CN113224751A - Method for optimizing line capacity in high wind power ratio system - Google Patents

Method for optimizing line capacity in high wind power ratio system Download PDF

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CN113224751A
CN113224751A CN202110479056.XA CN202110479056A CN113224751A CN 113224751 A CN113224751 A CN 113224751A CN 202110479056 A CN202110479056 A CN 202110479056A CN 113224751 A CN113224751 A CN 113224751A
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丁肇豪
余开媛
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North China Electric Power University
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Abstract

The invention discloses a method and a system for optimizing line capacity in a high wind power ratio system. The method comprises the following steps: constructing a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to basic data and wind speed data of a corresponding transmission line of a power system; determining constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model; and calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk. The invention describes the uncertainty of the dynamic ampere capacity of the transmission line in the form of an uncertainty set, takes the overload risk of the transmission line as an evaluation index, optimizes the admissible area of the dynamic ampere capacity and can realize the maximum capacity under the minimum risk.

Description

Method for optimizing line capacity in high wind power ratio system
Technical Field
The invention relates to the field of transmission line ampere capacity, in particular to a method and a system for optimizing the capacity of a data-driven high-wind power system line.
Background
Under the background of increasingly serious environmental pollution and energy crisis in the world at present, renewable energy represented by wind power is regarded as one of main energy sources capable of replacing traditional thermal power generation. Wind power is a pollution-free renewable resource, the output of the wind power has the characteristics of intermittency, volatility and randomness, and the difficulty of system operation scheduling is increased by large-scale wind power access. In a power transmission network, the capacity of an overhead transmission line is often set by using relatively conservative environmental parameters. The wind speed, the output of the wind power plant and the ampere capacity of the transmission line have a fixed mapping relation, if the capacity of the transmission line is dynamically set by using environmental parameters, a dynamic ampere capacity mechanism is adopted, the transmission potential of the line can be effectively excavated, the wind abandon phenomenon caused by line blockage is reduced, and the consumption of renewable energy is promoted.
Most of the existing literature researches how to properly improve the current-carrying capacity by using dynamic environment parameters in the scheduling process so as to achieve the purpose of improving the line capacity. However, due to uncertainty of factors such as weather, prediction errors of dynamic ampacity cannot be avoided, and if the prediction error of the dynamic ampacity of the transmission line is too large, transmission line overload can be caused, and even cascading failure of a power system can be caused seriously. There is also literature to study the effect of dynamic ampacity in high wind power ratios for uncertainty in wind power and dynamic ampacity. Therefore, on the premise of considering the uncertainty of the dynamic ampere capacity of the transmission line, the method has very important engineering significance for evaluating the overload risk of the high wind power ratio power system.
Although the influence of uncertainty of dynamic ampacity on system operation in the quantitative analysis of power system operation has been studied, under a given operation strategy, the evaluation of potential overload risk of a power transmission line caused by uncertainty of dynamic ampacity still is a problem to be solved urgently. The power transmission line tide limit for avoiding risks should be provided for power system operators, and the power transmission line tide limit is similar to the wind power output schedulable area for providing the risks so as to visualize the safety margin and assist the scheduling operation of the power system.
It should be noted that the conventional model evaluates the steady operation state of the power system, and there are also a few studies discussing the reasons for the overload of the lines in the system. In consideration of a high wind power ratio system, uncertainty of a dynamic ampere capacity predicted value enables transmission line overload risk assessment to have higher engineering application value.
Disclosure of Invention
In order to solve the above-mentioned technical problems, the invention provides a data-driven high wind power system line capacity optimization method and system, which consider the uncertainty of transmission line dynamic ampere capacity and realize capacity maximization when the risk is minimum.
In order to achieve the purpose, the invention provides the following scheme:
a data-driven high wind power system line capacity optimization evaluation method comprises the following steps:
acquiring wind speed data near a power transmission line;
acquiring basic data of a transmission line corresponding to a power system;
constructing a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data;
determining constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model;
and calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
Optionally, the expression of the data-driven transmission line overload risk assessment model is as follows:
Figure BDA0003047982470000021
