CN111106631B - Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network - Google Patents

Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network Download PDF

Info

Publication number
CN111106631B
CN111106631B CN202010038346.6A CN202010038346A CN111106631B CN 111106631 B CN111106631 B CN 111106631B CN 202010038346 A CN202010038346 A CN 202010038346A CN 111106631 B CN111106631 B CN 111106631B
Authority
CN
China
Prior art keywords
preset
power
reactive power
distribution network
distributed
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010038346.6A
Other languages
Chinese (zh)
Other versions
CN111106631A (en
Inventor
吴文传
许桐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Original Assignee
Tsinghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University filed Critical Tsinghua University
Priority to CN202010038346.6A priority Critical patent/CN111106631B/en
Publication of CN111106631A publication Critical patent/CN111106631A/en
Application granted granted Critical
Publication of CN111106631B publication Critical patent/CN111106631B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The embodiment of the invention relates to the technical field of power distribution networks, and discloses a distributed reactive power scheduling method, a distributed reactive power scheduling system, distributed reactive power scheduling equipment and a storage medium for the power distribution networks. In the embodiment of the invention, a preset power grid control model is converted into an augmented Lagrange function; performing iterative operation of the feasible values in the augmented Lagrange function by a preset improved alternative direction multiplier method to obtain the feasible values after iteration; if the convergence degree value corresponding to the iterated feasible value is within the preset convergence degree range, obtaining the reactive power output information output by the preset power grid control model; and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation. The embodiment of the invention provides a real-time scheduling mode of a distributed power distribution network, the operation condition of the distributed power distribution network is controlled by scheduling the reactive power output of a photovoltaic inverter in real time, and the real-time fluctuation of photovoltaic power generation is further dealt with, so that the technical problem of the real-time fluctuation of the photovoltaic power generation in the operation process is solved.

