CN108565865B - Risk assessment method for wind power-containing alternating current-direct current hybrid system - Google Patents

Risk assessment method for wind power-containing alternating current-direct current hybrid system Download PDF

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CN108565865B
CN108565865B CN201810409494.7A CN201810409494A CN108565865B CN 108565865 B CN108565865 B CN 108565865B CN 201810409494 A CN201810409494 A CN 201810409494A CN 108565865 B CN108565865 B CN 108565865B
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陈页
郭创新
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Zhejiang University ZJU
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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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H02J3/386—
    • 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
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract

The invention discloses a risk assessment method for an alternating current-direct current hybrid system containing wind power. The invention comprises the following steps: step 1, modeling the wind speed distribution of a wind power plant by using a Weibull distribution double-parameter curve according to the historical wind speed statistical data of the wind power plant; step 2, sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method; step 3, establishing a wind speed and wind turbine generator output model of the wind power plant, and obtaining the corresponding wind turbine generator output according to the wind speed of the wind power plant sampled by a non-sequential Monte Carlo method; step 4, performing state analysis on the determined system state, and solving the power flow of the series-parallel system by using an alternating current-direct current system unified iterative method; and 5, calculating a variance coefficient, judging whether the non-sequential Monte Carlo method convergence condition is met, if not, returning to the step 2, and if so, calculating a comprehensive risk value. The method is reliable, easy to implement and convenient to popularize.

Description

Risk assessment method for wind power-containing alternating current-direct current hybrid system
Technical Field
The invention belongs to the field of load flow calculation and risk assessment of a power system, and particularly relates to a risk assessment method of an alternating current-direct current hybrid system containing wind power.
Background
Risk is a comprehensive measure of the probability and severity of occurrence of an uncertain operating scenario. With the large-scale penetration of new energy, the randomness and the intermittency of the new energy require that a power grid needs to be operated closer to an extreme state under multiple uncertain conditions, and the complexity of scheduling is further increased. How to reasonably evaluate and control the risk of the power grid and ensure the safe and stable operation of the power grid becomes the focus of the operation of the power system.
With the continuous increase of the scale of an electric power system and the continuous progress of a power grid technology, the power generation trend of renewable energy sources is rapidly expanded, non-fossil energy sources are developed vigorously, energy sources in the middle and western parts of China are rich, the east economy is developed, direct current transmission has many advantages in the aspects of large-capacity and long-distance transmission, and the risk assessment of alternating current-direct current series-parallel coordination operation accessed by the renewable energy sources is necessary.
The current risk assessment method ignores the influence of wind power access and an alternating current-direct current hybrid power transmission mode on a power grid system, and after the wind power access and the alternating current-direct current hybrid power transmission mode are considered, a sequential Monte Carlo method is adopted to simulate the random process of system operation, a quantized power system operation risk index is established, and the power system risk assessment after the wind power access is realized.
Disclosure of Invention
The invention provides a risk assessment method of an alternating current-direct current hybrid system containing wind power, aiming at the problems that the current risk assessment method ignores the influence of wind power access and an alternating current-direct current hybrid transmission mode on a power grid system and the like, and solves the problems that the wind power access and the influence of the alternating current-direct current hybrid transmission mode on the power grid system are not considered in the existing assessment method, a quantized power system operation risk index is established, and the risk assessment of the power system after the wind power access is realized.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1, modeling the wind speed distribution of a wind power plant by using a Weibull distribution double-parameter curve according to the historical wind speed statistical data of the wind power plant;
the historical wind speed statistical data are derived from hour average wind speed data collected by a wind measuring tower of a local wind power plant;
the general expression of the two-parameter Weibull distribution is as follows:
Figure BDA0001647606600000021
wherein k is a shape parameter of the two-parameter Weibull distribution and reflects the skewness of the Weibull distribution, and c is a scale parameter of the two-parameter Weibull distribution and reflects the average wind speed in the wind speed distribution model;
further, a probability distribution function of the Weibull distribution can be calculated, which is generally expressed as follows:
Figure BDA0001647606600000022