wherein Risk is an index for transmission line overload Risk assessment,F ltrepresenting transmission line capacity, Q, taking into account overload riskltThe overload capacity of the transmission line is shown, l represents the l-th section of the transmission line, and t represents the selected time interval.
Optionally, the expression of the data-driven transmission line overload Risk assessment index Risk is as follows:
Figure BDA0003047982470000031
wherein, clRepresents a transmission line overload penalty factor,F ltrepresenting transmission line capacity, δ, taking into account overload riskltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,
Figure BDA0003047982470000032
representing predicted dynamic Ampere Capacity, Pr(δlt) Representing a dynamic ampacity prediction error probability distribution function.
Optionally, the expression of the data-driven transmission line dynamic ampacity optimization model is as follows:
Figure BDA0003047982470000033
wherein the content of the first and second substances,F ltrepresenting transmission line capacity, Δ F, taking into account overload riskltIndicating a shortage of transmission line capacity, vltThe uncertainty of the dynamic ampere capacity is represented and is a Boolean variable, phi represents a decision variable set, l represents an l-th transmission line, and t represents a selected time interval.
Optionally, the constraint conditions of the data-driven transmission line overload risk assessment model are as follows:
Figure BDA0003047982470000034
Figure BDA0003047982470000035
wherein Q isltIndicating the amount of overload of the transmission line, dltj/eltjThe segment coefficients representing the time t risk linearization,F ltindicating the transmission line capacity taking into account the risk of overload,
Figure BDA0003047982470000036
the predicted ampacity of the line is shown, l represents the l-th transmission line, and t represents the selected time interval.
Optionally, the constraints of the data-driven transmission line dynamic ampacity optimization model are as follows:
Figure BDA0003047982470000037
Figure BDA0003047982470000038
Figure BDA0003047982470000039
Figure BDA0003047982470000041
Figure BDA0003047982470000042
Figure BDA0003047982470000043
Figure BDA0003047982470000044
Figure BDA0003047982470000045
Figure BDA0003047982470000046
Figure BDA0003047982470000047
Figure BDA0003047982470000048
Figure BDA0003047982470000049
Figure BDA00030479824700000410
Figure BDA00030479824700000411
Figure BDA00030479824700000412
Figure BDA00030479824700000413
wherein the content of the first and second substances,
Figure BDA00030479824700000414
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltIndicating a shortage of transmission line capacity, pltWhich represents the power transmitted by the power line,F ltrepresenting the transmission line capacity taking into account the risk of overload. Pr,Pc,PsRespectively represents the radiation heat dissipation power of the lead, the convection heat dissipation power of the lead, the sunshine heat absorption power of the lead, RTAs resistance of the transmission line, EUltIs Euler number, Re is Reynolds number, and the value is related to wind speed. v. ofltRepresenting the dynamic ampacity uncertainty as a boolean variable. p is a radical ofgtIs representative of the power of the generator set,
Figure BDA0003047982470000051
representing a genset output upper/lower bound. p is a radical ofwtThe power of the wind turbine is represented,
Figure BDA0003047982470000052
the rated power of the wind turbine generator is shown,
Figure BDA0003047982470000053
and representing the predicted output of the wind turbine.
Figure BDA0003047982470000054
Indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure BDA0003047982470000055
the transmission line admittance is represented as a function of,
Figure BDA0003047982470000056
θn2representing the phase angle theta of two end nodes of the power line in the time period tref,tIs the reference node phase value. w is aci,wr,wcoThe cut-in wind speed, the rated wind speed, and the cut-out wind speed are respectively indicated.
The invention also provides a data-driven high wind power system line capacity optimization system, which comprises:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the power system and wind speed data related to the transmission line;
the model building module is used for building a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data;
the constraint condition determining module is used for determining the constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model;
and the solving module is used for calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a data-driven high wind power system line capacity optimization method and system. The method comprises the following steps: constructing a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to basic data of a transmission line corresponding to the power system and wind speed data related to the transmission line; determining constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model; and calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk. The invention describes the uncertainty of the dynamic ampere capacity in the form of an uncertainty set, takes the overload risk of the transmission line as an evaluation index, optimizes the admissible area of the dynamic ampere capacity and can realize the maximum capacity under the minimum risk.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described 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 without inventive exercise.
FIG. 1 is a flow chart of a method for optimizing line capacity of a high wind power ratio system according to the present invention;
FIG. 2 is a schematic view of a 5-node test system topology employed in the present invention;
