CN105811409B - A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile - Google Patents

A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile Download PDF

Info

Publication number
CN105811409B
CN105811409B CN201610273527.0A CN201610273527A CN105811409B CN 105811409 B CN105811409 B CN 105811409B CN 201610273527 A CN201610273527 A CN 201610273527A CN 105811409 B CN105811409 B CN 105811409B
Authority
CN
China
Prior art keywords
microgrid
power
generation unit
grid
accumulator
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201610273527.0A
Other languages
Chinese (zh)
Other versions
CN105811409A (en
Inventor
程启明
黄山
褚思远
张强
杨小龙
张海清
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai University of Electric Power
Original Assignee
Shanghai University of Electric Power
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai University of Electric Power filed Critical Shanghai University of Electric Power
Priority to CN201610273527.0A priority Critical patent/CN105811409B/en
Publication of CN105811409A publication Critical patent/CN105811409A/en
Application granted granted Critical
Publication of CN105811409B publication Critical patent/CN105811409B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION 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
    • B60L55/00Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381Dispersed generators
    • H02J3/382Dispersed generators the generators exploiting renewable energy
    • H02J3/383Solar energy, e.g. photovoltaic energy
    • H02J3/385Maximum power point tracking control for photovoltaic sources
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381Dispersed generators
    • H02J3/382Dispersed generators the generators exploiting renewable energy
    • H02J3/386Wind energy
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381Dispersed generators
    • H02J3/387Dispersed generators using fuel cells
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling of the sharing of output between the generators, converters, or transformers
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/76Power conversion electric or electronic aspects
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/10Internal combustion engine [ICE] based vehicles
    • Y02T10/40Engine management systems

Abstract

The present invention relates to a kind of microgrid multiple target traffic control methods containing hybrid energy storage system of electric automobile, which is characterized in that includes the following steps:1) consider electric vehicle simultaneously as load and generator unit, and on the basis of meter and cogeneration of heat and power heating income, build energy internet;2) the microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile is built according to energy internet, considers economy and environmental benefit, obtain the object function and constraints of the model;3) microgrid Multiobjective Optimal Operation model is solved by NSGA II multi-objective optimization algorithms, obtains Pareto front ends solution, and obtain the optimization traffic control scheme of the micro-capacitance sensor in 24 hours.Compared with prior art, the present invention have many advantages, such as it is good in economic efficiency, improvement environment, reasonable distribution output, peak load shifting, prolong the service life.

Description

A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile
Technical field
The present invention relates to micro-capacitance sensor field, more particularly, to a kind of microgrid multiple target containing hybrid energy storage system of electric automobile Traffic control method.
Background technology
Micro-capacitance sensor (Micro-Grid) is also translated into microgrid, is a kind of new network structure, is one group of micro battery, load, storage The system unit that energy system and control device are formed.Micro-capacitance sensor, which is one, can realize self-contr ol, protection and the autonomy of management System can both be incorporated into the power networks with external electrical network, can also isolated operation.Micro-capacitance sensor is one of relatively traditional bulk power grid general It reads, refers to the network that multiple distributed generation resources and its related load are formed according to certain topological structure, and pass through static switch It is associated with to normal grid.
Exploitation and extension micro-capacitance sensor can sufficiently promote the extensive access of distributed generation resource and regenerative resource, realization pair The highly reliable supply of load various energy resources form is a kind of effective means for realizing active power distribution network, is traditional power grid to intelligence It can power grid transition.
With the development of national economy, electricity needs increases rapidly, and investment is concentrated on thermoelectricity, water power by power department mostly And in the construction of the large-scale centralizeds such as nuclear power power supply and super-pressure remote conveying power grid.But with the continuous expansion of power grid scale Greatly, it is also increasingly shown especially the drawbacks of ultra-large electric system, of high cost, operation difficulty is big, it is difficult to it is higher and higher to adapt to user Security and reliability requires and diversified power demands.
Under the overall background changed in recent years in electricity, microgrid energy optimum management increasingly receives attention.Microgrid energy optimizes Scheduling is worked as by micro- source, energy-storage units, load and the power grid to distributed power generation (distributed generation, DG) Preceding operating status and historical data are analyzed, and then make the assessment and prediction of science;According to all types of points in micro-grid system The different selections of the classification of priority scheduling power, load rating and major network system electricity price type that cloth power supply is enjoyed, different energy Scheduling strategy is measured, determines corresponding Optimal Operation Model, the optimal fortune of following different dispatching cycles is solved using effective algorithm Row plan, including to degree type unit adjustable in microgrid the plan of output a few days ago, energy-storage units operation plan and Real-Time Scheduling a few days ago Plan provides electric energy and thermal energy to user, realizes cogeneration of heat and power (combined heat and power, CHP) simultaneously.For Electric vehicle (electric vehicles, EV) user is both the load of microgrid and can be used as power supply.EV is rationally accessed Microgrid is of great significance for economy, environment and energy security problem etc..The scheduling problem of micro-capacitance sensor is just increasingly subject to close Note.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind is good in economic efficiency, changes Kind environment, reasonable distribution output, peak load shifting, the microgrid multiple target containing hybrid energy storage system of electric automobile to prolong the service life Traffic control method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile, includes the following steps:
1) consider electric vehicle simultaneously as load and generator unit, and meter and cogeneration of heat and power heat the basis of income On, build energy internet;
2) the microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile is built according to energy internet, it is comprehensive It closes and considers economy and environmental benefit, obtain the object function and constraints of the model;
3) microgrid Multiobjective Optimal Operation model is solved by NSGA-II multi-objective optimization algorithms, obtained Pareto front ends solve, and obtain the optimization traffic control scheme of the micro-capacitance sensor in 24 hours.
The target letter of microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile in the step 2) Number is:
min F3=wF1+(1-w)F2
Ci Install=Ri Install×Si
Ci,t Fuel=Ri,t Fuel×Pi,t
Ci Grid=RGrid,t×PGrid,t
Wherein, F3To consider the target of financial cost and Environmental costs, F1Totle drilling cost, F are run for microgrid2For pollution Object control expense, w are weights, and 0≤w≤1,For the mounting cost of i-th kind of distributed generation unit,It is i-th kind The operation and maintenance cost of distributed generation unit,For the fuel used expense of i-th kind of distributed generation unit,It is micro- Exchange the expense of power between net and bulk power grid, N is the installation sum of distributed generation unit, and t is current time, TiFor emulation Optimize total duration, μjFor the processing cost of jth kind pollutant, KijFor i-th of distributed generation unit jth pollutant discharge system Number, KGrid,jFor jth pollutant emission factor, pollutant emission is not present in regenerative resource distributed generation unit, this takes 0, W is total for pollutant kind, Ri InstallFor the unit installation cost of i-th kind of distributed generation unit, SiFor i-th kind of distribution The initial installed capacity of generator unit, Ri,t OMFor the unit operation and maintenance cost of i-th distributed generation unit, Pi,tIt is i-th Generated energy or battery cell's time charge-discharge electric power, R in the distributed generation unit unit intervali,t FuelFor i-th distribution The unit of fuel expense of generator unit, for regenerative resource distributed generation unit due to not needing to using fuel, this takes 0, RGrid,tFor the Spot Price of t moment external power grid, PGrid,tElectricity is interacted with external power grid for t moment microgrid, when microgrid is to external power grid It is taken during power purchase just, when microgrid is negative to being taken during external power grid sale of electricity.
The constraint item of microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile in the step 2) Part is:
Equality constraint:
Active power balance constraint:
Discharge and recharge of accumulator constrains:
SOCt-PSB,t/BATcap=SOCt+1
Inequality constraints:
Generated output power constrains:
Accumulator operation constraint:
PSB,mi≤PSB(t)≤PSB,ma
-Sinv,SB≤PSB(t)≤Sinv,SB
Electric vehicle charge and discharge constrain:
PEV, mi≤PEV(t)≤PEV, ma
-Sinv,EV≤PEV(t)≤Sinv,EV
Microgrid allows to interact power constraint with outer net:
Wherein, Pi,tIt contributes for i-th kind of distributed generation unit of t moment, PSB,tFor t moment accumulator cell charging and discharging electricity, PGrid,tPower, P are exchanged with external power grid for t moment microgridLoad,tFor t moment load, and t=1,2 ..., Ti, TiFor simulation optimization Total duration, N are that the installation of distributed generation unit is total, SOCtFor the SOC value of accumulator t moment, BATcapAlways hold for accumulator Amount, SOCt+1For the SOC value at accumulator t+1 moment,Respectively i-th kind of distributed generation unit t moment is contributed Upper and lower limit, PSB,mi、PSB,maRespectively accumulator minimum and maximum active power, Sinv,SBFor accumulator inverter capacity, PEV, mi、PEV, maRespectively accumulator of electric car minimum and maximum active power, Sinv,EVFor electric vehicle inverter capacity,Respectively microgrid interacts power upper and lower limit with external power grid in the t periods.
The pollutant includes CO2, SO2 and NOx
Micro-capacitance sensor in the step 3) in 24 hours optimizes traffic control scheme:
(1) it is preferentially handed over using the clean energy resource inside microgrid to meet workload demand and free power can be carried out with major network It changes;
(2) WT and PV power generation operations are in MPPT maximum power point tracking pattern;
(3) co-generation unit works in the method for operation of electricity determining by heat, and the active power output of MT is determined by thermic load;
(4) it when the active power output of WT, PV and MT meet whole electric loads, charges first to electric vehicle and accumulator, Monitor the charging and discharging state of accumulator simultaneously and then charge to fuel cell, when EV is full of according to cost of electricity-generating by It is high to Low to cut off WT or PV successively;
(5) EV improves microgrid benefit according to by different periods charge and discharge control, charges when microgrid electricity abundance to EV, When micro-grid power source deficiency is to major network power purchase, EV is not allowed to charge, and by the remaining charge transports of EV to microgrid;
(6) when the active power output of WT, PV and MT can not meet all loads of microgrid, battery discharging is preferentially selected, if There are still active vacancy, then call FC active power of output, and interior remaining capacity is transferred to microgrid and obtained by EV user during this period Take income;
(7) if all micro- sources cannot still meet microgrid safe and reliable operation in the range of output, according to the important of load Degree is cut off successively.
Compared with prior art, the present invention has the following advantages:
The beneficial effects of the present invention are:
First, the whole economic efficiency for making micro-capacitance sensor is more preferable, saves financial cost.
2nd, improve micro-capacitance sensor environmental situation, using new energy equipment, reduce and generated electricity using traditional contamination type Equipment generates electricity;Reduce the discharge of pernicious gas (CO2, SO2, NOx), improve ambient conditions.
3rd, the service efficiency in each micro- source in micro-capacitance sensor, the output in each micro- source of rational management, according to negative in real time are improved Lotus situation reasonable distribution is contributed.
4th, making full use of electric vehicle, in the peak time of load, electricity price is higher so as to play the role of " peak load shifting " Electric vehicle can sell electricity to micro-capacitance sensor, and when trough period, and new energy output has more than needed, and battery completely fills, then to electronic Automobile charges.
5th, extend the service life of accumulator and fuel cell, avoid overcharging for battery and either put caused damage excessively It is bad, extend battery life.
6th, cogeneration of heat and power operational mode improves the economy and the feature of environmental protection of micro-capacitance sensor, miniature gas turbine operate in Under the pattern of the fixed electricity of heat, the economy and the feature of environmental protection of micro-capacitance sensor are improved.
Description of the drawings
Fig. 1 is the micro-grid system structure chart described in description of the invention;
Fig. 2 is thermic load and the situation of electric load in the microgrid described in description of the invention;
Fig. 3 is the output power of the PV and WT described in description of the invention;
Fig. 4 is the NSGA-II algorithm flow charts described in description of the invention;
Fig. 5 is thermic load-electric power curves described in description of the invention;
Fig. 6 is the accumulator SOC curves described in description of the invention;
Fig. 7 is to contain electric vehicle and battery mixed energy storage system power curve described in description of the invention;
Fig. 8 is microgrid optimum results under the grid-connect mode described in description of the invention;
Fig. 9 is microgrid scheduling result under the grid-connect mode described in description of the invention;
Figure 10 contributes for each micro- source of microgrid under the grid-connect mode described in description of the invention;
Figure 11 is that the NSGA-II algorithm evolution processes described in description of the invention compare figure.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment:
The microgrid structure that the example of this paper uses is as shown in Figure 1.Microgrid in figure is by industrial load 4, thermoelectricity load 5, micro- Type gas turbine MT6, resident load 9, Commercial Load 10, wind-driven generator WT11, fuel cell FC12, photovoltaic cell PV14, Electric vehicle EV16 allows interruptible load 17 and 18 grade of accumulator to form, and microgrid is in grid-connect mode operating status.Accumulator Rated capacity is 500kWh, and depth of discharge is charged as most preferably when being 50%-75%, therefore initial quantity of electricity is set as 60%, fills Discharging efficiency is 1, ignores self discharge, the inverter capacity of accumulator is 40kVA;The inverter capacity of fuel cell is 30kVA; The power upper and lower limit of electric vehicle is set to 30kW, -30kW, and inverter capacity is 30kVA;Operation of fuel cells in The fixed electricity of heat, thus it to send out power with miniature gas turbine in a linear relationship, heating income is 0.12 yuan/(kWh);Electricity consumption peak When being 8~20 when, when being 20 during electricity consumption paddy~8 when.
1 electric vehicle model
It is ultracapacitor to provide energy for EV operations in this patent, ignores the self-discharge rate of super capacitor, then EV The model of energy-storage units is:
The energy stored in EV is:
In formula:S is the remaining capacity after EV charge and discharge;Q is the practical quantity of electric charge of super capacitor storage;QNFor super capacitor The maximum amount of charge of storage;Umax、UminRespectively ultracapacitor maximum operating voltage and minimum operating voltage;U0For super electricity Container initial voltage;ICFor charging and discharging currents.
Miniature gas turbine model:
Miniature gas turbine mathematical model is:
QMT(t)=Pe(t)(1-ηe(t)-η1)/ηe(t)
In formula:QMT(t) it is t moment gas turbine excess heat;Pe(t) electrical power for the output of t moment gas turbine;ηe (t) it is the generating efficiency of t moment gas turbine;η1Radiation loss coefficient for t moment gas turbine.
Qhe(t)=QMT(t)Khe
In formula:Qhe(t) heating capacity for the supply of t moment gas turbine flue gas waste heat;KheFor bromine cooling machine heating efficiency.
VMT=∑ (Pe(t)Δt/(ηe(t)L))
In formula:VMTAmount of natural gas for gas turbine consumption;T is the run time of gas turbine;L is natural gas low-heat Value, is taken as 9.7kWh/m3
The fuel cost calculation of miniature gas turbine is:
CMT=(Cn1/L)∑(Pe(t)Δt/(ηe(t))
In formula:CMTFor gas turbine fuel cost;Cn1For Gas Prices, this patent takes 0.5 $/m3
The Sino-Japan electric load of microgrid and day thermic load situation it is as shown in Figure 2;Each distributed electrical source dates are shown in Table 1;It is each The installation cost and energy cost in micro- source are shown in Table 2;The active power curves of intermittence power generation micro- source PV, WT are as shown in Figure 3;It is each The emissions data of micro- source pollutants is shown in Table 3.
The basic parameter in each micro- source of table 1
The installation cost and energy cost in each micro- source of table 2
The emissions data of each micro- source pollutants of table 3
The scheduling strategy that this patent is formulated is as follows:
(1) preferential wind-powered electricity generation (WT), photovoltaic (PV) using inside microgrid the clean energy resourcies such as generates electricity to meet workload demand simultaneously And free Power Exchange can be carried out with major network;
(2) WT and PV power generation operations are in MPPT maximum power point tracking pattern;
(3) co-generation unit (MT) works in the electricity determining by heat method of operation, and the active power output of MT is determined by thermic load;
(4) when the active power output of WT, PV and MT meet whole electric loads, first to electric vehicle (EV) and accumulator (SB) it charges, while monitors the charging and discharging state of accumulator and then charge to fuel cell (FC), it can when EV is full of To consider cut-out cost of electricity-generating higher WT or PV successively;
(5) EV improves microgrid benefit according to by different periods charge and discharge control, can be to EV when microgrid electricity abundance Arbitrarily charging;When micro-grid power source deficiency is to major network power purchase, consider for economy and stability, EV does not allow to charge, and EV can Remaining electricity is sold to microgrid;
It is (6) preferential that accumulator is selected to discharge when the active power output of WT, PV and MT can not meet microgrid so during load, FC is recalled if there are still active vacancy and carrys out active power of output, interior remaining capacity can be sold to by EV user during this period Microgrid is so as to obtain income;
(7) if all micro- sources cannot still meet microgrid safe and reliable operation in the range of output, according to the important of load Degree is cut off successively.
This patent solves micro-capacitance sensor models using NSGA-II algorithms in solution procedure.NSGA-II is NSGA The improvement of algorithm.NSGA-II multi-objective optimization algorithms are in engineering extensive utilization in practice.NSGA improvements mainly have:
(1) quick non-dominated ranking algorithm is proposed, on the one hand reduces the complexity of calculating, on the other hand it is by parent Population merges with progeny population so that follow-on population is chosen from double space, so as to remain the most Outstanding all individuals;
(2) elitism strategy is introduced, ensures that certain excellent population at individual will not be dropped during evolution, so as to improve The precision of optimum results;
(3) using crowding and crowding comparison operator, not only overcoming needs artificially to specify shared parameter in NSGA Defect, and as the standard of comparison between individual in population so that a physical efficiency in quasi- Pareto domains extends equally to Entire Pareto domains, ensure that the diversity of population.
Fig. 4 is NSGA-II multiple-objection optimization principle flow charts.The detailed process of NSGA-II is described as follows:
(1) initial population P is randomly generated0, non-bad sequence then is carried out to population, each individual is endowed order;Again to first Beginning population performs binary system algorithm of tournament selection, intersection and variation, obtains new population Q0, enable t=0.
(2) new group R is formedt=Pt∪Qt, to population Rt.Non- bad sequence is carried out, obtains non-bad front end F1, F2....
(3) to all Fi< is relatively operated by crowdednIt is ranked up, and wherein best N number of body is selected to form population Pt+1
(4) to population Pt+1Duplication is performed, intersects and makes a variation, forms population Qt+1
(5) if end condition is set up, terminate;Otherwise, t=t+1 is gone to (2).
Using financial cost and Environmental costs as target, above-mentioned microgrid is optimized.Thermic load-electric power curves such as Fig. 5 Shown, accumulator SOC is as shown in Figure 6.As seen from the figure, 0 when~8 when microgrid load it is also relatively light, distributed generation resource will be remaining Electric energy give accumulator (SB) charging;At 8~20 when, there are active vacancy, the output of PV, WT and MT in microgrid to meet At this moment workload demand in microgrid calls SB electric discharges, ensures micro net power quality;At 8~15 when between, SB can meet microgrid In workload demand, it is possible to reduce call the active power output of FC.At 20~6 when, SB charges, due to the Environmental costs of FC It is relatively low, it is preferential that FC is called to provide active power.FC works in electricity determining by heat pattern, therefore it is in a linear relationship with WT to send out power.
It is as shown in Figure 7 containing electric vehicle (EV) and battery mixed energy storage system power curve.As seen from the figure, SB, FC and EV It is that the mutual cooperation of both battery system and EV makes the power in microgrid more gentle collectively as backup power supply;And in microgrid When there is active vacancy, reduce microgrid from the purchase of electricity in outer net, improve the economic benefit of microgrid.Meanwhile EV is in microgrid Occur to charge when power abundance in microgrid to microgrid sale of electricity, playing one well during active shortage of power The effect of a " peak load shifting ".
Microgrid optimum results are as shown in Figure 8 under grid-connect mode.Microgrid scheduling result is as shown in Figure 9 under grid-connect mode.By scheming As it can be seen that the energy cost of PV, WT, SB and EV are than relatively low, it is required to meet that microgrid should preferentially call free of contamination PV and WT Power;Since the energy cost of FC is apparently higher than SB, should preferentially call SB, at the same EV user can to microgrid transmission power, The charge and discharge number of SB can be reduced in this way, extends the SB service life, and FC can be recalled when active vacancy occurs in microgrid;WT costs are most High, environmental pollution maximum, should finally consider to call.
The optimum results that each micro- source is contributed in microgrid under grid-connect mode are as shown in Figure 10.As seen from the figure, PV and WT are worked in MPPT maximum power point tracking pattern;Since SB depth of discharges for 50%~75% are that charging is best, from 7 when~18 when before SB put Power mode is maintained at 75%;When 6~18 when, EV user carries out selling electricity to microgrid according to microgrid load condition;At 0~7 when With 18 when~24 when, load is lighter in microgrid, and PV and WT charge after optimized distributionl requirement is met to SB, and SB is in charging mould Formula;FC is preferentially called at this time, recalls MT to meet active vacancy.At 7~18 when, electricity consumption is substantially increased in microgrid, SB In discharge mode, EV user also conveys electric energy to microgrid, reduces the output of FC and MT.
In order to illustrate the advantage of IQGA algorithms used herein, below by it and traditional GA algorithms and basis QGA algorithms It is compared.
Simulation parameter is as follows, the population of modified quantum genetic algorithm, common genetic algorithm and basic quantum genetic algorithm Scale is 100, and maximum genetic algebra is 200, and the binary length of each variable is 20, and weight coefficient w is taken as 0.8.3 kinds of algorithms Evolution curve it is as shown in figure 11.Table 4 gives 3 kinds of algorithm simulating results.
4 simulation result of table

Claims (4)

  1. A kind of 1. microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile, which is characterized in that including following Step:
    1) electric vehicle is considered simultaneously as load and generator unit, and on the basis of meter and cogeneration of heat and power heating income, structure Build energy internet;
    2) the microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile is built according to energy internet, synthesis is examined Consider economy and environmental benefit, obtain the object function and constraints of the model, described contains hybrid energy storage system of electric automobile The object function of microgrid Multiobjective Optimal Operation model be:
    min F3=wF1+(1-w)F2
    Ci Install=Ri Install×Si
    Ci,t Fuel=Ri,t Fuel×Pi,t
    Ci Grid=RGrid,t×PGrid,t
    Wherein, F3To consider the target of financial cost and Environmental costs, F1Totle drilling cost, F are run for microgrid2It is controlled for pollutant Reason expense, w are weights,For the mounting cost of i-th kind of distributed generation unit,For i-th kind of distributed generation unit Operation and maintenance cost,For the fuel used expense of i-th kind of distributed generation unit,Between microgrid and bulk power grid The expense of power is exchanged, N is the installation sum of distributed generation unit, and t is current time, TiFor simulation optimization total duration, μjFor The processing cost of jth kind pollutant, KijFor i-th of distributed generation unit jth pollutant emission factor, KGrid,jFor jth class Pollutant discharge coefficient, W is total for pollutant kind, Ri InstallFor the unit installation cost of i-th kind of distributed generation unit, Si For the initial installed capacity of i-th kind of distributed generation unit, Ri,t OMUnit operation and maintenance expense for i-th distributed generation unit With Pi,tFor generated energy in i-th distributed generation unit unit interval or battery cell's time charge-discharge electric power, Ri,t Fuel For the unit of fuel expense of i-th distributed generation unit, RGrid,tFor the Spot Price of t moment external power grid, PGrid,tFor t moment Microgrid interacts electricity with external power grid;
    3) microgrid Multiobjective Optimal Operation model is solved by NSGA-II multi-objective optimization algorithms, before obtaining Pareto End solution, and obtain the optimization traffic control scheme of the micro-capacitance sensor in 24 hours.
  2. 2. a kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile according to claim 1, It is characterized in that, in the step 2) the microgrid Multiobjective Optimal Operation model containing hybrid energy storage system of electric automobile constraint Condition is:
    Equality constraint:
    Active power balance constraint:
    Discharge and recharge of accumulator constrains:
    SOCt-PSB,t/BATcap=SOCt+1
    Inequality constraints:
    Generated output power constrains:
    Accumulator operation constraint:
    PSB,mi≤PSB(t)≤PSB,ma
    -Sinv,SB≤PSB(t)≤Sinv,SB
    Electric vehicle charge and discharge constrain:
    PEV,mi≤PEV(t)≤PEV,ma
    -Sinv,EV≤PEV(t)≤Sinv,EV
    Microgrid allows to interact power constraint with outer net:
    Wherein, Pi,tIt contributes for i-th kind of distributed generation unit of t moment, PSB,tFor t moment accumulator cell charging and discharging electricity, PGrid,tFor T moment microgrid exchanges power, P with external power gridLoad,tFor t moment load, and t=1,2 ..., Ti, TiFor simulation optimization total duration, N is that the installation of distributed generation unit is total, SOCtFor the SOC value of accumulator t moment, BATcapFor accumulator total capacity, SOCt+1For the SOC value at accumulator t+1 moment,Respectively i-th kind of distributed generation unit t moment contribute it is upper, Lower limit, PSB,mi、PSB,maRespectively accumulator minimum and maximum active power, Sinv,SBFor accumulator inverter capacity, PEV,mi、 PEV,maRespectively accumulator of electric car minimum and maximum active power, Sinv,EVFor electric vehicle inverter capacity,Respectively microgrid interacts power upper and lower limit with external power grid in the t periods.
  3. 3. a kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile according to claim 1, It is characterized in that, the pollutant includes CO2, SO2 and NOx.
  4. 4. a kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile according to claim 1, It is characterized in that, the micro-capacitance sensor optimization traffic control scheme in the step 3) in 24 hours is:
    (1) preferentially meet workload demand using the clean energy resource inside microgrid and free Power Exchange can be carried out with major network;
    (2) WT and PV power generation operations are in MPPT maximum power point tracking pattern;
    (3) co-generation unit works in the method for operation of electricity determining by heat, and the active power output of MT is determined by thermic load;
    (4) it when the active power output of WT, PV and MT meet whole electric loads, charges first to electric vehicle and accumulator, simultaneously Monitor the charging and discharging state of accumulator and then charge to fuel cell, when EV is full of according to cost of electricity-generating by height to It is low to cut off WT or PV successively;
    (5) EV improves microgrid benefit according to by different periods charge and discharge control, charges when microgrid electricity abundance to EV, when micro- When network source deficiency is to major network power purchase, EV is not allowed to charge, and by the remaining charge transports of EV to microgrid;
    (6) when the active power output of WT, PV and MT can not meet all loads of microgrid, battery discharging is preferentially selected, if still depositing In active vacancy, then FC active power of output is called, interior remaining capacity is transferred to microgrid and obtains receipts by EV user during this period Benefit;
    (7) if all micro- sources cannot still meet microgrid safe and reliable operation in the range of output, according to the significance level of load It cuts off successively.
CN201610273527.0A 2016-04-28 2016-04-28 A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile Active CN105811409B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610273527.0A CN105811409B (en) 2016-04-28 2016-04-28 A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610273527.0A CN105811409B (en) 2016-04-28 2016-04-28 A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile

Publications (2)

Publication Number Publication Date
CN105811409A CN105811409A (en) 2016-07-27
CN105811409B true CN105811409B (en) 2018-06-26

Family

ID=56457743

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610273527.0A Active CN105811409B (en) 2016-04-28 2016-04-28 A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile

Country Status (1)

Country Link
CN (1) CN105811409B (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106339778A (en) * 2016-09-30 2017-01-18 安徽工程大学 Optical storage microgrid operation optimization method considering multiple objectives
CN106444424B (en) * 2016-10-20 2020-03-27 易事特集团股份有限公司 Simulation test platform for micro-grid energy management system
CN106684868A (en) * 2017-02-23 2017-05-17 南京航空航天大学 Electric power distribution method of hierarchical Internet micro-grid
CN106887841B (en) * 2017-03-23 2020-09-11 东北大学 Multi-population genetic particle swarm optimization method containing electric automobile microgrid capacity configuration
CN107609693A (en) * 2017-08-31 2018-01-19 安徽大学 Micro-capacitance sensor Multipurpose Optimal Method based on Pareto archives particle cluster algorithms
CN107813718A (en) * 2017-09-30 2018-03-20 中车青岛四方机车车辆股份有限公司 A kind of on-vehicle fuel hybrid power system design method and device
CN107769237B (en) * 2017-11-30 2019-11-08 南方电网科学研究院有限责任公司 Multi-energy system coordinated dispatching method and device based on electric car access
CN108400585B (en) * 2018-02-08 2021-04-02 东华大学 Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system
CN108944517A (en) * 2018-07-20 2018-12-07 湖北理工学院 The online charging method of electric car
CN110943465B (en) * 2019-12-16 2020-10-30 贵州电网有限责任公司 Energy storage system site selection and volume fixing optimization method

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105160451A (en) * 2015-07-09 2015-12-16 上海电力学院 Electric-automobile-contained micro electric network multi-target optimization scheduling method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160111920A1 (en) * 2013-03-19 2016-04-21 Electricite De France Energy management device and its associated method

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105160451A (en) * 2015-07-09 2015-12-16 上海电力学院 Electric-automobile-contained micro electric network multi-target optimization scheduling method

Also Published As

Publication number Publication date
CN105811409A (en) 2016-07-27

Similar Documents

Publication Publication Date Title
Zhang et al. A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints
Liu et al. A review on electric vehicles interacting with renewable energy in smart grid
Fathima et al. Optimization in microgrids with hybrid energy systems–A review
Kavousi-Fard et al. Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids
Zhao et al. Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system
CN103903073B (en) A kind of micro-capacitance sensor Method for optimized planning containing distributed power source and energy storage and system
Khan et al. Multi-agent based distributed control architecture for microgrid energy management and optimization
Moradi et al. Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system
Moghaddam et al. Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study
CN104362677B (en) A kind of active distribution network distributes structure and its collocation method rationally
CN102710013B (en) Park energy-network energy optimizing management system based on microgrids and implementing method thereof
Zheng et al. Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation
Wu et al. Optimal coordinate operation control for wind–photovoltaic–battery storage power-generation units
CN103840457B (en) Consider DG Optimal Configuration Method in the power distribution network that electric automobile discharge and recharge affects
CN103793758B (en) Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system
CN103151797B (en) Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode
Wang et al. PSO-based multi-criteria optimum design of a grid-connected hybrid power system with multiple renewable sources of energy
CN105591406B (en) A kind of optimized algorithm of the microgrid energy management system based on non-cooperative game
CN103346562B (en) Multi-time scale microgrid energy control method considering demand response
CN104135025B (en) Microgrid connection economic optimization method based on fuzzy particle swarm algorithm
Niknam et al. Impact of thermal recovery and hydrogen production of fuel cell power plants on distribution feeder reconfiguration
CN105811397B (en) A kind of microgrid dispatching method of providing multiple forms of energy to complement each other based on Multiple Time Scales
CN105160451B (en) A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle
Ye et al. Towards a 90% renewable energy future: A case study of an island in the South China Sea
CN104269849B (en) Energy management method based on building photovoltaic micro and system

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant