CN113517698A - Optimal power flow convexity control method and device for active distribution network - Google Patents

Abstract

The invention provides a method and a device for controlling optimal power flow convexity of an active power distribution network. The method includes: establishing an optimal power flow model of the active power distribution network according to power grid parameters; Convex processing; perform solution processing on the optimal power flow model after the convexization process to generate solution result data; perform active distribution network parameter optimization control according to the solution result data and a preset optimal control model. The invention solves the situation in the prior art that the solution speed is slow or even the optimal solution cannot be obtained. The invention convexizes the model so as to solve the problem quickly and improve the solution speed of the optimal power flow mathematical model of the active distribution network.

Description

Optimal power flow convexity control method and device for active distribution network

technical field

The invention relates to power control technology, in particular to a method and a device for controlling optimal power flow convexity of an active distribution network.

Background technique

In recent years, active distribution network has received extensive attention due to its friendly and active consumption of renewable energy. In most cases, renewable energy such as wind and solar will be fed into the distribution network through the microgrid-nodal distribution network (MDN) platform and hub. Some nodes of the distribution network are connected to the microgrid, which has become a typical network topology and a wide range of energy consumption patterns, which is one of many topologies of the active distribution network (ADN).

The decision-making commands required for the scheduling and operation of the active distribution network require the solution result of the optimal power flow, and the speed and accuracy of the solution of the optimal power flow have high requirements. Since the mathematical model of a typical distribution network contains line loss constraints, which are nonlinear constraints, the mathematical model of the distribution network is non-convex and nonlinear, and cannot be solved quickly using theories such as convex optimization. Artificial intelligence algorithms such as annealing are used to solve the problem, and the higher the accuracy, the longer the solution time, especially when the scale of the problem to be solved is large, it is time-consuming and generally cannot meet the needs of the scheduling department.

The linear programming method, nonlinear programming method and artificial intelligence algorithm in the existing technology are used to solve the optimal power flow of the active distribution network. The solution accuracy can generally meet the needs of practical problems, but the solution speed is unsatisfactory. There is room for improvement in the speed of finding the optimal solution.

SUMMARY OF THE INVENTION

In view of the defects existing in the prior art, the present invention provides an optimal power flow convexity control method for an active distribution network, including:

The optimal power flow model of the active distribution network is established according to the grid parameters;

Convex the power flow nonlinear constraints of the established optimal power flow model;

Solve the optimal power flow model after convexization processing to generate the solution result data;

The active distribution network parameter optimization control is performed according to the solution result data and the preset optimization control model.

In the embodiment of the present invention, the establishment of the optimal power flow model of the active distribution network according to the power grid parameters includes:

According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network;

According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established;

According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established.

In the embodiment of the present invention, the convexity processing on the nonlinear constraints of the established optimal power flow model includes:

Convex the nonlinear constraints of the established optimal power flow model by using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving terminal voltage amplitude of branch j;

P _r,j is the network loss power of the jth AC line;

Q _r,j is the receiving end power of the jth AC line.

In the embodiment of the present invention, the preset optimal control model includes:

Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the preset optimization target, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1;

V _e , the average rated voltage of the node;

ΔV, the average node voltage deviation of the distribution network in a single day;

P _loss , active network loss;

E, operating cost;

R, the operating profit of the microgrid;

P ₀ , the optimal solution of active network loss for a single-objective optimization solution within a single day under preset operating conditions;

E ₀ , the optimal solution of the operating cost of the single-objective optimization solution in a single day under the preset working conditions;

R ₀ , the optimal solution of the operating profit of the microgrid for the single-objective optimization solution within a single day under the preset operating conditions.

In the embodiment of the present invention, the solution result data includes:

In each optimization interval on a single day, the active power input, reactive power input of the balance node of the distribution network, the voltage power angle and reactive power input of the PV node, and the voltage amplitude and voltage power angle of the PQ node;

Active power transmission and reactive power transmission of the distribution network and the microgrid connection line in each optimization interval on a single day;

In each optimization interval on a single day, the active power emitted or absorbed by the distributed power generation in the microgrid, and the active power emitted or absorbed by the energy storage device.

At the same time, the present invention also provides an optimal power flow convexity control device for an active distribution network, including:

The model building module is used to establish the optimal power flow model of the active distribution network according to the power grid parameters;

The convexity processing module is used to perform convexity processing on the nonlinear constraints of the power flow of the established optimal power flow model;

The solution processing module is used to solve the optimal power flow model after convexization processing to generate solution result data;

The optimization control module is used for performing active distribution network parameter optimization control according to the solution result data and the preset optimization control model.

