CN112561732B - Method and device for optimizing active power scheduling of power system and readable storage medium - Google Patents
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Abstract
The invention provides a method, a device and a readable storage medium for active scheduling optimization of an electric power system, wherein historical data of new energy output and active scheduling parameters are obtained from the electric power system, a reasonable distribution function set of new energy output probability components is established by mining the historical data of the new energy output and the active scheduling parameters, an optimal value function and a distribution set robust optimization model are established, the distribution set robust optimization model is converted into a deterministic optimal model, and the limit distribution of the new energy output is solved, wherein the limit distribution can describe the probability distribution situation of the new energy output, the active scheduling parameters are optimized through the limit distribution of the new energy output, the economy and the reliability of the electric power system can be balanced, the absorption level of energy is improved, and the method, the device and the readable storage medium are widely applied to active scheduling of the electric power system.
Description
Technical Field
The invention relates to the field of operation control of a power system, in particular to a method and a device for optimizing active power scheduling of the power system and a readable storage medium.
Background
The access of wind power and photovoltaic brings more cleanliness and sustainability to the power system, but large-scale intermittent new energy power generation causes great uncertainty in active scheduling of the power system. How to take up new energy in an economical way and deal with its volatility and randomness in a reliable way is one of the major challenges in the control of the operation of power systems.
Since the new energy output level and the probability distribution thereof cannot be accurately predicted in the future, in order to make the active scheduling scheme have certain robustness, it is usually necessary to grasp the extreme conditions of new energy output and reserve certain active balance resources.
The common rotating standby method and the limit scenario method may have the situation that the estimation of the standby amount is over-conservative or under-conservative, and it is difficult to effectively balance the economy of the active scheduling scheme and the reliability of the system.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention provides a method, an apparatus, and a readable storage medium for optimizing active scheduling of an electric power system, which can optimize active scheduling parameters of the electric power system, and balance the economy of active scheduling and the reliability of the system.
The method for optimizing the active power scheduling of the power system provided by the embodiment of the invention comprises the following steps:
acquiring historical data of new energy output of the power system and active scheduling parameters;
estimating a first moment and a second moment of the empirical distribution of the historical data, and establishing a distribution function set of the new energy output probability distribution;
establishing an optimal value function of active scheduling parameters of the power system, and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
converting the distribution set robust optimization model into a deterministic optimization model;
solving the deterministic optimization model, and establishing the limit distribution of new energy output by using dual variables;
and calculating active scheduling optimization parameters according to the limit distribution, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
As a preferred mode, the estimating a first moment and a second moment of the empirical distribution of the historical data, and establishing a distribution function set of the new energy output probability distribution specifically includes:
estimating first and second moments of the empirical distribution of the case time data using an unbiased moment estimation method:
establishing a distribution function set of the new energy output probability distribution by using the first moment and the second moment:
wherein S is the sample capacity of the historical data,
for the s-th historical data sample, vector
Is a first order moment, the matrix sigma is a second order moment,
in order to distribute the set of functions,
which is representative of the function of the distribution,
is a sigma algebra on a supporting set xi of the random vector xi of the new energy output, the supporting set xi is a hyper-cuboid which is accurately defined according to the upper limit and the lower limit of the new energy output,
which represents the mathematical expectation that,
representing generalized unequal signs.
As a preferred mode, the establishing an optimal value model of an active scheduling parameter of the power system, and the establishing a robust optimization model of a distribution set of the optimal value function specifically include:
and constructing an optimization function Q (x, xi) of the active scheduling parameters, wherein the Q (x, xi) is expressed as:
and establishing the robust optimization model of the distribution set according to an optimization function Q (x, xi)
Wherein C is an active cost vector, x is the active scheduling parameter vector, y is an active output vector or an adjustment quantity of the optimized active scheduling, ξ is a random vector of new energy output, A, B and C are coefficient matrixes, d is a coefficient vector and comprises an active predicted value of the system; z primal (x) Representing the cost value of active scheduling under the most extreme new energy output probability distribution in the subsequent time period by taking the active scheduling parameter as an input parameter, and representing Q (x, xi) as an optimal value function by taking the current active scheduling parameter as an input parameter and taking the minimum cost of active scheduling in the subsequent time period as output; scalar f ξ The probability density of the distribution function at the position where the random vector value is xi is a decision variable of the maximization problem; d xi is the differential of the random vector; a supporting set of random vectors is defined xi = { xi | xi = min ≤ξ≤ξ max Is where ξ min And xi max Respectively the minimum and maximum values of the new energy output; scalar h 0 The vector H and the matrix H are dual variables of corresponding constraint conditions in the distribution set robust optimization model respectively.
