CA2864463A1 - Computing method for continuous power flow based on wind power fluctuation rules - Google Patents
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Abstract
A continuous power flow calculation method based on a wind power fluctuation rule comprises: analyzing an active power fluctuation feature of a wind power field by adopting an analytical method of a Poisson process, and establishing a probability model; determining a time interval of wind power output fluctuation and an output variation situation of power of the wind power field according to the probability model and a statistical result of wind power variations, to obtain a fluctuation rule of the wind power output; performing continuous power flow calculation within the same time interval according to the fluctuation rule, to obtain a state variable value; and analyzing the state variable value, and analyzing an influence of large-scale wind photoelectricity access on the power flow, to obtain the influence of wind power on the power flow. The above calculation method has the advantages of wind power fluctuation compatibility and high calculation speed; the method is applied according to actual power grid data, and the correctness and the practicability are proved.
Description
Computing method for continuous power flow based on wind power fluctuation rules Technical field The present invention relates to the field of grid connection techniques of wind photoelectricity, specifically relates to a computing method for continuous power flow based on wind power fluctuation rules.
BackEround Along the adjustment of the energy structure in our country, green energy has attached importance increasingly in recent years. Wind photoelectricity is the renewable green energy; meanwhile, the randomness and intermittent of the wind photoelectricity cannot be adjusted, so new requirements are put forwards to the power flow computation containing wind power generators.
The common computing method for power flow containing wind power generators comprises:
(1) The fan access point is regarded as PQ node (the active power P and reactive power Q of PQ node are given; and node voltage V and phase 6 are the quantities to be obtained); the active power and reactive power of the wind power generator are computed according to the given wind speed and power factor; however, the method does not consider the reactive real-time variation of the fan.
BackEround Along the adjustment of the energy structure in our country, green energy has attached importance increasingly in recent years. Wind photoelectricity is the renewable green energy; meanwhile, the randomness and intermittent of the wind photoelectricity cannot be adjusted, so new requirements are put forwards to the power flow computation containing wind power generators.
The common computing method for power flow containing wind power generators comprises:
(1) The fan access point is regarded as PQ node (the active power P and reactive power Q of PQ node are given; and node voltage V and phase 6 are the quantities to be obtained); the active power and reactive power of the wind power generator are computed according to the given wind speed and power factor; however, the method does not consider the reactive real-time variation of the fan.
(2) The wind power plant adopts PX model which is more improved than other models fully considering the characteristics of output power of the wind power generator. The iterative process in of the PX model is divided into two steps:
iterative computation for normal power flow and slip frequency iterative computation for asynchronous wind power generator; and it has more total iterations and slower convergence rate.
Therefore, it is important to select the power flow computing method considering the fluctuation characteristic of the wind power and the computing speed.
During the process for realizing the invention, the inventor discovered the current technology without considering the fluctuation characteristic of the wind power and the slower computing speed.
Summary of the invention For solving the above problems, the invention aims at providing a computing method for continuous power flow based on wind power fluctuation rules which has the fluctuation characteristic of the wind power and the faster computing speed.
In order to realize the above purpose, the technical solution adopted in the invention is a computing method for continuous power flow based on wind power fluctuation rules which comprises:
a. analyzing the fluctuation characteristics of active power in a wind power plant by the analytical method of Poisson process and setting up a probability model;
b. confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step a and the wind power variation so as to obtain the fluctuation rules of the wind power output;
c. computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step b so as to obtain the state variable value;
and d. analyzing the state variable value obtained in step c and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
Further, in step b, the time interval of the wind power output fluctuation is obtained by the analytical method of Poisson process; and the output variation situation of the power in the wind power plant complies with the normal distribution.
Further, in step b, the operation for confirming the time interval of the wind power output fluctuation comprises confirming the time interval of the power flow computation and the wind power output situation of the power flow computation according to the probability statistics analysis.
Further, in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process comprises the steps of:
al. Obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation PB at the moment t in a probability space [m1 ,m2], selecting a counting process (s) , if S 0, it meets N(0) = 0 The above process is called as the Poisson process and defined as:
(1)N(õ) =0 (2) The increment in the non-intersect area is independent;
iterative computation for normal power flow and slip frequency iterative computation for asynchronous wind power generator; and it has more total iterations and slower convergence rate.