the shape parameter k and the scale parameter c in the Weibull distribution double-parameter curve are obtained by using a least square estimation method through wind speed historical statistical data of the wind power plant, and the method specifically comprises the following steps:
(1) converting the probability distribution function of the Weibull distribution into a linear form, namely taking the logarithm twice of the probability distribution function (2) of the Weibull distribution, wherein the expression is as follows:
Figure BDA0001647606600000023
(2) dividing the wind speed of a wind farm into n wind speed intervals, [0, v1]，(v1,v2]，…，(vi-1,vi]，…，(vn-1,vn]Counting the frequency f of the wind speed sample value in each wind speed interval1,f2,...,fi,...,fnAnd calculating the cumulative frequency p1,p2,...,pi,...,pnWherein p is1＝f1,pi＝pi-1+fi；
(3) Let xi＝lnvi，yi＝ln[-ln(1-pi)]Then, the shape parameter k and the scale parameter c can be obtained from the following equations (4) and (5):
Figure BDA0001647606600000024
Figure BDA0001647606600000025
step 2, sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method;
the method comprises the following steps of sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method:
(1) determining the inverse of the probability distribution function of a Weibull distribution
Figure BDA0001647606600000026
Wherein u isiIs [0,1 ]]Uniformly distributed random numbers of the intervals;
(2) when u isiObey [0,1]When the intervals are uniformly distributed, 1-uiAlso obey [0,1]The interval is evenly distributed, and the random sampling value of the wind speed per hour can be obtained as
Figure BDA0001647606600000031
Step 3, establishing a wind speed and wind turbine generator output model of the wind power plant, and obtaining the corresponding wind turbine generator output according to the wind speed of the wind power plant sampled by a non-sequential Monte Carlo method;
the relationship between the wind power plant wind motor output characteristic and the wind speed is as follows:
Figure BDA0001647606600000032
wherein, PwindFor wind turbine generator output power, PrRated power, v, of a wind turbinetIs the wind speed v of the current wind power plant at the moment tciIndicating the cut-in wind speed, v, of the wind turbinerIndicating rated wind speed, v, of the wind turbinecoThe cut-out wind speed of the wind turbine generator is represented, A, B, C are parameters in a wind turbine output characteristic model formula (7-2) of the wind power plant, and the parameters can be calculated in the following mode:
Figure BDA0001647606600000033
Figure BDA0001647606600000034
Figure BDA0001647606600000035
step 4, performing state analysis on the system state determined by the primary sampling, and solving the power flow of the hybrid system by using an alternating current-direct current system unified iterative method;
the unified iterative method is used for solving the power flow of the alternating current-direct current hybrid system, and specifically comprises the following steps:
4-1, expanding the traditional power flow calculation problem, wherein the direct current voltage, the direct current, the converter voltage converter transformation ratio, the converter power factor and the converter control angle variable in the expanded converter equation, the direct current network equation and the control equation can be respectively written into the following forms:
Figure BDA0001647606600000036
4-2, the load flow calculation correction equation of the alternating current-direct current hybrid system can be written as follows:
Figure BDA0001647606600000041
wherein the column vector Δ PaAnd Δ PtActive power deviation of pure AC and DC nodes, respectively, column vector Δ Qa、Δθa、ΔVaAnd Δ Qt、Δθt、ΔVtRespectively the reactive power, phase angle and node voltage deviation of a pure alternating current node and a direct current node, J is a Jacobian matrix of a load flow calculation equation of an alternating current-direct current hybrid system, and delta d1And Δ d2、Δd3、Δd4And Δ d5Respectively a converter basic equation, a direct current network basic equation and a control equation;
4-3, judging whether the load flow calculation of the alternating current-direct current series-parallel system is converged according to the calculation result of the correction amount, wherein the convergence criterion is as follows:
||Δdi||＜(i＝1,2,...,5)
||ΔP||＜
||ΔQ||＜
and if the convergence condition is reached, finishing the load flow calculation, otherwise, performing the next cycle calculation.
Step 5, calculating a variance coefficient, judging whether a non-sequential Monte Carlo method convergence condition is met, if not, returning to the step 2, and if so, calculating a comprehensive risk value;
if the beta is smaller than the given value of the convergence criterion, the iteration is finished, and a risk value is calculated;
the variance coefficient β can be calculated as follows:
Figure BDA0001647606600000042
wherein, X is a risk index with the slowest convergence speed, sigma (X) is the standard deviation of X, E (X) is the expected value of X, and NS is the simulation times and is the given value of the convergence criterion;
the comprehensive risk value is comprehensively measured through two risk indexes of voltage out-of-limit and load flow out-of-limit, and is specifically calculated as follows:
(1) voltage out-of-limit risk value calculation
Figure BDA0001647606600000043
Where NS is the total number of state samples, Risk(Ui) The voltage state experimental result is the ith state sampling of the system.
(2) Load flow out-of-limit risk value calculation
Figure BDA0001647606600000051
Where NS is the total number of state samples, Risk(Si) And calculating an experimental result for the j-th load flow of the system for state sampling.
