CN110707702A - Optimal power flow calculation method and device considering uncertainty of renewable energy source - Google Patents
Optimal power flow calculation method and device considering uncertainty of renewable energy source Download PDFInfo
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Abstract
The invention discloses an optimal power flow calculation method considering uncertainty of renewable energy sources, which comprises the following steps: respectively calculating uncertainty of wind speed and illumination intensity; calculating a wind speed and illumination intensity scene value considering the correlation; calculating scene values of wind power generation and photovoltaic output; calculating a system cost value based on scene values of wind power generation and photovoltaic output; constructing a system safety constraint function, and establishing an optimal power flow model considering the uncertainty of renewable energy sources and system safety constraints; and carrying out micro transformation on the optimal power flow model, and solving the transformed optimal power flow model to obtain a calculation result of the uncertainty of the renewable energy source. The optimal power flow calculation method considering the uncertainty of the renewable energy sources can simulate the uncertainty of the renewable energy sources and improve the running safety of a power system. The invention also discloses an optimal power flow calculation device and a storage medium considering the uncertainty of the renewable energy source.
Description
Technical Field
The invention relates to the technical field of operation, analysis and scheduling of a power system, in particular to an optimal power flow calculation method and device considering uncertainty of renewable energy sources.
Background
With the continuous and high-speed development of global economy, energy and environmental problems facing countries in the world are becoming more and more serious. On the one hand, traditional fossil fuels such as coal, oil and natural gas are becoming exhausted; on the other hand, the ecological environment problem caused by the emission of greenhouse gases and the combustion of fossil fuels is becoming more serious. Therefore, the position of clean renewable energy as an alternative energy source of the conventional fossil fuel is continuously increasing in energy strategy of each country. Among them, wind power and solar power hold a very important position among a lot of renewable energy sources.
However, wind energy and solar energy have strong volatility and intermittency, and the uncertainty factor of a power grid is increased after the grid connection. And with the higher and higher installed proportion of wind power and photovoltaic power, the permeability of wind power and photovoltaic power in the power grid is also increased continuously, and the access of large-scale wind power and photovoltaic power can have great influence on the safe operation and economic dispatch of the power system.
Therefore, a method of simulating the uncertainty of renewable energy is needed.
Disclosure of Invention
The embodiment of the invention provides an optimal power flow calculation method considering the uncertainty of renewable energy sources, which can simulate the uncertainty of the renewable energy sources and improve the running safety of a power system.
The embodiment of the invention provides an optimal power flow calculation method considering uncertainty of renewable energy sources, which comprises the following steps:
respectively calculating uncertainty of wind speed and illumination intensity;
calculating a wind speed and illumination intensity scene value considering the correlation;
calculating scene values of wind power generation and photovoltaic output according to output power of a fan and a photovoltaic power station;
calculating a system cost value based on the scene values of the wind power generation and photovoltaic output;
constructing a system safety constraint function, and establishing an optimal power flow model considering the uncertainty of renewable energy sources and system safety constraints;
and carrying out micro transformation on the optimal power flow model, and solving the transformed optimal power flow model to obtain a calculation result of the uncertainty of the renewable energy source.
As an improvement of the above scheme, the calculating the uncertainty of the wind speed and the uncertainty of the illumination intensity respectively specifically includes:
establishing an uncertainty model of the wind speed at a single moment by the following formula (1):
where v is the predicted value of wind speed, fw(v) C and k represent probability distribution shape parameters and scale parameters respectively corresponding to the predicted value v of the wind speed, and c>0，k>0；
An uncertainty model of the illumination intensity is established by the following formula (2):
wherein E is a predicted value of the light intensity, fPV(E) And the probability value corresponding to the predicted value E of the illumination intensity is Emax, Г, the gamma function and alpha and beta are both shape parameters.
As an improvement of the above scheme, the calculating the wind speed and illumination intensity scene value considering the correlation specifically includes:
constructing an edge distribution function of wind speed and illumination intensity variables according to historical data of a wind power plant and a photovoltaic power station;
and describing a correlation structure between the wind speed and the illumination intensity variable, fitting to determine parameters of the correlation structure, and calculating to obtain an equivalent correlation coefficient matrix rho.
Sampling the uncertainty model of the wind speed and the illumination intensity without considering the correlation to obtain a sampling matrix R consisting of Ns groups of sampling values of Nrv random variables: rNrv*RNsAnd calculating said sampling matrix RA correlation coefficient matrix T;
decomposing the equivalent correlation coefficient matrix rho to obtain a lower triangular matrix P: ρ ═ P × P'; decomposing the correlation coefficient matrix T to obtain a lower triangular matrix Q: t ═ Q × Q';
let S be P Q-1R ═ RS', the matrix of correlation coefficients of R is then the same as the matrix ρ of equivalent correlation coefficients;
rearranging the element arrangement sequence of each column vector in the scene matrix R according to the random variable pairing condition of R to obtain a corrected scene matrix R ', and calculating the wind speed and photovoltaic intensity scene value considering the correlation through the corrected scene matrix R'.
As an improvement of the above scheme, the calculating the scene values of the wind power output and the photovoltaic output according to the output powers of the fan and the photovoltaic power station specifically includes:
the following formula (3) is a relation expression of nonlinear wind speed and output power:
in the formula, C0、C1、C2And C3All are parameters obtained by fitting;
the following formula (4) is an output power expression of a photovoltaic array:
PPV＝EAτ (4)
in the formula, E, A and tau are respectively the illumination intensity, the effective area of the photovoltaic array and the photoelectric conversion rate of the cell panel;
and converting the wind speed and illumination intensity sampling values into scene values of wind power output and photovoltaic output through formulas (3) and (4).
As an improvement of the above solution, the calculating a system cost value based on the scene values of the wind power generation and the photovoltaic output specifically includes:
calculating a system cost value based on the scene values of wind power generation and photovoltaic output by the following equation (5):
wherein the content of the first and second substances,
symbol [ 2 ]]+Indicates that the value of "Zheng", "if", "is]If the internal number is less than zero, taking zero;
in the formula, crAnd csRespectively are a standby coefficient and a saving coefficient which are positive numbers; ns is the scene number of renewable energy output; pREjA force value is planned for the jth new energy power station;is the active output value of the jth new energy power station in the scene s
Is that it is
A corresponding probability; ED (electronic device)jFor new energy plants with less than planned output expected, ESjAnd the new energy power station is more than the expected value of the planned output.
As an improvement of the above scheme, the constructing a system safety constraint function and establishing an optimal power flow model considering the uncertainty of renewable energy and the system safety constraint specifically includes:
establishing a generator rotation backup constraint of the following equation (6):
in the formula, USR and DSR are respectively used for upward and downward rotation of the system without considering renewable energy source units, and USiAnd DSiFor respectively providing an upward and downward rotation reserve for a conventional unit, the USiAnd DSiThe value of (A) is as follows (7):
in the formula, NGIndicating the number of conventional units in the system, PG maxAnd PG minThe maximum and minimum output values of the traditional unit are respectively, and d is a rotary spare capacity parameter;
establishing a system operating voltage constraint as follows (8):
Vi min≤Vi≤Vi maxi∈NB(8)
in the formula, NBRepresenting the node number of the system, and V represents the node voltage amplitude;
establishing a line thermal limit constraint as follows (9):
in the formula, Pij maxActive maximum allowed for the line;
establishing a power generation cost objective function and a power flow equation constraint as follows (10) and (11):
wherein B represents a cost parameter, θ represents a node voltage phase angle, QGAnd QLRespectively the reactive output and node reactive load of the traditional unit; pGAnd PLRespectively the active output of the traditional unit and the active load of the node;
and obtaining an optimal power flow model considering the uncertainty of the renewable energy source and the safety constraint of the system according to the formulas (7), (8), (9), (10) and (11).
As an improvement of the above scheme, the micro-transforming the optimal power flow model specifically includes:
let c (x) be an n-segment linear function, which if convex, is transformed by the following equation (12):
piecewise linear function EDjThe following formula (13):
in the formula, mdj sAnd cdj sRenewable energy output scenario PsAnd frequency f of the samesDerived if EDjFor convex constraint, then EDjTo the following formula (14):
and obtaining the transformed continuous micro model.
Correspondingly, an embodiment of the present invention provides an optimal power flow calculation apparatus considering uncertainty of renewable energy, including: the system comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize an optimal power flow calculation method considering uncertainty of renewable energy sources according to the first embodiment of the invention.
The third embodiment of the present invention correspondingly provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the optimal power flow calculation method according to the first embodiment of the present invention, where uncertainty of renewable energy is considered.
The optimal power flow calculation method considering the uncertainty of the renewable energy source provided by the embodiment of the invention has the following beneficial effects:
in order to introduce the uncertainty of renewable energy into a system model, a scene idea is adopted, a sampling technology is utilized to generate scene values of wind speed and illumination intensity considering correlation, and finally the uncertainty of the output of the renewable energy is quantized into opportunity cost and added into a target function; meanwhile, an optimal power flow model is established according to safety constraints such as generator rotation standby constraint, system operation voltage constraint, line thermal limit and the like, the safety requirement of the system is considered, and the safety and the reliability of the model are improved; for piecewise linear components of an optimal power flow model caused by introduction of renewable energy generator opportunity cost and uncertainty of unit rotation standby, a convex transformation method is adopted to simplify solution, micro-transformation is carried out on an original model, and finally a traditional nonlinear optimization method is adopted to solve the transformed model to obtain an accurate and reliable calculation result so as to improve operation safety of a power system.
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Fig. 1 is a schematic flowchart of an optimal power flow calculation method considering uncertainty of renewable energy according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a system wiring according to an embodiment of the present invention.
Fig. 3 is a US of an optimal power flow calculation method considering uncertainty of renewable energy according to an embodiment of the present inventioniAnd DSiAnd (4) value obtaining schematic diagrams.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flowchart of an optimal power flow calculation method considering uncertainty of renewable energy according to an embodiment of the present invention is shown, including:
s101, respectively calculating uncertainty of wind speed and illumination intensity;
s102, calculating a wind speed and illumination intensity scene value considering correlation;
s103, calculating scene values of wind power generation and photovoltaic output according to output power of the fan and the photovoltaic power station;
s104, calculating a system cost value based on the scene values of the wind power generation and the photovoltaic output;
s105, constructing a system safety constraint function, and establishing an optimal power flow model considering the uncertainty of renewable energy sources and the system safety constraint;
and S106, carrying out micro transformation on the optimal power flow model, and solving the transformed optimal power flow model to obtain a calculation result of the uncertainty of the renewable energy source.
Further, there is usually a certain deviation between the predicted values of wind speed and illumination intensity and the actual values, which increases the uncertainty of the system, and the calculating the uncertainty of wind speed and illumination intensity respectively specifically includes:
establishing an uncertainty model of the wind speed at a single moment by the following formula (1):
where v is the predicted value of wind speed, fw(v) C and k represent probability distribution shape parameters and scale parameters respectively corresponding to the predicted value v of the wind speed, and c>0，k>0；
Preferably, c and k reflect the extent and degree to which the predicted wind speed fluctuates. Historical wind speed data of the wind field comprise a large number of discrete wind speed values v and frequency f thereof, and values of parameters c and k can be obtained through fitting of the relationship between f and v;
an uncertainty model of the illumination intensity is established by the following formula (2):
wherein E is a predicted value of the light intensity, fPV(E) Is light illuminationThe probability value corresponding to the predicted value E of the intensity, Emax is the maximum illumination intensity, Г is a gamma function, and both alpha and beta are shape parameters and can be obtained by fitting according to historical data.
Further, the calculating the wind speed and illumination intensity scene value considering the correlation specifically includes:
constructing an edge distribution function of wind speed and illumination intensity variables according to historical data of a wind power plant and a photovoltaic power station;
and describing a correlation structure between the wind speed and the illumination intensity variable, fitting to determine parameters of the correlation structure, and calculating to obtain an equivalent correlation coefficient matrix rho.
In the equivalent correlation coefficient matrix ρ, the parameters in the ith row and the jth column represent the correlation coefficient between the variable i and the variable j, the matrix is a symmetric matrix with all diagonal elements being 1, and the matrix is also a positive definite matrix.
The scene analysis method can clearly embody the probability characteristics of the uncertain quantity, and can construct a random optimization model on the basis of the probability characteristics, so that the method becomes one of important methods for processing uncertainty. In this embodiment, a latin hypercube sampling technique considering the equivalent correlation coefficient matrix ρ is adopted to sample a probabilistic model of wind speed and illumination intensity considering correlation, so as to obtain Ns 1000 sets of scene values.
Sampling the uncertainty model of the wind speed and the illumination intensity without considering the correlation by utilizing a Latin hypercube sampling technology to obtain a sampling matrix R consisting of Ns sampling values of Nrv random variables: rNrv*RNsAnd calculating a correlation coefficient matrix T of the sampling matrix R;
decomposing the equivalent correlation coefficient matrix rho by using a Cholesky decomposition method to obtain a lower triangular matrix P: ρ ═ P × P'; decomposing the correlation coefficient matrix T to obtain a lower triangular matrix Q: t ═ Q × Q';
let S be P Q-1R ═ RS', the matrix of correlation coefficients of R is then the same as the matrix ρ of equivalent correlation coefficients;
rearranging the element arrangement sequence of each column vector in the scene matrix R according to the random variable pairing condition of R to obtain a corrected scene matrix R ', and calculating the wind speed and photovoltaic intensity scene value considering the correlation through the corrected scene matrix R'.
Further, the calculating the scene values of the wind power output and the photovoltaic output according to the output powers of the fan and the photovoltaic power station specifically includes:
the output power of the fan is related to the wind speed and the power characteristic of the fan, and when the wind speed v reaches the cut-in wind speed vinWhen the fan is started, the power P is outputwIncrease with increasing wind speed; when the wind speed v reaches the rated wind speed vnThe output power is also maintained at the rated value Pn(ii) a When the wind speed v reaches the cut-out wind speed voutAnd (3) when the wind turbine exits from the power grid, fitting a nonlinear wind speed and output power relational expression of the following formula (3) according to a wind speed and power relation given in a wind turbine technical manual:
in the formula, C0、C1、C2And C3All are parameters obtained by fitting;
preferably, the output power of the photovoltaic power station is related to the effective area of the photovoltaic array, the illumination radiation intensity of the surface of the cell panel, the photoelectric conversion efficiency of the cell panel and the like, the photovoltaic active power output is basically in direct proportion to the illumination intensity,
the following formula (4) is an output power expression of a photovoltaic array:
PPV＝EAτ (4)
in the formula, E, A and tau are respectively the illumination intensity, the effective area of the photovoltaic array and the photoelectric conversion rate of the cell panel;
and converting the wind speed and illumination intensity sampling values into scene values of wind power output and photovoltaic output through formulas (3) and (4).
Further, the calculating a system cost value based on the scene values of the wind power generation and the photovoltaic output specifically includes:
calculating a system cost value based on the scene values of wind power generation and photovoltaic output by the following equation (5):
wherein the content of the first and second substances,
symbol [ 2 ]]+Indicates that the value of "Zheng", "if", "is]If the internal number is less than zero, taking zero;
in the formula, crAnd csThe spare coefficient and the saving coefficient are positive numbers respectively and can be set according to requirements; ns is the scene number of renewable energy output; pREjThe planned output value of the jth new energy power station (wind field or photovoltaic power station);
is the active output value of the jth new energy power station in the scene s
Is that it isA corresponding probability; ED (electronic device)jFor new energy plants with less than planned output expected, ESjAnd the new energy power station is more than the expected value of the planned output.
Preferably, the purpose of the optimal power flow calculation is to determine the planned output of each unit under the condition of meeting the economic and operation constraints. When the planned output of the wind farm and the photovoltaic power plant is determined, the actual output may be greater or less than the planned output value due to uncertainty in the wind and photovoltaic outputs. If the actual output is less than the planned output, the system must take necessary backup to maintain the system power balance, and the running cost of the whole system increases corresponding backup cost; if the actual output is larger than the planned output, the output of a conventional unit in the system can be correspondingly reduced, and the actual cost is reduced because the wind power generation cost is generally lower.
Further, in order to ensure the safety of the system, while constructing the uncertainty opportunity cost by using the renewable energy output scene, the system safety constraint function is constructed, and an optimal power flow model considering the renewable energy uncertainty and the system safety constraint is established, which specifically includes:
establishing a generator rotation backup constraint of the following equation (6):
in the formula, USR and DSR are respectively used for upward and downward rotation of the system without considering renewable energy source units, and USiAnd DSiRespectively provides upward and downward rotation for standby of the traditional unit,
preferably, USiAnd DSiThe values are shown in FIG. 3 as the area of the slanted shading, the USiAnd DSiThe value of (A) is as follows (7):
in the formula, NGIndicating the number of conventional units in the system, PG maxAnd PG minThe maximum and minimum output values of the traditional unit are respectively, and d is a rotary spare capacity parameter;
establishing a system operating voltage constraint as follows (8):
Vi min≤Vi≤Vi maxi∈NB(8)
in the formula, NBRepresenting the node number of the system, and V represents the node voltage amplitude;
establishing a line thermal limit constraint as follows (9):
in the formula, Pij maxActive maximum allowed for the line;
establishing a power generation cost objective function and a power flow equation constraint as follows (10) and (11):
wherein B represents a cost parameter, θ represents a node voltage phase angle, QGAnd QLRespectively the reactive output and node reactive load of the traditional unit; pGAnd PLRespectively the active output of the traditional unit and the active load of the node;
preferably, the power generation cost function of the conventional unit is quadratic, and the power generation cost of the renewable energy source is expressed by a linear equation;
and obtaining an optimal power flow model considering the uncertainty of the renewable energy source and the safety constraint of the system according to the formulas (7), (8), (9), (10) and (11).
Further, the micro-transforming the optimal power flow model specifically includes:
in the optimal power flow model established in S105, due to the introduction of the opportunity cost of the renewable energy power generator and the uncertainty of the unit rotation standby, the model has a piecewise linear component EDj，ESj，USiAnd DSi. The invention adopts a convex transformation method to carry out micro-transformation on the original model.
Let c (x) be an n-segment linear function, if it is convex, then transform c (x) into an optimization problem with variable y and n linear constraints by:
piecewise linear function EDjThe following formula (13):
in the formula, mdj sAnd cdj sRenewable energy output scenario PsAnd frequency f of the samesDerived if EDjFor convex constraint, then EDjTo the following formula (14):
similarly, the linear component ES can be segmentedj，USiAnd DSiConvex transformation is carried out, and finally the transformed continuous micro model is obtained.
The IEEE57 node system is described in detail as a specific embodiment, and the connection thereof is shown in fig. 2. To take into account the access to renewable energy, two wind farms are connected to node 20 and node 25, and two photovoltaic plants are connected to node 40 and node 50, so in this example the number of random variables Nrv is 4. In a particular embodiment, NB 57.
Substituting the parameters into the expression provided in the first embodiment can calculate the uncertainty of the IEEE57 node system to the renewable energy source.
The optimal power flow calculation method considering the uncertainty of the renewable energy source provided by the embodiment of the invention has the following beneficial effects:
in order to introduce the uncertainty of renewable energy into a system model, a scene idea is adopted, a sampling technology is utilized to generate scene values of wind speed and illumination intensity considering correlation, and finally the uncertainty of the output of the renewable energy is quantized into opportunity cost and added into a target function; meanwhile, an optimal power flow model is established according to safety constraints such as generator rotation standby constraint, system operation voltage constraint, line thermal limit and the like, the safety requirement of the system is considered, and the safety and the reliability of the model are improved; for piecewise linear components of an optimal power flow model caused by introduction of renewable energy generator opportunity cost and uncertainty of unit rotation standby, a convex transformation method is adopted to simplify solution, micro-transformation is carried out on an original model, and finally a traditional nonlinear optimization method is adopted to solve the transformed model to obtain an accurate and reliable calculation result so as to improve operation safety of a power system.
The embodiment of the invention correspondingly provides an optimal power flow calculation device considering the uncertainty of renewable energy sources, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize the optimal power flow calculation method considering the uncertainty of renewable energy sources according to the first embodiment of the invention. The optimal power flow calculation device considering the uncertainty of the renewable energy sources can be a desktop computer, a notebook computer, a palm computer, a cloud server and other calculation devices. The optimal power flow calculation device considering the uncertainty of the renewable energy source can comprise, but is not limited to, a processor and a memory.
The third embodiment of the present invention correspondingly provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the optimal power flow calculation method according to the first embodiment of the present invention, where uncertainty of renewable energy is considered.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the optimal power flow calculation apparatus considering uncertainty of renewable energy, and various interfaces and lines are used to connect various parts of the entire optimal power flow calculation apparatus considering uncertainty of renewable energy.
The memory may be used for storing the computer program and/or the module, and the processor may implement various functions of the optimal power flow calculation apparatus considering uncertainty of renewable energy by executing or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the module/unit integrated by the optimal power flow calculation device considering the uncertainty of renewable energy can be stored in a computer readable storage medium if it is realized in the form of software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (9)
1. An optimal power flow calculation method considering uncertainty of renewable energy sources is characterized by comprising the following steps:
respectively calculating uncertainty of wind speed and illumination intensity;
calculating a wind speed and illumination intensity scene value considering the correlation;
calculating scene values of wind power generation and photovoltaic output according to output power of a fan and a photovoltaic power station;
calculating a system cost value based on the scene values of the wind power generation and photovoltaic output;
constructing a system safety constraint function, and establishing an optimal power flow model considering the uncertainty of renewable energy sources and system safety constraints;
and carrying out micro transformation on the optimal power flow model, and solving the transformed optimal power flow model to obtain a calculation result of the uncertainty of the renewable energy source.
2. The optimal power flow calculation method considering the uncertainty of renewable energy according to claim 1, wherein the separately calculating the uncertainty of wind speed and illumination intensity comprises:
establishing an uncertainty model of the wind speed at a single moment by the following formula (1):
where v is the predicted value of wind speed, fw(v) C and k represent probability distribution shape parameters and scale parameters respectively corresponding to the predicted value v of the wind speed, and c>0，k>0；
An uncertainty model of the illumination intensity is established by the following formula (2):
wherein E is a predicted value of the light intensity, fPV(E) And the probability value corresponding to the predicted value E of the illumination intensity is Emax, Г, the gamma function and alpha and beta are both shape parameters.
3. The method according to claim 2, wherein the calculating of the optimal power flow considering uncertainty of renewable energy includes:
constructing an edge distribution function of wind speed and illumination intensity variables according to historical data of a wind power plant and a photovoltaic power station;
and describing a correlation structure between the wind speed and the illumination intensity variable, fitting to determine parameters of the correlation structure, and calculating to obtain an equivalent correlation coefficient matrix rho.
Sampling the uncertainty model of the wind speed and the illumination intensity without considering the correlation to obtain a sampling matrix R consisting of Ns groups of sampling values of Nrv random variables: rNrv*RNsAnd calculating a correlation coefficient matrix T of the sampling matrix R;
decomposing the equivalent correlation coefficient matrix rho to obtain a lower triangular matrix P: ρ ═ P × P'; decomposing the correlation coefficient matrix T to obtain a lower triangular matrix Q: t ═ Q × Q';
let S be P Q-1R ═ RS', the matrix of correlation coefficients of R is then the same as the matrix ρ of equivalent correlation coefficients;
rearranging the element arrangement sequence of each column vector in the scene matrix R according to the random variable pairing condition of R to obtain a corrected scene matrix R ', and calculating the wind speed and photovoltaic intensity scene value considering the correlation through the corrected scene matrix R'.
4. The optimal power flow calculation method considering the uncertainty of the renewable energy according to claim 3, wherein the calculating the scene values of the wind power output and the photovoltaic output according to the output powers of the wind turbine and the photovoltaic power station specifically comprises:
the following formula (3) is a relation expression of nonlinear wind speed and output power:
in the formula, C0、C1、C2And C3All are parameters obtained by fitting;
the following formula (4) is an output power expression of a photovoltaic array:
PPV＝EAτ (4)
in the formula, E, A and tau are respectively the illumination intensity, the effective area of the photovoltaic array and the photoelectric conversion rate of the cell panel;
and converting the wind speed and illumination intensity sampling values into scene values of wind power output and photovoltaic output through formulas (3) and (4).
5. The method according to claim 4, wherein the calculating of the system cost value based on the scene values of the wind power generation and photovoltaic output comprises:
calculating a system cost value based on the scene values of wind power generation and photovoltaic output by the following equation (5):
wherein the content of the first and second substances,symbol [ 2 ]]+Indicates that the value of "Zheng", "if", "is]If the internal number is less than zero, taking zero;
in the formula, crAnd csRespectively are a standby coefficient and a saving coefficient which are positive numbers; ns is the scene number of renewable energy output; pREjA force value is planned for the jth new energy power station;
is the active output value of the jth new energy power station in the scene sIs that it is
A corresponding probability; ED (electronic device)jFor new energy plants with less than planned output expected, ESjAnd the new energy power station is more than the expected value of the planned output.
6. The method according to claim 5, wherein the constructing a system safety constraint function and establishing an optimal power flow model considering the renewable energy uncertainty and the system safety constraint specifically comprises:
establishing a generator rotation backup constraint of the following equation (6):
in the formula, the USR and the DSR are respectively a system station without considering the renewable energy source unitRequiring up and down rotation for use, USiAnd DSiFor respectively providing an upward and downward rotation reserve for a conventional unit, the USiAnd DSiThe value of (A) is as follows (7):
in the formula, NGIndicating the number of conventional units in the system, PG maxAnd PG minThe maximum and minimum output values of the traditional unit are respectively, and d is a rotary spare capacity parameter;
establishing a system operating voltage constraint as follows (8):
Vi min≤Vi≤Vi maxi∈NB(8)
in the formula, NBRepresenting the node number of the system, and V represents the node voltage amplitude;
establishing a line thermal limit constraint as follows (9):
in the formula (I), the compound is shown in the specification,
active maximum allowed for the line;
establishing a power generation cost objective function and a power flow equation constraint as follows (10) and (11):
wherein B represents a cost parameter, θ represents a node voltage phase angle, QGAnd QLRespectively the reactive power output and the section of the traditional unitPoint reactive load; pGAnd PLRespectively the active output of the traditional unit and the active load of the node;
and obtaining an optimal power flow model considering the uncertainty of the renewable energy source and the safety constraint of the system according to the formulas (7), (8), (9), (10) and (11).
7. The method according to claim 6, wherein the performing the micro-transformation on the optimal power flow model comprises:
let c (x) be an n-segment linear function, which if convex, is transformed by the following equation (12):
piecewise linear function EDjThe following formula (13):
in the formula, mdj sAnd cdj sRenewable energy output scenario PsAnd frequency f of the samesDerived if EDjFor convex constraint, then EDjTo the following formula (14):
and obtaining the transformed continuous micro model.
8. An optimal power flow calculation apparatus considering uncertainty of renewable energy, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing an optimal power flow calculation method considering uncertainty of renewable energy according to any one of claims 1 to 7 when executing the computer program.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program, wherein when the computer program is executed, the computer-readable storage medium controls a device to execute an optimal power flow calculation method according to any one of claims 1 to 7, taking into account uncertainty of renewable energy.
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