CN113517698B - Active power distribution network optimal power flow salifying control method and device - Google Patents

Abstract

The present invention provides a method and device for convex control of the optimal power flow of an active distribution network, the method comprising: establishing an optimal power flow model of the active distribution network according to power grid parameters; convex processing the power flow nonlinear constraints of the established optimal power flow model; solving the optimal power flow model after convex processing to generate solution result data; and optimizing the parameters of the active distribution network according to the solution result data and a preset optimization control model. The present invention solves the problem that the solution speed is slow or even the optimal solution cannot be obtained in the prior art. The present invention convexifies the model, thereby quickly solving it and improving the solution speed of the optimal power flow mathematical model of the active distribution network.

Description

Convex control method and device for optimal power flow in active distribution network

Technical Field

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

Background technique

In recent years, active distribution networks have attracted widespread attention due to their friendliness and active absorption of renewable energy. In most cases, renewable energy such as wind and solar power will be fed into the distribution network through the microgrid (microgrid-nodal distribution network, MDN) platform and hub. Some nodes of the distribution network are connected to microgrids, which has become a typical network topology and a widespread energy consumption mode. It is one of the many topological types of active distribution networks (ADN).

The decision instructions required for the dispatch and operation of active distribution networks require the solution of the optimal power flow, and have high requirements for the speed and accuracy of the optimal power flow solution. Since the mathematical model of the typical distribution network contains line loss constraints, which are nonlinear constraints, the mathematical model of the distribution network is non-convex and nonlinear. It is impossible to use convex optimization and other theories for rapid solution. It needs to be solved with the help of artificial intelligence algorithms such as particle swarm and annealing. 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 dispatching department.

The existing technology uses linear programming, nonlinear programming and artificial intelligence algorithms to solve the optimal power flow of active distribution networks. The solution accuracy can generally meet the needs of practical problems, but the solution speed is unsatisfactory. There is still room for improvement in the speed of finding the optimal solution in the feasible domain.

Summary of the invention

In view of the defects of the prior art, the present invention provides a convex control method for optimal power flow of an active distribution network, comprising:

Establish the optimal power flow model of active distribution network according to the grid parameters;

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

Solving the optimal power flow model after convexification to generate solution result data;

Active distribution network parameter optimization control is performed based on the solution result data and a preset optimization control model.

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

Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network;

According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of 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 on power exchange with the distribution network are established.

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

The nonlinear constraints of the established optimal power flow model are convexified using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving end 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 optimization control model includes:

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

V _e , average rated voltage of the node;

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

P _loss , active network loss;

E, operating cost;

R, microgrid operating profit;

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

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

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

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

Active power input, reactive power input of distribution network balancing nodes, voltage power angle and reactive power input of PV nodes, voltage amplitude and voltage power angle of PQ nodes in each optimization interval of a single day;

Active and reactive transmission of the distribution network and microgrid tie lines within each optimization interval on a single day;

In each optimization interval of a single day, the active power generated or absorbed by the distributed power sources in the microgrid and the active power generated or absorbed by the energy storage device.

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

A model building module is used to build an optimal power flow model of the active distribution network according to the power grid parameters;

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

A solution processing module is used to solve the optimal power flow model after convexification and generate solution result data;

The optimization control module is used to perform 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 building module builds the optimal power flow model of the active power distribution network according to the power grid parameters, including:

Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network;

According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of 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 on power exchange with the distribution network are established.

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

The nonlinear constraints of the established optimal power flow model are convexified using the following formula;

For any small non-negative constant ε ≥ 0,

in,

V _r,j The receiving end 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 optimization control model includes:

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

V _e , average rated voltage of the node;

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

P _loss , active network loss;

E, operating cost;

R, microgrid operating profit;

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

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

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

At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above method is implemented when the processor executes the computer program.

At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program for executing the above method.

The present invention solves the problem in the prior art that when an active distribution network contains multiple distributed power sources and loads connected to the distribution network through a microgrid, the mathematical model of the optimal power flow of the active distribution network becomes a non-convex model, resulting in a slow solution speed or even failure to obtain an optimal solution. The present invention convexifies the model, thereby quickly solving the problem and improving the solution speed of the mathematical model.