(3) Integrated risk value
Figure BDA0001647606600000052
Wherein R isk(x) Is the outcome value under the k-th risk index of
Figure BDA0001647606600000054
The weight of the total risk under the kth risk indicator.
The invention has the following beneficial effects:
after wind power access and an alternating current-direct current hybrid power transmission mode are considered, a sequential Monte Carlo method is adopted to simulate a random process of system operation, a quantized power system operation risk index is established, and power system risk assessment after wind power access is achieved. The method is reliable, easy to implement and convenient to popularize.
Drawings
Fig. 1 is an implementation flow chart of a risk assessment method for an alternating current-direct current hybrid system containing wind power.
Fig. 2 is a flowchart of an implementation of a load flow calculation method of an ac/dc hybrid system.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, a risk assessment method for an ac/dc hybrid system including wind power specifically includes the following steps:
step 1, modeling the wind speed distribution of a wind power plant by using a Weibull distribution double-parameter curve according to the historical wind speed statistical data of the wind power plant;
the historical wind speed statistical data are derived from hour average wind speed data collected by a wind measuring tower of a local wind power plant;
the general expression of the two-parameter Weibull distribution is as follows:
Figure BDA0001647606600000053
wherein k is a shape parameter of the two-parameter Weibull distribution and reflects the skewness of the Weibull distribution, and c is a scale parameter of the two-parameter Weibull distribution and reflects the average wind speed in the wind speed distribution model;
further, a probability distribution function of the Weibull distribution can be calculated, which is generally expressed as follows:
Figure BDA0001647606600000061
the shape parameter k and the scale parameter c in the Weibull distribution double-parameter curve are obtained by using a least square estimation method through wind speed historical statistical data of the wind power plant, and the method specifically comprises the following steps:
(1) converting the probability distribution function of the Weibull distribution into a linear form, namely taking the logarithm twice of the probability distribution function (2) of the Weibull distribution, wherein the expression is as follows:
Figure BDA0001647606600000062
(2) dividing the wind speed of a wind farm into n wind speed intervals, [0, v1]，(v1,v2]，…，(vi-1,vi]，…，(vn-1,vn]Counting the frequency f of the wind speed sample value in each wind speed interval1,f2,...,fi,...,fnAnd calculating the cumulative frequency p1,p2,...,pi,...,pnWherein p is1＝f1,pi＝pi-1+fi；
(3) Let xi＝lnvi，yi＝ln[-ln(1-pi)]Then, the shape parameter k and the scale parameter c can be obtained from the following equations (4) and (5):
Figure BDA0001647606600000063
Figure BDA0001647606600000064
step 2, sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method;
the method comprises the following steps of sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method:
(1) determining the inverse of the probability distribution function of a Weibull distribution
Figure BDA0001647606600000065
Wherein u isiIs [0,1 ]]Uniformly distributed random numbers of the intervals;
(2) when u isiObey [0,1]When the intervals are uniformly distributed, 1-uiAlso obey [0,1]The interval is evenly distributed, and the random sampling value of the wind speed per hour can be obtained as
Figure BDA0001647606600000071
Step 3, establishing a wind speed and wind turbine generator output model of the wind power plant, and obtaining the corresponding wind turbine generator output according to the wind speed of the wind power plant sampled by a non-sequential Monte Carlo method;
the relationship between the wind power plant wind motor output characteristic and the wind speed is as follows:
Figure BDA0001647606600000072
wherein, PwindFor wind turbine generator output power, PrRated power, v, of a wind turbinetIs the wind speed v of the current wind power plant at the moment tciIndicating the cut-in wind speed, v, of the wind turbinerIndicating rated wind speed, v, of the wind turbinecoThe cut-out wind speed of the wind turbine generator is represented, A, B, C are parameters in a wind turbine output characteristic model formula (7-2) of the wind power plant, and the parameters can be calculated in the following mode:
Figure BDA0001647606600000073
Figure BDA0001647606600000074
Figure BDA0001647606600000075
step 4, performing state analysis on the determined system state, and solving the power flow of the series-parallel system by using an alternating current-direct current system unified iterative method;
as shown in fig. 2, the unified iterative method solves the power flow of the ac/dc hybrid system, and after selecting a file and reading in original data, nodes are renumbered according to the sequence of PQ nodes, PV nodes, and balance nodes to form a node admittance matrix, and after setting the treatment and iteration times of variables to be solved, the following steps are performed:
4-1, expanding the traditional power flow calculation problem, wherein the direct current voltage, the direct current, the converter voltage converter transformation ratio, the converter power factor and the converter control angle variable in the expanded converter equation, the direct current network equation and the control equation can be respectively written into the following forms:
Figure BDA0001647606600000076
4-2, the load flow calculation correction equation of the alternating current-direct current hybrid system can be written as follows:
Figure BDA0001647606600000081
wherein the column vector Δ PaAnd Δ PtActive power deviation of pure AC and DC nodes, respectively, column vector Δ Qa、Δθa、ΔVaAnd Δ Qt、Δθt、ΔVtRespectively the reactive power, phase angle and node voltage deviation of a pure alternating current node and a direct current node, J is a Jacobian matrix of a load flow calculation equation of an alternating current-direct current hybrid system, and delta d1And Δ d2、Δd3、Δd4And Δ d5Respectively a converter basic equation, a direct current network basic equation and a control equation;
4-3, judging whether the load flow calculation of the alternating current-direct current series-parallel system is converged according to the calculation result of the correction amount, wherein the convergence criterion is as follows:
||Δdi||＜(i＝1,2,...,5)
||ΔP||＜
||ΔQ||＜
and if the convergence condition is reached, finishing the load flow calculation, otherwise, performing the next cycle calculation.