fig. 3 is a schematic diagram of a test result considering the influence of the unit start-stop mode on the acceptable capacity of the transmission line in the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a data-driven high wind power system line capacity optimization method and system, which consider the uncertainty of the dynamic ampere capacity of a transmission line and realize the capacity maximization when the risk is minimum
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, a data-driven high wind power system line capacity optimization method includes the following steps:
step 101: basic data of the power system corresponding to the transmission line and wind speed data related to the transmission line are obtained.
Step 102: and constructing a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data.
Step 103: and determining the constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model.
Step 104: and calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
The specific principle of the invention is as follows:
in the operation scheduling process of the power system, aiming at the power system operation problem considering a DLR mechanism, the invention expresses the transmission line ampere capacity uncertainty caused by meteorological factors in a mode of combining a box set with a convex hull set, and constructs a transmission line overload risk assessment model based on two-stage robust optimization by combining the provided transmission line overload risk indexes, and the method comprises the following implementation steps:
(1) the method for acquiring the transmission line basic data corresponding to the power system comprises the following steps: the power system corresponds to power grid system topology data, load demand of each node in the power grid system and basic data of each generator set; wherein the power system corresponding power grid system topology data comprises: the mutual connection relation between the nodes and lines of the corresponding power grid system in the power system and the active power flow limit of each power transmission line are determined; the basic data of each generator set comprises: maximum/minimum generating capacity of the unit, maximum/minimum climbing rate of the unit and operation cost data of the unit; wind speed data associated with the transmission line.
(2) Selecting a line of the power system using a dynamic ampere capacity mechanism as L, selecting a time period as T, and respectively recording the total number of the lines, the total number of nodes and the total number of the time period in the system as L, B and T;
(3) initializing the system, setting an initial value k of iteration times to be 0, and setting a convergence error to be a very small constant epsilon;
(4) and constructing a data-driven transmission line overload risk assessment model considering a dynamic ampere capacity mechanism, assessing the overload risk of the system line and obtaining a line ampere capacity lower bound corresponding to no overload loss. The method comprises the following specific steps:
constructing an objective function of a data-driven transmission line overload risk assessment model, wherein the expression is as follows:
Figure BDA0003047982470000081
risk is a transmission line overload Risk assessment index, and the mathematical expression of the Risk is as follows:
Figure BDA0003047982470000082
in the formula (I), the compound is shown in the specification,F ltrepresenting transmission line capacity, Q, taking into account overload riskltIndicating the overload of the transmission line, l indicating the l-th transmission line, t indicating the selected time interval, clRepresenting transmission line overload penalty factor, deltaltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,
Figure BDA0003047982470000083
representing predicted dynamic Ampere Capacity, Pr(δlt) Representing a dynamic ampacity prediction error probability distribution function.
The expression of the data-driven transmission line dynamic ampacity optimization model is as follows:
Figure BDA0003047982470000084
wherein the content of the first and second substances,F ltrepresenting transmission line capacity, Δ F, taking into account overload riskltIndicating a shortage of transmission line capacity, vltRepresenting the dynamic ampacity uncertainty as a boolean variable, Φ ═ pgt，plt，θnt，ΔFltRepresents a min problem decision variable set.
The constraints for determining the data-driven assessment model considering the overload risk of the transmission line are as follows:
Figure BDA0003047982470000085
Figure BDA0003047982470000086
the constraints for determining the data-driven optimization model considering the dynamic ampacity of the transmission line are as follows:
Figure BDA0003047982470000087
Figure BDA0003047982470000088
Figure BDA0003047982470000091
Figure BDA0003047982470000092
Figure BDA0003047982470000093
Figure BDA0003047982470000094
Figure BDA0003047982470000095
Figure BDA0003047982470000096
Figure BDA0003047982470000097
Figure BDA0003047982470000098
Figure BDA0003047982470000099
Figure BDA00030479824700000910
Figure BDA00030479824700000911
Figure BDA00030479824700000912
Figure BDA00030479824700000913
Figure BDA00030479824700000914
in the above formula, the auxiliary constraint in the linearized overload risk assessment index is (4), the line capacity constraint considering a dynamic ampere capacity mechanism is (5), the transmission line capacity constraint is (6-11), the generator set output constraint is (12), the wind turbine generator set output constraint is (13), the generator set climbing constraints are (14) and (15), the power system node balance constraint is (16), the direct current power flow equation constraint is (17), the reference node phase angle constraint is (18), and the wind power maximum output constraint is (19-21). Wherein the content of the first and second substances,
Figure BDA00030479824700000915
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltIndicating a shortage of transmission line capacity, pltWhich represents the power transmitted by the power line,F ltrepresenting the transmission line capacity taking into account the risk of overload. Pr,Pc,PsRespectively represents the radiation heat dissipation power of the lead, the convection heat dissipation power of the lead, the sunshine heat absorption power of the lead, RTAs resistance of the transmission line, EUltIs Euler number, Re is Reynolds number, and the value is related to wind speed. v. ofltRepresenting the dynamic ampacity uncertainty as a boolean variable. p is a radical ofgtIs representative of the power of the generator set,
Figure BDA0003047982470000101
representing a genset output upper/lower bound. p is a radical ofwtThe power of the wind turbine is represented,
Figure BDA0003047982470000102
the rated power of the wind turbine generator is shown,
Figure BDA0003047982470000103
and representing the predicted output of the wind turbine.
Figure BDA0003047982470000104
Indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure BDA0003047982470000105
the transmission line admittance is represented as a function of,
Figure BDA0003047982470000106
θn2representing the phase angle theta of two end nodes of the power line in the time period tref,tIs the reference node phase value. w is aci,wr,wcoThe cut-in wind speed, the rated wind speed, and the cut-out wind speed are respectively indicated.
For ease of presentation, the data-driven transmission line overload risk assessment model is represented herein in a compact form as follows:
Figure BDA0003047982470000107
solving the model, and recording the optimal solution of c as c(k)The Risk optimal value is Risk(k)。
(5) In the step (4), the situation that the dynamic ampere capacity possibly deviates from the predicted value in the running process of the power system is considered. In the model constraint, the mathematical expression is in the form of a two-layer max-min model, which is named herein as a dynamic ampacity admissible criterion, i.e., assuming that the dynamic ampacity uncertainty is intended to maximize the dynamic ampacity overrun amount in the power system operational rescheduling phase. The max-min model is made to be a transmission line overload discrimination sub-problem.
Solving the transmission line discrimination subproblem model, and recording the optimal solution of v as v(k+1)The optimal value of the target function gamma is gamma(k+1)；
(6) If gamma is(k+1)If epsilon is less than epsilon, the step is terminated and c is output(k)(ii) a Otherwise, adding the vector and the corresponding constraint, making k equal to k +1, and returning to the step (4).
(7) And (4) finishing iteration, recording the overload risk of the transmission line caused by the uncertainty of the dynamic ampere capacity of the transmission line and the maximum allowable deviation of the ampere capacity of each transmission line, and ending the method.
The invention will be described in detail below with reference to model topology and example data. It should be emphasized that the examples, which are set forth below, are intended to be illustrative only and are not intended to limit the scope or application of the invention. The example models introduced in the present invention are each composed of a 5-node power network, as shown in fig. 2. Simulation tests were performed in MATLAB.
The method is used for evaluating the influence of a dynamic ampere capacity mechanism on overload risk of a system. Firstly, the starting and stopping states of different units in the system are changed, and the result that the outage of part of the units can cause the increase of the line overload risk under the mechanism is obtained. Secondly, the number of lines adopting a dynamic ampere capacity mechanism in the system is changed, and the result that the overload risk of the transmission line is gradually reduced along with the gradual increase of the number of the transmission lines adopting the dynamic ampere capacity mechanism is obtained.
Figure BDA0003047982470000111
As shown in fig. 3, in the test system, the blue bar graph represents the overload risk of the system, the green bar graph represents the running cost of the system, and the black solid line represents the marginal net gain of the system. According to the test result, when all transmission lines in the system do not adopt a dynamic ampere capacity mechanism, the transmission line overload risk is reached, and the value is maximum in the test; and when the number of the transmission lines is gradually increased, the overload risk value of the transmission line is gradually reduced, and the overload risk value reaches the minimum when a dynamic ampere capacity mechanism is adopted by all the lines. And the dynamic ampere capacity mechanism is adopted, so that the overload risk of the transmission line can be reduced for the whole system, and the method has practical application significance.
The invention also provides a data-driven high wind power system line capacity optimization system, which comprises:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the power system and wind speed data related to the transmission line;
the model building module is used for building a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data;
the constraint condition determining module is used for determining the constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model;
and the solving module is used for calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, the specific embodiments and the application range may be changed according to the idea of the present invention.

Claims (7)

1. A method for optimizing line capacity in a high wind power ratio system is characterized by comprising the following steps:
acquiring basic data of a power system corresponding to a transmission line and wind speed data related to the transmission line;
constructing a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data;
determining constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model;
and calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition, and determining the maximum capacity of the transmission line under the minimum risk.
2. The method for optimizing the line capacity in the high wind power ratio system according to claim 1, wherein the expression of the data-driven transmission line overload risk assessment model is as follows:
Figure FDA0003047982460000011
wherein Risk is an index for transmission line overload Risk assessment,F ltrepresenting transmission line capacity, Q, taking into account overload riskltThe overload capacity of the transmission line is shown, l represents the l-th section of the transmission line, and t represents the selected time interval.
3. The method for optimizing the line capacity in the high wind power ratio system according to claim 1, wherein the expression of the data-driven transmission line overload Risk assessment index Risk is as follows:
Figure FDA0003047982460000012
wherein the content of the first and second substances,clrepresents a transmission line overload penalty factor,F ltrepresenting transmission line capacity, δ, taking into account overload riskltRepresenting transmission line dynamic ampacity prediction error, L representing total number of transmission lines, T representing total number of segments,
Figure FDA0003047982460000013
representing predicted dynamic Ampere Capacity, Pr(δlt) Representing a dynamic ampacity prediction error probability distribution function.
4. The method for optimizing the line capacity in the high wind power ratio system according to claim 1, wherein the expression of the data-driven transmission line dynamic ampacity optimization model is as follows:
Figure FDA0003047982460000021
wherein the content of the first and second substances,F ltrepresenting transmission line capacity, Δ F, taking into account overload riskltIndicating a shortage of transmission line capacity, vltThe uncertainty of the dynamic ampere capacity is represented and is a Boolean variable, phi represents a decision variable set, l represents an l-th transmission line, and t represents a selected time interval.
5. The method for optimizing the line capacity in the high wind power ratio system according to claim 1, wherein the constraint conditions of the data-driven transmission line overload risk assessment model are as follows:
Figure FDA0003047982460000022
Figure FDA0003047982460000023
wherein Q isltIndicating overload of transmission lineAmount, dltj/eltjThe segment coefficients representing the time t risk linearization,F ltindicating the transmission line capacity taking into account the risk of overload,
Figure FDA0003047982460000024
the predicted ampacity of the line is shown, l represents the l-th transmission line, and t represents the selected time interval.
6. The method for optimizing the line capacity in the high wind power ratio system according to claim 1, wherein the constraint conditions of the dynamic ampacity optimization model of the transmission line are as follows:
Figure FDA0003047982460000025
Figure FDA0003047982460000026
Figure FDA0003047982460000027
Figure FDA0003047982460000031
Figure FDA0003047982460000032
Figure FDA0003047982460000033
Figure FDA0003047982460000034
Figure FDA0003047982460000035
Figure FDA0003047982460000036
Figure FDA0003047982460000037
Figure FDA0003047982460000038
Figure FDA0003047982460000039
Figure FDA00030479824600000310
Figure FDA00030479824600000311
Figure FDA00030479824600000312
Figure FDA00030479824600000313
wherein the content of the first and second substances,
Figure FDA00030479824600000314
representing the dynamic Ampere Capacity, Δ F, for the worst case scenarioltIndicating a shortage of transmission line capacity, pltWhich represents the power transmitted by the power line,F ltrepresenting transmission line capacity taking into account overload risk; pr,Pc,PsRespectively represents the radiation heat dissipation power of the lead, the convection heat dissipation power of the lead, the sunshine heat absorption power of the lead, RTAs resistance of the transmission line, EUltIs Euler number, Re is Reynolds number, and the value is related to wind speed; v. ofltRepresenting the uncertainty of the dynamic ampere capacity, which is a Boolean variable; p is a radical ofgtIs representative of the power of the generator set,
Figure FDA0003047982460000041
representing the upper/lower limit of the output of the generator set; p is a radical ofwtThe power of the wind turbine is represented,
Figure FDA0003047982460000042
the rated power of the wind turbine generator is shown,
Figure FDA0003047982460000043
representing the predicted output of the wind turbine;
Figure FDA0003047982460000044
indicating the positive/negative climbing capability of the generator set, ugtIndicating the start-stop condition of the generator set, pdtThe power of the load is represented by,
Figure FDA0003047982460000045
the transmission line admittance is represented as a function of,
Figure FDA0003047982460000046
θn2representing the phase angle theta of two end nodes of the power line in the time period tref,tIs a reference node phase value; w is aci,wr,wcoThe cut-in wind speed, the rated wind speed, and the cut-out wind speed are respectively indicated.
7. A line capacity optimizing system in a high wind power ratio system is characterized by comprising:
the data acquisition module is used for acquiring basic data of a transmission line corresponding to the power system and wind speed data related to the transmission line; the model building module is used for building a data-driven transmission line overload risk assessment model and a data-driven transmission line dynamic ampere capacity optimization model according to the basic data; the constraint condition determining module is used for determining the constraint conditions of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model; and the solving module is used for calculating the optimal solution of the data-driven transmission line overload risk assessment model and the data-driven transmission line dynamic ampere capacity optimization model under the constraint condition and determining the maximum capacity of the transmission line under the minimum risk.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434874A (en) * 2020-12-02 2021-03-02 华北电力大学 Line capacity optimization method and system for renewable energy consumption

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112434874A (en) * 2020-12-02 2021-03-02 华北电力大学 Line capacity optimization method and system for renewable energy consumption
CN112434874B (en) * 2020-12-02 2024-05-10 华北电力大学 Line capacity optimization method and system for renewable energy consumption

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