CN113517698B - Active power distribution network optimal power flow salifying control method and device - Google Patents
Active power distribution network optimal power flow salifying control method and device Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention provides a method and a device for controlling the saliency of an optimal power flow of an active power distribution network, wherein the method comprises the following steps: establishing an optimal power flow model of the active power distribution network according to the power grid parameters; carrying out salifying treatment on the nonlinear constraint of the power flow of the established optimal power flow model; solving the optimal power flow model after the salifying treatment to generate solving result data; and carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model. The invention solves the problems that the solving speed is low and even the optimal solution can not be obtained in the prior art, and the invention highlights the model so as to solve rapidly and promote the solving speed of the optimal power flow mathematical model of the active power distribution network.
Description
Technical Field
The invention relates to a power control technology, in particular to a method and a device for controlling the saliency of an optimal power flow of an active power distribution network.
Background
In recent years, active distribution networks have received a great deal of attention because of their friendly and positive digestion of renewable energy sources. In most cases, renewable energy sources such as wind and light are fed into the distribution network through a micro-grid (microgrid-nodal distribution network, MDN) platform and a hinge. Certain nodes of the power distribution network are connected with the micro-grid to form a typical network topology and a wide energy utilization mode, and the power distribution network is one of various topological types of active power distribution networks (active distribution network, ADN).
The decision instruction required by the dispatching and running of the active power distribution network needs the solving result of the optimal power flow, and has high requirements on the solving speed and the solving precision of the optimal power flow. Because the mathematical model of a typical power distribution network contains line loss constraint and nonlinear constraint, the mathematical model of the power distribution network is nonlinear and nonlinear, the theory such as convex optimization and the like cannot be used for quick solution in solving, the solution needs to be carried out by means of artificial intelligent algorithms such as particle swarm, annealing and the like, the higher the precision is, the longer the solution time is, and the time is consumed particularly when the solved problem is large in scale, so that the requirement of a dispatching department cannot be met generally.
In the prior art, a linear programming method, a nonlinear programming method, an artificial intelligence algorithm and the like are adopted to solve the optimal power flow of the active power distribution network, the solving precision can generally meet the requirements of real problems, but the solving speed is poor, the speed for searching the optimal solution in a feasible domain is also improved.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a control method for the saliency of the optimal power flow of an active power distribution network, which comprises the following steps:
Establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out salifying treatment on the nonlinear constraint of the power flow of the established optimal power flow model;
Solving the optimal power flow model after the salifying treatment to generate solving result data;
And carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
In the embodiment of the present invention, the establishing an optimal power flow model of the active power distribution network according to the power grid parameters includes:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
and establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
In the embodiment of the present invention, the salifying the nonlinear constraint of the established optimal power flow model includes:
carrying out salifying treatment on nonlinear constraint of the established optimal power flow model by utilizing the following formula;
For any small non-negative constant epsilon is more than or equal to 0,
Wherein,
The voltage amplitude of the receiving end of the V r,j branch j;
P r,j is the power loss of the j-th ac line;
q r,j is the power at the receiving end of the jth ac line.
In the embodiment of the present invention, the preset optimization control model includes:
Wherein, λ 1、λ2、λ3 and λ 4 are weight coefficients of a preset optimization target, and λ 1+λ2+λ3+λ4 =1;
v e, node average rated voltage;
Delta V, average node voltage deviation in a single day of the power distribution network;
p loss, active loss;
E, running cost;
r, operating profit of the micro-grid;
P 0, an active network loss optimal solution of single-objective optimization solution in a single day under a preset working condition;
e 0, a running cost optimal solution of single-objective optimization solution in a single day under a preset working condition;
R 0, a micro-grid operation profit optimal solution of single-objective optimization solution in a single day under a preset working condition.
In the embodiment of the invention, the solving result data comprises:
In each optimization interval of a single day, the active power input, the reactive power input, the voltage power angle and the reactive power input of the PV node of the balance node of the power distribution network, and the voltage amplitude and the voltage power angle of the PQ node;
in each optimization interval of a single day, the active transmission quantity and the reactive transmission quantity of the connecting line of the power distribution network and the micro-grid are calculated;
In each optimization interval of a single day, active power emitted or absorbed by a distributed power supply in the micro-grid and active power emitted or absorbed by an energy storage device.
