CN109818347B - Assessment method for wind power consumption capability of electric power system - Google Patents

Assessment method for wind power consumption capability of electric power system Download PDF

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CN109818347B
CN109818347B CN201811454403.8A CN201811454403A CN109818347B CN 109818347 B CN109818347 B CN 109818347B CN 201811454403 A CN201811454403 A CN 201811454403A CN 109818347 B CN109818347 B CN 109818347B
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王程
汪松
毕天姝
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North China Electric Power University
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Abstract

The invention discloses an assessment method of wind power consumption capability of an electric power system, which comprises the steps of firstly obtaining initial parameter information of the electric power system to be assessed; constructing a wind power receptibility evaluation model, and solving an initial wind power receptibility upper boundary and an initial wind power receptibility lower boundary; representing wind power uncertainty by using Boolean variables, and combining the wind power uncertainty with a wind power output predicted value to construct a wind power uncertainty set W; establishing wind power admissible criterion by taking the abandoned wind quantity and the load shedding quantity as optimal values, and judging whether the wind power uncertain set can generate abandoned wind and load shedding or not; setting model constraints of a power system and a natural gas system, and combining the model constraints with wind power receptivity criteria to form a wind power receptivity judging model; and solving the wind power acceptance judging model, obtaining a wind power extreme output scene, judging whether the convergence condition is met, and if the convergence condition is met, circularly terminating and outputting the upper and lower wind power acceptance boundaries. The method considers the constraint of the natural gas network on the gas supply of the gas turbine unit, so that the evaluation of the wind power consumption capability of the power system is more in line with the engineering practice.

Description

Assessment method for wind power consumption capability of electric power system
Technical Field
The invention relates to the technical field of power systems, in particular to an evaluation method for wind power consumption capacity of a power system.
Background
In recent years, new energy such as wind power and the like is developed rapidly, but wind power consumption becomes an important difficult problem to be solved urgently due to the volatility and uncertainty of wind power, and higher requirements are put forward on the operation flexibility of a power system. The gas turbine set has high flexibility, and the output power of the gas turbine set can quickly track the wind power output fluctuation, but on one hand, the gas turbine set is restrained by a natural gas network, on the other hand, natural gas resources in China are not abundant, the resource distribution is unbalanced, natural gas reserves are insufficient, pipe network facilities are lagged behind, and the priority of industrial gas such as gas power generation is not high. Therefore, the research on the characteristics of the natural gas network production, transmission, storage and consumption model is beneficial to accurately evaluating the influence of the gas supply constraint on the wind power consumption capability of the power system, so that the method is more in line with the engineering practice.
For the difficult problem of wind power consumption of the power system, researches of scholars mainly focus on power network modeling, uncertainty modeling of wind power, scheduling decision of the power system and the like. On one hand, the existing evaluation method only considers the power network model and does not consider the gas supply constraint, so that the evaluation result is obviously too optimistic and not in line with the engineering practice; on the other hand, the existing evaluation method mostly simulates the actual wind power output based on a scene method, has high dependency on the prediction precision of the wind power output, and cannot accurately evaluate the acceptable wind power output range of the power system.
Disclosure of Invention
The invention aims to provide an assessment method for wind power consumption capability of an electric power system, which considers the constraint of a natural gas network on gas supply of a gas turbine, so that the assessment of the wind power consumption capability of the electric power system is more in line with the actual engineering, and the loss caused by over optimism of the traditional assessment method can be avoided.