Therefore, it is important to select the power flow computing method considering the fluctuation characteristic of the wind power and the computing speed.
During the process for realizing the invention, the inventor discovered the current technology without considering the fluctuation characteristic of the wind power and the slower computing speed.
Summary of the invention For solving the above problems, the invention aims at providing a computing method for continuous power flow based on wind power fluctuation rules which has the fluctuation characteristic of the wind power and the faster computing speed.
In order to realize the above purpose, the technical solution adopted in the invention is a computing method for continuous power flow based on wind power fluctuation rules which comprises:
a. analyzing the fluctuation characteristics of active power in a wind power plant by the analytical method of Poisson process and setting up a probability model;
b. confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step a and the wind power variation so as to obtain the fluctuation rules of the wind power output;
c. computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step b so as to obtain the state variable value;
and d. analyzing the state variable value obtained in step c and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
Further, in step b, the time interval of the wind power output fluctuation is obtained by the analytical method of Poisson process; and the output variation situation of the power in the wind power plant complies with the normal distribution.
Further, in step b, the operation for confirming the time interval of the wind power output fluctuation comprises confirming the time interval of the power flow computation and the wind power output situation of the power flow computation according to the probability statistics analysis.
Further, in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process comprises the steps of:
al. Obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation PB at the moment t in a probability space [m1 ,m2], selecting a counting process (s) , if S 0, it meets N(0) = 0 The above process is called as the Poisson process and defined as:
(1)N(õ) =0 (2) The increment in the non-intersect area is independent;
(3)s,t 0; when P13 = )9(1+') P(l) given the monthly installed capacity of the whole wind power is PR,P = (P B I N)x 100%; it meets P1N(s +t)¨N(t) = 1c)=
(.1,$)k 1k!
and k = I n ;
a2. The arrival time interval of the Poisson process is the independently distributed random variable, the obtained counting process is called as the updating process, namely, T }Assuming n is a row of independent random variable with same distributed F
and n = 1'2. = = n ;
F(0) = P {Tn = 0} < 1 ; ,u = ET TdF(T) Assuming commanding 1' , it can be know that < from Tn 0, F(0) <1;
And assuming = So , Sn _ ¨ i=1 , the counting process is updated as N(t) = sup{n, St, t} and t Further, in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process further comprises the steps of:
a3. Obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal = e distribution, assumingT [0,GO,t Tthe relative variation p(t) of the power is the normal random process;
If random time frame t and t1 t. E T , At,,) is n-dimensional normal vector; the density function of n-dimensional normal distribution 19(a 'B) is F(x)= ____________ exp[¨ ¨1(x¨ a)B-1 (x ¨ a)11 (2700 21B1. - 2 Wherein B is the positive definite covariance matrix, the relative change rate of the (Põ I Põ)x100% =1(P(,,,)¨ P(0)1 Põ Ix 100%
power in any time frame is z If [m19m21 N(rn) 1 , if not, N(sn) = 0 , the rate at this time is Q = N On) /IN (s") ; in the above formula, PH is the wind power output fluctuation value in any time; PR is monthly installed capacity; An is fluctuation rate;
kni'md is probability interval; and N (sn) is the counting process.
The computing method for continuous power flow based on wind power fluctuation rules in each embodiment of the invention comprises steps of analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model, confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the obtained probability model and the wind power variation so as to obtain the fluctuation rules of the wind power output;
computing the continuous power flow in the same time interval according to the fluctuation rules so as to obtain the state variable value; analyzing the obtained state variable value and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow;
obtaining the detail data of the grid power flow influence of wind power with different capacities according to the probability accessing network by analyzing the fluctuation characteristic and power characteristic of the active power of the wind power, which is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc, thereby the defect of not considering the wind power fluctuation and the slow computing speed in the prior art can be overcame; and the advantages of considering the wind power fluctuation and the fast computing speed are realized.