Meanwhile, the invention also provides a device for controlling the saliency of the optimal power flow of the active power distribution network, which comprises the following components:
The model building module is used for building an optimal power flow model of the active power distribution network according to the power grid parameters;
The salifying processing module is used for carrying out salifying processing on the power flow nonlinear constraint of the established optimal power flow model;
The solution processing module is used for carrying out solution processing on the optimal power flow model after the salifying processing to generate solution result data;
And the optimization control module is used for carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
In the embodiment of the present invention, the model building module builds an optimal power flow model of the active power distribution network according to the power grid parameters, including:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
and establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
In the embodiment of the present invention, the salifying processing module performs salifying processing on the nonlinear constraint of the established optimal power flow model, including:
carrying out salifying treatment on nonlinear constraint of the established optimal power flow model by utilizing the following formula;
For any small non-negative constant epsilon is more than or equal to 0,
Wherein,
The voltage amplitude of the receiving end of the V r,j branch j;
P r,j is the power loss of the j-th ac line;
q r,j is the power at the receiving end of the jth ac line.
In the embodiment of the present invention, the preset optimization control model includes:
Wherein, λ 1、λ2、λ3 and λ 4 are weight coefficients of a preset optimization target, and λ 1+λ2+λ3+λ4 =1;
v e, node average rated voltage;
Delta V, average node voltage deviation in a single day of the power distribution network;
p loss, active loss;
E, running cost;
r, operating profit of the micro-grid;
P 0, an active network loss optimal solution of single-objective optimization solution in a single day under a preset working condition;
e 0, a running cost optimal solution of single-objective optimization solution in a single day under a preset working condition;
R 0, a micro-grid operation profit optimal solution of single-objective optimization solution in a single day under a preset working condition.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method.
Meanwhile, the invention also provides a computer readable storage medium which stores a computer program for executing the method.
According to the invention, the problem that in the prior art, when the active power distribution network contains a plurality of distributed power sources and loads and is connected into the power distribution network through the micro-grid, the mathematical model of the optimal power flow of the active power distribution network is changed into a non-male model, the situation that the solving speed is low and even the optimal solution cannot be obtained can be solved is solved, and the model is bulged, so that the solving speed of the mathematical model is improved.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an active power distribution network optimal power flow saliency control method provided by the invention;
FIG. 2 is a block diagram of an active power distribution network optimal power flow saliency control device provided by the invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, when the active power distribution network contains various distributed power sources and loads and is connected into the power distribution network through the micro-grid, a mathematical model of the optimal power flow of the active power distribution network can be changed into a non-male model, and the situation that the solving speed is low and even the optimal solution cannot be obtained can occur.
In this regard, the present invention provides a method for controlling the saliency of an optimal power flow of an active power distribution network, as shown in fig. 1, where the method includes:
Step S101, an optimal power flow model of an active power distribution network is established according to power grid parameters;
Step S102, salifying the nonlinear constraint of the power flow of the established optimal power flow model;
Step S103, solving the optimal power flow model after the salifying treatment to generate solving result data;
and step S104, performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
According to the active power distribution network optimal power flow salifying control method, salifying treatment is carried out on the power flow nonlinear constraint of the established optimal power flow model, the salifying is carried out by adopting second-order cone relaxation, the mathematical essence of the power distribution network optimal power flow model is converted into a second-order cone planning problem, so that the solving speed of the optimal power flow model is improved, the optimal control of power distribution network parameters is carried out according to the solving result, and the demands of a dispatching department are met.
In an active power distribution network optimal power flow model, the descriptions of active power flow and reactive power flow are represented in a power flow equation of constraint conditions. After some nodes of the power distribution network are connected with the micro-grid, constraint conditions of the optimization model comprise: and the constraint condition of the power distribution network and the constraint condition of the micro-grid are two parts.
The constraint condition of the active power distribution network is solved in the whole mathematical model to form a boundary of the running space of the whole active power distribution network, and the boundary is a feasible region boundary from the mathematical perspective. The process of solving the optimal solution of the optimization target of the active power distribution network is to search a specific position in the active power distribution network operation space formed by taking constraint conditions as boundary limitations, and the optimization target can obtain the maximum value or the minimum value when the active power distribution network operates on the position.