In the embodiment of the present invention, the model establishment module establishes the optimal power flow model of the active distribution network according to the grid parameters, including:

According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network;

According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established;

According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established.

In the embodiment of the present invention, the convexization processing module performing convexization processing on the nonlinear constraints of the established optimal power flow model includes:

Convex the nonlinear constraints of the established optimal power flow model by using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving terminal voltage amplitude of branch j;

P _r,j is the network loss power of the jth AC line;

Q _r,j is the receiving end power of the jth AC line.

In the embodiment of the present invention, the preset optimal control model includes:

Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the preset optimization target, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1;

V _e , the average rated voltage of the node;

ΔV, the average node voltage deviation of the distribution network in a single day;

P _loss , active network loss;

E, operating cost;

R, the operating profit of the microgrid;

P ₀ , the optimal solution of active network loss for a single-objective optimization solution within a single day under preset operating conditions;

E ₀ , the optimal solution of the operating cost of the single-objective optimization solution in a single day under the preset working conditions;

R ₀ , the optimal solution of the operating profit of the microgrid for the single-objective optimization solution within a single day under the preset operating conditions.

Meanwhile, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the above method when executing the computer program.

Meanwhile, the present invention also provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for executing the above method.

The invention solves the problem in the prior art that when the active distribution network contains a variety of distributed power sources and loads are connected to the distribution network through the microgrid, the mathematical model of the optimal flow of the active distribution network will become a non-convex model, and there will be In the case that the solution speed is slow or even the optimal solution cannot be obtained, the present invention makes the model convex, so as to solve the problem quickly and improve the solution speed of the mathematical model.

In order to make the above-mentioned and other objects, features and advantages of the present invention more obvious and easy to understand, the preferred embodiments are exemplified below, and are described in detail as follows in conjunction with the accompanying drawings.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

Fig. 1 is the flow chart of the optimal power flow convexity control method of the active distribution network provided by the present invention;

FIG. 2 is a block diagram of an optimal power flow convexity control device for an active distribution network provided by the present invention;

FIG. 3 is a schematic diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the prior art, when the active distribution network contains a variety of distributed power sources and loads are connected to the distribution network through the microgrid, the mathematical model of the optimal power flow of the active distribution network will become a non-convex model, and the solution speed will increase. Slow or even impossible to find the optimal solution.

In this regard, the present invention provides an optimal power flow convexity control method for an active 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 distribution network according to the power grid parameters;

Step S102, convexizing the power flow nonlinear constraints of the established optimal power flow model;

Step S103, performing a solution process on the optimal power flow model after the convexization process to generate solution result data;

Step S104, performing active distribution network parameter optimization control according to the solution result data and a preset optimization control model.

The optimal power flow convexization control method for an active distribution network provided by the present invention performs convexization processing on the nonlinear constraints of the power flow of the established optimal power flow model, and uses the second-order cone relaxation for convexization, so that the optimal power flow model of the distribution network is convexed. The mathematical essence of the power flow has been transformed into a second-order cone programming problem, so as to improve the solution speed of the optimal power flow model, and optimize the parameters of the distribution network according to the solution results to meet the needs of the dispatching department.

In the optimal power flow model of active distribution network, the characterization of active power flow and reactive power flow is reflected in the power flow equation of constraints. After some nodes of the distribution network are connected to the microgrid, the constraints of the optimization model include: distribution network constraints and microgrid constraints.

Among them, the role of the constraints of the active distribution network in solving the entire mathematical model is the boundary of the entire active distribution network operating space, which is the boundary of the feasible region from a mathematical point of view. The process of solving the optimal solution of the optimization objective of the active distribution network is to seek a specific location within the operating space of the active distribution network formed by the constraints as the boundary limit, and the optimization objective when the active distribution network operates at this location. A maximum or minimum value can be obtained.

In the embodiment of the present invention, establishing the optimal power flow model of the active distribution network according to the power grid parameters includes:

According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network;

According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established;

According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established.

Specifically, in the embodiment of the present invention, the constraints of the distribution network are described as follows:

The optimal power flow model based on the branch power flow model is adopted, and the detailed model modeling is as follows:

The active and reactive power balance equations of node i in the distribution network are:

Among them, P _Gi and Q _Gi are the active power and reactive power injected into node i, respectively;

P _Li and Q _Li are the active and reactive power of the load connected to node i, respectively;

M _PQ (i,j) and M _l (i,j) are the elements corresponding to node i and branch j of the correlation matrix of active and reactive power flow and network loss of AC distribution network;

P _r,j and Q _r,j are the active power and reactive power flowing into the receiving end of branch j, respectively;

P _loss,j and Q _loss,j are the active network loss and reactive network loss of branch j, respectively;

B _i,i are the diagonal components of the susceptance matrix, and V _i is the voltage amplitude at node i. n _l is the total number of lines.

