CN107480917A - A kind of probability load flow calculation method based on quasi-Monte Carlo simulation - Google Patents
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Abstract
A kind of probability load flow calculation method based on quasi-Monte Carlo simulation, to input stochastic variable as sample, is comprised the steps of：S1, obtain Load flow calculation deterministic data and input stochastic variable；S2, the selection method of sampling, it is determined that sampling scale N；S3, the sample matrix for obtaining input stochastic variable；S4, the I row based on sample matrix carry out probabilistic load flow successively, and preserve result（I=1,2,…,N）；S5, the data distribution and statistics for analyzing output variable.The advantage of the invention is that：Analog sampling number is unrelated with system scale, and is easily processed the influence of various actual motion control strategies and many objective factors.
Description
Technical field
The present invention relates to electric power network field, and in particular to a kind of probabilistic load flow side based on quasi-Monte Carlo simulation
Method.
Background technology
Shanghai Power Network is China megapolis power network, and its power load total amount is big, density is high, electricity consumption peak-valley difference is drawn year by year
Greatly, peak regulation is particularly thorny.Because Shanghai local power supply is less and is fired power generating unit, with the sustainable development of social economy,
Environmental protection, economic problems become increasingly conspicuous caused by a large amount of coal units.With the operation successively of the domestic extra-high voltage alternating current-direct current in Shanghai,
Entered a large amount of electric energy, actively consumption southwest cleaning water power from outside, be the great transition of the Shanghai energy.External electric power is especially clear
Clean electric power is on the increase, and Shanghai Power Network will face the external TV university amount feed-in in area, and the office of the local continuous reduction of unit generated energy
Face, power network step into the new normality of " the strong weak start of feed-in ".
Load flow calculation, short circuit calculation and stability Calculation are three big its main operationals of power system, and wherein Load flow calculation is three
Basis in class calculating, it provides initial value for short circuit calculation and stability Calculation, constitutes the foundation stone of Power System Analysis.Power train
The Load flow calculation of system is the running status that whole system is determined according to the network structure and service condition of system, is mainly to solve for
The voltage magnitude and phase angle of each node, the power distribution in network and power attenuation etc., its essence be to solve for one group it is non-linear
Equation group, equation group characterize the relation between Electric Power System Node Voltage and injecting power or Injection Current.Join in known network
In the case of number, and generated power in addition to balancing machine contribute it is known in the case of, solving power flow equation group can obtain
Obtain the voltage of each node in power system network, the active and reactive power of circuit conveying, generator reactive output and network damage
Consumption.
Load flow calculation does not restrain the foundation for causing operational planner to lack further adjustment flow data.Now can only root
Experience revised planning mode or the data repeatedly of personnel are arranged according to the method for operation, so as to obtain convergent flow solution.This method work
Work amount is larger, inefficient.Therefore need to study and the constringent calculation of large-scale electrical power system Load flow calculation is improved under special operation condition
Method, one is taken to have robustness and constringent tidal current computing method concurrently, to be carried out to electrical network parameter or operation controlled quentity controlled variable
Adjustment provides foundation.
In conventional electric power network analysis, due to the uncertainty of load prediction, the change and generating of power system operating mode
The factors such as the stoppage in transit of machine and other network original papers, the uncertainty of operation is brought to power system to a certain extent.Meanwhile
With the continuous development of power industry, the new energy using the wind energy with intermittent and randomness, solar energy as representative accesses electricity
Net, also it certainly will introduce uncertain factor to power network；The new ideas such as microgrid, electric automobile and distributed power source in power distribution network
Development also greatly strengthen interactive between " source-net-lotus ", the uncertain factor that result in power system significantly increases
Add.Therefore, above-mentioned a variety of uncertainties are described using probability theory, corresponding mathematical models of power system is inquired into and application is calculated
Method, also it is formed Probabilistic Load (PLF) research.PLF can be used for analysis circuit trend, the probability point of node voltage
Cloth, desired value, variance and limiting value, to have to the performance of whole power network at various operating conditions one comprehensively, it is comprehensive
Evaluation, and quantization point is made to weak link existing for power network, these information are to planning and the great reference of decision-making of traffic department
Value, thus it is widely used in medium-term and long-term Electric Power Network Planning and short-term operation planning, state estimation and measuring point arrangement, transmission system
Transmission capacity and idle planning etc..
The content of the invention
It is an object of the invention to provide a kind of probability load flow calculation method based on quasi-Monte Carlo simulation, applied to hair
Weak link in existing power network, the control reference of the person's of being scheduled for scheduler routine, and planning, bouw/infra are proposed to improve grid structure,
Accelerate the suggestion of capital construction progress.
Probabilistic load flow is in the nature to solve the Nonlinear System of Equations containing random parameter, and its key element includes：Generally
Rate power flow equation, input stochastic variable and output stochastic variable.Probabilistic Load Flow equation is based on power flow equation, including node work(
Rate equation and Line Flow equation.It is Power System Planning and operating uncertain factor to input stochastic variable, is mainly included：
Node load fluctuates, the maintenance of generator and circuit or failure, fluctuation that generation of electricity by new energy is contributed etc..Export stochastic variable bag
Include：The voltage magnitude and phase angle of PQ nodes, the voltage phase angle of PV node, Line Flow it is active and idle etc..
Disclosure sets forth it is a kind of based on quasi-Monte Carlo simulation probability load flow calculation method, using input stochastic variable as
Sample, comprise the steps of：
S1, obtain Load flow calculation deterministic data and input stochastic variable；
S2, the selection method of sampling, it is determined that sampling scale N；
S3, the sample matrix for obtaining input stochastic variable；
S4, the I row based on sample matrix carry out probabilistic load flow successively, and preserve result (I=1,2 ... ..., N)；
S5, the data distribution and statistics for analyzing output variable.
A kind of above-mentioned probability load flow calculation method based on quasi-Monte Carlo simulation, input described in the step S1 with
Machine variable is independent between each other.
A kind of above-mentioned probability load flow calculation method based on quasi-Monte Carlo simulation, the Probabilistic Load Flow in the step S4
The model of calculating is：
X=g (y), z=h (y)
X={ x1,x2,...,xs}
ui=Fi(xi) i=1,2 ..., s
A kind of above-mentioned probability load flow calculation method based on quasi-Monte Carlo simulation, the step S5 are specifically included：
S51, the frequency histogram for establishing accurate result；
S52, according to the block count nb set in step S51, obtain the frequency histogram M of accurate data；
S53, frequency histogram M in step S52 method of partition, obtain the frequency histogram P of test data；
The difference of S54, the frequency histogram that definition distribution error is test data and accurate data frequency histogram.
The advantages of the present invention are：Analog sampling number is unrelated with system scale, and is easily processed various realities
Run the influence of control strategy and many objective factors in border.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, institute in being described below to the embodiment of the present invention
The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only embodiments of the invention, right
For those of ordinary skill in the art, on the premise of not paying creative work, can also be according to embodiments of the present invention
Content and these accompanying drawings obtain other accompanying drawings.
Fig. 1 is the flow chart of the present invention.
Embodiment
Below in conjunction with accompanying drawing, by describing a preferable specific embodiment in detail, the present invention is further elaborated.
In probabilistic load flow, determine Nonlinear System of Equations and establish the master that input stochastic variable is Probabilistic Load Flow modeling
Content is wanted, and exports stochastic variable then by Probabilistic Load Flow equation, stochastic variable is inputted and probability load flow calculation method together decides on.
Three key elements of probabilistic load flow are Probabilistic Load Flow equation, input stochastic variable and output stochastic variable.Probability
Power flow equation is contact input stochastic variable and the tie for exporting stochastic variable based on traditional power flow equation.It is random in input
In variate model, the information of single stochastic variable is represented using probability-distribution function, using correlation matrix represent it is multiple with
Correlation between machine variable, correlation matrix is not needed between separate input stochastic variable.Given input is random
The probability distribution information of variable, based on Probabilistic Load Flow equation, the purpose of probabilistic load flow is to obtain the system of output stochastic variable
Count word and probability-distribution function.
As shown in figure 1, be it is a kind of based on quasi-Monte Carlo simulation probability load flow calculation method, using input stochastic variable as
Sample, comprise the steps of：
S1, obtain Load flow calculation deterministic data and input stochastic variable；
S2, the selection method of sampling, it is determined that sampling scale N；
S3, the sample matrix for obtaining input stochastic variable；
S4, the I row based on sample matrix carry out probabilistic load flow successively, and preserve result (I=1,2 ... ..., N)；
S5, the data distribution and statistics for analyzing output variable.
Select the input stochastic variable described in step S1 independent between each other in the present embodiment.
The model of probabilistic load flow in step S4 is：
X=g (y), z=h (y)
X={ x1,x2,...,xs}
ui=Fi(xi) i=1,2 ..., s
Step S5 is specifically included：
S51, the frequency histogram for establishing accurate result, frequency histogram is obtained by Freedman-Diaconis methods
Block count nb：
ω=2 (Q3(data)-Q1(data))/N1/3 (1)
In formula (1), data is accurate data；ω is the width of each block in frequency histogram；Square brackets represent to take upwards
It is whole；Q3() and Q1() represents data upper and lower quartile respectively.
S52, according to the block count nb set in step S51, obtain the frequency histogram M of accurate data；
S53, frequency histogram M in step S52 method of partition, obtain the frequency histogram P of test data；
The difference of S54, the frequency histogram that definition distribution error is test data and accurate data frequency histogram：
In formula (2), MiAnd PiRespectively i-th piece of sample number accounts in the frequency histogram of accurate data and test data
The frequency of total number of samples.
The evaluation index ε examined based on bivariate KSksIt is defined as Kolmogorov-Smirnov statistics, its calculation formula
For：
In formula (3)：Sup is max-value function；WithThe empirical distribution function of the sample of respectively two variables.
Assuming that stochastic variable x sample is x{i}(i=1,2 ..., N), then stochastic variable x empirical distribution function is：
In formula (4)：I[-∞,x](x{i}) it is indicator function, work as x{i}During≤x, its value is 1；Otherwise it is 0.
Technical scheme is described in detail above in association with accompanying drawing, the present invention proposes one kind and is based on quasi-Monte Carlo
The probability load flow calculation method of simulation, by sketching Load flow calculation and appraisal procedure, give weak ring in a kind of discovery power network
Section, the control reference of the person's of being scheduled for scheduler routine, and planning, bouw/infra are proposed to improve grid structure, accelerate capital construction progress
It is recommended that.
Although present disclosure is discussed in detail by above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read the above, for the present invention's
A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (4)
1. a kind of probability load flow calculation method based on quasi-Monte Carlo simulation, it is characterised in that to input stochastic variable as sample
This, comprises the steps of：
S1, obtain Load flow calculation deterministic data and input stochastic variable；
S2, the selection method of sampling, it is determined that sampling scale N；
S3, the sample matrix for obtaining input stochastic variable；
S4, the I row based on sample matrix carry out probabilistic load flow successively, and preserve result (I=1,2 ... ..., N)；
S5, the data distribution and statistics for analyzing output variable.
A kind of 2. probability load flow calculation method based on quasi-Monte Carlo simulation as claimed in claim 1, it is characterised in that institute
State and stochastic variable is inputted described in step S1 between each other independently.
A kind of 3. probability load flow calculation method based on quasi-Monte Carlo simulation as claimed in claim 1, it is characterised in that institute
The model for stating the probabilistic load flow in step S4 is：
A kind of 4. probability load flow calculation method based on quasi-Monte Carlo simulation as claimed in claim 1, it is characterised in that institute
Step S5 is stated specifically to include：
S51, the frequency histogram for establishing accurate result；
S52, according to the block count nb set in step S51, obtain the frequency histogram M of accurate data；
S53, frequency histogram M in step S52 method of partition, obtain the frequency histogram P of test data；
The difference of S54, the frequency histogram that definition distribution error is test data and accurate data frequency histogram.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111563637A (en) * | 2019-02-13 | 2020-08-21 | 株洲中车时代电气股份有限公司 | Multi-target probability optimal power flow calculation method and device based on demand response |
| CN110334366B (en) * | 2019-03-14 | 2023-07-14 | 华电电力科学研究院有限公司 | Building instantaneous cold load prediction method based on Monte Carlo method using Latin hypercube sampling |
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| CN105162112A (en) * | 2015-08-25 | 2015-12-16 | 许继集团有限公司 | Faure sequence based photovoltaic system power flow calculation and probabilistic power flow statistical method |
| CN106712037A (en) * | 2016-11-28 | 2017-05-24 | 武汉大学 | Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit |
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| CN105162112A (en) * | 2015-08-25 | 2015-12-16 | 许继集团有限公司 | Faure sequence based photovoltaic system power flow calculation and probabilistic power flow statistical method |
| CN106712037A (en) * | 2016-11-28 | 2017-05-24 | 武汉大学 | Electric power system static voltage stability assessment method considering electric automobile charging characteristic and load fluctuation limit |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111563637A (en) * | 2019-02-13 | 2020-08-21 | 株洲中车时代电气股份有限公司 | Multi-target probability optimal power flow calculation method and device based on demand response |
| CN111563637B (en) * | 2019-02-13 | 2023-09-12 | 株洲中车时代电气股份有限公司 | Multi-objective probability optimal power flow calculation method and device based on demand response |
| CN110334366B (en) * | 2019-03-14 | 2023-07-14 | 华电电力科学研究院有限公司 | Building instantaneous cold load prediction method based on Monte Carlo method using Latin hypercube sampling |
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Application publication date: 20171215 |