Other characteristics and advantages of the invention shall be stated in the follow specification; partial characteristics and advantages are obvious from the specification or known by implementing the invention. The purposes and other advantages of the invention can be realized and obtained by the specification, claims and structure special pointed in the attached drawings.
The technical solution of the invention is further described in details by the attached drawings and embodiments.
Brief description of the drawings
(.1,$)k 1k!
and k = I n ;
a2. The arrival time interval of the Poisson process is the independently distributed random variable, the obtained counting process is called as the updating process, namely, T }Assuming n is a row of independent random variable with same distributed F
and n = 1'2. = = n ;
F(0) = P {Tn = 0} < 1 ; ,u = ET TdF(T) Assuming commanding 1' , it can be know that < from Tn 0, F(0) <1;
And assuming = So , Sn _ ¨ i=1 , the counting process is updated as N(t) = sup{n, St, t} and t Further, in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process further comprises the steps of:
a3. Obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal = e distribution, assumingT [0,GO,t Tthe relative variation p(t) of the power is the normal random process;
If random time frame t and t1 t. E T , At,,) is n-dimensional normal vector; the density function of n-dimensional normal distribution 19(a 'B) is F(x)= ____________ exp[¨ ¨1(x¨ a)B-1 (x ¨ a)11 (2700 21B1. - 2 Wherein B is the positive definite covariance matrix, the relative change rate of the (Põ I Põ)x100% =1(P(,,,)¨ P(0)1 Põ Ix 100%
power in any time frame is z If [m19m21 N(rn) 1 , if not, N(sn) = 0 , the rate at this time is Q = N On) /IN (s") ; in the above formula, PH is the wind power output fluctuation value in any time; PR is monthly installed capacity; An is fluctuation rate;
kni'md is probability interval; and N (sn) is the counting process.
The computing method for continuous power flow based on wind power fluctuation rules in each embodiment of the invention comprises steps of analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model, confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the obtained probability model and the wind power variation so as to obtain the fluctuation rules of the wind power output;
computing the continuous power flow in the same time interval according to the fluctuation rules so as to obtain the state variable value; analyzing the obtained state variable value and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow;
obtaining the detail data of the grid power flow influence of wind power with different capacities according to the probability accessing network by analyzing the fluctuation characteristic and power characteristic of the active power of the wind power, which is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc, thereby the defect of not considering the wind power fluctuation and the slow computing speed in the prior art can be overcame; and the advantages of considering the wind power fluctuation and the fast computing speed are realized.
Other characteristics and advantages of the invention shall be stated in the follow specification; partial characteristics and advantages are obvious from the specification or known by implementing the invention. The purposes and other advantages of the invention can be realized and obtained by the specification, claims and structure special pointed in the attached drawings.
The technical solution of the invention is further described in details by the attached drawings and embodiments.
Brief description of the drawings
4 The attached drawings are used for understanding the invention further or forming a part of the specification; the attached drawings and the embodiments are used for explaining the invention and do not limit the invention. In the attached drawings:
FIG.1 is the flow diagram of the computing method for continuous power flow based on wind power fluctuation rules;
FIG.2 is the schematic diagram of probability statistics of the wind power fluctuation rules in some area (probability distribution of power change rate of wind power in a certain domestic wind power plant within 5min).
Specific embodiments The optimized embodiment of the invention is explained by combining the attached drawings; it is understood that the optimized embodiment described here is only used for explaining and stating the invention rather than limiting the invention.
The embodiment of the invention provides a computing method for continuous power flow based on wind power fluctuation rules. As shown in figure 1, the embodiment comprises:
Step 100: analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model;
In the step 100, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process comprises the steps of:
(1). obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation PB at the moment t in a probability space [m, m2], selecting a counting process N ( > 0 i0, if s ¨
, t meets N( ) = 0 =
The above process is called as the Poisson process and defined as:
(1)N(0) =0 (2) The increment in the non-intersect area is independent;
(3)s,t P =
when B ("-') (`) given the monthly installed capacity of the whole wind power is P R P it meets = (pB 1 põ)x100% pIN (s + t) ¨ N (t)= k} = CA' (As)k 1k!
and k = 1 ¨ n ;
(II). the arrival time interval of the Poisson process is the independently distributed
FIG.1 is the flow diagram of the computing method for continuous power flow based on wind power fluctuation rules;
FIG.2 is the schematic diagram of probability statistics of the wind power fluctuation rules in some area (probability distribution of power change rate of wind power in a certain domestic wind power plant within 5min).
