi/2. In the invention, the physical attack resources which need to be consumed by setting to successfully attack any line or any unit are all 1.
In specific implementation, the defensive-conventional coordination planning model considering wind power of the power generation and transmission system comprises an upper layer model based on a power generation and transmission system planning staff angle, a middle layer model based on a power generation and transmission system attacker angle and a lower layer model based on a power generation and transmission system operating staff angle; the decision variables of the upper layer model are the building judgment values of the candidate power transmission line, the candidate conventional unit and the candidate wind turbine, the decision variables of the middle layer model are a cooperative attack scheme, a load scene and a wind power output scene, and the decision variables of the lower layer model are the output of the conventional unit, the wind turbine air curtailment and the node load cutting.
The extended planning is widely applied in the power system and is regarded as one of the important means for solving the problems facing the current situation. In the background of the current information physical system, attack risks and uncertainties of renewable energy sources, users and the like possibly suffered by the power grid are considered before the power grid is expanded and planned, and a more reasonable decision is made. Therefore, the defensive-conventional coordination planning model of the power generation and transmission system is established. In the defensive-conventional coordination planning model for the power generation and transmission system provided by the embodiment of the invention, the planning model is implemented according to the following personnel: system planners, operators (defenders) and attackers (attackers) and the rest: the power users and the natural environment are based on the uncertainty of a robust optimization processing attack scheme, and a three-layer defensive robust planning model based on planners, attack personnel, users, the natural environment and operators is provided. Through three-layer optimization framework, can accurately describe the game relation between the three, embody influence and influenced mechanism, promptly: the decision of each object can affect other objects and be affected by the decisions of other objects. In addition, the most serious operation scene can be optimized through the middle-lower layer model, so that the planning result has stronger robustness.
An upper optimization model: the layer model is used for determining investment planning and determining the construction of candidate wind turbines, conventional turbines and lines. In the layer model, the real operation condition of the planning system after misleading by the false data under the normal operation condition and the worst operation condition is completely considered.
A middle-layer optimization model: the layer model tries to identify the worst operation scene causing the planning system to generate the maximum load shedding based on the planning system determined by the upper layer model, and the worst operation scene comprises a cooperative attack scheme, load and wind power output.
The lower optimization model is as follows: the layer model optimally schedules a planning system based on the attack scheme, the load and the wind-power output combined state determined by the middle layer model, and determines the optimal unit output and load shedding scheme under the visual angle of operators.
In the upper-layer optimization model provided by the embodiment of the invention, the conditions of investment cost, conventional planning requirements, scenes in rated operation, real operation scenes after cooperative attack and the like are considered in advance, the lowest total cost in a planning period is taken as an objective function, and a decision variable is a binary variable for whether a candidate wind turbine generator, a candidate conventional generator and a candidate power transmission line are constructed or not.
In the middle-layer optimization model provided by the embodiment of the invention, uncertainties of attack schemes, loads and wind-power output are coordinately considered, and the worst combined operation scene of a planning system is determined through optimization. The objective function is to maximize the operation load shedding of the planning system, and the decision variables are a cooperative attack scheme, a load scene and a wind power output scene.
In the middle-lower layer optimization model provided by the embodiment of the invention, the operation simulation behavior of the planning system is described. And under the scenes of the planning scheme determined by the upper layer model and the attack scheme, the load and the wind and electricity output determined by the middle layer model, the operator carries out optimal scheduling on the planning system based on the acquired data and other operation conditions. The objective function is to minimize the operation load shedding of the planning system, and the decision variables are the output of the conventional unit, the wind curtailment amount of the wind turbine unit and the node load shedding.