As a preferred mode, the converting the robust optimization model of the distribution set into the deterministic optimization model specifically includes:
piecewise affine function form of the optimal value function Q (x, xi)
Substitution into Z primal (x) In the distribution set robust optimization model represented, the distribution set robust optimization model is converted into the deterministic optimization model
Wherein Q is newIntroduced free variable, vector lambda i 、μ i And a scalar v i Corresponding to the vertex of the feasible region where the active scheduling cost is minimized,
a set of vertex indices for the feasible region; the matrix theta is
The operator tr (-) is the trace of the matrix; matrix sigma i Vector pi i Scalar ρ i Dual variables of constraints of the model are optimized for the determinism.
As a preferred mode, solving the deterministic optimization model, and establishing a limit distribution of new energy output by using a dual variable specifically includes:
solving Z by using semi-definite programming solver SDP (x) Representing the deterministic optimization model, obtaining the optimal value of the dual variable from the solver, and recording the optimal value as
Optimal value based on the dual variable
Establishing limit distribution of new energy output
As a preferred mode, the calculating an active scheduling optimization parameter according to the limit distribution, and optimizing an active scheduling of the power system according to the active scheduling optimization parameter specifically includes:
replacing Z with said limiting distribution primal (x) The distribution set represented by the robust optimization model obtains a distribution mode of an optimal value function Q (x, xi)
Authentication postSaid distribution pattern and said deterministic optimization model such that
And calculating active scheduling optimization parameters of the new energy output meeting the distribution mode, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
The invention provides a method for optimizing active scheduling of an electric power system, which comprises the steps of acquiring historical data of new energy output and active scheduling parameters from the electric power system, mining the historical data of the new energy output and the active scheduling parameters, establishing a reasonable distribution function set of new energy output probability components, establishing an optimal value function and distribution set robust optimization model, converting the distribution set robust optimization model into a deterministic optimal model, and solving the limit distribution of the new energy output, wherein the limit distribution can describe the probability distribution condition of the new energy output, the active scheduling parameters are optimized through the limit distribution of the new energy output, the economy and the reliability of the electric power system can be balanced, the consumption level of energy is improved, and the method is widely applied to the active scheduling of the electric power system.
An embodiment of the present invention further provides a device for optimizing active power scheduling of an electric power system, including: the system comprises an electric power data acquisition module, a distribution function set building module, a distribution set robust optimization model building module, a deterministic optimization model building module, a limit distribution building module and an active scheduling optimization module;
the power data acquisition module is used for acquiring historical data of new energy output of the power system and active scheduling parameters;
the distribution function set establishing module is used for estimating a first moment and a second moment of the empirical distribution of the historical data and establishing a distribution function set of the new energy output probability distribution;
the distribution set robust optimization model establishing module is used for establishing an optimal value function of active scheduling parameters of the power system and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
the deterministic optimization model building module is used for converting the distribution set robust optimization model into a deterministic optimization model;
the limit distribution establishing module is used for solving the deterministic optimization model and establishing the limit distribution of the new energy output by utilizing a dual variable;
the active scheduling optimization module is used for calculating active scheduling optimization parameters according to the limit distribution and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
As a preferred mode, the function of the distribution function set establishing module specifically includes:
estimating first and second moments of the empirical distribution of the case time data using an unbiased moment estimation method:
establishing a distribution function set of the new energy output probability distribution by using the first moment and the second moment:
wherein S is the sample capacity of the historical data,
for the s-th historical data sample, vector
Is a first order moment, the matrix sigma is a second order moment,
in order to distribute the set of functions,
which is representative of the function of the distribution,