Description

Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a distributed reactive power scheduling method, a distributed reactive power scheduling system, distributed reactive power scheduling equipment and a storage medium for the power distribution networks.
Background
In recent years, in order to reduce greenhouse gas emissions and improve environmental conditions, a power generation system that generates power from renewable energy has been increasingly emphasized.
Meanwhile, along with the reduction of the production cost of the renewable photovoltaic panels, more and more distributed photovoltaic panels capable of generating power are connected into the power distribution network, so that the effect of protecting the environment is achieved.
However, in terms of the photovoltaic power generation technology, the photovoltaic power generation is often accompanied by strong volatility and randomness, and these characteristics can cause huge impact on the power distribution network, and phenomena such as voltage out-of-limit are easy to occur.
Therefore, the overall operation safety of the power distribution network can be influenced by the real-time fluctuation of the renewable energy sources.
However, at present, there is no specific scheme for better coping with real-time fluctuations of photovoltaic power generation.
Disclosure of Invention
In order to solve the technical problem of real-time fluctuation of photovoltaic power generation in the operation process, the embodiment of the invention provides a distributed reactive power scheduling method, a distributed reactive power scheduling system, distributed reactive power scheduling equipment and a storage medium for a power distribution network.
In a first aspect, an embodiment of the present invention provides a distributed reactive power scheduling method for a power distribution network, including:
acquiring a preset power grid control model;
converting the preset power grid control model into an augmented Lagrange function;
performing iterative operation of the feasible values in the augmented Lagrangian function by a preset improved alternative direction multiplier method to obtain an iterated feasible value;
if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range, obtaining reactive power output information output by the preset power grid control model;
and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation.
Preferably, before the obtaining of the preset power grid control model, the distributed reactive power scheduling method for the power distribution network further includes:
acquiring a preset electric energy loss function;
acquiring a preset power flow constraint, wherein the preset power flow constraint is used for constraining electric energy to flow;
acquiring a preset operation constraint, wherein the preset operation constraint is used for constraining the operation state of the distributed power distribution network;
and constructing a preset power grid control model according to the preset electric energy loss function, the preset power flow constraint and the preset operation constraint.
Preferably, the preset power flow constraint comprises an active power flow constraint, a reactive power flow constraint and a magnitude power flow constraint;
the active power flow constraint is used for determining active power injection corresponding to a second branch according to active power injection corresponding to a first branch, first branch information, first current amplitude information and active power injection corresponding to a first node in the distributed power distribution network;
and the reactive power flow constraint is used for determining reactive power injection corresponding to the second branch according to the reactive power injection corresponding to the first branch, second branch information, first current amplitude information and the reactive power injection corresponding to the first node in the distributed power distribution network.
And the amplitude power flow constraint is used for determining the voltage amplitude information of the first node according to the first branch information, active power injection, second branch information, reactive power injection, first current amplitude information and the voltage amplitude of the second node corresponding to the first branch.
Preferably, the preset power flow constraint comprises a first constraint relation;
before the preset power flow constraint is obtained, the distributed reactive power scheduling method for the power distribution network further comprises the following steps:
and adjusting constraint relations among active power injection, reactive power injection, first current amplitude information and voltage amplitude information corresponding to the second node corresponding to the first branch by a second-order conical convex relaxation technology, and recording the adjusted constraint relations as first constraint relations.
Preferably, the constructing a preset power grid control model according to the preset power loss function, the preset power flow constraint and the preset operation constraint specifically includes:
determining a feasible region of the input quantity of the preset power loss function according to the preset power flow constraint and the preset operation constraint;
and constructing a preset power grid control model according to the preset power loss function of the input quantity in the feasible domain.
Preferably, after the preset power grid control model is converted into the augmented lagrangian function, the distributed reactive power scheduling method for the power distribution network further includes:
determining a current convergence degree value according to the augmented Lagrange function;
and if the current convergence degree value is smaller than or equal to a preset convergence threshold value, acquiring reactive power output information output by the preset power grid control model, and executing the dispatching of the photovoltaic inverter according to the reactive power output information so as to cope with the real-time fluctuation condition of photovoltaic power generation.
Preferably, the performing an iterative operation on the feasible values in the augmented lagrangian function by using a preset improved alternative direction multiplier method to obtain the feasible values after the iteration includes:
iteration of the feasible value simulation increment is carried out through a preset improved alternative direction multiplier method to obtain the iterated feasible value simulation increment;
iterating the practical increment of the feasible value according to the iterated practical value simulation increment to obtain the iterated practical increment of the feasible value;
and performing iterative operation of the feasible values in the augmented Lagrangian function according to the iterated feasible value actual increment to obtain the iterated feasible value.
In a second aspect, an embodiment of the present invention provides a distributed reactive power scheduling system for a power distribution network, including:
the model determining module is used for acquiring a preset power grid control model;
the function conversion module is used for converting the preset power grid control model into an augmented Lagrange function;
the feasible value iteration module is used for carrying out iterative operation on the feasible values in the augmented Lagrangian function through a preset improved alternative direction multiplier method so as to obtain the feasible values after iteration;
the reactive power output determining module is used for acquiring reactive power output information output by the preset power grid control model if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range;
and the power distribution network scheduling module is used for scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the distributed reactive power scheduling method for a power distribution network provided in the first aspect of the present invention when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the distributed reactive power scheduling method for an electrical distribution network provided in the first aspect of the present invention.
According to the distributed reactive power scheduling method, the distributed reactive power scheduling system, the distributed reactive power scheduling equipment and the storage medium of the power distribution network, a preset power grid control model is obtained firstly; converting a preset power grid control model into an augmented Lagrange function; performing iterative operation of the feasible values in the augmented Lagrange function by a preset improved alternative direction multiplier method to obtain the feasible values after iteration; if the convergence degree value corresponding to the iterated feasible value is within the preset convergence degree range, obtaining the reactive power output information output by the preset power grid control model; and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation. The embodiment of the invention provides a real-time scheduling mode of a distributed power distribution network, the operation condition of the distributed power distribution network is controlled by scheduling the reactive power output of a photovoltaic inverter in real time, and the real-time fluctuation of photovoltaic power generation is further dealt with, so that the technical problem of the real-time fluctuation of the photovoltaic power generation in the operation process is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to an embodiment of the present invention;
fig. 2 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to another embodiment of the present invention;
fig. 3 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to still another embodiment of the present invention;
FIG. 4 is a diagram illustrating an exemplary region provided in accordance with yet another embodiment of the present invention;
fig. 5 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of an exemplary iterative manner provided by another embodiment of the present invention;
fig. 7 is a schematic structural diagram of a distributed reactive power scheduling system of a power distribution network according to an embodiment of the present invention;
fig. 8 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
and S1, acquiring a preset power grid control model.
It can be understood that, as the active power generation resources are gradually connected to the conventional passive distribution network, the passive distribution network becomes an active distribution network, and how to effectively transfer the controllable resources in the distribution network to ensure the voltage of the active distribution network becomes more and more important.