In specific implementation, the objective function of the upper model includes:
C＝min(Cinv+Cope+Cperf) (1)
wherein C represents an objective function with the lowest total cost in the planning period, Cinv、CopeAnd CperfRespectively representing investment cost, rated operation cost and system performance loss cost;
Figure BDA0002651238360000131
Figure BDA0002651238360000132
Figure BDA0002651238360000133
in the formula, xL-l、xG-gAnd xW-gRespectively representing the construction judgment values of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g, wherein the construction judgment value is 1 to represent construction, the construction judgment value is 0 to represent non-construction, and CL-l、CG-gAnd CW-gRespectively representing year values of construction investment costs and the like, omega ', of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g'L、Ω'GAnd omega'WRespectively representing a set of candidate power transmission lines, a set of candidate conventional units and a set of candidate wind generation units; t represents the annual running time, ΩGAnd ΩDRespectively representing the set of the original conventional unit and the set of the original load node,
Figure BDA0002651238360000141
and
Figure BDA0002651238360000142
respectively representing the active power output of the conventional unit g and the load shedding amount of the load node d under the normal operation condition, CP-gAnd CS-dRespectively representing the annual value of the unit cost of g output of the conventional unit and the unit cost of d load shedding of the load node; gamma represents the coefficient of converting the power failure capacity into the economic cost under the worst operation scene obtained according to the optimization model, SdRepresenting the load shedding amount of the load node d in the worst operation scene;
the constraints of the upper layer model include:
Figure BDA0002651238360000143
Figure BDA0002651238360000144
Figure BDA0002651238360000145
Figure BDA0002651238360000146
Figure BDA0002651238360000147
Figure BDA0002651238360000148
Figure BDA0002651238360000149
Figure BDA00026512383600001410
Figure BDA00026512383600001411
Figure BDA0002651238360000151
Figure BDA0002651238360000152
Figure BDA0002651238360000153
Figure BDA0002651238360000154
Figure BDA0002651238360000155
Figure BDA0002651238360000156
Figure BDA0002651238360000157
Fl＝Bl(θS(l)-θE(l))vL-l l∈ΩL (21)
Fl＝Bl(θS(l)-θE(l))xL-l l∈Ω'L (22)
Figure BDA0002651238360000158
θr＝0 (24)
Figure BDA0002651238360000159
-Fl max≤Fl≤Fl max l∈ΩL∪Ω'L (26)
in the formula, CtotalRepresents the maximum investment cost; v represents the wind speed corresponding to the hub of the fan, vin、vrateAnd voutRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, PwindAnd PrateRespectively representing the output and rated power of a single wind turbine; omegaWThe set of the original wind turbine generator is shown,
Figure BDA00026512383600001510
represents the maximum capacity of the conventional unit g,
Figure BDA00026512383600001511
the capacity of the wind turbine g is represented,
Figure BDA00026512383600001512
representing the peak load of the load node d, and eta represents the lowest standby rate allowed by the system; rgThe peak-load capacity ratio of the conventional unit g is represented, the peak-load capacity ratio is the ratio of the peak-load capacity of the conventional unit to the rated installed capacity of the conventional unit, beta represents the peak-valley difference rate of the load demand, and the peak-valley difference rate is the ratio of the peak-valley difference of the load to the peak load;
Figure BDA00026512383600001513
representing the lowest wind power permeability allowed by the system; fl 0Representing the power flow of the transmission line i in normal operation, BlThe susceptance of the transmission line/is represented,
Figure BDA00026512383600001514
represents the corresponding phase angle of S (l) in normal operation,
Figure BDA00026512383600001515
representing a corresponding phase angle E (l) in normal operation, and respectively representing a starting node and a terminating node of the transmission line l by S (l) and E (l);
Figure BDA0002651238360000161
representing the air abandoning amount of the wind turbine generator g when the system is in normal operation,
Figure BDA0002651238360000162
representing a typical value of the output of the wind turbine g,
Figure BDA0002651238360000163
a typical value of the load of the node d is represented,
Figure BDA0002651238360000164
representing the active power output of the unit g in normal operation,
Figure BDA0002651238360000165
the air abandon quantity of the unit g in normal operation is shown,
Figure BDA0002651238360000166
and
Figure BDA0002651238360000167
respectively representing the set of original wind turbines connected at the node b and the set of candidate wind turbines,
Figure BDA0002651238360000168
representing the set of units, Ω, connected to node bBRepresenting an original system node set;
Figure BDA0002651238360000169
representing the phase angle of a reference node when the planning system operates normally; fl maxRepresenting the maximum allowable active power flow of the transmission line l;
Figure BDA00026512383600001610
and
Figure BDA00026512383600001611
respectively representing the phase angle, the maximum allowed phase angle and the minimum allowed phase angle of the node b in normal operation;W-grepresenting the maximum wind abandon rate of the wind turbine generator g allowed by the planning system; flRepresenting the power flow, theta, of the transmission line lS(l)Denotes the phase angle, θ, corresponding to S (l)E(l)Denotes the corresponding phase angle of E (l), SW-gRepresents the wind abandon amount of the wind turbine generator g when the system is in normal operation, PW-gRepresenting the active power output, L, of the wind turbine gdRepresenting the load value, P, of node dgRepresenting the active output of the unit g, SgRepresenting the air curtailment quantity of the unit g, K representing a node-load incidence matrix, and K (b, d) representing the elements of the row b and the column d of the node-load incidence matrix; thetarRepresenting a phase angle of a reference node; thetabRepresenting the phase angle at node b.
The constraint (5) is used to limit the total investment costs of the newly built line and the unit to reflect its economic requirements of the plan. And the constraint (6) represents the relation between the output of the wind turbine generator and the wind speed. Constraints (7) limit the capacity of the generator set and ensure that there is sufficient spare capacity for the planning system to supply the load. Constraint (8) means that the regulation capacity of a conventional generator set can offset the fluctuation of intermittent energy power generation (wind energy, solar energy and the like) and load requirements, and the flexibility requirement of the system is met. And constraint (9) ensures enough capacity of the renewable energy generator set so as to achieve the purposes of energy conservation and emission reduction. The power flow constraint under the worst operation scene is (10) - (20), and at the moment, the planning system is under the normal condition, the planning system is not attacked, and the load and the wind-power output are fixed to be typical values. The power flow constraints under the real operation of the planning system are shown in (21) to (26), and at the moment, the planning system is in the worst operation scene after the wind turbine generator output, load and cooperative attack scheme optimized by the middle-layer model. The planner should know and consider ahead of time in the event that the operator is misled by spurious data therein.