Specific embodiments The optimized embodiment of the invention is explained by combining the attached drawings; it is understood that the optimized embodiment described here is only used for explaining and stating the invention rather than limiting the invention.
The embodiment of the invention provides a computing method for continuous power flow based on wind power fluctuation rules. As shown in figure 1, the embodiment comprises:
Step 100: analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model;
In the step 100, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process comprises the steps of:
(1). obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation PB at the moment t in a probability space [m, m2], selecting a counting process N ( > 0 i0, if s ¨
, t meets N( ) = 0 =
The above process is called as the Poisson process and defined as:
(1)N(0) =0 (2) The increment in the non-intersect area is independent;
(3)s,t P =
when B ("-') (`) given the monthly installed capacity of the whole wind power is P R P it meets = (pB 1 põ)x100% pIN (s + t) ¨ N (t)= k} = CA' (As)k 1k!
and k = 1 ¨ n ;
(II). the arrival time interval of the Poisson process is the independently distributed
5 random variable; the obtained counting process is called as the updating process, namely:
IT i Assuming n s a row of independent random variable with same distributed F, n = 1,2== = n Assuming F(0)= P{Tn = 0} <1 p = ET
, commanding n=
TdF (T) , it can be know that 0 </1 Go from Tn 0, F(0) <1 .
And assuming so = o Sn = t=1 , the counting process is updated as N(t) = sup{n, Sn t} and t O.
(III). obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal distribution, assuming T = [0 , co) , t e T the relative variation p(t) of the power is the normal random process;
If random time frame t and ti tn T p(t1) no( ) = is n-dimensional normal vector; the density function of n-dimensional normal distribution p(a,B) is F (x) =-112 exp[- ¨1(x¨ a)B1 (x ¨ a)11 (27r)n/21B1 2 Wherein B is the positive definite covariance matrix; the relative change rate of the 2õ = (P, I Põ) x 100% =[(P(,õ) ¨ P(0)1 Põ ix 100%
power in any time frame is A E [rn M 1N =1 If it 1, 2 ( vti) , if not N (sn) = 0 , the rate at this time is Q = N0,01EN("0 ; in the above formula, pB is the wind power output fluctuation value in any time; PR is monthly installed capacity; /1 ' is fluctuation rate;
[m1 ,m2]
is probability interval; and N (sn) is the counting process.
Step 101: confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step 100 and the wind power variation so as to obtain the fluctuation rules of the wind power output;
In step 101, the time interval of the wind power output fluctuation is obtained by
IT i Assuming n s a row of independent random variable with same distributed F, n = 1,2== = n Assuming F(0)= P{Tn = 0} <1 p = ET
, commanding n=
TdF (T) , it can be know that 0 </1 Go from Tn 0, F(0) <1 .
And assuming so = o Sn = t=1 , the counting process is updated as N(t) = sup{n, Sn t} and t O.
(III). obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal distribution, assuming T = [0 , co) , t e T the relative variation p(t) of the power is the normal random process;
If random time frame t and ti tn T p(t1) no( ) = is n-dimensional normal vector; the density function of n-dimensional normal distribution p(a,B) is F (x) =-112 exp[- ¨1(x¨ a)B1 (x ¨ a)11 (27r)n/21B1 2 Wherein B is the positive definite covariance matrix; the relative change rate of the 2õ = (P, I Põ) x 100% =[(P(,õ) ¨ P(0)1 Põ ix 100%
power in any time frame is A E [rn M 1N =1 If it 1, 2 ( vti) , if not N (sn) = 0 , the rate at this time is Q = N0,01EN("0 ; in the above formula, pB is the wind power output fluctuation value in any time; PR is monthly installed capacity; /1 ' is fluctuation rate;
[m1 ,m2]
is probability interval; and N (sn) is the counting process.
