CN109510234A - A kind of the hybrid energy-storing capacity configuration optimizing method and device of micro-capacitance sensor energy-accumulating power station - Google Patents
A kind of the hybrid energy-storing capacity configuration optimizing method and device of micro-capacitance sensor energy-accumulating power station Download PDFInfo
- Publication number
- CN109510234A CN109510234A CN201811237650.2A CN201811237650A CN109510234A CN 109510234 A CN109510234 A CN 109510234A CN 201811237650 A CN201811237650 A CN 201811237650A CN 109510234 A CN109510234 A CN 109510234A
- Authority
- CN
- China
- Prior art keywords
- ban
- energy
- supercapacitor
- battery
- power
- 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
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
-
- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Abstract
The present invention relates to a kind of hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station and devices, belong to microgrid energy-storage system configuration optimization technical field, determine prepackage type micro-capacitance sensor energy-accumulating power station structure and power supply reliability index, it determines using hybrid energy-storing life period of an equipment cost minimization as objective function and constraint condition, using the chaos cloud particle swarm optimization solving optimization allocation models of fusion AEA algorithm.The present invention overcomes the dependences to initial value, improve algorithm optimizing performance, search out optimal solution, and the optimal solution battery shown and the capacity and quantity configuration of supercapacitor are reasonable, on the basis of meeting system safe and reliable stable operation, keep the Life cycle cost of hybrid accumulator minimum, greatly improves the economy of hybrid accumulator in micro-capacitance sensor.
Description
Technical field
The invention belongs to microgrid energy-storage system configuration optimization technical fields, and in particular to a kind of micro-capacitance sensor energy-accumulating power station mixes
Close stored energy capacitance Optimal Configuration Method and device.
Background technique
Increasingly serious with energy and environment problem, energy-saving and emission-reduction problem has obtained extensive concern, wind energy, solar energy etc.
Specific gravity of the clean energy resource in China's non-renewable energy steps up, and corresponding wind-power electricity generation, photovoltaic power generation also obtain in recent years
It greatly develops.Wind-force and photovoltaic power generation depend on the meteorological condition of variation, the output power of power generation there are fluctuation, with
The problems such as machine and intermittence, in order to solve this problem, micro-capacitance sensor comes into being, and micro-capacitance sensor is a small-sized controllable system,
Distributed generation resource, energy-storage units, load and some controllables etc. are organically combined together by it, convey electricity to user
Can, the permeability of distributed generation resource can be greatly improved.Energy-storage system is as the essential key component of micro-capacitance sensor, in micro-capacitance sensor
When being incorporated into the power networks have stabilize power swing caused by distributed generation resource in micro-capacitance sensor, smoothing system load fluctuation, storage it is extra
Electric energy, as functions such as system backup power supplys, for micro-capacitance sensor in isolated power grid, energy-storage system, which has, for distributed generation resource provides electricity
Potential source support balances power distribution in micro-capacitance sensor, as functions such as the backup power supplies of isolated network.
Currently, generally selecting battery, battery for stabilizing the energy storage device of distributed generation resource power generation fluctuation in engineering
As energy type energy storage device, have the characteristics that energy density is big, storage capacity is strong, but that there is also charging-discharging cycles simultaneously is long,
The disadvantages of service life is short, at high cost, thus limit the application of energy storage technology.Supercapacitor is as being at present most widely used
Power-type energy storage device has the characteristics that power density is big, charging-discharging cycle is short, energy storage efficiency is high, has extended cycle life, can be effective
Ground stabilize distributed generation resource power generation in short time, power swing by a small margin.Usually by energy type energy storage device and power-type
Energy storage device is used in conjunction with, and forms the hybrid accumulator of mutual supplement with each other's advantages, can optimize accumulator charging and discharging state, bright
The aobvious battery that reduces is charged and discharged number, prolongs the service life, reduces system operation cost.
The hybrid accumulator of large capacity is generally expensive, and the cost of hybrid accumulator often accounts for microgrid totle drilling cost
20%-30%, in order to further increase the economy of hybrid accumulator in micro-capacitance sensor, lot of domestic and international scholar has carried out mixing
The numerous studies of capacity of energy storing device configuration, most of first acquisition expenses for only considering hybrid accumulator, and do not examine
Consider purchase, operation, maintenance and processing etc. the required cost in hybrid accumulator use process, i.e., Life cycle at
This.Therefore, using the Life cycle cost of hybrid accumulator as optimization aim, pass through optimization algorithm, reasonable disposition battery
With the capacity and quantity of supercapacitor, become one of the research direction of micro-capacitance sensor mixed energy storage system, especially with full life
The minimum target of life cycle costing establishes prepackage type micro-capacitance sensor energy-accumulating power station mixed energy storage system capacity Optimal Allocation Model.
In optimization algorithm, particle swarm optimization algorithm itself has the characteristics that parallel search, strong robustness, can be with relatively probably
Rate searches objective function globally optimal solution, and more much higher than the computational efficiency of traditional random algorithm method, such as application is public
The Chinese patent application that cloth number is CN106251005A proposes " a kind of excellent based on the hybrid energy-storing capacity for improving particle swarm algorithm
Change configuration method ", but the disadvantage of this method is that: 1. only consider that the miss rate of the energy of load does not consider distributed electrical
Source generated energy loss late objective cannot reflect comprehensively that energy storage system capacity configures reasonability；2. accumulator cell charging and discharging does not have
It carries out tactic and protects prolonging service life of battery；3. particle initialization is random, optimal solution cannot or be found more slowly；4. inertia is weighed
Weight is linearly updated with Studying factors, can not be adjusted according to state where particle group phylogenetic scale, 5. can not be according to particle
Quality state takes specific aim evolution strategy, limits a possibility that finding optimal solution, 6. local optimal searching is indifferent, precision
It is relatively low；Population is easy to lose diversity in search process, falls into the drawbacks such as local optimum；The final storage because of configuration
The capacity and quantity of battery and supercapacitor are unreasonable, and the Life cycle cost of the hybrid accumulator caused is still
It is relatively high, it is unfavorable for the economy of hybrid accumulator in micro-capacitance sensor.
Summary of the invention
The object of the present invention is to provide a kind of hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station and device,
For solving the prior art because the battery of configuration and the capacity and quantity of supercapacitor are unreasonable, and lead to hybrid energy-storing
The problem of Life cycle cost of device is still relatively high, is unfavorable for the economy of hybrid accumulator in micro-capacitance sensor.
In order to solve the above technical problems, the present invention proposes that a kind of hybrid energy-storing capacity of micro-capacitance sensor energy-accumulating power station is distributed rationally
Method, including following settlement steps to deal:
1) Optimal Allocation Model of micro-capacitance sensor energy-accumulating power station is obtained, the structure of micro-capacitance sensor energy-accumulating power station includes: wind-power electricity generation
Unit and/or photovoltaic array, the hybrid accumulator being made of battery group and supercapacitor group；The Optimal Allocation Model
Including with the objective function of the Life cycle cost minimization of hybrid accumulator, and can according to the power supply of micro-capacitance sensor energy-accumulating power station
The constraint condition determined by property index；
2) following modified particle swarm optiziation solving optimization allocation models is used:
Chaos intialization 2-1) is carried out to particle, according to the fitness of primary after chaos intialization by primary group
At least it is divided into two groups；
The optimization algorithm that optimizing is carried out to respective sets primary 2-2) is selected according to the fitness of respective sets primary,
Determine the optimal solution of the Optimal Allocation Model.
In order to solve the above technical problems, the present invention also proposes that a kind of hybrid energy-storing capacity optimization of micro-capacitance sensor energy-accumulating power station is matched
Device, including processor are set, processor is for executing above-mentioned hybrid energy-storing capacity configuration optimizing method when computer program.
Above-mentioned hybrid energy-storing capacity configuration optimizing method and device have the beneficial effect that
The present invention carries out chaos intialization to particle first, secondly the update to particle after obtaining Optimal Allocation Model
Iteration introduces grouping strategy, different evolution strategies is taken respectively according to the fitness of corresponding group primary, to respective sets
Primary carries out optimizing, and the final optimal solution for determining Optimal Allocation Model improves algorithm optimizing performance, searches out optimal
Solution, and the optimal solution battery shown and the capacity and quantity configuration of supercapacitor are reasonable, make the full life of hybrid accumulator
It is minimum to order life cycle costing, greatly improves the economy of hybrid accumulator in micro-capacitance sensor.