In the embodiment of the invention, the establishment of the optimal power flow model of the active power distribution network according to the power grid parameters comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
and establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
Specifically, in the embodiment of the present invention, the constraint condition part of the power distribution network is described as follows:
An optimal power flow model based on a branch power flow model is adopted, and the detailed model is modeled as follows:
The active and reactive balance equation of the node i in the power distribution network is as follows:
Wherein, 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, respectively, of the load to which node i is connected;
M PQ (i, j) and M l (i, j) are elements of a corresponding node i and a corresponding branch j of an incidence matrix of active and reactive power flows and network losses of the alternating-current power distribution network 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 and reactive losses of branch j, respectively;
B i,i is the diagonal component of the susceptance matrix and V i is the voltage magnitude at node i. n l is the total number of lines.
In this embodiment, the correlation matrix elements related to the power flow and loss are defined as follows:
the voltage drop of line j is as follows:
gamma in formula (6) refers to imaginary units;
V s,j and V r,j are the voltage amplitudes of the transmitting end and the receiving end of the branch j respectively;
θ s,j and θ r,j are the voltage phase angles of the transmitting end and the receiving end of the branch j respectively;
R j,j and X j,j are the resistance, reactance, respectively, of branch j.
Carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
Specifically, the constraint conditions for induction formation are as follows:
(1) Linear constraint of tide:
PMG+PG-σPL-MPQPr-MlPloss＝0 (4)
QMG+QG-σQL-MPQQr-MlQloss+BW＝0 (5)
2RPr+2XQr+RPloss+XQloss-MWW＝0 (6)
θsr-XPr+RQr＝0 (7)
XPloss-RQloss＝0 (8)
wherein, formulas (7) and (8) are power distribution network load flow balance constraints;
Equation (9) is an 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 losses.
Wherein: p MG and Q MG are active and reactive power column vectors, respectively, injected by the microgrid into the connected nodes;
p G and Q G are column vectors of active and reactive power injections, respectively, of the network node;
P L and Q L are network node active and reactive load column vectors, respectively;
Sigma is a load demand coefficient column vector of each period of the power distribution network;
M PQ and M l are respectively the incidence matrixes of active and reactive power flows and network losses of the alternating-current power distribution network;
P r and Q r are active and reactive power flow vectors in the network, respectively;
Active and reactive network loss column vectors in the P loss and Q loss networks;
r, X and B are diagonal matrices of resistance, reactance and susceptance, respectively;
W and M W are respectively the column vector and the associated matrix formed by the square of the voltage of each node;
θ sr is the phase angle difference column vector between the transmitting end and the receiving end of the ac line.
(2) Nonlinear constraint of tide:
For each ac line j=1..n l, line loss is expressed as follows:
wherein, the j-th alternating current line of P loss,j is used for network loss of active power;
the j-th alternating current line of Q loss,j is used for network loss reactive power;
Active power of the j-th alternating current line receiving end of P r,j;
reactive power at the receiving end of the jth alternating current line of Q r,j. W r,j is the square of V r,j.
(3) Upper and lower limit constraints for voltage and generator output
For node i=1..n b, where n b is the total number of ac nodes, there are the following constraints:
Wherein:
and V i are the upper and lower limits of the voltage at node i, respectively;
and/> The upper limit and the lower limit of the active output of the node i are adopted;
and/> The upper reactive power output limit and the lower reactive power output limit of the node i are respectively.
For line j=1..n l, there are:
Wherein: An active transmission upper limit for line j;
P r,j is the active transmission lower limit of line j;
And Q r,j are the upper active and lower reactive transmission limits of line j, respectively.
In order to avoid complexity, in the embodiment of the invention, the lower corner mark t representing the t-th optimization interval is ignored by the constraint conditions of the power distribution network.
Micro-grid constraints:
(1) Active balance constraint.
Dt+Wt+Sd,t-Sc,t＝PMG,t+Lt (13)
Wherein P MG,t is the power injected into the distribution network in the t-th section of the micro-grid.