Step 101: confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step 100 and the wind power variation so as to obtain the fluctuation rules of the wind power output;
In step 101, the time interval of the wind power output fluctuation is obtained by
6 the analytical method of Poisson process; and the output variation situation of the power in the wind power plant complies with the normal distribution;
In step 101, the operation for confirming the time interval of the wind power output fluctuation comprises confirming the interval time of the power flow computation and the wind power output situation of the power flow computation according to the probability statistics analysis;
Step 102, computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step 101 so as to obtain the state variable value;
Step 103: analyzing the state variable value obtained in step 102 and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
The computing method for continuous power flow based on wind power fluctuation rules in the above embodiment simulates the influence of the wind power with different capacities on the grid power flow according to the probability access network by analyzing the fluctuation characteristics and power characteristics of the active power of wind power. The method is the interval power flow computation by the probability statistics of wind power fluctuation under the preconditions of not changing the access model of the fan and ensuring the probability; provides more detail data to analyze the fluctuation of the wind power; fully analyzes the voltage and branch power of each node of the wind power plant integration system to provide reference to the integration scheme of the wind power plant.
The computing method for continuous power flow based on wind power fluctuation rules in the above embodiment computes the continuous power flow based on the statistic analysis of the wind power fluctuation rules. Compared with the normal power flow, the computing method considers the fluctuation characteristics of the wind power and the power flow computing speed; meanwhile, the method has the advantages of simple computation, easy realization, convenience for interfacing with general power flow program and a certain practicability; and the computing method is beneficial to increasing the accuracy and practicability of the grid power flow computation comprising large-scale wind photoelectricity.
For example, the probability distribution of power change rate of wind power in a certain domestic wind power plant within 5min is shown in FIG.2; the power flow is computed by taking 5min as the time interval and progressive manner of 1% of the wind power fluctuation capacity; and the results are as shown below:
In step 101, the operation for confirming the time interval of the wind power output fluctuation comprises confirming the interval time of the power flow computation and the wind power output situation of the power flow computation according to the probability statistics analysis;
Step 102, computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step 101 so as to obtain the state variable value;
Step 103: analyzing the state variable value obtained in step 102 and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
The computing method for continuous power flow based on wind power fluctuation rules in the above embodiment simulates the influence of the wind power with different capacities on the grid power flow according to the probability access network by analyzing the fluctuation characteristics and power characteristics of the active power of wind power. The method is the interval power flow computation by the probability statistics of wind power fluctuation under the preconditions of not changing the access model of the fan and ensuring the probability; provides more detail data to analyze the fluctuation of the wind power; fully analyzes the voltage and branch power of each node of the wind power plant integration system to provide reference to the integration scheme of the wind power plant.
The computing method for continuous power flow based on wind power fluctuation rules in the above embodiment computes the continuous power flow based on the statistic analysis of the wind power fluctuation rules. Compared with the normal power flow, the computing method considers the fluctuation characteristics of the wind power and the power flow computing speed; meanwhile, the method has the advantages of simple computation, easy realization, convenience for interfacing with general power flow program and a certain practicability; and the computing method is beneficial to increasing the accuracy and practicability of the grid power flow computation comprising large-scale wind photoelectricity.
For example, the probability distribution of power change rate of wind power in a certain domestic wind power plant within 5min is shown in FIG.2; the power flow is computed by taking 5min as the time interval and progressive manner of 1% of the wind power fluctuation capacity; and the results are as shown below:
7 Time Output level of wind power Voltage of access point of plant group wind power plant group 0 61% 760.507kV
5min 62% 740.99kV
10min 63% 727.168kV
It can be seen that the wind power fluctuation has great influence on the voltage of the access point; it is considered that the voltage of the access point is a fixed value at this time in the traditional power flow computation, so the method reflects the influence of the wind power fluctuation on the grid accurately and helps the safe and steady operation of the grid.
The actual grid data of some province can be shown in figure 2; the example is counted by the wind power fluctuation probability obtained by the method of the above embodiment; the power flow is computed according to the statistical result and the same time interval so as to obtain the detail influence of the wind power fluctuation on the grid.