If distributed photovoltaic power generation is carried out on the active power distribution network, the photovoltaic power generation units and the power grid are connected together through the photovoltaic inverters, and obviously, decoupling control of active power and reactive power is achieved through the photovoltaic inverters.
In daily operation, the photovoltaic power generation unit often has surplus reactive power resources for calling, and meanwhile, the photovoltaic inverter rapidly has the potential of rapidly adjusting voltage fluctuation.
Therefore, in order to better cope with real-time fluctuation of photovoltaic power generation, the voltage of the power distribution network can be controlled by adjusting the reactive power of the photovoltaic inverter.
In order to better cope with the real-time fluctuation of the photovoltaic power generation, the embodiment of the invention provides a better real-time scheduling mode of the power distribution network to cope with the real-time fluctuation of the photovoltaic power generation.
Specifically, the real-time scheduling mode of the power distribution network is that the voltage of the power distribution network is controlled by scheduling the reactive power of the photovoltaic inverter in real time, and then real-time fluctuation of photovoltaic power generation is responded.
The execution main body of the embodiment of the invention is electronic equipment which can be a distributed power distribution network, wherein the distributed power distribution network comprises a plurality of photovoltaic panels and a plurality of photovoltaic inverters; the electronic equipment can also be a single photovoltaic inverter, and the photovoltaic inverter can operate independently and perform information interaction with the adjacent photovoltaic inverter.
In a specific implementation, a preset power grid control model may be established first, where the preset power grid control model is a control model based on a distributed power distribution network, the distributed power distribution network is an active power distribution network, and the distributed power distribution network includes a photovoltaic inverter.
The preset power grid control model is used for distributed control of the distributed power distribution network, and specifically, voltage optimization of the distributed power distribution network is performed.
And S2, converting the preset power grid control model into an augmented Lagrangian function.
The default grid control model may then be converted to an Augmented Lagrangian (AL) function.
And S3, performing iterative operation of the feasible values in the augmented Lagrangian function by a preset improved alternative direction multiplier method to obtain the feasible values after iteration.
Specifically, a lagrangian multiplier exists in the augmented lagrangian function, and the lagrangian multiplier can be marked as a feasible value and used for representing consistency constraint among all regions in the distributed power distribution network.
Wherein the possible values can be recorded as
Figure BDA0002366822140000072
And representing the consistency constraint under the connection of the region n and the region m, wherein n and m represent the sequence number of the region.
Then, by continuously iterating each parameter in the augmented lagrangian function, an iterated feasible value can be obtained.
Wherein the iterated feasible values can be recorded as
Figure BDA0002366822140000071
The feasible values after k +1 iterations are shown.
As for the specific iteration method, an alternating direction multiplier (ADMM) method may be introduced.
The preset improved alternative direction multiplier method can additionally perform iterative operation for increasing the feasible value in the Lagrangian function by improving the alternative direction multiplier method so as to improve the operation efficiency.
And S4, if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range, acquiring the reactive power output information output by the preset power grid control model.
After one or more iterations, the convergence degree value corresponding to the feasible value at the time, that is, the convergence degree value corresponding to the augmented Lagrange function at the time, can obtain the reactive power output information at the time if the feasible value is within the preset convergence degree range.
And S5, scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation.
The reactive power output information can schedule the reactive power of the photovoltaic inverter, so that the photovoltaic inverter can be rapidly scheduled through the reactive power output information, and the real-time fluctuation condition of photovoltaic power generation can be further dealt with.
In addition, the embodiment of the invention not only talks about an active power distribution network, but also about a distributed power distribution network.
It should be appreciated that embodiments of the present invention relate to distributed control of a distributed power distribution network, rather than traditional centralized regulation.
After all, the traditional centralized regulation and control has the defects of slow optimization and solution, low communication efficiency, large communication burden and the like, and the distributed regulation and control strategy can better deal with the defects of the centralized regulation and control by decomposing the global problem into sub-problems and then realizing the local solution of each sub-problem.
The distributed reactive power scheduling method for the power distribution network, provided by the embodiment of the invention, comprises the steps of firstly obtaining a preset power grid control model; converting a preset power grid control model into an augmented Lagrange function; performing iterative operation of the feasible values in the augmented Lagrange function by a preset improved alternative direction multiplier method to obtain the feasible values after iteration; if the convergence degree value corresponding to the iterated feasible value is within the preset convergence degree range, obtaining the reactive power output information output by the preset power grid control model; and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation. The embodiment of the invention provides a real-time scheduling mode of a distributed power distribution network, the operation condition of the distributed power distribution network is controlled by scheduling the reactive power output of a photovoltaic inverter in real time, and the real-time fluctuation of photovoltaic power generation is further dealt with, so that the technical problem of the real-time fluctuation of the photovoltaic power generation in the operation process is solved.
Fig. 2 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to another embodiment of the present invention, where the another embodiment of the present invention is based on the embodiment shown in fig. 1.
In this embodiment, before S1, the method for distributed reactive power scheduling of a power distribution network further includes:
and S01, acquiring a preset power loss function.
The embodiment of the invention can provide a construction mode of a preset power grid control model.
It will be appreciated that the predetermined power loss function is used to limit the power loss condition of the distributed power distribution network.
And S02, acquiring a preset power flow constraint, wherein the preset power flow constraint is used for constraining the flow of electric energy.
It will be appreciated that the predetermined power flow constraints are used to constrain the overall flow of electrical energy, which may relate to active power, reactive power, voltage magnitude, and current magnitude, among others.
And S03, acquiring preset operation constraints, wherein the preset operation constraints are used for constraining the operation state of the distributed power distribution network.
It can be understood that the preset operation constraint is used for constraining the operation state of the distributed power distribution network, and the constrained operation condition may relate to each node in the distributed power distribution network and the distributed power distribution network of each branch, where the branch is a connection path between the nodes, and the node includes different types of power distribution network nodes such as a photovoltaic inverter.
And S04, constructing a preset power grid control model according to the preset power loss function, the preset power flow constraint and the preset operation constraint.
The preset power grid control model is constructed through the constraint effects of the preset power loss function, the preset power flow constraint and the preset operation constraint, so that the constructed preset power grid control model can balance the constraint effects of the preset power grid control model, the preset power flow constraint and the preset operation constraint, and the three constraint effects are achieved simultaneously.
The distributed reactive power scheduling method for the power distribution network, provided by the embodiment of the invention, provides a construction mode of a class of preset power grid control model, so that the three types of constraint effects can be considered simultaneously when the photovoltaic inverter is scheduled, and the power grid operation is stabilized.
Fig. 3 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to still another embodiment of the present invention, where still another embodiment of the present invention is based on the embodiment shown in fig. 2.
In this embodiment, before S01, the method for distributed reactive power scheduling of a power distribution network further includes:
and S001, constructing a preset electric energy loss function through active power loss and action loss of the photovoltaic inverter.
The embodiment of the invention can provide a construction mode of a preset electric energy loss function, but is not limited to the construction mode.
It should be appreciated that the constrained power loss condition may include active power loss and photovoltaic inverter activity loss, given that a preset power loss function is used to limit the power loss condition of the distributed power distribution grid.
A specific example of the preset power loss function can be given, but is not limited thereto, as follows,
Figure BDA0002366822140000101
wherein F represents the objective function, QGgRepresenting the reactive power output of the g photovoltaic inverter;
Figure BDA0002366822140000102
representing the sum of active power losses during operation of the distribution network, lijRepresenting the square of the magnitude of the current, r, of the branch connecting node i and node jijRepresenting the resistance value of the branch between the connection node i and the node j, and representing a branch set;