a sigma algebra on a supporting set xi of the random vector xi of the new energy output, wherein the supporting set xi is a superlong one accurately defined according to the upper limit and the lower limit of the new energy outputThe square body is provided with a plurality of square bodies,
which represents the mathematical expectation that,
represents a generalized unequal sign;
the distribution set robust optimization model building module has the specific functions of:
and constructing an optimization function Q (x, xi) of the active scheduling parameter, wherein Q (x, xi) is expressed as:
and establishing the robust optimization model of the distribution set according to an optimization function Q (x, xi)
Wherein C is an active cost vector, x is the active scheduling parameter vector, y is an active output vector or an adjustment quantity of the optimized active scheduling, ξ is a random vector of new energy output, A, B and C are coefficient matrixes, d is a coefficient vector and comprises an active predicted value of the system; z is a linear or branched member primal (x) Representing the cost value of active scheduling under the most extreme new energy output probability distribution in the subsequent time period by taking the active scheduling parameter as an input parameter, and representing Q (x, xi) as an optimal value function by taking the current active scheduling parameter as an input parameter and taking the minimum cost of active scheduling in the subsequent time period as output; scalar f ξ The probability density of the distribution function at the position where the random vector value is xi is a decision variable of the maximization problem; d xi is the differential of the random vector; a supporting set of random vectors is defined xi = { xi | xi = min ≤ξ≤ξ max Is where ξ min And xi max Respectively the minimum and maximum values of the new energy output; scalar h 0 The vector H and the matrix H are dual variables of corresponding constraint conditions in the robust optimization model of the distribution set respectively;
the deterministic optimization model building module specifically comprises the following functions:
piecewise affine function form of the optimal value function Q (x, xi)
Substitution into Z primal (x) In the represented robust optimization model of the distribution set, converting the robust optimization model of the distribution set into the deterministic optimization model
Where Q is a newly introduced free variable, vector λ i 、μ i And a scalar v i Corresponding to the vertex of the feasible region where the active scheduling cost is minimized,
a set of vertex indices for the feasible region; the matrix theta is
Operator tr (-) is a trace of the matrix; matrix sigma i Vector pi i Scalar ρ i Dual variables of constraints for the deterministic optimization model;
solving Z using a semi-positive definite programming solver SDP (x) The deterministic optimization model represented, the optimal values of the dual variables obtained from the solver, are noted
Optimal value based on the dual variable
Establishing limit distribution of new energy output
The limit distribution establishing module has the specific functions of:
solving Z using a semi-positive definite programming solver SDP (x) Said certainty of representationOptimizing the model, obtaining the optimal value of the dual variable from the solver, and recording the optimal value as
Optimal value based on the dual variable
Establishing limit distribution of new energy output
The active scheduling optimization module has the specific functions of:
replacing Z with said limit profile primal (x) The represented distribution set robustness optimization model obtains the distribution mode of an optimal value function Q (x, xi)
Verifying said distribution pattern and said deterministic optimization model such that
And calculating active scheduling optimization parameters of the new energy output meeting the distribution mode, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
The embodiment of the present invention further provides an apparatus for optimizing power scheduling of an electric power system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements a method for optimizing power scheduling of an electric power system according to any one of the above embodiments
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the method for optimizing the active scheduling of the power system according to any one of the foregoing embodiments.
The invention provides a method, a device and a readable storage medium for active scheduling optimization of a power system, wherein historical data of new energy output and active scheduling parameters are obtained from the power system, a reasonable distribution function set of new energy output probability components is established by mining the historical data of the new energy output and the active scheduling parameters, an optimal value function and distribution set robust optimization model is established, the distribution set robust optimization model is converted into a deterministic optimal model, and the limit distribution of the new energy output is solved, wherein the limit distribution can describe the probability distribution condition of the new energy output, the active scheduling parameters are optimized through the limit distribution of the new energy output, the economy and the reliability of the power system can be balanced, the absorption level of energy is improved, and the method, the device and the readable storage medium are widely applied to active scheduling of the power system.
