CN109586384A - The optimal adjustment method and device of high renewable energy infiltration in a kind of power grid - Google Patents
The optimal adjustment method and device of high renewable energy infiltration in a kind of power grid Download PDFInfo
- Publication number
- CN109586384A CN109586384A CN201810693187.6A CN201810693187A CN109586384A CN 109586384 A CN109586384 A CN 109586384A CN 201810693187 A CN201810693187 A CN 201810693187A CN 109586384 A CN109586384 A CN 109586384A
- Authority
- CN
- China
- Prior art keywords
- energy
- storage system
- wind turbines
- power grid
- population
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000008595 infiltration Effects 0.000 title claims abstract description 23
- 238000001764 infiltration Methods 0.000 title claims abstract description 23
- 238000004146 energy storage Methods 0.000 claims abstract description 130
- 238000009826 distribution Methods 0.000 claims abstract description 13
- 230000002068 genetic effect Effects 0.000 claims description 20
- 230000007704 transition Effects 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000002950 deficient Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims 1
- 230000005611 electricity Effects 0.000 abstract description 9
- 230000008569 process Effects 0.000 abstract description 9
- 238000005457 optimization Methods 0.000 abstract description 8
- 230000006870 function Effects 0.000 description 25
- 239000000243 solution Substances 0.000 description 9
- 238000003860 storage Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000009434 installation Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 239000002803 fossil fuel Substances 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/14—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle
-
- H02J3/386—
-
- 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
- H02J3/48—Controlling the sharing of the in-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
- H02J3/38—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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- 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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/10—Flexible AC transmission systems [FACTS]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention provides a kind of optimal adjustment method and devices of renewable energy high in power grid infiltration, the present invention, which first passes through, optimizes the installed capacity of Wind turbines and energy-storage system, obtain the optimal installed capacity distribution of the two, result again based on the first suboptimization, the position of Wind turbines and energy-storage system in network system is optimized, the optimal installed capacity of final the Wind turbines group for determining electric system assembly and battery energy storage system, and in optimization process, consider the factor of multiple influence Wind turbines access power grids, influence of the large-scale wind power unit access to power distribution network safety and the influence with electricity quality can be reduced to the maximum extent.
Description
Technical field
The present invention relates in generation of electricity by new energy and distribution network electric energy sacurity dispatching and optimization field more particularly to a kind of power grid
The optimal adjustment method and device of high renewable energy infiltration.
Background technique
The energy largely to generate electricity at present is from fossil fuel, and the burning of fossil fuel is to environment band for the survival of mankind
Carry out serious pollution, the renewable energy such as wind energy, solar energy are pollution-free and reserves are huge, therefore generate electricity using renewable energy
Most attention by countries in the world.In recent years, wind generating technology is fast-developing, but wind-force Wind turbines group is intermittent
, although access power grid can improve current not reasonable energy resource structure, its fluctuation, intermittence, randomness on a large scale
With demodulate the features such as peak, the stability of power grid can be seriously affected.
Therefore it provides after a kind of solution Wind turbines are incorporated to interconnected network, the method for caused grid power fluctuation problem
The technical issues of as those skilled in the art.
Summary of the invention
The embodiment of the invention provides a kind of optimal adjustment method and devices of renewable energy high in power grid infiltration, can
Influence of the large-scale wind power unit access to power distribution network safety and matching electricity quality is reduced to the maximum extent.
According to an aspect of the present invention, a kind of optimal adjustment method of high renewable energy infiltration in power grid, packet are provided
It includes:
Determine the Wind turbines and energy-storage system of preset quantity in network system；
It take the installed capacity of Wind turbines and energy-storage system as the individual of the first population, and with the efficiency of management of network system
The first fitness function is established for target, the iterative calculation of genetic algorithm is carried out to first population, until obtaining largest tube
Manage the installed capacity of efficiency corresponding Wind turbines and energy-storage system；
Based on the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system, with Wind turbines and energy storage system
The position of system is the individual of the second population, and with feeder line energy loss, opposite tide numerical value, battery energy storage system energy mistake/owe benefit
The combined influence that dosage, battery energy storage system transition loss and node voltage deviate is that target establishes the second fitness function, right
Second population carries out the iterative calculation of genetic algorithm, until obtaining the corresponding Wind turbines of minimum combined influence and energy storage system
The position of system.
Preferably, first fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity,For
The installed capacity of jth platform Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.
Preferably, the preset quantity is 3.