Step 2-1) specifically: chaos intialization is carried out to particle, according to the fitness of primary after chaos intialization
By primary group be divided into it is excellent, in, it is three groups poor, the fitness of excellent group of primary is less than the first setting value, middle group initial
The fitness of particle is between the first setting value and the second setting value, the fitness of the primary of difference group is greater than the second setting
Value, the first setting value is less than the second setting value；
Step 2-2) specifically: particle optimizing is carried out using AEA algorithm for excellent group of primary, at the beginning of middle group
Beginning particle carries out particle optimizing using cloud generator, carries out particle using chaotic optimization algorithm for the primary of difference group and seeks
It is excellent；The optimal solution of the Optimal Allocation Model is determined according to AEA algorithm, cloud generator and chaotic optimization algorithm.
The present invention carries out chaos intialization to particle first, secondly the update to particle after obtaining Optimal Allocation Model
Iteration introduce grouping strategy, i.e., by primary group divide it is excellent, in, it is three groups poor, different evolution strategies is taken respectively, to excellent group
Primary takes AEA algorithm, is only excavated with small range, to locally find more preferably particle；The primary of centering group
Take cloud generator carry out optimizing, using cloud generator convenience, randomness and stability the characteristics of, improve algorithmic statement speed
Degree；The primary of difference group takes chaotic optimization algorithm, to continue optimizing on a large scale, traverses entire solution space；Then, excellent,
In, poor three groups of particles by fixed algebra iterative cycles, terminate until meeting condition of convergence algorithm, obtain Optimal Allocation Model
Optimal solution, the present invention overcomes the dependence to initial value, (solution of Nonlinear System of Equations is in weather forecast, petroleum geology exploration, meter
Calculating in the engineering fields such as mechanics, control has stronger application background.But solving to it is still a problem, Newton iteration method and
Its improved form is current widely used method, and such convergence is largely dependent upon initial value, uncomfortable
Initial value will lead to algorithm failure；And the selection not a duck soup of initial value, for this reason, it is necessary to explore the algorithm of high efficient and reliable；The present invention
Initial value be exactly single battery number m and supercapacitor number of monomers n composition particle (m, n), can be made by chaos intialization
Several primaries group is uniformly throughout entire solution space, and finding optimal solution than random initializtion, faster precision is higher), it improves
Algorithm optimizing performance searches out optimal solution, and the optimal solution battery shown and the capacity and quantity configuration of supercapacitor close
Reason, keeps the Life cycle cost of hybrid accumulator minimum, greatly improves the economy of hybrid accumulator in micro-capacitance sensor.
For the reasonable grouping for realizing primary group, the calculating formula of first setting value and the second setting value is as follows:
In formula, fG1For the first setting value, fG2For the second setting value, two grouping point fg1、fg2It is calculated by the following formula
It arrives:
fg1=1.618favg
frange=fmax-fmin
fg2=fmin+frange/1.618
In formula, fiFor primary group, i=1,2 ..., n, favg、fmin、fmaxRespectively primary group fiIt is average suitable
Response, minimum fitness, maximum adaptation degree, frangeFor intermediate variable.
For the specific power supply reliability index of determination, the power supply reliability index includes distributed generation resource rate of energy loss
fLPPP, load and energy short of electricity rate fLPSP, calculating formula is as follows:
fLPPP=Elpp/Eg
fLPSP=Elps/El
In formula, ElppFor the off-energy of distributed generation resource, EgFor the generated energy of distributed generation resource, ElpsFor load short of electricity
Amount, ElFor workload demand electricity.
The objective function of above-mentioned Optimal Allocation Model are as follows:
LCCmin=Civ+Coc+Cmc+Cdc=(1+fob+fmb+fdb)mPb+(1+foc+fmc+fdc)nPc
Wherein, LCC is the Life cycle cost of hybrid accumulator；CivFor the buying expenses of hybrid accumulator；Coc
For the operating cost of hybrid accumulator；CmcFor the maintenance cost of hybrid accumulator；CdcFor the handling of hybrid accumulator
With, including scraping expense and residual value expense；M, n is the quantity of battery, supercapacitor respectively；Pb、PcBe respectively battery,
The unit price of supercapacitor；fob、focIt is the operating factor of battery, supercapacitor respectively；fmb、fmcIt is battery respectively, super
The maintenance factor of grade capacitor；fdb、fdcIt is the processing coefficient of battery, supercapacitor respectively.
Based on above-mentioned power supply reliability index, the following constraint condition of Optimal Allocation Model is determined, comprising:
fLPPP(m,n)≤fLPPPmax
fLPSP(m,n)≤fLPSPmax
|(Pba(k,m)+Puc(k,n))·ηc|=| Pl(k)-[Ppv(k)+Pw(k)]·ηc|
Wherein, m, n respectively indicate single battery number and supercapacitor number of monomers, and k is moment, fLPPPmax、fLPSPmaxPoint
Not Biao Shi distributed generation resource rate of energy loss, load and energy short of electricity rate maximum permissible value；Ppv(k)、Pw(k)、Pl(k) it is respectively
The photovoltaic generation power at k moment, wind-power electricity generation power, workload demand power, ηcIt is the power conversion efficiency of inverter, PbaTo store
The power of battery pack, PucFor the power of supercapacitor group.
Increase following constraint condition to keep the solution of objective function more accurate, in Optimal Allocation Model:
The energy storage capacity of energy-storage system and the constraint condition of power:
Pba(k, m)=0 or Pba(k, m)=Pban(m)
Puc(k,n)≤Puc max(n)
Eba min(m)≤Eba(k,m)≤Eban(m)
Euc min(n)≤Euc(k,n)≤Euc max(n)
Wherein, k is moment, PbaFor the power of battery group, PbanFor the rated power of battery group, PucFor super capacitor
The power of device group, Puc maxFor the maximum power of supercapacitor group, Eba min、Eba、EbanThe minimum of respectively battery group is surplus
Current energy storage capacity, specified energy storage capacity after remaining energy storage capacity, charge/discharge；Eucmin、Euc、Euc maxRespectively supercapacitor group
Current energy storage capacity and maximum energy storage capacity after minimum energy storage capacity, charge/discharge；
The constraint condition of the output power of battery group:
Pban(m) < μ Pl
Wherein, μ is specific gravity, PlFor total load rated power.
