CN108665098A - A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network - Google Patents
A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network Download PDFInfo
- Publication number
- CN108665098A CN108665098A CN201810421860.0A CN201810421860A CN108665098A CN 108665098 A CN108665098 A CN 108665098A CN 201810421860 A CN201810421860 A CN 201810421860A CN 108665098 A CN108665098 A CN 108665098A
- Authority
- CN
- China
- Prior art keywords
- distributed generation
- generation resource
- distribution network
- power distribution
- capacity
- 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.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 238000013528 artificial neural network Methods 0.000 claims abstract description 40
- 238000004364 calculation method Methods 0.000 claims abstract description 17
- 238000004088 simulation Methods 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000002945 steepest descent method Methods 0.000 claims abstract description 8
- 238000003860 storage Methods 0.000 claims description 22
- 238000009434 installation Methods 0.000 claims description 13
- 230000000694 effects Effects 0.000 claims description 9
- 230000002411 adverse Effects 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 230000003068 static effect Effects 0.000 claims description 5
- 238000013515 script Methods 0.000 claims description 4
- 210000002569 neuron Anatomy 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims description 2
- 230000006870 function Effects 0.000 description 62
- 238000004422 calculation algorithm Methods 0.000 description 13
- 238000012545 processing Methods 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000452 restraining effect Effects 0.000 description 4
- 230000003416 augmentation Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses the addressing constant volume optimization method and devices that a kind of distributed generation resource accesses power distribution network, including：Receive the object function formulated according to distributed generation resource and power distribution network location data；Receive distributed generation resource and distribution network data, build the power distribution network simulation model containing distributed generation resource based on OpenDSS, sample calculation analysis is carried out to this area using the model and obtains the voltage of each node in power distribution network, current data progress Load flow calculation, determines the constraints of an inequality form；Constraints is added in object function according to common Lagrangian, and energy function is determined in conjunction with Hopfield neural networks, the value for violating energy function described in constraints increases；Gradient of the energy function to capacity and Lagrange multiplier is calculated separately according to discrete time steepest descent method, determines that update iterative equation trains discrete hopfield neural network, obtains position and the capacity of distributed generation resource access power distribution network.
Description
Technical field
The invention belongs to the technical fields of Distributed Generation in Distribution System, are accessed more particularly, to a kind of distributed generation resource
The addressing constant volume optimization method and device of power distribution network.
Background technology
Currently, many Intelligent Information Processing algorithms can be used for addressing constant volume of the distributed generation resource before accessing power distribution network
In optimization, such as genetic algorithm, neural network algorithm, ant group algorithm, fish-swarm algorithm and particle cluster algorithm etc..Neural network
Computing capability with highly-parallel can be used for solving unconstrained optimization problem and with constrained optimization problem.It utilizes
Hopfield neural networks solve the problems, such as that the basic thought of the optimization problem of belt restraining is to establish energy letter according to Different Optimization
Number, the function contain two parts of optimization problem desired value and optimization problem constraints, it is only necessary to ask minimum to energy function
Point can be solved the optimization problem.
But if directly solving the problems, such as that distributed generation resource addressing constant volume exists using the algorithm is easily trapped into local minimum
The defect of value.It is connect therefore, it is necessary to Hopfield neural network methods are used in combination with other methods obtain distributed generation resource
Enter optimum position and the capacity of distribution.For example,《Distributed generation resource based on argument Lagrange-Hopfield neural networks is most
The research of excellent configuration》In disclose based on Hopfield neural networks and Lagrangian Relaxation
(LagrangianRelaxation) to handle, distributed generation resource is optimal to match argument Lagrange-Hopfield neural networks
Set problem.But constraints is written by augmentation Lagrangian in object function this method, though it can solve to be absorbed in
The problem of local minimum point, but the selection of the punishment parameter of augmentation Lagrangian is improper, algorithm can be prevented from restraining or
Algorithm shakes near solution.And augmentation Lagrangian is very sensitive to multiplier, needs to obtain before being restrained suitable
The iterative step of quantity.
In conclusion for how to use simple algorithm in the addressing of distributed generation resource access power distribution network in the prior art
The case where improvement asks optimization problem to be easily absorbed in local minimum point with neural network in constant volume, and effectively solution annual reporting law cannot
Convergence or algorithm still lack effective solution scheme the problem of solution oscillates about.
Invention content
For the deficiencies in the prior art, the present invention provides the addressings that a kind of distributed generation resource accesses power distribution network
Common method of Lagrange multipliers is used in combination constant volume optimization method and device with discrete hopfield neural network, improves and uses
The case where neural network asks optimization problem to be easily absorbed in local minimum point, and effectively solution annual reporting law cannot restrain or algorithm
The problem of solution oscillates about, accesses distribution for distributed generation resource and select suitable position and capacity, reach reduction distributed electrical
The target of the investment cost of distribution is accessed in source, and algorithm is simple, it is easy to accomplish.
The first object of the present invention is to provide a kind of addressing constant volume optimization method of distributed generation resource access power distribution network.
To achieve the goals above, the present invention is using a kind of following technical solution：
A kind of addressing constant volume optimization method of distributed generation resource access power distribution network, including：
Receive the object function formulated according to distributed generation resource and power distribution network location data；The object function is
The cost of unit capacity distributed generation resource, the function of installed capacity and installation number；
Distributed generation resource and distribution network data are received, the distribution network simulation containing distributed generation resource based on OpenDSS is built
Model carries out sample calculation analysis to this area using the model and obtains the voltage of each node in power distribution network, current data progress trend
It calculates, determines the constraints of an inequality form；
Constraints is added in object function according to common Lagrangian, and combines Hopfield nerve nets
Network determines energy function, and the value for violating energy function described in constraints increases；
Gradient of the energy function to capacity and Lagrange multiplier is calculated separately according to discrete time steepest descent method, really
Surely update iterative equation trains discrete hopfield neural network, obtains position and the capacity of distributed generation resource access power distribution network.
Scheme as a further preference, in the method, the specific steps for the function that sets objectives include：
Determine that distributed generation resource is grid-connected to power distribution network band according to the concrete condition of distributed generation resource and power distribution network location
The adverse effect come；
By adverse effect in conjunction with the present situation and policy requirements of this area, the target letter to tally with the actual situation with policy is formulated
Number；The object function be the cost of unit capacity distributed generation resource, installed capacity and installation number function；The unit
The cost of capacity distributed generation resource be fixed Annual Percentage Rate, per kilowatt dynamic investment expense, per kilowatt static investment expense and
The function of distributed generation resource service life.
Scheme as a further preference, in the method, the distributed generation resource and power distribution network data of reception include feeder line
Relevant parameter and load-related parameter record the distributed generation resource and distribution network data of reception in OpenDSS scripts, structure
The power distribution network simulation model containing distributed generation resource based on OpenDSS.
Scheme as a further preference, in the method, the constraints of an inequality form meet power grid
The voltage of each node, current data and distributed generation resource capacity in trend, power distribution network.
Scheme as a further preference, in the method, the constraints of an inequality form includes distribution
The constraints of formula power supply capacity and the constraints of voltage perunit value；Wherein, the range constraint of distributed generation resource capacity is
The range constraint of (0,6000), voltage perunit value is (0.95,1.05).
Scheme as a further preference, in the method, the specific steps packet of training discrete hopfield neural network
It includes：
Receive parameter, maximum iteration and the default error of discrete hopfield neural network；
Initialize distributed generation resource capacity and Lagrange multiplier；
The dynamic behaviour that neuron is calculated according to iteration renewal equation, determines the structure and parameter of neural network；
Worst error is calculated, is more than the maximum iteration set when worst error is less than default error or iterations
When, terminate training；Otherwise, update iterations return to previous step and continue to iterate to calculate.
Scheme as a further preference, in the method, the parameter of the discrete hopfield neural network include the
One Study rate parameter, the second Study rate parameter and third Study rate parameter, the first Study rate parameter, the second Study rate parameter are equal
It is 0.01, third Study rate parameter is set as 0.
Scheme as a further preference initializes distributed generation resource capacity and Lagrange multiplier is in the method
Zero initial condition.
The second object of the present invention is to provide a kind of computer readable storage medium.
To achieve the goals above, the present invention is using a kind of following technical solution：
A kind of computer readable storage medium, wherein being stored with a plurality of instruction, described instruction is suitable for by terminal device
Reason device loads and executes a kind of addressing constant volume optimization method of distributed generation resource access power distribution network.