It can be known from the instance analysis shown in figure 2 that the computing method for continuous power flow based on wind power fluctuation rules in the above embodiment can overcome the defects of not considering the reactive real-time variation of fan, more algorithm iterations and slow convergence rate in the traditional method, obtain the influence of the wind power output fluctuation on the access point of the wind power plant group and the regional grid voltage; it is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc; and it has reference value to guide the generation schedule of large-scale wind power network operation.
In conclusion, the computing method for continuous power flow based on wind power fluctuation rules in the each embodiment of the invention relates to the field of the wind photoelectricity grid connection techniques; and the method comprises the steps of analyzing the fluctuation characteristics of the wind power plant by adopting the
5min 62% 740.99kV
10min 63% 727.168kV
It can be seen that the wind power fluctuation has great influence on the voltage of the access point; it is considered that the voltage of the access point is a fixed value at this time in the traditional power flow computation, so the method reflects the influence of the wind power fluctuation on the grid accurately and helps the safe and steady operation of the grid.
The actual grid data of some province can be shown in figure 2; the example is counted by the wind power fluctuation probability obtained by the method of the above embodiment; the power flow is computed according to the statistical result and the same time interval so as to obtain the detail influence of the wind power fluctuation on the grid.
It can be known from the instance analysis shown in figure 2 that the computing method for continuous power flow based on wind power fluctuation rules in the above embodiment can overcome the defects of not considering the reactive real-time variation of fan, more algorithm iterations and slow convergence rate in the traditional method, obtain the influence of the wind power output fluctuation on the access point of the wind power plant group and the regional grid voltage; it is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc; and it has reference value to guide the generation schedule of large-scale wind power network operation.
In conclusion, the computing method for continuous power flow based on wind power fluctuation rules in the each embodiment of the invention relates to the field of the wind photoelectricity grid connection techniques; and the method comprises the steps of analyzing the fluctuation characteristics of the wind power plant by adopting the
8 analytical method of Poisson process so as to set up a probability model, confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the model and the statistical result so as to obtain the fluctuation rules of the wind power output; computing the continuous power flow according to the fluctuation rules so as to obtain the state variable value;
analyzing the obtained state variable value and the influence of large-scale wind photoelectricity access on the power flow; the detail data of the grid power flow influence of wind power with different capacities according to the probability accessing network can be obtained by analyzing the fluctuation characteristic and power characteristic of the active power of the wind power; it is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc.
What to be explained finally is that the above embodiment is the optimized embodiment of the invention rather than limiting the invention. The invention is explained in details by referring to the above embodiment; the technician in the field can modify the technical solution recorded in the above embodiment or replace partial technical characteristics equally. Any modification, equal replacement, improvement and the like in the spirit and principle of the invention should be contained in the protection scope of the invention.
analyzing the obtained state variable value and the influence of large-scale wind photoelectricity access on the power flow; the detail data of the grid power flow influence of wind power with different capacities according to the probability accessing network can be obtained by analyzing the fluctuation characteristic and power characteristic of the active power of the wind power; it is of great importance to reduce the voltage fluctuation of the system caused by the wind power online, improve the system stability margin and voltage level of wind power integration point, reduce transmission losses, etc.
What to be explained finally is that the above embodiment is the optimized embodiment of the invention rather than limiting the invention. The invention is explained in details by referring to the above embodiment; the technician in the field can modify the technical solution recorded in the above embodiment or replace partial technical characteristics equally. Any modification, equal replacement, improvement and the like in the spirit and principle of the invention should be contained in the protection scope of the invention.
9
Claims (5)
1. A computing method for continuous power flow based on wind power fluctuation rules, comprising the steps of:
a. analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model;
b. confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step a and the wind power variation so as to obtain the fluctuation rules of the wind power output;
c. computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step b so as to obtain the state variable value;
and d. analyzing the state variable value obtained in step c and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
a. analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process so as to set up a probability model;
b. confirming the time interval of wind power output fluctuation and the output variation situation of the wind power plant power according to the statistical result of the probability model obtained in step a and the wind power variation so as to obtain the fluctuation rules of the wind power output;
c. computing the continuous power flow in the same time interval according to the fluctuation rules obtained in step b so as to obtain the state variable value;
and d. analyzing the state variable value obtained in step c and the influence of large-scale wind photoelectricity access on the power flow so as to obtain the influence of the wind power on the power flow.