Drawings
Fig. 1 is a flowchart of a method for optimizing active scheduling of an electric power system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of active scheduling limit distribution of an electric power system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an active scheduling optimization device of an electric power system according to an embodiment of the present invention.
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 some embodiments, not all embodiments, of the present invention. 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 flowchart of a method for optimizing active scheduling of an electric power system according to an embodiment of the present invention is shown. The embodiment of the invention provides a method for optimizing active power dispatching of a power system, which comprises the steps of S101-S106;
s101, acquiring historical data of new energy output of a power system and active scheduling parameters;
s102, estimating a first moment and a second moment of the empirical distribution of the historical data, and establishing a distribution function set of the output probability distribution of the new energy;
s103, establishing an optimal value function of an active scheduling parameter of the power system, and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
s104, converting the robust optimization model of the distribution set into a deterministic optimization model;
s105, solving the deterministic optimization model, and establishing the limit distribution of new energy output by using a dual variable;
and S106, calculating an active scheduling optimization parameter according to the limit distribution, and optimizing the active scheduling of the power system according to the active scheduling optimization parameter.
In this embodiment, step S102 specifically includes:
the new energy output refers to clean energy of wind power and photovoltaic access electric power systems;
estimating first and second moments of the empirical distribution of the case time data using an unbiased moment estimation method:
wherein S is the sample capacity of the historical data,
for the s-th historical data sample, vector
Is a first order moment, and the matrix Σ is a second order moment;
establishing a distribution function set of the new energy output probability distribution by using the first moment and the second moment:
wherein,
in order to distribute the set of functions,
which is representative of the function of the distribution,
is a sigma algebra on a supporting set xi of the random vector xi of the new energy output, the supporting set xi is a hyper-cuboid which is accurately defined according to the upper limit and the lower limit of the new energy output,
representing the mathematical expectation that,
representing generalized unequal signs.
The first row in the above distribution function set definition represents that the total probability mass of the probability distribution of the random vector is 1, the second row represents that the expected value of the random vector is exactly equal to ξ, and the third row represents that the covariance of the random vector may be equal to or less than Σ.
In this embodiment, step S103 is specifically:
marking current active scheduling parameters such as a unit starting and stopping state, an energy storage charging and discharging state, a unit reference output and the like as a vector x, constructing an active scheduling optimization problem Q (x, xi) by using acquired relevant parameters of active scheduling such as a power system network, a unit, a load and the like, writing the active scheduling optimization problem Q (x, xi) into a standardized linear programming model, constructing an optimization function Q (x, xi) of the active scheduling parameters, and expressing the Q (x, xi) as follows:
wherein C is an active cost vector, x is the active scheduling parameter vector, y is an active output vector or an adjustment quantity of the optimized active scheduling, ξ is a random vector of new energy output, A, B and C are coefficient matrixes, d is a coefficient vector and comprises an active predicted value of the system;
and establishing the robust optimization model of the distribution set according to an optimization function Q (x, xi)
Wherein Z is primal (x) Representing the cost value of active scheduling under the most extreme new energy output probability distribution in the subsequent time period by taking the active scheduling parameter as an input parameter, and representing Q (x, xi) as an optimal value function by taking the current active scheduling parameter as an input parameter and taking the minimum cost of active scheduling in the subsequent time period as output; scalar f ξ The probability density of the distribution function at the position where the random vector value is xi is a decision variable of the maximization problem; d xi is the differential of the random vector; a supporting set of random vectors is defined xi = { xi | xi = min ≤ξ≤ξ max Is where ξ min And xi max Respectively the minimum value and the maximum value of the new energy output; scalar h 0 The vector H and the matrix H are dual variables of corresponding constraint conditions in the distribution set robust optimization model respectively.
The distribution set robust optimization model is a semi-infinite cone optimization model containing integral, and specifically, the model contains infinite decision variables f ξ And a limited number of constraints; the objective function of the model comprises an optimal value function Q (x, xi), and Q (x, xi) is a minimized linear programming problem, in particular a minimized active scheduling cost problem in the subsequent period.
In this embodiment, step S104 is specifically:
an optimal value function Q (x, xi) of an active scheduling optimization problem in a distribution set robust optimization model is expressed into a piecewise affine function of x and xi, namely, the form of a point-by-point maximum value of a finite number of affine functions
Wherein Q is a newly introduced free variable; vector lambda i ，μ i And a scalar v i Corresponding to the vertex of the feasible domain of the active scheduling cost minimization problem;
a set of vertex indices representing a feasible domain.
Substituting the piecewise affine function form of the optimal value function Q (x, xi) into the distribution set robust optimization model, and converting the distribution set robust optimization model into the following optimization problem containing a finite number of decision variables and an infinite number of constraint conditions by using a semiinfinite dimension optimization dual method:
wherein the matrix Θ represents
The operator tr (-) represents the trace of the matrix.
And (3) utilizing a dual technology and a Schur complement technology in the static robust optimization, equivalently rewriting the constraint conditions into the following constraints:
wherein, the vector h i (u i )＝h-μ i +u 2i -u 1i (ii) a Scalar quantity
Vector u 1i And u 2i The vectors have the same dimensions as the random vectors, and are dual variables related to the lower boundary and upper boundary constraints of the support set respectively.
Equivalently converting the robust optimization model of the distribution set into a deterministic optimization model as follows:
wherein the matrix σ i The vector pi i Scalar ρ i Are dual variables of the constraint.
In this embodiment, step S105 is specifically:
solving by using semi-positive definite plan solverSolving the deterministic optimization model, obtaining the optimal value of the dual variable from a solver, and recording the optimal value as
Optimal value based on dual variables
Constructing new energy output limit distribution:
wherein,
represents a limiting distribution; distribution function delta ξ Representing the Dirac distribution that xi has the probability of 1 at the position and the probability of 0 at the rest positions;
a value representing new energy output, event i;
representing the probability of the event i, the distribution can be verified
Has a covariance of less than or equal to
In this embodiment, step S106 is specifically:
replacing Z with said limit profile primal (x) The distribution set represented by the robust optimization model obtains a distribution mode of an optimal value function Q (x, xi)
Verifying said distribution pattern and said deterministic optimization model such that
And calculating active scheduling optimization parameters of which the new energy output meets the distribution mode, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
Referring to fig. 2, the active scheduling limit distribution schematic diagram of the power system provided by the embodiment of the present invention is a wind power parameter index-wind power diagram, which represents the limit distribution of 2 wind power plants in an active scheduling model with 24 time periods in the power system, and the power generation capacity of each wind power plant is about 620MW. The expected value of the wind power output obtained by historical data statistics is shown as a square dotted line in the attached drawing. The 4 sub-graphs show 8 events of the limit distribution in total, two solid lines in the graph represent 2 events of each sub-graph, the probability of each event is contained, the probability size of the event is represented by the thickness of the solid line, the total probability of 8 events is 1, and the probability of the event with larger fluctuation is lower.
The invention provides a method for optimizing active power dispatching of a power system, which comprises the following steps: acquiring historical data of new energy output of a power system and active scheduling parameters; estimating a first moment and a second moment of the empirical distribution of the historical data, and establishing a distribution function set of the new energy output probability distribution; establishing an optimal value function of active scheduling parameters of the power system, and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set; converting the distribution set robust optimization model into a deterministic optimization model; solving the deterministic optimization model, and establishing the limit distribution of new energy output by using dual variables; and calculating an active scheduling optimization parameter according to the limit distribution, and optimizing the active scheduling of the power system according to the active scheduling optimization parameter.
The method comprises the steps of acquiring historical data of new energy output and active scheduling parameters from an electric power system, mining the historical data of the new energy output and the active scheduling parameters, establishing a reasonable distribution function set of new energy output probability components, establishing an optimal value function and a distribution set robust optimization model, converting the distribution set robust optimization model into a deterministic optimal model, solving the limit distribution of the new energy output, describing the probability distribution condition of the new energy output, optimizing the active scheduling parameters through the limit distribution of the new energy output, balancing the economy and reliability of the electric power system, improving the energy consumption level, and being widely applied to active scheduling of the electric power system.
Referring to fig. 3, it is a schematic structural diagram of an apparatus for optimizing power scheduling of an electric power system according to an embodiment of the present invention, where the apparatus includes: the system comprises a power data acquisition module, a distribution function set building module, a distribution set robust optimization model building module, a deterministic optimization model building module, a limit distribution building module and an active scheduling optimization module;
the power data acquisition module is used for acquiring historical data of new energy output of the power system and active scheduling parameters;
the distribution function set establishing module is used for estimating a first moment and a second moment of the empirical distribution of the historical data and establishing a distribution function set of the new energy output probability distribution;
the distribution set robust optimization model establishing module is used for establishing an optimal value function of active scheduling parameters of the power system and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
the deterministic optimization model building module is used for converting the distribution set robust optimization model into a deterministic optimization model;
the limit distribution establishing module is used for solving the deterministic optimization model and establishing the limit distribution of the new energy output by utilizing a dual variable;
and the active scheduling optimization module is used for calculating active scheduling optimization parameters according to the limit distribution and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
In specific implementation, the device for optimizing the active power scheduling of the power system can complete specific functions of the method for optimizing the active power scheduling of the power system provided in any embodiment, and a specific implementation process is specifically described in any embodiment of the method for optimizing the active power scheduling of the power system, which is not described in detail in this embodiment.
The embodiment of the present invention further provides an apparatus for optimizing power scheduling of an electric power system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements a method for optimizing power scheduling of an electric power system described in any of the above embodiments.
The device for optimizing the active scheduling of the electric power system can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The power system active scheduling optimization device/terminal equipment can comprise, but is not limited to, a processor and a memory.
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 power system active scheduling optimization device, and various interfaces and lines are used for connecting various parts of the whole power system active scheduling optimization device.
The memory may be used for storing the computer programs and/or modules, and the processor implements various functions of the power system active scheduling optimization apparatus/terminal device by running or executing the computer programs and/or modules stored in the memory and calling the 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, etc. 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.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the method for optimizing the active scheduling of the power system according to any of the foregoing embodiments. The device integrated module for power dispatching optimization of the power system can be stored in a computer readable storage medium if the module is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the embodiments of the method for implementing the power scheduling optimization of the power system according to the present invention can also be implemented by a computer program, which can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the method can 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 said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Claims (9)
1. A method for optimizing active scheduling of a power system is characterized by comprising the following steps:
acquiring historical data of new energy output of the power system and active scheduling parameters;
estimating a first moment and a second moment of the empirical distribution of the historical data, and establishing a distribution function set of the new energy output probability distribution;
establishing an optimal value function of active scheduling parameters of the power system, and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
converting the distribution set robust optimization model into a deterministic optimization model;
solving the deterministic optimization model, and establishing the limit distribution of the new energy output by using a dual variable;
calculating active scheduling optimization parameters according to the limit distribution, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters;
the estimating of the first moment and the second moment of the empirical distribution of the historical data and the establishing of the distribution function set of the new energy output probability distribution specifically include:
estimating first and second moments of an empirical distribution of the historical data using an unbiased moment estimation method:
establishing a distribution function set of the new energy output probability distribution by using the first moment and the second moment:
wherein S is the sample capacity of the historical data,
for the s-th historical data sample, vector
Is a first order moment, the matrix sigma is a second order moment,
in order to distribute the set of functions,
which is representative of the function of the distribution,
is a sigma algebra on a supporting set xi of the random vector xi of the new energy output, the supporting set xi is a hyper-cuboid which is accurately defined according to the upper limit and the lower limit of the new energy output,
which represents the mathematical expectation that,
representing generalized unequal signs.
2. The method according to claim 1, wherein the establishing of the optimal value model of the active scheduling parameter of the power system and the establishing of the robust optimization model of the distribution set of the optimal value function specifically include:
constructing an optimal value function Q (x, xi) of the active scheduling parameter, wherein Q (x, xi) is expressed as:
and establishing the robust optimization model of the distribution set according to an optimal value function Q (x, xi)
Wherein C is an active cost vector, x is the active scheduling parameter vector, y is an active output vector or an adjustment quantity of the optimized active scheduling, ξ is a random vector of new energy output, A, B and C are coefficient matrixes, d is a coefficient vector and comprises an active predicted value of the system; z is a linear or branched member primal (x) Representing the active scheduling parameters as input parameters, and the active scheduling parameters in the subsequent time period are under the most extreme new energy output probability distributionThe value Q (x, xi) represents an optimal value function which takes the current active scheduling parameter as an input parameter and takes the minimum cost of active scheduling in the subsequent time interval as output; scalar f ξ The probability density of the distribution function at the position where the random vector value is xi is a decision variable of the maximization problem; d xi is the differentiation of the random vector; a supporting set of random vectors is defined xi = { xi | xi = min ≤ξ≤ξ max Is of which ξ min And xi max Respectively the minimum and maximum values of the new energy output; scalar h 0 The vector H and the matrix H are dual variables of corresponding constraint conditions in the distribution set robust optimization model respectively.
3. The method for optimizing power dispatching of a power system according to claim 2, wherein the converting the robust optimization model of the distribution set into the deterministic optimization model specifically comprises:
piecewise affine function form of the optimal value function Q (x, xi)
Substitution into Z primal (x) In the represented robust optimization model of the distribution set, converting the robust optimization model of the distribution set into the deterministic optimization model
Where Q is a newly introduced free variable, vector λ i 、μ i And a scalar v i Corresponding to the vertex of the feasible region where the active scheduling cost is minimized,
a set of vertex indices for the feasible region; the matrix theta is
The operator tr (-) is the trace of the matrix; matrix sigma i Vector pi i Scalar ρ i Optimizing the model for said determinismDual variables of the constraint; u. of 1i And u 2i Dual variables, h, constrained by the upper and lower boundaries of the random vector supporting the XI respectively i For the replication variable of vector h corresponding to the ith vertex of the feasible region, h 0i Is a scalar h 0 A copy variable corresponding to the ith vertex of the feasible region.
4. The method according to claim 3, wherein the deterministic optimization model is solved, and the extreme distribution of the new energy output is established by using dual variables, and the method specifically comprises the following steps:
solving Z by using semi-definite programming solver SDP (x) The deterministic optimization model represented, the optimal values of the dual variables obtained from the solver, are noted
Optimal value based on the dual variable
Establishing limit distribution of new energy output
5. The method according to claim 4, characterized in that the calculating an active scheduling optimization parameter according to the limit distribution and optimizing the active scheduling of the power system according to the active scheduling optimization parameter specifically includes:
replacing Z with said limit profile primal (x) The represented distribution set robustness optimization model obtains the distribution mode of an optimal value function Q (x, xi)
Verifying the distribution pattern and the deterministic optimization patternType, such that
And calculating active scheduling optimization parameters of the new energy output meeting the distribution mode, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
6. An apparatus for optimizing power scheduling in a power system, comprising: the system comprises a power data acquisition module, a distribution function set building module, a distribution set robust optimization model building module, a deterministic optimization model building module, a limit distribution building module and an active scheduling optimization module;
the power data acquisition module is used for acquiring historical data of new energy output of the power system and active scheduling parameters;
the distribution function set establishing module is used for estimating a first moment and a second moment of the empirical distribution of the historical data and establishing a distribution function set of the new energy output probability distribution;
the distribution set robust optimization model establishing module is used for establishing an optimal value function of active scheduling parameters of the power system and establishing a distribution set robust optimization model of the optimal value function according to the distribution function set;
the deterministic optimization model building module is used for converting the distribution set robust optimization model into a deterministic optimization model;
the limit distribution establishing module is used for solving the deterministic optimization model and establishing limit distribution of new energy output by utilizing dual variables;
the active scheduling optimization module is used for calculating active scheduling optimization parameters according to the limit distribution and optimizing the active scheduling of the power system according to the active scheduling optimization parameters;
the functions of the distribution function set establishing module specifically include:
estimating first and second moments of an empirical distribution of the historical data using an unbiased moment estimation method:
wherein S is the sample capacity of the historical data,
for the s-th historical data sample, vector
Is a first order moment, the matrix sigma is a second order moment,
in order to distribute the set of functions,
the function of the distribution is represented by,
a sigma algebra on a supporting set xi of the random vector xi of the new energy output, the supporting set xi is a super-rectangular body accurately defined according to the upper limit and the lower limit of the new energy output,
which represents the mathematical expectation that,
representing generalized unequal signs.
7. The active scheduling optimization device of the power system according to claim 6, wherein the distribution set robust optimization model building module specifically functions as:
construction ofThe optimal value function Q (x, ξ) of the active scheduling parameter, Q (x, ξ) is expressed as:
and establishing the robust optimization model of the distribution set according to an optimal value function Q (x, xi)
∫ Ξ f ξ dξ＝1：h 0
Wherein C is an active cost vector, x is the active scheduling parameter vector, y is an active output vector or an adjustment quantity of the optimized active scheduling, ξ is a random vector of new energy output, A, B and C are coefficient matrixes, d is a coefficient vector and comprises an active predicted value of the system; z primal (x) Representing the cost value of active scheduling under the most extreme new energy output probability distribution in the subsequent time period by taking the active scheduling parameter as an input parameter, and Q (x, xi) represents an optimal value function by taking the current active scheduling parameter as an input parameter and taking the minimum cost of active scheduling in the subsequent time period as output; scalar f ξ The probability density of the distribution function at the position where the random vector value is xi is a decision variable of the maximization problem; d xi is the differential of the random vector; a supporting set of random vectors is defined xi = { xi | xi = min ≤ξ≤ξ max Is where ξ min And xi max Respectively the minimum and maximum values of the new energy output; scalar h 0 Vector H and matrix H are dual variables of corresponding constraint conditions in the distribution set robust optimization model respectively;
the deterministic optimization model building module specifically comprises the following functions:
piecewise affine function form of the optimal value function Q (x, xi)
Substitution into Z primal (x) In the represented robust optimization model of the distribution set, converting the robust optimization model of the distribution set into the deterministic optimization model
Where Q is a newly introduced free variable, vector λ i 、μ i And a scalar v i Corresponding to the vertex of the feasible region where the active scheduling cost is minimized,
a set of vertex indices for the feasible region; the matrix theta is
The operator tr (-) is the trace of the matrix; matrix sigma i The vector pi i Scalar ρ i Dual variables of constraints for the deterministic optimization model; u. u 1i And u 2i Dual variables, h, constrained by the upper and lower boundaries of the random vector supporting the XI respectively i For the replication variable of vector h corresponding to the ith vertex of the feasible region, h 0i Is a scalar h 0 A copy variable corresponding to the ith vertex of the feasible region;
solving Z using a semi-positive definite programming solver SDP (x) The deterministic optimization model represented, the optimal values of the dual variables obtained from the solver, are noted
Based on said dual variablesOptimum value
Establishing limit distribution of new energy output
The limit distribution establishing module has the specific functions of:
solving Z using a semi-positive definite programming solver SDP (x) The deterministic optimization model represented, the optimal values of the dual variables obtained from the solver, are noted
Optimal value based on the dual variable
Establishing a limit distribution of new energy output
The active scheduling optimization module has the specific functions of:
replacing Z with said limit profile primal (x) The represented distribution set robustness optimization model obtains the distribution mode of an optimal value function Q (x, xi)
Verifying said distribution pattern and said deterministic optimization model such that
And calculating active scheduling optimization parameters of the new energy output meeting the distribution mode, and optimizing the active scheduling of the power system according to the active scheduling optimization parameters.
8. An apparatus for power system active scheduling optimization, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a method for power system active scheduling optimization according to any one of claims 1 to 5.
9. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the method for optimizing power scheduling in an electric power system according to any one of claims 1 to 5.
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