The purpose of the invention is realized by the following technical scheme:
a method for evaluating wind power consumption capability of a power system, the method comprising:
step 1, firstly, obtaining initial parameter information of an electric power system to be evaluated, wherein the initial parameter information comprises a wind power output predicted value
Figure BDA0001887418560000011
A unit combination strategy;
step 2: constructing a wind power receptibility evaluation model, adding wind power receptibility upper and lower boundary constraints, and solving initial wind power receptibility upper and lower boundaries;
step 3, representing wind power uncertainty by using a Boolean variable, and combining the wind power uncertainty with the wind power output predicted value to construct a wind power uncertainty set W; wherein the wind power uncertain set W is defined by a Boolean variable vu/vlUpper and lower boundaries w receivable with wind poweru/wlJointly forming;
step 4, constructing a wind power admissible criterion by taking the abandoned wind quantity and the load shedding quantity as optimal values, and judging whether the wind power uncertain set can generate abandoned wind and load shedding or not;
step 5, setting model constraints of a power system and a natural gas system, and combining the model constraints with the wind power acceptance criterion to form a wind power acceptance judging model;
step 6, solving a wind power acceptability judging model, obtaining a wind power extreme output scene, judging whether a convergence condition is met, if so, circularly terminating and outputting corresponding wind power acceptability upper and lower boundaries;
and 7, if the wind power extreme output scene is not met, generating new wind power output upper and lower boundary constraints according to the wind power extreme output scene, adding the new wind power output upper and lower boundary constraints into the wind power receptibility evaluation model, and solving to obtain new wind power receptibility upper and lower boundaries.
According to the technical scheme provided by the invention, the constraint of the natural gas network on the gas supply of the gas turbine is considered, so that the evaluation of the wind power consumption capability of the power system is more consistent with the actual engineering, and the loss caused by over optimism of the traditional evaluation method can be avoided.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an evaluation method for wind power consumption capability of an electric power system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a wind power receivable area according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a coupling system topology according to an exemplary embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a comparison between the difference between the acceptable wind power upper and lower boundaries and the predicted value and the accumulated operation risk in 24 time periods a day in the conventional evaluation method and the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The following will describe an embodiment of the present invention in further detail with reference to the accompanying drawings, and as shown in fig. 1, a schematic flow chart of an evaluation method for wind power consumption capability of an electric power system provided by the embodiment of the present invention is shown, where the method includes:
step 1, firstly, obtaining initial parameter information of an electric power system to be evaluated, wherein the initial parameter information comprises a wind power output predicted value
Figure BDA0001887418560000038
A unit combination strategy;
step 2, constructing a wind power receptibility evaluation model, adding wind power receptibility upper and lower boundary constraints, and solving initial wind power receptibility upper and lower boundaries;
here, the process of constructing the wind power receptibility evaluation model specifically includes:
multiplying the abandoned air quantity and the load shedding quantity by corresponding penalty coefficients respectively and then adding the multiplied abandoned air quantity and the load shedding quantity to obtain the running loss of the power system;
and integrating the operation loss with the wind power output probability density function to obtain the operation risk of the power system, wherein the mathematical expression is as follows:
Figure BDA0001887418560000031
in the formula, alphat/βtThe corresponding penalty coefficient of the air abandon amount/load shedding amount is shown,
Figure BDA0001887418560000032
representing wind power output predicted value/upper bound/lower bound/installed capacity;
Figure BDA0001887418560000033
representing the wind power prediction error, the obedience mean value is 0, and the mean square error is
Figure BDA0001887418560000034
The normal distribution of (2) is calculated as follows:
Figure BDA0001887418560000035
in the formula, sigma represents a wind power output prediction error coefficient, and the wind power output prediction error coefficient is not only related to wind power prediction output, but also gradually increases along with the increase of a time scale;
Figure BDA0001887418560000036
and representing a wind power prediction error probability distribution function.
In specific implementation, the formula (21) is difficult to calculate efficiently and accurately, so that the following friendly model is converted for solving:
Figure BDA0001887418560000037
Figure BDA0001887418560000041
Figure BDA0001887418560000042
Figure BDA0001887418560000043
Figure BDA0001887418560000044
Figure BDA0001887418560000045
the above equations (23) to (28) actually form a two-stage robust optimization model, the first stage is formed by equations (23) to (27) and aims at optimizing a wind power receivable area, wherein equations (24) to (25) are wind power receivable boundary constraints, and equations (26) to (27) are auxiliary constraints added by a piecewise linearization operation risk assessment index; and the second stage is a wind power admissible criterion shown in the formula (28), the optimal value F is required to be 0, namely, no operation loss is caused in any wind power output scene in the current wind power output region, and the optimal wind power admissible region can be obtained by solving the two-stage optimization model.
The wind power admissible assessment model belongs to a two-stage optimization model mathematically, and the first stage is a main wind power admissible region assessment problem taking an equation (21) as a target function and equations (24) - (27) as constraints; and in the second stage, the wind power acceptance judgment sub-problem with the formula (3) as a target function and the formula (28) as a constraint is used for verifying whether the wind power area generates operation loss.