2. The computing method for continuous power flow based on wind power fluctuation rules according to claim 1, wherein in step b, the time interval of the wind power output fluctuation is obtained by the analytical method of Poisson process; and the output variation situation of the power in the wind power plant complies with the normal distribution.
3. The computing method for continuous power flow based on wind power fluctuation rules according to claim 1, wherein in step b, the operation for confirming the time interval of the wind power output fluctuation comprises confirming the interval time of the power flow computation and the wind power output situation then according to the probability statistics analysis.
4. The computing method for continuous power flow based on wind power fluctuation rules according to any one of claims 1-3, wherein in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process comprises the steps of:
a1. Obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation P B at the moment t in a probability space [m1,m2], selecting a counting process N(s), if s >= 0, it meets N(0) = 0 ;
The above process is called as the Poisson process and defined as:
(1) N(0) = 0 ;
(2) The increment in the non-intersect area is independent;
(3) s,t >= 0, when P B = P(t+s) - P(t), the monthly installed capacity of the whole wind power is P R, p = (p B / p R)× 100%, it meets p{N(s + t)- N(t)= k}
= e-.lambda.s (.lambda.s)k / k!
and k =1 ~ n ;
a2. The arrival time interval of the Poisson process is the independently distributed random variable; the obtained counting process is called as the updating process, namely:
Assuming {T n} is a row of independent random variable with same distributed F
, n = 1,2.multidot.n ;
Assuming F(0) = P{Tn = 0} <1, commanding µ = ET n .intg.~ TdF (T) it can be know that 0 < µ <= .infin. from T n >= 0, F(0) < 1 ; and Assuming S0 = 0 , Sn = the counting process is updated as N(t) = sup{n, S n <= t} and if t >= 0.
a1. Obtaining the fluctuation characteristics of wind power based on the time interval of the power fluctuation in the wind power plant, namely:
Observing the occurrence times of power fluctuation P B at the moment t in a probability space [m1,m2], selecting a counting process N(s), if s >= 0, it meets N(0) = 0 ;
The above process is called as the Poisson process and defined as:
(1) N(0) = 0 ;
(2) The increment in the non-intersect area is independent;
(3) s,t >= 0, when P B = P(t+s) - P(t), the monthly installed capacity of the whole wind power is P R, p = (p B / p R)× 100%, it meets p{N(s + t)- N(t)= k}
= e-.lambda.s (.lambda.s)k / k!
and k =1 ~ n ;
a2. The arrival time interval of the Poisson process is the independently distributed random variable; the obtained counting process is called as the updating process, namely:
Assuming {T n} is a row of independent random variable with same distributed F
, n = 1,2.multidot.n ;
Assuming F(0) = P{Tn = 0} <1, commanding µ = ET n .intg.~ TdF (T) it can be know that 0 < µ <= .infin. from T n >= 0, F(0) < 1 ; and Assuming S0 = 0 , Sn = the counting process is updated as N(t) = sup{n, S n <= t} and if t >= 0.
5. The computing method for continuous power flow based on wind power fluctuation rules according to claim 4, wherein in step a, the operation for analyzing the fluctuation characteristics of active power in a wind power plant by adopting the analytical method of Poisson process further comprises the steps of:
a3. Obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal distribution, assuming T = [0,.infin.),t .EPSILON. T, the relative variation p(t) of the power is the normal random process;
If random time frame t and t1 ~ t n .EPSILON. T, p(t1) ... p(t n ) is n-dimensional normal vector; the density function of n-dimensional normal distribution p(a, B) is Wherein B is the positive definite covariance matrix; the relative change rate of the .lambda. n= (P B/ P R)×100% =[(P (t+s) - P (t))/ P R] ×100%
power in any time frame is If .lambda.n .epsilon.[m1,m2],N(sn) = 1, if not, N(sn)= 0 , the rate at this time is Q = N (sn) / .SIGMA. N (SN) In the above formula, P B is the wind power output fluctuation value in any time;
P R is monthly installed capacity; .lambda. n is fluctuation rate; [m1,m2] is probability interval; and N(sn) is the counting process.