In this embodiment, the elements of the correlation matrix related to power flow and loss are defined as follows:

where the voltage drop on line j is as follows:

γ in formula (6) refers to the imaginary unit;

V _s,j and V _r,j are the voltage amplitudes of the sending and receiving terminals of branch j, respectively;

θ _s,j and θ _r,j are the voltage phase angles of the sending and receiving terminals of branch j, respectively;

R _j,j and X _j,j are the resistance and reactance of branch j, respectively.

According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established;

Specifically, the constraints formed by induction are as follows:

(1) Power flow linear constraints:

P _MG +P _G -σP _L -M _PQ P _r -M _l P _loss = 0 (4)

Q _MG +Q _G -σQ _L -M _PQ Q _r -M _l Q _loss +BW=0 (5)

2RP _r +2XQ _r +RP _loss +XQ _loss -MW _W =0 (6)

θ _sr -XP _r +RQ _r =0 (7)

XP _loss -RQ _loss = 0 (8)

Among them, equations (7) and (8) are the power flow balance constraints of the distribution network;

Equation (9) is the AC line voltage drop equation constraint;

Equation (10) is the power angle equation constraint of the AC line;

Equation (11) represents the relationship between active and reactive network losses.

Among them: _PMG and _QMG are the active and reactive power column vectors injected by the microgrid to the connected nodes, respectively;

P _G and Q _G are the column vectors of active and reactive power injection of network nodes, respectively;

_PL and _QL are the network node active and reactive load column vectors, respectively;

σ is the column vector of load demand coefficients in each period of distribution network;

M _PQ and M _l are the correlation matrices of active and reactive power flow and network loss in AC distribution network, respectively;

P _r and Q _r are the active and reactive power flow train vectors in the network, respectively;

Column vectors of active and reactive network losses in P _loss and Q _loss networks;

R, X, and B are diagonal matrices of resistance, reactance, and susceptance, respectively;

W and M _W are the column vector and its correlation matrix composed of the square of the voltage of each node, respectively;

θ _sr is the column vector of the phase angle difference between the sending end and the receiving end of the AC line.

(2) Power flow nonlinear constraints:

For each AC line j=1,...,n _l , the line loss is expressed as follows:

In the formula, P _loss,j is the active power loss of the jth AC line;

Q _loss,j jth AC line network loss reactive power;

P _r,j Active power at the receiving end of the jth AC line;

Q _r,j Reactive power at the receiving end of the jth AC line. W _r,j is the square of V _r,j .

(3) Upper and lower limit constraints of voltage and generator output

For nodes i=1,...,n _b , where n _b is the total number of AC nodes, the following constraints exist:

where:

和V _i分别是节点i的电压的上限和电压的下限；

and V _i are the upper and lower voltage limits of node i, respectively;

和

是节点i的有功出力上限和有功出力下限；

and

is the upper limit and lower limit of active power output of node i;

和

分别是节点i的无功出力的上限和无功出力下限。

and

are the upper limit and lower limit of reactive power output of node i, respectively.

For lines j=1,...,n _l , there are:

为线路j的有功传输上限；where:

is the upper limit of active transmission of line j;

P _r,j is the lower limit of active power transmission of line j;

和Q _r,j分别是线路j的有功传输上限和无功传输下限。

and Q _r,j are the upper limit of active transmission and the lower limit of reactive transmission of line j, respectively.

In order to avoid complexity, in the embodiment of the present invention, the above distribution network constraints all ignore the subscript t representing the t-th optimization interval.

Microgrid constraints:

(1) Active power balance constraints.

D _t +W _t +S _d,t -S _c,t =P _MG,t +L _t (13)

Among them, _PMG,t is the power injected into the distribution network in the t-th interval of the microgrid.

D _t is the output of the controllable DG in the micro-grid in the t-th interval, W _t is the output of the uncontrollable DG in the micro-grid in the t-th interval, S _d,t is the energy storage system in the micro-grid in the t-th interval The discharge power in the interval, S _c,t is the charging power of the energy storage system in the microgrid in the t-th interval, and L _t is the load in the t-th interval in the microgrid.