Step 3, representing wind power uncertainty by using a Boolean variable, and combining the wind power uncertainty with the wind power output predicted value to construct a wind power uncertainty set W;
here, the wind power uncertainty set W is represented by a Boolean variable vu/vlUpper and lower boundaries w receivable with wind poweru/wlFormed jointly, the upper and lower boundaries of W are Wu/wlWherein v isu/vlThe different values represent three conditions of the wind power value, namely an upper boundary or a lower boundary or a predicted value.
In this step, the constructed wind power uncertainty set W is composed of the following formulas 1 and 2:
Figure BDA0001887418560000046
Figure BDA0001887418560000047
wherein the content of the first and second substances,
Figure BDA0001887418560000048
the uncertainty of the wind power is represented,
Figure BDA0001887418560000049
for Boolean variables, 1/0 is taken to represent that the wind power output reaches the output intervalUpper bound/predicted value;
Figure BDA00018874185600000410
taking 1/0 as a Boolean variable to indicate that the wind power output reaches the lower bound/predicted value of the output interval;
Figure BDA0001887418560000051
representing the wind power output upper/lower boundary;
Figure BDA0001887418560000052
belonging to wind power uncertain set W.
Step 4, constructing a wind power admissible criterion by taking the abandoned wind quantity and the load shedding quantity as optimal values, and judging whether the wind power uncertain set can generate abandoned wind and load shedding or not;
in this step, the established wind power acceptance criterion is shown as the following formula 3:
Figure BDA0001887418560000053
wherein the content of the first and second substances,
Figure BDA0001887418560000054
representing the abandoned air quantity/cut load quantity, and if the optimal value F is equal to 0, representing that the wind power uncertain set W can be accepted; otherwise, if F>0, representing that the wind power uncertain set W is not acceptable; phi represents a min problem decision variable set, and X represents a feasible domain of a min problem decision variable.
Step 5, setting model constraints of a power system and a natural gas system, and combining the model constraints with the wind power acceptance criterion to form a wind power acceptance judging model;
in this step, the set power system and natural gas system model constraints are expressed as:
Figure BDA0001887418560000055
Figure BDA0001887418560000056
Figure BDA0001887418560000057
Figure BDA0001887418560000058
Figure BDA0001887418560000059
Figure BDA00018874185600000510
Figure BDA00018874185600000511
Figure BDA00018874185600000512
Figure BDA00018874185600000513
Figure BDA00018874185600000514
Figure BDA00018874185600000515
Figure BDA0001887418560000061
Figure BDA0001887418560000062
Figure BDA0001887418560000063
Figure BDA0001887418560000064
Figure BDA0001887418560000065
Figure BDA0001887418560000066
Figure BDA0001887418560000067
Figure BDA0001887418560000068
Figure BDA00018874185600000610
in each of the above formulae, neAnd NeRepresenting the index and the number of the conventional units; n isegAnd NegRepresenting the index and the number of the gas units; n isgAnd NgRepresenting the index and the number of the natural gas wells; n iswAnd NwRepresenting the index and the number of the natural gas wells; t and T represent the ordinal number and the number of the time period; leAnd LeRespectively representing the index and the number of transmission lines of the power system; lgAnd LgRespectively representing the indexes and the quantity of the transmission lines of the natural gas system; i.e. ieAnd IeRespectively representing node ordinal number and number of electric power system；igAnd IgRespectively representing natural gas system node ordinal number and number; deAnd DeRepresenting the power system load index and quantity; dgAnd DgIndicating natural gas load index and quantity; c and C represent the ordinal number and the number of the natural gas system compressor;
Figure BDA00018874185600000611
representing the running state of a conventional unit/gas unit;
Figure BDA00018874185600000612
representing the upper/lower boundary of the output of the conventional unit;
Figure BDA00018874185600000613
representing the upper/lower limit of the output of the gas turbine unit;
Figure BDA00018874185600000614
representing the output of a conventional unit/gas unit;
Figure BDA00018874185600000615
representing the positive climbing capacity of the conventional unit/gas unit;
Figure BDA00018874185600000616
the negative climbing capacity of the conventional unit/gas unit is represented; thetareftRepresenting a reference node phase angle;
Figure BDA00018874185600000617
representing power line transmission capacity;