In specific implementation, the objective function of the middle layer model includes:
Figure BDA00026512383600001612
the constraints of the middle layer model include:
Figure BDA00026512383600001613
-τ·Ld≤ΔLd≤τ·Ldd∈ΩD (29)
Figure BDA0002651238360000171
Figure BDA0002651238360000172
Figure BDA0002651238360000173
L＝L0+sLDLeL (33)
Figure BDA0002651238360000174
Figure BDA0002651238360000175
Figure BDA0002651238360000176
in the formula,. DELTA.LdRepresenting the load measurement value tampering amount of the load node d after LR attack, wherein the load measurement value is increased to be positive and decreased to be negative; tau represents the ratio upper limit of the load data tampering amount relative to the original load value; v. ofL-l、vG-gAnd vW-gRespectively representing whether the original transmission line l, the original conventional unit g and the original wind turbine unit g are selected as binary variables of physical attack objects, wherein the value 0 represents attacked, the value 1 represents not attacked, and r representsL-l、rG-gAnd rW-gRespectively representing physical attack resources, R, consumed by attacking the original transmission line l, the original conventional unit g and the original wind turbine unit gmaxRepresenting the upper limit of physical attack resources; f. oflRepresenting the virtual SC flow of the first power transmission line, and being used for judging whether the system has an island or not, NBRepresenting the number of system nodes; a and (b, l) respectively represent the b row and l column elements of the node-line incidence matrix and the node-line incidence matrix of the power system; l represents the load level, L0Representing the load level in normal operation, sLIndicating the magnitude of the load deviation, DLRepresents the lower triangular matrix of the load covariance matrix after Cholesky decomposition, eLAn error vector representing the load; xiLRepresenting a conservative coefficient describing the uncertainty of the load, NLRepresents the total number of loads; pWAnd
Figure BDA0002651238360000177
respectively representing the output of the wind turbine generator in the worst scene and the output of the wind turbine generator in the rated operation scene, wherein the element sequence is that the original wind turbine generator is firstly replaced by a candidate wind turbine generator, H represents a conversion matrix, and sWIndicating deviation amplitude of wind power output, DWRepresenting a lower triangular matrix, e, of the wind turbine generator covariance matrix after Cholesky decompositionWError vector, ξ, representing the wind turbine outputWConservative parameters representing uncertainty of fan output; n is a radical ofWAnd N'WAnd respectively representing the total number of the original wind turbines and the total number of the candidate wind turbines.
The LR attack is a special way of False Data Injection (FDI) attack. The FDI attack is an attack mode for destroying the integrity of the power grid information by tampering with the measurement and control data, and has strong accessibility, concealment and interference. The FDI attack causes the state estimation result to deviate from the normal operation state by maliciously tampering the measurement data of the metering device in the power grid. And the data volume is reasonably designed and tampered according to a state estimation mechanism to successfully avoid bad data detection, so that adverse consequences are brought to the operation control of the power system. The LR attack is an attack form that an attacker misdirects an operator to make wrong scheduling by maliciously modifying the load, the trend and other measurement data in the information physical system, so that the system suffers great loss. In an actual power system, lawless persons add error data and tamper initial real data to the SCADA system, so that considerable controllability of the system is lost for operating personnel and electrical equipment, the operation of a power grid is seriously affected, and therefore the study on the attack type has important practical significance.
The middle-layer objective function (27) enables the planning system to cut the load to the maximum in the operation scenes determined by the screened attack scheme, load scene and wind power output scene, and is not limited to the cooperative attack scheme independently. The decision variables are: cooperative attack scheme vL-l、vG-g、vW-gAnd Δ LdLoad level L, wind turbine output PW. Constraints (28) - (32) are cooperative attack constraints,the equation constraint corresponding to the formula (28) is used for limiting the sum of the change amounts of all the modified load measurement values to be 0, ensuring the active balance of the planning system after LR attack, and avoiding the detection and discovery of false data caused by large frequency fluctuation; equation (29) is used to limit the amount of malicious tampering of the load measurement value, so as to prevent the excessive change from being discovered by the operator. Equation (30) is a physical attack constraint. The formula considers practical situations and limits the upper limit of the number of devices under physical attack. Equations (31) - (32) are cooperative attack constraints. The two constraints adopt an SC power flow method to indicate that an attacked power system does not generate an isolated island, otherwise false data of an LR attack design is detected and found, and a cooperative attack is degenerated into a pure physical attack, so that the attack effect is reduced. Constraints (33) - (36) determine the wind power output and the uncertainty set of the load, and the invention describes the load and uncertainty of the wind power output based on Cholesky decomposition of the variable covariance matrix.