a3. Obtaining the output variable situation of wind power plant, namely:
The relative variation of the action power of the wind power is in normal distribution, assuming T = [0,.infin.),t .EPSILON. T, the relative variation p(t) of the power is the normal random process;
If random time frame t and t1 ~ t n .EPSILON. T, p(t1) ... p(t n ) is n-dimensional normal vector; the density function of n-dimensional normal distribution p(a, B) is Wherein B is the positive definite covariance matrix; the relative change rate of the .lambda. n= (P B/ P R)×100% =[(P (t+s) - P (t))/ P R] ×100%
power in any time frame is If .lambda.n .epsilon.[m1,m2],N(sn) = 1, if not, N(sn)= 0 , the rate at this time is Q = N (sn) / .SIGMA. N (SN) In the above formula, P B is the wind power output fluctuation value in any time;
P R is monthly installed capacity; .lambda. n is fluctuation rate; [m1,m2] is probability interval; and N(sn) is the counting process.
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| CN102682222B (en) * | 2012-05-23 | 2014-03-12 | 甘肃省电力公司电力科学研究院 | Continuous power flow calculation method based on wind power fluctuation rule |
| CN104537485B (en) * | 2014-12-23 | 2018-01-26 | 国网北京经济技术研究院 | The modeling method of newly-built Large Scale Wind Farm Integration output wave characteristic and application |
| CN104574216A (en) * | 2015-01-22 | 2015-04-29 | 国家电网公司 | Wind power output characteristic analysis method based on WAMS data |
| CN104636631B (en) * | 2015-03-09 | 2018-06-05 | 江苏中康软件有限责任公司 | A kind of device using diabetes system big data prediction diabetes |
| CN104933301A (en) * | 2015-06-05 | 2015-09-23 | 三峡大学 | Calculation method for calculating available capacity of wind power plant |
| CN104933315B (en) * | 2015-06-30 | 2018-05-08 | 中国电力科学研究院 | A kind of method of photovoltaic generation output joint probability distribution modeling |
| CN106709221B (en) * | 2015-07-27 | 2021-01-15 | 中国电力科学研究院有限公司 | Probability statistics-based new energy output discrete probability sequence generation method |
| CN105699806B (en) * | 2016-01-26 | 2018-06-29 | 云南电网有限责任公司电力科学研究院 | A kind of multi-source harmonic contributions division methods |
| CN106056305B (en) * | 2016-06-14 | 2020-04-03 | 中国电力工程顾问集团中南电力设计院有限公司 | Power generation system reliability rapid evaluation method based on state clustering |
| CN110518865B (en) * | 2019-08-09 | 2021-07-20 | 华南理工大学 | Industrial-scale stable hydrogen supply system based on wind power-photoelectric coupling |
| CN110601204B (en) * | 2019-10-14 | 2024-02-02 | 国网辽宁省电力有限公司盘锦供电公司 | Random variable state time sequence simulation-based probability power flow analysis method for photovoltaic grid-connected system |
| CN110909308B (en) * | 2019-11-08 | 2023-10-13 | 广东电网有限责任公司电力调度控制中心 | Monitoring and analyzing method and system for new energy plant station |
| CN114880624B (en) * | 2021-11-12 | 2024-09-27 | 中国电力科学研究院有限公司 | Method and system for generating wind power random time sequence output sequence meeting autocorrelation |
| CN113904338A (en) * | 2021-11-19 | 2022-01-07 | 广东电网有限责任公司 | Wind power grid-connected system and frequency characteristic probability load flow calculation method and system |
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| CN102136036B (en) * | 2011-03-23 | 2013-01-16 | 天津大学 | Double-feed wind power station equivalent modeling method applied to analysis on small signal stability of power system |
| CN102682222B (en) * | 2012-05-23 | 2014-03-12 | 甘肃省电力公司电力科学研究院 | Continuous power flow calculation method based on wind power fluctuation rule |
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