Figure BDA00018874185600000618
representing a transmission line admittance;
Figure BDA00018874185600000619
representing node phase angles at two ends of an electric power line le;
Figure BDA00018874185600000620
representing the wind abandoning amount of each wind power plant;
Figure BDA00018874185600000621
representing the maximum wind power output of each wind power plant;
Figure BDA00018874185600000622
representing load shedding quantity of each load node of the power system;
Figure BDA00018874185600000623
representing the load of each load node of the power system;
Figure BDA00018874185600000624
representing the natural gas production;
Figure BDA00018874185600000625
representing the upper/lower bound of natural gas production; r isstRepresenting the gas storage capacity of the natural gas storage device;
Figure BDA0001887418560000071
representing the maximum/minimum of the gas storage quantity in the gas storage device; q. q.sst in/qst outIndicating natural gas inflow/outflow;
Figure BDA0001887418560000072
representing the upper limit of the air input/output of the air storage device;
Figure BDA0001887418560000073
representing nodes i of respective natural gas systemsgNode air pressure;
Figure BDA0001887418560000074
representing node air pressure upper/lower bound;
Figure BDA0001887418560000075
indicating natural gas inventory of each pipeline;
Figure BDA0001887418560000076
the relation coefficient between the pipe stock and the node pressure is obtained;
Figure BDA0001887418560000077
representing the gas pressure of the nodes at two ends of the natural gas pipeline;
Figure BDA0001887418560000078
represents the inflow/outflow of the natural gas pipeline lg;
Figure BDA0001887418560000079
is the compressor compression factor;
Figure BDA00018874185600000710
respectively representing the air pressure of the nodes at the two ends of the compressor;
Figure BDA00018874185600000711
representing the average natural gas flow in the pipeline;
Figure BDA00018874185600000712
representing the relation coefficient of the pipeline power flow and the air pressure of the nodes at the two ends of the pipeline power flow; n is a radical ofe(ie)/Nw(ie)/De(ie)/Neg(ie)/Le(ie) Presentation and power system node ieThe connected conventional unit/wind power plant/electric load/gas unit/transmission line set; l isg(ig)/S(ig)/Ng(ig)/Dg(ig) Representation and natural gas system node igAnd the connected natural gas transmission pipeline/gas storage device/natural gas well/natural gas load set.
Since the equation (18) represents the nonlinear constraint, it is linearized by a piecewise linearization method.
Step 6, solving a wind power acceptability judging model, obtaining a wind power extreme output scene, judging whether a convergence condition is met, if so, circularly terminating and outputting corresponding wind power acceptability upper and lower boundaries;
and 7, if the wind power extreme output scene is not met, generating new wind power output upper and lower boundary constraints according to the wind power extreme output scene, adding the new wind power output upper and lower boundary constraints into the wind power receptibility evaluation model, and solving to obtain new wind power receptibility upper and lower boundaries.
In the concrete implementation, a nested C & CG algorithm can be used for solving a two-stage model formed by a wind power receptivity evaluation model and a wind power receptivity judgment model, and the concrete process is as follows:
firstly, solving the two-stage model, and performing the following model equivalence transformation on the two-stage model:
wind power acceptance discriminant problem:
Figure BDA00018874185600000713
s.t.Ex+Gz+Rv≤hs (30)
in the formula (I), the compound is shown in the specification,
Figure BDA00018874185600000714
represents a constant coefficient matrix or vector; v represents a wind power uncertainty vector; x and z represent continuous and boolean decision variable vectors, respectively;
and the wind power receptibility evaluation main problem is as follows:
Figure BDA00018874185600000715
s.t.Kw+Lμ≤hm (32)
in the formula, K, L, hmIs a constant coefficient matrix or vector; w represents a boundary vector of the wind power receivable area; mu represents a system operation risk vector;
based on the above conversion, the process of solving by the nested C & CG algorithm is specifically as follows:
firstly, setting the iteration number k to be 0 and the convergence error xi, and extracting the predicted value of the wind power output
Figure BDA0001887418560000081
A unit combination strategy;
then, taking the risk evaluation index as an objective function and the wind power admissible boundary constraint as a constraint condition, solving a first-stage main problem, and optimizing a wind power admissible region:
Figure BDA0001887418560000082
s.t.
Figure BDA0001887418560000083
wherein, the optimal solution of the upper and lower boundaries of the wind power receivable area is recorded as wkRecord OriskOptimum value is Ok riskIn the formula
Figure BDA0001887418560000084
Representing a Hadamamed product; equation (16) generates extreme scenario added constraints for the wind power admissible criterion sub-problem.
Solving the wind power receptivity discriminant sub-problem, and recording the optimal solution of v as vk+1Noting that the optimal value of the objective function F is FkIf F isk<ξ, the algorithm is stopped and the upper and lower boundaries w of the wind power receivable area are outputk(ii) a Otherwise, add vector xk+1,zk+1And constraint to the first phase Main problem as follows
Figure BDA0001887418560000085
Then, the iteration times k are changed into k +1, and the operation is carried out by returning to the step;
where ξ is a very small positive number; k is the current iteration number; E/G/R/Λ/h represents a constant coefficient matrix or vector; o isriskRepresenting a system operational risk value; fkCutting the load for the abandoned wind; mu is a system operation risk vector; x and z respectively represent continuous variables and integer variables in min problem decision variables in the second-stage wind power acceptance discriminant sub-problem; v represents the wind uncertainty.
In addition, in order to obtain the wind power admissible range, a wind power admissible range evaluation index may be proposed first, as shown in fig. 2, which is a schematic diagram of a wind power admissible area according to an embodiment of the present invention, as can be seen from fig. 2: the ordinate is the ratio of wind power output to installed capacity of the wind power plant, dotted lines of dots in the graph represent a wind power output prediction curve, and a shaded part III represents a wind power receivable area, namely, no wind power abandoning or load shedding of the power system can be caused in any wind power output scene in the area. The solid square line represents a wind power actual output curve, represents a certain wind power output scene, and is not easily found to be enveloped by a shadow region III, the parts exceeding the upper and lower boundaries of the shadow region respectively cause wind abandoning and load shedding of the power system, and the parts exceeding the upper and lower boundaries of the shadow region III and the regions surrounded by the upper and lower boundaries of the shadow region III of the wind power actual output curve respectively represent the wind abandoning amount and the load shedding amount of the power system in the current wind power output scene, as shown by a shadow region I, II in the figure.
In concrete implementation, when the wind power receptibility evaluation model is solved, the wind power receptibility upper and lower bounds wu/wlIs variable, and the wind power extreme scene is a known parameter; when the wind power receivable distinguishing model is solved, the wind power receivable upper and lower boundaries are known quantities, and the wind power extreme output scene vu/vlIs a boolean variable; thus solving circularly.
The process of the above-described evaluation method is described in detail below with specific examples, and a simulation test is performed in MATLAB according to parameters of a coupled system of a certain 5-node power network and a 7-node natural gas network, as shown in fig. 3, a topological schematic diagram of the coupled system of the example of the present invention, and generator parameters are shown in the following table:
TABLE 1 Generator parameter Table
Figure BDA0001887418560000091
1 blower W1The installed capacity is 250 MW; 3 electric loads PL1-PL3(ii) a 6 power transmission lines L1-L6(ii) a 2 gas wells GW1And GW2(ii) a 5 conventional natural gas transmission lines Gline1-Gline5(ii) a 1 compressor C1(ii) a 3 gas loads GL1-GL3. It is detailedThe detailed parameters are shown in Table 1, wherein G1-G3Is always in the power-on state.
The test results were as follows:
Figure BDA0001887418560000092
it can be seen that the operation risk of the system is increased after the natural gas network constrains the gas turbine unit, which shows that the natural gas network does influence the fuel supply of the gas turbine unit, and when the wind power fluctuation is large, the gas turbine unit with the rapid adjusting capability is constrained by the natural gas network, which causes the insufficient adjusting capability of the power system and causes more operation risks.
The difference value between the wind power admissible upper and lower boundaries and the predicted value and the accumulated operation risk in 24 periods of a day in the traditional evaluation method (without considering the gas supply constraint) and the method of the invention (with considering the gas supply constraint) are shown in fig. 4, and can be seen from fig. 4: the upper and lower acceptable wind power boundaries are obviously reduced by considering the constraint of gas supply, and the upper and lower acceptable wind power boundaries are especially obvious from the time interval 6 to the time interval 12, which shows that the constraint of gas supply has important influence on the acceptable wind power range. In addition, the risk of wind abandoning and load shedding is known, the risk is reduced after the constraint of gas supply is considered in 24 days, the increase of partial time is obvious, the difference between the corresponding wind power receivable upper and lower boundaries is obvious, and the condition that the fuel of a gas turbine unit is limited by a natural gas network and the regulating capacity of a system is reduced is shown, so that the running loss of the system is caused.
It is noted that those skilled in the art will recognize that embodiments of the present invention are not described in detail herein.
In summary, the method of the embodiment of the present invention has the following advantages:
1. the constraint of a natural gas network on the gas supply of the gas turbine unit is considered, so that the wind power consumption capability evaluation of the power system is more consistent with the actual engineering, and the loss caused by over optimism of the traditional evaluation method can be avoided;
2. the wind power admissible region evaluation index considering the wind power prediction error is provided, so that the wind power admissible region evaluation is more accurate;
3. the modeling method is oriented to the operation time scale, friendly in calculation and considering the gas supply constraint, high in solving efficiency and easy to realize in engineering.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A method for evaluating wind power consumption capability of a power system is characterized by comprising the following steps:
step 1, firstly, obtaining initial parameter information of an electric power system to be evaluated, wherein the initial parameter information comprises a wind power output predicted value
Figure FDA0002719255640000011
A unit combination strategy;
step 2: constructing a wind power receptibility evaluation model, adding wind power receptibility upper and lower boundary constraints, and solving initial wind power receptibility upper and lower boundaries; the process of constructing the wind power receptibility evaluation model specifically comprises the following steps:
multiplying the abandoned air quantity and the load shedding quantity by corresponding penalty coefficients respectively and then adding the multiplied abandoned air quantity and the load shedding quantity to obtain the running loss of the power system;
and integrating the operation loss with the wind power output probability density function to obtain the operation risk of the power system, wherein the mathematical expression is as follows:
Figure FDA0002719255640000012
in the formula, alphat/βtThe corresponding penalty coefficient of the air abandon amount/load shedding amount is shown,
Figure FDA0002719255640000013
representing wind power output predicted value/upper bound/lower bound/installed capacity;
Figure DEST_PATH_FDA0002698191810000013
representing the wind power prediction error, the obedience mean value is 0, and the mean square error is
Figure FDA0002719255640000014
The normal distribution of (2) is calculated as follows:
Figure FDA0002719255640000015
in the formula, sigma represents a wind power output prediction error coefficient, and the wind power output prediction error coefficient is not only related to wind power prediction output, but also gradually increases along with the increase of a time scale;
Figure FDA0002719255640000016
representing a wind power prediction error probability distribution function;
step 3, representing wind power uncertainty by using a Boolean variable, and combining the wind power uncertainty with the wind power output predicted value to construct a wind power uncertainty set W; wherein the wind power uncertain set W is represented by a Boolean variable vu/vlUpper and lower boundaries w receivable with wind poweru/wlJointly forming;
step 4, constructing a wind power admissible criterion by taking the abandoned wind quantity and the load shedding quantity as optimal values, and judging whether the wind power uncertain set can generate abandoned wind and load shedding or not; the constructed wind power acceptance criterion is shown as the following formula 3:
Figure FDA0002719255640000017
wherein the content of the first and second substances,
Figure FDA0002719255640000018
representing the abandoned air quantity/cut load quantity, and if the optimal value F is equal to 0, representing that the wind power uncertain set W can be accepted; otherwise, if F>0, representing that the wind power uncertain set W is not acceptable; phi represents a min problem decision variable set, and X represents a feasible domain of a min problem decision variable;
step 5, setting model constraints of a power system and a natural gas system, and combining the model constraints with the wind power acceptance criterion to form a wind power acceptance judging model;
step 6, solving a wind power acceptability judging model, obtaining a wind power extreme output scene, judging whether a convergence condition is met, if so, circularly terminating and outputting corresponding wind power acceptability upper and lower boundaries;
and 7, if the wind power extreme output scene is not met, generating new wind power output upper and lower boundary constraints according to the wind power extreme output scene, adding the new wind power output upper and lower boundary constraints into the wind power receptibility evaluation model, and solving to obtain new wind power receptibility upper and lower boundaries.