In specific implementation, the objective function of the lower model includes:
Figure BDA0002651238360000181
the constraint functions of the underlying model include:
Figure BDA0002651238360000182
Figure BDA0002651238360000183
Figure BDA0002651238360000184
Figure BDA0002651238360000191
Figure BDA0002651238360000192
Figure BDA0002651238360000193
Figure BDA0002651238360000194
0≤Sd≤Ld+ΔLd d∈ΩD (45)
Figure BDA0002651238360000195
0≤SW-g≤W-gPW-g g∈ΩW (47)
0≤SW-g≤xW-g W-gPW-g g∈Ω'W (48)
in the formula (I), the compound is shown in the specification,
Figure BDA0002651238360000196
representing a spurious power flow of the transmission line/,
Figure BDA0002651238360000197
represents the corresponding spurious phase angle of s (l),
Figure BDA0002651238360000198
represents the corresponding spurious phase angle e (l),
Figure BDA0002651238360000199
and
Figure BDA00026512383600001910
respectively representing the node phase angle and the reference node phase angle determined by the operator after the system is attacked by LR and analyzed based on the measured false load data.
The objective function corresponding to the formula (37) is to minimize the cutting load of the planning system under the worst operation scene through optimizing operation; equation (38) is the power flow constraint of the original line, wherein the physically attacked line is shut down and the active power flow is 0; the formula (39) is the active power flow limit of the candidate line, and the power flow of the non-constructed line is 0; equation (40) is the node power balance constraint; equations (41) - (48) respectively limit the phase angle of the reference node, the active power flow of the power transmission line, the output of the original conventional unit, the output of the candidate conventional unit, the load shedding amount of the load node, the phase angle of the node, the wind curtailment amount of the original wind turbine and the wind curtailment amount of the candidate wind turbine in the worst operation scene.
After the planning system is subjected to cooperative attack, scheduling operators can perform optimized scheduling by combining measured false load data, unit and line outage conditions and wind turbine generator output, and determine the output of a conventional unit, wind turbine generator abandoned air volume and load quantity to be reduced. The load of the load node d measured by the operator is Ld+ΔLdTherefore, the line power flow calculated by the layer model
Figure BDA00026512383600001911
Is spurious.
In specific implementation, the defensive robust planning model aiming at the information-physical cooperative attack is a three-layer mixed integer optimization problem. In general, a robust optimization model is difficult to solve because its multi-layer optimization structure often leads to NP-hard problems. At present, the Benders method and the C & CG method are mostly used for solving the three-layer optimization problem. In contrast to Benders' method, the C & CG algorithm generates a new set of constraints using a cut-plane strategy in each iteration, involving only the original decision variables. In addition, the C & CG algorithm does not generally require the micromanipulation of the problem, so it is generally better than the corresponding calculation performance of the Benders algorithm based on dual information, so the C & CG algorithm is adopted to solve the three-layer optimization model.
The C & CG algorithm needs to split an original problem into a main problem and a sub problem to be solved, and the main problem model of the model provided by the invention is as follows:
Figure BDA0002651238360000201
Figure BDA0002651238360000202
Figure BDA0002651238360000203
Figure BDA0002651238360000204
Figure BDA0002651238360000205
Figure BDA0002651238360000206
Figure BDA0002651238360000207
Figure BDA0002651238360000208
Figure BDA0002651238360000209
Figure BDA00026512383600002010
Figure BDA00026512383600002011
Figure BDA00026512383600002012
Figure BDA00026512383600002013
Figure BDA00026512383600002014
Figure BDA00026512383600002015
Figure BDA00026512383600002016
Figure BDA0002651238360000211
Figure BDA0002651238360000212
Figure BDA0002651238360000213
Figure BDA0002651238360000214
Figure BDA0002651238360000215
-Fl max≤Fl (m)≤Fl max l∈ΩL∪Ω'L (70)
Figure BDA0002651238360000216
Figure BDA0002651238360000217
Figure BDA0002651238360000218
Figure BDA0002651238360000219
Figure BDA00026512383600002110
Figure BDA00026512383600002111
Figure BDA00026512383600002112
Figure BDA00026512383600002113
Figure BDA00026512383600002114
Figure BDA00026512383600002115
Figure BDA00026512383600002116
Figure BDA0002651238360000221
m＝1,…,k (83)
where eta represents an intermediate variable, k is the number of iterations,
Figure BDA0002651238360000222
and
Figure BDA0002651238360000223
for known quantities, they are obtained by solving sub-problems iteratively, and the remaining variables all need to be solved optimally.
The decision variables of the sub-problem model of the model provided by the invention are as follows: the kth iterative solution
Figure BDA0002651238360000224
Figure BDA0002651238360000225
And
Figure BDA0002651238360000226
the subproblem is a two-layer optimization model, and the objective function is shown as follows:
Figure BDA0002651238360000227
the upper layer constraint is shown by the following formula:
Figure BDA0002651238360000228
the lower layer constraint is shown as follows:
Figure BDA0002651238360000231
sub-problem solvingIn the process of the preparation, the raw materials are mixed,
Figure BDA0002651238360000232
and
Figure BDA0002651238360000233
is a known quantity, obtained by solving a main problem. The other variables need to be optimized, and the double-layer optimization model can be converted into a single-layer optimization model through a KKT method to be solved.
