CN116345463A - Main and distribution network integrated system random power flow calculation method - Google Patents
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Abstract
The invention discloses a random power flow calculation method of a main distribution network integrated system, which comprises the following steps of; step 1: collecting network parameters of a main distribution network; step 2: dividing a main distribution network according to line voltage levels, network topology structures and the like, and determining boundary nodes; step 3: establishing wind power, photovoltaic output power and load probability density functions according to the wind speed, illumination intensity, load and other data; step 4: taking wind power, photovoltaic and load randomness into consideration, and carrying out random power flow calculation on the distribution network by utilizing a point estimation method; step 5: obtaining boundary node voltage amplitude expectation and phase angle expectation according to the tide result in the step 4; step 6: calculating the sum of absolute values of corresponding voltage amplitude differences of n boundary nodes of two adjacent iterations; step 7: and (5) solving a probability density function of the output load flow variable by using Gram-Charlier series expansion. According to the invention, the load randomness and the main-distribution integrated power grid load flow probability distribution after a large amount of new energy sources such as wind power, photovoltaic and the like are accessed are accurately estimated and considered, and the static safety analysis is more effectively carried out.
Description
Technical Field
The invention belongs to the field of steady-state analysis of power systems, and relates to a random power flow calculation method of a main distribution network integrated system.
Background
The traditional trend calculation is to calculate the network topology structure, line impedance parameters, transformer transformation ratio, active and reactive power output of generator nodes, active and reactive power of load nodes and other parameters as known quantities, and is generally called deterministic trend calculation. In recent years, however, clean energy sources such as wind power and photovoltaic and the like are largely connected into a power grid, the trend distribution of the system is changed, the output of the wind power and the photovoltaic is easily influenced by wind speed, illumination intensity, weather and other environmental factors, and the randomness and uncertainty of the wind power and the photovoltaic are caused to lead the output of the wind power and the photovoltaic to be not constant any more, but are random variables obeying a certain probability distribution. In addition, in the actual running of the power grid, the load demand is also changed at moment, and the deterministic power flow calculation cannot accurately reflect the power flow distribution of the power grid in real time. The random power flow energy effectively considers various uncertainty factors in the system, obtains probability characteristics of the power flow of the system, and reflects the power flow distribution of the power grid more truly.
For the current calculation of the traditional power grid, a unified model is generally established, and a certain method is used for obtaining a current calculation result, for example, a Newton-Lapherson method, a PQ decomposition method and the like can be used for a main network; the power distribution network can adopt forward-push back generation, Z-Bus method and the like, and the result meeting the convergence accuracy can be obtained. However, as the power grid in China adopts a layered and partitioned management system, each level of dispatching mechanism models the power grid in the jurisdiction range in detail, the distributed management causes the information island phenomenon, and only limited information can be exchanged between systems in different levels, so that the problem that a unified model is difficult to build to perform load flow calculation on the main distribution network is fundamentally caused. Even if sufficient information can be obtained, the main-distribution integrated model is built at the expense of precision, the huge node and branch number of the whole system can cause the abnormal huge calculation scale, massive calculation resources are required to be occupied, and the requirement of on-line calculation is difficult to meet. The data of the main distribution network have obvious difference, for example, the reactance-resistance ratio in the main network is far greater than that of the distribution network; the network parameter values of the main distribution network and the branch power have the order-of-magnitude level difference and the like, so that the problems of poor convergence of a nonlinear equation set of power flow calculation, easy singular jacobian matrix and the like are caused, and the difficulty of power flow solving is greatly increased. The master-slave split method is used as the most common master-slave integrated power flow calculation method, the master-slave network is respectively modeled and calculated, the problems are fundamentally solved, the power distribution network is equivalent to a constant power load when the power flow of the master network is calculated, the master network is equivalent to a constant voltage source when the power flow of the power distribution network is calculated, the master power distribution network is connected through boundary nodes, and convergence is finally achieved through alternate iterative operation, so that the master-slave split method is widely applied. However, with the large amount of access of new energy sources such as wind power, photovoltaic and the like and considering the randomness of loads, the main network and the distribution network are more tightly coupled, and bidirectional energy flow possibly occurs, and the main network and the distribution network are continuously and simply equivalent to a constant voltage source and a constant power load, so that the calculation result and the actual value error are increased, and the traditional master-slave split method also has corresponding problems in calculating the random tide of the main-distribution integrated system.