Preferably, second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy
Amount loss,For opposite tide numerical value,For battery energy storage system transition loss.
Preferably, constraint condition corresponding with second fitness function include: Wind turbines generated energy restriction,
Energy-storage system energy distributes restriction, energy-storage system charge and discharge restriction, energy-storage system state-of-charge restriction, feeder line
Current limit constraint, node power Constraints of Equilibrium and state-of-charge Constraints of Equilibrium.
According to another aspect of the present invention, a kind of optimal adjustment device of high renewable energy infiltration in power grid, packet are provided
It includes:
Determining module, for determining the Wind turbines and energy-storage system of preset quantity in network system；
First computing module, for taking the installed capacity of Wind turbines and energy-storage system as the individual of the first population, and with
The efficiency of management of network system is that target establishes the first fitness function, and the iteration meter of genetic algorithm is carried out to first population
It calculates, until obtaining the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system；
Second computing module, for the installed capacity based on the maximum efficiency of management corresponding Wind turbines and energy-storage system,
It take the position of Wind turbines and energy-storage system as the individual of the second population, and with feeder line energy loss, opposite tide numerical value, battery storage
The combined influence that energy system capacity mistake/deficient utilization, battery energy storage system transition loss and node voltage deviates is target foundation
Second fitness function, the iterative calculation of genetic algorithm is carried out to second population, until it is corresponding to obtain minimum combined influence
Wind turbines and energy-storage system position.
Preferably, first fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity,For
The installed capacity of jth platform Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.
Preferably, the preset quantity is 3.
Preferably, second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy
Amount loss,For opposite tide numerical value,For battery energy storage system transition loss.
Preferably, constraint condition corresponding with second fitness function include: Wind turbines generated energy restriction,
Energy-storage system energy distributes restriction, energy-storage system charge and discharge restriction, energy-storage system state-of-charge restriction, feeder line
Current limit constraint, node power Constraints of Equilibrium and state-of-charge Constraints of Equilibrium.
As can be seen from the above technical solutions, the embodiment of the present invention has the advantage that
The present invention provides a kind of optimal adjustment method and device of renewable energy high in power grid infiltration, this method packets
It includes: determining the Wind turbines and energy-storage system of preset quantity in network system；With the installed capacity of Wind turbines and energy-storage system
For the individual of the first population, and establish the first fitness function by target of the efficiency of management of network system, to the first population into
The iterative calculation of row genetic algorithm, until obtaining the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system；
Based on the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system, with the position of Wind turbines and energy-storage system
For the individual of the second population, and with feeder line energy loss, opposite tide numerical value, battery energy storage system energy mistake/owe utilization, battery
The combined influence that energy-storage system transition loss and node voltage deviate is that target establishes the second fitness function, to the second population into
The iterative calculation of row genetic algorithm, until obtaining the position of minimum combined influence corresponding Wind turbines and energy-storage system.This hair
Bright first pass through optimizes the installed capacity of Wind turbines and energy-storage system, obtains the optimal installed capacity distribution of the two, then
It is based on the first suboptimization as a result, optimized to Wind turbines and energy-storage system in the position of power distribution network, finally determine electric power
The Wind turbines group of system assembly and the optimal installed capacity of battery energy storage system, and in optimization process, it is contemplated that Duo Geying
Ring Wind turbines access power grid factor, can reduce to the maximum extent large-scale wind power unit access to power distribution network safety with match
The influence of electricity quality.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other attached drawings according to these attached drawings.
Fig. 1 is one of the optimal adjustment method of high renewable energy infiltration in a kind of power grid provided in an embodiment of the present invention
The flow diagram of embodiment.
Fig. 2 is the schematic diagram of the first suboptimization.
Fig. 3 is the schematic diagram of the second suboptimization.
Fig. 4 is a kind of structural schematic diagram of the optimal adjustment device of high renewable energy infiltration provided by the invention.
Specific embodiment
The embodiment of the invention provides a kind of optimal adjustment method and devices of renewable energy high in power grid infiltration, can
Influence of the large-scale wind power unit access to power distribution network safety and matching electricity quality is reduced to the maximum extent.
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
Those of ordinary skill's all other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, one of the optimal adjustment method that high renewable energy is permeated in a kind of power grid provided by the invention
Embodiment, comprising:
101, the Wind turbines and energy-storage system of preset quantity in network system are determined；
It should be noted that Wind turbines and energy-storage system are installed on multiple nodes by having in network system, in order to
Realize optimal adjusting, the present invention optimizes adjusting by randomly selecting a certain number of Wind turbines and energy-storage system, should
Quantity can usually be chosen to three Wind turbines and three energy-storage systems by presetting, in the present embodiment, determine
After Wind turbines and energy-storage system, its installed capacity can be optimized.
It 102, is the individual of the first population with the installed capacity of Wind turbines and energy-storage system, and with the management of network system
Efficiency is that target establishes the first fitness function, and the iterative calculation of genetic algorithm is carried out to the first population, until obtaining largest tube
Manage the installed capacity of efficiency corresponding Wind turbines and energy-storage system；
In the present embodiment, in order to finally determine three Wind turbines and three energy-storage systems in network system
Optimal installed capacity, the present invention calculates its separately optimizing, that is, the installation for first passing through optimization Wind turbines and energy-storage system is held
Amount, the method for optimizing calculating is genetic algorithm, detailed process are as follows: with the installed capacity of Wind turbines and energy-storage system for first
The individual of population, and the first fitness function is established by target of the efficiency of management of network system, gene is carried out to the first population
The iterative calculation of algorithm, until obtaining the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system.
Specifically, the first fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity,For
The installed capacity of jth platform Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.It can manage
Solution, by taking n=3 as an example, j represents three Wind turbines in individual, any one in three energy-storage systems herein, and matches
Node location in network system is unrelated.
The detailed process of step 102 needs first to be arranged Wind turbines, energy storage system as shown in Fig. 2, before optimizing calculating
The state-of-charge of system is 50%, and sets 0 for the management threshold efficiency of network system, initializes the individual value of the first population
Afterwards, then the optimization that can carry out genetic algorithm calculates, and the process of genetic algorithm is as being the mistake in a-quadrant in dotted line frame in Fig. 3
Journey, it should be noted that genetic algorithm is the common knowledge of those skilled in the art, does not do specific introduction herein.Optimization calculates
After, the maximum individual of the efficiency of management can be selected in all the number of iterations, at this point, the individual is optimal Wind turbines
With the installed capacity of energy-storage system.
103, the installed capacity based on the maximum efficiency of management corresponding Wind turbines and energy-storage system, with Wind turbines and storage
Can system position be the second population individual, and with feeder line energy loss, opposite tide numerical value, battery energy storage system energy mistake/
Owing the combined influence that utilization, battery energy storage system transition loss and node voltage deviate is that target establishes the second fitness letter
Number, the iterative calculation of genetic algorithm is carried out to the second population, until obtaining the corresponding Wind turbines of minimum combined influence and energy storage
The position of system.
After the optimal installed capacity for determining Wind turbines, energy-storage system, then need to determine its node in network system
Position, based on the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system, with Wind turbines and energy-storage system
Position be the second population individual, and with feeder line energy loss, opposite tide numerical value, battery energy storage system energy mistake/owe utilize
The combined influence that amount, battery energy storage system transition loss and node voltage deviate is that target establishes the second fitness function, to the
Two populations carry out the iterative calculation of genetic algorithm, until obtaining the position of minimum combined influence corresponding Wind turbines and energy-storage system
It sets.
Specifically, the second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy
Amount loss,For opposite tide numerical value,For battery energy storage system transition loss.
Wherein, the penalty that node voltage deviates are as follows:
Battery energy storage system energy mistake/deficient utilization penalty are as follows:
Feeder line energy loss:
In formula,With
Opposite tide numerical value are as follows:
Battery energy storage system transition loss are as follows:
Wherein, i-th of node in network system, when h hours,It is node voltage size,For generator rotor angle,For active injection,For idle injection,For the state-of-charge of battery energy storage system,For battery energy storage system
Charge and discharge scheduling.
rijIt is the resistance between node i and node j, η is battery energy storage system efficiency for charge-discharge, and φ is -year conversion system
Number, N are system node sum, ISpc.For power grid head tide restriction value,VFor the minimum limit value of node voltage,For node voltage highest limit
Value,SOCFor energy-storage system state-of-charge minimum limit value,For energy-storage system state-of-charge threshold limit value,PB For energy-storage system
Minimum discharge and recharge limit value,For energy-storage system maximum discharge and recharge limit value,For the installed capacity of energy-storage system.
Specifically, constraint condition corresponding with the second fitness function includes:
Wind turbines generated energy restriction:
Energy-storage system energy distributes restriction:
Energy-storage system charge and discharge restriction:
Energy-storage system state-of-charge restriction:
Feeder current restriction:
Node is active/reactive power equilibrium constraint:
State-of-charge Constraints of Equilibrium:
Wherein,WBi、WithThe active power of the Wind turbines of respectively i-th node, energy-storage system
The ceiling capacity of energy distribution, the maximum power limitation of Wind turbines and energy-storage system limits.θijFor impedance angle, YijFor Y bus
Matrix element,For h hours current values,For maximum current limit value.
Wind turbine model:
Whereinvcutin、vcutout、vr、PR, iRespectively the i-th node, h hours wind speed, wind cutting speed, wind are cut out
Speed, the installed capacity of Wind turbines rated wind speed and Wind turbines.
The optimization calculating process of step 103 as shown in figure 3, after generating the second initial population the (dress in individual at this time
Machine capacity determines that determination to be optimized is the node location of Wind turbines, energy-storage system via in step 102), then
Into in the Optimized Iterative progress of genetic algorithm, in all iteration, each individual is calculated by the second fitness function
The combined influence f arrived, it is final to determine the smallest individual of combined influence as Wind turbines, energy-storage system in network system most
Excellent position.
The present invention, which first passes through, optimizes the installed capacity of Wind turbines and energy-storage system, obtains the optimal installation of the two
Capacity distribution, then it is based on the first suboptimization as a result, excellent to the position progress of Wind turbines and energy-storage system in network system
Change, the optimal installed capacity of final the Wind turbines group for determining electric system assembly and battery energy storage system, and in optimization process
In, it is contemplated that multiple factors for influencing Wind turbines access power grid can reduce the access of large-scale wind power unit to the maximum extent
Influence to power distribution network safety and matching electricity quality.
Be above the optimal adjustment method of renewable energy infiltration high in a kind of power grid provided by the invention is carried out it is detailed
It describes in detail bright, the structure of the optimal adjustment device of renewable energy infiltration high in a kind of power grid provided by the invention will be carried out below
Illustrate, referring to Fig. 4, an implementation of the optimal adjustment device that high renewable energy is permeated in a kind of power grid provided by the invention
Example, comprising:
Determining module 401, for determining the Wind turbines and energy-storage system of preset quantity in network system；
First computing module 402, for taking the installed capacity of Wind turbines and energy-storage system as the individual of the first population, and
The first fitness function is established by target of the efficiency of management of network system, the iteration meter of genetic algorithm is carried out to the first population
It calculates, until obtaining the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system；
Second computing module 403 holds for the installation based on the corresponding Wind turbines of the maximum efficiency of management and energy-storage system
Amount take the position of Wind turbines and energy-storage system as the individual of the second population, and with feeder line energy loss, opposite tide numerical value, electricity
Pond energy-storage system energy mistake/owe the combined influence that utilization, battery energy storage system transition loss and node voltage deviate as target
The second fitness function is established, the iterative calculation of genetic algorithm is carried out to the second population, until it is corresponding to obtain minimum combined influence
Wind turbines and energy-storage system position.
Optionally, the first fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity,For
The installed capacity of jth platform Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.
Optionally, preset quantity 3.
Optionally, the second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy
Amount loss,For opposite tide numerical value,For battery energy storage system transition loss.
Optionally, constraint condition corresponding with the second fitness function includes: Wind turbines generated energy restriction, energy storage
System capacity distributes restriction, energy-storage system charge and discharge restriction, energy-storage system state-of-charge restriction, feeder current
Restriction, node power Constraints of Equilibrium and state-of-charge Constraints of Equilibrium.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the present invention
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although referring to before
Stating embodiment, invention is explained in detail, those skilled in the art should understand that: it still can be to preceding
Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
It modifies or replaces, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of optimal adjustment method of high renewable energy infiltration in power grid characterized by comprising
Determine the Wind turbines and energy-storage system of preset quantity in network system；
It take the installed capacity of Wind turbines and energy-storage system as the individual of the first population, and using the efficiency of management of network system as mesh
Mark establishes the first fitness function, and the iterative calculation of genetic algorithm is carried out to first population, until obtaining maximum management effect
The installed capacity of rate corresponding Wind turbines and energy-storage system；
Based on the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system, with Wind turbines and energy-storage system
Position be the second population individual, and with feeder line energy loss, opposite tide numerical value, battery energy storage system energy mistake/owe utilization,
The combined influence that battery energy storage system transition loss and node voltage deviate is that target establishes the second fitness function, to described the
Two populations carry out the iterative calculation of genetic algorithm, until obtaining the position of minimum combined influence corresponding Wind turbines and energy-storage system
It sets.
2. the optimal adjustment method of high renewable energy infiltration in power grid according to claim 1, which is characterized in that described
First fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity, Pr jFor jth platform
The installed capacity of Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.
3. the optimal adjustment method of high renewable energy infiltration in power grid according to claim 2, which is characterized in that described
Preset quantity is 3.
4. the optimal adjustment method of high renewable energy infiltration in power grid according to claim 1, which is characterized in that described
Second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy damage
Consumption,For opposite tide numerical value,For battery energy storage system transition loss.
5. the optimal adjustment method of high renewable energy infiltration in power grid according to claim 3, which is characterized in that with institute
Stating the corresponding constraint condition of the second fitness function includes: Wind turbines generated energy restriction, energy-storage system energy distribution limit
Restrict beam, energy-storage system charge and discharge restriction, energy-storage system state-of-charge restriction, feeder current restriction, node
Power-balance constraint and state-of-charge Constraints of Equilibrium.
6. the optimal adjustment device of high renewable energy infiltration in a kind of power grid characterized by comprising
Determining module, for determining the Wind turbines and energy-storage system of preset quantity in network system；
First computing module, for taking the installed capacity of Wind turbines and energy-storage system as the individual of the first population, and with power grid
The efficiency of management of system is that target establishes the first fitness function, and the iterative calculation of genetic algorithm is carried out to first population,
Until obtaining the installed capacity of the maximum efficiency of management corresponding Wind turbines and energy-storage system；
Second computing module, for the installed capacity based on the maximum efficiency of management corresponding Wind turbines and energy-storage system, with wind
The position of motor group and energy-storage system is the individual of the second population, and with feeder line energy loss, opposite tide numerical value, battery energy storage system
The combined influence that system energy mistake/deficient utilization, battery energy storage system transition loss and node voltage deviates is that target establishes second
Fitness function, the iterative calculation of genetic algorithm is carried out to second population, until obtaining the corresponding wind of minimum combined influence
The position of motor group and energy-storage system.
7. the optimal adjustment device of high renewable energy infiltration in power grid according to claim 6, which is characterized in that described
First fitness function are as follows:
In formula,For the generated output of the jth platform Wind turbines in the individual of the first population, n is preset quantity, Pr jFor jth platform
The installed capacity of Wind turbines,State-of-charge variable quantity for jth platform energy-storage system at h hours.
8. the optimal adjustment device of high renewable energy infiltration in power grid according to claim 7, which is characterized in that described
Preset quantity is 3.
9. the optimal adjustment device of high renewable energy infiltration in power grid according to claim 6, which is characterized in that described
Second fitness function are as follows:
In formula, Δ VhFor node voltage deviation, Δ WBFor battery energy storage system energy mistake/owe utilization,For feeder line energy damage
Consumption,For opposite tide numerical value,For battery energy storage system transition loss.
10. the optimal adjustment device of high renewable energy infiltration in power grid according to claim 9, which is characterized in that with
The corresponding constraint condition of second fitness function includes: Wind turbines generated energy restriction, the distribution of energy-storage system energy
Restriction, energy-storage system charge and discharge restriction, energy-storage system state-of-charge restriction, feeder current restriction, section
Point power-balance constraint and state-of-charge Constraints of Equilibrium.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810693187.6A CN109586384B (en) | 2018-06-29 | 2018-06-29 | Optimal adjustment method and device for high renewable energy permeation in power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810693187.6A CN109586384B (en) | 2018-06-29 | 2018-06-29 | Optimal adjustment method and device for high renewable energy permeation in power grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109586384A true CN109586384A (en) | 2019-04-05 |
| CN109586384B CN109586384B (en) | 2022-03-04 |
Family
ID=65919629
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810693187.6A Active CN109586384B (en) | 2018-06-29 | 2018-06-29 | Optimal adjustment method and device for high renewable energy permeation in power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109586384B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102801156A (en) * | 2011-05-25 | 2012-11-28 | 株式会社日立制作所 | System state arithmetic device, method and system, system control device and method, and power distribution trend simulation device and method |
| KR20130066320A (en) * | 2011-12-12 | 2013-06-20 | 주식회사 포스코 | Floating offshore power generation plant |
| CN104836247A (en) * | 2015-05-18 | 2015-08-12 | 国家电网公司 | Optical storage micro grid system for realizing energy storage capacity dynamic optimization |
| CN106786700A (en) * | 2017-01-17 | 2017-05-31 | 无锡协鑫分布式能源开发有限公司 | User's sidelight stores up integral system off-grid operation capacity configuration software algorithm |
| CN108054751A (en) * | 2017-12-11 | 2018-05-18 | 国网江苏省电力有限公司经济技术研究院 | A kind of method of the optimal access capacity of regenerative resource in definite network system |
-
2018
- 2018-06-29 CN CN201810693187.6A patent/CN109586384B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102801156A (en) * | 2011-05-25 | 2012-11-28 | 株式会社日立制作所 | System state arithmetic device, method and system, system control device and method, and power distribution trend simulation device and method |
| KR20130066320A (en) * | 2011-12-12 | 2013-06-20 | 주식회사 포스코 | Floating offshore power generation plant |
| CN104836247A (en) * | 2015-05-18 | 2015-08-12 | 国家电网公司 | Optical storage micro grid system for realizing energy storage capacity dynamic optimization |
| CN106786700A (en) * | 2017-01-17 | 2017-05-31 | 无锡协鑫分布式能源开发有限公司 | User's sidelight stores up integral system off-grid operation capacity configuration software algorithm |
| CN108054751A (en) * | 2017-12-11 | 2018-05-18 | 国网江苏省电力有限公司经济技术研究院 | A kind of method of the optimal access capacity of regenerative resource in definite network system |
Non-Patent Citations (3)
| Title |
|---|
| JUNLEI LIU等: "Rapid evaluation of short-term voltage stability considering integration of renewable power generations", 《 2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2)》 * |
| 吴晨曦: "风光发电及电动汽车充放电随机性对配电系统的影响研究", 《万方数据》 * |
| 王彦虹等: "含大规模风光电源的配电网储能电池选址定容优化方案", 《电力科学与技术学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109586384B (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108599206B (en) | Power distribution network hybrid energy storage configuration method under high-proportion uncertain power supply scene | |
| CN106130004B (en) | A kind of grid entry point new energy comprehensively considering stability characteristic (quality) receives the appraisal procedure of ability | |
| CN106684905B (en) | A kind of wind power plant Dynamic Equivalence considering wind-powered electricity generation uncertainty in traffic | |
| CN107196333B (en) | distributed photovoltaic cluster division method based on modularization index | |
| CN106159944B (en) | Multi-stage transmission expansion planning method under low-carbon environment based on bilevel programming model | |
| CN105322535A (en) | Two-stage optimal power flow calculation method for power supply containing unified power flow controller | |
| CN109560574A (en) | A kind of intelligent distribution network space truss project method considering uncertain factor | |
| CN108281959A (en) | A kind of bulk transmission grid optimization method of high proportion type power system of renewable energy | |
| CN112671035A (en) | Virtual power plant energy storage capacity configuration method based on wind power prediction | |
| CN106385055B (en) | A kind of power distribution network Security Checking method containing distributed generation resource | |
| Wu et al. | Optimized capacity configuration of an integrated power system of wind, photovoltaic and energy storage device based on improved particle swarm optimizer | |
| CN116402210A (en) | Multi-objective optimization method, system, equipment and medium for comprehensive energy system | |
| CN106712060A (en) | Multi-agent-based hundred-megawatt level battery energy storage system control method and system | |
| Yammani et al. | Optimal placement and sizing of DER's with load models using BAT algorithm | |
| CN108233412A (en) | A kind of low-carbon builds system optimized operation method of providing multiple forms of energy to complement each other | |
| CN109873419B (en) | Water-light storage system operation optimization method considering similarity and economic benefits | |
| CN116865271A (en) | Digital twin-drive-based micro-grid multi-agent coordination optimization control strategy | |
| CN109586384A (en) | The optimal adjustment method and device of high renewable energy infiltration in a kind of power grid | |
| CN109713720A (en) | A kind of balance of electric power and ener method of new-energy grid-connected operation | |
| CN115021241A (en) | Self-recovery method of power distribution network based on distributed resources | |
| CN114977247A (en) | Particle swarm algorithm applied to energy routing management and used for expanding time axis | |
| CN108022055A (en) | A kind of micro-capacitance sensor economic load dispatching method based on particle group model | |
| Li et al. | Dynamic penetration allocation for distributed generators based on PSO initialized with K-means cluster | |
| CN114626180A (en) | Power distribution network centralized energy storage optimal configuration method and device | |
| CN113177860A (en) | Improved ant lion algorithm-based micro-grid optimization scheduling method with electric automobile participation |
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 |