In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a convex control method for optimal power flow in an active distribution network provided by the present invention;

FIG2 is a block diagram of a device for convexifying optimal power flow in 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 following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

In the prior art, when the active distribution network contains multiple distributed power sources and loads connected to the distribution network through microgrids, the mathematical model of the optimal power flow of the active distribution network will become a non-convex model, resulting in a slow solution speed or even failure to obtain the optimal solution.

In this regard, the present invention provides a convex control method for optimal power flow of an active distribution network. As shown in FIG1 , 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, convexifying the power flow nonlinear constraints of the established optimal power flow model;

Step S103, solving the optimal power flow model after convexification 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 convexification control method for active distribution network provided by the present invention convexifies the power flow nonlinear constraints of the established optimal power flow model, adopts second-order cone relaxation for convexification, and transforms the mathematical essence of the optimal power flow model of the distribution network into a second-order cone programming problem, thereby improving the solution speed of the optimal power flow model, and optimizing the distribution network parameters according to the solution results, thereby meeting the needs of the dispatching department.

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

Among them, the role of solving the constraints of the active distribution network in the entire mathematical model is to determine the boundary of the entire active distribution network operation space, which is the feasible domain boundary from a mathematical point of view. The process of solving the optimal solution of the optimization target of the active distribution network is to find a specific position within the active distribution network operation space formed by the constraints as the boundary restrictions, where the optimization target can achieve the maximum or minimum value when the active distribution network operates.

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

Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network;

According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of 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 on power exchange with the distribution network are established.

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

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

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

Where 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 powers of the load connected to node i, respectively;

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

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;

_Bi,i is the diagonal component of the susceptance matrix, Vi _is the voltage amplitude at node i, _{and nl} is the total number of lines.

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

The voltage drop of line j is as follows:

In formula (6), γ refers to the imaginary unit;

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

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

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

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

Specifically, the constraints formed by induction are as follows:

(1) Power flow linear constraints:

_PMG +PG _{-σPL -MPQPr- MlPloss = 0 (} 4)

_QMG + _{QG -} σQL _{-MPQQr - MlQloss} + _BW ＝0 (5)

2RP _r + 2XQ _r + RP _loss + XQ _loss - M _W 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;

Formula (10) is the power angle equality constraint of the AC line;

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

Where: P _MG and Q _MG are the active and reactive power column vectors injected by the microgrid into 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 column vectors of active and reactive loads of network nodes, respectively;

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

_MPQ and M _l are the correlation matrices of active and reactive power flows and network losses in the AC distribution network, respectively;

P _r and Q _r are the column vectors of active and reactive power flows in the network, respectively;

P _loss and Q _loss are the column vectors of active and reactive network losses in the network;

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

W and _MW are the column vectors composed of the squares of the node voltages and their correlation matrices respectively;

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

(2) Power flow nonlinear constraints:

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

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

Q _loss,j The reactive power of the jth AC line loss;

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

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

(3) Voltage and generator output upper and lower limit constraints

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

Where:

and Vi are the upper and lower limits of the voltage at node i _, respectively;

and/> is the upper and lower limits of active output of node i;

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

For line j=1,...,n _l , we have:

Where: is the upper limit of active transmission of line j;

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

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

To avoid complexity, in the embodiment of the present invention, the above distribution network constraints all ignore the subscript t representing the tth optimization interval.

Microgrid constraints:

(1) Active power balance constraint.

D _t + W _t + S _{d, t} - S _{c, t} = P _{MG, t} + L _t (13)

Among them, P _MG,t is the power injected into the distribution network by the tth interval of the microgrid.

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

(2) Power exchange constraints with the distribution network.

-P _MGmax ≤P _MG,t ≤P _MGmax (14)

Among them, P _MGmax is the line transmission capacity between the distribution network and the microgrid connected to a certain node.

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

In an embodiment of the present invention, the nonlinear constraints of the power flow of the established optimal power flow model are convexified, that is, in this embodiment, according to the method provided in the following part, some nonlinear equality constraints in the original constraints are converted into inequality constraints that conform to the second-order cone programming form and can then be solved.

Based on the branch power flow model, the second-order cone relaxation method is adopted to deal with the 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 that conform to the second-order cone programming form.