Disclosure of Invention
The invention provides a random power flow calculation method of a main distribution network integrated system, which aims to overcome the defects in the prior art. Based on the principle of a master-slave splitting method, carrying out random power flow calculation on a main power distribution network respectively, firstly giving boundary node voltage, and calculating the random power flow of the power distribution network by considering wind power, photovoltaic output and load randomness to obtain boundary node voltage amplitude
And the expected value of phase angle->
Then, calculating the random power flow of the main network by taking the boundary node as a relation to obtain the expected +.>
And the expected variance of phase angle>
And calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of adjacent iterations meet convergence accuracy. If all of them meet, then the global random is reachedConverging tide; otherwise, the steps 4 to 6 are alternately iterated until convergence; after the convergence of the power flow is judged, the moment of each step of the current main and distribution network power flow variable is considered as the moment of each step of the main and distribution network integrated system power flow variable, and the probability density function of the output power flow variable is obtained by utilizing Gram-Charlier series expansion, so that the probability distribution of the main and distribution integrated system power flow can be finally obtained, and the method can be used for static security analysis of the main and distribution network integrated system.
The invention adopts the following technical scheme for solving the technical problems:
the random power flow calculation method of the main and distribution network integrated system comprises the following steps:
step 1: collecting network parameters of a main distribution network, including parameters such as network topology, line parameters, active power and reactive power of generator nodes and the like; historical data such as wind speed, illumination intensity, load and the like;
step 2: dividing a main distribution network according to line voltage levels, network topological structures and the like, determining boundary nodes, forming a set B by the boundary nodes, setting boundary node voltage amplitude convergence criteria epsilon 1 and phase angle convergence criteria epsilon, wherein the number of the nodes is n 2 ；
Step 3: establishing wind power, photovoltaic output power and load probability density functions according to data such as wind speed, illumination intensity and load, and obtaining digital characteristics such as expected and variance;
step 4: taking wind power, photovoltaic and load randomness into consideration, carrying out random power flow calculation on the distribution network by using a point estimation method to obtain each moment of a power flow variable of the distribution network;
step 5: obtaining the expected voltage amplitude of the boundary node from the tide result in the step 4
And phase angle expectation
Calculating the random power flow of the main network by taking the boundary nodes as relations to obtain the amplitude expectation of the boundary node voltage
Is about to phase angle expectation>
Step 6: calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision or not, and reaching convergence after the sum of absolute values meets the precision, otherwise, alternately iterating the steps 4 to 6 until convergence;
step 7: after the convergence of the power flow is judged, the moment of each step of the current main power flow variable and the moment of each step of the current power flow variable of the distribution network are the moment of each step of the power flow variable of the main power flow and distribution network integrated system, and the probability density function of the output power flow variable is obtained by means of Gram-Charlier series expansion;
a random power flow calculation method of a main distribution network integrated system is characterized in that the step 3 is carried out according to the following steps:
for wind power generation, the wind speed is considered to be compliant with Weibull distribution, and when the wind speed changes, the relation between the output power of the wind power generator and the wind power generation active power and the probability density function are as follows:
k in 1 ＝P r /(v r -v ci )，k 2 ＝-k 1 v ci Is a constant coefficient; v is wind speed; v ci Is the cut-in wind speed; v co Cutting out wind speed; v r Is the rated wind speed; k. c is the shape parameter and the scale parameter of Weibull distribution respectively; the method can be obtained by the average value and standard deviation of the collected historical wind speed data; p (P) r The rated output power of the wind driven generator is obtained.
For photovoltaic power generation, the illumination intensity is considered to approximately follow the Beta distribution, and the probability density function of the output power of the photovoltaic array is as follows:
wherein: p (P) m And P m,max Respectively outputting an actual value and a maximum value of active power; a and b are shape parameters of Beta distribution, and are determined by the average value and standard deviation of collected historical illumination intensity data; f is a gamma function. In the distribution simulation of the actual historical data of the load, the load is considered to be approximately subjected to normal distribution, and then the load active power P load 、
Reactive power Q load The probability density function of (2) is:
in the middle, mu P 、μ Q The method is characterized by respectively absorbing the expectations of active power and reactive power for the load; sigma (sigma) P 、σ Q The variances of the active power and the reactive power are absorbed by the load respectively.
A random power flow calculation method of a main distribution network integrated system is characterized in that the point estimation method of the step 4 is carried out according to the following steps:
the expected mu is obtained by wind power, photovoltaic output and load probability density functions k Variance sigma k Equal digital characteristic, calculating position coefficient xi of each input variable k,i Probability coefficient p k,i 。
Determining three value points x according to the mean value and variance of the random variable k,i ，i＝1,2,3
x k,i ＝μ k +ξ k,i σ k ,i＝1,2,3 (7)
Each value point value of the random variable isx k,i And taking the average value of the rest random variables, and carrying out deterministic power flow calculation by utilizing a Newton-Laporton method to obtain power flow distribution X (i, k) of the main-distribution integrated system.
For each random variable, three values are needed, and 3 deterministic power flow calculations are needed until the calculation is completed for all random variables. And calculating each moment of the distribution network power flow X by using the following formula.
A random power flow calculation method of a main distribution network integrated system is characterized in that the step 6 is carried out according to the following steps:
the random power flow calculation is carried out on the distribution network and the main network respectively in the steps 4 and 5 to obtain the expectation of calculating the voltage amplitude and the phase angle of the boundary node in two adjacent iterations
And->
Wherein i=1, 2, …, n, n is the number of boundary nodes; calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision, and reaching convergence after the convergence precision is met, otherwise, alternately iterating the steps 4 to 6 until convergence, wherein the convergence criterion is as follows:
further, an apparatus, comprising:
one or more processors;
a memory for storing one or more programs;
when one or more of the programs are executed by one or more of the processors, the one or more of the processors implement a method for calculating random power flow of a main-distribution network integrated system as described above.
Further, a storage medium containing computer executable instructions, which when executed by a computer processor, are for performing a method of random power flow calculation for a primary distribution network integrated system as described above.
The beneficial effects of the invention are as follows:
the invention provides a random power flow calculation method of a main and distribution network integrated system based on a principle of a master-slave splitting method and a point estimation random power flow algorithm, which is used for calculating random power flow of a power distribution network and a main power network respectively by taking boundary nodes as relations to obtain the voltage amplitude and phase angle expectations of the boundary nodes of two adjacent iterations.
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FIG. 1 is a schematic flow chart of the present invention;
Detailed Description
The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to fall within the scope of this disclosure.
The invention will be described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, a method for calculating random power flow of a main-distribution network integrated system specifically includes the following steps:
step 1: collecting network parameters of a main distribution network, including network topology, line parameters, active power, reactive power and other deterministic parameters of generators and load nodes; historical data such as wind speed, illumination intensity, load and the like;
step 2: dividing a main distribution network according to line voltage levels, line topological structures and the like, and determining boundary nodes, wherein the number of the boundary nodes is n;
as a bridge for the connection of the main distribution network, the boundary nodes can be understood as load nodes in the main network as well as generator nodes of the distribution network. And establishing a boundary node set B, removing residual nodes of the boundary nodes by the main network node to form a set T, and forming a set D by the residual distribution network nodes.
Step 3: establishing wind power, photovoltaic output power and load probability density functions according to data such as wind speed, illumination intensity and load, and obtaining digital characteristics such as expected and variance;
for wind power generation, the wind speed is considered to be compliant with Weibull distribution, and when the wind speed changes, the relation between the output power of the wind power generator and the wind power generation active power and the probability density function are as follows:
k in 1 ＝P r /(v r -v ci )，k 2 ＝-k 1 v ci Is a constant coefficient; v is wind speed; v ci Is the cut-in wind speed; v co Cutting out wind speed; v r Is the rated wind speed; k. c is the shape parameter and the scale parameter of Weibull distribution respectively; the method can be obtained by the average value and standard deviation of the collected historical wind speed data; p (P) r The rated output power of the wind driven generator is obtained.
For photovoltaic power generation, the illumination intensity is considered to approximately follow the Beta distribution, and the probability density function of the output power of the photovoltaic array is as follows:
wherein: p (P) m And P m,max Respectively outputting an actual value and a maximum value of active power; a and b are shape parameters of Beta distribution, and are determined by the average value and standard deviation of collected historical illumination intensity data; f is a gamma function.
In the distribution simulation of the actual history data of the load, the load is considered to be approximately compliant with normal distribution, and the load absorbs the active power P load Reactive power Q load Can be described as:
wherein mu P 、σ P 、μ Q 、σ Q The expected and variance of the collected historical load active power and reactive power, respectively.
Step 4: giving boundary node voltage amplitude and phase angle, taking wind power, photovoltaic and load randomness into consideration, and carrying out random power flow calculation on the distribution network by using a point estimation method to obtain each moment of a power flow variable of the distribution network;
the distribution network tide equation is as follows: s is S D -S DB -S DD ＝0
Wherein: s is S D Injecting complex power vectors for nodes in the set D, S DT Vector formed by branch complex power flowing into node set T for node in node set D, S DD And (3) a branch complex power vector flowing into the node set for the node in the node set D. Because of randomness of wind power, photovoltaic output and load, the number of random variables contained in power flow calculation of a power distribution network is set as m, and three value points x are determined for one random variable according to expectations and variances of the random variable k,i ，i＝1,2,3，k＝1,2,…m。
x k，i ＝μ k +ξ k，i σ k ,i＝1,2,3 (5)
Wherein:
for each value point, the residual variable is averaged, and a deterministic power flow calculation is performed to obtain a deterministic power flow result f (x) k,i ). 3 deterministic power flow calculations are performed on a random variable. Repeating the calculation process until all random variables are calculated to obtain each moment E (X) of distribution network power flow distribution j )：
The expected voltage amplitude of n boundary nodes can be obtained through each moment of power flow distribution of the power distribution network
Is about to phase angle expectation>
Where i=1, 2, …, n. n is the number of boundary nodes. k is the distinction between adjacent iterations of the distribution network and the main network. B represents a set of border nodes.
Step 5: calculating the random power flow of the main network by taking the boundary nodes as the relations to obtain the expectation of the voltage amplitude of the boundary nodes
And phase angle->
The specific process is as follows: the main network tide equation is:
wherein S is T 、S B Complex power vectors respectively injected for nodes of the corresponding node set; s is S XY Is the complex power vector of each node on the node set X flowing into the node set Y; s is S XX The complex power vector of the own branch flows into the nodes of the node set X.
The calculation of the main network random power flow still adopts a point estimation method, and the process is consistent as described in the step 4, and is not repeated here. Wherein the boundary nodes correspond to load nodes and are treated as random variables. Obtaining each moment of the main network tide variable after the calculation is completed, namely obtaining the expected voltage amplitude of the boundary node
And the expected variance of phase angle>
Where i=1, 2, …, n. n is the number of boundary nodes. k+1 is the distinction between adjacent iterations of the distribution network and the main network. B represents a set of border nodes.
Step 6: and calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of adjacent iterations meet convergence accuracy. If both the power flows are satisfied, the convergence of the global random power flow is achieved; otherwise, the steps 4 to 6 are alternately iterated until convergence, and the convergence criterion is as follows:
i.e. if at the same time satisfy
And->
Considering that the overall random power flow convergence is achieved, exiting the loop, and performing step 7; otherwise, returning to the step 4, the step 4 and the step 6 are alternately iterated until convergence.
Step 7: after the convergence of the power flow is judged, the moment of each step of the current main power flow variable and the moment of each step of the current power flow variable of the distribution network are regarded as the moment of each step of the power flow variable of the main power flow and distribution network integrated system, and the probability density function of the output power flow variable is obtained by utilizing Gram-Charlier series expansion, and the specific process is as follows:
firstly, the main and distribution network integrated power flow X is standardized:
where μ and σ are the expectation and variance of X, respectively. Then->
The probability density function of (2) is:
wherein the method comprises the steps of
Is a probability density function that obeys a standard normal distribution.
The probability density function of the main distribution network integrated power flow X is:
based on the same inventive concept, the present invention also provides a computer apparatus comprising: one or more processors, and memory for storing one or more computer programs; the program includes program instructions and the processor is configured to execute the program instructions stored in the memory. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), field-Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal for implementing one or more instructions, in particular for loading and executing one or more instructions within a computer storage medium to implement the methods described above.
It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which when executed by a processor performs the above method. The storage media may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features, and advantages of the present disclosure. It will be understood by those skilled in the art that the present disclosure is not limited to the embodiments described above, which have been described in the foregoing and description merely illustrates the principles of the disclosure, and that various changes and modifications may be made therein without departing from the spirit and scope of the disclosure, which is defined in the appended claims.
Claims (6)
1. A random power flow calculation method of a main distribution network integrated system comprises the following steps:
step 1: collecting network parameters of a main distribution network, including network topology, line parameters and active and reactive power parameters of generator nodes; wind speed, illumination intensity and load history data;
step 2: dividing a main distribution network according to line voltage levels and a network topological structure, determining boundary nodes, wherein the boundary nodes form a set B, the number of the boundary nodes is n, and setting boundary node voltage amplitude convergence criteria epsilon 1 and phase angle convergence criteria epsilon 2 ；
Step 3: building wind power, photovoltaic output power and load probability density functions according to wind speed, illumination intensity and load data to obtain expected variance digital characteristics;
step 4: taking wind power, photovoltaic and load randomness into consideration, carrying out random power flow calculation on the distribution network by using a point estimation method to obtain each moment of a power flow variable of the distribution network;
step 5: obtaining the expected voltage amplitude of the boundary node from the tide result in the step 4
And phase angle expectation
Calculating the random power flow of the main network by taking the boundary nodes as relations to obtain the amplitude expectation of the boundary node voltage
Is about to phase angle expectation>
Step 6: calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision or not, and reaching convergence after the sum of absolute values meets the precision, otherwise, alternately iterating the steps 4 to 6 until convergence;
step 7: after the convergence of the power flow is judged, the moment of each step of the current main power flow variable and the moment of each step of the current power flow variable of the distribution network are the moment of each step of the power flow variable of the main power flow and distribution network integrated system, and the probability density function of the output power flow variable is obtained by means of Gram-Charlier series expansion.
2. The method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the step 3 comprises:
for wind power generation, the wind speed is considered to follow Weibull distribution, and when the wind speed changes, the relation between the output power of the wind power generator and the wind speed and the probability density function of the active power of wind power generation are as follows:
k in 1 ＝P r /(v r -v ci )，k 2 ＝-k 1 v ci Is a constant coefficient; v is wind speed; v ci Is the cut-in wind speed; v co Cutting out wind speed; v r Is the rated wind speed; k. c is the shape parameter and the scale parameter of Weibull distribution respectively; the method can be obtained by the average value and standard deviation of the collected historical wind speed data; p (P) r Rated output power of the wind driven generator;
for photovoltaic power generation, the illumination intensity is considered to obey beta distribution, and the probability density function of the output power of the photovoltaic array is as follows:
wherein: p (P) m And P m,max Respectively outputting an actual value and a maximum value of active power; a and b are shape parameters of Beta distribution, and are determined by the average value and standard deviation of collected historical illumination intensity data; f is a gamma function; in the distribution simulation of the actual historical data of the load, the load is considered to be approximately subjected to normal distribution, and then the load active power P load Reactive power Q load The probability density function of (2) is:
in the middle, mu P 、μ Q The method is characterized by respectively absorbing the expectations of active power and reactive power for the load; sigma (sigma) P 、σ Q The variances of the active power and the reactive power are absorbed by the load respectively.
3. The method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the point estimation method of the step 4 is performed according to the following steps:
the expected mu is obtained by wind power, photovoltaic output and load probability density functions k Variance sigma k Equal digital characteristic, calculating position coefficient xi of each input variable k,i Probability coefficient p k,i ；
Determining three value points x according to the mean value and variance of the random variable k,i ，i＝1,2,3
x k,i ＝μ k +ξ k,i σ k ,i＝1,2,3 (7)
Each value point value of the random variable is x k,i Taking the average value of the rest random variables, and carrying out deterministic power flow calculation by utilizing a Newton-Lapherson method to obtain power flow distribution X (i, k) of the main-distribution integrated system;
three values are needed for each random variable, and 3 deterministic power flow calculations are needed until calculation is completed for all random variables; calculating each moment of the distribution network power flow X by using the following formula;
4. the method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the step 6 is performed as follows:
the random power flow calculation is carried out on the distribution network and the main network respectively in the step 4 and the step 5 to obtain the expectation of calculating the voltage amplitude and the phase angle of the boundary node in two adjacent iterations
And->
Wherein i=1, 2, …, n, n is the number of boundary nodes; calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision, and reaching convergence after the convergence precision is met, otherwise, alternately iterating the steps 4 to 6 until convergence, wherein the convergence criterion is as follows:
5. an apparatus, the apparatus comprising:
one or more processors;
a memory for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform a method of stochastic load flow calculation of a primary distribution network integration system according to any of claims 1-4.
6. A computer readable storage medium storing a computer program, wherein the program when executed by a processor implements a method for calculating a random power flow of a main distribution network integrated system according to any one of claims 1 to 4.
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