D t is the output of the controllable DG in the micro-grid in the t-th zone, W t is the output of the uncontrollable DG in the micro-grid in the t-th zone, S d,t is the discharge power of the energy storage system in the micro-grid in the t-th zone, S c,t is the charge power of the energy storage system in the micro-grid in the t-th zone, and L t is the load of the non-controllable DG in the micro-grid in the t-th zone.
(2) And (3) power exchange constraint with the distribution network.
-PMGmax≤PMG,t≤PMGmax (14)
Wherein P MGmax is the line transmission capacity of the micro-grid connected with the distribution network and a certain node.
The method and the device establish the optimal power flow model of the active power distribution network in the embodiment of the invention, and the mathematical model of the active power distribution network is nonlinear and non-convex in nature, so that the speed is low when solving a large-scale problem.
In the embodiment of the invention, the salifying treatment is carried out on the power flow nonlinear constraint of the established optimal power flow model, namely, in the embodiment, the partial nonlinear equation constraint in the original constraint condition is converted into the inequality constraint conforming to the second-order cone programming form according to the method provided by the following parts, so that the solution can be realized.
Based on the branch power flow model, a second order cone relaxation method is adopted to carry out, and nonlinear constraint exists in power flow constraint, as shown in formulas (12) and (13). In this embodiment, the two quadratic equation constraints are converted into an inequality constraint conforming to the form of a second order cone plan.
In other words, the nonlinear equation constraint in the original distribution network constraint, namely the equation (12) (13), can be replaced by the equation (20) through the conversion process of the equation (18) (19), and the inequality constraint accords with the second order cone planning form.
The active distribution network model with the formula (12) (13) replaced by the formula (20) can be subjected to convex solving by adopting second-order cone relaxation.
Converting the original constraint formulas (12) and (13) into a formula (18) and then into a formula (20); namely, the formulas (12) and (13) in the original constraint are replaced by the formula (20).
For any small non-negative constant epsilon is more than or equal to 0,
The conversion to the 2-norm form, i.e., the line loss constraint to the convex constraint, is as follows:
Through the above processing, the mathematical nature of the optimal power flow model of the power distribution network in this embodiment has been converted into a second order cone planning problem, and the solver in the prior art can be used to effectively solve the problem.
According to the method and the device for solving the optimal power flow model of the power distribution network, the solving result comprises the following steps:
firstly, in each optimization interval of a single day, the active power and reactive power of a balance node of a power distribution network are input, the voltage power angle and reactive power of a PV node are input, and the voltage amplitude and the voltage power angle of a PQ node are input;
secondly, in each optimization interval of a single day, the active and reactive transmission quantity of the connecting lines of the power distribution network and the micro-grid is calculated;
thirdly, in each optimization interval of a single day, active power is emitted or absorbed by a distributed power supply and an energy storage device in the micro-grid.
The solving result is used as a reference value for optimizing the control parameters of the power distribution network, a dispatching reference value can be provided for a dispatching department, and the dispatching department can execute dispatching according to the reference value.
Specifically, the following control parameters of the power grid are optimally controlled: reactive compensation quantity of reactive compensation devices in the micro-grid, actual active power sent or absorbed by distributed power supplies and energy storage devices in the micro-grid, reactive power sent by grid-connected inverters of the distributed power supplies and the energy storage devices in the micro-grid, and taps of voltage-adjustable transformers in the distribution network.
Specifically, the optimization objective may be a single objective, such as a minimum deviation min Δv of average node voltage in a single day of the power distribution network, a minimum active network loss minP loss, a minimum running cost mineand a maximum operating profit maxR of the micro-grid; the multi-objective optimization expression of the normalization process can also be multi-objective, such as the following:
Wherein: lambda 1、λ2、λ3 and lambda 4 are respectively weight coefficients of four optimization targets of minimum deviation of average node voltage, minimum active network loss, minimum running cost and maximum operating profit of the micro-grid in a single day of the power distribution network, and lambda 1+λ2+λ3+λ4 =1.
V e is the node average rated voltage.