Figure BDA0002366822140000103
representing the loss of operation of the photovoltaic inverter, GDGRepresents a collection of photovoltaic power generation devices; alpha is alpha1The weight coefficient is typically 10 to 50.
Therefore, the preset electric energy loss function can be a type of objective function and is used for carrying out optimization control on distributed voltage of the active power distribution network.
The distributed reactive power scheduling method for the power distribution network, provided by the embodiment of the invention, provides a construction mode of a type of preset electric energy loss function, and the constructed preset electric energy loss function can be used for limiting active power loss and action loss of a photovoltaic inverter of the distributed power distribution network in a scheduling process.
On the basis of the above embodiment, preferably, the preset power flow constraint includes an active power flow constraint, a reactive power flow constraint, and a magnitude power flow constraint;
the active power flow constraint is used for determining active power injection corresponding to a second branch according to active power injection corresponding to a first branch, first branch information, first current amplitude information and active power injection corresponding to a first node in the distributed power distribution network;
and the reactive power flow constraint is used for determining reactive power injection corresponding to the second branch according to the reactive power injection corresponding to the first branch, second branch information, first current amplitude information and the reactive power injection corresponding to the first node in the distributed power distribution network.
And the amplitude power flow constraint is used for determining the voltage amplitude information of the first node according to the first branch information, active power injection, second branch information, reactive power injection, first current amplitude information and the voltage amplitude of the second node corresponding to the first branch.
It can be understood that the preset power flow constraint can be used for constraining the whole electric energy to flow, and if the preset power flow constraint is refined, the refinement is carried out in a manner of refining into an active power flow constraint, a reactive power flow constraint and an amplitude value power flow constraint.
The active power flow constraint restrains active power, the reactive power flow constraint restrains reactive power, and the amplitude flow constraint restrains voltage amplitude.
A specific example of a preset power flow constraint may be given, but is not limited to, see the following,
Figure BDA0002366822140000111
Figure BDA0002366822140000112
vj＝vi-2(rijPij+xijQij)+(rij 2+xij 2)lij
from top to bottom, there are active power flow constraint, reactive power flow constraint and amplitude power flow constraint.
In addition, reference may also be made to the following formula,
Figure BDA0002366822140000113
Figure BDA0002366822140000114
the pure load node refers to a node which is not connected with photovoltaic and does not perform power generation operation, and the distributed photovoltaic access node can be a photovoltaic inverter.
Wherein, PjAnd QjRespectively representing the active and reactive power injection, P, of the j-th nodeDjAnd QDjRespectively representing the load active power demand and the load reactive power demand, P, of the node jGgActive power output, Q, of the g-th distributed photovoltaicGgAnd the reactive power output of the distributed photovoltaic of the g-th station.
Wherein i and j both represent serial numbers.
Wherein, as for the active power flow constraint, PijRepresents the active power injection corresponding to the branch (i, j), which may be denoted as the first branch, i.e. the branch between the ith and jth nodes, rijAnd xijRespectively representing the resistance and reactance values of the branch (I, j), which can be recorded as branch information, IijRepresenting the current amplitude of the branch (i, j),
Figure BDA0002366822140000121
the jth node can be marked as the first node, PjkRepresenting active power injection corresponding to the branch (j, k), wherein the branch (j, k) can be marked as a second branch;
as for reactive power flow constraints, QijRepresenting the reactive power injection, Q, corresponding to branch (i, j)jkRepresenting the reactive power injection corresponding to the branch (j, k);
as for the magnitude-flow constraint,
Figure BDA0002366822140000122
Virepresenting the voltage amplitude of the node i, and the node i can be marked as a second node; v. ofjWhich represents the square of the voltage magnitude of the node j, i.e., the voltage magnitude information of the first node.
On the basis of the foregoing embodiment, preferably, the preset power flow constraint includes a first constraint relation;
before the preset power flow constraint is obtained, the distributed reactive power scheduling method for the power distribution network further comprises the following steps:
and adjusting constraint relations among active power injection, reactive power injection, first current amplitude information and voltage amplitude information corresponding to the second node corresponding to the first branch by a second-order conical convex relaxation technology, and recording the adjusted constraint relations as first constraint relations.
The first constraint relationship may also be introduced in the preset power flow constraint, which takes into account that if the following conventional constraint relationship is used in the preset power flow constraint,
Figure BDA0002366822140000123
non-convexity is brought to the finally constructed preset power grid control model, so that the preset power grid control model is difficult to solve efficiently when being solved, therefore, the conventional constraint relation can be adjusted to the following first constraint relation,
Figure BDA0002366822140000124
wherein the content of the first and second substances,
Figure BDA0002366822140000131
indicating first current amplitude information, P, corresponding to the first branchijRepresenting active power injection, Q, corresponding to the first branchijIndicating reactive power injection, v, corresponding to the first branchiTo representThe square of the voltage amplitude of the node i, i.e., the voltage amplitude information of the second node.
Therefore, after the conventional constraint relation is adjusted to be the first constraint relation through the second-order cone-convex relaxation technology, the solving efficiency of the model is greatly improved, and the defect of low operation efficiency caused by non-convexity is overcome.
In addition, as for the preset operation constraints, in order to constrain the operation state of the distributed power distribution network, the preset operation constraints may include a first preset operation constraint corresponding to active power output, a second preset operation constraint corresponding to reactive power output, a third preset operation constraint corresponding to operation safety, and the like.
The first preset operating constraint, which is used to constrain the active power output, may be expressed as,
Figure BDA0002366822140000132
wherein the content of the first and second substances,
Figure BDA0002366822140000133
the active power predicted value of the g-th distributed photovoltaic is represented, and if the working mode of the distributed photovoltaic is assumed to be a maximum power tracking point mode, the active power predicted value is consistent with the power generation operation mode of most of the distributed photovoltaics at present;
the second preset operating constraint, which is used to constrain the reactive power contribution of the distributed photovoltaic, may be expressed as follows,
Figure BDA0002366822140000134
wherein S isGgRepresenting apparent power, Q, of the g-th distributed photovoltaic power plantGgRepresenting the reactive power output of the ith photovoltaic inverter;
the third preset operation constraint is used for ensuring the operation safety of the system, can constrain each node and line in the whole network, and can be expressed as the following formula,
Figure BDA0002366822140000135
wherein, Vi、V iAnd
Figure BDA0002366822140000136
respectively representing the voltage of a node i, the lower limit and the upper limit of the voltage; pij、P ijAnd
Figure BDA0002366822140000141
respectively representing the active power of the branch (i, j), the lower limit and the upper limit of the active power; qij、Q ijAnd, and
Figure BDA0002366822140000142
the reactive power, the lower reactive power limit and the upper reactive power limit of the branch (i, j) are respectively represented.
On the basis of the foregoing embodiment, preferably, the constructing a preset power grid control model according to the preset power loss function, the preset power flow constraint, and the preset operation constraint specifically includes:
determining a feasible region of the input quantity of the preset power loss function according to the preset power flow constraint and the preset operation constraint;
and constructing a preset power grid control model according to the preset power loss function of the input quantity in the feasible domain.
It can be understood that, in order to construct the preset power grid control model, an example of model construction is given in the embodiment of the present invention.
For example, an objective function of the preset power loss function may be written as f (x), and feasible fields of all variables to which the preset power flow constraint and the preset operation constraint are constrained may be recorded as f (x)
Figure BDA0002366822140000143
Therefore, the generated preset grid control model can be briefly expressed as follows,
Figure BDA0002366822140000144
where x is an input quantity, and in particular may be a vector comprising a set of multiple controlled variables, the controlled variables relating to { Q }Gg,Vi,Iij,Pij,QijFor the variable meanings of the controlled variables, see above, and are not described herein.
In addition, for ease of understanding, reference may be made to the exemplary illustration of regions shown in fig. 4, which shows three regions, regions A, B and C, respectively, with lines and devices within different dashed boxes belonging to benefit principals of different regions, with tie lines between regions being common to adjacent connected regions. In actual operation, the division of the area is determined by the coverage area of a specific energy service company.
As for the numbers shown in fig. 4, they are serial numbers for indicating common connection points or nodes. The distributed photovoltaic power generation equipment access node is the distributed photovoltaic access node.