Step 5, calculating a variance coefficient, judging whether a non-sequential Monte Carlo method convergence condition is met, if not, returning to the step 2, and if so, calculating a comprehensive risk value;
if the beta is smaller than the given value of the convergence criterion, the iteration is finished, and a risk value is calculated;
the variance coefficient β can be calculated as follows:
Figure BDA0001647606600000082
wherein, X is a risk index with the slowest convergence speed, sigma (X) is the standard deviation of X, E (X) is the expected value of X, and NS is the simulation times and is the given value of the convergence criterion;
the comprehensive risk value is comprehensively measured through two risk indexes of voltage out-of-limit and load flow out-of-limit, and is specifically calculated as follows:
(1) voltage out-of-limit risk value calculation
Figure BDA0001647606600000083
Where NS is the total number of state samples, Risk(Ui) The voltage state experimental result is the ith state sampling of the system.
(2) Load flow out-of-limit risk value calculation
Figure BDA0001647606600000091
Where NS is the total number of state samples, Risk(Si) And calculating an experimental result for the j-th load flow of the system for state sampling.
(3) Integrated risk value
Figure BDA0001647606600000092
Wherein R isk(x) Is the outcome value under the k-th risk index of
Figure BDA0001647606600000093
The weight of the total risk under the kth risk indicator.

Claims (1)

1. A risk assessment method for an alternating current-direct current hybrid system containing wind power is characterized by comprising the following steps:
step 1, modeling wind speed probability distribution of a wind power plant by using a Weibull distribution double-parameter curve according to historical wind speed statistical data of the wind power plant;
step 2, sampling wind speed according to a probability distribution model of wind speed of a wind power plant by adopting a non-sequential Monte Carlo method;
step 3, establishing a wind speed and wind turbine generator output model of the wind power plant, and obtaining the corresponding wind turbine generator output according to the wind speed of the wind power plant sampled by a non-sequential Monte Carlo method;
step 4, performing state analysis on the system state determined by the primary sampling, and solving the power flow of the hybrid system by using an alternating current-direct current system unified iterative method;
step 5, calculating a variance coefficient, judging whether a non-sequential Monte Carlo method convergence condition is met, if not, returning to the step 2, and if so, calculating a comprehensive risk value;
the historical wind speed statistical data in the step 1 are derived from hour average wind speed data collected by a wind measuring tower of a local wind power plant;
the expression of the two-parameter Weibull distribution in the step 1 is as follows:
Figure FDA0002651168220000011
wherein k is a shape parameter of the two-parameter Weibull distribution and reflects the skewness of the Weibull distribution, and c is a scale parameter of the two-parameter Weibull distribution and reflects the average wind speed in the wind speed distribution model;
further, a probability distribution function of the Weibull distribution can be calculated, and the expression of the probability distribution function is as follows:
Figure FDA0002651168220000012
the shape parameter k and the scale parameter c in the Weibull distribution double-parameter curve are obtained by using a least square estimation method through wind speed historical statistical data of the wind power plant, and the method specifically comprises the following steps:
(1) converting the probability distribution function of the Weibull distribution into a linear form, namely taking the logarithm twice of the probability distribution function (2) of the Weibull distribution, wherein the expression is as follows:
Figure FDA0002651168220000021
(2) dividing the wind speed of a wind farm into n wind speed intervals, [0, v1]，(v1,v2]，…，(vi-1,vi]，…，(vn-1,vn]Counting the frequency f of the wind speed sample value in each wind speed interval1,f2,...,fi,...,fnAnd calculating the cumulative frequency p1,p2,...,pi,...,pnWherein p is1＝f1,pi＝pi-1+fi；
(3) Let xi＝ln vi，yi＝ln[-ln(1-pi)]Then, the shape parameter k and the scale parameter c can be obtained from the following equations (4) and (5):
Figure FDA0002651168220000022
Figure FDA0002651168220000023
step 2, sampling the wind speed according to the probability distribution model of the wind speed of the wind power plant by adopting a non-sequential Monte Carlo method, which comprises the following steps:
2-1. solving the inverse of the probability distribution function of the Weibull distribution
Figure FDA0002651168220000024
Wherein u isiIs [0,1 ]]Uniformly distributed random numbers of the intervals;
2-2, when uiObey [0,1]When the intervals are uniformly distributed, 1-uiAlso obey [0,1]The interval is evenly distributed, and the random sampling value of the wind speed per hour can be obtained as
Figure FDA0002651168220000025
3, establishing a wind speed and wind turbine generator output model of the wind power plant, and accordingly obtaining the corresponding wind turbine generator output according to the wind speed of the wind power plant sampled by a non-sequential Monte Carlo method;
the relationship between the wind power plant wind motor output characteristic and the wind speed is as follows:
Figure FDA0002651168220000026
wherein, PwindFor wind turbine generator output power, PrRated power, v, of a wind turbinetIs the wind speed v of the current wind power plant at the moment tciIndicating the cut-in wind speed, v, of the wind turbinerIndicating rated wind speed, v, of the wind turbinecoThe cut-out wind speed of the wind turbine generator is represented by A, B, C, which are parameters in a wind turbine output characteristic model formula (7-2) of the wind power plant, and the cut-out wind speed can be calculated by the following formula:
Figure FDA0002651168220000031
Figure FDA0002651168220000032
Figure FDA0002651168220000033
and 4, performing state analysis on the system state determined by the primary sampling, and solving the power flow of the hybrid system by using an alternating current-direct current system unified iterative method, wherein the method for solving the power flow of the alternating current-direct current hybrid system by using the unified iterative method specifically comprises the following steps:
4-1, expanding the traditional power flow calculation problem, wherein the direct current voltage, the direct current, the converter voltage converter transformation ratio, the converter power factor and the converter control angle variable in the expanded converter equation, the direct current network equation and the control equation can be respectively written into the following forms:
Figure FDA0002651168220000034
4-2, the load flow calculation correction equation of the alternating current-direct current hybrid system can be written as follows:
Figure FDA0002651168220000035
wherein the column vector Δ PaAnd Δ PtActive power deviation of pure AC and DC nodes, respectively, column vector Δ Qa、Δθa、ΔVaAnd Δ Qt、Δθt、ΔVtRespectively the reactive power, phase angle and node voltage deviation of a pure alternating current node and a direct current node, J is a Jacobian matrix of a load flow calculation equation of an alternating current-direct current hybrid system, and delta d1And Δ d2、Δd3、Δd4And Δ d5Respectively a converter basic equation, a direct current network basic equation and a control equation;
4-3, judging whether the load flow calculation of the alternating current-direct current series-parallel system is converged according to the calculation result of the correction amount, wherein the convergence criterion is as follows:
||Δdi||＜,i＝1,2,...,5
||ΔP||＜
||ΔQ||＜
if the convergence condition is reached, the load flow calculation is finished, otherwise, the next cycle calculation is carried out;
the variance coefficient in the step 5 is beta, if the beta is larger than the given value of the convergence criterion, the step 2 is returned, if the beta is smaller than the given value of the convergence criterion, the iteration is finished, and a risk value is calculated;
the variance coefficient β can be calculated by the following formula:
Figure FDA0002651168220000041
wherein, X is a risk index with the slowest convergence speed, sigma (X) is the standard deviation of X, E (X) is the expected value of X, and NS is the simulation times and is the given value of the convergence criterion;
the comprehensive risk value is comprehensively measured through two risk indexes of voltage out-of-limit and load flow out-of-limit, and is specifically calculated as follows:
(1) voltage out-of-limit risk value calculation
Figure FDA0002651168220000042
Where NS is the total number of state samples, Risk(Ui) Voltage state experimental results for the ith state sampling of the system;
(2) load flow out-of-limit risk value calculation
Figure FDA0002651168220000043
Where NS is the total number of state samples, Risk(Si) Calculating an experimental result for the j-th load flow of state sampling of the system;
(3) integrated risk value
Figure FDA0002651168220000044
Wherein R isk(x) For the outcome value at the kth risk indicator,
Figure FDA0002651168220000045
is the weight of the total risk under the kth risk indicator.
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