2. The method for evaluating the wind power consumption capability of the power system according to claim 1, wherein the wind power uncertain set W constructed in the step 3 is composed of the following formulas 1 and 2:
Figure FDA0002719255640000021
Figure FDA0002719255640000022
wherein the content of the first and second substances,
Figure FDA0002719255640000023
the uncertainty of the wind power is represented,
Figure FDA0002719255640000024
for a Boolean variable, take1/0, the wind power output reaches the upper bound/predicted value of the output interval;
Figure FDA0002719255640000025
taking 1/0 as a Boolean variable to indicate that the wind power output reaches the lower bound/predicted value of the output interval;
Figure FDA0002719255640000026
representing the wind power output upper/lower boundary;
Figure FDA0002719255640000027
belonging to wind power uncertain set W.
3. The method for evaluating the wind power consumption capability of the power system according to claim 1, wherein the power system and natural gas system model constraint set in the step 5 is represented as:
Figure FDA0002719255640000028
Figure FDA0002719255640000029
Figure FDA00027192556400000210
Figure FDA00027192556400000211
Figure FDA00027192556400000212
Figure FDA00027192556400000213
Figure FDA00027192556400000214
Figure FDA00027192556400000215
Figure FDA00027192556400000216
Figure FDA0002719255640000031
Figure FDA0002719255640000032
Figure FDA0002719255640000033
Figure FDA0002719255640000034
Figure FDA0002719255640000035
Figure FDA0002719255640000036
Figure FDA0002719255640000037
Figure FDA0002719255640000038
Figure FDA0002719255640000039
Figure FDA00027192556400000310
Figure FDA00027192556400000311
in the above formulae (4) to (20), neAnd NeRepresenting the index and the number of the conventional units; n isegAnd NegRepresenting the index and the number of the gas units; n isgAnd NgRepresenting the index and the number of the natural gas wells; n iswAnd NwRepresenting the index and the number of the natural gas wells; t and T represent the ordinal number and the number of the time period; leAnd LeRepresenting the index and the number of transmission lines of the power system; lgAnd LgIndicating the index and the number of the transmission lines of the natural gas system; i.e. ieAnd IeRepresenting the ordinal number and the number of the nodes of the power system; i.e. igAnd IgRepresenting the ordinal number and the number of the natural gas system nodes; deAnd DeRepresenting the power system load index and quantity; dgAnd DgIndicating natural gas load index and quantity; c and C represent the ordinal number and the number of the natural gas system compressor;
Figure FDA00027192556400000312
representing the running state of a conventional unit/gas unit;
Figure FDA00027192556400000313
representing the upper/lower boundary of the output of the conventional unit;
Figure FDA00027192556400000314
representing the upper/lower limit of the output of the gas turbine unit;
Figure FDA00027192556400000315
representing the output of a conventional unit/gas unit;
Figure FDA00027192556400000316
representing the positive climbing capacity of the conventional unit/gas unit;
Figure FDA00027192556400000317
the negative climbing capacity of the conventional unit/gas unit is represented; thetareftRepresenting a reference node phase angle;
Figure FDA00027192556400000318
representing power line transmission capacity;
Figure FDA00027192556400000319
representing a transmission line admittance;
Figure FDA0002719255640000041
representing node phase angles at two ends of an electric power line le;
Figure FDA0002719255640000042
representing the wind abandoning amount of each wind power plant;
Figure FDA0002719255640000043
representing the maximum wind power output of each wind power plant;
Figure FDA0002719255640000044
representing load shedding quantity of each load node of the power system;
Figure FDA0002719255640000045
representing the load of each load node of the power system;
Figure FDA0002719255640000046
representing the natural gas production;
Figure FDA0002719255640000047
representing the upper/lower bound of natural gas production; r isstRepresenting the gas storage capacity of the natural gas storage device;
Figure FDA0002719255640000048
representing the maximum/minimum of the gas storage quantity in the gas storage device;
Figure FDA0002719255640000049
indicating natural gas inflow/outflow;
Figure FDA00027192556400000410