As shown in fig. 2, in specific implementation, the solving process of the model provided by the present invention based on the C & CG algorithm is as follows:
the method comprises the following steps: and inputting electrical parameters such as system network topology, units, lines, transformers and the like, positions of candidate lines and candidate units, electrical parameters, economic parameters and the like. Let the upper bound UB be a larger positive number and the lower bound LB be 0; initializing convergence precision; let the iteration number k equal to 1, initialize the optimization decision vector:
Figure BDA0002651238360000234
Figure BDA0002651238360000235
Figure BDA0002651238360000236
order to
Figure BDA0002651238360000237
And is
Figure BDA00026512383600002313
N’LAnd N'GAnd respectively representing the number of the candidate lines and the number of the candidate conventional units, and the superscript (k) represents a variable value corresponding to the kth iteration.
Step two: will be provided with
Figure BDA0002651238360000239
And
Figure BDA00026512383600002310
solving the sub-problem to obtain the optimal combination state:
Figure BDA00026512383600002311
Figure BDA00026512383600002312
Figure BDA0002651238360000241
Figure BDA0002651238360000242
Figure BDA0002651238360000243
Figure BDA0002651238360000244
order to
Figure BDA0002651238360000245
Step three: and judging whether UB-LB is equal to or less than the standard value, if so, turning to the sixth step, and otherwise, turning to the fourth step.
Step four: will be provided with
Figure BDA0002651238360000246
ΔL(k)、
Figure BDA0002651238360000247
L(k)And PW(k)AsSolving a main problem by substituting a known quantity into the main problem
Figure BDA0002651238360000248
And
Figure BDA0002651238360000249
let LB equal eta.
Step five: and (5) enabling k to be k +1, and turning to the step two.
Step six: output of
Figure BDA00026512383600002410
And
Figure BDA00026512383600002411
and coordinating the planning results.
The embodiments of the invention will now be illustrated:
the original IEEE-RTS79 system included 32 generators, 33 return transmission lines, and 5 transformers (i.e., 38 branches), with a total installed capacity of 3405MW and a maximum annual peak load of 2850 MW. The invention carries out test analysis based on an improved IEEE-RTS79 system, in order to reflect the characteristics of a renewable energy power system, a renewable energy unit is assumed to exist in the improved system, and a 76MW conventional unit of each node 1 and 2 and a 100MW conventional unit of a node 7 are replaced by wind power units with the same equivalent capacity. And a candidate power transmission line, a candidate conventional unit and a candidate wind turbine are added, as shown in fig. 3 (the dotted line is the candidate power transmission line, and the dotted line is connected to the candidate conventional unit and the candidate wind turbine), and the specific parameter setting conditions are shown in tables 1 and 2.
To analyze the advantages of the defensive-conventional coordinated planning model proposed by the present invention, consider the following four scenarios:
scene 1: the coordination planning model provided by the invention is adopted to deal with uncertainty of cooperative attack, load and wind-power output;
scene 2: adopting a defensive planning model to deal with uncertainty of cooperative attack, load and wind-power output;
scene 3: adopting a conventional planning model to deal with uncertainty of cooperative attack, load and wind-power output;
scene 4: and combining the planning strategies obtained from the scene 2 and the scene 3 to form a combined planning strategy so as to deal with the uncertainty of the cooperative attack, the load and the wind-power output.
The results of these four planning scenarios are shown in table 3. Wherein, the 'L' represents a newly added line, the number is shown in the table 1, and (2) represents that the line is constructed as a double-circuit line; "G'" indicates a newly added unit, the following numbers indicate the unit type number, and (a × b) indicates that a unit corresponding to b stations is built at the node a. For example, "G' 4(11 × 2, 12 × 2)" indicates that two sets of wind turbines are built at the node 11 and two sets of wind turbines are built at the node 12. Fig. 4 and 5 list the results of these four planning schemes in the worst scenario corresponding to coordinated planning herein, from the point of view of load shedding and economic cost, respectively.
Analyzing table 3, fig. 4 and fig. 5, the following conclusions can be drawn:
(1) the planning schemes determined by the scenarios 2, 3 and 4 generate larger cutting load when applied to the worst operation situation, and the coordinated planning is better in terms of the effect of coping with future uncertainty.
(2) In terms of the total cost in the planning period corresponding to the planning scheme, scenario 1 is also far less than the other three scenarios. Therefore, the coordinated planning is better from the economical point of view.
(3) Although the new transmission capacity and the unit capacity of the planning system corresponding to the scene 4 are higher than those of the scenes 2 and 3, the uncertain performance of the planning system is not better than that of the scene 2. This indicates that the ability of the combined planning strategy to cope with future uncertainty is not ideal and a scientific planning strategy needs to be developed.
(4) Comparing scenarios 1 and 2, it can be seen that the planning investment cost increases after considering renewable energy. Because the planning strategy needs to add a conventional unit and a circuit to deal with the attack, and additionally builds a new energy unit to meet the conventional planning requirement.