The third object of the present invention is to provide a kind of terminal device.
To achieve the goals above, the present invention is using a kind of following technical solution：
A kind of terminal device, including processor and computer readable storage medium, processor is for realizing each instruction；Meter
Calculation machine readable storage medium storing program for executing is suitable for being loaded by processor and being executed described one kind point for storing a plurality of instruction, described instruction
The addressing constant volume optimization method of cloth plant-grid connection power distribution network.
Beneficial effects of the present invention：
1, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, uses
The optimization method that common method of Lagrange multipliers and discrete hopfield neural network are combined is incorporated to distribution to distributed generation resource
Installation site and capacity when net optimize, and reduce due to distributed generation resource installation site, capacity is unreasonable leads to capital cost
With higher problem, less expense is invested as much as possible, obtains optimal effect, ensures to contain distributed power distribution network
Safety in production and stable operation.
2, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, is locating
When managing the distributed generation resource addressing constant volume problem of belt restraining, there is very significantly advantage, it is bright to combine common glug in mathematics
Day operator, merges into penalty term by constraint and is attached in object function, substantially reduce processing time, has good reality
Application value, can be by making adjustment appropriate to the weight coefficient in object function so that this method is applied to different regions
Distributed generation resource addressing constant volume is analyzed.
3, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, improves
The problem of Hopfield neural networks are easily trapped into local minimum point, can be adjusted flexibly parameter as needed, can be distribution
The position of formula plant-grid connection distribution and capacity provide highly beneficial reference, advantageously reduce distribution network loss.
Description of the drawings
The accompanying drawings which form a part of this application are used for providing further understanding of the present application, the application's
Illustrative embodiments and their description do not constitute the improper restriction to the application for explaining the application.
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the specific method flow chart in the embodiment of the present invention 1.
Specific implementation mode：
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless
Otherwise indicated, all technical and scientific terms that the present embodiment uses have the ordinary skill with the application technical field
The normally understood identical meanings of personnel.
It should be noted that term used herein above is merely to describe specific implementation mode, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular shape
Formula is also intended to include plural form, additionally, it should be understood that, when in the present specification use term "comprising" and/or
When " comprising ", existing characteristics, step, operation, device, component and/or combination thereof are indicated.
It should be noted that flowcharts and block diagrams in the drawings show methods according to various embodiments of the present disclosure
With the architecture, function and operation in the cards of system.It should be noted that each box in flowchart or block diagram can be with
A part for a module, program segment, or code is represented, a part for the module, program segment, or code may include one
A or multiple executable instructions for realizing the logic function of defined in each embodiment.It should also be noted that in some works
Function in alternative realization, to be marked in box can also occur according to the sequence different from being marked in attached drawing.Example
Such as, two boxes succeedingly indicated can essentially be basically executed in parallel or they sometimes can also be according to opposite
Sequence executes, this depends on involved function.It should also be noted that each box in flowchart and or block diagram,
And the combination of the box in flowchart and or block diagram, the dedicated based on hard of functions or operations as defined in executing can be used
The system of part is realized, or can make to combine using a combination of dedicated hardware and computer instructions to realize.
In the absence of conflict, the features in the embodiments and the embodiments of the present application can be combined with each other knot below
Closing attached drawing, the invention will be further described with embodiment.
Embodiment 1：
The purpose of the present embodiment 1 is to provide a kind of addressing constant volume optimization method of distributed generation resource access power distribution network.
To achieve the goals above, the present invention is using a kind of following technical solution：
As Figure 1-Figure 2,
A kind of addressing constant volume optimization method of distributed generation resource access power distribution network, including：
Step (1)：Receive the object function formulated according to distributed generation resource and power distribution network location data；The mesh
Scalar functions be the cost of unit capacity distributed generation resource, installed capacity and installation number function；
Step (2)：Distributed generation resource and distribution network data are received, the matching containing distributed generation resource based on OpenDSS is built
Power system simulation model carries out sample calculation analysis to this area using the model and obtains the voltage of each node, current data in power distribution network
Load flow calculation is carried out, determines the constraints of an inequality form；
Step (3)：Constraints is added in object function according to common Lagrangian, and is combined
Hopfield neural networks determine energy function, and the value for violating energy function described in constraints increases；
Step (4)：Energy function is calculated separately to capacity and Lagrange multiplier according to discrete time steepest descent method
Gradient determines that update iterative equation trains discrete hopfield neural network, obtains the position of distributed generation resource access power distribution network
And capacity.
The present embodiment the step of in (1), the specific steps for the function that sets objectives include：
Determine that distributed generation resource is grid-connected to power distribution network band according to the concrete condition of distributed generation resource and power distribution network location
The adverse effect come；
By adverse effect in conjunction with the present situation and policy requirements of this area, the target letter to tally with the actual situation with policy is formulated
Number；Object function is the investment cost of distributed generation resource, and the object function is the cost of unit capacity distributed generation resource, peace
The function of dressing amount and installation number：
Wherein, CiFor the investment cost of unit capacity distributed photovoltaic；PiInstallation for the distributed generation resource of node i is held
Amount.N is the number that distributed photovoltaic installs distributed electrical source node.
The cost of the unit capacity distributed generation resource is fixed Annual Percentage Rate, per kilowatt dynamic investment expense, unit thousand
The function of watt static investment expense and distributed generation resource service life.According to policy requirements, unit capacity distributed photovoltaic at
This CiMeet：
Wherein, r is fixed Annual Percentage Rate；C1 is unit kilowatt dynamic investment；C2 is unit kilowatt static investment.N is to use
The time limit.
Due to the inequality constraints that is constrained to of installed capacity, constraints is become by increasing penalty factor first
Formula constrains, then constraints is integrated into object function by common method of Lagrange multipliers, writes out Lagrangian,
And write out energy function formula.To energy function minimizing, the minimum found out corresponds to minimum investment cost.Energy letter
The independent variable of object function when number minimalization corresponds to optimum capacity and the on-position of distributed generation resource.
The specific steps of (2) include the present embodiment the step of：
Step (2-1)：Receive distributed generation resource and distribution network data, structure based on OpenDSS containing distributed generation resource
Power distribution network simulation model, the distributed generation resource and power distribution network data of reception include feeder line relevant parameter and load-related parameter,
The distributed generation resource and distribution network data of reception are recorded in OpenDSS scripts, structure contains distributed generation resource based on OpenDSS
Power distribution network simulation model.
In specific operation process, the data of local controller switching equipment and distributed generation resource are collected, in OpenDSS scripts
The middle relevant parameter of determination, for example, feeder line relevant parameter, load-related parameter etc. build the distributed electrical based on OpenDSS
Source model and the distribution network simulation containing distributed generation resource and analog platform.
Step (2-2)：Sample calculation analysis is carried out to this area using the model and obtains the voltage of each node, electricity in power distribution network
Flow data carries out Load flow calculation.
In the present embodiment, distributed electrical is modeled and contained using the distributed generation resource built based on OpenDSS built
The distribution network simulation in source carries out sample calculation analysis with analog platform to experiment area, obtains the voltage of each node, electric current etc. in network
Data carry out Load flow calculation.
Step (2-3)：According to above-mentioned data, the constraints for meeting voltage and distributed generation resource capacity is determined.At this
In embodiment, the constraints of an inequality form meets the voltage of each node, electric current in electric network swim, power distribution network
Data and distributed generation resource capacity.The constraints of inequality form includes the constraint of distributed generation resource capacity
The constraints of condition and voltage perunit value；Wherein, the range constraint of distributed generation resource capacity is (0,6000), voltage mark
The range constraint of one value is (0.95,1.05), specific constraints：
0<Pi<6000
0.95<Pu<1.05
Wherein, PiFor capacity, PuFor voltage perunit value.
The specific steps of (3) include the present embodiment the step of：
Step (3-1)：Constraints is added in object function according to common Lagrangian；
In the present embodiment, common Lagrangian L (P, λ), the glug are write out according to object function and constraints
The effect of bright day operator is to convert the not equal constraints in constraints to equality constraint, and constraints is attached to target
In function；
L (P, λ)=Fcost+λ(P+θ-6000)
Wherein, P is distributed generation resource capacity, and λ is Lagrange multiplier, and θ is the parameter more than zero.
Step (3-2)：In order to get rid of the value for not meeting constraints, glug is determined in conjunction with Hopfield neural networks
The energy function E of Lang-Hopfield neural networks：
E (P, λ)=Fcost+k/2(P+θ-6000)T(P+θ-6000)+λT(P+θ-6000)
When violating constraints, the value of energy function can be increased.
The specific steps of (4) include the present embodiment the step of：
Step (4-1)：Energy function is calculated separately to capacity and Lagrange multiplier according to discrete time steepest descent method
Gradient.
In the present embodiment, using discrete time steepest descent method, gradient of the energy function to p and λ is calculated separately.
Remember h=Ap+ θ -6000
A is the coefficient matrix of constraints, and in the present embodiment, A is unit matrix.
Step (4-2)：Determine update iterative equation：
λ (k+1)=λ (k)+v (k) h (k)
Wherein, μ (k), v (k), K are respectively the first Study rate parameter, the second Study rate parameter and third of neural network
Study rate parameter.
Step (4-3)：Training discrete hopfield neural network, obtain distributed generation resource access power distribution network position and
Capacity.
In the present embodiment, the specific steps of training discrete hopfield neural network include：
Step (4-3-1)：Receive parameter, the maximum iteration n of discrete hopfield neural networkmaxWith default error
e；Set the first Study rate parameter, the second Study rate parameter is 0.01, third Study rate parameter is set as 0.
Step (4-3-2)：Initialize distributed generation resource capacity and Lagrange multiplier；Initialize iterations；In this reality
It applies in example, it is zero initial condition to initialize distributed generation resource capacity and Lagrange multiplier.Iterations n=1 is set.
Step (4-3-3)：The dynamic behaviour that neuron is calculated according to iteration renewal equation, determines the structure of neural network
And parameter；
Step (4-3-4)：Calculate worst error Ermax, as worst error ErmaxIt is super less than default error e or iterations
Cross the maximum iteration n of settingmaxWhen, terminate training；Otherwise, update iterations return to step (4-3-3) continues iteration
It calculates.
Embodiment 2：
The purpose of the present embodiment 2 is to provide a kind of computer readable storage medium.
To achieve the goals above, the present invention is using a kind of following technical solution：
A kind of computer readable storage medium, wherein being stored with a plurality of instruction, described instruction is suitable for by terminal device equipment
Processor load and execute following processing：
Step (1)：Receive the object function formulated according to distributed generation resource and power distribution network location data；The mesh
Scalar functions be the cost of unit capacity distributed generation resource, installed capacity and installation number function；
Step (2)：Distributed generation resource and distribution network data are received, the matching containing distributed generation resource based on OpenDSS is built
Power system simulation model carries out sample calculation analysis to this area using the model and obtains the voltage of each node, current data in power distribution network
Load flow calculation is carried out, determines the constraints of an inequality form；
Step (3)：Constraints is added in object function according to common Lagrangian, and is combined
Hopfield neural networks determine energy function, and the value for violating energy function described in constraints increases；
Step (4)：Energy function is calculated separately to capacity and Lagrange multiplier according to discrete time steepest descent method
Gradient determines that update iterative equation trains discrete hopfield neural network, obtains the position of distributed generation resource access power distribution network
And capacity.
Embodiment 3：
The purpose of the present embodiment 3 is to provide a kind of construction device of patent data knowledge mapping.
To achieve the goals above, the present invention is using a kind of following technical solution：
A kind of construction device of patent data knowledge mapping, including processor and computer readable storage medium, processor
For realizing each instruction；Computer readable storage medium is suitable for by processor load simultaneously for storing a plurality of instruction, described instruction
Execute following processing：
Step (1)：Receive the object function formulated according to distributed generation resource and power distribution network location data；The mesh
Scalar functions be the cost of unit capacity distributed generation resource, installed capacity and installation number function；
Step (2)：Distributed generation resource and distribution network data are received, the matching containing distributed generation resource based on OpenDSS is built
Power system simulation model carries out sample calculation analysis to this area using the model and obtains the voltage of each node, current data in power distribution network
Load flow calculation is carried out, determines the constraints of an inequality form；
Step (3)：Constraints is added in object function according to common Lagrangian, and is combined
Hopfield neural networks determine energy function, and the value for violating energy function described in constraints increases；
Step (4)：Energy function is calculated separately to capacity and Lagrange multiplier according to discrete time steepest descent method
Gradient determines that update iterative equation trains discrete hopfield neural network, obtains the position of distributed generation resource access power distribution network
And capacity.
These computer executable instructions make the equipment execute according to each reality in the disclosure when running in a device
Apply method or process described in example.
In the present embodiment, computer program product may include computer readable storage medium, containing for holding
The computer-readable program instructions of row various aspects of the disclosure.Computer readable storage medium can be kept and deposit
Store up the tangible device of the instruction used by instruction execution equipment.Computer readable storage medium for example can be-- but it is unlimited
In-- storage device electric, magnetic storage apparatus, light storage device, electromagnetism storage device, semiconductor memory apparatus or above-mentioned
Any appropriate combination.The more specific example (non exhaustive list) of computer readable storage medium includes：Portable computing
Machine disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM or
Flash memory), static RAM (SRAM), Portable compressed disk read-only memory (CD-ROM), digital versatile disc
(DVD), memory stick, floppy disk, mechanical coding equipment, the punch card for being for example stored thereon with instruction or groove internal projection structure, with
And above-mentioned any appropriate combination.Computer readable storage medium used herein above is not interpreted instantaneous signal sheet
The electromagnetic wave of body, such as radio wave or other Free propagations, the electromagnetic wave propagated by waveguide or other transmission mediums
(for example, the light pulse for passing through fiber optic cables) or the electric signal transmitted by electric wire.
Computer-readable program instructions described herein can download to each meter from computer readable storage medium
Calculation/processing equipment, or outer computer is downloaded to by network, such as internet, LAN, wide area network and/or wireless network
Or External memory equipment.Network may include copper transmission cable, optical fiber transmission, wireless transmission, router, fire wall, exchange
Machine, gateway computer and/or Edge Server.Adapter or network interface in each calculating/processing equipment are from net
Network receives computer-readable program instructions, and forwards the computer-readable program instructions, for being stored in each calculating/processing
In computer readable storage medium in equipment.
Computer program instructions for executing present disclosure operation can be assembly instruction, instruction set architecture (ISA)
Instruction, machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data or with one or more programmings
Language arbitrarily combines the source code or object code write, and the programming language includes that the programming language-of object-oriented is all
Such as C++, and conventional procedural programming languages-such as " C " language or similar programming language.Computer-readable program
Instruction can be executed fully, partly be executed on the user computer, as an independent software on the user computer
Packet executes, part executes or on the remote computer completely in remote computer or server on the user computer for part
Upper execution.In situations involving remote computers, remote computer can include LAN by the network-of any kind
(LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as using because of spy
Service provider is netted to be connected by internet).In some embodiments, by using the shape of computer-readable program instructions
State information comes personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or programmable
Logic array (PLA), the electronic circuit can execute computer-readable program instructions, to realize each of present disclosure
Aspect.
It should be noted that although being referred to several modules or submodule of equipment in the detailed description above, it is this
Division is merely exemplary rather than enforceable.In fact, in accordance with an embodiment of the present disclosure, it is above-described two or more
The feature and function of module can embody in a module.Conversely, the feature and function of an above-described module can
It is embodied by multiple modules with being further divided into.
Beneficial effects of the present invention：
1, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, uses
The optimization method that common method of Lagrange multipliers and discrete hopfield neural network are combined is incorporated to distribution to distributed generation resource
Installation site and capacity when net optimize, and reduce due to distributed generation resource installation site, capacity is unreasonable leads to capital cost
With higher problem, less expense is invested as much as possible, obtains optimal effect, ensures to contain distributed power distribution network
Safety in production and stable operation.
2, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, is locating
When managing the distributed generation resource addressing constant volume problem of belt restraining, there is very significantly advantage, it is bright to combine common glug in mathematics
Day operator, merges into penalty term by constraint and is attached in object function, substantially reduce processing time, has good reality
Application value, can be by making adjustment appropriate to the weight coefficient in object function so that this method is applied to different regions
Distributed generation resource addressing constant volume is analyzed.
3, the addressing constant volume optimization method and device of a kind of distributed generation resource access power distribution network of the present invention, improves
The problem of Hopfield neural networks are easily trapped into local minimum point, can be adjusted flexibly parameter as needed, can be distribution
The position of formula plant-grid connection distribution and capacity provide highly beneficial reference, advantageously reduce distribution network loss.
The foregoing is merely the preferred embodiments of the application, are not intended to limit this application, for the skill of this field
For art personnel, the application can have various modifications and variations.Within the spirit and principles of this application, any made by
Modification, equivalent replacement, improvement etc., should be included within the protection domain of the application.Therefore, the present invention will not be limited
In the embodiments shown herein, and it is to fit to consistent with the principles and novel features disclosed in this article widest
Range.
Claims (10)
1. a kind of addressing constant volume optimization method of distributed generation resource access power distribution network, which is characterized in that including：
Receive the object function formulated according to distributed generation resource and power distribution network location data；The object function holds for unit
Measure the cost of distributed generation resource, the function of installed capacity and installation number；
Distributed generation resource and distribution network data are received, the power distribution network simulation model containing distributed generation resource based on OpenDSS is built,
Sample calculation analysis is carried out to this area using the model and obtains the voltage of each node in power distribution network, current data progress Load flow calculation,
Determine the constraints of an inequality form；
Constraints is added in object function according to common Lagrangian, and Hopfield neural networks is combined to determine
Energy function, the value for violating energy function described in constraints increase；
Gradient of the energy function to capacity and Lagrange multiplier is calculated separately according to discrete time steepest descent method, determines update
Iterative equation trains discrete hopfield neural network, obtains position and the capacity of distributed generation resource access power distribution network.
2. the method as described in claim 1, which is characterized in that in the method, the specific steps for the function that sets objectives include：
It determines that distributed generation resource is grid-connected according to the concrete condition of distributed generation resource and power distribution network location to bring to power distribution network
Adverse effect；
By adverse effect in conjunction with the present situation and policy requirements of this area, the object function to tally with the actual situation with policy is formulated；Institute
Object function is stated as the cost of unit capacity distributed generation resource, the function of installed capacity and installation number；The unit capacity point
The cost of cloth power supply is fixed Annual Percentage Rate, per kilowatt dynamic investment expense, per kilowatt static investment expense and distribution
The function of power supply service life.
3. the method as described in claim 1, which is characterized in that in the method, the distributed generation resource and distribution netting index of reception
According to including feeder line relevant parameter and load-related parameter, the distributed generation resource and power distribution network of reception are recorded in OpenDSS scripts
Data build the power distribution network simulation model containing distributed generation resource based on OpenDSS.
4. the method as described in claim 1, which is characterized in that in the method, the constraint item of an inequality form
Part meets the voltage of each node in electric network swim, power distribution network, current data and distributed generation resource capacity.
5. the method as described in claim 1, which is characterized in that in the method, the constraint item of an inequality form
Part includes the constraints of distributed generation resource capacity and the constraints of voltage perunit value；Wherein, distributed generation resource capacity
Range constraint is (0,6000), and the range constraint of voltage perunit value is (0.95,1.05).
6. the method as described in claim 1, which is characterized in that in the method, training discrete hopfield neural network
Specific steps include：
Receive parameter, maximum iteration and the default error of discrete hopfield neural network；
Initialize distributed generation resource capacity and Lagrange multiplier；
The dynamic behaviour that neuron is calculated according to iteration renewal equation, determines the structure and parameter of neural network；
Worst error is calculated, when worst error is less than default error or iterations are more than the maximum iteration of setting, eventually
Only train；Otherwise, update iterations return to previous step and continue to iterate to calculate.
7. method as claimed in claim 6, which is characterized in that in the method, the discrete hopfield neural network
Parameter includes the first Study rate parameter, the second Study rate parameter and third Study rate parameter, the first Study rate parameter, the second study
Rate parameter is 0.01, and third Study rate parameter is set as 0.
8. method as claimed in claim 6, which is characterized in that in the method, initialize distributed generation resource capacity and glug
Bright day multiplier is zero initial condition.
9. a kind of computer readable storage medium, wherein being stored with a plurality of instruction, which is characterized in that described instruction is suitable for by terminal
The processor of equipment loads and executes the method according to any one of claim 1-8.
10. a kind of terminal device, including processor and computer readable storage medium, processor is for realizing each instruction；It calculates
Machine readable storage medium storing program for executing is for storing a plurality of instruction, which is characterized in that described instruction is appointed for executing according in claim 1-8
Method described in one.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810421860.0A CN108665098A (en) | 2018-05-04 | 2018-05-04 | A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810421860.0A CN108665098A (en) | 2018-05-04 | 2018-05-04 | A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108665098A true CN108665098A (en) | 2018-10-16 |
Family
ID=63781884
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810421860.0A Pending CN108665098A (en) | 2018-05-04 | 2018-05-04 | A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108665098A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191497A (en) * | 2021-05-28 | 2021-07-30 | 国家电网有限公司 | Knowledge graph construction method and system for substation stepping exploration site selection |
| CN116345470A (en) * | 2023-05-31 | 2023-06-27 | 国网冀北电力有限公司 | Knowledge-graph-fused power system power flow optimization method and device |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170003702A1 (en) * | 2013-12-19 | 2017-01-05 | International Business Machines Corporation | Parallel Technique For Computing Problem Functions In Solving Optimal Power Flow |
-
2018
- 2018-05-04 CN CN201810421860.0A patent/CN108665098A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170003702A1 (en) * | 2013-12-19 | 2017-01-05 | International Business Machines Corporation | Parallel Technique For Computing Problem Functions In Solving Optimal Power Flow |
Non-Patent Citations (3)
| Title |
|---|
| 刘跃文等: ""基于增广Lagrange-Hopfield神经网络的分布式电源最优配置的研究"", 《电力科学与工程》 * |
| 张宗梅: ""利用神经网络求解组合优化问题"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 聂耸: ""含分布式电源的智能配电网分析与重构"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191497A (en) * | 2021-05-28 | 2021-07-30 | 国家电网有限公司 | Knowledge graph construction method and system for substation stepping exploration site selection |
| CN113191497B (en) * | 2021-05-28 | 2024-04-23 | 国家电网有限公司 | Knowledge graph construction method and system for substation site selection |
| CN116345470A (en) * | 2023-05-31 | 2023-06-27 | 国网冀北电力有限公司 | Knowledge-graph-fused power system power flow optimization method and device |
| CN116345470B (en) * | 2023-05-31 | 2023-08-08 | 国网冀北电力有限公司 | Knowledge-graph-fused power system power flow optimization method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gamarra et al. | Computational optimization techniques applied to microgrids planning: A review | |
| Rajan et al. | Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem | |
| CN109240818A (en) | Task discharging method based on user experience in a kind of edge calculations network | |
| CN103365727B (en) | Host load forecasting method in cloud computing environment | |
| Jasmin et al. | Reinforcement learning approaches to economic dispatch problem | |
| Fu et al. | Qos-aware and resource efficient microservice deployment in cloud-edge continuum | |
| Chen et al. | Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties | |
| CN106934497A (en) | Wisdom cell power consumption real-time predicting method and device based on deep learning | |
| CN104037761B (en) | AGC power multi-objective random optimization distribution method | |
| CN109800929A (en) | A kind of Load Forecasting, device and calculate equipment | |
| CN110383689A (en) | Uncertainty-flexibility for across time electric energy product matches engine | |
| CN108665098A (en) | A kind of addressing constant volume optimization method and device of distributed generation resource access power distribution network | |
| CN109543890A (en) | Power grid based on load estimation equilibrium takes control Optimization Scheduling, apparatus and system | |
| CN108988325A (en) | A kind of distribution network planning method counted and distributed generation resource and electric car access | |
| CN109272353A (en) | Meter and integration requirement, which respond probabilistic system dynamic probability, can flow analysis method | |
| CN111864742B (en) | Active power distribution system extension planning method and device and terminal equipment | |
| CN109947558A (en) | Host resource utilization rate calculation method and resource regulating method | |
| Konda et al. | Investigating the impact of load profile attributes on demand response exchange | |
| CN109063978A (en) | Energy Internet resources dispatching method and system, readable storage medium storing program for executing and terminal | |
| CN108876091A (en) | A kind of virtual plant realized based on software definition power grid | |
| Ghafouri et al. | Mobile-kube: Mobility-aware and energy-efficient service orchestration on kubernetes edge servers | |
| CN109742805A (en) | A kind of consumption optimization method and Related product for the power distribution network of wind-powered electricity generation containing distributing | |
| CN105426247B (en) | A kind of HLA federal members programming dispatching method | |
| CN109861295A (en) | Multiple target new energy method and device for planning | |
| CN110163255A (en) | A kind of data stream clustering method and device based on density peaks |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181016 |