CN107196586A - Micro-grid system optimizing operation method is stored up containing the light bavin that electric automobile is accessed - Google Patents
Micro-grid system optimizing operation method is stored up containing the light bavin that electric automobile is accessed Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000005457 optimization Methods 0.000 claims abstract description 130
- 230000006870 function Effects 0.000 claims abstract description 88
- 238000004146 energy storage Methods 0.000 claims abstract description 20
- 230000007613 environmental effect Effects 0.000 claims abstract description 14
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 82
- 229910052744 lithium Inorganic materials 0.000 claims description 82
- 230000005611 electricity Effects 0.000 claims description 41
- 238000003860 storage Methods 0.000 claims description 19
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- 238000009434 installation Methods 0.000 claims description 16
- 238000007599 discharging Methods 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 8
- 238000010248 power generation Methods 0.000 claims description 6
- 238000011084 recovery Methods 0.000 claims description 6
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- 238000012360 testing method Methods 0.000 description 3
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- 238000011217 control strategy Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
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- 238000000342 Monte Carlo simulation Methods 0.000 description 1
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- ZZUFCTLCJUWOSV-UHFFFAOYSA-N furosemide Chemical compound C1=C(Cl)C(S(=O)(=O)N)=CC(C(O)=O)=C1NCC1=CC=CO1 ZZUFCTLCJUWOSV-UHFFFAOYSA-N 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S10/00—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power
- H02S10/10—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power including a supplementary source of electric power, e.g. hybrid diesel-PV energy systems
- H02S10/12—Hybrid wind-PV energy systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
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- H02J3/382—
-
- 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
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/30—Electrical components
- H02S40/38—Energy storage means, e.g. batteries, structurally associated with PV modules
-
- 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
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
-
- 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
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention is directed to the light bavin accessed containing electric automobile and stores up micro-grid system there is provided a kind of optimizing operation method, including：Multiple optimization object functions are set up, the optimization object function is used for the factor to be optimized for optimizing influence micro-grid system optimization operation；Determine the constraints of micro-grid system；According to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is converted into single-object problem, and micro-grid system operation is optimized.The present invention surrounds micro-grid system environmental protection and economy running optimizatin problem, under grid-connected and island operation state, the generating expense, amortization charge and environmental improvement expense for establishing generator unit in a distributed manner are target, consider that micro-grid system runs the economic optimization moving model of constraints, form multiple target constrained optimization problem.Power output and energy storage device charge/discharge Optimization Solution to distributed power source in micro-grid system, optimum results demonstrate the validity of put forward model, strategy and algorithm.
Description
Technical field
The present invention relates to micro-grid system environmental protection and economy running optimizatin correlative technology field, specifically, it is related to a kind of containing electricity
The light bavin storage micro-grid system optimizing operation method of electrical automobile access.
Background technology
Distributed power generation is increasingly becoming important electrical energy production mode, is to solve the problems such as energy crisis, environmental pollution
Important channel.By distributed power source (Distributed Generator, DG) and energy storage system (Energy Storage
System, ESS) power network is accessed in microgrid (Micro-grid, MG) form, supported each other with power network, distributed electrical can be improved
The utilization rate in source, continues power supply to important load when helping power network catastrophe, it is to avoid influence of the fitful power to the quality of power supply, is
Play the most effective mode of distributed electrical source efficiency.The constraints normally run in each distributed power source for meeting trend constraint
Under, the discharge and recharge to the exerting oneself of distributed power source, energy storage facility carries out reasonable arrangement, can optimize its economic function, ring
Border performance simultaneously makes system obtain more preferable reliability.
Electric automobile (Electric Vehicle, EV) is as controllable load, and can dissolve various forms of renewable energies
Source.EV will produce influence in terms of reliability, the quality of power supply, economical operation when accessing microgrid on it, can participate in microgrid
The Optimized Operation of system, improves the economy of system operation.In the prior art, have the single electric vehicle of document analysis, it is electronic
The charge control strategy of three kinds of research aspects of automobile group and regional power system electric automobile；Also document analysis divides to reduce
Cloth grid net loss is target, to the grid-connected carry out real-time optimization of plug-in hybrid automobile；Also other documents are also discussed
Under V2G (vehicle-to-grid) control mode, using electric automobile energy storage resource, how charge and discharge process is adjusted, be electricity
Net provides assistant service.
Lithium battery has that operational voltage level is high, specific energy and specific volume are big, self-discharge rate is low, memory-less effect, environmental protection
Property good and nonstaining property the advantages of, but the influence for avoiding frequent discharge and recharge to battery life is needed, in selection lithium battery group conduct
During the energy-storage travelling wave tube of microgrid, isolated island and be incorporated into the power networks under pattern, containing photovoltaic, wind-powered electricity generation, electric power storage access microgrid economic optimization fortune
Row turns into study hotspot both domestic and external, rarely has preferable micro-grid system optimization operational solution.
The content of the invention
The present invention containing the light bavin that electric automobile is accessed stores up micro-grid system for existing, isolated island and is being incorporated into the power networks under pattern,
Containing photovoltaic, wind-powered electricity generation, electric power storage access microgrid economic optimization operation, rarely have preferably optimization operational solution the problem of there is provided
It is a kind of to store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed.
The technical problem solved required for of the invention, can be achieved through the following technical solutions：
A kind of to store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, the micro-grid system includes, as
The photovoltaic generating system and diesel power generation system of distributed power source, for the lithium battery group energy-storage system of energy storage, and by electricity
Electrical automobile accesses micro-grid system and the charging pile charged to electric automobile, it is characterised in that：
Multiple optimization object functions are set up, the optimization object function is used to optimize treating for influence micro-grid system optimization operation
Optimizing factors；
Determine the constraints of micro-grid system；
According to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is converted into single goal
Optimization problem, and micro-grid system operation is optimized.
In the present invention, the factor to be optimized, including micro-grid system operating cost, micro-grid system depreciable cost, microgrid system
The Environmental costs of system, the electric income of repurchase of power network to micro-grid system, the power failure rejection penalty of micro-grid system.
In the present invention, the optimization object function, including the first optimization object function, what it was used to optimizing micro-grid system is
System operating cost so that system operation cost is minimum, and first optimization object function is expressed as：
Wherein, Pi(t) it is the active power output of i-th of distributed power source in micro-grid system；CfFor disappearing for distributed power source
Consume cost；ComFor micro-grid system operational management cost；CgridCost, P are interacted with power network for micro-grid systemgrid(t) it is microgrid system
The power that interacts united with power network, micro-grid system defers to purchase electricity price c from power network during absorbed powerp(t), micro-grid system is to power network
Sale of electricity electricity price c is deferred to during power outputs(t), expression is as follows：
In the present invention, the optimization object function, including the second optimization object function, what it was used to optimizing micro-grid system is
System depreciable cost so that system depreciable cost is minimum, when system depreciable cost disregards the depreciable cost of lithium battery, and described second is excellent
Change object function to be expressed as：
Wherein,CACC=CINSfcr,
Pi(t) it is the active power output of i-th of distributed power source in system；CfFor the consuming cost of distributed power source；
ComFor micro-grid system operational management cost；CDG-DERFor the installation depreciable cost of distributed power source；CACCFor the peace of distributed power source
Dress up this Average Annual Cost；PrFor the rated power of distributed power source；fcfFor capacity factor measure；CINSFor the installation of distributed power source
Cost；fcrFor capital recovery factor；D is interest rate or allowance for depreciation；L is the life-span of distributed power source；Pbat,iRepresent period i lithium electricity
The average charge-discharge electric power in pond, positive sign represents electric discharge, and negative sign represents charging；γ is the new lithium battery SOC constraint penalty factors, can be with
It is described as lithium battery increase or reduces the expense of unit quantity of electricity；ηbat,c,ηbat,dFor charging and discharging lithium battery efficiency.
In the present invention, the optimization object function, including the second optimization object function, what it was used to optimizing micro-grid system is
System depreciable cost so that system depreciable cost is minimum, when system depreciable cost is included in the depreciable cost of lithium battery, and described second is excellent
Change object function to be expressed as：
Wherein,CbwFor charging and discharging lithium battery depreciable cost；Cbat,repFor lithium battery replacement cost；
QlifetimeTotal electricity is exported for the battery cell life-cycle；
CACC=CINSfcr,
Pi(t) it is the active power output of i-th of distributed power source in system；CfFor the consuming cost of distributed power source；
ComFor micro-grid system operational management cost；CDG-DERFor the installation depreciable cost of distributed power source；CACCFor the peace of distributed power source
Dress up this Average Annual Cost；PrFor the rated power of distributed power source；fcfFor capacity factor measure；CINSFor the installation of distributed power source
Cost；fcrFor capital recovery factor；D is interest rate or allowance for depreciation；L is the life-span of distributed power source；Pbat,iRepresent period i lithium electricity
The average charge-discharge electric power in pond, positive sign represents electric discharge, and negative sign represents charging；γ is the new lithium battery SOC constraint penalty factors, can be with
It is described as lithium battery increase or reduces the expense of unit quantity of electricity；ηbat,c,ηbat,dFor charging and discharging lithium battery efficiency.
In the present invention, the optimization object function, including the 3rd optimization object function, what it was used to optimizing micro-grid system is
System Environmental costs so that system environments cost is minimum, and the 3rd optimization object function is expressed as：
Wherein, kiFor the CO of i-th of distributed power source2Emission factor, kgridFor the CO of power network2Emission factor, Pgrid(t) it is
T micro-grid system is to the purchase of electricity of power network, Pi(t) it is the active power output of i-th of distributed power source in micro-grid system.
In the present invention, the constraints of the micro-grid system is one or more than one in following constraints
Meaning combination：
1) the output constraint condition of distributed power source
Pi,min≤Pi(t)≤Pi,max
Wherein, Pi,min、Pi,maxThe minimum and maximum power output of respectively i-th distributed power source, Pi(t) it is microgrid system
The active power output of i-th of distributed power source in system；
2) lithium battery memory capacity constraints
SOCmin≤SOC(t)≤SOCmax
Wherein, SOC (t) is the state-of-charge of t lithium battery, SOCminThe minimum value allowed for lithium battery charge state,
SOCmaxThe maximum allowed for lithium battery charge state；
3) micro-grid system interacts power constraints with power network
Pgrid,min≤Pgrid(t)≤Pgrid,max
Wherein, Pgrid,min,Pgrid,maxRespectively micro-grid system and power network minimum and maximum transimission power, Pgrid(t) to be micro-
Net system interacts power with power network；
4) electric automobile access constraints
The power that electric automobile absorbs from micro-grid system is constrained by charging pile power output and charging pile number：
N·PEV,min≤PEV(t)≤N·PEV,max
Wherein, N is charging pile number, P in micro-grid systemEV,min、PEV,maxRespectively charging pile open circuit loss and maximum are defeated
Go out power, PEV(t) power absorbed for electric automobile from micro-grid system；
5) micro-grid system power-balance constraint condition
Wherein, PLoadFor all electric loads of whole micro-grid system, PgridFor interact power of the micro-grid system with power network, NG
For distributed electrical Source Type, PDG,iFor the power output of i-th of distributed power source.
In the present invention, according to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is changed
For into single-object problem, using fuzzy overall evaluation, including：
Set up the evaluation factor collection U={ μ of multi-objective optimization question1,μ2,...,μi}；
Set up weight sets A={ a1,a2,...,ai, wherein,
Set up opinion rating V={ opinion rating 1, opinion rating 2 ..., opinion rating m }；
The evaluation R of i-th of objective optimisation problems in multi-objective optimization questioniIt is the fuzzy subset R on Vi={ ri1,
ri2,...,rim, the evaluation vector of multi-objective optimization question constitutes i × m rank Judgement Matrixes R；
Using the synthesis computing of fuzzy matrix, the comprehensive evaluation model B=A ο R of single-object problem are obtained；
The final appraisal results Z=BF of single-object problem, wherein, F=[f1,f2,...,fi]T, fiTo be multiple excellent
Change i-th of actual value in object function, Fuzzy computings are using M (, ⊕) operator.
The μiUsing the membership function of drop half Γ distributions, it is expressed as：
Wherein, fi,minFor i-th of minimum value under constraints in multiple optimization object functions.
In the present invention, according to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is changed
For into single-object problem, Lagrange functions, the list changed into are constructed to multi-objective optimization question using weigthed sums approach
Objective optimisation problems：
L(F,λ1,λ2,...,λi)=λ1F1(θ)+λ2F2(θ)+...+λiFi(θ)
Wherein, F is the expression of single-object problem, FiFor the expression formula of i-th of optimization object function, λiFor i-th
The weight of optimization object function.
The present invention's stores up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, and is passed through around micro-grid system environmental protection
Ji running optimizatin problem, under grid-connected and island operation state, establishes generating expense, the depreciation cost of generator unit in a distributed manner
It is target with environmental improvement expense, it is considered to which micro-grid system runs the economic optimization moving model of constraints, forms multiple target
Constrained optimization problem.Using comprehensive study particle cluster algorithm, power output and energy storage to distributed power source in micro-grid system
Device charge/discharge Optimization Solution, to include photovoltaic, diesel-driven generator, lithium battery, electric automobile charging pile and the alternating current-direct current of load
Mixing micro-capacitance sensor is specific research object, and optimum results demonstrate the validity of put forward model, strategy and algorithm.
Brief description of the drawings
The present invention is further illustrated below in conjunction with the drawings and specific embodiments.
Fig. 1 is that light bavin stores up micro-grid system electrical structure schematic diagram.
Fig. 2 is 1 time light bavin storage micro-grid system economic optimization operation result of scene.
Fig. 3 is 2 times light bavin storage micro-grid system economic optimization operation results of scene.
Fig. 4 is 3 times light bavin storage micro-grid system economic optimization operation results of scene.
Fig. 5 is 4 times light bavin storage micro-grid system economic optimization operation results of scene.
Fig. 6 is light bavin storage micro-grid system winter typical daily load and cloudy photovoltaic data.
Fig. 7 is light bavin storage micro-grid system winter typical daily load and fine day photovoltaic data.
Embodiment
In order that technological means, creation characteristic, reached purpose and effect of the present invention are easy to understand, with reference to tool
Body is illustrated, and the present invention is expanded on further.
Idea of the invention is that, by the existing light bavin storage micro-grid system model accessed containing electric automobile and optimization
The analysis of strategy, is found in isolated island and is incorporated into the power networks under pattern, and the microgrid economic optimization containing photovoltaic, wind-powered electricity generation, electric power storage access is run,
The problem of in the presence of rarely having preferably optimization operational solution, provide a kind of light bavin of the access containing electric automobile by the present invention and store up
Micro-grid system optimizing operation method is to solve the above problems.
The light bavin storage micro-grid system of the present invention is similar with traditional light bavin storage micro-grid system, including photovoltaic generating system and bavin
The class distributed power source of hydro power generation system two, and the lithium battery group energy-storage system for energy storage, the present invention mainly study be
In the case where being accessed containing electric automobile, the optimization operation of light bavin storage micro-grid system, therefore, micro-grid system of the invention is also set
Have and electric automobile is accessed into micro-grid system and the charging pile charged to electric automobile.For the ease of being said to the present invention
It is bright, first the model of foregoing various systems is briefly introduced herein.
For photovoltaic generating system, simplified photovoltaic module steady state power output model, it is believed that photovoltaic cell is exerted oneself
It is only related to environment temperature with solar radiation value.
T (t)=Tair(t)+0.0138[1+0.031Tair(t)](1-0.042Vw)G(t) (2)
In formula：fpvFor photovoltaic array derating factor；PSTCFor standard test condition (Standard Test Conditions,
STC), i.e. sunshine incident intensity 1000W/m2, the peak power output at 25 DEG C of environment temperature, kW；G (t) is actual illumination
Intensity, W/m2；GSTCFor STC intensities of illumination, 1000W/m is taken2；K is temperature power coefficient, take -0.45%/DEG C；T(t)、Tair(t)
For the surface temperature and environment temperature of t photovoltaic array, DEG C；TSTCFor STC photovoltaic array temperature, 25 DEG C are taken；Tmax,TminPoint
Not Wei the degree/day maximum and minimum value；tpAt the time of for mean temperature；VmFor current wind speed, m/s.
The depletion charge of diesel power generation system is mainly relevant with its power output, can be represented with quadratic polynomial：
CDE=aPDE 2+bPDE+c (4)
Wherein, CDEFor the fuel cost of diesel-driven generator；PDEFor the power output of diesel-driven generator；A, b, c send out for diesel oil
The coefficient of motor fuels cost function, it is relevant with specific diesel-driven generator type.
Lithium battery energy storage battery system is mainly made up of lithium battery, the state-of-charge (state of charge, SOC) of lithium battery
The dump energy of lithium battery is described, is the important parameter of battery management, its expression formula
In formula：CNFor lithium battery nominal capacity；Ie(t) it is the actual charging and discharging currents of t lithium battery (A)；Pbat(t) it is t
Moment lithium battery is exerted oneself (kW)；U is lithium battery group terminal voltage (V)；SOC (t) is state-of-charge of the lithium battery in t
(%)；ηchFor lithium cell charging efficiency；ηdisFor lithium battery discharging efficiency.
Electric automobile is, using vehicle power as power, to be started with the energy being stored in lithium battery, with it is pollution-free, make an uproar
The features such as sound is low, energy utilization rate is high.Electric automobile daily travel S approximately meets logarithm normal distribution, i.e. S~logN (μt,
σt 2), its probability density function is
State-of-charge (SOC) when electric automobile networks can be obtained by electric automobile daily travel S, so as to obtain its charging
Duration：
In formula, W100For hundred km power consumption, ηC_EVFor the automobile charge efficiency in power station.By monte carlo method, add up
The daily load for obtaining certain amount charging electric vehicle is：
In formula, PEVload(t) it is total charge power of t periods, t=1,2 ..., 24；N is the sum of schedulable electric automobile
Amount；Pi(t) it is charge power of i-th electric automobile in the t periods.
Because the demand of micro-grid system is different with the effect that lithium battery energy storage battery unit is played in systems, lithium battery energy storage battery list
Element type and its operation function are also different.The present invention stores up micro-grid system for centralized control type light bavin, to microgrid level center
Controller carries out economic optimization scheduling, so that lithium battery group realizes peak load shifting function in systems as an example, establishes comprising lithium electricity
Pond energy storage and the micro-grid system economical operation Optimized model of electric automobile charging pile access.
Micro-grid system is stored up containing the light bavin that electric automobile is accessed for the present invention, there are many influence micro-grid system optimization operations
Factor to be optimized, for example, micro-grid system operating cost, micro-grid system depreciable cost, Environmental costs of micro-grid system etc., if
Allow micro-grid system to the electric income of repurchase of power network sale of electricity, in addition to power network to micro-grid system, for independent microgrid system, go back
Need the power failure rejection penalty of consideration micro-grid system.So, those factors to be optimized are chosen as micro-grid system running optimizatin
It can account for according to actual needs.
It is to be handled by the way of multiple optimization object functions are set up for factor to be optimized, in the present invention, Mei Geyou
Change one or more factor to be optimized that object function is used to optimize influence micro-grid system optimization operation, then form multiple target
Optimization problem.In present embodiment, 3 optimization object functions are employed illustrative.
First optimization object function, it is used for the system operation cost for optimizing micro-grid system so that system operation cost is most
Low, the first optimization object function is expressed as：
Wherein, F1For the system operation cost of micro-grid system, Pi(t) it is the active of i-th distributed power source in micro-grid system
Power output；CfFor the consuming cost of distributed power source；ComFor micro-grid system operational management cost；CgridFor micro-grid system and electricity
Net interaction cost, Pgrid(t) it is interact power of the micro-grid system with power network, micro-grid system defers to purchase from power network during absorbed power
Electricity price cp(t), micro-grid system to power network power output when defer to sale of electricity electricity price cs(t), expression is as follows：
Second optimization object function, it is used for the system depreciable cost for optimizing micro-grid system so that system depreciable cost is most
It is low, the optimization aim mainly describe the installation cost depreciation of micro-grid system operating cost and each distributed power source and energy storage device because
Element.The service life of lithium battery group can be reduced in view of frequent discharge and recharge, and then increases the operating cost of micro-grid system indirectly, will
The replacement cost of lithium battery is converted into operating cost, can more realistically reflect the life of storage battery to operating cost and actual receipts
The influence of benefit.Therefore, the present invention devises the optimization object function for the depreciable cost for disregarding lithium battery and meter lithium battery.
When system depreciable cost disregards the depreciable cost of lithium battery, second optimization object function is expressed as：
Wherein,CACC=CINSfcr,
F2For the system depreciable cost of micro-grid system, Pi(t) it is the active power output of i-th of distributed power source in system；
CfFor the consuming cost of distributed power source；ComFor micro-grid system operational management cost；CDG-DERFor the installation depreciation of distributed power source
Cost；CACCFor the installation cost Average Annual Cost of distributed power source；PrFor the rated power of distributed power source；fcfFor capacity because
Son；CINSFor the installation cost of distributed power source；fcrFor capital recovery factor；D is interest rate or allowance for depreciation；L is distributed power source
Life-span；Pbat,iThe average charge-discharge electric power of period i lithium battery is represented, positive sign represents electric discharge, and negative sign represents charging；γ is new lithium electricity
The pond SOC constraint penalty factors, can be described as lithium battery increase or reduce the expense of unit quantity of electricity；ηbat,c,ηbat,dFor lithium electricity
Pond efficiency for charge-discharge.
When system depreciable cost is included in the depreciable cost of lithium battery, second optimization object function is expressed as：
Wherein,
CbwFor charging and discharging lithium battery depreciable cost；Cbat,repFor lithium battery replacement cost；QlifetimeFor the battery cell full longevity
Life output total electricity；
CACC=CINSfcr,
Pi(t) it is the active power output of i-th of distributed power source in system；CfFor the consuming cost of distributed power source；
ComFor micro-grid system operational management cost；CDG-DERFor the installation depreciable cost of distributed power source；CACCFor the peace of distributed power source
Dress up this Average Annual Cost；PrFor the rated power of distributed power source；fcfFor capacity factor measure；CINSFor the installation of distributed power source
Cost；fcrFor capital recovery factor；D is interest rate or allowance for depreciation；L is the life-span of distributed power source；Pbat,iRepresent period i lithium electricity
The average charge-discharge electric power in pond, positive sign represents electric discharge, and negative sign represents charging；γ is the new lithium battery SOC constraint penalty factors, can be with
It is described as lithium battery increase or reduces the expense of unit quantity of electricity；ηbat,c,ηbat,dFor charging and discharging lithium battery efficiency.
3rd optimization object function, it is used for the system environments cost for optimizing micro-grid system so that system environments cost is most
Low, the 3rd optimization object function is expressed as：
Wherein, kiFor the CO of i-th of distributed power source2Emission factor, kgridFor the CO of power network2Emission factor, Pgrid(t) it is
T micro-grid system is to the purchase of electricity of power network, Pi(t) it is the active power output of i-th of distributed power source in system.
It is understood that Existence restraint condition during the operation of micro-grid system, it, which optimizes operation, needs in constraints
Limitation under carry out, in the present invention, the constraints of micro-grid system is one in following constraints or more than one
Any combination：
1) the output constraint condition of distributed power source
Pi,min≤Pi(t)≤Pi,max (17)
Wherein, Pi,min、Pi,maxIn the minimum and maximum power output of respectively i-th distributed power source, micro-grid system
The active power output of i distributed power source；
2) lithium battery memory capacity constraints
SOCmin≤SOC(t)≤SOCmax (18)
Wherein, SOC (t) is the state-of-charge of t lithium battery, SOCminThe minimum value allowed for lithium battery charge state,
SOCmaxThe maximum allowed for lithium battery charge state；
3) micro-grid system interacts power constraints with power network
Pgrid,min≤Pgrid(t)≤Pgrid,max (19)
Wherein, Pgrid,min,Pgrid,maxRespectively micro-grid system and power network minimum and maximum transimission power, Pgrid(t) to be micro-
Net system interacts power with power network；
4) electric automobile access constraints
The power that electric automobile absorbs from micro-grid system is constrained by charging pile power output and charging pile number：
N·PEV,min≤PEV(t)≤N·PEV,max (20)
Wherein, N is charging pile number, P in micro-grid systemEV,min、PEV,maxRespectively charging pile open circuit loss and maximum are defeated
Go out power, PEV(t) power absorbed for electric automobile from micro-grid system.
5) micro-grid system power-balance constraint condition
Wherein, PLoadFor all electric loads of whole micro-grid system, PgridFor interact power of the micro-grid system with power network, NG
For distributed electrical Source Type, PDG,iFor the power output of i-th of distributed power source.
By foregoing processing and analysis, each optimization object function of the invention characterizes an objective optimization and asked
Topic, then the present invention light bavin accessed containing electric automobile can be stored up the operation of micro-grid system economic optimization be summarized as Constrained (with
Exemplified by aforementioned constraint condition considers) multi-objective optimization question, its mathematical expression is as follows：
MinF=min (F1,F2,F3) (22)
It is understood that there is great difficulty in multi-objective optimization question, in solution therefore, by multiple-objection optimization
Problem is converted into single-object problem, is with realistic meaning.The present invention runs many mesh to solve micro-grid system optimization
Optimization problem is marked, the Optimized model based on satisfaction principle is introduced, with the fuzzy overall evaluation in ambiguity function, by fuzzy
Appraisal process changes into single-object problem.
So-called satisfaction refers to the gratifying degree of performance of solution.By micro-grid system energy management and expiring that optimization is run
Meaning degree is defined as one group of photovoltaic/bavin and sends power, battery charging and discharging, interacted with bulk power grid power etc. under state variable, to be optimized
System operation cost, depreciable cost, the satisfaction of Environmental costs are directed in the satisfaction of factor, such as present embodiment.
Solve the single goal optimal solution of Different Optimization target under the constraints respectively first.Secondly the optimal solution tried to achieve is substituted into
Respective membership function, each sub-goal function obfuscation is occured simultaneously as newly with weight sets combined structure membership function
Fitness function.Last foundation principle of fuzzy, using comprehensive study particle cluster algorithm (Comprehensive
Learning Particle Swarm Optimization, CLPSO), ask for causing the solution that fuzzy evaluation result is optimal, then should
Solution is the optimal solution under multi-objective optimization question.
It is the first step to set up extent function, and setting up the method for extent function has using neural network, based on mesh
Scalar functions are directly set up, set up using fuzzy logic, setting up 4 kinds according to the search cost of solution.Application is based on fuzzy theory herein
Fuzzy comprehensive evoluation process processing micro-grid system optimization operation multi-objective optimization question.By sub-goal function obfuscation, principle
Defer on the premise of system requirements is met, factor to be optimized, such as operating cost, maintenance cost, reduction ring are reduced as far as possible
Border protection conversion cost, then desired value have a higher limit and without lower limit, therefore the membership function of selection half shape of drop.Using drop half
The membership function of Γ distributions, is expressed as：
Wherein, fi,minFor i-th of minimum value under constraints in multiple optimization object functions, fiFor multiple optimization mesh
I-th of actual value in scalar functions.
Based on above-mentioned membership function, according to the constraints in the present invention, multiple optimization object functions are characterized
Multi-objective optimization question is converted into single-object problem, using fuzzy overall evaluation, including：
Set up the evaluation factor collection U={ μ of multi-objective optimization question1,μ2,...,μi}
Set up weight sets A={ a1,a2,...,ai, wherein,
Set up opinion rating V={ opinion rating 1, opinion rating 2 ..., opinion rating m }；
The evaluation R of i-th of objective optimisation problems in multi-objective optimization questioniIt is the fuzzy subset R on Vi={ ri1,
ri2,...,rim, the evaluation vector of multi-objective optimization question constitutes i × m rank Judgement Matrixes R；
Using the synthesis computing of fuzzy matrix, the comprehensive evaluation model B=A ο R of single-object problem are obtained；
The final appraisal results Z=BF of single-object problem, wherein, F=[f1,f2,...,fi]T, fiTo be multiple excellent
Change i-th of actual value in object function, Fuzzy computings are using M (, ⊕) operator.
In order to make it easy to understand, for system operation cost that in the present embodiment, 3 optimization object functions are characterized, depreciation into
Originally, the satisfaction of Environmental costs is illustrative.
Evaluation factor collection is comprising the index set U={ μ for characterizing system operation cost, depreciable cost, Environmental costs1,μ2,
μ3}.Weight sets A={ a in appraisement system1,a2,a3,Wherein aiCharacterize objective optimisation problems FiIn whole index body
Significance level in system.Entropy weight method is combined with subjective weight, determines that weight sets takes a1=0.4, a2=0.4, a1=0.3.Mould
Paste is divided to be divided using Pyatyi, opinion rating V=it is excellent, it is good, in, it is poor, bad.R is evaluated to i-th of objective optimizationiIt is the mould on V
Paste subset Ri={ ri1,ri2,ri3,ri4,ri5, three single index evaluation vectors constitute 3 × 5 rank Judgement Matrix R.
If real data integrates as F=[f1,f2,f3]T, using the synthesis computing of fuzzy matrix, obtain comprehensive evaluation model B
And final appraisal results Z.
Z=BF (26)
Wherein Fuzzy computings are using M (, ⊕) operator.
Certainly, more simple mode, many mesh that multiple optimization object functions are characterized can also be used for the present invention
Mark optimization problem is converted into single-object problem, for example, being constructed using weigthed sums approach to multi-objective optimization question
Lagrange functions, the single-object problem changed into：
L(F,λ1,λ2,...,λi)=λ1F1(θ)+λ2F2(θ)+...+λiFi(θ)
Wherein, F is the expression of single-object problem, FiFor the expression formula of i-th of optimization object function, λiFor i-th
The weight of optimization object function.
Using comprehensive study particle cluster algorithm (Comprehensive Learning Particle Swarm
Optimization, CLPSO) above mentioned problem is solved.Basic thought is：The renewal of particle rapidity is considered not only and worked as
Preceding itself desired positions pbest and global desired positions gbest, also learns to the past desired positions of other particle.So
The diversity of particle can be ensured, it is to avoid PSO Premature Convergence.
In order to be better understood from the present invention, using positioned at the Hefei City, Anhui Province Anhui of 31 ° 52 " of north latitude, 117 ° 17 " of east longitude
University uses up in the school district of garden.Include the exemplary micro-grid systems of 400V/100kW and 400V/20kW test-type micro-grid system two parts, electricity
Gas structural representation such as Fig. 1.Using alternating current-direct current mixing bus, including photovoltaic generating system, diesel power generation system, lithium battery group
It is energy-storage system, charging pile (10), the two-way inverter of two-way DC/DC, AC/DC, two-way grid simulator, electronic load, conventional
Load is illustrative.
In this example, distributed power source (DG) relevant information such as table 1,2.
The distributed power source of table 1 and schedulable load cost and correlative charges coefficient
The diesel-driven generator emission factor of table 2
Economical operation model, research microgrid is in grid-connected/two kinds of isolated island operational mode, it is considered under typical daily load, different
Multi-objective optimization question in illuminance scene.Micro-grid system under different operational modes and scene is passed through using CLPSO optimized algorithms
Help running optimizatin.Take maximum iteration 500, population number 30, distributed energy species 3 kinds of (diesel-driven generator DG, lithium batteries
BT, solar energy photovoltaic panel PV), acceleration parameter c1=c2=2, weight factor Wmin=0.4, Wmax=0.9.According to different fortune
Row mode and meteorological condition, are divided into four kinds of different scenes.Light bavin stores up micro-grid system economic optimization operation result under each scene, please
Referring to Fig. 2 to 5.
The smooth bavin of table 3 storage micro-grid system optimization Run-time scenario
Distributed power source output power is paid the utmost attention to when control strategy defers to grid-connected；Power purchase and sale of electricity expense, with average electricity
Take calculating.Depreciable cost is fallen into a trap and lithium battery depreciable cost.Under island operation state, in the object function of system operation cost
Add power failure penalty term.
The load of this example light bavin storage micro-grid system is the floor heat light and power electricity consumption of University of Anhui's science and engineering building B buildings one, because having
Motor experiment platform etc. tests electricity consumption, when load peak is generally present in 11,15 when and when 23, such as Fig. 6 and 7.Be incorporated into the power networks mould
Under formula, such as illuminance is not good, then needs from bulk power grid absorbed power, as shown in Grid curves in Fig. 1；Such as illumination quality well,
Photovoltaic power output can meet system loading up to 17 when 9, during this period except supply load, also toward charging storage in lithium battery
Energy.Because considering Environmental costs optimization aim, diesel-driven generator DG is simultaneously not cut into system；Because considering that lithium battery BT service lifes are related to
Depreciable cost, it is to avoid frequent discharge and recharge, a charging and discharging is distinguished in one day.
Under islet operation pattern, there is no energetic interaction with bulk power grid.Rely primarily on diesel-driven generator and lithium battery in microgrid
Load and energy is provided.In the case that illuminance is not good, powered by diesel-driven generator DG to load.Under the good scene of illuminance,
PV provides load energy and gives energy-storage lithium battery charging；19 when 24, and lithium battery group is exerted oneself, but by the power output upper limit about
Beam, the remaining load and energy of DG supplements.
Only preferred embodiments of the present invention are described above, but are not to be construed as limiting the scope of the invention.This
Invention is not only limited to above example, and its concrete structure allows to change.In a word, all guarantors in independent claims of the present invention
The various change made in the range of shield is within the scope of the present invention.
Claims (10)
1. storing up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, the micro-grid system includes, and is used as distribution
The photovoltaic generating system and diesel power generation system of power supply, for the lithium battery group energy-storage system of energy storage, and by electric automobile
Access micro-grid system and the charging pile charged to electric automobile, it is characterised in that：
Multiple optimization object functions are set up, the optimization object function is used to optimize the to be optimized of influence micro-grid system optimization operation
Factor；
Determine the constraints of micro-grid system；
According to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is converted into single object optimization
Problem, and micro-grid system operation is optimized.
2. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The factor to be optimized, including micro-grid system operating cost, micro-grid system depreciable cost, the Environmental costs of micro-grid system, electricity
The electric income of repurchase of net to micro-grid system, the power failure rejection penalty of micro-grid system.
3. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The optimization object function, including the first optimization object function, it is used for the system operation cost for optimizing micro-grid system, made
System operation cost it is minimum, first optimization object function is expressed as：
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Sale of electricity electricity price c is deferred to during powers(t), expression is as follows：
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4. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The optimization object function, including the second optimization object function, it is used for the system depreciable cost for optimizing micro-grid system, made
Obtain system depreciable cost minimum, when system depreciable cost disregards the depreciable cost of lithium battery, the second optimization object function table
Up to for：
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Wherein,CACC=CINSfcr,
Pi(t) it is the active power output of i-th of distributed power source in system；CfFor the consuming cost of distributed power source；ComTo be micro-
Net system operation management cost；CDG-DERFor the installation depreciable cost of distributed power source；CACCFor the installation cost of distributed power source
Average Annual Cost；PrFor the rated power of distributed power source；fcfFor capacity factor measure；CINSFor the installation cost of distributed power source；fcr
For capital recovery factor；D is interest rate or allowance for depreciation；L is the life-span of distributed power source；Pbat,iRepresent that period i lithium battery averagely fills
Discharge power, positive sign represents electric discharge, and negative sign represents charging；γ is the new lithium battery SOC constraint penalty factors, can be described as lithium
Battery increases or reduced the expense of unit quantity of electricity；ηbat,c,ηbat,dFor charging and discharging lithium battery efficiency.
5. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The optimization object function, including the second optimization object function, it is used for the system depreciable cost for optimizing micro-grid system, made
Obtain system depreciable cost minimum, when system depreciable cost is included in the depreciable cost of lithium battery, the second optimization object function table
Up to for：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
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<mi>min</mi>
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<msub>
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<mi>&Sigma;</mi>
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</mfenced>
Wherein,CbwFor charging and discharging lithium battery depreciable cost；Cbat,repFor lithium battery replacement cost；Qlifetime
Total electricity is exported for the battery cell life-cycle；
CACC=CINSfcr,
Pi(t) it is the active power output of i-th of distributed power source in system；CfFor the consuming cost of distributed power source；ComTo be micro-
Net system operation management cost；CDG-DERFor the installation depreciable cost of distributed power source；CACCFor the installation cost of distributed power source
Average Annual Cost；PrFor the rated power of distributed power source；fcfFor capacity factor measure；CINSFor the installation cost of distributed power source；fcr
For capital recovery factor；D is interest rate or allowance for depreciation；L is the life-span of distributed power source；Pbat,iRepresent that period i lithium battery averagely fills
Discharge power, positive sign represents electric discharge, and negative sign represents charging；γ is the new lithium battery SOC constraint penalty factors, can be described as lithium
Battery increases or reduced the expense of unit quantity of electricity；ηbat,c,ηbat,dFor charging and discharging lithium battery efficiency.
6. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The optimization object function, including the 3rd optimization object function, it is used for the system environments cost for optimizing micro-grid system, made
System environments cost it is minimum, the 3rd optimization object function is expressed as：
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</mrow>
</msub>
<mo>(</mo>
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</mrow>
</mrow>
Wherein, kiFor the CO of i-th of distributed power source2Emission factor, kgridFor the CO of power network2Emission factor, Pgrid(t) when being t
Carve purchase of electricity of the micro-grid system to power network, Pi(t) it is the active power output of i-th of distributed power source in micro-grid system.
7. according to claim 1 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The constraints of the micro-grid system is one or more than one any combination in following constraints：
1) the output constraint condition of distributed power source
Pi,min≤Pi(t)≤Pi,max
Wherein, Pi,min、Pi,maxThe minimum and maximum power output of respectively i-th distributed power source, Pi(t) in micro-grid system
The active power output of i-th of distributed power source；
2) lithium battery memory capacity constraints
SOCmin≤SOC(t)≤SOCmax
Wherein, SOC (t) is the state-of-charge of t lithium battery, SOCminThe minimum value allowed for lithium battery charge state,
SOCmaxThe maximum allowed for lithium battery charge state；
3) micro-grid system interacts power constraints with power network
Pgrid,min≤Pgrid(t)≤Pgrid,max
Wherein, Pgrid,min,Pgrid,maxRespectively micro-grid system and power network minimum and maximum transimission power, Pgrid(t) it is microgrid system
System interacts power with power network；
4) electric automobile access constraints
The power that electric automobile absorbs from micro-grid system is constrained by charging pile power output and charging pile number：
N·PEV,min≤PEV(t)≤N·PEV,max
Wherein, N is charging pile number, P in micro-grid systemEV,min、PEV,maxRespectively charging pile open circuit loss and maximum work output
Rate, PEV(t) power absorbed for electric automobile from micro-grid system；
5) micro-grid system power-balance constraint condition
<mrow>
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<mi>P</mi>
<mrow>
<mi>L</mi>
<mi>o</mi>
<mi>a</mi>
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<mo>=</mo>
<msub>
<mi>P</mi>
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<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
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</mrow>
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<mo>&Sigma;</mo>
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<mi>G</mi>
</mrow>
</munderover>
<msub>
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<mrow>
<mi>D</mi>
<mi>G</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
</mrow>
Wherein, PLoadFor all electric loads of whole micro-grid system, PgridFor interact power of the micro-grid system with power network, NG is to divide
Cloth power supply type, PDG,iFor the power output of i-th of distributed power source.
8. micro-grid system optimizing operation method is stored up according to the light bavin of any described access containing electric automobile of claim 1 to 7, its
It is characterised by：According to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is converted into monocular
Optimization problem is marked, using fuzzy overall evaluation, including：
Set up the evaluation factor collection U={ μ of multi-objective optimization question1,μ2,...,μi}；
Set up weight sets A={ a1,a2,...,ai, wherein,
Set up opinion rating V={ opinion rating 1, opinion rating 2 ..., opinion rating m }；
The evaluation R of i-th of objective optimisation problems in multi-objective optimization questioniIt is the fuzzy subset R on Vi={ ri1,ri2,...,
rim, the evaluation vector of multi-objective optimization question constitutes i × m rank Judgement Matrixes R；
Using the synthesis computing of fuzzy matrix, the comprehensive evaluation model of single-object problem is obtained
The final appraisal results Z=BF of single-object problem, wherein, F=[f1,f2,...,fi]T, fiFor multiple optimization mesh
I-th of actual value in scalar functions, Fuzzy computings are usedOperator.
9. according to claim 8 store up micro-grid system optimizing operation method containing the light bavin that electric automobile is accessed, its feature exists
In：The μiUsing the membership function of drop half Γ distributions, it is expressed as：
<mrow>
<msub>
<mi>&mu;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
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<mi>f</mi>
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<mi>f</mi>
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<mi>n</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, fi,minFor i-th of minimum value under constraints in multiple optimization object functions.
10. according to any described light bavin storage micro-grid system optimizing operation method accessed containing electric automobile of claim 1 to 7,
It is characterized in that：According to the constraints, the multi-objective optimization question that multiple optimization object functions are characterized is converted into list
Objective optimisation problems, construct Lagrange functions, the single goal changed into is excellent using weigthed sums approach to multi-objective optimization question
Change problem：
L(F,λ1,λ2,...,λi)=λ1F1(θ)+λ2F2(θ)+...+λiFi(θ)
Wherein, F is the expression of single-object problem, FiFor the expression formula of i-th of optimization object function, λiFor i-th of optimization
The weight of object function.
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| CN108656999A (en) * | 2018-06-05 | 2018-10-16 | 江苏景源旭新能源科技有限公司 | A kind of two-way charging pile |
| CN109149561A (en) * | 2018-08-13 | 2019-01-04 | 国网江苏省电力有限公司南京供电分公司 | A kind of power distribution network static optimization method storing up charging tower access based on light |
| CN110297456A (en) * | 2018-03-23 | 2019-10-01 | 中国石油化工股份有限公司 | A kind of regulator control system and method for the electrical integrated supply process of oil |
| CN110896246A (en) * | 2019-12-05 | 2020-03-20 | 西南交通大学 | Configuration optimization method of hybrid energy storage type tramcar system |
| CN114865673A (en) * | 2022-05-31 | 2022-08-05 | 国网湖北省电力有限公司荆门供电公司 | Micro-grid charge-storage cooperative optimization method, device, equipment and storage medium |
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| CN115513949A (en) * | 2022-11-02 | 2022-12-23 | 南方电网数字电网研究院有限公司 | Method, device and program product for controlling operation of power distribution system with microgrid access |
| CN117613959A (en) * | 2023-11-28 | 2024-02-27 | 国网江苏省电力有限公司镇江供电分公司 | Novel power distribution system hybrid energy storage optimal configuration method considering electric automobile |
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Application publication date: 20170922 |