Finally, with the importance of society on energy conservation and emission reduction, a certain amount of renewable energy permeability needs to be ensured in a future power system. Therefore, the lowest permeability was investigated
Figure BDA0002651238360000251
The influence on the planning result is significant. Increasing the maximum investment cost to 1500M $, and changing the parameters
Figure BDA0002651238360000252
The results obtained are shown in Table 4. The results of the analysis can be concluded as follows:
(1) with following
Figure BDA0002651238360000253
The investment costs required for planning the project tend to increase. This is because
Figure BDA0002651238360000254
The increase of the number of the wind power generation sets can require to be expanded, and at the moment, more conventional wind power generation sets and lines also need to be expanded to ensure peak shaving, so that the investment cost is increased.
(2)
Figure BDA0002651238360000255
And when the rate is 35%, the coordination planning result has no feasible solution. Although building all candidate wind turbines within the investment cost limits can achieve this permeability target, the result of building wind turbines separately is not scientific due to constraints such as peak shaving, wind curtailment, etc., requiring additional conventional turbines and lines to be built accordingly. On the premise, the investment cost is not sufficient, so that no feasible solution is available. This further proves necessary to reconcile the consideration of new energy units and conventional units, routes in the planning problem.
TABLE 3
Figure BDA0002651238360000256
Figure BDA0002651238360000261
TABLE 4
Figure BDA0002651238360000262
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. The defensive-conventional coordination planning method for the power generation and transmission system considering wind power is characterized by comprising the following steps of:
s1, acquiring information of the original power transmission and transmission system and the candidate power transmission and transmission system;
s2, establishing a defensive-conventional coordination planning model of the power generation and transmission system considering wind power;
s3, converting the defensive-conventional coordination planning model of the power transmission system into a main problem model and a sub-problem model;
and S4, iteratively solving the main problem model and the sub problem model based on the information of the original power transmission system and the candidate power transmission system to obtain an optimal planning scheme.
2. The wind power generation and transmission system defensive-conventional coordination planning method considering as claimed in claim 1 wherein the wind power generation and transmission system defensive-conventional coordination planning model considering includes an upper layer model based on a transmission system planner perspective, a middle layer model based on a transmission system attacker perspective and a lower layer model based on a transmission system operator perspective; the decision variables of the upper layer model are the building judgment values of the candidate power transmission line, the candidate conventional unit and the candidate wind turbine, the decision variables of the middle layer model are a cooperative attack scheme, a load scene and a wind power output scene, and the decision variables of the lower layer model are the output of the conventional unit, the wind turbine air curtailment and the node load cutting.
3. The defensive-conventional coordination planning method of wind power generation and transmission system considering wind power as claimed in claim 2, wherein the objective function of the upper model comprises:
C＝min(Cinv+Cope+Cperf)
wherein C represents an objective function with the lowest total cost in the planning period, Cinv、CopeAnd CperfRespectively representing investment cost, rated operation cost and system performance loss cost;
Figure FDA0002651238350000011
Figure FDA0002651238350000012
Figure FDA0002651238350000013
in the formula, xL-l、xG-gAnd xW-gRespectively representing the construction judgment values of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g, wherein the construction judgment value is 1 to represent construction, the construction judgment value is 0 to represent non-construction, and CL-l、CG-gAnd CW-gRespectively representing year values of construction investment costs and the like, omega ', of the candidate power transmission line l, the candidate conventional unit g and the candidate wind turbine unit g'L、Ω'GAnd omega'WRespectively representing a set of candidate power transmission lines, a set of candidate conventional units and a set of candidate wind generation units; t represents the annual running time, ΩGAnd ΩDRespectively representing the set of the original conventional unit and the set of the original load node,
Figure FDA0002651238350000021
and
Figure FDA0002651238350000022
respectively representing the active power output of the conventional unit g and the load shedding amount of the load node d under the normal operation condition, CP-gAnd CS-dRespectively representing the annual value of the unit cost of g output of the conventional unit and the unit cost of d load shedding of the load node; gamma represents the coefficient of converting the power failure capacity into the economic cost under the worst operation scene obtained according to the optimization model, SdRepresenting the load shedding amount of the load node d in the worst operation scene;
the constraints of the upper layer model include:
Figure FDA0002651238350000023
Figure FDA0002651238350000024
Figure FDA0002651238350000025
Figure FDA0002651238350000026
Figure FDA0002651238350000027
Figure FDA0002651238350000028
Figure FDA0002651238350000029
Figure FDA00026512383500000210
Figure FDA00026512383500000211
-Fl max≤Fl 0≤Fl max l∈ΩL∪Ω'L
Figure FDA00026512383500000212
Figure FDA00026512383500000213
Figure FDA00026512383500000214
Figure FDA00026512383500000215
Figure FDA0002651238350000031
Figure FDA0002651238350000032
Fl＝Bl(θS(l)-θE(l))vL-l l∈ΩL
Fl＝Bl(θS(l)-θE(l))xL-l l∈Ω'L
Figure FDA0002651238350000033
θr＝0
Figure FDA0002651238350000034
-Fl max≤Fl≤Fl max l∈ΩL∪Ω'L
in the formula, CtotalRepresents the maximum investment cost; v represents the wind speed corresponding to the hub of the fan, vin、vrateAnd voutRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan, PwindAnd PrateRespectively representing the output and rated power of a single wind turbine; omegaWThe set of the original wind turbine generator is shown,
Figure FDA0002651238350000035
represents the maximum capacity of the conventional unit g,
Figure FDA0002651238350000036
the capacity of the wind turbine g is represented,
Figure FDA0002651238350000037
representing the peak load of the load node d, and eta represents the lowest standby rate allowed by the system; rgThe peak-load capacity ratio of the conventional unit g is represented, the peak-load capacity ratio is the ratio of the peak-load capacity of the conventional unit to the rated installed capacity of the conventional unit, beta represents the peak-valley difference rate of the load demand, and the peak-valley difference rate is the ratio of the peak-valley difference of the load to the peak load;
Figure FDA0002651238350000038
representing the lowest wind power permeability allowed by the system; fl 0Representing the power flow of the transmission line i in normal operation, BlThe susceptance of the transmission line/is represented,
Figure FDA0002651238350000039
represents the corresponding phase angle of S (l) in normal operation,
Figure FDA00026512383500000310
representing a corresponding phase angle E (l) in normal operation, and respectively representing a starting node and a terminating node of the transmission line l by S (l) and E (l);
Figure FDA00026512383500000311
representing the air abandoning amount of the wind turbine generator g when the system is in normal operation,
Figure FDA00026512383500000312
representing a typical value of the output of the wind turbine g,
Figure FDA00026512383500000313
a typical value of the load of the node d is represented,
Figure FDA00026512383500000314
representing the active power output of the unit g in normal operation,
Figure FDA00026512383500000315
the air abandon quantity of the unit g in normal operation is shown,
Figure FDA00026512383500000316
and
Figure FDA00026512383500000317
respectively representing the set of original wind turbines connected at the node b and the set of candidate wind turbines,
Figure FDA00026512383500000318
representing the set of units, Ω, connected to node bBRepresenting an original system node set;
Figure FDA00026512383500000319
representing the phase angle of a reference node when the planning system operates normally; fl maxRepresenting the maximum allowable active power flow of the transmission line l;
Figure FDA00026512383500000320
and
Figure FDA00026512383500000321
respectively representing the phase angle, the maximum allowed phase angle and the minimum allowed phase angle of the node b in normal operation;W-grepresenting the maximum wind abandon rate of the wind turbine generator g allowed by the planning system; flRepresenting the power flow, theta, of the transmission line lS(l)Denotes the phase angle, θ, corresponding to S (l)E(l)Denotes the corresponding phase angle of E (l), SW-gRepresents the wind abandon amount of the wind turbine generator g when the system is in normal operation, PW-gRepresenting the active power output, L, of the wind turbine gdRepresenting the load value, P, of node dgRepresenting the active output of the unit g, SgRepresenting the air curtailment quantity of the unit g, K representing a node-load incidence matrix, and K (b, d) representing the elements of the row b and the column d of the node-load incidence matrix; thetarRepresenting a phase angle of a reference node; thetabRepresenting the phase angle at node b.
4. The defensive-conventional coordination planning method of wind power generation and transmission system considering wind power as claimed in claim 3, wherein the objective function of the middle layer model comprises:
Figure FDA0002651238350000041
the constraints of the middle layer model include:
Figure FDA0002651238350000042
-τ·Ld≤ΔLd≤τ·Ld d∈ΩD
Figure FDA0002651238350000043
-(NB-1)·vL-l≤fl≤(NB-1)·vL-l l∈ΩL
Figure FDA0002651238350000044
L＝L0+sLDLeL
||eL||1≤ξL,
Figure FDA0002651238350000045
Figure FDA0002651238350000046
||eW||1≤ξW,
Figure FDA0002651238350000047
in the formula,. DELTA.LdRepresenting the load measurement value tampering amount of the load node d after LR attack, wherein the load measurement value is increased to be positive and decreased to be negative; tau represents the ratio upper limit of the load data tampering amount relative to the original load value; v. ofL-l、vG-gAnd vW-gRespectively representing whether the original transmission line l, the original conventional unit g and the original wind turbine unit g are selected as binary variables of physical attack objects, wherein the value 0 represents attacked, the value 1 represents not attacked, and r representsL-l、rG-gAnd rW-gRespectively representing physical attack resources, R, consumed by attacking the original transmission line l, the original conventional unit g and the original wind turbine unit gmaxRepresenting the upper limit of physical attack resources;flrepresenting the virtual SC flow of the first power transmission line, and being used for judging whether the system has an island or not, NBRepresenting the number of system nodes; a and (b, l) respectively represent the b row and l column elements of the node-line incidence matrix and the node-line incidence matrix of the power system; l represents the load level, L0Representing the load level in normal operation, sLIndicating the magnitude of the load deviation, DLRepresents the lower triangular matrix of the load covariance matrix after Cholesky decomposition, eLAn error vector representing the load; xiLRepresenting a conservative coefficient describing the uncertainty of the load, NLRepresents the total number of loads; pWAnd
Figure FDA0002651238350000051
respectively representing the output of the wind turbine generator in the worst scene and the output of the wind turbine generator in the rated operation scene, wherein the element sequence is that the original wind turbine generator is firstly replaced by a candidate wind turbine generator, H represents a conversion matrix, and sWIndicating deviation amplitude of wind power output, DWRepresenting a lower triangular matrix, e, of the wind turbine generator covariance matrix after Cholesky decompositionWError vector, ξ, representing the wind turbine outputWConservative parameters representing uncertainty of fan output; n is a radical ofWAnd N'WAnd respectively representing the total number of the original wind turbines and the total number of the candidate wind turbines.
5. The defensive-conventional coordination planning method of wind power generation and transmission system considering wind power as claimed in claim 4, wherein the objective function of the lower model comprises:
Figure FDA0002651238350000052
the constraint functions of the underlying model include:
Figure FDA0002651238350000053
Figure FDA0002651238350000054
Figure FDA0002651238350000055
Figure FDA0002651238350000056
Figure FDA0002651238350000057
Figure FDA0002651238350000058
Figure FDA0002651238350000059
0≤Sd≤Ld+ΔLd d∈ΩD
Figure FDA00026512383500000510
0≤SW-g≤W-gPW-g g∈ΩW
0≤SW-g≤xW-g W-gPW-g g∈Ω'W
in the formula (I), the compound is shown in the specification,
Figure FDA0002651238350000061
representing a spurious power flow of the transmission line/,
Figure FDA0002651238350000062
represents the corresponding spurious phase angle of s (l),
Figure FDA0002651238350000063
represents the corresponding spurious phase angle e (l),
Figure FDA0002651238350000064
and
Figure FDA0002651238350000065
respectively representing the node phase angle and the reference node phase angle determined by the operator after the system is attacked by LR and analyzed based on the measured false load data.
6. The wind power generation and transmission system defensive-conventional coordination planning method of claim 5, wherein,
the objective function of the master problem model includes:
Figure FDA0002651238350000066
the constraints of the main problem model include:
Figure FDA0002651238350000067
Figure FDA0002651238350000068
Figure FDA0002651238350000069
Figure FDA00026512383500000610
Figure FDA00026512383500000611
Figure FDA00026512383500000612
Figure FDA00026512383500000613
Figure FDA00026512383500000614
Figure FDA00026512383500000615
Figure FDA00026512383500000616
Figure FDA00026512383500000617
Figure FDA00026512383500000618
Figure FDA0002651238350000071
Figure FDA0002651238350000072
Figure FDA0002651238350000073
Figure FDA0002651238350000074
Figure FDA0002651238350000075
Figure FDA0002651238350000076
Figure FDA0002651238350000077
-Fl max≤Fl (m)≤Fl max l∈ΩL∪Ω'L
Figure FDA0002651238350000078
Figure FDA0002651238350000079
Figure FDA00026512383500000710
Figure FDA00026512383500000711
Figure FDA00026512383500000712
Figure FDA00026512383500000713
Figure FDA00026512383500000714
Figure FDA00026512383500000715
Figure FDA00026512383500000716
Figure FDA00026512383500000717
Figure FDA00026512383500000718
Figure FDA00026512383500000719
m＝1,…,k
k is the iteration number, and the superscript (m) is the mth iteration of the corresponding parameter;
the objective function of the sub-problem model includes:
Figure FDA0002651238350000081
the constraints of the sub-problem model include:
and (4) upper layer constraint:
Figure FDA0002651238350000082
and (3) lower layer constraint:
Figure FDA0002651238350000091
7. the defensive-conventional coordination planning method of wind power generation and transmission system considering wind power as claimed in claim 6, wherein the step S4 includes:
s401, setting an upper bound UB as a preset value and setting a lower bound LB as 0; initializing convergence precision; let the iteration number k equal to 1, initialize the optimization decision vector:
Figure FDA0002651238350000092
Figure FDA0002651238350000093
Figure FDA0002651238350000094
order to
Figure FDA0002651238350000095
And is
Figure FDA00026512383500000913
N’LAnd N'GRespectively representing the number of the candidate transmission lines and the number of the candidate conventional units, and a superscript (k) tableShowing the variable value corresponding to the kth iteration;
s402, mixing
Figure FDA0002651238350000097
And
Figure FDA0002651238350000098
solving the sub-problem to obtain the optimal combination state:
Figure FDA0002651238350000099
Figure FDA00026512383500000910
Figure FDA00026512383500000911
Figure FDA00026512383500000912
Figure FDA0002651238350000101
Figure FDA0002651238350000102
order to
Figure FDA0002651238350000103
S403, judging whether UB-LB is not more than or equal to the established value, if so, executing step S406, otherwise, executing step S404;
s404, mixing
Figure FDA0002651238350000104
ΔL(k)、
Figure FDA0002651238350000105
L(k)And PW(k)Solving the main problem by substituting it as a known quantity
Figure FDA0002651238350000106
And
Figure FDA0002651238350000107
let LB equal to η;
s405, let k equal to k +1, and execute step S402;
and S406, outputting a coordination planning result.
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