In other words, the nonlinear equality constraint in the original distribution network constraint, i.e., equation (12) (13), can be replaced by equation (20) through the conversion process of equation (18) (19). The inequality constraint conforms to the second-order cone programming form.

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

The original constraint formula (12) and (13) are first converted into formula (18), and then converted into formula (20); that is, the original constraint formulas (12) and (13) are replaced by formula (20).

For any small non-negative constant ε ≥ 0,

Converting the line loss constraint into a convex constraint by converting it into a 2-norm is 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 using the solver in the prior art.

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

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

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

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

The above solution results can be used as the benchmark values for optimizing the distribution network control parameters, and can provide dispatching benchmark values for the dispatching department. The dispatching department can then use the above benchmark values as the basis for the optimization of the distribution network control parameters.

Specifically, the following control parameters of the power grid are optimized and controlled, including: the reactive compensation amount of the reactive compensation device in the microgrid, the actual active power emitted or absorbed by the distributed power source and energy storage device in the microgrid, the reactive power emitted by the grid-connected inverter of the distributed power source and energy storage device in the microgrid, 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 the distribution network within a single day, 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, such as the normalized multi-objective optimization expression as follows:

Where: λ ₁ , λ ₂ , λ ₃ and λ ₄ are the weight coefficients of the four optimization objectives of the minimum deviation of the average node voltage of the distribution network within a single day, 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.

_Ve is the average rated voltage at the node.

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

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

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

R, microgrid operating profit in a single day;

P ₀ , the optimal solution of active power loss of distribution network solved by single-objective optimization within a single day under preset working conditions;

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

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

The present invention solves the problem in the prior art that when the active distribution network contains multiple distributed power sources and loads connected to the distribution network through a microgrid, the mathematical model of the optimal power flow of the active distribution network will become a non-convex model, resulting in a slow solution speed or even failure to find the optimal solution. The second-order cone relaxation method adopted by the present invention convexifies the model, thereby quickly solving the problem, overcoming the time-consuming problem and improving the dispatch response speed.

At the same time, the present invention also provides an active distribution network optimal power flow convex control device, as shown in FIG2, comprising:

A model building module 201 is used to build an optimal power flow model of the active distribution network according to the power grid parameters;

A convex processing module 202 is used to perform convex processing on the power flow nonlinear constraints of the established optimal power flow model;

A solution processing module 203 is used to solve the optimal power flow model after convexification to generate solution result data;

The optimization control module 204 is used to perform 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 building module builds the optimal power flow model of the active power distribution network according to the power grid parameters, including:

Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network;

According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of 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 on power exchange with the distribution network are established.

This embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the electronic device may refer to the embodiments of the aforementioned method and device, the contents of which are incorporated herein, and the repeated parts are not repeated.

FIG3 is a schematic block diagram of a system structure of an electronic device 600 according to an embodiment of the present invention. As shown in FIG3 , the electronic device 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. It is worth noting that the figure is exemplary; other types of structures may also be used to supplement or replace the structure to implement telecommunication functions or other functions.

In one embodiment, the optimal power flow convexification control function of the active power distribution network can be integrated into the central processor 100. The central processor 100 can be configured to perform the following control:

Establish the optimal power flow model of active distribution network according to the grid parameters;

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

Solving the optimal power flow model after convexification to generate solution result data;

Active distribution network parameter optimization control is performed based on the solution result data and the preset optimization control model.

In another embodiment, the optimal power flow convexity control device for the active distribution network can be configured separately from the central processor 100. For example, the optimal power flow convexity control device for the active distribution network can be configured as a chip connected to the central processor 100, and the optimal power flow convexity control function of the active distribution network can be realized through the control of the central processor.

As shown in FIG3 , 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 FIG3 ; in addition, the electronic device 600 may also include components not shown in FIG3 , and reference may be made to the prior art.

As shown in FIG. 3 , the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and/or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600 .

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 may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

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 display objects such as images and text. 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 a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and/or for executing other functions of the electronic device (such as messaging applications, address book applications, 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 input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

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 LAN module, etc. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

An embodiment of the present invention also provides 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 optimal power flow convexity control method as described in the above embodiment in the electronic device.

