CN106709641A - Monte-Carlo simulation based small interference probability risk analysis and simulation method - Google Patents
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Abstract
The invention discloses a Monte-Carlo simulation based small interference probability risk analysis and simulation method. According to the invention, a corresponding probability distribution model is built so as to be used for studying the small interference probability stability. A large number of operating scenarios are generated by adopting a Monte-Carlo method, and certainty small interference stability analysis is performed at different cross sections, wherein the certainty small interference stability analysis specifically comprises system state space linearization, eigenvalue analysis, key oscillation mode recognition, damping ratio sensitivity calculation and the like. Through performing simulation, calculation and analysis on difference scenarios, a probability density distribution function of the key modal damping ratio is formed by using a mathematical statistics method, and thus the probability of small interference safe and stable operations of a system is acquired by calculation. On the basis, risk evaluation is performed on the system by a risk index based on the damping ratio sensitivity. According to the analysis and simulation method, the small interference probability stability of the system can be evaluated effectively, effectively risk evaluation is performed on the system, and a decision-making basis can be provided for safe and stable operations of the system.
Description
Technical field
The invention belongs to power system small signal stability technical field, more particularly to consider the small dry of uncertain factor
Disturb Probabilistic Stability and risk assessment.
Background technology
With China's AC extra high voltage power grid construction, ultra-large interconnected network will finally constitute the unified joint of China
Power network.The free-revving engine of Power System Interconnection is extensive transimission power at a distance, improves whole Operation of Electric Systems economy, but
The structure of interacted system is more complicated, and the security margin of system may be caused to diminish, and influencing each other more after accident
Aggravation, easily causes dominant eigenvalues fluctuation, induces interregional low-frequency oscillation.Interconnected network low-frequency oscillation problem turns into
The key factor of Large-Scale Interconnected power system conveying capacity is restricted, also determines that can whole system stable operation.
In addition, as a large amount of grid-connected and electricity market of the new energy such as wind-powered electricity generation is relaxed control, power system is faced not
Certainty factor is dramatically increased, the running status variation of system.The increase of many uncertain factors so that conventional electric power system
The deterministic parsing of system is theoretical and method receives new challenge.When carrying out analysis on Small Disturbance Stability to power system, it is considered to
The influence that uncertain factor is brought seems particularly necessary.Therefore study and assessment wind-electricity integration, load fluctuation etc. it is uncertain because
Influence of the element to interacted system low-frequency oscillation, the small probability of interference stability for controlling and improving large-scale interconnected power system has become me
An important topic in state's power system research, is also the important foundation of China's strong intelligent grid of construction.
At present, power system uncertain problem is mainly studied using probability analysis method, be summed up it is main have with
Lower method：Monte Carlo simulation approach, analytic method and approximation method.Wherein Monte Carlo simulation approach is based on probability theory and mathematical statistics
Principle, is solved using statistical experiment is repeated and considers probabilistic system problem, by simulating the not true of various enchancement factors
It is qualitative, a large amount of Run-time scenarios are generated, different running statuses are analyzed and index is calculated.Certain point is obeyed its essence is utilizing
The stochastic variable of cloth carrys out the chance phenomenon being likely to occur in simulating reality system.
The content of the invention
Goal of the invention：The present invention provides a kind of modeling and small probability of interference Simulation of stability and analysis method, and proposes one
Plant effective risk indicator carries out quantitative probability distributive function to system, is that effective suppression of Study system low-frequency oscillation is arranged
Apply, safeguards system safe and stable operation provides decision-making foundation.
Technical scheme
A kind of small probability of interference risk analysis and emulation mode based on Monte Carlo simulation, it is characterised in that：Including suitable
The following steps that sequence is performed：
Step 1, correlation of being exerted oneself according to Wind turbines in the same area and different node load horizontal correlations, are set
Relative coefficient, sets up uncertain system model；
Step 2, for set up uncertain system model, using in Monte Carlo simulation simulation system it is various it is random because
The uncertainty of element carrys out simulation system state, so as to generate n Run-time scenario；
Step 3, under each Run-time scenario to system being determined property Small signal stability analysis, to n Run-time scenario
Analysis result carries out the probability density function of the crucial oscillation mode damping ratio of mathematical statistics generation, and then obtains the small interference peace of system
The probability of full stable operation；
Step 4, based on said system it is small interference safe and stable operation probability, and combine it is small interference unstability loss based on
The risk indicator of specific sensitivity is damped, risk assessment is carried out to system.
Further, in the present invention, the certainty analysis on Small Disturbance Stability is specific as follows：By system state space
Carry out linearizing near steady state equilibrium point obtaining standard state equation, its characteristic value is calculated according to standard state equation and is solved
Electromechanical oscillations mode, using time-domain-simulation and Eigenvalues analysis, the crucial oscillation mode characteristic value in judgement system, and then obtains
Crucial oscillation mode dampingratioζ is, for specific run scene, only to need primary line as simulation result, the advantage of said process
Propertyization can be obtained for analyzing simultaneously, the related data of risk assessment, can also be provided for the control of further Study system
Related data；Then the simulation result of different scenes is obtained into crucial oscillation mode dampingratioζ by probability theory and mathematical statistics
Probability density function P (ζ), and then according to default damping ratio threshold value ζsetAsk for the small interference safe and stable operation of power system
Probability PSteady。
Further, in general, crucial oscillation mode damping ratio is more than 0.05 system absolute stability, and key is shaken
Swinging mode damping ratio has the risk of unstability less than 0.05, therefore damping ratio threshold value is set as ζset=0.05, and system is small dry
The probability for disturbing safe and stable operation can be calculated by following formula：PSteady=∫ζ≥0.05P(ζ)dζ；Based on theory of risk assessment,
The probability density function P (ζ) of the crucial oscillation mode dampingratioζ of the small probability of interference stability of comprehensive characterization system and the system of sign
Small interference stability severity function S (ζ) of each Run-time scenario is defined in the simulation model of the loss that small interference unstability is brought
Risk indicator Risk is
Risk=∫ P (ζ) S (ζ) d ζ
Wherein：
It is describedThe inverse of the sensitivity for crucial oscillation mode damping ratio to the method for operation；
Above-mentioned risk indicator shows：During system core oscillation mode dampingratioζ≤0.05, system may occur small interference and lose
Surely, linear, severity function is approximately considered near analysis breakpoint(0.05- ζ) is approximately characterized and adjusted by active power
Section loses damping ratio regulation to generator active power adjustment amount when 0.05, namely the active power of power network, is lost with system
The active power that may be brought when steady loses to characterize order of severity during small interference unstability.
Further, in the present invention, sensitivity of the crucial oscillation mode damping ratio in the S (ζ) to the method for operation
InverseCalculated by the following method：
Step 4.1, by the correlation factor of the relatively crucial oscillation mode of generator by order sequence from big to small, and select
The preceding m generator for meeting following formula：
In formula：
piRepresent the correlation factor of the relatively crucial oscillation mode of i-th generator；
pjRepresent the correlation factor of the relatively crucial oscillation mode of jth platform generator；
nGIt is all generator set in system；
RsetIt is the proportion threshold value for setting；
Step 4.2, in the m platform generators selected, screening meets spirit of the crucial oscillation mode damping ratio to the method for operation
Sensitivity is reciprocalGenerator, number of units is designated as N, PkIt is the active power of kth platform generator in generator；
The sensitivity of step 4.3, the crucial oscillation mode eigenvalue λ of acquisition to changes of operating modes μ
Step 4.4 obtains sensitivity of the crucial oscillation mode dampingratioζ to changes of operating modes μ according to step 4.3Wherein The reality of respectively crucial oscillation mode Eigenvalue Sensitivity
Portion and imaginary part, δ0, ω0Be illustrated respectively in choose calculate special scenes under system core oscillation mode characteristic value real part and void
Portion；
Step 4.5, changes of operating modes μ is set as above-mentioned screening N platform generators active power output change △ P,
So as to obtainExpression formula, it is inverted and then obtainExpression formula.
Further, in the present invention, in the step 1, in order to substantially utilize wind energy, wind park is typically using most
Big wind energy tracking strategy, under this strategy, the level of exerting oneself of Wind turbines is determined that Wind turbines are exerted oneself and wind speed by wind speed
Relation is calculated according to equation below：
And Wind turbines in the same area are set according to wind power source geographical position exert oneself relative coefficient ρij, so as to build
It is vertical to include the m correlation matrix [ρ in wind power sourceij]m×m；If enough air speed datas are for analysis, same area
Wind turbines are exerted oneself relative coefficient ρ in domainijSpecifically can be calculated according to equation below：
Wherein：
v、vr、vci、vcoRespectively the actual wind speed of Wind turbines, rated wind speed, incision wind speed and cut-out wind speed, the reality
Border wind speed Follow Weibull Distribution, the probability density function of its actual wind speed is as follows：
PwIt is Wind turbines actually active output；PrIt is the rated active power of Wind turbines；
vi、vjWind speed variable respectively related to wind power source i, j；Cov(vi,vj) it is viAnd vjCovariance,The respectively wind speed deviation of wind power source i, j；
K and c are the form parameter for describing Weibull distributions, and meet k in the probability density letter of actual wind speed>0, v>0, c
>0；
Further, in the present invention, it is to describe negative as precisely as possible in the step 1, it is considered to which actual load fluctuates
Lotus level changes, on the basis of the historical load data of research real system, using just too distributed model describes load fluctuation,
And according to relative coefficient ρ (x, y) between following formula calculated load x and load y：
Wherein：
σr() represents the standard deviation of load model normal distribution, and cov (x, y) is represented by bearing that historical data analysis are obtained
The covariance of lotus x and load y.
Beneficial effect
On the basis of Monte Carlo Analogue Method, wind-powered electricity generation, the load fluctuation model for considering correlation are set up, to Monte Carlo mould
Intend the different Run-time scenarios for producing, it is likely to occur in simulating reality system using the stochastic variable for obeying setting distribution
Chance phenomenon, and then being determined property Small signal stability analysis obtain corresponding key damping ratios, then unite by probability
Meter method obtains the probability density function of crucial damping ratios.And theory of risk assessment is combined, with one kind based on damping ratio spirit
The risk indicator of sensitivity quantifies the risk of the small probability of interference stability of power system, and probability distributive function is carried out to system, can be with
Further provided fundamental basis and decision-making to consider the effective measures of uncertain factor research raising system safe and stable operation
Foundation.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the method for the present invention；
Fig. 2 is the probability density distribution schematic diagram of crucial oscillation mode damping ratio in embodiment；
Fig. 3 is that the probability density distribution of East China Power Grid low load levels key oscillation mode damping ratio in embodiment is illustrated
Figure.
Specific embodiment
Illustrated with reference to specific embodiment.
It is simulation example with East China Power Grid in 2012, is considering that probabilistic primal system and subregion drop are negative respectively
Small probability of interference risk analysis is carried out during lotus level with emulation.In Jiangsu, Anhui, Fujian region work is separately added into by normal distribution
Industry, resident load stochastic wave simulation actual load are uncertain, it is considered that load of the power factor less than 0.9 is industrial load,
Remaining is resident load.According to correlation analysis, correlation coefficient ρ=0.8 between resident load is taken, coefficient correlation between industrial load
ρ=0.4, the coefficient correlation between resident load and industrial load is ρ=0.2.Wind-powered electricity generation mould is separately added into Jiangsu, Zhejiang region
Type, each region adds three Fans, mean wind speed herein to be taken as 10m/s, and parameter value is as follows：C=10.88, k=
5.004, vr=14m/s, vci=3m/s, vco=25m/s, and set relative coefficient according to historical data analysis.To East China electricity
Host wants the low-frequency oscillation between survey region, chooses crucial vibration of the Fujian mode as research observation by time-domain-simulation herein
Pattern.By many scene simulations based on Monte Carlo simulation, the probability density distribution such as accompanying drawing 2 of Fujian damping ratios is obtained
It is shown.
It can be seen that crucial oscillation mode damping ratio is substantially distributed between 0 to 0.07, with certain safety and stability
Nargin, but still there is unstability.
Generator is sorted according to correlation factor, only the generator big to correlation factor carries out Calculation of Sensitivity, to reduce
The amount of calculation of whole algorithm, crucial oscillation mode damping ratio is big to the sensitivity data amount of the method for operation, and following table is to randomly select
Several scenes in Fujian modal damping compare part generator and load initial sensitivity result of calculation：
Table 1
Damping ratio is much smaller compared to active to idle sensitivity, it is therefore assumed that load or burden without work with burden with power according to perseverance
Fixed power-factor cos Φ changes, i.e.,：△QL=△ PLTan Φ, take cos Φ=0.9.
According to aforementioned invention process, can be in the hope of the probability of the small interference safe and stable operation of the power system of the system
PSteady, risk indicator is Risk=0.9679.
Load level is reduced respectively for Jiangsu, Fujian regional power grid, and part of nodes load reduction 20% is carried out to system
Correspondence is changed and exerted oneself after adjustment, re-starts simulation analysis, calculates the data such as damping specific sensitivity.Obtain the resistance of Fujian mode
Buddhist nun than probability density distribution such as 3：
Found out by Fig. 3, crucial oscillation mode damping ratio is basic between 0.03 to 0.09, abundant with larger safety and stability
Degree.According to aforementioned invention process, can in the hope of Fujian, Jiangsu Province reduce load level after, the small probability of interference of the system
Stability Psteady=0.8410, risk indicator is Risk=0.1782.
By the East China specific emulation and analysis of system in 2012, it can be seen that the invention simulating analysis can be applicable
Specific small probability of interference stability analysis and effective risk assessment are carried out in the system of different scales.Comparison system is in difference
Risk indicator under load level, it can be seen that reducing the load level of subregion can significantly improve the small interference of system
Probabilistic stability, reduces system risk, and this can also be further steady to consider the small interference of uncertain factor research raising system
Qualitatively effective measures are provided fundamental basis and decision-making foundation.
Claims (6)
1. a kind of small probability of interference risk analysis and emulation mode based on Monte Carlo simulation, it is characterised in that：Including order
The following steps of execution：
Step 1, correlation of being exerted oneself according to Wind turbines in the same area and different node load horizontal correlations, set related
Property coefficient, sets up uncertain system model；
Step 2, the uncertain system model for foundation, are simulated using Monte Carlo simulation, generate n Run-time scenario；
Step 3, under each Run-time scenario to system being determined property Small signal stability analysis, to the n analysis of Run-time scenario
Result carries out the probability density function of the crucial oscillation mode damping ratio of mathematical statistics generation, and then it is steady to obtain the small interference safety of system
The probability of fixed operation；
Step 4, the probability based on the small interference safe and stable operation of said system, with reference to small interference unstability loss based on damping ratio
The risk indicator Risk of sensitivity, risk assessment is carried out to system.
2. small probability of interference risk analysis and emulation mode based on Monte Carlo simulation according to claim 1, it is special
Levy and be：The certainty analysis on Small Disturbance Stability is specific as follows：System state space is carried out near steady state equilibrium point
Linearisation obtains standard state equation, calculates its characteristic value according to standard state equation and solves electromechanical oscillations mode, during utilization
Domain emulates and Eigenvalues analysis, the crucial oscillation mode characteristic value in judgement system, and then obtains crucial oscillation mode dampingratioζ
As simulation result；The simulation result of different scenes is obtained into crucial oscillation mode dampingratioζ by probability theory and mathematical statistics
Probability density function P (ζ), and then according to default damping ratio threshold value ζsetAsk for the general of the small interference safe and stable operation of system
Rate PSteady。
3. small probability of interference risk analysis and emulation mode based on Monte Carlo simulation according to claim 2, it is special
Levy and be：In step 4, defining risk indicator Risk is
Risk=∫ P (ζ) S (ζ) d ζ
Wherein：
S (ζ) is the small interference stability severity function of each Run-time scenario in simulation model；
The inverse of the sensitivity for crucial oscillation mode damping ratio to the method for operation.
4. small probability of interference risk analysis and emulation mode based on Monte Carlo simulation according to claim 3, it is special
Levy and be：The inverse of the sensitivity of crucial oscillation mode damping ratio in the S (ζ) to the method for operationBy the following method
Calculated：
Step 4.1, by the correlation factor of the relatively crucial oscillation mode of generator by order sequence from big to small, and select preceding m
The individual generator for meeting following formula：
In formula：
piRepresent the correlation factor of the relatively crucial oscillation mode of i-th generator；
pjRepresent the correlation factor of the relatively crucial oscillation mode of jth platform generator；
nGIt is all generator set in system；
RsetIt is the proportion threshold value for setting；
Step 4.2, in the m platform generators selected, screening meets sensitivity of the crucial oscillation mode damping ratio to the method for operation
It is reciprocalGenerator, number of units is designated as N, PkIt is the active power of kth platform generator in generator；
The sensitivity of step 4.3, the crucial oscillation mode eigenvalue λ of acquisition to changes of operating modes μ
Step 4.4 obtains sensitivity of the crucial oscillation mode dampingratioζ to changes of operating modes μ according to step 4.3WhereinThe reality of respectively crucial oscillation mode Eigenvalue Sensitivity
Portion and imaginary part, δ0, ω0Be illustrated respectively in choose calculate special scenes under system core oscillation mode characteristic value real part and void
Portion；
Step 4.5, changes of operating modes μ is set as above-mentioned screening N platform generators active power output change △ P so that
ObtainExpression formula, it is inverted and then obtainExpression formula.
5. small probability of interference risk analysis and emulation mode based on Monte Carlo simulation according to claim 1, it is special
Levy and be：In the step 1, Wind turbines are exerted oneself and are calculated according to equation below with the relation of wind speed：
And Wind turbines in the same area are set according to wind power source geographical position exert oneself relative coefficient ρij, specifically according to such as
Lower formula is calculated：
Wherein：
v、vr、vci、vcoRespectively the actual wind speed of Wind turbines, rated wind speed, incision wind speed and cut-out wind speed, the actual wind
Fast Follow Weibull Distribution；PwIt is Wind turbines actually active output；PrIt is the rated active power of Wind turbines；
vi、vjWind speed variable respectively related to wind power source i, j；Cov(vi,vj) it is viAnd vjCovariance,
The respectively wind speed deviation of wind power source i, j.
6. small probability of interference risk analysis and emulation mode based on Monte Carlo simulation according to claim 1, it is special
Levy and be：In the step 1, load fluctuation is described using just too distributed model, and according to following formula calculated load x and load y it
Between relative coefficient ρ (x, y)：
Wherein：
σr() represent load model normal distribution standard deviation, cov (x, y) represent by historical data analysis obtain load x with
The covariance of load y.
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| CN109871640A (en) * | 2019-03-07 | 2019-06-11 | 常州大学 | A kind of dust subsequent explosion Risk Forecast Method based on Monte Carlo simulation |
| CN110518627A (en) * | 2019-05-28 | 2019-11-29 | 国网辽宁省电力有限公司电力科学研究院 | Meter and the probabilistic power system steady state voltage stability probability evaluation method of failure of wind-powered electricity generation |
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| CN111177851B (en) * | 2019-12-27 | 2023-05-02 | 北航（四川）西部国际创新港科技有限公司 | Assessment method for ground risk in unmanned aerial vehicle operation safety risk assessment |
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| US11592788B2 (en) * | 2020-03-23 | 2023-02-28 | Battelle Memorial Institute | Enhanced dynamic contingency analysis for power systems |
| CN112508324A (en) * | 2020-10-14 | 2021-03-16 | 浙江大学 | Electric power system characteristic value evaluation method based on complex plane regionalization |
| CN112508324B (en) * | 2020-10-14 | 2024-02-23 | 浙江大学 | Power system characteristic value evaluation method based on complex planar regionalization |
| CN113725910A (en) * | 2021-10-08 | 2021-11-30 | 南通大学 | Stability analysis and quantitative evaluation method for wind power plant grid-connected system |
| CN116613751A (en) * | 2023-07-19 | 2023-08-18 | 国网江西省电力有限公司电力科学研究院 | Small interference stability analysis method and system for new energy grid-connected system |
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