(2) Power exchange constraints with the distribution network.

-P _{MGmax ≤P MG,t ≤P MGmax} (14)

Among them, _PMGmax is the line transmission capacity of the distribution network and the microgrid connected to a node.

The above is the optimal power flow model for establishing the active distribution network in the embodiment of the present invention. The mathematical model of the active distribution network is essentially nonlinear and non-convex, and the speed of solving large-scale problems is slow.

In this embodiment of the present invention, convexization is performed on the nonlinear constraints of the power flow of the established optimal power flow model, that is, in this embodiment, the partial nonlinear equation constraints in the original constraints are converted into The inequality constraints in the form of second-order cone programming can then be solved.

Based on the branch power flow model, the second-order cone relaxation method is adopted, and there are nonlinear constraints in the power flow constraints, as shown in equations (12) and (13). In this embodiment, the two quadratic equality constraints are converted into inequality constraints conforming to the form of second-order cone programming.

In other words, the nonlinear equality constraints in the original distribution network constraints, that is, formulas (12) and (13), can be replaced by formulas (20) through the conversion process of formulas (18) and (19). This inequality constraint conforms to the second-order cone. planning form.

The active distribution network model that replaces equations (12) and (13) with (20) can be solved by convexity using second-order cone relaxation.

Convert the original constraint equations (12) and (13) into equation (18) first, and then into equation (20); that is, replace equations (12) and (13) in the original constraint with equation (20).

For any small non-negative constant ε ≥ 0,

Converting to the form of 2-norm converts the line loss constraint into a convex constraint as follows:

After the above processing, the mathematical essence of the optimal power flow model of the distribution network in this embodiment has been transformed into a second-order cone programming problem, which can be effectively solved by using the solver of the prior art.

The optimal power flow model of the distribution network is solved according to the embodiment of the present invention, and the solution results include:

First, in each optimization interval on a single day, the active and reactive power input of the balance node of the distribution network, the voltage power angle and reactive power input of the PV node, and the voltage amplitude and voltage power angle of the PQ node;

Second, the amount of active and reactive power transmission between the distribution network and the microgrid tie line in each optimization interval on a single day;

Third, the active power emitted or absorbed by the distributed power sources and energy storage devices in the microgrid in each optimization interval on a single day.

The above solution results are used as the reference value for the optimization of distribution network control parameters, which can provide the dispatching reference value for the dispatching department, and the dispatching department can base on the above reference value.

Specifically, the optimal control of the following control parameters of the power grid includes: the reactive power compensation amount of the reactive power compensation device in the microgrid, the actual active power emitted or absorbed by the distributed power supply and the energy storage device in the microgrid, and the distribution in the microgrid. The reactive power generated by the grid-connected inverter of the type power supply and energy storage device, and the tap of the adjustable voltage transformer in the distribution network.

Specifically, the optimization objective can be a single objective, such as the minimum deviation minΔV of the average node voltage in a single day of the distribution network, the minimum active network loss minP _loss , the minimum operating cost minE and the maximum operating profit maxR of the microgrid; it can also be multi-objective, For example, the multi-objective optimization of normalized processing is expressed as follows:

In the formula: λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the four optimization objectives of the average node voltage in a single day, the minimum deviation of the distribution network, the minimum active network loss, the minimum operating cost and the maximum operating profit of the microgrid, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1.

V _e is the node average rated voltage.

ΔV, the average node voltage deviation of the distribution network in a single day;

P _loss , the active power loss of the distribution network in a single day;

E, the operating cost of the distribution network in a single day;

R, the operating profit of the microgrid in a single day;

P ₀ , the optimal solution of the active power network loss of the distribution network obtained by the single-objective optimization solution in a single day under the preset operating conditions;

E ₀ , the optimal solution of the distribution network operating cost for the single-objective optimization solution within a single day under the preset working conditions;

R ₀ , the optimal solution of the operating profit of the microgrid for the single-objective optimization solution within a single day under the preset operating conditions.

The invention solves the problem in the prior art that when the active distribution network contains a variety of distributed power sources and loads are connected to the distribution network through the microgrid, the mathematical model of the optimal power flow of the active distribution network will become a non-convex model, and the There are cases where the solution speed is slow or even the optimal solution cannot be obtained. The second-order cone relaxation method adopted in the present invention makes the model convex, so as to solve the problem quickly, overcome the problem of time-consuming, and improve the dispatching response speed.

At the same time, the present invention also provides an optimal power flow convexity control device for an active distribution network, as shown in FIG. 2 , including:

The model establishment module 201 is used for establishing the optimal power flow model of the active distribution network according to the grid parameters;

The convexity processing module 202 is used for convexizing the power flow nonlinear constraints of the established optimal power flow model;

The solution processing module 203 is configured to perform solution processing on the optimal power flow model after the convexization process to generate solution result data;

The optimization control module 204 is configured to perform active distribution network parameter optimization control according to the solution result data and a preset optimization control model.

In the embodiment of the present invention, the model establishment module establishes the optimal power flow model of the active distribution network according to the grid parameters, including:

According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network;

According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established;

According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established.

This embodiment also provides an electronic device, and the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but this embodiment is not limited thereto. In this embodiment, for the electronic device, reference may be made to the foregoing embodiments of the method and apparatus, the contents of which are incorporated herein, and repeated descriptions are not repeated here.

FIG. 3 is a schematic block diagram of a system configuration of an electronic device 600 according to an embodiment of the present invention. As shown in FIG. 3 , the electronic device 600 may include a central processing unit 100 and a memory 140 ; the memory 140 is coupled to the central processing unit 100 . Notably, this figure is exemplary; other types of structures may be used in addition to or in place of this structure to implement telecommunication functions or other functions.

In one embodiment, the active distribution grid optimal power flow convexity control function may be integrated into the central processing unit 100 . Wherein, the central processing unit 100 can be configured to perform the following controls:

The optimal power flow model of the active distribution network is established according to the grid parameters;

Convex the power flow nonlinear constraints of the established optimal power flow model;

Solve the optimal power flow model after convexization processing to generate the solution result data;

According to the solution result data and the preset optimization control model, the active distribution network parameter optimization control is carried out

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 connection to the central processing unit 100 . The chip realizes the optimal power flow convexity control function of the active distribution network through the control of the central processing unit.

As shown in FIG. 3 , the electronic device 600 may further include: a communication module 110 , an input unit 120 , an audio processing unit 130 , a display 160 , and a power supply 170 . It is worth noting that the electronic device 600 does not necessarily include all the components shown in FIG. 3 ; in addition, the electronic device 600 may also include components not shown in FIG. 3 , and reference may be made to the prior art.

As shown in FIG. 3 , the central processing unit 100 , sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, and the central processing unit 100 receives input and controls various aspects of the electronic device 600 component operation.

Wherein, the memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure can be stored, and a program executing the related information can also be stored. And the central processing unit 100 can execute the program stored in the memory 140 to realize information storage or processing.

The input unit 120 provides input to the central processing unit 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 for displaying display objects such as images and characters. The display can be, for example, but not limited to, an LCD display.

The memory 140 may be solid state memory, eg, read only memory (ROM), random access memory (RAM), SIM card, and the like. There may also be memories that retain information even when powered off, selectively erased and provided with more data, examples of which are sometimes referred to as EPROMs or the like. 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 part 142 for storing application programs and function programs or for performing operations of the electronic device 600 through the central processing unit 100 .

The memory 140 may also include a 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 section 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for executing other functions of the electronic device (eg, a messaging application, a contact book application, etc.).

The communication module 110 is the transmitter/receiver 110 that transmits and receives signals via the antenna 111 . A communication module (transmitter/receiver) 110 is coupled to the central processing unit 100 to provide input signals and receive output signals, as may be the case with conventional mobile communication terminals.

Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and/or a wireless local area network module. Communication module (transmitter/receiver) 110 is also coupled to speaker 131 and microphone 132 via audio processor 130 to provide audio output via speaker 131 and to receive audio input from microphone 132 for general telecommunication functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers, and the like. In addition, the audio processor 130 is also coupled to the central processing unit 100 so as to enable recording on the local unit through the microphone 132 and to play back the sound stored on the local unit through the speaker 131 .

Embodiments of the present invention also provide a computer-readable program, wherein when the program is executed in an electronic device, the program enables a computer to execute the active distribution network optimization described in the above embodiments in the electronic device Power flow convexity control method.

Embodiments of the present invention further provide a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the optimal power flow convexity control of the active distribution network described in the above embodiments in an electronic device.

The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. The many features and advantages of these embodiments are apparent from this detailed description, and the appended claims are therefore intended to cover all such features and advantages of these embodiments as fall within their true spirit and scope. Furthermore, since many modifications and changes will readily occur to those skilled in the art, the embodiments of the present invention are not intended to be limited to the precise construction and operation illustrated and described, but are intended to cover all suitable modifications and equivalents falling within the scope thereof thing.

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, etc.) 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 in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

In the present invention, the principles and implementations of the present invention are described by using specific embodiments, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention; The idea of the invention will have changes in the specific implementation and application scope. To sum up, the content of this specification should not be construed as a limitation to the present invention.

Claims (11)

1. an active distribution network optimal power flow convexity control method, is characterized in that, described method comprises: The optimal power flow model of the active distribution network is established according to the grid parameters; Convex the power flow nonlinear constraints of the established optimal power flow model; Solve the optimal power flow model after convexization processing to generate the solution result data; The active distribution network parameter optimization control is performed according to the solution result data and the preset optimization control model. 2. The optimal power flow convexity control method for an active distribution network as claimed in claim 1, wherein said establishing an optimal power flow model of the active distribution network according to power grid parameters comprises: According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network; According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established; According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established. 3. The optimal power flow convexity control method for an active distribution network according to claim 1, wherein the convexity processing on the nonlinear constraints of the established optimal power flow model comprises: Convex the nonlinear constraints of the established optimal power flow model by using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving terminal voltage amplitude of branch j; P _r,j is the network loss power of the jth AC line; Q _r,j is the receiving end power of the jth AC line. 4. The optimal power flow convexity control method for an active distribution network according to claim 1, wherein the preset optimal control model comprises:

Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the preset optimization target, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1; V _e , the average rated voltage of the node; ΔV, the average node voltage deviation of the distribution network in a single day; P _loss , active network loss; E, operating cost; R, the operating profit of the microgrid; P ₀ , the optimal solution of active network loss for a single-objective optimization solution within a single day under preset operating conditions; E ₀ , the optimal solution of the operating cost of the single-objective optimization solution in a single day under the preset working conditions; R ₀ , the optimal solution of the operating profit of the microgrid for the single-objective optimization solution within a single day under the preset operating conditions. 5. The optimal power flow convexity control method for an active distribution network according to claim 1, wherein the solution result data comprises: In each optimization interval on a single day, the active power input, reactive power input of the balance node of the distribution network, the voltage power angle and reactive power input of the PV node, and the voltage amplitude and voltage power angle of the PQ node; Active power transmission and reactive power transmission of the distribution network and the microgrid connection line in each optimization interval on a single day; In each optimization interval on a single day, the active power emitted or absorbed by the distributed power generation 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 distribution network, characterized in that the device comprises: The model building module is used to establish the optimal power flow model of the active distribution network according to the power grid parameters; The convexity processing module is used to perform convexity processing on the nonlinear constraints of the power flow of the established optimal power flow model; The solution processing module is used to solve the optimal power flow model after convexization processing to generate solution result data; The optimization control module is used for performing active distribution network parameter optimization control according to the solution result data and the preset optimization control model. 7. The optimal power flow convexity control device for an active distribution network as claimed in claim 6, wherein the model establishment module establishes the optimal power flow model of the active distribution network according to power grid parameters, comprising: According to the grid parameters of the distribution network, establish the active power balance equation and reactive power balance equation of the nodes in the distribution network; According to the grid parameters of the distribution network, the power flow linear constraints, the power flow nonlinear constraints and the upper and lower limit constraints of the voltage and generator output are established; According to the grid parameters of the microgrid, the active power balance constraints of the microgrid and the constraints of power exchange with the distribution network are established. 8. The optimal power flow convexity control device for an active distribution network according to claim 6, wherein the convexity processing module performs convexity processing on the nonlinear constraints of the established optimal power flow model, comprising: Convex the nonlinear constraints of the established optimal power flow model by using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving terminal voltage amplitude of branch j; P _r,j is the network loss power of the jth AC line; Q _r,j is the receiving end power of the jth AC line. 9 . The optimal power flow convexity control device for an active distribution network according to claim 6 , wherein the preset optimal control model comprises: 10 .

Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the preset optimization target, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1; V _e , the average rated voltage of the node; ΔV, the average node voltage deviation of the distribution network in a single day; P _loss , active network loss; E, operating cost; R, the operating profit of the microgrid; P ₀ , the optimal solution of active network loss for a single-objective optimization solution within a single day under preset operating conditions; E ₀ , the optimal solution of the operating cost of the single-objective optimization solution in a single day under the preset working conditions; R ₀ , the optimal solution of the operating profit of the microgrid for the single-objective optimization solution within a single day under the preset operating conditions. 10. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any of claims 1 to 5 when the processor executes the computer program method described in item. 11. A computer-readable storage medium, wherein 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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