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

The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are clear from this detailed description, and therefore the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. In addition, since many modifications and changes are readily apparent to those skilled in the art, the embodiments of the present invention are not intended to be limited to the precise structures and operations illustrated and described, but all suitable modifications and equivalents that fall within their scope may be included.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims (6)

1. A method for convex control of optimal power flow in an active distribution network, characterized in that the method comprises: Establish the optimal power flow model of active distribution network according to the grid parameters; Convexify the nonlinear constraints of the established optimal power flow model; Solving the optimal power flow model after convexification to generate solution result data; Active distribution network parameter optimization control is performed according to the solution result data and the preset optimization control model; The optimal power flow model for establishing the active distribution network according to the power grid parameters includes: Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network; According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of voltage and generator output are established; Establish the active balance constraint of the microgrid and the constraint of power exchange with the distribution network according to the grid parameters of the microgrid; The preset optimization control model includes: Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are weight coefficients of preset optimization objectives, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1; V _e , average rated voltage of the node; ΔV, average node voltage deviation of the distribution network in a single day; P _loss , active network loss; E, operating cost; R, microgrid operating profit; P ₀ , the optimal solution of active network loss solved by single-objective optimization within a single day under preset working conditions; E ₀ , the optimal solution of operating cost for single-objective optimization within a single day under preset working conditions; R ₀ , the optimal solution of microgrid operating profit solved by single-objective optimization within a single day under preset working conditions; The solution result data includes: Active power input, reactive power input of distribution network balancing nodes, voltage power angle and reactive power input of PV nodes, voltage amplitude and voltage power angle of PQ nodes in each optimization interval of a single day; Active and reactive transmission of the distribution network and microgrid tie lines within each optimization interval on a single day; The active power generated or absorbed by the distributed power sources in the microgrid and the active power generated or absorbed by the energy storage device in each optimization interval of a single day; The power flow linear constraints include: _PMG + _PG - _{σPL - MPQPr - MlPloss} =0 _QMG + _QG -σQL _{- MPQQr - MlQloss} + _BW ＝0 2RP _r + 2XQ _r + RP _loss + XQ _loss - M _W W = 0 θ _sr -XP _r +RQ _r = 0 XP _loss - RQ _loss = 0 Where: P _MG and Q _MG are the active and reactive power column vectors injected by the microgrid into 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 column vectors of active and reactive loads of network nodes, respectively; σ is the column vector of load demand coefficients in each period of the distribution network; _MPQ and M _l are the correlation matrices of active and reactive power flows and network losses in the AC distribution network, respectively; P _r and Q _r are the column vectors of active and reactive power flows in the network, respectively; P _loss and Q _loss are the column vectors of active and reactive network losses in the network; R, X, and B are the diagonal matrices of resistance, reactance, and susceptance, respectively; W and _MW are the column vectors composed of the squares of the node voltages and their correlation matrices respectively; θ _sr is the phase angle difference column vector between the sending end and the receiving end of the AC line; Microgrid active power balance constraints: D _t + W _t + S _{d, t} - S _{c, t} = P _{MG, t} + L _t Wherein, P _MG,t is the power injected into the distribution network by the microgrid in the tth interval, D _t is the output of the controllable DG in the microgrid in the tth interval, W _t is the output of the uncontrollable DG in the microgrid in the tth interval, S _d,t is the discharge power of the energy storage system in the microgrid in the tth interval, S _c,t is the charging power of the energy storage system in the microgrid in the tth interval, and L _t is the load in the tth interval of the microgrid; Constraints on power exchange with the distribution network: -P _MGmax ≤P _MG,t ≤P _MGmax Among them, P _MGmax is the line transmission capacity between the distribution network and the microgrid connected to a certain node. 2. The method for convexifying the optimal power flow control of an active power distribution network according to claim 1, wherein the convexifying the nonlinear constraints of the established optimal power flow model comprises: The nonlinear constraints of the established optimal power flow model are convexified using the following formula; For any small non-negative constant ε ≥ 0, in, V _r,j The receiving end 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. 