Delta V, average node voltage deviation in a single day of the power distribution network;
P loss, active power loss of the power distribution network in a single day;
E, running cost of the power distribution network in a single day;
R, operating profits of the micro-grid in a single day;
p 0, a single-objective optimal solution of the active network loss of the power distribution network in a single day under a preset working condition;
e 0, a single-objective optimal solution of the operation cost of the power distribution network in a single day under a preset working condition;
R 0, a micro-grid operation profit optimal solution of single-objective optimization solution in a single day under a preset working condition.
According to the invention, the problem that in the prior art, when the active power distribution network contains a plurality of distributed power sources and loads and is connected into the power distribution network through the micro-grid, the mathematical model of the optimal power flow of the active power distribution network is changed into a non-male model, and the situation that the solving speed is low and even the optimal solution cannot be obtained can be solved. The second order cone relaxation method adopted by the invention can be used for carrying out convexity on the model, so that the problem of long time consumption is solved rapidly, and the dispatching response speed is improved.
Meanwhile, the invention also provides a device for controlling the saliency of the optimal power flow of the active power distribution network, as shown in fig. 2, comprising:
the model building module 201 is configured to build an optimal power flow model of the active power distribution network according to the power grid parameters;
The salifying module 202 is configured to perform salifying on the power flow nonlinear constraint of the established optimal power flow model;
the solution processing module 203 is configured to perform a solution process on the salinized optimal power flow model to generate solution result data;
and the optimization control module 204 is used for performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model.
In the embodiment of the present invention, the model building module builds an optimal power flow model of the active power distribution network according to the power grid parameters, including:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
and establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to the grid parameters of the micro-grid.
The present embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, or the like, and the present embodiment is not limited thereto. In this embodiment, the electronic device may refer to the embodiments of the foregoing method and apparatus, and the content thereof is incorporated herein, and the repetition is not repeated.
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 processor 100 and a memory 140; memory 140 is coupled to central processor 100. Notably, the diagram is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the active power distribution network optimal power flow characterization control function may be integrated into the central processor 100. Wherein the central processor 100 may be configured to control as follows:
Establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out salifying treatment on the nonlinear constraint of the power flow of the established optimal power flow model;
Solving the optimal power flow model after the salifying treatment to generate solving result data;
performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model
In another embodiment, the active power distribution network optimal power flow saliency control device may be configured separately from the central processing unit 100, for example, the active power distribution network optimal power flow saliency control device may be configured as a chip connected to the central processing unit 100, and the active power distribution network optimal power flow saliency control function is implemented by 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, a power supply 170. It is noted that the electronic device 600 need not include all of the components shown in fig. 3; in addition, the electronic device 600 may further include components not shown in fig. 3, to which reference is made to the prior art.
As shown in fig. 3, the central processor 100, sometimes also referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 100 receives inputs and controls the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 100 can execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides an input to the central processor 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 may be, for example, but not limited to, an LCD display.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, or the like. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. Memory 140 may also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage 142, the application/function storage 142 for storing application programs and function programs or a flow for executing operations of the electronic device 600 by the central processor 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. A communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and to receive audio input from the microphone 132 to implement usual telecommunication functions. The audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 130 is also coupled to the central processor 100 so that sound can be recorded locally through the microphone 132 and so that sound stored locally can be played through the speaker 131.
The embodiment of the invention also provides a computer readable program, wherein when the program is executed in an electronic device, the program causes a computer to execute the active power distribution network optimal power flow saliency control method in the electronic device.
The embodiment of the invention also provides a storage medium storing a computer readable program, wherein the computer readable program enables a computer to execute the active power distribution network optimal power flow saliency control described in the above embodiment in electronic equipment.
Preferred embodiments of the present invention are described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the 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 a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (6)
1. The method for controlling the convexity of the optimal power flow of the active power distribution network is characterized by comprising the following steps:
Establishing an optimal power flow model of the active power distribution network according to the power grid parameters;
carrying out salifying treatment on the nonlinear constraint of the power flow of the established optimal power flow model;
Solving the optimal power flow model after the salifying treatment to generate solving result data;
Performing active power distribution network parameter optimization control according to the solving result data and a preset optimization control model;
the establishing the optimal power flow model of the active power distribution network according to the power grid parameters comprises the following steps:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
Establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to grid parameters of the micro-grid;
The preset optimal control model comprises the following steps:
Wherein, λ 1、λ2、λ3 and λ 4 are weight coefficients of a preset optimization target, and λ 1+λ2+λ3+λ4 =1;
v e, node average rated voltage;
Delta V, average node voltage deviation in a single day of the power distribution network;
p loss, active loss;
E, running cost;
r, operating profit of the micro-grid;
P 0, an active network loss optimal solution of single-objective optimization solution in a single day under a preset working condition;
e 0, a running cost optimal solution of single-objective optimization solution in a single day under a preset working condition;
r 0, optimizing and solving the optimal solution of the operation profit of the micro-grid in a single day by a single target under a preset working condition;
The solving result data comprises:
In each optimization interval of a single day, the active power input, the reactive power input, the voltage power angle and the reactive power input of the PV node of the balance node of the power distribution network, and the voltage amplitude and the voltage power angle of the PQ node;
in each optimization interval of a single day, the active transmission quantity and the reactive transmission quantity of the connecting line of the power distribution network and the micro-grid are calculated;
in each optimization interval of a single day, active power emitted or absorbed by a distributed power supply in the micro-grid and active power emitted or absorbed by an energy storage device are generated;
the power flow linear constraint comprises:
PMG+PG-σPL-MPQPr-MlPloss＝0
QMG+QG-σQL-MPQQr-MlQloss+BW＝0
2RPr+2XQr+RPloss+XQloss-MWW＝0
θsr-XPr+RQr＝0
XPloss-RQloss＝0
wherein: p MG and Q MG are active and reactive power column vectors, respectively, injected by the microgrid into the connected nodes;
p G and Q G are column vectors of active and reactive power injections, respectively, of the network node;
P L and Q L are network node active and reactive load column vectors, respectively;
Sigma is a load demand coefficient column vector of each period of the power distribution network;
M PQ and M l are respectively the incidence matrixes of active and reactive power flows and network losses of the alternating-current power distribution network;
P r and Q r are active and reactive power flow vectors in the network, respectively;
Active and reactive network loss column vectors in the P loss and Q loss networks;
r, X and B are diagonal matrices of resistance, reactance and susceptance, respectively;
W and M W are respectively the column vector and the associated matrix formed by the square of the voltage of each node;
θ sr is the phase angle difference column vector of the sending end and the receiving end of the alternating current line;
micro-grid active balance constraint:
Dt+Wt+Sd,t-Sc,t＝PMG,t+Lt
wherein P MG,t is the power injected into the distribution network in the t-th section of the micro-grid, D t is the output of controllable DG in the t-th section of the micro-grid, W t is the output of uncontrollable DG in the t-th section of the micro-grid, S d,t is the discharge power of an energy storage system in the t-th section of the micro-grid, S c,t is the charge power of the energy storage system in the t-th section of the micro-grid, and L t is the load of the t-th section of the micro-grid;
constraints on power exchange with distribution networks:
-PMGmax≤PMG,t≤PMGmax
Wherein P MGmax is the line transmission capacity of the micro-grid connected with the distribution network and a certain node.
2. The method for controlling the saliency of the optimal power flow of the active power distribution network according to claim 1, wherein the saliency of the nonlinear constraint of the established optimal power flow model comprises:
carrying out salifying treatment on nonlinear constraint of the established optimal power flow model by utilizing the following formula;
For any small non-negative constant epsilon is more than or equal to 0,
Wherein,
The voltage amplitude of the receiving end of the V r,j branch j;
P r,j is the power loss of the j-th ac line;
q r,j is the power at the receiving end of the jth ac line.
