CN113904338A - Wind power grid-connected system and frequency characteristic probability load flow calculation method and system - Google Patents
Wind power grid-connected system and frequency characteristic probability load flow calculation method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The invention discloses a wind power grid-connected system and a method and a system for calculating probability load flow of frequency characteristics, wherein the method comprises the following steps: generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed, and converting the offshore wind speed sample into wind power output; inputting the wind power output into a preset linear power flow model for rapid probability power flow calculation to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics. By the method, a more accurate and comprehensive steady-state probability power flow analysis result can be obtained.
Description
Technical Field
The invention relates to the technical field of power system load flow calculation, in particular to a wind power grid-connected system and a method and a system for calculating the probability load flow of frequency characteristics.
Background
Wind power generation is a mature renewable energy power generation form, and the development is rapid in recent years. The wind power output has randomness and volatility, and more uncertainty can be brought to the system tide after the grid connection, so that the analysis of the influence of large-scale offshore wind power grid connection on the operation of a power system has important significance.
The conventional power flow model used by the existing power flow calculation method ignores an important index, namely frequency, which influences the quality of electric energy, so that the power flow result usually has no frequency information; in addition, most of conventional power flow models have the problems of nonlinearity, difficult convergence, low calculation efficiency and the like, and an accurate and comprehensive steady-state probability power flow analysis result is often difficult to obtain.
Disclosure of Invention
In order to solve the technical problems, the invention provides a wind power grid-connected system and a probability load flow calculation method and system based on frequency characteristics.
In a first aspect, the invention provides a wind power grid-connected system and a frequency characteristic probability power flow calculation method, which includes:
generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed, and converting the offshore wind speed sample into wind power output;
inputting the wind power output into a preset linear power flow model for rapid probability power flow calculation to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
Optionally, the marine wind speed sample is generated based on the probability distribution condition of the marine wind speed and the correlation coefficient, and specifically includes:
describing probability distribution characteristics of offshore wind speed by using double-parameter Weibull distribution, and describing a correlation relation of wind speeds among a plurality of wind power plants by using Pearson correlation coefficients;
and generating an offshore wind speed sample which has correlation and obeys preset distribution by applying a Nataf transformation method.
Optionally, the converting the offshore wind speed sample into wind power output specifically includes:
converting the offshore wind speed sample into wind power output based on a conversion relation between wind speed and power, wherein the conversion relation is expressed as:
in the formula, PrIndicating rated output power, v, of the fanci、vrAnd vcoRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan.
Optionally, the load flow calculation result includes: the voltage phase angle, the voltage amplitude, the system frequency, the active output of the conventional generator and the active power of the branch circuit of the node in the power system.
Optionally, the static frequency characteristic of the power system includes: a static frequency characteristic of the generator set and a static frequency characteristic of the load.
In a second aspect, the present invention provides a wind power integration system and a frequency characteristic probabilistic power flow calculation system, including:
the first calculation unit is used for generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed and converting the offshore wind speed sample into wind power output;
the second calculation unit is used for inputting the wind power output into a preset linear power flow model to perform rapid probability power flow calculation so as to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
Optionally, in the first computing unit, the generating an offshore wind speed sample based on the probability distribution of the offshore wind speed and the correlation coefficient specifically includes:
describing probability distribution characteristics of offshore wind speed by using double-parameter Weibull distribution, and describing a correlation relation of wind speeds among a plurality of wind power plants by using Pearson correlation coefficients;
and generating an offshore wind speed sample which has correlation and obeys preset distribution by applying a Nataf transformation method.
Optionally, in the first computing unit, the converting the offshore wind speed sample into wind power output specifically includes:
converting the offshore wind speed sample into wind power output based on a conversion relation between wind speed and power, wherein the conversion relation is expressed as:
in the formula, PrIndicating rated output power, v, of the fanci、vrAnd vcoRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan.
In a third aspect, the present invention provides a data processing device, including a processor, where the processor is coupled with a memory, where the memory stores a program, and the program is executed by the processor, so that the data processing device executes the method for calculating a probabilistic power flow of a wind power grid connection system and frequency characteristics according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for calculating a probabilistic power flow of a wind power grid-connected system and frequency characteristics is implemented.
Compared with the prior art, the invention has the beneficial effects that:
according to the wind power grid-connected system and frequency characteristic probabilistic power flow calculation method, the power flow is calculated by utilizing the linear probabilistic power flow model considering the active-frequency static characteristics of the generator and the load of the power system, and compared with a conventional power flow algorithm, the obtained probabilistic power flow calculation result is more comprehensive and accurate, can be used for analyzing the influence of offshore wind power grid connection on the system frequency, and has an important value for steady-state probabilistic power flow analysis.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a wind power integration system and a probability power flow calculation method of frequency characteristics according to an embodiment of the present invention;
fig. 2 is a block diagram of a structure of a wind power integration system and a frequency characteristic probabilistic power flow calculation system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, in a first aspect, an embodiment of the present invention provides a wind power grid connection system and a method for calculating a probability power flow of a frequency characteristic, which specifically includes the following steps S1 and S2.
S1: and generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed, and converting the offshore wind speed sample into wind power output.
It will be appreciated that short term wind speeds are important for wind farm planning and operational research, and that in the absence of a sufficiently long time series of wind speeds, a probability distribution of offshore wind speeds may be used instead.
In the embodiment, the probability distribution characteristic of the offshore wind speed is described by using double-parameter Weibull distribution, the correlation relation of the wind speeds among a plurality of wind power plants is described by using Pearson correlation coefficients, and then an offshore wind speed sample which has correlation and obeys preset distribution is generated by applying a Nataf transformation method.
Specifically, the probability density function and cumulative distribution function of a two-parameter Weibull distribution can be expressed as:
f(v)＝k/c(v/c)k-1exp[-(v/c)k] v≥0
F(v)＝1-exp[-(v/c)k] v≥0
wherein f (v) is a probability density function of an observed wind speed v, F (v) is a cumulative distribution function of v, k and c respectively represent a shape parameter and a scale parameter, and the probability characteristic of the offshore wind speed can be more accurately described by using double-parameter Weibull distribution.
The correlation of the wind speeds among the wind power plants is related to the geographical distance, the wind speeds of the wind power plants with close distances have strong correlation due to the influence of the same weather condition, and the correlation of the wind speeds of different wind power plants is represented by using Pearson correlation coefficients in the embodiment.
The Pearson correlation coefficient is a common method for quantifying the interdependence relation between random input variables in the probability analysis of the power system, and can describe any two random variables XiAnd XjThe linear relationship between the two can be specifically represented by the following formula:
ρx(i,j)＝(μij-μiμj)/σiσj
wherein, mui、μj、σiAnd σjAre each XiAnd XjMean and standard deviation of (D), muijIs XiAnd XjThe average of the products.
Considering that it is difficult for the Pearson correlation coefficient to directly generate a sample satisfying the preset correlation in the original probability space, the embodiment adopts a natural af transformation method to realize the conversion of the Pearson correlation coefficient from the original domain to the gaussian domain, so as to generate the offshore wind speed sample having correlation and complying with the preset distribution.
Since the output power of the wind turbine mainly depends on the wind speed, the embodiment converts the generated offshore wind speed sample into the wind power output based on the conversion relationship between the wind speed and the power. Wherein the conversion relationship is expressed as:
in the formula, PrIndicating rated output power, v, of the fanci、vrAnd vcoRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan.
S2: inputting the wind power output into a preset linear power flow model for rapid probability power flow calculation to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
In the embodiment, a linear power flow model which is constructed in advance is used for performing rapid probabilistic power flow calculation, and it should be noted that, for the linear power flow model, the embodiment only considers the active-frequency static characteristics of a conventional generator and a load in a power system, does not consider the reactive-voltage static characteristics, and ignores the weak coupling relation between the active-voltage and the reactive-frequency.
Specifically, when the active balance of the power system is broken, a conventional generator with frequency modulation capability will vary its output and participate in primary frequency modulation according to its own active-frequency static characteristics and adjustable capacity. The active-frequency static characteristic of a conventional generator can be expressed as:
PGi＝KGi(fi0-f) i＝1,2,...,g
wherein, PGiIs the active power output of unit i, KGiIs the active-frequency static characteristic coefficient, f, of the unit ii0The no-load frequency of the unit i, f the system frequency and g the number of the units.
For the active-frequency static characteristic of the load, the present embodiment is represented by the following formula:
PLi＝PLi0[1+KPf(f-1)] i＝1,2,...,l
wherein, PLiTo be active of load i, PLi0For rated active power of load i, KPfThe number of the loads is expressed by l, which is the active-frequency static characteristic coefficient of the loads.
For a power system with n nodes, the active power equation in polar coordinate form is shown as follows:
specifically, the node admittance matrix Y of the power system may be represented as:
similarly, for an approximate nodal admittance matrix Y' without considering the parallel admittances, can be expressed as:
wherein, YijIs the ith row and the jth column element of the node admittance matrix Y; y isijIs the admittance of the line between node i and node j; y isiiIs the parallel admittance of node i; y'ijAre the i-th row and j-th column elements of the approximate node admittance matrix Y'.
Substituting the node admittance matrix Y into the formula of the active power equation to obtain:
considering that in most practical operation scenarios of power system, the node voltage amplitude is generally about 1.0p.u. and the absolute value of the node voltage phase angle difference does not substantially exceed 30 ° (mostly within 10 °), the following assumptions are made in this embodiment: vi 2≈Vi,cosθij≈1,sinθij≈θi-θj
Thus, for the non-linear part of the active power equation substituted into the node admittance matrix Y, a linear approximation can be made:
gijVi(Vi-Vjcosθij)≈gijVi(Vi-Vj)＝gij(1+ΔVi)(ΔVi-ΔVj)≈
gij(ΔVi-ΔVj)＝gij(1+ΔVi-1-ΔVj)＝gij(Vi-Vj)
wherein, is Δ ViUsually an order of magnitude smaller than V, the quadratic term av produced in the intermediate stepi 2And Δ ViΔVjAnd can be ignored.
Further, the linear approximation formula is substituted back to the active power equation to achieve decoupling of the node voltage amplitude and the phase angle, and obtain a decoupling linear expression of the node active power:
for the same reason, for Q in the active power equationiThe linear approximation described above is also performed in part. Considering giiMuch less than biiSuppose Gij'≈GijAnd obtaining the decoupling linearity of the reactive power of the node:
in conclusion, the decoupling linear expression of the active power and the reactive power of the node forms the linear power flow model for voltage amplitude and phase angle decoupling provided by the embodiment.
Further, the linear power flow model can be expressed in the form of the following matrix:
wherein θ and V are respectively composed of 3 sub-column vectors and respectively correspond to a V θ node, a PV node, and a PQ node, and the present embodiment assumes that the nodes are in the order of V θ, PV, and PQ.
The admittance matrix Y is divided according to the same idea to obtain:
at this time, the node voltage phase angle θ is balanced based on a known amountvθAmplitude VvθAnd PV node voltage amplitude VpvThe active power vector P of the PV and PQ nodes can be obtained by utilizing the matrix form of the linear power flow modelpv、PpqAnd PQ node reactive power vector Qpq：
It can be understood that the active injection vector P of the PV, PQ node can be obtained based on the static frequency characteristics of the conventional generator and loadInj,pv、PInj,pqAnd reactive injection vector Q of PQ nodeInj,pqExpressed as:
therefore, the phase angle theta of the PV node voltage can be adjustedpvPQ node voltage phase angle thetapqPQ node voltage amplitude VpqAnd taking the system frequency f as an unknown variable of the linear power flow model to obtain a uniformly expanded linear power flow equation set:
in the formula (I), the compound is shown in the specification,
and
the part which is left after the unknown quantity f is converted to the right side of the equal sign by the node power injection vector is a known quantity; F. k is related to the static frequency characteristic, and is 0 since the weak reactive-frequency coupling relationship is neglected in the embodiment; the elements of the other part are as follows:
it is worth noting that the linear power flow model taking the static frequency characteristic of the system into account in the implementation only adds 1 equation and 1 unknown quantity compared with the conventional active power equation.
In the linear power flow model solving process, the embodiment performs calculation by using general matrix multiplication which is based on a block algorithm and performs matrix multiplication and accumulation so as to meet the high-performance calculation requirement.
In the calculation process, the linear power flow model is expressed in the following form:
[y]＝[A][x]+[C]
wherein y is an output variable of the linear power flow model, and comprises PV, a PQ node voltage phase angle, a PQ node voltage amplitude and a system frequency f; x is an input variable of the linear power flow model, specifically the offshore wind power output; a and C are the coefficient matrices of the model.
Further, for a power system with n nodes, m PQ nodes and k offshore wind farms, a in the linear power flow model is an (n + m) × k-dimensional matrix, C is an (n + m) × 1-dimensional vector, and x is a k × 1-dimensional vector; when linear power flow calculation is carried out once, a (n + m) multiplied by 1-dimensional vector y can be obtained.
It will be appreciated that if an ns Monte Carlo simulation is performed, then when using a conventional power flow model, the ns loop calculations need to be repeated to obtain a (n + m). times.ns dimensional matrix y. Based on the characteristics of the linear power flow model, when the coefficient matrixes a and C are known, when the input variable x is expanded to the dimension of k × ns, the result y of the dimension of (n + m) × ns can be obtained only by one-time calculation.
After the linear power flow model is solved, the obtained calculation results comprise the voltage phase angle, the voltage amplitude, the system frequency, the active output of the conventional generator and the active power of the branch circuit of the node in the power system.
The general matrix multiplication used in the embodiment has the advantage that the matrix operation speed is much faster than the cycle operation speed, and the time consumption of probability load flow calculation can be obviously reduced.
The embodiment of the invention generates an offshore wind speed sample which has Pearson correlation and obeys weibull distribution based on Nataf transformation, and converts the offshore wind speed sample into wind power output; meanwhile, under the condition of considering the conventional generator and the load static frequency characteristic, a linear power flow model is constructed in advance based on the idea of voltage amplitude and phase angle decoupling, and on the basis, the model characteristics and the linear algebra idea are combined to enable one-time calculation of the model to be equivalent to multiple Monte Carlo simulations, so that the calculation efficiency of the model in the probabilistic power flow analysis is remarkably improved.
Compared with a conventional power flow algorithm, the power flow result obtained by the wind power grid-connected system and the frequency characteristic probability power flow calculation method provided by the embodiment of the invention has the following three advantages:
firstly, frequency distribution information can be obtained by using the method, and the obtained trend result is more comprehensive.
Secondly, the true value of the tidal current result influenced by the frequency can be reflected more accurately based on the method, and the obtained result is higher in accuracy.
Thirdly, the conventional power flow algorithm is a nonlinear algorithm, in a probability power flow scene, repeated calculation is often required for many times, and the consumed time is long; the method is a linear method, based on a linear algebra idea, only one calculation is needed to simulate the probability load flow of tens of thousands of scenes, the calculation process is faster, and the efficiency is higher.
Referring to fig. 2, in a second aspect, an embodiment of the present invention further provides a wind power grid connection system and a probabilistic power flow calculation system of frequency characteristics, including a first calculation unit 101 and a second calculation unit 102.
The first calculation unit 101 is configured to generate an offshore wind speed sample based on a probability distribution of an offshore wind speed and a correlation coefficient, and convert the offshore wind speed sample into a wind power output.
The second calculating unit 102 is configured to input the wind power output into a preset linear power flow model for performing fast probabilistic power flow calculation to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
Since the contents of information interaction, execution process, and the like between the units in the system are based on the same concept as the method embodiment of the first aspect of the present invention, specific contents may refer to the description in the method embodiment of the present invention, and are not described herein again.
The above described system embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the units can be selected according to actual needs to achieve the purpose of the method of the embodiment.
According to the embodiment of the invention, various load flow calculation results including the steady-state frequency of the system can be obtained through the constructed linear load flow model taking the static frequency characteristic of the system into account, and the linear load flow model is used for calculating the steady-state probability load flow under the large-scale offshore wind power grid connection.
In a third aspect, the present invention provides a data processing device, including a processor, where the processor is coupled with a memory, where the memory stores a program, and the program is executed by the processor, so that the data processing device executes the method for calculating a probabilistic power flow of a wind power grid connection system and frequency characteristics according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for calculating a probabilistic power flow of a wind power grid-connected system and frequency characteristics is implemented.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and may include the processes of the embodiments of the methods when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A wind power grid-connected system and a frequency characteristic probability power flow calculation method are characterized by comprising the following steps:
generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed, and converting the offshore wind speed sample into wind power output;
inputting the wind power output into a preset linear power flow model for rapid probability power flow calculation to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
2. The wind power integration system and frequency characteristic probabilistic power flow calculation method according to claim 1, wherein the offshore wind speed sample is generated based on a probability distribution condition and a correlation coefficient of an offshore wind speed, and specifically comprises:
describing probability distribution characteristics of offshore wind speed by using double-parameter Weibull distribution, and describing a correlation relation of wind speeds among a plurality of wind power plants by using Pearson correlation coefficients;
and generating an offshore wind speed sample which has correlation and obeys preset distribution by applying a Nataf transformation method.
3. The wind power integration system and frequency characteristic probabilistic power flow calculation method according to claim 1, wherein the converting the offshore wind speed sample into a wind power output specifically comprises:
converting the offshore wind speed sample into wind power output based on the conversion relation between wind speed and power; wherein the conversion relationship is expressed as:
in the formula, PrIndicating rated output power, v, of the fanci、vrAnd vcoRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan.
4. The wind power integration system and frequency characteristic probabilistic power flow calculation method according to claim 1, wherein the power flow calculation result includes:
the voltage phase angle, the voltage amplitude, the system frequency, the active output of the conventional generator and the active power of the branch circuit of the node in the power system.
5. The wind power grid-connected system and the frequency characteristic probability power flow calculation method according to claim 1, wherein the static frequency characteristic of the power system comprises:
a static frequency characteristic of the generator set and a static frequency characteristic of the load.
6. A wind power grid-connected system and a frequency characteristic probability power flow calculation system are characterized by comprising:
the first calculation unit is used for generating an offshore wind speed sample based on the probability distribution condition and the correlation coefficient of the offshore wind speed and converting the offshore wind speed sample into wind power output;
the second calculation unit is used for inputting the wind power output into a preset linear power flow model to perform rapid probability power flow calculation so as to obtain a power flow calculation result; wherein the linear power flow model accounts for power system static frequency characteristics.
7. The wind power integration system and frequency characteristic probabilistic power flow calculation system according to claim 6, wherein in the first calculation unit, the offshore wind speed sample is generated based on a probability distribution condition of an offshore wind speed and a correlation coefficient, and specifically:
describing probability distribution characteristics of offshore wind speed by using double-parameter Weibull distribution, and describing a correlation relation of wind speeds among a plurality of wind power plants by using Pearson correlation coefficients;
and generating an offshore wind speed sample which has correlation and obeys preset distribution by applying a Nataf transformation method.
8. The wind power integration system and frequency characteristic probabilistic power flow calculation system according to claim 6, wherein in the first calculation unit, the converting the offshore wind speed sample into a wind power output specifically is:
converting the offshore wind speed sample into wind power output based on a conversion relation between wind speed and power, wherein the conversion relation is expressed as:
in the formula, PrIndicating rated output power, v, of the fanci、vr、vcoRespectively representing the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan.
9. A data processing apparatus, characterized by comprising:
a processor coupled to a memory, the memory storing a program for execution by the processor to cause the data processing apparatus to perform the method of calculating a probabilistic power flow of a wind power grid system and frequency characteristics according to any one of claims 1 to 5.
10. A computer storage medium, characterized in that the computer storage medium stores computer instructions for executing the wind power integration system and the frequency characteristic probabilistic power flow calculation method according to any one of claims 1 to 5.
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| CN116613759A (en) * | 2023-05-18 | 2023-08-18 | 广东电网有限责任公司 | Power distribution network tide calculation method and device, electronic equipment and storage medium |
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