Further, if the preset grid control model is explained more specifically, while combining the partitioning for the area, the input quantity may be labeled xn，xnFor the controlled variable column vector of the distributed photovoltaic in the nth zone, x ═ xnL N belongs to N, wherein N is a set of all areas in the active power distribution network;
Figure BDA0002366822140000151
is the feasible field of the column vector of the controlled variable in the nth region,
Figure BDA0002366822140000152
since the objective function is decoupled for each region, fn(xn) As an objective function within the nth region, i.e.
Figure BDA0002366822140000153
It can be seen that the preset power grid control model may correspond to a region.
At the same time, differ from xnCan also give
Figure BDA0002366822140000154
And representing boundary variable column vectors connected with the region m in the region n, namely representing boundary node voltage variables, connecting line branch current variables, branch active power variables, branch reactive power variables and the like of adjacent regions connected with each other.
The controlled variable column vectors are of the same data type as the boundary variable column vectors, except that the scene is different, one is within the region and one is at the region boundary.
Based on the consistency principle, the coupling relation of adjacent distributed photovoltaic tie lines can be expressed as
Figure BDA0002366822140000155
Wherein s isn,mFor the auxiliary global variable column vector in the case where region n is connected to region m,
Figure BDA0002366822140000156
and representing boundary variable column vectors connected with the region n in the region m, namely representing boundary node voltage variables, connecting line branch current variables, branch active power variables, branch reactive power variables and the like of adjacent regions connected with each other.
Based on the coupling relationship of the distributed photovoltaic tie lines, a preset power grid control model can be finally expressed as the following formula,
Figure BDA0002366822140000157
Figure BDA0002366822140000158
wherein N isnIs a set of regions adjacent to region n.
Fig. 5 is a flowchart of a distributed reactive power scheduling method for a power distribution network according to another embodiment of the present invention, where another embodiment of the present invention is based on the embodiment shown in fig. 1.
In this embodiment, after S2, the method for distributed reactive power scheduling of a power distribution network further includes:
and S31, determining the current convergence degree value according to the augmented Lagrangian function.
And S32, if the current convergence degree value is less than or equal to a preset convergence threshold value, obtaining the reactive power output information output by the preset power grid control model.
After S32, S5 is performed.
It can be understood that, if the preset power grid control models respectively correspond to the regions, the preset power grid control model corresponding to each region can be solved through communication with the adjacent regions.
As regards the transformed augmented Lagrangian function, this can be noted
Figure BDA0002366822140000161
Wherein L isnRepresenting the corresponding augmented lagrange function for region n,
Figure BDA0002366822140000162
and representing a Lagrange multiplier of consistency constraint under the connection of the region n and the region m, wherein rho is a penalty coefficient and can be generally taken from 200 to 600.
After determining the augmented Lagrangian function, a current convergence level value may be determined, which may be recorded as
Figure BDA0002366822140000163
k denotes the number of iterations, n denotes the correspondence to the region, and the preset convergence threshold can be denoted as σ.
Therefore, if
Figure BDA0002366822140000164
The optimized convergence of the characterization region n can be finished, and at the moment, a regulation plan can be output to carry out photovoltaic inversionAnd scheduling the photovoltaic generator to cope with the real-time fluctuation condition of the photovoltaic generator.
Wherein the control plan includes reactive power output information.
The distributed reactive power scheduling method for the power distribution network, provided by the embodiment of the invention, provides a scheduling mode of a class of photovoltaic inverters, and the photovoltaic inverters are more efficiently scheduled through optimized convergence.
Of course, if the current convergence degree value is greater than the preset convergence threshold, the step of performing iterative operation on the feasible value in the augmented lagrangian function by using a preset improved alternative direction multiplier method to obtain an iterated feasible value is performed.
Therefore, if the optimization convergence does not meet the requirement, iteration processing can be performed on the feasible values until the optimization convergence meets the requirement.
The preset convergence degree range is a degree range in which the convergence degree value is less than or equal to a preset convergence threshold value.
Further, if the iterative process is described in more detail, an exemplary iterative manner will be given below, but is not limited thereto.
This exemplary iterative approach can be seen in part in fig. 6.
For example, after determining the augmented lagrange function, initial values of various types of variables may be set, where the initial values are initial values, and the types of variables that may be set include
Figure BDA0002366822140000171
And
Figure BDA0002366822140000172
the number 0 in the upper right corner of each variable type is used to characterize this time the 0 th iteration.
Wherein, k is used as an iteration flag bit and is set to be 0;
Figure BDA0002366822140000173
setting each variable as an average value of the summation of the upper and lower limits of each variable; is provided with
Figure BDA0002366822140000174
Figure BDA0002366822140000175
Setting as an average value after summing the variables of the boundary between the region m and the region n; symmetric matrix
Figure BDA0002366822140000176
Can be set as an identity matrix with 2D dimensionn×2DnWherein D isnRepresenting the total number of links for region n and other regions.
After setting the initial value, the convergence metric may be calculated and may be expressed as a convergence flag
Figure BDA0002366822140000177
As for
Figure BDA0002366822140000178
In particular to a preparation method of the compound shown in the formula,
Figure BDA0002366822140000179
wherein the original residual error
Figure BDA00023668221400001710
Is a vector
Figure BDA00023668221400001711
Dual residual error
Figure BDA00023668221400001712
Is a vector
Figure BDA00023668221400001713
“|| ||2"represents a two-norm, i.e., the sum of the squares of all the elements is summed and then squared.
If it is
Figure BDA00023668221400001714
Therefore, if
Figure BDA00023668221400001715
And finishing the optimization convergence of the characterization region n, and at the moment, outputting a regulation and control plan to schedule the photovoltaic inverter so as to deal with the real-time fluctuation condition of the photovoltaic power generation.
Wherein, σ is used as convergence standard and can take 10-4。
On the basis of the foregoing embodiment, preferably, the performing an iterative operation on the feasible values in the augmented lagrangian function by using a preset improved alternating direction multiplier method to obtain the feasible values after the iteration specifically includes:
iteration of the feasible value simulation increment is carried out through a preset improved alternative direction multiplier method to obtain the iterated feasible value simulation increment;
iterating the practical increment of the feasible value according to the iterated practical value simulation increment to obtain the iterated practical increment of the feasible value;
and performing iterative operation of the feasible values in the augmented Lagrangian function according to the iterated feasible value actual increment to obtain the iterated feasible value.
It can be understood that, if the current convergence degree value is greater than the preset convergence threshold, the iterative operation of the feasible values in the augmented lagrangian function may be performed by a preset improved alternative direction multiplier method to obtain the feasible values after the iteration.
The embodiment of the present invention may give an exemplary iterative flow of feasible values, but is not limited thereto.
In contrast to the conventional alternative direction multiplier method, a specific preset improved alternative direction multiplier method can be provided, but is not limited to this, and after all, various improvements exist for optimizing the operation efficiency.
In the preset improved alternative direction multiplier method provided by the embodiment of the invention, the dual multiplier, namely the lagrangian multiplier, can be updated by adopting the preset improved alternative direction multiplier method, and the physical characteristic of the photovoltaic inverter for quick action at the second level can be effectively exerted.
As for a conventional distributed regulation strategy, the method is mostly based on a gradient method, has slow convergence speed and low solving efficiency, can only be used for minute-level real-time scheduling at the fastest speed, cannot effectively deal with the problem of real-time fluctuation of renewable energy sources, and influences the overall operation safety of the power distribution network. However, the embodiment of the invention greatly improves the situation, and the embodiment of the invention can achieve real-time scheduling of second level.
In particular implementations, the feasible values are updated
Figure BDA0002366822140000181
The process can be decomposed into three steps to solve
Figure BDA0002366822140000182
Solving for (Δ λ)n)kAnd updating
Figure BDA0002366822140000183
Where k +1 outside the parenthesis indicates the k +1 th iteration.
The first step is to simulate the increment by the feasible value
Figure BDA0002366822140000184
Is in fact
Figure BDA0002366822140000185
Wherein the content of the first and second substances,
Figure BDA0002366822140000186
to augment the Hessian matrix, "()-1"is the inversion operation;
Figure BDA0002366822140000187
a pre-set column vector is represented,
Figure BDA0002366822140000188
wherein the content of the first and second substances,
Figure BDA0002366822140000189
is dimension DnX 1 column vector(ii) a k denotes the kth iteration, and similarly, k +1 denotes the kth +1 iteration.
As for the above-mentioned,
Figure BDA00023668221400001810
the elements are represented by the following formula,