3. The utility model provides an initiative distribution network optimal power flow saliency controlling means which characterized in that, the device includes:
The model building module is used for building an optimal power flow model of the active power distribution network according to the power grid parameters;
The salifying processing module is used for carrying out salifying processing on the power flow nonlinear constraint of the established optimal power flow model;
The solution processing module is used for carrying out solution processing on the optimal power flow model after the salifying processing to generate solution result data;
the optimization control module is used for carrying out active power distribution network parameter optimization control according to the solving result data and a preset optimization control model;
The model building module builds an optimal power flow model of the active power distribution network according to the power grid parameters, and the model building module comprises:
establishing an active balance equation and a reactive balance equation of nodes in the power distribution network according to power grid parameters of the power distribution network;
carrying out induction processing according to power grid parameters of the power distribution network to establish a power flow linear constraint, a power flow nonlinear constraint and upper and lower limit constraints of voltage and generator output;
Establishing active balance constraint of the micro-grid and constraint of power exchange with the power distribution network according to grid parameters of the micro-grid;
The preset optimal control model comprises the following steps:
Wherein, λ 1、λ2、λ3 and λ 4 are weight coefficients of a preset optimization target, and λ 1+λ2+λ3+λ4 =1;
v e, node average rated voltage;
Delta V, average node voltage deviation in a single day of the power distribution network;
p loss, active loss;
E, running cost;
r, operating profit of the micro-grid;
P 0, an active network loss optimal solution of single-objective optimization solution in a single day under a preset working condition;
e 0, a running cost optimal solution of single-objective optimization solution in a single day under a preset working condition;
r 0, optimizing and solving the optimal solution of the operation profit of the micro-grid in a single day by a single target under a preset working condition;
The solving result data comprises:
In each optimization interval of a single day, the active power input, the reactive power input, the voltage power angle and the reactive power input of the PV node of the balance node of the power distribution network, and the voltage amplitude and the voltage power angle of the PQ node;
in each optimization interval of a single day, the active transmission quantity and the reactive transmission quantity of the connecting line of the power distribution network and the micro-grid are calculated;
in each optimization interval of a single day, active power emitted or absorbed by a distributed power supply in the micro-grid and active power emitted or absorbed by an energy storage device are generated;
the power flow linear constraint comprises:
PMG+PG-σPL-MPQPr-MlPloss＝0
QMG+QG-σQL-MPQQr-MlQloss+BW＝0
2RPr+2XQr+RPloss+XQloss-MWW＝0
θsr-XPr+RQr＝0
XPloss-RQloss＝0
wherein: p MG and Q MG are active and reactive power column vectors, respectively, injected by the microgrid into the connected nodes;
p G and Q G are column vectors of active and reactive power injections, respectively, of the network node;
P L and Q L are network node active and reactive load column vectors, respectively;
Sigma is a load demand coefficient column vector of each period of the power distribution network;
M PQ and M l are respectively the incidence matrixes of active and reactive power flows and network losses of the alternating-current power distribution network;
P r and Q r are active and reactive power flow vectors in the network, respectively;
Active and reactive network loss column vectors in the P loss and Q loss networks;
r, X and B are diagonal matrices of resistance, reactance and susceptance, respectively;
W and M W are respectively the column vector and the associated matrix formed by the square of the voltage of each node;
θ sr is the phase angle difference column vector of the sending end and the receiving end of the alternating current line;
micro-grid active balance constraint:
Dt+Wt+Sd,t-Sc,t＝PMG,t+Lt
wherein P MG,t is the power injected into the distribution network in the t-th section of the micro-grid, D t is the output of controllable DG in the t-th section of the micro-grid, W t is the output of uncontrollable DG in the t-th section of the micro-grid, S d,t is the discharge power of an energy storage system in the t-th section of the micro-grid, S c,t is the charge power of the energy storage system in the t-th section of the micro-grid, and L t is the load of the t-th section of the micro-grid;
constraints on power exchange with distribution networks:
-PMGmax≤PMG,t≤PMGmax
Wherein P MGmax is the line transmission capacity of the micro-grid connected with the distribution network and a certain node.
4. The active power distribution network optimal power flow salifying control device according to claim 3, wherein the salifying processing module performs salifying processing on the nonlinear constraint of the established optimal power flow model, and the method comprises the following steps:
carrying out salifying treatment on nonlinear constraint of the established optimal power flow model by utilizing the following formula;
For any small non-negative constant epsilon is more than or equal to 0,
Wherein,
The voltage amplitude of the receiving end of the V r,j branch j;
P r,j is the power loss of the j-th ac line;
q r,j is the power at the receiving end of the jth ac line.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of 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 of claim 1 or 2.
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