representing the upper limit of the air input/output of the air storage device;
Figure FDA00027192556400000411
representing nodes i of respective natural gas systemsgNode air pressure;
Figure FDA00027192556400000412
representing node air pressure upper/lower bound;
Figure FDA00027192556400000413
indicating natural gas inventory of each pipeline;
Figure FDA00027192556400000414
the relation coefficient between the pipe stock and the node pressure is obtained;
Figure FDA00027192556400000415
showing a natural gas pipelineEnd node air pressure;
Figure FDA00027192556400000416
represents the inflow/outflow of the natural gas pipeline lg;
Figure FDA00027192556400000417
is the compressor compression factor;
Figure FDA00027192556400000418
respectively representing the air pressure of the nodes at the two ends of the compressor;
Figure FDA00027192556400000419
representing the average natural gas flow in the pipeline;
Figure FDA00027192556400000420
representing the relation coefficient of the pipeline power flow and the air pressure of the nodes at the two ends of the pipeline power flow; n is a radical ofe(ie)/Nw(ie)/De(ie)/Neg(ie)/Le(ie) Presentation and power system node ieThe connected conventional unit/wind power plant/electric load/gas unit/transmission line set; l isg(ig)/S(ig)/Ng(ig)/Dg(ig) Representation and natural gas system node igAnd the connected natural gas transmission pipeline/gas storage device/natural gas well/natural gas load set.
4. The method for evaluating the wind power absorption capacity of the power system according to claim 1, wherein in the step 7, the process of solving the two-stage model formed by the wind power absorption capacity evaluation model and the wind power absorption discrimination model by using the nested C & CG algorithm specifically comprises the following steps:
firstly, solving the two-stage model, and performing the following model equivalence transformation on the two-stage model:
wind power acceptance discriminant problem:
Figure FDA00027192556400000421
s.t.Ex+Gz+Rv≤hs (30)
in the formula (I), the compound is shown in the specification,
Figure FDA00027192556400000422
represents a constant coefficient matrix or vector; v represents a wind power uncertainty vector; x and z represent continuous and boolean decision variable vectors; s.t. represents a constraint;
and the wind power receptibility evaluation main problem is as follows:
Figure FDA00027192556400000423
s.t.Kw+Lμ≤hm (32)
in the formula, K, L, hmIs a constant coefficient matrix or vector; w represents a boundary vector of the wind power receivable area; mu represents a system operation risk vector;
based on the model equivalent transformation, the process of generating the C & CG algorithm solution by nesting columns and constraints is specifically as follows:
firstly, setting the iteration number k to be 0 and the convergence error xi, and extracting the predicted value of the wind power output
Figure FDA0002719255640000051
A unit combination strategy;
then, taking the risk evaluation index as an objective function and the wind power admissible boundary constraint as a constraint condition, solving a first-stage main problem, and optimizing a wind power admissible region:
Figure FDA0002719255640000052
s.t.
Figure FDA0002719255640000053
wherein, the optimal solution of the upper and lower boundaries of the wind power receivable area is recorded as wkRecord OriskOptimum value is Ok riskIn the formula
Figure FDA0002719255640000054
Representing a Hadamamed product;
solving the wind power receptivity discriminant sub-problem, and recording the optimal solution of v as vk+1Noting that the optimal value of the objective function F is FkIf F isk<ξ, the algorithm is stopped and the upper and lower boundaries w of the wind power receivable area are outputk(ii) a Otherwise, add vector xk+1,zk+1And constraint to the first phase Main problem as follows
Figure FDA0002719255640000055
Then, the iteration times k are set as k +1, and the process of solving by the C & CG algorithm is returned to operate;
where ξ is a very small positive number; k is the current iteration number; E/G/R/Λ/h represents a constant coefficient matrix or vector; o isriskRepresenting a system operational risk value; fkCutting the load for the abandoned wind; mu is a system operation risk vector; x and z represent continuous and boolean decision variable vectors; v represents a wind power uncertainty vector.
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