For the off-energy for determining distributed generation resource, determined by following steps:
(1) work as Pleft·ηc≥Pban+Puc max, battery and the equal quota of supercapacitor charge, and Eba> EbanWhen, institute
State the calculating formula of off-energy are as follows:
Elpp=[Pleft·ηc-(Pban+Puc max)]t+[Pban(1-ηbac)+Puc max(1-ηucc)]t+(Eba-Eban)
(2) work as Pleft·ηc≥Pban+Puc max, battery and the equal quota of supercapacitor charge, and Eba< EbanWhen, institute
State the calculating formula of off-energy are as follows:
Elpp=[Pleft·ηc-(Pban+Puc max)]·t+[Pban(1-ηbac)+Puc max(1-ηucc)]t
(3) work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥Pban, battery preferentially charges, fills after supercapacitor
Electricity, and Eba> Eban、Pleft·ηc≥Puc maxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Puc max(t-t1)](1-ηucc)+(Pleft·ηc-Puc max)(t-t1)
(4) work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥Pban, battery preferentially charges, fills after supercapacitor
Electricity, and Eba> Eban、Pleft·ηc< Puc maxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Pleft·ηc(t-t1)](1-ηucc)
(5) work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥Pban, battery preferentially charges, fills after supercapacitor
Electricity, and Eba< EbanWhen, the calculating formula of the off-energy are as follows:
Elpp=[Pban(1-ηbac)+(Pleft·ηc-Pban)(1-ηucc)]t
(6) work as Pleft·ηc< Pban+Puc max, Pleft·ηc< Pban, battery does not charge, only supercapacitor charges,
And Pleft·ηc≥Puc maxWhen, the calculating formula of the off-energy are as follows:
Elpp=(Pleft·ηc-Puc max)t+Puc max(1-ηucc)t
(7) work as Pleft·ηc< Pban+Puc max, Pleft·ηc< Pban, battery does not charge, only supercapacitor charges,
And Pleft·ηc< Puc maxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pleft·ηc(1-ηucc)t
In formula, PleftFor the dump power of distributed generation resource, ηcIt is the power conversion efficiency of inverter, PbanFor battery group
Rated power, Puc maxFor the maximum power of supercapacitor group, Eba、EbanRespectively after the current charge and discharge of battery group
Energy storage capacity, specified energy storage capacity, ηbac、ηuccRespectively battery, supercapacitor charge efficiency, t is the time (for the unit time
Interval), t1=(Eban-Ebar)/(Pbanηbac), and t1<t。
To determine load short of electricity amount, determined using following steps:
(1) work as Plack≥(Pban+Puc max)·ηc, battery and the equal quota of supercapacitor are discharged, and Eba< Ebanmin
When, the calculating formula of the load short of electricity amount are as follows:
Elps=[Plack-(Pban+Puc max)ηc]t+(Eba min-Eba)ηbadηc
(2) work as Plack≥(Pban+Puc max)·ηc, battery and the equal quota of supercapacitor are discharged, and Eba> Eban min
When, the calculating formula of the load short of electricity amount are as follows:
Elps=[Plack-(Pban+Puc max)ηc]t
(3) work as Plack< (Pban+Puc max)·ηc、Plack≥Pban·ηc, put after battery preferential discharge, supercapacitor
Electricity, and Eba< EbanminWhen, the calculating formula of the load short of electricity amount are as follows:
Elps=(Plack-Pucηc)(t-t1)
(4) work as Plack< (Pban+Puc max)·ηc、Plack≥Pban·ηc, put after battery preferential discharge, supercapacitor
Electricity, and Eba> EbanminWhen, the calculating formula of the load short of electricity amount are as follows:
Elps=0
(5) work as Plack< (Pban+Puc max)·ηc、Plack< Pban·ηc, battery does not discharge, only supercapacitor is put
Electricity, the calculating formula of the load short of electricity amount are as follows:
Elps=(Plack-Pucηc)t
In formula, PlackIt is lacked for the power of distributed generation resource, ηcIt is the power conversion efficiency of inverter, PbanFor battery group
Rated power, PucFor the power of supercapacitor group, Puc maxFor the maximum power of supercapacitor group, Eba、EbanminRespectively
Current residual energy storage capacity, least residue energy storage capacity for battery group, ηbad、ηucdRespectively battery, supercapacitor are put
Electrical efficiency, t are unit time interval, t1=(Ebar-Eba min)/(Pbanηbad), and t1<t。
Detailed description of the invention
Fig. 1 is the hybrid energy-storing capacity optimization method flow chart of micro-capacitance sensor energy-accumulating power station of the invention；
Fig. 2 is the schematic diagram of prepackage type micro-capacitance sensor energy-accumulating power station structure in the prior art；
Fig. 3 is distributed generation resource rate of energy loss and load and energy miss rate calculation flow chart of the invention；
Fig. 4 (a) is the charging flow figure of hybrid accumulator of the invention；
Fig. 4 (b) is the discharge flow path figure of hybrid accumulator of the invention；
Fig. 5 is Studying factors coupling convergence step curve synoptic diagram of the invention；
Fig. 6 is that cloud model and its numerical characteristic of the invention show schematic diagram；
Fig. 7 is the chaos cloud particle colony optimization algorithm flow chart of fusion AEA algorithm of the invention；
Fig. 8 is AEA algorithm flow chart of the invention；
Fig. 9 is chaotic optimization algorithm flow chart of the invention.
Specific embodiment
A specific embodiment of the invention is further described with reference to the accompanying drawing.
The hybrid energy-storing capacity optimization method of micro-capacitance sensor energy-accumulating power station as shown in Figure 1, includes the following steps:
Step 1: prepackage type micro-capacitance sensor energy-accumulating power station structure is determined；
Step 2: power supply reliability index is determined；
Step 3: it determines using hybrid energy-storing life period of an equipment cost minimization as objective function；
Step 4: constraint condition is determined；
Step 5: using fusion AEA algorithm, (Alopex-based evolutionary algorithm is based on Alopex
Colony intelligence evolution algorithm) chaos cloud particle swarm optimization (i.e. modified particle swarm optiziation) solving optimization allocation models.
Specific implementation process is as follows:
In order to improve the economy of prepackage type micro-capacitance sensor energy-accumulating power station, its operating cost is reduced, the present invention proposes prepackage type
The hybrid energy-storing capacity Optimal Allocation Model of micro-capacitance sensor energy-accumulating power station is used as using battery and supercapacitor and pre-installs the electricity that declines
Net energy-accumulating power station hybrid accumulator is constraint with reliability index of powering etc. using its Life cycle cost minimization as target
Condition establishes hybrid energy-storing capacity Optimal Allocation Model；Secondly, propose a kind of chaos cloud particle swarm optimization for merging AEA algorithm,
Utilize the algorithm solving optimization allocation models.Detailed process is as follows:
1) prepackage type micro-capacitance sensor energy-accumulating power station structure is determined:
It regard battery group and the combination of supercapacitor group as hybrid accumulator, prepackage type micro-capacitance sensor energy-accumulating power station structure
It is made of wind power generating set, photovoltaic array, Chai Fa, battery group, supercapacitor group, current transformer and load.Such as Fig. 2 institute
Show.
2) system power supply reliability index is determined:
Choose distributed generation resource rate of energy loss fLPPP, load and energy short of electricity rate fLPSPAs system power supply reliability index:
That is:
fLPPP=Elpp/Eg
fLPSP=Elps/El
Wherein, distributed generation resource rate of energy loss fLPPPFor the off-energy E of distributed generation resourcelppIt is sent out with distributed generation resource
Electricity EgThe ratio of (distributed generation resource including photovoltaic and wind-powered electricity generation)；Load short of electricity rate fLPSPFor load short of electricity amount ElpsIt is needed with load
Seek electricity ElRatio.
In conjunction with where prepackage type micro-capacitance sensor energy-accumulating power station ground fan year power generation prediction data, photovoltaic year power generation prediction data,
Year load power prediction data, distributed generation resource rate of energy loss fLPPP, load and energy short of electricity rate fLPSPCalculating process it is as follows:
Remember power difference Δ P=(Ppv(k)+Pw(k))·ηc-Pl(k)
Wherein, Ppv(k)、Pw(k)、Pl(k) be respectively the k moment photovoltaic generation power, wind-power electricity generation power, workload demand
Power；ηcIt is the power conversion efficiency of inverter.
When photovoltaic and wind-powered electricity generation generated output meet workload demand, i.e. when Δ P > 0, there are dump powers for distributed generation resource
Pleft, at this time if the residual capacity E of hybrid accumulatorcnrIt is not in quota state Ecnmax, then it is mixed for controlling distributed generation resource
Close energy storage device charging；If the residual capacity E of hybrid accumulatorcnrIn quota state Ecnmax, distributed generation resource will generate energy
Amount loss, the calculating formula of the off-energy of distributed generation resource are as follows: Elpp(k)={ [Ppv(k)+Pw(k)]·ηc-Pl(k) } t,
In, ηcIndicate the transfer efficiency of inverter.
When photovoltaic and wind-powered electricity generation generated output are unable to satisfy workload demand, i.e. when Δ P < 0, then there is function in distributed generation resource
Rate lacks PlackIf the dump energy E of hybrid accumulatorcnrGreater than least residue ENERGY Ecnmin, controlling hybrid accumulator is
Distributed generation resource electric discharge, to make up power missing；If the dump energy E of hybrid accumulatorcnrNo more than least residue energy
Ecnmin, then energy missing, the calculating formula of the energy missing amount (i.e. load short of electricity amount) of distributed generation resource are made up by power grid are as follows:
Elps(k)={ Pl(k)-[Ppv(k)+Pw(k)]·ηc}·t。
Calculate distributed generation resource rate of energy loss fLPPP, load and energy short of electricity rate fLPSPProcess is as shown in figure 3, wherein Ecnr、
Ebar、EucrDump energy respectively before hybrid accumulator, battery group, supercapacitor group charge/discharge；Eba、EucTable
Show the current energy storage capacity of battery group and supercapacitor group after charge/discharge；Ecnmax、EcnminRespectively indicate hybrid energy-storing
The Energy maximum value and minimum value of device, T are moment sum (be subject to the hourage 8760 in year, hour is unit).
The specified energy storage capacity of battery is Eban(unit MWh), the least residue energy storage capacity of battery are Eba min,
That is:
Eban=mCba·Uba/106
Eba min=mCba·Uba·(1-DOD)/106
In formula, m is battery number, UbaIt (V) is the voltage rating of battery, unit V；CbaIt (Ah) is rated capacitance,
Unit Ah；DOD is maximum depth of discharge.
In a practical situation, the operating voltage range of supercapacitor is Uucmin~Uuc max, then the supercapacitor group
Maximum energy storage capacity are as follows:
Minimum energy storage capacity are as follows:
In formula, n is supercapacitor number；UucFor the end voltage of supercapacitor；CucFor capacitance.
Shown in the charging flow of hybrid accumulator such as Fig. 4 (a), the damage of the corresponding distributed generation resource calculated of different situations
Disability amount is also different, is divided into T1~T7Kind situation, is described as follows:
T1: work as Pleft·ηc≥Pban+Puc maxWhen, system spare power is greater than battery group rated power PbanWith super electricity
Container group maximum power Puc maxThe sum of, battery group is with rated power PbanCharging, supercapacitor is with maximum power Puc max
It charges；And in this case battery group has reached specified energy storage capacity E by unit time t chargingban, when current
Carve energy storage capacity E after chargingbaReach EbanWhen, battery charging is interrupted, the energy part of extra waste is with (Eba-Eban) form
It is calculated；Battery energy loss amount ElppIncluding the effect generated in directly discarded distributed electrical source power, charging process
Rate is lost and the waste after being full of because of battery, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=[Pleft·ηc-(Pban+Puc max)]t+[Pban(1-ηbac)+Puc max(1-ηucc)]t+(Eba-Eban)
T2: work as Pleft·ηc≥Pban+Puc maxWhen, system spare power is greater than battery group rated power PbanWith super electricity
Container group maximum power Puc maxThe sum of, battery group is with rated power PbanCharging, supercapacitor is with maximum power Puc max
It charges；And in this case battery group is not up to specified energy storage capacity E by unit time t chargingban, without more
The energy part of remaining waste, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=[Pleft·ηc-(Pban+Puc max)]·t+[Pban(1-ηbac)+Puc max(1-ηucc)]t
T3: work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥PbanWhen, battery group is with rated power PbanPreferentially fill
Electricity, supercapacitor group is with power Puc=Pleft·ηc-PbanIt charges, by time t1Afterwards, wherein t1< t, t1=(Eban-
Ebar)/(Pbanηbac), battery group reaches rated capacity Eban, battery charging is interrupted, at this time Pleft·ηc≥Puc max,
Subsequent t-t1Period only has supercapacitor charging, and charge power is with the progress of supercapacitor maximum power, i.e. Puc=Puc max, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Puc max(t-t1)](1-ηucc)+(Pleft·ηc-Puc max)(t-t1)
T4: work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥PbanWhen, battery group is with rated power PbanPreferentially fill
Electricity, supercapacitor group is with power Puc=Pleft·ηc-PbanIt charges, by time t1Afterwards, wherein t1< t, t1=(Eban-
Ebar)/(Pbanηbac), battery group reaches rated capacity Eban, battery charging is interrupted, at this time Pleft·ηc< Puc max,
Subsequent t-t1Period only has supercapacitor charging, charge power Puc=Pleft·ηc, the meter of the off-energy of distributed generation resource
Calculate formula are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Pleft·ηc(t-t1)](1-ηucc)
T5: work as Pleft·ηc< Pban+Puc max, Pleft·ηc≥PbanWhen, battery group is with rated power PbanPreferentially fill
Electricity, supercapacitor group is with power Puc=Pleft·ηc-PbanIt charges, by the t time, battery group is not up to specified storage
ENERGY Eban, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=[Pban(1-ηbac)+(Pleft·ηc-Pban)(1-ηucc)]t
T6: work as Pleft·ηc< Pban+Puc max, Pleft·ηc< Pban, Pleft·ηc≥Puc maxWhen, system spare power is small
In battery rated power, it is greater than supercapacitor group maximum power Puc max, only charge at this time to supercapacitor group, and fill
Electrical power Puc=Puc max, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=(Pleft·ηc-Puc max)t+Puc max(1-ηucc)t
T7: work as Pleft·ηc< Pban+Puc max, Pleft·ηc< Pban, Pleft·ηc< Puc maxWhen, system spare power is small
In battery rated power, it is less than supercapacitor group maximum power Puc max, only charge at this time to supercapacitor group, and fill
Electrical power Puc=Pleft·ηc, the calculation formula of the off-energy of distributed generation resource are as follows:
Elpp=Pleft·ηc(1-ηucc)t
It is likely to occur T according to the actual situation1~T7Any one, detects supercapacitor group state itself: 1. working as Euc>
Euc maxWhen, i.e., supercapacitor group has reached maximum energy storage capacity E by unit time t charginguc max, after current time charging
Energy storage capacity EucReach Euc maxWhen, the charging of supercapacitor group is interrupted, the energy part of extra waste is with (Euc-Euc max) form
It is calculated；And in T1~T7Calculate ElppOn the basis of add up；2. working as Euc≤Euc maxWhen, there is no the energy of extra waste
Measure part；So far charging terminates.
In formula, PleftFor the dump power of distributed generation resource, ηcIt is the power conversion efficiency of inverter, PbanFor battery group
Rated power, Puc maxFor the maximum power of supercapacitor group, Eba、EbanIt is respectively current after the charge/discharge of battery group
Energy storage capacity, specified energy storage capacity, ηbac、ηuccRespectively battery, supercapacitor charge efficiency, the unit interval t, herein
For the charging time, and t > t1, t1=(Eban-Ebar)/(Pbanηbac), EbarFor the dump energy before battery charging.
Shown in the discharge flow path of hybrid accumulator such as Fig. 4 (b), the energy of the corresponding distributed generation resource calculated of different situations
It is also different to measure missing amount, is equally divided into T1~T5Kind situation, is described as follows:
T1: work as Plack≥(Pban+Puc max)·ηcWhen, systematic absence power is greater than battery group rated power PbanWith it is super
Capacitor group maximum power Puc maxThe sum of, battery group is with rated power PbanElectric discharge, supercapacitor is with maximum power Puc maxElectric discharge；And in this case battery group has reached minimum energy storage capacity E by unit time t electric dischargeba min, when current
Carve energy storage capacity E after dischargingbaReach Eba minWhen, the electric discharge of battery group is interrupted, the energy part of extra missing is with (Eba min-Eba)
Form calculus obtains；The energy missing amount E of distributed generation resourcelpsCalculation formula are as follows:
Elps=[Plack-(Pban+Puc max)ηc]t+(Eba min-Eba)ηbadηc
Wherein, ηbad、ηucdIt is the discharging efficiency of battery, supercapacitor respectively.
T2: work as Plack≥(Pban+Puc max)·ηcWhen, systematic absence power is greater than battery group rated power PbanWith it is super
Capacitor group maximum power Puc maxThe sum of, battery group is with rated power PbanElectric discharge, supercapacitor is with maximum power Puc maxIt discharges；And in this case battery group is not up to minimum energy storage capacity E by unit time t electric dischargeba min, no
There are the energy part of extra missing, the energy missing amount E of distributed generation resourcelpsCalculation formula are as follows:
Elps=[Plack-(Pban+Puc max)ηc]t
T3: work as Plack< (Pban+Puc max)·ηc, Plack≥Pban·ηcWhen, battery group is with rated power PbanPreferentially put
Electricity, supercapacitor group is with power Puc=Plack/ηc-PbanIt discharges, by time t1Afterwards, wherein t1< t, t1=(Ebar-
Eba min)/(Pbanηbad), energy storage capacity E after current time electric dischargebaReach Eba minWhen, the electric discharge of battery group is interrupted, P is 1. worked aslack
≥Puc maxηc, in subsequent t-t1Period only has supercapacitor electric discharge, and discharge power with supercapacitor maximum power into
Row, i.e. Puc=Puc max；2. working as Plack< Puc maxηc, in subsequent t-t1Period only has supercapacitor electric discharge, and the function that discharges
Rate is with Puc=Plack/ηcIt carries out；Due to noenergy missing in 2., by the 1. and 2. energy of two kinds of situation joint account distributed generation resources
Measure missing amount, the energy missing amount E of distributed generation resourcelpsCalculation formula are as follows:
Elps=(Plack-Pucηc)(t-t1)
T4: work as Plack< (Pban+Puc max)·ηc, Plack≥Pban·ηcWhen, battery group is with rated power PbanPreferentially put
Electricity, supercapacitor group is with power Puc=Plack/ηc-PbanIt discharges, after time t, energy storage capacity after the electric discharge of battery group
EbaNot up to Eba min, this stage, there is no two stages electric discharges, therefore noenergy lacks, i.e. Elps=0.
T5: work as Plack< (Pban+Puc max)·ηc, Plack< Pban·ηcWhen, systematic absence power is less than battery group volume
Determine power Pban, therefore only supercapacitor group is discharged:
1. working as Plack≥Puc maxηcWhen, supercapacitor discharge power is carried out with maximum power, i.e. Puc=Puc max；2. when
Plack< Puc maxηc, supercapacitor discharge power is with Puc=Plack/ηcIt carries out；It 1. and 2., will due to noenergy missing in 2.
The energy missing amount of two kinds of situation joint account distributed generation resources, the energy missing amount E of distributed generation resourcelpsCalculation formula are as follows:
Elps=(Plack-Pucηc)t
In formula, PlackIt is lacked for the power of distributed generation resource, ηbad、ηucdThe respectively electric discharge of battery, supercapacitor
Efficiency is unit time interval, t in discharge regime t1For t1=(Ebar-Eba min)/(Pbanηbad), and t1< t, EbarFor battery
Dump energy before group electric discharge.
It is likely to occur T according to the actual situation1~T5Any one, detects supercapacitor group state itself: 1. working as Euc<
EucminWhen, i.e., supercapacitor group has reached minimum energy storage capacity E by unit time t electric dischargeucmin, stored up after current time electric discharge
ENERGY EucReach EucminWhen, the electric discharge of supercapacitor group is interrupted, the energy part of extra missing is with (Eucmin-Euc)(1-ηucdηc)
Form calculus obtains；And in T1~T5Calculate ElpsOn the basis of add up；2. working as Euc≥EucminWhen, extra missing is not present
Energy part；So far electric discharge terminates.
3) it determines using the Life cycle cost minimization of hybrid accumulator as objective function:
Life cycle cost refers in the life cycle of equipment, from the purchase of hybrid accumulator, operation, maintenance,
The sum of all expenses paid during processing etc..The objective function of the Life cycle cost minimization of hybrid accumulator
Are as follows:
LCCmin=Civ+Coc+Cmc+Cdc=(1+fob+fmb+fdb)mPb+(1+foc+fmc+fdc)nPc
In formula, LCC is the Life cycle cost of hybrid accumulator, and min indicates to seek the minimum value of LCC；CivFor mixing
The buying expenses of energy storage device；CocFor the operating cost (including experiment, installation, loss, labour cost etc.) of hybrid accumulator；
CmcFor the maintenance cost of hybrid accumulator (including the maintenance cost before and after failure)；CdcFor the handling of hybrid accumulator
With (including scraping expense and residual value expense)；M, n is the quantity of battery, supercapacitor respectively；Pb、PcBe respectively battery,
The unit price of supercapacitor；fob、focIt is the operating factor of battery, supercapacitor respectively；fmb、fmcIt is battery respectively, super
The maintenance factor of grade capacitor；fdb、fdcIt is the processing coefficient of battery, supercapacitor respectively.
4) constraint condition is determined:
(1) system power supply reliability index:
|(Pba(k,m)+Puc(k,n))·ηc|=| Pl(k)-[Ppv(k)+Pw(k)]·ηc|
fLPPP(m,n)≤fLPPPmax
fLPSP(m,n)≤fLPSPmax
Wherein: above-mentioned first formula represents system power conservation, to carrying out power-balance constraint during energy storage charge and discharge；
M, n respectively indicate single battery number and supercapacitor number of monomers in second, third above-mentioned formula；fLPPPmax、fLPSPmaxPoint
It is not the maximum permissible value of distributed generation resource rate of energy loss, load and energy short of electricity rate.
(2) energy storage capacity and power constraints of energy-storage system:
Pba(k, m)=0 or Pba(k, m)=Pban(m)
Puc(k,n)≤Puc max(n)
Eba min(m)≤Eba(k,m)≤Eban(m)
Euc min(n)≤Euc(k,n)≤Euc max(n)
Wherein, above-mentioned first formula represents battery power and keeps fully charged state or completely put state, can extend storage
Battery, PbaFor the power of battery group, k is moment, PbanFor the rated power of battery group；Above-mentioned second formula
Filial generation table supercapacitor group power is less than or equal to its maximum power, PucFor the power of supercapacitor group, Puc maxIt is super
The maximum power of grade capacitor group；Eban min、Eba、EbanRespectively after the least residue energy storage capacity of battery group, charge/discharge
Current energy storage capacity and specified energy storage capacity；Eucmin、Euc、Euc maxRespectively after the minimum energy storage capacity of supercapacitor group, charge/discharge
Current energy storage capacity and maximum energy storage capacity.
(3) power output of battery group should meet:
Pban(m) < μ Pl
Wherein, the power output of battery group maintains base load (base load refers to total load rated power PlMultiplied by
Specific gravity μ) within, μ is specific gravity, PlFor total load rated power.
5) using the chaos cloud particle swarm optimization solving optimization allocation models of fusion AEA algorithm:
In order to allow particle in the search initial stage possible entire search space of leap maximum to the greatest extent, realizes diversity, avoid too early
Converge on local extremum.Thus in initial ranging, Dynamic Packet strategy is introduced, is adapted to according to the primary after chaotization
Degree, by primary group be divided into it is excellent, in, it is three groups poor, excellent group of particle is known as AEA particle, with AEA algorithm process, because AEA
The quality of son preferably close to global optimum, is only excavated with small range, to find more excellent solution；Middle group of particle is known as cloud particle
Son, position is moderate, takes cloud inertia weight adaptive, using the convenience, randomness and stability of cloud generator, improves algorithm
Convergence rate, associative learning factor coupling adaptive, it is ensured that algorithmic statement boosting algorithm performance；The particle of difference group claims chaos grain
Son needs to be traversed on a large scale using chaotic optimization algorithm, to find more preferable solution；Three kinds of particles are followed by fixed algebra iteration
Ring terminates until meeting condition of convergence algorithm.Using above-mentioned three kinds of algorithms (AEA algorithm, cloud generator, chaotic optimization algorithm) purport
In boosting algorithm optimizing performance, overcomes and initial value is relied on, it is precocious insufficient during evolution, search out more excellent solution.Specific mistake
Journey is as follows:
(1) prepackage type micro-capacitance sensor energy-accumulating power station initial data is inputted, system and facility information are obtained.Determine optimized variable
The parameters such as algorithm population scale and iterations max are arranged in several and value.
(2) population scale particle and its position and speed is randomly generated in initialization of population, calculates current all particles and adapts to
It is optimal optimal with population to obtain individual for value.
Wherein, cloud particle takes nonlinear decreasing strategy using inertia weight coefficient ω, relative to linear decrease strategy, is used to
Property weight decreases in non-linear initial stage ω value it is larger, it is fast to reduce speed, particle can quickly throughout search space can determine it is global most
The probable ranges of excellent solution；Later period ω value is smaller, and reduction speed is slow, and particle rapidity does not change substantially, with this speed optimal
Search global optimum nearby.Under ω decreases in non-linear formula:
ω (t)=ωmin/[1+(ωmin/ωmax-1)exp(-rt)] (1)
In formula, ωmax、ωminMaximum value, the minimum value of inertia weight coefficient are respectively indicated, attenuation rate r is constant.
Wherein, the Studying factors of cloud particle take coupling convergence step, and wherein convergence curve is as shown in figure 5, convergence step
Formula it is as follows:
In formula, γminFor the initial angle of convergence curve.
(3) population chaos intialization, to initialization population optimal particle Pg=(pg1,pg2,···,pgD) use formula (4)
It carries out mapping and generates a n-dimensional vector x1=(x11,x12,···,x1n), per the one-dimensional value all between (0~1).According to
Tent chaotic maps formula (i.e. formula (3)) obtains N number of chaos vector, its inverse mapping to optimized variable value interval is used formula
(4), using its global ergodic, initial value insensitivity and distribution more uniform property the characteristics of, the initialization of population particle is completed.
Tent chaotic maps formula are as follows:
In formula, Xn∈ [0,1], n=1,2,3.
Optimized variable x can be usedi∈(ai,bi) mapped back and forth with Chaos Variable, mapping equation are as follows:
In this way by the initialization of the achievable population of ergodic using Chaos Variable, the more of population are increased well
Sample.
(4) calculating of respective objects function is carried out to M particle, calculate all particles fitness and be compared, and obtained
The optimal pBest of current individualidAnd the current global optimum gBest of populationid。
(5) population's fitness f is calculatedavg,fg1And fg2, population is grouped according to Dynamic Packet strategy, determine AEA particle,
Cloud particle and chaotic particle turn to step (6) if chaotic particle；If cloud particle, turn to step (7)；If AEA particle,
It turns to step (8).
Dynamic Packet strategy are as follows: firstly, calculating population minimum fitness fmin, average fitness favg, maximum adaptation degree
fmax, according to golden section, determine two grouping point fg1And fg2, calculate such as formula (5), it may be assumed that
In formula, n is population scale, fiFor primary group, i=1,2 ..., n, favg、fmin、fmaxRespectively primary
Group fiAverage fitness, minimum fitness, maximum adaptation degree, frangeFor intermediate variable.
Following Dynamic Packet decision criteria is given below:
In formula, fG1For the first setting value, fG2For the second setting value.
Then particle types are divided, particle fitness is lower than fG1Particle divide AEA particle into, carry out the update of AEA algorithm,
Because the quality of AEA particle preferably close to global optimum, is only excavated with small range, to find more excellent solution；Higher than fG2Grain
Son divides chaotic particle into, needs to traverse on a large scale, therefore uses Chaos Search, to find preferably solution；And between fG1、fG2Between
Particle position it is moderate, referred to as cloud particle introduces inertia weight coefficient, utilizes the convenience, randomness and stabilization of cloud generator
Property, improve algorithm the convergence speed, associative learning factor coupling adaptive, it is ensured that algorithmic statement boosting algorithm performance.
(6) treatment process of chaotic optimization algorithm is as follows:
By Tent chaotic maps formula to chaotic particle optimal location PgIt is mapped to section [0,1] per one-dimensional, that is, is utilized
Formula cxi=(xi-ai)/(bi-ai) converted (aiAnd biIt is optimization section minimax value respectively)；It is mapped further according to Tent
S Chaos Variable sequence is generated, that is, utilizes formula (3)；And inverse mapping utilizes formula x to optimized variable sectioni=ai+
cxi·(bi-ai) obtain s particle.
(7) treatment process of cloud particle is as follows:
Using X condition cloud generator, dynamic adjusts inertia weight ω, updatesWithAnd calculate fitness；Between
fG1And fG2Between be known as cloud particle, position is moderate, and with X condition cloud generator, dynamic adjusts inertia weight, and X condition cloud occurs
The numerical characteristic of device is as shown in fig. 6, the calculating of X condition cloud generator is as follows:
(8) treatment process of AEA particle is as follows:
New population is generated by AEA algorithmThe calculation process of AEA algorithm is as follows:
1. the parameters such as population total, maximum number of iterations and majorized function optimal conditions are arranged；
2. AEA particle is as initialization populationEnable iteration numeration t=1；
3. generating populationPopulationMiddle individual is numbered from 1 to K, and an integer I is randomly choosed in value [2, K], is pressed
According to [I, I+1, K, 1,2 .I-1] number order generation populationK is population number；
4. calculating populationMiddle correspondence individual is obtained with its target function value difference according to formula (9) per one-dimensional difference
It arrives, the often one-dimensional and relevance of corresponding target function value of each individual is calculated according to formula (8)；
5. calculating this evolution annealing temperature according to formula (10) and calculating direction of travel probability according to formula (11)；
6. calculating variable direction of travel by formula (12), intermediate individual is generated by formula (13)；
7. calculating intermediate individual fitness, executes operation formula (14) and calculate, select optimal replacement, obtain population
8. judging whether to meet termination condition, if so, exporting current AEA particle, otherwise iteration numeration t=t+1 turns to step
Suddenly 3. continue to evolve.
(9) pass through fixed number of iterations (fixed number of iterations < maximum number of iterations), calculate all particles of new population and adapt to
Degree；Compare and obtains the optimal pBest of current individualidAnd global optimum gBestid。
(10) algorithm evolution terminates；Reach and sets greatest iteration number and the continuous constant (precision 10 of optimal particle-4) maximum
Algebra turns to step (11), otherwise turns to step (5).
(11) it exports particle optimal solution and target function value, whole flow process is as shown in Figure 7, Figure 8 and Figure 9.
Three kinds of state selections (completely fill or completely put or do not fill and do not put) are carried out to battery group in the present embodiment, can effectively be extended
Battery service life saves energy storage cost of investment；Chaos intialization is carried out to particle in the present embodiment, makes primary group
Throughout entire solution space, it can faster search out and more preferably solve；Cloud particle subdivision in the present embodiment uses cloud inertia weight certainly
It adapts to couple convergence step with Studying factors, using cloud algorithm convenience, randomness and stability, improves algorithm the convergence speed,
Associative learning factor coupling adaptive, it is ensured that algorithmic statement boosting algorithm performance；The present invention moves full population particle evolution
State divide, according to mass particle state be divided into it is excellent, in, poor three kinds of regions, Different Evolutionary strategy is taken to different zones particle, with
Phase finds more excellent solution.
It is the embodiment that primary group is divided into three groups above, it, can also be according to chaos as other embodiments
Primary group is divided into two groups by the fitness of primary after initialization, i.e., excellent group and poor group, excellent group of primary use
AEA algorithm carries out particle optimizing, carries out particle optimizing using chaotic optimization algorithm for the primary of difference group；It can also will be first
Beginning population is divided into three groups or more, and the optimization algorithm of every group of primary progress optimizing is selected according to the fitness of primary,
And then the optimal solution of Optimal Allocation Model.
Hybrid energy-storing capacity configuration optimizing method of the invention is applicable not only to above by wind power generating set, photovoltaic battle array
The micro-capacitance sensor energy-accumulating power station that column, hybrid accumulator are constituted, when micro-capacitance sensor energy-accumulating power station does not have wind power generating set or photovoltaic
When array, it need to will only join in above-mentioned hybrid energy-storing capacity configuration optimizing method in relation to wind power generating set or the related of photovoltaic array
The removal of several and condition.
The present invention also proposes that a kind of hybrid energy-storing capacity of micro-capacitance sensor energy-accumulating power station distributes device, including processor rationally,
Processor is for executing above-mentioned hybrid energy-storing capacity configuration optimizing method when computer program.The hybrid energy-storing capacity is distributed rationally
Device, is actually based on a kind of computer solution of the method for the present invention process, i.e., a kind of software architecture can be applied to
In computer, above-mentioned apparatus is treatment progress corresponding with method flow, since the introduction to the above method is enough
It is clear and complete, therefore be no longer described in detail.
Claims (10)
1. a kind of hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station, which comprises the following steps:
1) Optimal Allocation Model of micro-capacitance sensor energy-accumulating power station is obtained, the structure of micro-capacitance sensor energy-accumulating power station includes: wind power generating set
And/or photovoltaic array, the hybrid accumulator being made of battery group and supercapacitor group；The Optimal Allocation Model includes
With the objective function of the Life cycle cost minimization of hybrid accumulator, and the power supply reliability according to micro-capacitance sensor energy-accumulating power station
The constraint condition that index determines；
2) following modified particle swarm optiziation solving optimization allocation models is used:
2-1) to particle carry out chaos intialization, according to the fitness of primary after chaos intialization by primary group at least
It is divided into two groups；
The optimization algorithm for carrying out optimizing to respective sets primary 2-2) is selected according to the fitness of respective sets primary, is determined
The optimal solution of the Optimal Allocation Model.
2. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 1, which is characterized in that
Step 2-1) specifically: chaos intialization is carried out to particle, according to the fitness of primary after chaos intialization by initial grain
Subgroup be divided into it is excellent, in, it is three groups poor, the fitness of excellent group of primary less than the first setting value, middle group of primary it is suitable
Response is between the first setting value and the second setting value, the fitness of the primary of difference group is greater than the second setting value, and first sets
Definite value is less than the second setting value；
Step 2-2) specifically: particle optimizing is carried out using AEA algorithm for excellent group of primary, for middle group of initial grain
Son carries out particle optimizing using cloud generator, carries out particle optimizing using chaotic optimization algorithm for the primary of difference group；Root
The optimal solution of the Optimal Allocation Model is determined according to AEA algorithm, cloud generator and chaotic optimization algorithm.
3. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 2, which is characterized in that
The calculating formula of first setting value and the second setting value is as follows:
In formula, fG1For the first setting value, fG2For the second setting value, two grouping point fg1、fg2It is calculated by the following formula to obtain:
fg1=1.618favg
frange=fmax-fmin
fg2=fmin+frange/1.618
In formula, fiFor primary group, i=1,2 ..., n, favg、fmin、fmaxRespectively primary group fiAverage fitness,
Minimum fitness, maximum adaptation degree, frangeFor intermediate variable.
4. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 2, which is characterized in that
The power supply reliability index includes distributed generation resource rate of energy loss fLPPP, load and energy short of electricity rate fLPSP, calculating formula is as follows:
fLPPP=Elpp/Eg
fLPSP=Elps/El
In formula, ElppFor the off-energy of distributed generation resource, EgFor the generated energy of distributed generation resource, ElpsFor load short of electricity amount, ElFor
Workload demand electricity.
5. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 4, which is characterized in that
Include: according to the constraint condition that the power supply reliability index determines
fLPPP(m,n)≤fLPPPmax
fLPSP(m,n)≤fLPSPmax
|(Pba(k,m)+Puc(k,n))·ηc|=| Pl(k)-[Ppv(k)+Pw(k)]·ηc|
Wherein, m, n respectively indicate single battery number and supercapacitor number of monomers, and k is moment, fLPPPmax、fLPSPmaxTable respectively
Show the maximum permissible value of distributed generation resource rate of energy loss, load and energy short of electricity rate；Ppv(k)、Pw(k)、Pl(k) when being respectively k
The photovoltaic generation power at quarter, wind-power electricity generation power, workload demand power, ηcIt is the power conversion efficiency of inverter, PbaFor electric power storage
The power of pond group, PucFor the power of supercapacitor group.
6. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 5, which is characterized in that
The constraint condition that the Optimal Allocation Model includes further include:
The energy storage capacity of energy-storage system and the constraint condition of power:
Pba(k, m)=0 or Pba(k, m)=Pban(m)
Puc(k,n)≤Pucmax(n)
Ebamin(m)≤Eba(k,m)≤Eban(m)
Eucmin(n)≤Euc(k,n)≤Eucmax(n)
Wherein, k is moment, PbaFor the power of battery group, PbanFor the rated power of battery group, PucFor supercapacitor group
Power, PucmaxFor the maximum power of supercapacitor group, Ebamin、Eba、EbanThe respectively least residue energy storage of battery group
Current energy storage capacity, specified energy storage capacity after amount, charge/discharge；Eucmin、Euc、EucmaxThe respectively minimum energy storage of supercapacitor group
Current energy storage capacity and maximum energy storage capacity after amount, charge/discharge；
The constraint condition of the output power of battery group:
Pban(m) < μ Pl
Wherein, μ is specific gravity, PlFor total load rated power.
7. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 4, which is characterized in that
The off-energy of distributed generation resource is determined by following steps:
(1) work as Pleft·ηc≥Pban+Pucmax, battery and the equal quota of supercapacitor charge, and Eba> EbanWhen, the loss
The calculating formula of energy are as follows:
Elpp=[Pleft·ηc-(Pban+Pucmax)]t+[Pban(1-ηbac)+Pucmax(1-ηucc)]t+(Eba-Eban)
(2) work as Pleft·ηc≥Pban+Pucmax, battery and the equal quota of supercapacitor charge, and Eba< EbanWhen, the loss
The calculating formula of energy are as follows:
Elpp=[Pleft·ηc-(Pban+Pucmax)]·t+[Pban(1-ηbac)+Pucmax(1-ηucc)]t
(3) work as Pleft·ηc< Pban+Pucmax, Pleft·ηc≥Pban, battery preferentially charges, charges after supercapacitor, and Eba
> Eban、Pleft·ηc≥PucmaxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Pucmax(t-t1)](1-ηucc)+(Pleft·ηc-Pucmax)(t-
t1)
(4) work as Pleft·ηc< Pban+Pucmax, Pleft·ηc≥Pban, battery preferentially charges, charges after supercapacitor, and Eba
> Eban、Pleft·ηc< PucmaxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pban(1-ηbac)t1+[(Pleft·ηc-Pban)t1+Pleft·ηc(t-t1)](1-ηucc)
(5) work as Pleft·ηc< Pban+Pucmax, Pleft·ηc≥Pban, battery preferentially charges, charges after supercapacitor, and Eba
< EbanWhen, the calculating formula of the off-energy are as follows:
Elpp=[Pban(1-ηbac)+(Pleft·ηc-Pban)(1-ηucc)]t
(6) work as Pleft·ηc< Pban+Pucmax, Pleft·ηc< Pban, battery does not charge, only supercapacitor charges, and
Pleft·ηc≥PucmaxWhen, the calculating formula of the off-energy are as follows:
Elpp=(Pleft·ηc-Pucmax)t+Pucmax(1-ηucc)t
(7) work as Pleft·ηc< Pban+Pucmax, Pleft·ηc< Pban, battery does not charge, only supercapacitor charges, and
Pleft·ηc< PucmaxWhen, the calculating formula of the off-energy are as follows:
Elpp=Pleft·ηc(1-ηucc)t
In formula, PleftFor the dump power of distributed generation resource, ηcIt is the power conversion efficiency of inverter, PbanFor the volume of battery group
Determine power, PucmaxFor the maximum power of supercapacitor group, Eba、EbanCurrent storage respectively after the charge/discharge of battery group
Energy, specified energy storage capacity, ηbac、ηuccRespectively battery group, the charge efficiency of supercapacitor group, time t > t1, t1=
(Eban-Ebar)/(Pbanηbac), EbarFor the dump energy before battery charging.
8. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 4, which is characterized in that
Load short of electricity amount is determined by following steps:
(1) work as Plack≥(Pban+Pucmax)·ηc, battery and the equal quota of supercapacitor are discharged, and Eba< EbanminWhen, it is described
The calculating formula of load short of electricity amount are as follows:
Elps=[Plack-(Pban+Pucmax)ηc]t+(Ebamin-Eba)ηbadηc
(2) work as Plack≥(Pban+Pucmax)·ηc, battery and the equal quota of supercapacitor are discharged, and Eba> EbanminWhen, it is described
The calculating formula of load short of electricity amount are as follows:
Elps=[Plack-(Pban+Pucmax)ηc]t
(3) work as Plack< (Pban+Pucmax)·ηc、Plack≥Pban·ηc, it discharges after battery preferential discharge, supercapacitor, and
Eba< EbanminWhen, the calculating formula of the load short of electricity amount are as follows:
Elps=(Plack-Pucηc)(t-t1)
(4) work as Plack< (Pban+Pucmax)·ηc、Plack≥Pban·ηc, it discharges after battery preferential discharge, supercapacitor, and
Eba> EbanminWhen, the calculating formula of the load short of electricity amount are as follows:
Elps=0
(5) work as Plack< (Pban+Pucmax)·ηc、Plack< Pban·ηc, battery does not discharge, only supercapacitor is discharged, described
The calculating formula of load short of electricity amount are as follows:
Elps=(Plack-Pucηc)t
In formula, PlackIt is lacked for the power of distributed generation resource, ηcIt is the power conversion efficiency of inverter, PbanFor the volume of battery group
Determine power, PucFor the power of supercapacitor group, PucmaxFor the maximum power of supercapacitor group, Eba、EbanminRespectively store
Energy storage capacity, least residue energy storage capacity, η after the current charge and discharge of battery packbad、ηucdRespectively battery group, supercapacitor group
Discharging efficiency, time t > t1, and t1=(Ebar-Ebamin)/(Pbanηbad), EbarFor the residual energy before the electric discharge of battery group
Amount.
9. the hybrid energy-storing capacity configuration optimizing method of micro-capacitance sensor energy-accumulating power station according to claim 2, which is characterized in that
The objective function are as follows:
LCCmin=Civ+Coc+Cmc+Cdc=(1+fob+fmb+fdb)mPb+(1+foc+fmc+fdc)nPc
Wherein, LCC is the Life cycle cost of hybrid accumulator, and min indicates to seek the minimum value of LCC；CivFor hybrid energy-storing
The buying expenses of device；CocFor the operating cost of hybrid accumulator；CmcFor the maintenance cost of hybrid accumulator；CdcIt is mixed
Close the processing cost of energy storage device, including scraping expense and residual value expense；M, n is the quantity of battery, supercapacitor respectively；
Pb、PcIt is the unit price of battery, supercapacitor respectively；fob、focIt is the operating factor of battery, supercapacitor respectively；fmb、
fmcIt is the maintenance factor of battery, supercapacitor respectively；fdb、fdcIt is the processing coefficient of battery, supercapacitor respectively.
10. a kind of hybrid energy-storing capacity of micro-capacitance sensor energy-accumulating power station distributes device rationally, which is characterized in that including processor, place
Such as the described in any item hybrid energy-storing capacity of the claim 1-9 side of distributing rationally is realized when reason device is for executing computer program
Method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811237650.2A CN109510234B (en) | 2018-10-23 | 2018-10-23 | Hybrid energy storage capacity optimal configuration method and device for micro-grid energy storage power station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811237650.2A CN109510234B (en) | 2018-10-23 | 2018-10-23 | Hybrid energy storage capacity optimal configuration method and device for micro-grid energy storage power station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109510234A true CN109510234A (en) | 2019-03-22 |
| CN109510234B CN109510234B (en) | 2020-09-18 |
Family
ID=65746146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811237650.2A Active CN109510234B (en) | 2018-10-23 | 2018-10-23 | Hybrid energy storage capacity optimal configuration method and device for micro-grid energy storage power station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109510234B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109842158A (en) * | 2019-03-28 | 2019-06-04 | 广东工业大学 | A kind of micro-capacitance sensor Optimal Configuration Method |
| CN111628558A (en) * | 2020-05-21 | 2020-09-04 | 南京工程学院 | System and method for optimizing energy management and capacity configuration of hybrid energy storage system |
| CN112564147A (en) * | 2020-11-11 | 2021-03-26 | 长江勘测规划设计研究有限责任公司 | Off-grid micro-grid energy storage capacity engineering calculation method |
| CN112668764A (en) * | 2020-12-22 | 2021-04-16 | 国网福建省电力有限公司 | Offshore wind farm energy storage system optimization configuration method based on cloud model and FCM algorithm |
| CN112886624A (en) * | 2021-02-05 | 2021-06-01 | 国网新疆电力有限公司经济技术研究院 | Planning and designing system and method for energy storage device of three-station-in-one transformer substation |
| CN114662387A (en) * | 2022-03-19 | 2022-06-24 | 特斯联科技集团有限公司 | Method and system for optimizing wind and light energy storage of low-carbon economic park based on GAN |
| CN117543706A (en) * | 2024-01-08 | 2024-02-09 | 国网江西省电力有限公司经济技术研究院 | Hybrid energy storage configuration method and system based on micro-grid wind-solar energy storage system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058900A (en) * | 2016-07-21 | 2016-10-26 | 南京工程学院 | Hybrid energy storage capacity optimization configuration method in grid connected wind-solar generation |
| CN106230012A (en) * | 2016-09-19 | 2016-12-14 | 华北电力大学（保定） | Ultracapacitor and the Optimal Configuration Method of accumulator capacity in grid-connected photovoltaic system |
| CN106251005A (en) * | 2016-07-21 | 2016-12-21 | 南京工程学院 | A kind of based on the hybrid energy-storing capacity configuration optimizing method improving particle cluster algorithm |
| CN106887841A (en) * | 2017-03-23 | 2017-06-23 | 东北大学 | A kind of genetic particle group optimizing method on multiple populations of the capacity configuration of micro-capacitance sensor containing electric automobile |
-
2018
- 2018-10-23 CN CN201811237650.2A patent/CN109510234B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106058900A (en) * | 2016-07-21 | 2016-10-26 | 南京工程学院 | Hybrid energy storage capacity optimization configuration method in grid connected wind-solar generation |
| CN106251005A (en) * | 2016-07-21 | 2016-12-21 | 南京工程学院 | A kind of based on the hybrid energy-storing capacity configuration optimizing method improving particle cluster algorithm |
| CN106230012A (en) * | 2016-09-19 | 2016-12-14 | 华北电力大学（保定） | Ultracapacitor and the Optimal Configuration Method of accumulator capacity in grid-connected photovoltaic system |
| CN106887841A (en) * | 2017-03-23 | 2017-06-23 | 东北大学 | A kind of genetic particle group optimizing method on multiple populations of the capacity configuration of micro-capacitance sensor containing electric automobile |
Non-Patent Citations (4)
| Title |
|---|
| 刘 凯, 杜红彬, 金宇辉,等: "基于AEA 和PSO 的双层同步换热网络综合方法研究", 《高 校 化 学 工 程 学 报》 * |
| 杨国华，朱向芬，马玉娟，等: "基于改进粒子群算法的混合储能系统容量优化", 《电测与仪表》 * |
| 祁莹，苏宏升: "基于黄金分割准则的混沌云粒子群算法", 《计算机工程与应用》 * |
| 许国根、贾瑛、韩启龙: "《模式识别与智能计算的MATLAB实现》", 31 August 2017 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109842158A (en) * | 2019-03-28 | 2019-06-04 | 广东工业大学 | A kind of micro-capacitance sensor Optimal Configuration Method |
| CN111628558A (en) * | 2020-05-21 | 2020-09-04 | 南京工程学院 | System and method for optimizing energy management and capacity configuration of hybrid energy storage system |
| CN112564147A (en) * | 2020-11-11 | 2021-03-26 | 长江勘测规划设计研究有限责任公司 | Off-grid micro-grid energy storage capacity engineering calculation method |
| CN112668764A (en) * | 2020-12-22 | 2021-04-16 | 国网福建省电力有限公司 | Offshore wind farm energy storage system optimization configuration method based on cloud model and FCM algorithm |
| CN112886624A (en) * | 2021-02-05 | 2021-06-01 | 国网新疆电力有限公司经济技术研究院 | Planning and designing system and method for energy storage device of three-station-in-one transformer substation |
| CN112886624B (en) * | 2021-02-05 | 2023-05-05 | 国网新疆电力有限公司经济技术研究院 | Three-station-in-one substation energy storage device planning and designing system and method |
| CN114662387A (en) * | 2022-03-19 | 2022-06-24 | 特斯联科技集团有限公司 | Method and system for optimizing wind and light energy storage of low-carbon economic park based on GAN |
| CN114662387B (en) * | 2022-03-19 | 2022-12-09 | 特斯联科技集团有限公司 | Method and system for optimizing wind and light energy storage of low-carbon economic park based on GAN |
| CN117543706A (en) * | 2024-01-08 | 2024-02-09 | 国网江西省电力有限公司经济技术研究院 | Hybrid energy storage configuration method and system based on micro-grid wind-solar energy storage system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109510234B (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109510234A (en) | A kind of the hybrid energy-storing capacity configuration optimizing method and device of micro-capacitance sensor energy-accumulating power station | |
| Wong et al. | Review on the optimal placement, sizing and control of an energy storage system in the distribution network | |
| CN110311421B (en) | Micro-grid multi-time scale energy management method based on demand side response | |
| Rana et al. | A review on hybrid photovoltaic–Battery energy storage system: Current status, challenges, and future directions | |
| CN104092231A (en) | Method for optimal configuration of independent micro grid mixed energy storage capacity | |
| CN106058900B (en) | Hybrid energy-storing capacity configuration optimizing method in a kind of grid-connected wind light generation | |
| CN109586325A (en) | A kind of new energy energy storage Optimal Configuration Method | |
| CN110135662B (en) | Energy storage site selection constant volume multi-objective optimization method considering reduction of peak-valley difference | |
| CN109886472B (en) | Power distribution area capacity method with uncertain distributed photovoltaic and electric automobile access | |
| CN109242271A (en) | Distributed photovoltaic accesses the node Sensitivity Analysis Method that distribution network electric energy quality is administered | |
| CN106251005A (en) | A kind of based on the hybrid energy-storing capacity configuration optimizing method improving particle cluster algorithm | |
| CN111628558B (en) | System and method for optimizing energy management and capacity configuration of hybrid energy storage system | |
| Wang et al. | An improved min-max power dispatching method for integration of variable renewable energy | |
| CN109193729A (en) | The site selecting method of energy-storage system in a kind of distribution automation system | |
| CN107732945A (en) | A kind of energy-storage units optimization method based on simulated annealing particle cluster algorithm | |
| CN109829228B (en) | Optimization method for capacity allocation of hybrid energy storage power supply in renewable energy system | |
| CN111614110A (en) | Receiving-end power grid energy storage optimization configuration method based on improved multi-target particle swarm optimization | |
| Fan et al. | A conditional depreciation balancing strategy for the equitable operation of extended hybrid energy storage systems | |
| CN115940284B (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| Liu et al. | Effects of reliability index on optimal configuration of hybrid solar/battery energy system by optimization approach: a case study | |
| CN115764849A (en) | Hybrid energy storage capacity optimal configuration method and configuration system thereof | |
| CN109510242A (en) | A kind of micro-grid energy storage system method for optimizing configuration and storage medium | |
| CN107046294A (en) | Combined accumulation energy capacity collocation method based on probability statistics | |
| CN113555901A (en) | Hybrid energy storage capacity optimization method based on improved S-shaped function particle swarm optimization algorithm | |
| AL-Maaitah et al. | Particle Swarm Optimizer for BESS Operation to Mitigate Voltage Deviation of the Modified IEEE 9-bus Power System |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |