CN113517698A - Optimal power flow convexity control method and device for active power distribution network - Google Patents
Optimal power flow convexity control method and device for active power distribution network Download PDFInfo
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Abstract
The invention provides an optimal power flow convexity control method and device for an active power distribution network, wherein the method comprises the following steps: establishing an optimal power flow model of the active power distribution network according to the power grid parameters; carrying out convex processing on the nonlinear constraints of the power flow of the established optimal power flow model; solving the optimal power flow model after the convex processing to generate solving result data; and performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model. The method solves the problem that the solving speed is slow or even the optimal solution can not be solved in the prior art, and the method can be used for carrying out the projection on the model so as to rapidly solve and improve the solving speed of the optimal power flow mathematical model of the active power distribution network.
Description
Technical Field
The invention relates to a power control technology, in particular to an optimal power flow convexity control method and device for an active power distribution network.
Background
In recent years, active power distribution networks have received much attention due to their friendly and active consumption of renewable energy sources. In most cases, renewable energy such as wind and light can be fed into a power distribution network through a platform and a hub of a micro-grid (MDN). Some nodes of the power distribution network are connected with the microgrid to form a typical network topology and a wide energy utilization mode, and the power distribution network is one of a plurality of topology types of an Active Distribution Network (ADN).
Decision instructions required by the scheduling and running of the active power distribution network need the solving result of the optimal power flow, and have higher requirements on the solving speed and the solving precision of the optimal power flow. Because the mathematical model of a typical power distribution network contains line loss constraints and nonlinear constraints, the mathematical model of the power distribution network is non-convex and nonlinear, can not be rapidly solved by using theories such as convex optimization and the like in the solving process, needs to be solved by means of artificial intelligence algorithms such as particle swarm optimization, annealing and the like, has higher precision and longer solving time, and particularly when the solved problem has larger scale, is time-consuming and generally can not meet the requirements of a dispatching department.
In the prior art, a linear programming method, a nonlinear programming method, an artificial intelligence algorithm and the like are adopted to solve the optimal power flow of the active power distribution network, the solving precision can generally meet the requirements of practical problems, but the solving speed is high, and the speed for searching the optimal solution in the feasible region also improves the space.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an optimal power flow convexity control method for an active power distribution network, which comprises the following steps:
establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out convex processing on the nonlinear constraints of the power flow of the established optimal power flow model;
solving the optimal power flow model after the convex processing to generate solving result data;
and performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
In the embodiment of the present invention, the establishing of the optimal power flow model of the active power distribution network according to the power grid parameters includes:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
In the embodiment of the present invention, the process of performing convex processing on the nonlinear constraint of the established optimal power flow model includes:
carrying out convex processing on the nonlinear constraint of the established optimal power flow model by using the following formula;
for any small non-negative constant epsilon is more than or equal to 0,
wherein,
Vr,jthe receiving end voltage amplitude of the branch j;
Pr,jis the network loss power of the jth alternating current line;
Qr,jis the receiving end power of the jth ac line.
In an embodiment of the present invention, the preset optimization control model includes:
wherein λ is1、λ2、λ3And λ4Weight coefficient of a preset optimization target, and1+λ2+λ3+λ4＝1；
Vethe average rated voltage of the node;
Δ V, average node voltage deviation within a single day of the power distribution network;
Plossactive network loss;
e, operating cost;
r, micro-grid operation profit;
P0the optimal solution of the active network loss is obtained through single-target optimization solution in a single day under the preset working condition;
E0the optimal solution of the operation cost of the single-target optimization solution in a single day under the preset working condition is obtained;
R0and the optimal solution of the operation profit of the micro-grid is obtained by optimizing and solving the single target in a single day under the preset working condition.
In an embodiment of the present invention, the solution result data includes:
in each optimization interval of a single day, the active power input and the reactive power input of a power distribution network balance node, the voltage power angle and the reactive power input of a PV node, and the voltage amplitude and the voltage power angle of a PQ node are carried out;
in each optimization interval of a single day, the active transmission quantity and the reactive transmission quantity of the power distribution network and the microgrid connecting line;
and in each optimization interval in a single day, the active power emitted or absorbed by the distributed power supply in the microgrid and the active power emitted or absorbed by the energy storage device.
Meanwhile, the invention also provides an optimal power flow convexity control device for the active power distribution network, which comprises the following steps:
the model establishing module is used for establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
the convex processing module is used for carrying out convex processing on the nonlinear load flow constraint of the established optimal load flow model;
the solving processing module is used for solving the optimal power flow model after the convex processing to generate solving result data;
and the optimization control module is used for carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
In the embodiment of the invention, the model establishing module establishes the optimal power flow model of the active power distribution network according to the power grid parameters, and the optimal power flow model comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
In the embodiment of the present invention, the process of performing the convex processing on the nonlinear constraint of the established optimal power flow model by the convex processing module includes:
carrying out convex processing on the nonlinear constraint of the established optimal power flow model by using the following formula;
for any small non-negative constant epsilon is more than or equal to 0,
wherein,
Vr,jthe receiving end voltage amplitude of the branch j;
Pr,jis the network loss power of the jth alternating current line;
Qr,jis the receiving end power of the jth ac line.
In an embodiment of the present invention, the preset optimization control model includes:
wherein λ is1、λ2、λ3And λ4Weight coefficient of a preset optimization target, and1+λ2+λ3+λ4＝1；
Vethe average rated voltage of the node;
Δ V, average node voltage deviation within a single day of the power distribution network;
Plossactive network loss;
e, operating cost;
r, micro-grid operation profit;
P0the optimal solution of the active network loss is obtained through single-target optimization solution in a single day under the preset working condition;
E0the optimal solution of the operation cost of the single-target optimization solution in a single day under the preset working condition is obtained;
R0under a preset working conditionAnd (4) optimizing the operation profit of the micro-grid by using a single target within a single day.
Meanwhile, the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the method when executing the computer program.
Meanwhile, the invention also provides a computer readable storage medium, and a computer program for executing the method is stored in the computer readable storage medium.
The method solves the problems that in the prior art, when the active power distribution network contains various distributed power supplies and loads and is connected to the power distribution network through the micro-grid, a mathematical model of the optimal power flow of the active power distribution network becomes a non-convex model, and the situation that the solving speed is slow or even the optimal solution cannot be solved occurs.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an optimal power flow convexity control method for an active power distribution network according to the present invention;
fig. 2 is a block diagram of an active power distribution network optimal power flow convexity control device provided by the invention;
fig. 3 is a schematic diagram of an electronic device 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 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.
In the prior art, when an active power distribution network contains various distributed power supplies and loads and is connected to the power distribution network through a microgrid, a mathematical model of the optimal power flow of the active power distribution network becomes a non-convex model, and the situation that the solving speed is slow or even the optimal solution cannot be solved occurs.
In view of the above, the present invention provides an optimal power flow convexity control method for an active power distribution network, as shown in fig. 1, the method of the present invention includes:
step S101, establishing an optimal power flow model of the active power distribution network according to power grid parameters;
step S102, carrying out convex processing on the nonlinear load flow constraint of the established optimal load flow model;
step S103, solving the optimal power flow model after the convex processing to generate solving result data;
and S104, performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
According to the optimal power flow convexity control method for the active power distribution network, disclosed by the invention, the power flow nonlinear constraint of the established optimal power flow model is subjected to convexity treatment, the second-order cone relaxation is adopted for convexity treatment, the mathematical essence of the optimal power flow model of the power distribution network is converted into a second-order cone planning problem, so that the solving speed of the optimal power flow model is increased, the optimal control of the parameters of the power distribution network is carried out according to the solving result, and the requirements of a dispatching department are further met.
In the optimal power flow model of the active power distribution network, active power flow and reactive power flow are depicted in a power flow equation of a constraint condition. After some nodes of the power distribution network are accessed to the microgrid, the constraint conditions of the optimization model comprise: the method comprises two parts of a power distribution network constraint condition and a microgrid constraint condition.
The constraint condition of the active power distribution network is solved in the whole mathematical model to serve as the boundary of the operation space of the whole active power distribution network, and the boundary is a feasible domain boundary in mathematical terms. The process of solving the optimal solution of the optimization target of the active power distribution network is that a specific position is sought in an active power distribution network operation space formed by limiting with a constraint condition as a boundary, and the optimization target can obtain the maximum value or the minimum value when the active power distribution network operates on the position.
In the embodiment of the invention, the establishment of the optimal power flow model of the active power distribution network according to the power grid parameters comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
Specifically, in the embodiment of the present invention, the constraint condition part of the power distribution network is described as follows:
an optimal power flow model based on a branch power flow model is adopted, and the detailed model is modeled as follows:
the active and reactive balance equation of the node i in the power distribution network is as follows:
wherein, PGiAnd QGiActive power and reactive power injected into the node i respectively;
PLiand QLiRespectively the active power and the reactive power of the load connected with the node i;
MPQ(i, j) and Ml(i, j) are respectively active and reactive power flow and network loss of the AC power distribution networkThe incidence matrix of (a) corresponds to the elements of the node i and the branch j;
Pr,jand Qr,jRespectively the active power and the reactive power flowing into the receiving end of the branch j;
Ploss,jand Qloss,jBranch j active network loss and reactive network loss respectively;
Bi,iis the diagonal upper component, V, of the susceptance matrixiIs the voltage magnitude at node i. n islIs the total number of lines.
In this embodiment, the elements of the correlation matrix related to power flow and loss are defined as follows:
wherein the voltage drop of line j is as follows:
γ in formula (6) denotes an imaginary unit;
Vs,jand Vr,jThe voltage amplitudes of the sending end and the receiving end of the branch j are respectively;
θs,jand thetar,jThe voltage angles of the transmitting end and the receiving end of the branch j are respectively;
Rj,jand Xj,jRespectively the resistance and reactance of branch j.
Carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
specifically, the constraint conditions for the induction formation are as follows:
(1) linear constraint of power flow:
PMG+PG-σPL-MPQPr-MlPloss＝0 (4)
QMG+QG-σQL-MPQQr-MlQloss+BW＝0 (5)
2RPr+2XQr+RPloss+XQloss-MWW＝0 (6)
θsr-XPr+RQr＝0 (7)
XPloss-RQloss＝0 (8)
wherein, the formulas (7) and (8) are power distribution network power flow balance constraints;
equation (9) is the ac line voltage drop equality constraint;
equation (10) is the power angle equation constraint of the ac line;
equation (11) represents the relationship between active and reactive network losses.
Wherein: pMGAnd QMGRespectively injecting active power column vectors and reactive power column vectors into the connected nodes by the micro-grid;
PGand QGColumn vectors for active and reactive power injection of network nodes, respectively;
PLand QLRespectively are network node active and reactive load column vectors;
sigma is a load demand coefficient column vector of each time period of the power distribution network;
MPQand MlRespectively are incidence matrixes of active and reactive power flow and network loss of the alternating-current power distribution network;
Prand QrRespectively are active power flow column vectors and reactive power flow column vectors in the network;
Plossand QlossActive and reactive network loss column vectors in the network;
r, X and B are diagonal matrices of resistance, reactance, and susceptance, respectively;
w and MWColumn vectors consisting of the squares of the voltages of the nodes, respectively, and their correlationsA joint matrix;
θsris the phase angle difference column vector of the sending end and the receiving end of the AC line.
(2) And (3) nonlinear constraint of power flow:
for each ac line j ═ 1.. multidot.nlThe line loss is expressed as follows:
in the formula, Ploss,jThe net loss active power of the jth alternating current line;
Qloss,jthe net loss reactive power of the jth alternating current line;
Pr,jactive power of a receiving end of the jth alternating current line;
Qr,jreactive power of a receiving end of the jth alternating current line. Wr,jIs Vr,jSquare of (d).
(3) Voltage and generator output upper and lower limit constraints
1.. n for node ibWherein n isbFor the total number of AC nodes, the following constraints exist:
in the formula:
1.. multidot.n for line jlThe method comprises the following steps:
in the formula:
is the active transmission upper limit of line j;
P r,jis the active transmission lower limit of line j;
In order to avoid complexity, in the embodiment of the invention, the lower corner mark t representing the tth optimization interval is ignored in the constraint conditions of the power distribution network.
Constraint conditions of the microgrid:
(1) and (5) active power balance constraint.
Dt+Wt+Sd,t-Sc,t＝PMG,t+Lt (13)
Wherein, PMG,tAnd injecting power to the distribution network for the tth interval of the micro-grid.
DtIs the output of the controllable DG in the t-th interval in the microgrid, WtThe uncontrollable DGs in the microgrid are within the t-th intervalForce of (S)d,tIs the discharge power S of the energy storage system in the microgrid in the t-th intervalc,tIs the charging power of the energy storage system in the microgrid in the t-th interval, LtIs the load in the t-th interval within the microgrid.
(2) And exchanging constraint with the distribution network power.
-PMGmax≤PMG,t≤PMGmax (14)
Wherein, PMGmaxThe line transmission capacity of the microgrid connected with the distribution network and a certain node is disclosed.
The optimal power flow model of the active power distribution network is established in the embodiment of the invention, and the mathematical model of the active power distribution network is nonlinear and non-convex in nature, so that the speed of solving a large-scale problem is low.
In the embodiment of the invention, the nonlinear power flow constraint of the established optimal power flow model is subjected to convex processing, that is, part of nonlinear equality constraint in the original constraint condition is converted into inequality constraint conforming to a second-order cone programming form according to the method provided by the following part in the embodiment, so that the solution can be realized.
Based on the branch power flow model, a second-order cone relaxation method is adopted, and nonlinear constraints exist in power flow constraints, as shown in formulas (12) and (13). In this embodiment, the two quadratic equality constraints are converted into inequality constraints conforming to a second order cone programming form.
In other words, the nonlinear equality constraint in the original distribution network constraint, namely equation (12) (13), can be replaced by equation (20) through the conversion process of equations (18) (19), and the inequality constraint conforms to the second-order cone programming form.
The active distribution network model with equations (12) (13) and (20) can be solved for the convexity by using the second-order cone relaxation.
Converting the original constraint formulas (12) and (13) into a formula (18) and then a formula (20); namely, the equations (12) and (13) in the original constraint are changed to the equation (20).
For any small non-negative constant epsilon is more than or equal to 0,
the transformation into a 2-norm form transforms the line loss constraint into a convex constraint as follows:
through the processing, the mathematical essence of the optimal power flow model of the power distribution network in the embodiment is converted into a second-order cone planning problem, and the solver in the prior art can be used for effectively solving the problem.
According to the method for solving the optimal power flow model of the power distribution network, provided by the embodiment of the invention, the solved result comprises the following steps:
firstly, in each optimization interval of a single day, inputting active power and reactive power of a power distribution network balance node, inputting voltage power angle and reactive power of a PV node, and inputting voltage amplitude and voltage power angle of a PQ node;
secondly, in each optimization interval of a single day, the active and reactive transmission quantity of the power distribution network and the microgrid interconnection line is calculated;
and thirdly, in each optimization interval of a single day, the active power emitted or absorbed by the distributed power supply and the energy storage device in the microgrid.
The solving result is used as a reference value for optimizing the control parameters of the power distribution network, a scheduling reference value can be provided for a scheduling department, and the scheduling department can provide the reference value according to the reference value.
Specifically, the optimal control of the following control parameters of the power grid includes: the reactive compensation amount of a reactive compensation device in the microgrid, actual active power sent or absorbed by a distributed power source and an energy storage device in the microgrid, reactive power sent by a grid-connected inverter of the distributed power source and the energy storage device in the microgrid, and a tap of a voltage-adjustable transformer in a distribution network.
In particular, the optimization objective may be a single objective, such as a distribution gridMinimum deviation min delta V of average node voltage in a single day and minimum active network loss minPlossThe minimum running cost minE and the maximum operating profit maxR of the micro-grid; it may also be multi-objective, such as the multi-objective optimization of the normalization process is expressed as follows:
in the formula: lambda [ alpha ]1、λ2、λ3And λ4The weight coefficients of four optimization targets of the minimum deviation of the average node voltage in a single day, the minimum active network loss, the minimum operation cost and the maximum operation profit of the micro-grid are respectively, and the lambda is1+λ2+λ3+λ4＝1。
VeIs the node average nominal voltage.
Δ V, average node voltage deviation within a single day of the power distribution network;
Plossthe active network loss of the power distribution network in a single day;
e, the running cost of the power distribution network in a single day;
r, operating profit of the micro-grid within a single day;
P0the optimal solution of the active network loss of the power distribution network is obtained through single-target optimization solution in a single day under the preset working condition;
E0the optimal solution of the operation cost of the power distribution network is solved by single-target optimization within a single day under the preset working condition;
R0and the optimal solution of the operation profit of the micro-grid is obtained by optimizing and solving the single target in a single day under the preset working condition.
The method solves the problems that in the prior art, when the active power distribution network contains various distributed power supplies and loads and is connected to the power distribution network through the micro-grid, a mathematical model of the optimal power flow of the active power distribution network becomes a non-convex model, and the situation that the solving speed is slow or even the optimal solution cannot be solved occurs. The second-order cone relaxation method adopted by the invention is used for carrying out the convex treatment on the model, thereby rapidly solving, overcoming the problem of long time consumption and improving the dispatching response speed.
Meanwhile, the invention also provides an optimal power flow convexity control device for the active power distribution network, as shown in fig. 2, comprising:
the model establishing module 201 is used for establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
the convexity processing module 202 is used for carrying out convexity processing on the nonlinear constraints of the power flow of the established optimal power flow model;
the solving processing module 203 is used for solving the optimal power flow model after the convex processing to generate solving result data;
and the optimization control module 204 is configured to perform active power distribution network parameter optimization control according to the solution result data and a preset optimization control model.
In the embodiment of the invention, the model establishing module establishes the optimal power flow model of the active power distribution network according to the power grid parameters, and the optimal power flow model comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
The present embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, and the like, but is not limited thereto. In this embodiment, the electronic device may refer to the embodiments of the method and the apparatus, and the contents thereof are incorporated herein, and repeated descriptions are omitted.
Fig. 3 is a schematic block diagram of a system configuration of an electronic apparatus 600 according to an embodiment of the present invention. As shown in fig. 3, the electronic device 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. Notably, this diagram is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the active power distribution network optimal power flow convexity control function can be integrated into the central processor 100. The central processor 100 may be configured to control as follows:
establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out convex processing on the nonlinear constraints of the power flow of the established optimal power flow model;
solving the optimal power flow model after the convex processing to generate solving result data;
performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model
In another embodiment, the active distribution network optimal power flow convexity control device may be configured separately from the central processing unit 100, for example, the active distribution network optimal power flow convexity control device may be configured as a chip connected with the central processing unit 100, and the active distribution network optimal power flow convexity control function is realized through the control of the central processing unit.
As shown in fig. 3, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in fig. 3; furthermore, the electronic device 600 may also comprise components not shown in fig. 3, which may be referred to in the prior art.
As shown in fig. 3, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
The embodiment of the present invention further provides a computer-readable program, where when the program is executed in an electronic device, the program causes a computer to execute the method for controlling the optimal power flow convexity of the active power distribution network in the electronic device according to the above embodiment.
The embodiment of the present invention further provides a storage medium storing a computer readable program, where the computer readable program enables a computer to execute the optimal power flow projection control of the active power distribution network in the electronic device according to the above embodiment.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments that fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. An optimal power flow convexity control method for an active power distribution network is characterized by comprising the following steps:
establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out convex processing on the nonlinear constraints of the power flow of the established optimal power flow model;
solving the optimal power flow model after the convex processing to generate solving result data;
and performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
2. The method for controlling convex optimization power flow of the active power distribution network according to claim 1, wherein the step of establishing the optimal power flow model of the active power distribution network according to the power grid parameters comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
3. The optimal power flow convexity control method for the active power distribution network according to claim 1, wherein the convexity processing of the nonlinear constraints of the established optimal power flow model comprises:
carrying out convex processing on the nonlinear constraint of the established optimal power flow model by using the following formula;
for any small non-negative constant epsilon is more than or equal to 0,
wherein,
Vr,jthe receiving end voltage amplitude of the branch j;
Pr,jis the network loss power of the jth alternating current line;
Qr,jis the receiving end power of the jth ac line.
4. The optimal power flow convexity control method for the active power distribution network according to claim 1, wherein the preset optimization control model comprises:
wherein λ is1、λ2、λ3And λ4Weight coefficient of a preset optimization target, and1+λ2+λ3+λ4＝1；
Vethe average rated voltage of the node;
Δ V, average node voltage deviation within a single day of the power distribution network;
Plossactive network loss;
e, operating cost;
r, micro-grid operation profit;
P0the optimal solution of the active network loss is obtained through single-target optimization solution in a single day under the preset working condition;
E0the optimal solution of the operation cost of the single-target optimization solution in a single day under the preset working condition is obtained;
R0and the optimal solution of the operation profit of the micro-grid is obtained by optimizing and solving the single target in a single day under the preset working condition.
5. The optimal power flow convexity control method for the active power distribution network according to claim 1, wherein the solution result data comprises:
in each optimization interval of a single day, the active power input and the reactive power input of a power distribution network balance node, the voltage power angle and the reactive power input of a PV node, and the voltage amplitude and the voltage power angle of a PQ node are carried out;
in each optimization interval of a single day, the active transmission quantity and the reactive transmission quantity of the power distribution network and the microgrid connecting line;
and in each optimization interval in a single day, the active power emitted or absorbed by the distributed power supply in the microgrid and the active power emitted or absorbed by the energy storage device.
6. An optimal power flow convexity control device for an active power distribution network is characterized by comprising the following components:
the model establishing module is used for establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
the convex processing module is used for carrying out convex processing on the nonlinear load flow constraint of the established optimal load flow model;
the solving processing module is used for solving the optimal power flow model after the convex processing to generate solving result data;
and the optimization control module is used for carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
7. The active power distribution network optimal power flow convexity control device according to claim 6, wherein the model establishing module establishes the optimal power flow model of the active power distribution network according to the power grid parameters, and comprises:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to the power grid parameters of the power distribution network;
carrying out induction processing according to the power grid parameters of the power distribution network to establish power flow linear constraint, power flow nonlinear constraint and upper and lower voltage and generator output limit constraints;
and establishing the active balance constraint of the micro-grid and the constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
8. The optimal power flow convexity control device for the active power distribution network according to claim 6, wherein the convexity processing module for carrying out convexity processing on the nonlinear constraints of the established optimal power flow model comprises:
carrying out convex processing on the nonlinear constraint of the established optimal power flow model by using the following formula;
for any small non-negative constant epsilon is more than or equal to 0,
wherein,
Vr,jthe receiving end voltage amplitude of the branch j;
Pr,jis the network loss power of the jth alternating current line;
Qr,jis the receiving end power of the jth ac line.
9. The optimal power flow convexity control device for the active power distribution network according to claim 6, wherein the preset optimization control model comprises:
wherein λ is1、λ2、λ3And λ4Weight coefficient of a preset optimization target, and1+λ2+λ3+λ4＝1；
Vethe average rated voltage of the node;
Δ V, average node voltage deviation within a single day of the power distribution network;
Plossactive network loss;
e, operating cost;
r, micro-grid operation profit;
P0the optimal solution of the active network loss is obtained through single-target optimization solution in a single day under the preset working condition;
E0the optimal solution of the operation cost of the single-target optimization solution in a single day under the preset working condition is obtained;
R0and the optimal solution of the operation profit of the micro-grid is obtained by optimizing and solving the single target in a single day under the preset working condition.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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