CN112001531A - Wind power short-term operation capacity credibility evaluation method based on effective load capacity - Google Patents

Wind power short-term operation capacity credibility evaluation method based on effective load capacity Download PDF

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CN112001531A
CN112001531A CN202010769779.9A CN202010769779A CN112001531A CN 112001531 A CN112001531 A CN 112001531A CN 202010769779 A CN202010769779 A CN 202010769779A CN 112001531 A CN112001531 A CN 112001531A
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陈光宇
王泽宇
张仰飞
郝思鹏
刘海涛
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Abstract

The invention discloses a wind power short-term operation capacity credibility assessment method based on effective load capacity, which comprises the following steps: acquiring wind speed data, predicting the wind speed of the next time period by using ARMA, and generating a predicted wind power output time sequence; acquiring load data and inputting a combination result of the conventional unit at each time interval; only considering the conventional units, calculating the load loss probability LOLP of the system; adding the wind turbine generator and then solving the system load loss probability LOLP again; and iteratively solving the short-term wind power running capacity reliability SOCC through a hybrid search algorithm. The scheme provides wind power short-term operation capacity reliability definition based on effective load capacity, the provided evaluation method considers the real-time operation state of the system and reflects the influence of the change of real-time operation conditions such as wind power output, load use and the like on the reliability, the provided evaluation method fully considers the real-time operation state of the system, and the wind power short-term operation credible capacity in different time periods can reflect the contribution degree of the wind power output to the reliability of the system.

Description

Wind power short-term operation capacity credibility evaluation method based on effective load capacity
Technical Field
The invention relates to a wind power short-term operation capacity credibility assessment method based on effective load capacity, and belongs to wind power capacity credibility assessment technologies.
Background
Intermittent energy represented by wind power is accessed on a large scale in the global range, so that the operation mode of a power system is greatly changed. The introduction of wind power with uncertainty and intermittency on the power generation side brings great challenges to the power system in maintaining the balance between the power generation amount and the load demand. The output of the wind turbine generator has the characteristic of intermittence, so that the load carrying capacity of the wind turbine generator with the same capacity is different from that of a conventional wind turbine generator. Therefore, in the adequacy measurement and analysis of the power system, the power department treats the wind power unit and the conventional unit differently.
The existing research on the reliability of the wind power capacity mainly focuses on a planning state and is not suitable for the field of power dispatching. The wind power capacity credibility in the planning state cannot be directly applied to the scheduling operation work, otherwise, the load distribution is improper, the standby is excessively arranged, the requirement of economic operation is contradictory, and the serious wind abandon phenomenon is caused. The existing wind power short-term operation capacity credibility assessment method omits the process of reliability index iterative solution, so that the assessment method is lack of accuracy.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a wind power short-term operation capacity credibility assessment method based on effective load capacity, and on the basis of the effective load capacity, the definition of the wind power short-term operation capacity credibility SOCC is provided; meanwhile, on the basis of the existing wind power prediction method, ARMA is used for predicting the wind speed and generating a wind power output time sequence; respectively solving the system load loss probability LOLP before and after the wind turbine generator is added by using an ACD method; and iteratively solving the SOCC of the short-term operation capacity of the wind power through a hybrid search algorithm.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a wind power short-term operation capacity credibility assessment method based on effective load capacity comprises the following steps:
s1, acquiring wind speed data, and predicting a wind speed time series V by using an ARMA modelt；
S2, converting the wind speed into a time sequence VtConverted into wind power output time sequence Pwt；
S3, acquiring a combination result of the load data and the thermal power generating units in each time period;
s4, only considering the initial system consisting of K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD methodt,K；
S5, adding M wind power generator sets into the initial system, and calculating the load loss probability LOLP of the new system by using an ACD methodt,M+K；
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithmW(t)；
S7, wind power short-term operation based credible capacity CW(t) solving short-term wind power operation capacity reliability SOCCt。
Preferably, in step S1, wind speed data is acquired, and the time series V of wind speeds is predicted using the ARMA modeltThe method comprises the following steps:
s11, preprocessing the wind speed data to obtain a time sequence { x }tFirst calculate { x }tH autocorrelation function ofkObserving whether the temperature rapidly decays to the vicinity of 0; if { xtJudging again after first-order difference processing if the stationarity requirement is not met; if the stationarity requirement can not be satisfied after twice difference, then { xtARMA cannot be used to predict wind speed;
s12, identifying and grading the model according to the { xtH autocorrelation coefficient ρ ofkSum partial autocorrelation coefficient
Figure BDA0002616158140000021
Selecting a proper model, and selecting a proper model order by using an AIC (automatic air interface) criterion;
the autocorrelation function (ACF) is defined as:
Figure BDA0002616158140000022
Figure BDA0002616158140000023
Figure BDA0002616158140000024
Figure BDA0002616158140000025
wherein: gamma raykIs covariance, gamma0And σx 2Is the variance, μxTaking the mean value, k is a lag order (positive integer), and n is the number of observed values (the number of observed values in the sequence);
the Partial Autocorrelation Coefficient (PACF) is defined as:
Figure BDA0002616158140000026
Figure BDA0002616158140000027
the AIC criterion is defined as:
Figure BDA0002616158140000031
wherein: n is the number of observed values; p and q are the order of the model; sigmaaIs the variance of the residual error when fitting the model; taking the order and the parameter corresponding to the minimum AIC value as the optimal order and parameter of the ARMA model;
s13, carrying out model parameter estimation by adopting maximum likelihood estimation, and calling an Estimate function in an MATLAB to realize a parameter estimation function;
s14, using DW Statistic (Durbin-Watson static) to test whether the model has first-order correlation, if the DW function value is close to 2, determining that the first-order correlation does not exist;
s15 prediction wind speed time series VtThe autoregressive moving average model ARMA mathematical expression is as follows:
xt＝a1xt-1+a2xt-2+…+apxt-p+t-b1 t-1-b2 t-2-…-bq t-q
t＝1,2,…,N
wherein:tis a white noise sequence; a is1,a2,…,apIs an autoregressive coefficient; b1,b2,…,bqIs a moving average coefficient; p and q are orders of the model; { xtIs the current time sequence value.
Preferably, in the step S2, the wind speed time series VtTime sequence P of wind power outputwtThe relationship between them is as follows:
Figure BDA0002616158140000032
wherein: vci、Vco、VrRespectively the cut-in wind speed, the cut-out wind speed and the rated wind speed of the wind turbine generator; vtThe wind speed at the current moment; A. b, C relates to VciAnd VrA function of (a); prThe power is output for the limit of the wind turbine; A. b, C is as follows:
Figure BDA0002616158140000033
Figure BDA0002616158140000034
Figure BDA0002616158140000035
preferably, in step S3, the load data is represented by a probability pulse of a time-series load; let the study period be T hours and the load level at T hours be LtT is 1,2, …, T, the probability of the occurrence of the load level at the tth hour is
Figure BDA0002616158140000041
(the purpose of this step is to simplify the solution of the reliability index in the ACD method), the load data during the study period is denoted as L ═ { L ═ L1,L2,…,Lt,…,LT}。
Preferably, in step S4, for the initial system, the load loss probability lopp of the initial system is calculated by using the ACD methodt,kThe method comprises the following steps:
s41, the effective capacity distribution of the thermal power generating unit i is represented as:
Figure BDA0002616158140000042
wherein: i-1, 2, …, K, CiIs the installed capacity of the thermal power generating unit i,
Figure BDA0002616158140000043
is the effective capacity distribution, P, of the thermal power generating unit iFOR,iThe probability of the random shutdown of the thermal power generating unit i is obtained;
s42 and installed capacity C of thermal power generating unit iiFor discrete random variables, a discrete random variable C is definediThe v-order moment of (a) is:
Figure BDA0002616158140000044
pi＝1-PFOR,i
wherein: p is a radical ofiIs the normal operation probability of the thermal power generating unit i,
Figure BDA0002616158140000045
v-moment, alpha, of installed capacity of thermal power generating unit ivThe variable-capacity thermal power generating unit is a v-order moment of the effective capacity of the thermal power generating unit, and v is a positive integer;
s43, converting each order moment into each order central moment:
Figure BDA0002616158140000046
Figure BDA0002616158140000047
wherein: mvThe v-order central moment of the effective capacity of the thermal power generating unit,
Figure BDA0002616158140000048
taking n permutation and combination for v;
s44, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure BDA0002616158140000051
wherein: kvThe accumulated quantity of the v order of the effective capacity of the thermal power generating unit is obtained;
and S45, the accumulated quantity of each order of the equivalent effective capacity of the K thermal power generating units is represented as:
Figure BDA0002616158140000052
wherein: KSvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, Ki,vThe accumulated quantity of the v order of the thermal power generating unit i is obtained;
s46, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after K thermal power generating units are loadedK(x) Represents:
Figure BDA0002616158140000053
Figure BDA0002616158140000054
Figure BDA0002616158140000055
Figure BDA0002616158140000056
wherein: fK(x) The probability that the generating capacity provided for the K thermal power generating units after being loaded is less than x, N (x) is a standard normal density function, and N(γ)(x) Is the gamma-order derivative of N (x), gvNormalizing the cumulative quantity for order v (the term is introduced for the purpose of simplifying the order number form), σ is the standard deviation;
s47, load level L for t hourtUsing FK(Lt) Indicating that the power generation capacity at the t-th hour is less than LtTo obtain the load loss probability LOLP of the t hourt,KAnd FK(Lt) Is LOLPt,K＝FK(Lt)。
Preferably, in step S5, M wind turbine generators are added to the initial system, and the load loss probability low of the new system is calculated by using the ACD methodt,m+kThe method comprises the following steps:
s51, the effective capacity distribution of the wind turbine generator j is represented as:
Figure BDA0002616158140000061
wherein: j is 1,2, …, M, PW,j(t) is the output of the wind turbine j at the moment t,
Figure BDA0002616158140000062
for the effective capacity distribution, P, of the wind turbine jFOR,WjThe forced outage probability of the wind turbine generator j;
s52 output power P of wind turbine generator jW,jFor discrete random variables, a discrete random variable P is definedW,jThe v-order moment of (a) is:
Figure BDA0002616158140000063
pWj＝1-PFOR,Wj
wherein: p is a radical ofWjIs the normal operation probability, P, of the wind turbine generator jW,jIs the output of the wind turbine generator j, betavThe variable-pitch wind turbine generator is a v-order moment of the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order central moment, wherein the formula is as follows:
Figure BDA0002616158140000064
Figure BDA0002616158140000065
wherein: mWvThe v-order central moment of the effective capacity of the wind turbine,
Figure BDA0002616158140000066
taking n permutation and combination for v;
s54, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure BDA0002616158140000067
wherein: kWvThe cumulative quantity of the v order of the effective capacity of the wind turbine generator is obtained;
the accumulated quantity of each order of equivalent effective capacity of S55, M wind power generating units and K thermal power generating units is represented as:
Figure BDA0002616158140000071
wherein: KWvIs the v-order cumulant, KS, of equivalent effective capacities of the M wind power generating units and the K thermal power generating unitsvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, KWj,vThe v-order cumulant of the wind turbine generator j is obtained;
s56, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after the M wind power units and the K thermal power units are loadedM+K(x) Represents:
Figure BDA0002616158140000072
Figure BDA0002616158140000073
Figure BDA0002616158140000074
Figure BDA0002616158140000075
wherein: fM+K(x) The probability that the power generation capacity provided for the M wind power generating units and the K thermal power generating units after loading is less than x; n (x) is a standard normal density function; n is a radical of(γ)(x) Is the gamma derivative of N (x); gWvNormalizing the cumulative quantity for order v (the term is introduced to simplify the form of the series), σ being the standard deviation;
s57, load level L for t hourtUsing FM+K(Lt) Indicating that the power generation capacity at the t-th hour is less than LtTo obtain the load loss probability LOLP of the t hourt,M+KAnd FM+K(Lt) Is/are as followsThe relationship is LOLPt,M+K＝FM+K(Lt)。
Preferably, in step S6, the wind power short-term operation credible capacity C is solvedW(t) comprises the steps of:
s61, calculating the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units
Figure BDA0002616158140000078
And FM+K(x) The following relationships exist:
Figure BDA0002616158140000076
wherein:
Figure BDA0002616158140000077
the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s62, calculating the intermediate value of the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units
Figure BDA0002616158140000081
And FM+K(x) The following relationships exist:
Figure BDA0002616158140000082
wherein:
Figure BDA0002616158140000083
is the middle value of the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s63, assigning the load loss probability data of the t hour:
R0(t)＝LOLPt,K
RW(t)＝LOLPt,M+K
Figure BDA0002616158140000084
Figure BDA0002616158140000085
s64, by bringing in different Δ LtIterative solution of Rq(t); let the number of iterations q equal to 1, compare R0(t) and RmidMagnitude of (t) value: if the absolute value of the difference is greater than 5, i.e. | R0(t)-Rmid(t) | equal to or greater than 5, then proceed to step S65; otherwise, go to step S66; wherein, for the precision requirement of iterative solution, the delta L is the total installed capacity added into the initial system and is CWThe wind turbine generator can bear more load;
s65, solving the target value R by using a chord cutting methodq(t), if | Rq(t)-R0(t)|＞|Rq-1(t)-R0(t) |, which indicates that the oscillation phenomenon occurs at the qth iteration, the process proceeds to step S66; otherwise, q is q +1, the step S65 is repeated, and the intercept method is continuously used to iteratively solve the target value Rq(t)；
S66, solving the target value R by using a dichotomyq(t) wherein Rq(t) the reliability index solved by the qth iteration in the tth hour is obtained, and the step S67 is performed;
s67, if | Rq(t)-R0(t) | ≧ q ═ q +1, repeat step S66, continue to use dichotomy to iteratively solve target value Rq(t); otherwise, go to step S68;
s68, taking the slave load side as an entry point, calculating the ratio of the load amount capable of being loaded more to the installed capacity of the wind power under the condition that the reliability level of the system is kept unchanged before and after the wind turbine generator is accessed:
R(Cg,L)＝R(Cg+CW,L+ΔL)
CW(t)＝ΔLt
wherein: cW(t) the short-term operation credible capacity of the wind power in the tth hour; Δ LtAdding total installed capacity C to initial systemWThe load capacity that the wind turbine generator can bear more in the tth hour, CgThe total installed capacity of the initial system (the total installed capacity of the thermal power generating unit), R (C)gL) is the reliability level of the initial system when facing the load L, R (C)g+CWL + Δ L) is the reliability level of the new system facing the load L + Δ L after the wind turbine set was added to the original system.
Preferably, in step S7, the wind power short-term operation capacity reliability SOCCtSolving according to the following formula:
Figure BDA0002616158140000091
wherein: SOCCtReliability of short-term wind power operation capacity in the tth hour, CWIs the total installed capacity, Delta L, of the wind turbinetAdding total installed capacity C to initial systemWThe load capacity which can be borne by the wind turbine generator in the last t hour.
Has the advantages that: the wind power short-term operation capacity credibility assessment method based on the effective load capacity can perform reliability assessment on the premise of considering the real-time operation state of the system, uses a hybrid search algorithm for iterative solution of the wind power short-term operation credibility capacity, can well avoid the influence of chord-intercept method search oscillation on the iteration times, and can accelerate the solution speed and precision; according to the method, the reliability analysis is carried out according to the real-time running state of the system, and the influence of the wind power output time sequence characteristics and the uncertainty thereof on the reliability of the short-term running capacity of the wind power can be well reflected; and the wind power short-term operation credible capacity at different time intervals can reflect the contribution degree of the wind power output to the system reliability.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram illustrating the geometry of the trusted capacity based on payload capacity in the present invention;
FIG. 3 is a flow chart of a hybrid search algorithm of the present invention;
FIG. 4 is a comparison graph of reliability indexes before and after wind power access;
FIG. 5 is a comparison graph of SOCC and wind power short-term operation credible capacity in each time period.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1 to 3, a method for evaluating reliability of short-term operation capacity of wind power based on payload capacity includes the following steps:
s1, acquiring wind speed data, and predicting a wind speed time series V by using an ARMA modelt。
S11, when the ARMA model carries out data prediction, the time sequence of the sample data needs to meet the requirement of stationarity; stationarity is the requirement that a fitted curve obtained through a sample time sequence can continue along the existing form inertially in the future, namely the mean value and the variance of the fitted curve are required not to generate obvious changes; therefore, before wind speed prediction modeling, whether an original wind speed sequence is a stable random sequence needs to be detected; preprocessing the wind speed data to obtain a time sequence { x }tFirst calculate { x }tH autocorrelation function ofkObserving whether the temperature rapidly decays to the vicinity of 0; if { xtJudging again after first-order difference processing if the stationarity requirement is not met; if the stationarity requirement can not be satisfied after twice difference, then { xtARMA cannot be used to predict wind speed;
s12, identifying and grading the model according to the { xtH autocorrelation coefficient ρ ofkSum partial autocorrelation coefficient
Figure BDA0002616158140000101
Selecting a proper model; in the actual data processing and analyzing process, the values of the orders p and q of the ARMA model are not unique, and if the ARMA model is artificially estimated, errors are inevitably generated, so that the AIC criterion is used for selecting a proper model order:
the autocorrelation function (ACF) is defined as:
Figure BDA0002616158140000102
Figure BDA0002616158140000103
Figure BDA0002616158140000104
Figure BDA0002616158140000105
wherein: gamma raykIs covariance, gamma0And σx 2Is the variance, μxTaking the mean value as k, the lag order as k, and the number of observed values as n;
the Partial Autocorrelation Coefficient (PACF) is defined as:
Figure BDA0002616158140000106
Figure BDA0002616158140000107
selecting a proper model order by using an Akaichi information criterion (AIC criterion), wherein the criterion determines the model order by using the principle that the likelihood function estimation value is maximum; the AIC criterion is defined as:
Figure BDA0002616158140000111
wherein: n is the number of observed values; p and q are the order of the model; sigmaaIs the variance of the residual error when fitting the model; taking the order and the parameter corresponding to the minimum AIC value as the optimal order and parameter of the ARMA model;
s13, determining the structure and the order of the model, and building an ARMA model, and then estimating the parameters of the model; the common parameter estimation method comprises moment estimation, maximum likelihood estimation and least square estimation, wherein the moment estimation only uses p + q sample information, so that the information redundancy is excessive, and the estimation precision is poor; according to the scheme, model parameter estimation is carried out by adopting maximum likelihood estimation, and an Estimate function is called in an MATLAB to realize a parameter estimation function;
s14, the common ARMA model checking method comprises a stable reversibility check, an overfitting check and a residual error analysis check, some documents adopt the residual error analysis check, the method checks whether the model residual error has certain randomness, and if the model residual error does not have randomness, the selected model needs to be modified; in the scheme, DW statistics (Durbin-Watson static) are used for detecting whether a model has first-order correlation, and if the output DW function value is close to 2, the first-order correlation is not considered to exist;
s15 prediction wind speed time series VtThe autoregressive moving average model ARMA mathematical expression is as follows:
xt＝a1xt-1+a2xt-2+…+apxt-p+t-b1 t-1-b2 t-2-…-bq t-q
t＝1,2,…,N
wherein:tis a white noise sequence; a is1,a2,…,apIs an autoregressive coefficient; b1,b2,…,bqIs a moving average coefficient; p and q are orders of the model; { xtIs the current time sequence value.
S2, converting the wind speed into a time sequence VtConverted into wind power output time sequence Pwt。
Predicting the wind speed V at the t moment according to the ARMA modeltThen, the wind power output power at the time t can be calculated; since the wind speed uncertainty is included in the ARMA model using the wind power prediction method described above, the wind speed V is determined at a certain time ttIs relatively definite, wind speed time series VtWith the windElectrical output time series PwtThe relationship between them is as follows:
Figure BDA0002616158140000112
wherein: vci、Vco、VrRespectively the cut-in wind speed, the cut-out wind speed and the rated wind speed of the wind turbine generator; vtThe wind speed at the current moment; A. b, C relates to VciAnd VrA function of (a); prThe power is output for the limit of the wind turbine; A. b, C is as follows:
Figure BDA0002616158140000121
Figure BDA0002616158140000122
Figure BDA0002616158140000123
and S3, acquiring the load data and the combined result of the thermal power generating units in each time period.
The load data is represented by probability pulses of time sequence loads; let the study period be T hours and the load level at T hours be LtT is 1,2, …, T, the probability of the occurrence of the load level at the tth hour is
Figure BDA0002616158140000124
(the purpose of this step is to simplify the solution of the reliability index in the ACD method), the load data during the study period is denoted as L ═ { L ═ L1,L2,…,Lt,…,LT}。
S4, only considering the initial system consisting of K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD methodt,K。
S41, representing the thermal power generating unit by a common two-state model, wherein for the thermal power generating unit i, the model includesThe effective capacity distribution is expressed as
Figure BDA0002616158140000125
The thermal power generating units are sequenced according to the power generation cost and are sequentially loaded, so that the effective capacity of the front K thermal power generating units
Figure BDA0002616158140000126
And
Figure BDA0002616158140000127
the relationship is as follows:
Figure BDA0002616158140000128
Figure BDA0002616158140000129
wherein: i-1, 2, …, K, CiIs the installed capacity of the thermal power generating unit i,
Figure BDA00026161581400001210
is the effective capacity distribution, P, of the thermal power generating unit iFOR,iThe probability of the random shutdown of the thermal power generating unit i is obtained;
s42, along with the enlargement of the scale of the power system, the convolution and deconvolution calculation amount is too large each time, so the ACD method describes the load level of each time period of the system and the random shutdown condition of the unit by each order of accumulated amount, the method simplifies the convolution calculation into the addition operation of several accumulated amounts, greatly simplifies the calculation difficulty, and after each order of accumulated amount of the effective capacity distribution curve is obtained, the function value of each point on the curve can be obtained through the Edgeworth series expansion formula, thereby calculating the required reliability index; charging capacity C of thermal power generating unit iiFor discrete random variables, a discrete random variable C is definediThe v-order moment of (a) is:
Figure BDA0002616158140000131
pi＝1-PFOR,i
wherein: p is a radical ofiIs the normal operation probability of the thermal power generating unit i,
Figure BDA0002616158140000132
v-moment, alpha, of installed capacity of thermal power generating unit ivThe variable-capacity thermal power generating unit is a v-order moment of the effective capacity of the thermal power generating unit, and v is a positive integer;
s43 cumulative quantity KvA digital feature which is also a random variable, which can be derived from moments of orders not higher than the corresponding order; to simplify the calculation, each order moment is converted into each order central moment:
Figure BDA0002616158140000133
Figure BDA0002616158140000134
wherein: mvThe v-order central moment of the effective capacity of the thermal power generating unit,
Figure BDA0002616158140000135
taking n permutation and combination for v;
s44, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure BDA0002616158140000136
wherein: kvThe accumulated quantity of the v order of the effective capacity of the thermal power generating unit is obtained;
s45, when the effective capacity distributions of the thermal power generating unit are independent from each other, the distribution function of equivalent effective capacity can be realized through addition operation of the cumulant to replace convolution operation, so that the calculation difficulty is simplified; therefore, the accumulated quantity of each order of the equivalent effective capacity of the K thermal power generating units is represented as:
Figure BDA0002616158140000137
wherein: KSvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, Ki,vThe accumulated quantity of the v order of the thermal power generating unit i is obtained;
s46, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after K thermal power generating units are loadedK(x) Represents:
Figure BDA0002616158140000141
Figure BDA0002616158140000142
Figure BDA0002616158140000143
Figure BDA0002616158140000144
wherein: fK(x) The probability that the generating capacity provided for the K thermal power generating units after being loaded is less than x, N (x) is a standard normal density function, and N(γ)(x) Is the gamma-order derivative of N (x), gvNormalizing the cumulative quantity for order v (the term is introduced for the purpose of simplifying the order number form), σ is the standard deviation;
s47, load level L for t hourtUsing FK(Lt) Indicating that the power generation capacity at the t-th hour is less than LtTo obtain the load loss probability LOLP of the t hourt,KAnd FK(Lt) Is LOLPt,K＝FK(Lt)。
S5, adding M wind turbine generators in the initial system, and using ACLOLP (loss of load probability) of new system calculated by D methodt,M+K。
S51, after the wind power plant is added into the initial system, the wind power generator set is loaded preferentially, and then the conventional generator sets are loaded in sequence; describing the wind turbine by using a two-state model, wherein the effective capacity distribution of a wind turbine j is represented as:
Figure BDA0002616158140000145
wherein: j is 1,2, …, M, PW,j(t) is the output of the wind turbine j at the moment t,
Figure BDA0002616158140000146
for the effective capacity distribution, P, of the wind turbine jFOR,WjThe forced outage probability of the wind turbine generator j;
s52 output power P of wind turbine generator jW,jFor discrete random variables, a discrete random variable P is definedW,jThe v-order moment of (a) is:
Figure BDA0002616158140000147
pWj＝1-PFOR,Wj
wherein: p is a radical ofWjIs the normal operation probability, P, of the wind turbine generator jW,jIs the output of the wind turbine generator j, betavThe variable-pitch wind turbine generator is a v-order moment of the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order central moment, wherein the formula is as follows:
Figure BDA0002616158140000151
Figure BDA0002616158140000152
wherein: mWvThe v-order central moment of the effective capacity of the wind turbine,
Figure BDA0002616158140000153
taking n permutation and combination for v;
s54, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure BDA0002616158140000154
wherein: kWvThe cumulative quantity of the v order of the effective capacity of the wind turbine generator is obtained;
the accumulated quantity of each order of equivalent effective capacity of S55, M wind power generating units and K thermal power generating units is represented as:
Figure BDA0002616158140000155
wherein: KWvIs the v-order cumulant, KS, of equivalent effective capacities of the M wind power generating units and the K thermal power generating unitsvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, KWj,vThe v-order cumulant of the wind turbine generator j is obtained;
s56, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after the M wind power units and the K thermal power units are loadedM+K(x) Represents:
Figure BDA0002616158140000156
Figure BDA0002616158140000157
Figure BDA0002616158140000158
Figure BDA0002616158140000161
wherein: fM+K(x) The probability that the power generation capacity provided for the M wind power generating units and the K thermal power generating units after loading is less than x; n (x) is a standard normal density function; n is a radical of(γ)(x) Is the gamma derivative of N (x); gWvNormalizing the cumulative quantity for order v (the term is introduced to simplify the form of the series), σ being the standard deviation;
s57, load level L for t hourtUsing FM+K(Lt) Indicating that the power generation capacity at the t-th hour is less than LtTo obtain the load loss probability LOLP of the t hourt,M+KAnd FM+K(Lt) Is LOLPt,M+K＝FM+K(Lt)。
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithmW(t)。
When the wind turbine generator is not considered, obtaining an equivalent effective capacity distribution curve F of the K thermal power generating units by an ACD methodK(x) (ii) a After M wind turbine generators are added, an equivalent effective capacity distribution curve F of a new system is obtainedM+K(x) (ii) a Facing the load L of the t hourtThe loss probability is LOLPt,KAnd LOLPt,M+KWhen the system reliability index is solved by the ACD method, the unit effective capacity distribution function F (x) is complex, and when the target value LOLP is knownt,KThe needed x cannot be solved by a method of solving an inverse function, so that iterative calculation can be carried out by modifying x; solving LOLPt,M+K′＝LOLPt,KL of timet+ΔLtAt this time,. DELTA.LtNamely the wind power short-term operation credible capacity.
S61, calculating the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units by using the method of the step S5
Figure BDA0002616158140000162
By definition, assume that the system reliability index of the conventional unit is considered only at the tth hour as LOLPt,KAddition force is PWtThe reliability index of the rear system of the wind turbine generator is LOLPt,M+K(ii) a If the newly added power supply is an ideal unit with hundred percent reliability, the reliability of the newly added power supply is LOLPSt maxHowever, due to the existence of random outage rate of the units and other fault factors, the credible capacity of the newly added wind turbine generator is between 0 and the capacity C of the wind power generatorWTo (c) to (d);
Figure BDA0002616158140000163
and FM+K(x) The following relationships exist:
Figure BDA0002616158140000164
wherein:
Figure BDA0002616158140000165
the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s62, calculating the intermediate value of the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units by using the method of the step S5
Figure BDA0002616158140000171
And FM+K(x) The following relationships exist:
Figure BDA0002616158140000172
wherein:
Figure BDA0002616158140000173
is the middle value of the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s63, when the truncation method is used, the method vibrates when approaching the vicinity of the optimal value, so that the method cannot be converged to the optimal solution meeting the precision requirement, and the iteration times are increased; when the bisection method is used, the searching speed is lower compared with that of a truncation method, but the optimal solution can be found always finally as the searching range is continuously reduced; aiming at the advantages and disadvantages of a comprehensive search planning-state wind power capacity credibility algorithm, the scheme provides a hybrid search algorithm; assigning the load loss probability data in the t hour:
R0(t)＝LOLPt,K
RW(t)＝LOLPt,M+K
Figure BDA0002616158140000174
Figure BDA0002616158140000175
s64, by bringing in different Δ LtIterative solution of Rq(t); firstly, judging the interval position of a target value, and accurately searching by using a dichotomy when the interval position is close to the target value; if the distance from the target value is far, the search is accelerated by adopting a chord cutting method until the distance is close to the target value, and then the search is switched to a dichotomy accurate search; the method specifically comprises the following steps: let the number of iterations q equal to 1, compare R0(t) and RmidMagnitude of (t) value: if the absolute value of the difference is greater than 5, i.e. | R0(t)-Rmid(t) | equal to or greater than 5, then proceed to step S65; otherwise, go to step S66; wherein, for the precision requirement of iterative solution, the delta L is the total installed capacity added into the initial system and is CWThe wind turbine generator can bear more load;
s65, solving the target value R by using a chord cutting methodq(t), if | Rq(t)-R0(t)|＞|Rq-1(t)-R0(t) |, then, it means that oscillation occurs at the q-th iteration, Rq(t) since the target value is oscillated, convergence iteration operation is performed by using the dichotomy, and the process proceeds to step S66; otherwise, no oscillation is generated, q is q +1, and step S65 is repeated, continuing to use the truncating methodIteratively solving the target value Rq(t)；
S66, solving the target value R by using a dichotomyq(t) wherein Rq(t) the reliability index solved by the qth iteration in the tth hour is obtained, and the step S67 is performed;
s67, if | Rq(t)-R0(t) | ≧ which indicates that the calculation result satisfies the accuracy requirement, q ═ q +1, repeat step S66, continue to use dichotomy to iteratively solve the target value Rq(t); otherwise, go to step S68;
s68, taking the slave load side as an entry point, calculating the ratio of the load amount capable of being loaded more to the installed capacity of the wind power under the condition that the reliability level of the system is kept unchanged before and after the wind turbine generator is accessed:
R(Cg,L)＝R(Cg+CW,L+ΔL)
CW(t)＝ΔLt
wherein: cW(t) the short-term operation credible capacity of the wind power in the tth hour; Δ LtAdding total installed capacity C to initial systemWThe load capacity that the wind turbine generator can bear more in the tth hour, CgThe total installed capacity of the initial system (the total installed capacity of the thermal power generating unit), R (C)gL) is the reliability level of the initial system when facing the load L, R (C)g+CWL + Δ L) is the reliability level of the new system facing the load L + Δ L after the wind turbine set was added to the original system.
S7, wind power short-term operation based credible capacity CW(t) solving short-term wind power operation capacity reliability SOCCt。
The reliability level can be changed due to the change of real-time running conditions such as wind power output, load and the like, so that the reliability SOCC of the short-term running capacity of the wind powertThe real-time running state of the system is fully considered; the credibility expression of the short-term operation capacity of the wind power is as follows:
Figure BDA0002616158140000181
wherein: SOCCtReliability of short-term wind power operation capacity in the tth hour, CWIs the total installed capacity, Delta L, of the wind turbinetAdding total installed capacity C to initial systemWThe load capacity which can be borne by the wind turbine generator in the last t hour.
According to the method, the reliability of the final short-term operation capacity of the wind power can be obtained.
The specific embodiment of the wind power short-term operation capacity credibility assessment method based on the effective load capacity is as follows:
taking an IEEE-RTS79 reliability test system as an example to perform example simulation, removing the total installed capacity 3105MW of the system after the hydroelectric generating set in the system, and enabling the nuclear generating set to be equivalent to a conventional thermal generating set. The method comprises the steps of using wind speed prediction data of a certain place in Jiangsu, modeling wind power output, setting a total of 108 wind power units, wherein the total installed capacity is 162MW, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the units are respectively 3.33 m/s, 13.55 m/s and 22.22m/s, and the random outage rate is 0.04. The study period took 24 hours, with 400MW of spare capacity allocated per session.
TABLE 1 wind power output prediction data
Figure BDA0002616158140000191
The data in the table is measured wind speed data of a wind power plant in Jiangsu, sampled every 10 minutes and counted into 430 sampling points. Selecting the first 286 sampling point data as original wind speed data, and predicting wind speed data of 144 future sampling points; and (4) acquiring wind power output prediction data in the meter according to the functional relation between the wind speed and the wind power generator set output, wherein the wind power output prediction data is 24 hours in total.
TABLE 2 load data
Figure BDA0002616158140000192
The data in the table are data of the 23 rd week day of the IEEE-RTS79 reliability test system. The reliability indexes LOLP of the systems before and after wind power access are respectively calculated by using the data, and the result is shown in FIG. 4. Through comparison, the system reliability index LOLP is found to change along with the fluctuation of the load before the wind power is accessed, and the reliability of each time period is different; the system reliability is improved after the wind power is connected, but the LOLP value fluctuation in partial time period is larger due to the randomness of the wind power output. Although the wind power output is a predicted value and has a certain degree of error, the addition of a certain amount of wind power output is proved to be helpful for improving the reliability of the system.
TABLE 3 SOCC and wind power short-term operation credible capacity in each time period
Figure BDA0002616158140000193
The data in the table are obtained by using a hybrid search algorithm to iteratively solve the wind power short-term operation credible capacity according to the wind power short-term operation capacity credibility definition based on the effective load capacity. The wind power short-term credible capacity and the SOCC calculation result in each time period are shown in FIG. 5. And the wind power credible capacity of each time period represents the load amount which can be borne by newly-increased wind power output when the reliability of the system is equal before and after the wind power is accessed. Through comparison, the wind power short-term credible capacity is lower than the wind power predicted output under the dual influence of randomness of wind power and unchanged reliability level in each time period; comparing fig. 4 and fig. 5, it can be found that when the difference between the reliability levels of the system before and after the wind power is accessed at 14 th and 15 th, both the short-term operation credible capacity and the SOCC of the wind power are large; and when the difference between the reliability levels at 1 hour and 24 hours is small, the wind power short-term operation credible capacity and the SOCC are small, and the wind power utilization rate is over 95 percent.
TABLE 4 trusted Capacity search Algorithm comparison
Figure BDA0002616158140000201
The results of using the confidence capacity search algorithm proposed by the present invention to compare with the truncating method and the bisecting method for the above-mentioned SOCC solution are shown in tables 3-6. Through comparison, the difference of the required iteration times of the three methods is small when the precision is lower than the requirement; when the precision requirement is higher, the method can still search the optimal solution through the minimum iteration times. Therefore, the search algorithm provided by the invention has the advantages that the distance between the optimal solution and the iteration value is judged firstly, and then the chord-section method or the bisection method is used for searching according to the distance judgment, so that the oscillation problem generated by the chord-section method when the precision requirement is high is avoided to a great extent.
In summary, the invention provides a wind power short-term operation capacity credibility definition suitable for considering the real-time operation state of a system; the reliability evaluation is carried out based on an effective capacity distribution accumulation method, and the reliability evaluation can be carried out on the premise of considering the real-time running state of the system; the hybrid search algorithm is used for iterative solution of the short-term running credible capacity of the wind power, the influence of the search oscillation of a chord section method on the iteration times can be well avoided, and the solution speed and accuracy are increased; according to the method, the reliability analysis is carried out according to the real-time operation state of the system, and the influence of the wind power output time sequence characteristics and the uncertainty thereof on the reliability of the short-term operation capacity of the wind power can be well reflected; and the wind power short-term operation credible capacity at different time intervals can reflect the contribution degree of the wind power output to the system reliability.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (6)

1. A wind power short-term operation capacity credibility assessment method based on effective load capacity is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring wind speed data, and predicting a wind speed time series V by using an ARMA modelt；
S2, converting the wind speed into a time sequence VtConverted into wind power output time sequence Pwt；
S3, acquiring a combination result of the load data and the thermal power generating units in each time period;
s4, only considering the initial system consisting of K thermal power generating units, and calculating the load loss probability LOLP of the initial system by using an ACD methodt,K；
S5, adding M wind power generator sets into the initial system, and calculating the load loss probability LOLP of the new system by using an ACD methodt,M+K；
S6, iteratively solving wind power short-term operation credible capacity C through hybrid search algorithmW(t)；
S7, wind power short-term operation based credible capacity CW(t) solving short-term wind power operation capacity reliability SOCCt。
2. The wind power short-term operation capacity credibility assessment method based on payload capacity as claimed in claim 1, characterized in that: in step S3, the load data is represented by probability pulses of time-series loads; let the study period be T hours and the load level at T hours be LtT is 1,2, …, T, the probability of the occurrence of the load level at the tth hour is
Figure FDA0002616158130000011
The load data in the study period is denoted as L ═ L1,L2,…,Lt,…,LT}。
3. The wind power short-term operation capacity credibility assessment method based on payload capacity as claimed in claim 1, characterized in that: in step S4, for the initial system, the load loss probability lopp of the initial system is calculated by using the ACD methodt,kThe method comprises the following steps:
s41, the effective capacity distribution of the thermal power generating unit i is represented as:
Figure FDA0002616158130000012
wherein: i-1, 2, …, K, CiIs the installed capacity of the thermal power generating unit i,
Figure FDA0002616158130000013
is the effective capacity distribution, P, of the thermal power generating unit iFOR,iThe probability of the random shutdown of the thermal power generating unit i is obtained;
s42 and installed capacity C of thermal power generating unit iiFor discrete random variables, a discrete random variable C is definediThe v-order moment of (a) is:
Figure FDA0002616158130000014
pi＝1-PFOR,i
wherein: p is a radical ofiIs the normal operation probability of the thermal power generating unit i,
Figure FDA0002616158130000021
v-moment, alpha, of installed capacity of thermal power generating unit ivThe variable-capacity thermal power generating unit is a v-order moment of the effective capacity of the thermal power generating unit, and v is a positive integer;
s43, converting each order moment into each order central moment:
Figure FDA0002616158130000022
Figure FDA0002616158130000023
wherein: mvThe v-order central moment of the effective capacity of the thermal power generating unit,
Figure FDA0002616158130000024
taking n permutation and combination for v;
s44, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure FDA0002616158130000025
wherein: kvThe accumulated quantity of the v order of the effective capacity of the thermal power generating unit is obtained;
and S45, the accumulated quantity of each order of the equivalent effective capacity of the K thermal power generating units is represented as:
Figure FDA0002616158130000026
wherein: KSvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, Ki,vThe accumulated quantity of the v order of the thermal power generating unit i is obtained;
s46, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after K thermal power generating units are loadedK(x) Represents:
Figure FDA0002616158130000027
Figure FDA0002616158130000028
Figure FDA0002616158130000031
Figure FDA0002616158130000032
wherein: fK(x) The probability that the generating capacity provided for the K thermal power generating units after being loaded is less than x, N (x) is a standard normal density function, and N(γ)(x) Is the gamma-order derivative of N (x), gvNormalized cumulant of order v, σ is the standard deviation;
s47, load level L for t hourtUsing FK(Lt) Denotes the t-thPower generation capacity per hour less than LtTo obtain the load loss probability LOLP of the t hourt,KAnd FK(Lt) Is LOLPt,K＝FK(Lt)。
4. The wind power short-term operation capacity credibility assessment method based on payload capacity as claimed in claim 1, characterized in that: in step S5, M wind turbine generators are added to the initial system, and the load loss probability lopp of the new system is calculated by using the ACD methodt,m+kThe method comprises the following steps:
s51, the effective capacity distribution of the wind turbine generator j is represented as:
Figure FDA0002616158130000033
wherein: j is 1,2, …, M, PW,j(t) is the output of the wind turbine j at the moment t,
Figure FDA0002616158130000034
is the effective capacity distribution, P, of the wind turbine generator jFOR,WjThe forced outage probability of the wind turbine generator j;
s52 output power P of wind turbine generator jW,jFor discrete random variables, a discrete random variable P is definedW,jThe v-order moment of (a) is:
Figure FDA0002616158130000035
pWj＝1-PFOR,Wj
wherein: p is a radical ofWjIs the normal operation probability, P, of the wind turbine generator jW,jIs the output of the wind turbine generator j, betavThe variable-pitch wind turbine generator is a v-order moment of the effective capacity of the wind turbine generator, wherein v is a positive integer;
s53, converting each order moment into each order central moment, wherein the formula is as follows:
Figure FDA0002616158130000036
Figure FDA0002616158130000037
wherein: mWvThe v-order central moment of the effective capacity of the wind turbine,
Figure FDA0002616158130000041
taking n permutation and combination for v;
s54, obtaining the accumulated quantity of each order according to the center distance of each order, wherein the relation between the accumulated quantity of the first 8 orders and the center moment of each order is as follows:
Figure FDA0002616158130000042
wherein: kWvThe cumulative quantity of the v order of the effective capacity of the wind turbine generator is obtained;
the accumulated quantity of each order of equivalent effective capacity of S55, M wind power generating units and K thermal power generating units is represented as:
Figure FDA0002616158130000043
wherein: KWvIs the v-order cumulant, KS, of equivalent effective capacities of the M wind power generating units and the K thermal power generating unitsvIs the v-order cumulant of equivalent effective capacity of K thermal power generating units, KWj,vThe v-order cumulant of the wind turbine generator j is obtained;
s56, expressing the distribution of equivalent effective capacity by cumulant through the Edgeworth series expansion, and using F as the distribution function of the effective capacity after the M wind power units and the K thermal power units are loadedM+K(x) Represents:
Figure FDA0002616158130000044
Figure FDA0002616158130000045
Figure FDA0002616158130000046
Figure FDA0002616158130000047
wherein: fM+K(x) The probability that the power generation capacity provided for the M wind power generating units and the K thermal power generating units after loading is less than x; n (x) is a standard normal density function; n is a radical of(γ)(x) Is the gamma derivative of N (x); gWvNormalized cumulant of order v, σ is the standard deviation;
s57, load level L for t hourtUsing FM+K(Lt) Indicating that the power generation capacity at the t-th hour is less than LtTo obtain the load loss probability LOLP of the t hourt,M+KAnd FM+K(Lt) Is LOLPt,M+K＝FM+K(Lt)。
5. The wind power short-term operation capacity credibility assessment method based on payload capacity as claimed in claim 1, characterized in that: in the step S6, solving the wind power short-term operation credible capacity CW(t) comprises the steps of:
s61, calculating the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units
Figure FDA0002616158130000051
And FM+K(x) The following relationships exist:
Figure FDA0002616158130000052
wherein:
Figure FDA0002616158130000053
the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s62, calculating the intermediate value of the maximum load loss probability of the system aiming at the system consisting of the M wind power units and the K thermal power units
Figure FDA0002616158130000054
And FM+K(x) The following relationships exist:
Figure FDA0002616158130000055
wherein:
Figure FDA0002616158130000056
is the middle value of the maximum load loss probability, L, of a system consisting of M wind power generating units and K thermal power generating unitstAt the load level of the t hour, CWThe total installed capacity of the wind turbine generator is set;
s63, assigning the load loss probability data of the t hour:
R0(t)＝LOLPt,K
RW(t)＝LOLPt,M+K
Figure FDA0002616158130000057
Figure FDA0002616158130000058
s64, by bringing in different Δ LtIterative solution of Rq(t); let the number of iterations q equal to 1, compare R0(t) and RmidMagnitude of (t) value: if the absolute value of the difference is greater than 5, i.e. | R0(t)-Rmid(t) | equal to or greater than 5, then proceed to step S65; otherwise, go to step S66; wherein, for the precision requirement of iterative solution, the delta L is the total installed capacity added into the initial system and is CWThe wind turbine generator can bear more load;
s65, solving the target value R by using a chord cutting methodq(t), if | Rq(t)-R0(t)|＞|Rq-1(t)-R0(t) |, which indicates that the oscillation phenomenon occurs at the qth iteration, the process proceeds to step S66; otherwise, q is q +1, the step S65 is repeated, and the intercept method is continuously used to iteratively solve the target value Rq(t)；
S66, solving the target value R by using a dichotomyq(t) wherein Rq(t) the reliability index solved by the qth iteration in the tth hour is obtained, and the step S67 is performed;
s67, if | Rq(t)-R0(t) | ≧ q ═ q +1, repeat step S66, continue to use dichotomy to iteratively solve target value Rq(t); otherwise, go to step S68;
s68, taking the slave load side as an entry point, calculating the ratio of the load which can be loaded more to the installed capacity of the wind power under the condition that the reliability level of the system is kept unchanged before and after the wind turbine generator is accessed:
R(Cg,L)＝R(Cg+CW,L+ΔL)
CW(t)＝ΔLt
wherein: cW(t) the short-term operation credible capacity of the wind power in the tth hour; Δ LtAdding total installed capacity C to initial systemWThe load capacity that the wind turbine generator can bear more in the tth hour, CgTotal installed capacity, R (C), of the original systemgL) is the reliability level of the initial system when facing the load L, R (C)g+CWL + Δ L) is the reliability level of the new system facing the load L + Δ L after the wind turbine set was added to the original system.
6. According to claim 1The wind power short-term operation capacity credibility assessment method based on the effective load capacity is characterized by comprising the following steps: in the step S7, the short-term wind power operation capacity reliability SOCCtSolving according to the following formula:
Figure FDA0002616158130000061
wherein: SOCCtReliability of short-term wind power operation capacity in the tth hour, CWIs the total installed capacity, Delta L, of the wind turbinetAdding total installed capacity C to initial systemWThe load capacity which can be borne by the wind turbine generator in the last t hour.
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