Figure BDA00023668221400001811
here, it should be taken
Figure BDA00023668221400001812
The value of (c).
The second step is that the feasible value simulation increment of the kth iteration is obtained
Figure BDA0002366822140000191
Thereafter, the feasible value actual increment (Δ λ) for the kth iteration may be solvedn)k。
As for (Δ λ)n)kThe solving formula of (a) is as follows,
Figure BDA0002366822140000192
wherein (Δ λ)n)kIs dimension DnA x 1 column vector, in which each element can be represented as
Figure BDA0002366822140000193
Figure BDA0002366822140000194
Δ λ representing region nnThe portion of (a) corresponding to the region n itself;
Figure BDA0002366822140000195
Δ λ representing region mmCorresponding to the element connecting with the region n.
The third step is to update the feasible value
Figure BDA0002366822140000196
The feasible values may also be referred to as lagrangian multipliers.
As for
Figure BDA0002366822140000197
The update formula of (a) is as follows,
Figure BDA0002366822140000198
therefore, the dual multiplier, namely the Lagrange multiplier, is updated by the preset improved alternative direction multiplier method adopted by the embodiment of the invention, and the physical characteristic of the photovoltaic inverter for quick action at the second level can be effectively exerted.
Further, the embodiment of the present invention also provides a more specific iterative process, but is not limited to this.
This more specific iterative process can be seen in full fig. 6.
In the more specific iteration process, each photovoltaic inverter only needs to communicate with a neighbor node without a coordination layer, so that the algorithm has quasi-second-order convergence property, and second-order fast scheduling of reactive power of the photovoltaic inverters can be realized.
Meanwhile, each inverter only needs to exchange a small amount of boundary information with adjacent inverters, so that the privacy protection effect on important information exists; the global optimization problem can be solved quickly, and convergence is rapid.
For example, if
Figure BDA0002366822140000199
The input quantities, i.e. the vectors of the set of controllable variables, can be iterated first and can be written as
Figure BDA00023668221400001910
Wherein the content of the first and second substances,
Figure BDA00023668221400001911
and characterizing the input quantity corresponding to the nth region under the k +1 th iteration.
If k is greater than or equal to 1, i.e. in the stackAt the beginning of the generation, the feasible segmentations can be constrained
Figure BDA00023668221400001912
Joining to an existing feasible domain
Figure BDA00023668221400001913
In (1) to form
Figure BDA00023668221400001914
Obviously, the input quantities are iterated through the sectionable constraints to obtain iterated input quantities.
It is to be appreciated that by introducing a feasible cut constraint, the relaxed and original results can be made consistent.
Then, will
Figure BDA0002366822140000201
And
Figure BDA0002366822140000202
into LnIn order to solve the univariate optimization problem, i.e. as follows,
Figure BDA0002366822140000203
wherein the argmin function is used to solve
Figure BDA0002366822140000204
This optimization problem to get xnThe value of (c).
Similarly, the function argmax is used to solve the maximization optimization problem to obtain the variable values.
The single-variable optimization problem is a convex optimization problem, and a result can be obtained by calculation of any mature optimization solver.
It can be seen that the iterative process is obtained
Figure BDA0002366822140000205
At the same time, because
Figure BDA0002366822140000206
Is xnSo, solve for
Figure BDA0002366822140000207
While also obtaining
Figure BDA0002366822140000208
Where k +1 represents the value after k +1 iterations.
Then, obtaining
Figure BDA0002366822140000209
And
Figure BDA00023668221400002010
then, the auxiliary global variable column vector can be updated iteratively, namely the auxiliary global variable column vector is obtained by updating
Figure BDA00023668221400002011
The iterative updating mode is that the region n firstly updates
Figure BDA00023668221400002012
Transmitted to and received from adjacent regions m
Figure BDA00023668221400002013
Thus, according to
Figure BDA00023668221400002014
Solve out
Figure BDA00023668221400002015
As for the solving means, see the following formula,
Figure BDA00023668221400002016
then, the data is updated in iteration
Figure BDA00023668221400002017
Can then be updated
Figure BDA00023668221400002018
In contrast to the conventional alternative direction multiplier method, a specific preset improved alternative direction multiplier method can be provided, but is not limited to this, and after all, various improvements exist for optimizing the operation efficiency.
In the preset improved alternative direction multiplier method provided by the embodiment of the invention, the dual multiplier, namely the lagrangian multiplier, can be updated by adopting the preset improved alternative direction multiplier method, and the physical characteristic of the photovoltaic inverter for quick action at the second level can be effectively exerted.
Updating
Figure BDA0002366822140000211
The flow can be decomposed into four steps to solve
Figure BDA0002366822140000212
Solving for (Δ λ)n)kUpdate the data
Figure BDA0002366822140000213
And updating
Figure BDA0002366822140000214
The first step is that,
Figure BDA0002366822140000215
is in fact
Figure BDA0002366822140000216
Wherein the content of the first and second substances,
Figure BDA0002366822140000217
to augment the Hessian matrix, "()-1"is the inversion operation;
Figure BDA0002366822140000218
a pre-set column vector is represented,
Figure BDA0002366822140000219
wherein the content of the first and second substances,
Figure BDA00023668221400002110
is dimension DnA column vector of x 1; k denotes the kth iteration, and similarly, k +1 denotes the kth +1 iteration.
As for the above-mentioned,
Figure BDA00023668221400002111
the elements are represented by the following formula,
Figure BDA00023668221400002112
here, it should be taken
Figure BDA00023668221400002113
The value of (c).
The second step is that the feasible value simulation increment of the kth iteration is obtained
Figure BDA00023668221400002114
Thereafter, the feasible value actual increment (Δ λ) for the kth iteration may be solvedn)k。
As for (Δ λ)n)kThe solving formula of (a) is as follows,
Figure BDA00023668221400002115
wherein (Δ λ)n)kIs dimension DnA x 1 column vector, in which each element can be represented as
Figure BDA00023668221400002116
Figure BDA00023668221400002117
Δ λ representing region nnThe portion of (a) corresponding to the region n itself;
Figure BDA00023668221400002125
Δ λ representing region mmCorresponding to the element connecting with the region n.
The third step is to update
Figure BDA00023668221400002119
As for
Figure BDA00023668221400002120
The update formula of (a) is as follows,
Figure BDA00023668221400002121
as for
Figure BDA00023668221400002122
The update formula of (a) is as follows,
the fourth step is to update
Figure BDA00023668221400002123
As for
Figure BDA00023668221400002124
The update formula of (a) is as follows,
Figure BDA0002366822140000221
wherein (C)TA transpose operation of the representative matrix; gamma is a very small parameter greater than zero, and can generally take a value from 10-3To 10-5；vnRepresents the difference of variables, rnThe gradient difference is indicated.
In addition, BnAn averaged diagonal matrix representing the region n,dimension of 2Dn×2DnThen the diagonal elements in the averaged diagonal matrix are
Figure BDA0002366822140000222
Wherein, bnRepresenting the total number of regions connected to region n, bmRepresenting the total number of zones connected to zone m.
To determine
Figure BDA0002366822140000223
And
Figure BDA0002366822140000224
see the following formulas, respectively,
Figure BDA0002366822140000225
Figure BDA0002366822140000226
after the above iteration process is completed, the number of iterations may be accumulated, that is, k is k +1, and the convergence degree may be determined again until the optimization convergence of the region n is finished.
Fig. 7 is a schematic structural diagram of a distributed reactive power scheduling system of a power distribution network according to an embodiment of the present invention, and as shown in fig. 7, the system includes: the system comprises a model determining module 301, a function converting module 302, a feasible value iteration module 303, a reactive power output determining module 304 and a power distribution network scheduling module 305;
the model determining module 301 is configured to obtain a preset power grid control model;
a function conversion module 302, configured to convert the preset power grid control model into an augmented lagrangian function;
a feasible value iteration module 303, configured to perform iteration operation on the feasible values in the augmented lagrangian function by using a preset improved alternative direction multiplier method to obtain an iterated feasible value;
a reactive power output determining module 304, configured to obtain reactive power output information output by the preset power grid control model if a convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range;
and the power distribution network scheduling module 305 is configured to schedule the photovoltaic inverter according to the reactive power output information so as to deal with a real-time fluctuation condition of photovoltaic power generation.
The distributed reactive power dispatching system of the power distribution network, provided by the embodiment of the invention, comprises the steps of firstly obtaining a preset power grid control model; converting a preset power grid control model into an augmented Lagrange function; performing iterative operation of the feasible values in the augmented Lagrange function by a preset improved alternative direction multiplier method to obtain the feasible values after iteration; if the convergence degree value corresponding to the iterated feasible value is within the preset convergence degree range, obtaining the reactive power output information output by the preset power grid control model; and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation. The embodiment of the invention provides a real-time scheduling mode of a distributed power distribution network, the operation condition of the distributed power distribution network is controlled by scheduling the reactive power output of a photovoltaic inverter in real time, and the real-time fluctuation of photovoltaic power generation is further dealt with, so that the technical problem of the real-time fluctuation of the photovoltaic power generation in the operation process is solved.
The system embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the details, reference is made to the above method embodiments, which are not described herein again.
Fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 8, the electronic device may include: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the bus 404. The communication interface 402 may be used for information transfer of an electronic device. Processor 401 may call logic instructions in memory 403 to perform a method comprising:
acquiring a preset power grid control model;
converting the preset power grid control model into an augmented Lagrange function;
performing iterative operation of the feasible values in the augmented Lagrangian function by a preset improved alternative direction multiplier method to obtain an iterated feasible value;
if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range, obtaining reactive power output information output by the preset power grid control model;
and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-described method embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the method provided by the foregoing embodiments, for example, including:
acquiring a preset power grid control model;
converting the preset power grid control model into an augmented Lagrange function;
performing iterative operation of the feasible values in the augmented Lagrangian function by a preset improved alternative direction multiplier method to obtain an iterated feasible value;
if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range, obtaining reactive power output information output by the preset power grid control model;
and scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A distributed reactive power dispatching method for a power distribution network is characterized by comprising the following steps:
acquiring a preset power grid control model;
converting the preset power grid control model into an augmented Lagrange function;
performing iterative operation of the feasible values in the augmented Lagrangian function by a preset improved alternative direction multiplier method to obtain an iterated feasible value;
if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range, obtaining reactive power output information output by the preset power grid control model;
scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation;
before the preset power grid control model is obtained, the distributed reactive power scheduling method for the power distribution network further includes:
acquiring a preset electric energy loss function;
acquiring a preset power flow constraint, wherein the preset power flow constraint is used for constraining electric energy to flow;
acquiring a preset operation constraint, wherein the preset operation constraint is used for constraining the operation state of the distributed power distribution network;
constructing a preset power grid control model according to the preset electric energy loss function, the preset power flow constraint and the preset operation constraint;
the preset power flow constraint comprises an active power flow constraint, a reactive power flow constraint and an amplitude power flow constraint;
the active power flow constraint is used for determining active power injection corresponding to a second branch according to active power injection corresponding to a first branch, first branch information, first current amplitude information and active power injection corresponding to a first node in the distributed power distribution network;
the reactive power flow constraint is used for determining reactive power injection corresponding to the second branch according to reactive power injection corresponding to the first branch, second branch information, first current amplitude information and reactive power injection corresponding to the first node in the distributed power distribution network;
and the amplitude power flow constraint is used for determining the voltage amplitude information of the first node according to the first branch information, active power injection, second branch information, reactive power injection, first current amplitude information and the voltage amplitude of the second node corresponding to the first branch.
2. The distributed reactive power scheduling method of the power distribution network of claim 1 wherein the preset power flow constraint comprises a first constraint relationship;
before the preset power flow constraint is obtained, the distributed reactive power scheduling method for the power distribution network further comprises the following steps:
and adjusting constraint relations among active power injection, reactive power injection, first current amplitude information and voltage amplitude information corresponding to the second node corresponding to the first branch by a second-order conical convex relaxation technology, and recording the adjusted constraint relations as first constraint relations.
3. The distributed reactive power scheduling method for the power distribution network according to claim 1, wherein the constructing a preset power grid control model according to the preset power loss function, the preset power flow constraint and the preset operation constraint specifically includes:
determining a feasible region of the input quantity of the preset power loss function according to the preset power flow constraint and the preset operation constraint;
and constructing a preset power grid control model according to the preset power loss function of the input quantity in the feasible domain.
4. The distributed reactive power scheduling method of the power distribution network according to any one of claims 1 to 3, wherein after the converting the preset power grid control model into the augmented Lagrangian function, the distributed reactive power scheduling method of the power distribution network further comprises:
determining a current convergence degree value according to the augmented Lagrange function;
and if the current convergence degree value is smaller than or equal to a preset convergence threshold value, acquiring reactive power output information output by the preset power grid control model, and executing the dispatching of the photovoltaic inverter according to the reactive power output information so as to cope with the real-time fluctuation condition of photovoltaic power generation.
5. The distributed reactive power scheduling method for the power distribution network according to any one of claims 1 to 3, wherein the iterative operation of the feasible values in the augmented Lagrangian function is performed by a preset improved alternative direction multiplier method to obtain the iterated feasible values, and specifically comprises:
iteration of the feasible value simulation increment is carried out through a preset improved alternative direction multiplier method to obtain the iterated feasible value simulation increment;
iterating the practical increment of the feasible value according to the iterated practical value simulation increment to obtain the iterated practical increment of the feasible value;
and performing iterative operation of the feasible values in the augmented Lagrangian function according to the iterated feasible value actual increment to obtain the iterated feasible value.
6. A distributed reactive power scheduling system of a power distribution network, comprising:
the model determining module is used for acquiring a preset power grid control model;
the function conversion module is used for converting the preset power grid control model into an augmented Lagrange function;
the feasible value iteration module is used for carrying out iterative operation on the feasible values in the augmented Lagrangian function through a preset improved alternative direction multiplier method so as to obtain the feasible values after iteration;
the reactive power output determining module is used for acquiring reactive power output information output by the preset power grid control model if the convergence degree value corresponding to the iterated feasible value is within a preset convergence degree range;
the power distribution network scheduling module is used for scheduling the photovoltaic inverter according to the reactive power output information so as to deal with the real-time fluctuation condition of photovoltaic power generation;
wherein the system is further configured to: acquiring a preset electric energy loss function;
acquiring a preset power flow constraint, wherein the preset power flow constraint is used for constraining electric energy to flow;
acquiring a preset operation constraint, wherein the preset operation constraint is used for constraining the operation state of the distributed power distribution network;
constructing a preset power grid control model according to the preset electric energy loss function, the preset power flow constraint and the preset operation constraint;
the preset power flow constraint comprises an active power flow constraint, a reactive power flow constraint and an amplitude power flow constraint;
the active power flow constraint is used for determining active power injection corresponding to a second branch according to active power injection corresponding to a first branch, first branch information, first current amplitude information and active power injection corresponding to a first node in the distributed power distribution network;
the reactive power flow constraint is used for determining reactive power injection corresponding to the second branch according to reactive power injection corresponding to the first branch, second branch information, first current amplitude information and reactive power injection corresponding to the first node in the distributed power distribution network;
and the amplitude power flow constraint is used for determining the voltage amplitude information of the first node according to the first branch information, active power injection, second branch information, reactive power injection, first current amplitude information and the voltage amplitude of the second node corresponding to the first branch.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program realizes the steps of the distributed reactive power scheduling method of the power distribution network according to any of the claims 1 to 5.
8. A non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, implements the steps of the distributed reactive power scheduling method for the power distribution network according to any of the claims 1 to 5.
CN202010038346.6A 2020-01-14 2020-01-14 Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network Active CN111106631B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010038346.6A CN111106631B (en) 2020-01-14 2020-01-14 Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010038346.6A CN111106631B (en) 2020-01-14 2020-01-14 Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network

Publications (2)

Publication Number Publication Date
CN111106631A CN111106631A (en) 2020-05-05
CN111106631B true CN111106631B (en) 2021-03-12

Family

ID=70426673

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010038346.6A Active CN111106631B (en) 2020-01-14 2020-01-14 Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network

Country Status (1)

Country Link
CN (1) CN111106631B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115441463A (en) * 2022-09-30 2022-12-06 西南交通大学 Distributed photovoltaic power generation system voltage control method considering no communication connection

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104617577A (en) * 2015-02-10 2015-05-13 清华大学 Active power distribution network full-distributed type self-discipline economic dispatch method
CN104701849A (en) * 2015-03-02 2015-06-10 清华大学 Fully-distributed autonomic voltage control method for active distribution network
CN106208102A (en) * 2016-08-19 2016-12-07 东南大学 A kind of active distribution network new distributed optimal reactive power based on Auxiliary Problem Principle
CN106953359A (en) * 2017-04-21 2017-07-14 中国农业大学 A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic
CN109638873A (en) * 2018-10-24 2019-04-16 中国电力科学研究院有限公司 A kind of distributed photovoltaic cluster Optimization Scheduling and system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106159974B (en) * 2016-08-02 2019-01-15 清华大学 A kind of distributed reactive Voltage Optimum method that transmission & distribution are coordinated

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104617577A (en) * 2015-02-10 2015-05-13 清华大学 Active power distribution network full-distributed type self-discipline economic dispatch method
CN104701849A (en) * 2015-03-02 2015-06-10 清华大学 Fully-distributed autonomic voltage control method for active distribution network
CN106208102A (en) * 2016-08-19 2016-12-07 东南大学 A kind of active distribution network new distributed optimal reactive power based on Auxiliary Problem Principle
CN106953359A (en) * 2017-04-21 2017-07-14 中国农业大学 A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic
CN109638873A (en) * 2018-10-24 2019-04-16 中国电力科学研究院有限公司 A kind of distributed photovoltaic cluster Optimization Scheduling and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
主动配电网分区分布式无功优化控制方法;陈明;《现代电力》;20170707;第34卷(第6期);全文 *
主动配电网分布式无功优化控制方法;梁俊文;《电网技术》;20170725;第42卷(第1期);第230-237页 *

Also Published As

Publication number Publication date
CN111106631A (en) 2020-05-05

Similar Documents

Publication Publication Date Title
Kourounis et al. Toward the next generation of multiperiod optimal power flow solvers
Dominguez et al. Multistage security-constrained HVAC/HVDC transmission expansion planning with a reduced search space
CN110266038B (en) Distributed coordination regulation and control method for multiple virtual power plants
CN109950907B (en) Dispatching method and system for alternating current-direct current hybrid power distribution network containing power electronic transformer
CN107706921B (en) Micro-grid voltage regulation method and device based on Nash game
Preece et al. Probabilistic evaluation of damping controller in networks with multiple VSC-HVDC lines
Peng et al. Distributed algorithm for optimal power flow on an unbalanced radial network
El Helou et al. Fully decentralized reinforcement learning-based control of photovoltaics in distribution grids for joint provision of real and reactive power
Zhao et al. Distributed risk-limiting load restoration for wind power penetrated bulk system
CN111353910A (en) Distributed intelligent power grid economic dispatching method based on finite time consistency under directed topology
CN110661258B (en) Flexible resource distributed robust optimization method for power system
CN110232475A (en) A kind of distribution power transmission network power distribution network collaboration economic load dispatching method
Zhang et al. An efficient multi-agent negotiation algorithm for multi-period photovoltaic array reconfiguration with a hydrogen energy storage system
Zhai et al. Distributed optimal power flow for VSC-MTDC meshed AC/DC grids using ALADIN
CN115640963A (en) Offshore wind power access system robust planning method considering investment operation mode
Taheri et al. Adaptive robust optimal transmission switching considering the uncertainty of net nodal electricity demands
CN112467748A (en) Double-time-scale distributed voltage control method and system for three-phase unbalanced active power distribution network
CN111106631B (en) Distributed reactive power scheduling method, system, equipment and storage medium for power distribution network
Liu et al. A fully distributed economic dispatch method in DC microgrid based on consensus algorithm
CN112990596A (en) Distributed optimization method for cooperative operation of active power distribution network and virtual power plant
CN113097994A (en) Power grid operation mode adjusting method and device based on multiple reinforcement learning agents
Qiu et al. Optimality-feasibility-aware multistage unit commitment considering nonanticipative realization of uncertainty
CN113705964B (en) Method and device for formulating pre-disaster plan for toughness recovery of power distribution network
CN113421004A (en) Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method
CN107947153B (en) Method for minimizing active loss in alternating current-direct current hybrid power transmission system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant