CN104810826A - Bidirectional iteration parallel probability load flow calculation method combining Latin hypercube sampling - Google Patents
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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
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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
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Abstract
The invention discloses a bidirectional iteration parallel probability load flow calculation method combining Latin hypercube sampling. The method includes: using a Latin hypercube sampling method to sample new energy power generating power variables according to the accumulation distribution function of the new energy power generating power variables, and building the sample matrix of the new energy power generating power variables; aiming at the partitionable feature of a power grid, using a branch cutting and node tearing method to concentrate the power grid into meshes, and building a bidirectional iteration parallel load flow calculation model; using the sample matrix of the new energy power generating power variables as the input variable of the bidirectional iteration parallel load flow calculation model to perform probability load flow calculation so as to obtain the discrete result of output variables, and using kernel density estimation to fit the discrete result of the output variables to obtain the probability density function of the output variables. The method has the advantages that the Latin hypercube sampling is combined with the bidirectional iteration parallel load flow algorithm based on the concentrated meshes, sampling number is reduced, and parallel probability load flow calculation is achieved.
Description
Technical Field
The invention belongs to the field of electric power system analysis, and particularly relates to a bidirectional iterative parallel probabilistic power flow calculation method combining Latin hypercube sampling.
Background
New energy power generation such as wind power generation and photovoltaic power generation is gradually concerned due to the increasing maturity of power generation technology and huge environmental effect, but due to randomness of the new energy power generation, if a large number of new energy power generation devices are connected to a power grid, uncertainty of the power grid is aggravated, and risks are brought to safe operation of the power grid. The traditional deterministic power flow calculation method can only reflect the steady-state operation condition of the power system under a certain working condition and cannot be used for analyzing uncertainty factor scenes, and the probabilistic power flow calculation method is an effective tool for solving the problem.
The probabilistic power flow calculation method is firstly proposed by Borkowska in 1974, and the probabilistic power flow calculation is essentially to solve a power flow equation containing random parameters. The input random variables are active power and reactive power injected by the network structure and the nodes (the uncertainty of the input random variables is derived from the fluctuation of load, the fluctuation of renewable energy power generation output and the shutdown of the generator). The output random variables include the state output random variables (i.e., node voltage magnitude and phase angle) and the branch power flow. The process of solving the probabilistic power flow equation is to determine the expected value, variance or probability distribution of the output random variable according to the expected value, variance or probability distribution of the input random variable.
At present, probabilistic power flow calculation methods are roughly classified into a point estimation method, an analysis method, and a simulation method. The point estimation method is an approximate solving method, although the speed is high, the expectation and variance precision of the output variable are high, the high-order moment error of the output variable is large, and the probability distribution of the output variable is difficult to obtain; the analytic method comprises a fast Fourier transform method, a semi-invariant method and a first-order second-order moment method, although the calculation speed is high, the precision is not high as that of a simulation method, the simulation method is represented by a Monte Carlo simulation method, a traditional Monte Carlo simulation method utilizes a random sampling technology to extract samples of input variables, and after multiple deterministic load flow calculations, the probability distribution of output variables is obtained, the precision is high, and the time consumption is quite long.
Therefore, how to improve the calculation speed while ensuring the calculation accuracy is a problem to be solved by the current probabilistic power flow calculation method.
Disclosure of Invention
The purpose of the invention is as follows: the bidirectional iteration parallel probabilistic power flow calculation method combined with Latin hypercube sampling is provided to solve the problems in the prior art.
The technical scheme is as follows: a bidirectional iteration parallel probability power flow calculation method combined with Latin hypercube sampling comprises the following steps:
step 1, sampling a new energy power generation power variable by using a Latin hypercube sampling method according to an accumulated distribution function of the new energy power generation power variable, and establishing a sample matrix of the new energy power generation power variable;
step 2, aiming at the characteristic that the power grid can be partitioned, a branch cutting and node tearing method is utilized to concentrate the power grid into a grid, and a bidirectional iterative parallel power flow calculation model is established;
and 3, performing probability load flow calculation by taking the sample matrix of the new energy power generation variable as an input quantity of a bidirectional iterative parallel load flow calculation model to obtain a discrete result of an output variable, and fitting the discrete result of the output variable by utilizing kernel density estimation to obtain a probability density function of the output variable.
In a further embodiment, the process of establishing the sample matrix of the new energy power generation variables in step 1 further includes:
step 1.1, layering the new energy generated power variable according to the cumulative distribution function of the new energy generated power variable;
step 1.2, selecting a middle point or randomly selecting a point as a sample point in each layered subinterval;
and 1.3, sequencing the sample points, and establishing a sample matrix of the new energy power generation power variable.
The step 3 is further as follows:
step 3.1, taking the sample matrix of the new energy power generation variable as the input quantity of the bidirectional iterative parallel power flow calculation model to perform cyclic calculation, and extracting a certain column of vectors of the sample matrix of the new energy power generation variable as the input quantity in each cyclic calculation;
step 3.2, establishing a sample matrix of the output variables according to the results of the output variables obtained by the loop calculation;
and 3.3, fitting a probability density function of the output variable by utilizing kernel density estimation.
The step 2 is further as follows:
dividing a power grid into a plurality of sub-networks according to a branch cutting principle, associating each sub-network through a tie line, and tearing a tie line node into two sub-nodes according to a node tearing method, wherein one sub-node is merged on the side of the tie line, and the other sub-node is merged in the corresponding sub-network;
through the processing, the influence of each sub-network on the outside is summarized as the node interactive relation torn from the original node; the connecting lines are used as the edges of the grids, the sub-networks are used as the nodes of the grids to form a condensed grid, and each sub-network forms a computing node in one grid.
Has the advantages that: aiming at the characteristic that a power grid can be partitioned, the method combines the Latin hypercube sampling with the bidirectional iterative parallel power flow algorithm based on the concentration grid, reduces the sampling number and realizes the parallel probability power flow calculation.
Drawings
FIG. 1 is a simplified flow chart of the calculation method of the present invention.
FIG. 2 is a flow chart of a bidirectional iteration parallel probability power flow calculation method combining Latin hypercube sampling.
Detailed Description
As shown in fig. 1 and fig. 2, the bidirectional iterative parallel probabilistic power flow calculation method combining latin hypercube sampling of the present invention includes the following steps:
1) and sampling the new energy power generation power variable by utilizing a Latin hypercube sampling method according to the cumulative distribution function of the new energy power generation power variable, and establishing a sample matrix of the new energy power generation power variable.
Assume a random variable X1,X2,…XKFor K new energy power generation power variables, for random variable Xk(K-1, 2, …, K) having a cumulative distribution function of Fk(x)。
(1) Sampling
Firstly, to random variable XkCumulative distribution function Fk(x) Value range of [0, 1 ]]Dividing N equally to generate N sub-intervals(s 1, 2.. times.n), then in each subinterval(s 1, 2.. times.n) selecting a midpoint or randomly taking a pointFinally according to random variable XkCumulative distribution function Fk(x) Is inverse function ofCalculating the value of x corresponding to rX is to bek.rAs sub-intervalsThe sample point of (1). Sampling all random variables according to the method to generate an initial sample matrix of the new energy power generation power variables
(2) Sorting
Initial sample matrix for new energy power generation variableRandomly or by other improved sorting method to generate a sample matrix XK×N。
2) Aiming at the characteristic that the power grid can be partitioned, a branch cutting and node tearing method is utilized to concentrate the power grid into a grid, and a bidirectional iterative parallel power flow calculation model is established;
selecting a reasonable tie line according to a branch cutting principle to divide the power grid into a plurality of sub-networks, wherein all the sub-networks are related through the tie line, and the tie line node is (B)1,B2,…,Bu) Connecting line node B according to node tearing methodk(k-1, 2, …, u) into two child nodes B1kAnd B2kA child node B1kMerged on the contact side, another child node B2kAre merged into the corresponding subnet.
The influence of each sub-network on the outside of the sub-network can be reduced to the node interaction relationship torn from the original node after adopting the processing. The connecting lines are used as the edges of the grids, the sub-networks are used as the nodes of the grids to form a condensed grid, and each sub-network forms a computing node in one grid. Introducing a dummy currentTear node B1 representing a tiekAnd B2kThe interaction relationship between them.
The power flow equation of the calculation node is
Wherein a is an internal node of the subnet, b is a junctor tear-off node merged at the subnet side,to complex power, JabTo compute the jacobian matrix for a node,is a voltage, Δ is an offset, whereinIs the amount of complex power deviation of the internal node,is the voltage deviation amount of the internal node;the amount of complex power deviation for a torn node,to account for the amount of voltage deviation at the tear node,the voltage value of the tearing node is shown;is the virtual current offset.
Linear transformation of equation (1)
Wherein,is a coefficient of a linear relationship with respect to each other,is a constant of linear relationship.
The power flow equation of the grid is
Wherein c is a tie tearing node merged at the grid side, JcIs the Jacobian matrix of the grid.
Obtaining a virtual current deviation value according to a power flow equation (1) of a calculation nodeAmount of voltage deviation from tear nodeThen substituting the relation into a power flow equation (3) at the grid side, and calculating the voltage deviation amount of the tearing nodeAnd virtual current deviation amountFurther obtain a new tearing node voltage valueAnd novelVirtual current valueNew tearing node voltage valueAnd new virtual current valueSubstituting the power flow equation (1) of the calculation node for the next iteration calculation, and performing bidirectional iteration in such a way that the mark for stopping iteration isAndsmall enough to calculate the last iteration of the torn node voltage valueAnd a virtual current valueSubstituting the current into a computing node to carry out load flow calculation;
3) taking the sample matrix of the new energy power generation power obtained in the step 1) as the input quantity of the bidirectional iterative parallel power flow calculation model established in the step 2) to perform probability power flow calculation, and fitting the probability power flow calculation result by utilizing kernel density estimation to obtain a probability density function of an output variable.
Generating power variable sample matrix X of new energyK×NPerforming loop calculation as input quantity, and extracting sample matrix X for nth loop calculationK×NThe nth column vector of the power flow is taken as the input quantity of the bidirectional iterative parallel power flow calculation model established in the step 2) to carry out power flow calculation, and the result Z of the output variable is obtainednSetting n as n +1, and carrying out next cycle calculation until XK×NAll column vectors of (a) participate in the calculation and the loop ends.
Result Z from output variablesn(N ═ 1,2, …, N) establishes a sample matrix of output variables:
Z＝[Z1,Z2,…,ZN] (4)
fitting an output variable probability density function using kernel density estimation:
wherein N is the number of discrete data of the output variable, h is the window width, ZiFor discrete data of output variables, i ∈ 1,2, N, K (·) is a kernel function.
In a word, the improved Monte Carlo simulation method based on Latin hypercube sampling is based on the principle of layered sampling, can meet the probability characteristic of random variables by extracting fewer samples, is higher than the traditional Monte Carlo simulation method in speed, and can ensure the calculation precision.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention. It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition. In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (4)
1. A bidirectional iteration parallel probability power flow calculation method combined with Latin hypercube sampling is characterized by comprising the following steps:
step 1, sampling a new energy power generation power variable by using a Latin hypercube sampling method according to an accumulated distribution function of the new energy power generation power variable, and establishing a sample matrix of the new energy power generation power variable;
step 2, aiming at the characteristic that the power grid can be partitioned, a branch cutting and node tearing method is utilized to concentrate the power grid into a grid, and a bidirectional iterative parallel power flow calculation model is established;
and 3, performing probability load flow calculation by taking the sample matrix of the new energy power generation variable as an input quantity of a bidirectional iterative parallel load flow calculation model to obtain a discrete result of an output variable, and fitting the discrete result of the output variable by utilizing kernel density estimation to obtain a probability density function of the output variable.
2. The method for performing bi-directional iterative parallel probabilistic power flow calculation in combination with latin hypercube sampling according to claim 1 wherein the process of creating a sample matrix of new energy source generated power variables in step 1 further comprises:
step 1.1, layering the new energy generated power variable according to the cumulative distribution function of the new energy generated power variable;
step 1.2, selecting a middle point or randomly selecting a point as a sample point in each layered subinterval;
and 1.3, sequencing the sample points, and establishing a sample matrix of the new energy power generation power variable.
3. The method for bi-directional iterative parallel probabilistic power flow calculation in combination with latin hypercube sampling according to claim 1 wherein said step 3 is further:
step 3.1, taking the sample matrix of the new energy power generation variable as the input quantity of the bidirectional iterative parallel power flow calculation model to perform cyclic calculation, and extracting a certain column of vectors of the sample matrix of the new energy power generation variable as the input quantity in each cyclic calculation;
step 3.2, establishing a sample matrix of the output variables according to the results of the output variables obtained by the loop calculation;
and 3.3, fitting a probability density function of the output variable by utilizing kernel density estimation.
4. The method for bi-directional iterative parallel probabilistic power flow computation incorporating latin hypercube sampling of claim 1 wherein said step 2 is further characterized by:
dividing a power grid into a plurality of sub-networks according to a branch cutting principle, associating each sub-network through a tie line, and tearing a tie line node into two sub-nodes according to a node tearing method, wherein one sub-node is merged on the side of the tie line, and the other sub-node is merged in the corresponding sub-network;
through the processing, the influence of each sub-network on the outside is summarized as the node interactive relation torn from the original node;
the connecting lines are used as the edges of the grids, the sub-networks are used as the nodes of the grids to form a condensed grid, and each sub-network forms a computing node in one grid.
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| CN106485594A (en) * | 2016-05-10 | 2017-03-08 | 国网江苏省电力公司南京供电公司 | A kind of main distribution integration incident response decision method |
| CN106888112A (en) * | 2017-01-19 | 2017-06-23 | 四川大学 | A kind of node migration network blocks optimization method based on quick splitting algorithm |
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| CN111162537A (en) * | 2020-01-16 | 2020-05-15 | 广东工业大学 | Latin hypercube sampling method probability load flow calculation method based on combined Copula function |
| CN111682530A (en) * | 2020-06-11 | 2020-09-18 | 广东电网有限责任公司韶关供电局 | Method, device, equipment and medium for determining out-of-limit probability of voltage of power distribution network |
| CN113573406A (en) * | 2021-07-05 | 2021-10-29 | 江南大学 | Fingerprint information positioning method based on small amount of wireless signal strength |
| CN115173421A (en) * | 2022-07-30 | 2022-10-11 | 重庆大学 | Probability optimal power flow calculation method based on progressive Latin hypercube sampling |
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Cited By (16)
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| CN105656038A (en) * | 2016-03-15 | 2016-06-08 | 国电南瑞科技股份有限公司 | Probability load flow calculation method considering admitting ability of power grid |
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| CN105656038B (en) * | 2016-03-15 | 2018-02-13 | 国电南瑞科技股份有限公司 | A kind of meter and power network receive the probability load flow calculation method of ability |
| CN106485594A (en) * | 2016-05-10 | 2017-03-08 | 国网江苏省电力公司南京供电公司 | A kind of main distribution integration incident response decision method |
| CN107464007A (en) * | 2016-06-02 | 2017-12-12 | 南京理工大学 | Continuous time Probabilistic Load Flow Forecasting Methodology based on Markov theory and pro rate principle |
| CN106888112A (en) * | 2017-01-19 | 2017-06-23 | 四川大学 | A kind of node migration network blocks optimization method based on quick splitting algorithm |
| CN106888112B (en) * | 2017-01-19 | 2019-10-18 | 四川大学 | A kind of node migration network blocks optimization method based on quick splitting algorithm |
| CN107681685A (en) * | 2017-09-13 | 2018-02-09 | 国网甘肃省电力公司电力科学研究院 | A kind of Probabilistic Load computational methods for considering photovoltaic non-linear dependencies |
| CN111162537A (en) * | 2020-01-16 | 2020-05-15 | 广东工业大学 | Latin hypercube sampling method probability load flow calculation method based on combined Copula function |
| CN111162537B (en) * | 2020-01-16 | 2023-02-28 | 广东工业大学 | Latin hypercube sampling method probability load flow calculation method based on combined Copula function |
| CN111682530A (en) * | 2020-06-11 | 2020-09-18 | 广东电网有限责任公司韶关供电局 | Method, device, equipment and medium for determining out-of-limit probability of voltage of power distribution network |
| CN111682530B (en) * | 2020-06-11 | 2022-06-28 | 广东电网有限责任公司韶关供电局 | Method, device, equipment and medium for determining out-of-limit probability of voltage of power distribution network |
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| CN113573406B (en) * | 2021-07-05 | 2022-04-29 | 江南大学 | Fingerprint information positioning method based on wireless signal strength |
| CN115173421A (en) * | 2022-07-30 | 2022-10-11 | 重庆大学 | Probability optimal power flow calculation method based on progressive Latin hypercube sampling |
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