3. An active distribution network optimal power flow convexity control device, characterized in that the device comprises: A model building module is used to build an optimal power flow model of the active distribution network according to the power grid parameters; A convex processing module is used to perform convex processing on the power flow nonlinear constraints of the established optimal power flow model; A solution processing module is used to solve the optimal power flow model after convexification and generate solution result data; An optimization control module, used for performing active distribution network parameter optimization control according to the solution result data and a preset optimization control model; The model building module builds the optimal power flow model of the active distribution network according to the power grid parameters, including: Establish active power balance equations and reactive power balance equations of nodes in the distribution network according to the grid parameters of the distribution network; According to the power grid parameters of the distribution network, the linear constraints of power flow, nonlinear constraints of power flow and upper and lower constraints of voltage and generator output are established; Establish the active balance constraint of the microgrid and the constraint of power exchange with the distribution network according to the grid parameters of the microgrid; The preset optimization control model includes: Wherein, λ ₁ , λ ₂ , λ ₃ and λ ₄ are weight coefficients of preset optimization objectives, and λ ₁ +λ ₂ +λ ₃ +λ ₄ =1; V _e , average rated voltage of the node; ΔV, average node voltage deviation of the distribution network in a single day; P _loss , active network loss; E, operating cost; R, microgrid operating profit; P ₀ , the optimal solution of active network loss solved by single-objective optimization within a single day under preset working conditions; E ₀ , the optimal solution of operating cost for single-objective optimization within a single day under preset working conditions; R ₀ , the optimal solution of microgrid operating profit solved by single-objective optimization within a single day under preset working conditions; The solution result data includes: Active power input, reactive power input of distribution network balancing nodes, voltage power angle and reactive power input of PV nodes, voltage amplitude and voltage power angle of PQ nodes in each optimization interval of a single day; Active and reactive transmission of the distribution network and microgrid tie lines within each optimization interval on a single day; The active power generated or absorbed by the distributed power sources in the microgrid and the active power generated or absorbed by the energy storage device in each optimization interval of a single day; The power flow linear constraints include: _PMG + _PG - _{σPL - MPQPr - MlPloss} =0 _QMG + _QG -σQL _{- MPQQr - MlQloss} + _BW ＝0 2RP _r + 2XQ _r + RP _loss + XQ _loss - M _W W = 0 θ _sr -XP _r +RQ _r = 0 XP _loss - RQ _loss = 0 Where: P _MG and Q _MG are the active and reactive power column vectors injected by the microgrid into 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 column vectors of active and reactive loads of network nodes, respectively; σ is the column vector of load demand coefficients in each period of the distribution network; _MPQ and _Ml are the correlation matrices of active and reactive power flows and network losses in AC distribution networks, respectively; P _r and Q _r are the column vectors of active and reactive power flows in the network, respectively; P _loss and Q _loss are the column vectors of active and reactive network losses in the network; R, X, and B are the diagonal matrices of resistance, reactance, and susceptance, respectively; W and _MW are the column vectors composed of the squares of the node voltages and their correlation matrices respectively; θ _sr is the phase angle difference column vector between the sending end and the receiving end of the AC line; Microgrid active power balance constraints: D _t + W _t + S _{d, t} - S _{c, t} = P _{MG, t} + L _t Wherein, P _MG,t is the power injected into the distribution network by the microgrid in the tth interval, D _t is the output of the controllable DG in the microgrid in the tth interval, W _t is the output of the uncontrollable DG in the microgrid in the tth interval, S _d,t is the discharge power of the energy storage system in the microgrid in the tth interval, S _c,t is the charging power of the energy storage system in the microgrid in the tth interval, and L _t is the load in the tth interval of the microgrid; Constraints on power exchange with the distribution network: -P _MGmax ≤P _MG,t ≤P _MGmax Among them, P _MGmax is the line transmission capacity between the distribution network and the microgrid connected to a certain node. 4. The active distribution network optimal power flow convex control device according to claim 3, characterized in that the convex processing module convexifies the nonlinear constraints of the established optimal power flow model, comprising: The nonlinear constraints of the established optimal power flow model are convexified using the following formula; For any small non-negative constant ε ≥ 0, in, V _r,j The receiving end 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. 5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of claim 1 or 2 when executing the computer program. 6. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method according to claim 1 or 2.