CN105160451B - A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle - Google Patents
A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle Download PDFInfo
- Publication number
- CN105160451B CN105160451B CN201510400856.2A CN201510400856A CN105160451B CN 105160451 B CN105160451 B CN 105160451B CN 201510400856 A CN201510400856 A CN 201510400856A CN 105160451 B CN105160451 B CN 105160451B
- Authority
- CN
- China
- Prior art keywords
- micro
- electric vehicle
- capacitance sensor
- power
- soc
- 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
Links
Abstract
The micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle that the present invention relates to a kind of, which is characterized in that this approach includes the following steps：1) determine electric vehicle access micro-capacitance sensor pattern, put by the separate unit electric vehicle under different access modules fill load distribution performance superposition obtain putting for electric vehicle fill load distribution performance；2) it is added to electric vehicle as micro-capacitance sensor scheduler object in micro-capacitance sensor Optimized Operation, and the micro-capacitance sensor Optimal Operation Model for filling load distribution performance and establishing the extensive electric vehicle access of consideration is put according to electric vehicle；3) the micro-capacitance sensor Optimal Operation Model of extensive electric vehicle access is considered using the particle swarm optimization algorithm based on automatic recombination mechanism, and the micro-capacitance sensor under a variety of different scheduling strategies of comparative analysis dispatches economy, to obtain optimal scheduling strategy.Compared with prior art, the present invention has many advantages, such as to consider comprehensive, effective and feasible.
Description
Technical field
The present invention relates to micro-capacitance sensor scheduling fields, more particularly, to a kind of micro-capacitance sensor multiple-objection optimization tune containing electric vehicle
Degree method.
Background technology
Micro-capacitance sensor can be renewable energy power generation system as a kind of new distribution type electric power network management and provisioning technique
System access power distribution network provides facility, and realizes that the management of Demand-side Energy Efficient and major network electric power energy efficiently utilize.
The Optimal Scheduling of micro-capacitance sensor is similar to the Optimal Scheduling of traditional bulk power grid, but has its particularity and answer
Polygamy.Optimal Scheduling, main target are to realize that the cost of operation of power networks process is minimum.The problem of with environmental pollution
Increasingly cause the concern of people, in present the problem of much environmental pollution cost is put into optimizing scheduling by researchers.And
In the Optimized Operation of micro-capacitance sensor, reduces operating cost and reduce pollutant emission, having to the energy-saving and emission-reduction of entire power grid also has
Great meaning.
In recent years, with government's energy conservation and environmental protection and high and new technology relevant policies reinforcement and implement, use electric vehicle
The number of users of (Electric Vehicle, EV) is continuously increased, while the power storage amount of these electric vehicles quite may be used
It sees.However, electric vehicle access power grid is flexibly and to disperse, free from time and space restrictions, this feature will increase
The unstability of power grid, and influence the power quality of power grid.Similar to distributed generation resource, if electric vehicle is accessed micro- electricity
Net can avoid or reduce electric vehicle and be directly accessed influence to power grid.
Currently, access problem on a large scale for micro-capacitance sensor Multiobjective Optimal Operation and electric vehicle, scholar both domestic and external into
It has gone a series of research work, and has achieved the achievement in terms of some theory and practice.Chen Dawei and Zhu Guiping establish meter and
The Optimal Operation Model of the micro-capacitance sensor of environmental factor, but only to two object function the lowest coursing costs and pollution processing cost
It is minimum to be multiplied by fixed weights summation respectively, it is still single object optimization scheduling in fact.Yang Qi etc. mainly has studied four kinds and is related to
Grid-connected and islet operation micro-capacitance sensor economic load dispatching system hardware configuration, and analyze the effect of energy-storage units.S.W.Hadley
Etc. the statistics rule for having studied the EVs last time return moment and day travel distance, the statistics mould of EVs charge requirements is established
Type, and analyze EVs influences of the charging to network load at random.Han Haiying etc. considers electric vehicle by period charge and discharge
Journey, and the micro-capacitance sensor Optimal Operation Model containing the EVs that can network on a large scale is established, it has obtained a bill Unit Combination and has contributed.But
It is that these processing modes are relatively easy, many aspects require further study discussion.
Invention content
Consider comprehensively, effectively it is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of
The feasible micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle, which is characterized in that this method includes following step
Suddenly：
1) pattern for determining electric vehicle access micro-capacitance sensor, is put by the separate unit electric vehicle under different access modules and is filled
Load distribution performance superposition obtains putting for electric vehicle and fills load distribution performance；
2) it is added to electric vehicle as micro-capacitance sensor scheduler object in micro-capacitance sensor Optimized Operation, and according to electric vehicle
It puts and fills the micro-capacitance sensor Optimal Operation Model that load distribution performance establishes the extensive electric vehicle access of consideration；
3) the micro- of extensive electric vehicle access is considered using the particle swarm optimization algorithm based on automatic recombination mechanism
Optimal dispatch model, and the micro-capacitance sensor under a variety of different scheduling strategies of comparative analysis dispatches economy, it is optimal to obtain
Scheduling strategy.
Pattern in the step 1) includes the V2G moulds of the V0G patterns and two-way orderly charge and discharge of unidirectional unordered charging
Formula.
The optimization aim letter of the micro-capacitance sensor Optimal Operation Model of extensive electric vehicle access is considered in the step 2)
Number is：
Micro-capacitance sensor management and running cost Obj1It is minimum：
The processing cost Obj to discharge pollutants2It is minimum：
It is minimum that micro-capacitance sensor dispatches overall cost：
minObj3=m1Obj1+m2Obj2
Wherein, CGFor the fuel cost of distributed generation resource, COMFor operation expense, CDPFor generator unit depreciation at
This, CGridFor micro-capacitance sensor and bulk power grid electric energy switching cost, CEVFor micro-capacitance sensor and electric car electric energy switching cost, CLFor load
Stoppage in transit cost of compensation, Δ TtFor T is that j is that distributed generation unit is numbered in micro-capacitance sensor, and t is run the period, and k is discharged
Pollutant type, CkTo handle every kilogram of expense to discharge pollutants, γjk(Pjt) be in micro-capacitance sensor generator unit j export Pjt
The weight of the pollutant k generated when electric energy, γgridk(Pgridt) it is that power distribution network exports PgridtThe weight of the pollutant k generated when electric energy
Amount, m1、m2For the weight of operating cost and discharge costs；
Constraints is：
Power-balance constraint：
Cold heat power-balance constraint：
WLoad=WMT+WFC
Distributed generation unit active-power PjtBound constrains：
Micro-capacitance sensor exchanges power P with major networkgridLimit value constrains：
The power P of energy-storage unitsSBtConstraint and charged SOCSBtConstraint：
PSBmin≤PSBt≤PSBmax
SOCSBmin≤SOCSBt≤SOCSBmax
SOCend=SOC0
The power P of electric vehicleEvtConstraint and charged SOCEVtConstraint：
PEVmin≤PEVt≤PEVmax
SOCEVmin≤SOCEVt≤SOCEVmax
Wherein, PjtFor the power that generator unit j in t period micro-capacitance sensors is sent out, PgridtIt is t periods power distribution network to micro-capacitance sensor
The power of transmission, PbatterytFor the power that t period accumulators are sent out, PloadtFor the workload demand in t period micro-capacitance sensors, WLoadFor
The cold heat workload demand of entire micro-grid system, WMTFor the cold heat power that miniature gas turbine waste heat flue gas provides, WFCFor combustion
Expect that cell power generation generates the cold heat power that heat provides,For the minimum output power of distributed generation unit j,To divide
The peak power output of cloth generator unit j,The minimum that common point circuit can transmit between micro-capacitance sensor and power distribution network
Power,The maximum power that common point circuit can transmit between micro-capacitance sensor and power distribution network, PSBmin、PSBmaxRespectively store
The minimum power and maximum power of battery charging and discharging, SOCSBmin、SOCSBmaxIt is the minimum value and most of storage battery charge state respectively
Big value, SOC0And SOCendThe state-of-charge of 24 accumulator of initial time 0 and end time in a respectively dispatching cycle,
PEVmin、PEVmaxThe respectively minimum power and maximum power of electric vehicle charge and discharge, SOCmin、SOCmaxIt is electric vehicle respectively
The minimum value and maximum value of battery charge state.
Under the V0G patterns of unidirectional unordered charging, electric vehicle puts the electricity for filling electric vehicle in load distribution performance
Demand is：
EEV=ηEV·d
Wherein, ηEVFor the electrical demand coefficient of electric vehicle mileage, the mileage travelled d clothes of every electric vehicle
From logarithm normal distribution, probability density function is：
Charging time fs(x) meet normal distribution：
Wherein, μdAnd μsFor desired value, σdAnd σsFor standard deviation..
Under the V2G patterns of two-way orderly charge and discharge, when the putting of electric vehicle is filled the electric discharge in load distribution performance and is continued
Between Tdisc1For：
Duration of charge can be obtained in conjunction with the charge power of electric vehicle, to obtain continuing charging time Tdisc2For：
Charging load is by consuming gross energy, i.e. P needed for single electric vehicle among one dayEVFor：
Wherein, Tall-discIt is put when fully charged for electric vehicle to the total duration that discharges needed for state-of-charge lower limit, Pdisc
For electric vehicle discharge power, SOCmaxAnd SOCminThe respectively bound of storage battery charge state, D travel for electric vehicle day
Mileage, W100For hundred kilometers of power consumption of electric vehicle, Tend_discFor finish time of discharging, Tstart_discFor networking discharging time, Pc
For electric vehicle charge power.
A variety of different scheduling strategies in the step 3) include micro-grid connection operation reserve and micro-capacitance sensor isolated island
Operation reserve.
The step 3) specifically includes following steps：
31) according to the access module of electric vehicle, the model parameter of each distributed generation unit, each mesh in micro-capacitance sensor are set
Scalar functions parameter and each constraints parameter, and introduce uncontrollable predictable distributed generation resource and contribute and cold electric load parameter；
32) controllably output unit miniature gas turbine, fuel cell, diesel-driven generator, accumulator and major network work(will be exchanged
Rate sets particle cluster algorithm parameter as five dimension particles, including population, solution space dimension, maximum iteration, particle are most
Big speed and recombination index r；
33) fitness of each particle is calculated, and records the current individual extreme value of each particle and corresponding mesh
Offer of tender numerical value, and then all extreme values and corresponding target function value are obtained, and select individual optimal value and global optimum；
34) the current number of iteration adds one, and update population carries out position and speed；
35) whether judging result meets Premature Convergence standard and whether recombination number reaches preset value, if result
Meet Premature Convergence standard and recombination number do not reach preset value, then recombinates population and recombination index r=r+1,
And return to step 33), otherwise carry out step 36)；
36) judge whether to meet the condition of convergence, if so, obtaining global optimum or reaching maximum iteration, terminate
Iterative process, if it is not, then return to step 33), continue iterative operation.
Compared with prior art, the present invention has the following advantages：
The charge and discharge electrical characteristics of electric vehicle and the use habit of car owner are considered, electric vehicle has been formulated and list is respectively adopted
Micro-capacitance sensor is accessed to unordered V0G and two-way orderly V2G patterns, considers what electric vehicle largely accessed to establish one
Micro-capacitance sensor Optimized Operation mathematical model, while minimum, environmental benefit highest and comprehensive cost minimum three using operation expense
A optimization aim, comparative analysis difference electric vehicle access way is to micro-capacitance sensor economy under six kinds of preset scheduling strategies
The influence of operation accesses built micro-capacitance sensor Optimal Operation Model to verify electric vehicle with the access of V2G patterns and V0G patterns
Validity and feasibility.
Description of the drawings
Fig. 1 is micro-capacitance sensor Optimized Operation analysis process figure under the random charge mode of electric vehicle.
Fig. 2 is the electric vehicle charging carry calculation flow chart based on Monte Carlo Analogue Method.
Fig. 3 is electric automobile load curve graph under unidirectional unordered V0G patterns.
Fig. 4 is electric automobile load curve graph under two-way orderly V2G patterns.
Fig. 5 is the lower accumulator cell charging and discharging strategy that is incorporated into the power networks.
Fig. 6 is the prediction power curve graph of PV, WT.
Fig. 7 is the optimization output situation map of each generator unit under tactful 1 optimization aim three.
Fig. 8 is the optimization output situation map of each generator unit under tactful 3 optimization aims three.
Fig. 9 is the optimization output situation map of each generator unit under tactful 4 optimization aims three.
Specific implementation mode
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment：
After micro-capacitance sensor is added for electric vehicle in the present invention, the influence to Optimized Operation.The present invention contains distribution thus
Formula power supply include photovoltaic (Photovoltaic, PV), wind-powered electricity generation (Wind Turbine, WT), fuel cell (Fuel Cell, FC),
Miniature gas turbine (Micro Turbine, MT), diesel engine (Diesel Generator, DSG) and energy-storage units include
Accumulator (Battery, Bat), it is also contemplated that the access of electric vehicle.
The Optimal Scheduling of micro-capacitance sensor is an economical operation optimization problem, and the weighting to consider a problem is different, target letter
Number also differs.The present invention, which establishes, considers overall cost that operating cost and environmental benefit and the two are taken into account as an optimization
The micro-capacitance sensor multiple target economic load dispatching model of target.
1, optimization aim
(1) micro-capacitance sensor management and running cost minimization
Present invention is generally directed to the economic load dispatching cost in micro-capacitance sensor operational process, therefore each distributed generation resource initial investment
Construction cost is not counted in scheduling cost scope, and when only considering that distributed generation resource scheduling is contributed, operation expense and fuel
Cost etc.；In addition, electric vehicle belongs to car owner's private property, the purchase of electric vehicle and upkeep cost are voluntarily calculated by car owner to be held
Load, is not counted in micro-capacitance sensor operating cost, but this part expense is a shadow when formulating to the electricity price of electric vehicle power purchase
The factor of sound.So the optimization aim of micro-capacitance sensor operating cost includes the fuel cost C of distributed generation resourceG, operation expense
COM, generator unit depreciable cost CDP, micro-capacitance sensor and bulk power grid electric energy switching cost CGrid, micro-capacitance sensor and electric car electric energy hand over
Change this C intoEVAnd load synthesis cost of compensation CL, expression is：
(2) processing cost to discharge pollutants is minimum
Miniature gas turbine MT, fuel cell FC and diesel-driven generator DG operations in micro-capacitance sensor and bulk power grid unit generation
When will produce CO2、SO2、NOXEqual pollutants, to will produce pollutant emission processing cost.Micro-capacitance sensor emission treatment cost is most
Small object function is represented by：
In formula, j is that distributed generation unit is numbered in micro-capacitance sensor, 1~N；T is run the period, 1~T；K is to be discharged
Pollutant type (CO2、SO2、NOXDeng)；CkIt is every kilogram of expense (member/kg) to discharge pollutants of processing；γjk(Pjt) it is micro- electricity
Generator unit j exports P in netjtThe weight (kg/kW) of the pollutant k generated when electric energy；γgridk(Pgridt) power distribution network output
PgridtThe weight (kg/kW) of the pollutant k generated when electric energy.
(3) micro-capacitance sensor scheduling overall cost is minimum
min Obj3=m1Obj1+m2Obj2 (3)
In formula, Obj3For micro-capacitance sensor overall cost；Obj1For operating cost；Obj2For the processing cost that discharges pollutants；m1、m2
The respectively weight of operating cost and discharge costs assumes operating cost and discharge herein to balance the influence of the energy and environment
The weight of cost is identical, takes m here1=m2=1.
2, constraints
(1) power-balance constraint
PjtIt is the power (kW) that generator unit j is sent out in t period micro-capacitance sensors；PgridtIt is that t periods power distribution network is passed to micro-capacitance sensor
Defeated power (kW) indicates when negative value power by micro-capacitance sensor reverse transfer to power distribution network；PbatterytIt is that t period accumulators are sent out
Power (kW) indicates accumulator absorbed power when negative value；PloadtIt is the workload demand (kW) in t period micro-capacitance sensors.
(2) cold heat power-balance constraint
Cold heat electricity supply unit need to meet cooling and heating load constraint：
WLoad=WMT+WFC (5)
In formula：WLoadFor the cold heat workload demand of entire micro-grid system, kW；WMTFor miniature gas turbine waste heat flue gas
The cold heat power of offer, kW；WFCThe cold heat power that heat provides, kW are generated for fuel cell power generation.
(3) generator unit active power bound constrains
In formula,For the minimum output power of distributed generation unit j；It is defeated for the maximum of distributed generation unit j
Go out power.
(4) the Power Exchange limit value of micro-capacitance sensor and major network constrains
In formula,Be respectively common point circuit can transmit between micro-capacitance sensor and power distribution network minimum power and
Maximum power.
(5) power constraint of energy-storage units and charged constraint
The charge-discharge electric power and state-of-charge (SOC) that battery system should meet are constrained to：
PSBmin≤PSBt≤PSBmax (8)
SOCSBmin≤SOCSBt≤SOCSBmax (9)
In formula, PSBmin、PSBmaxThe respectively minimum power and maximum power of accumulator cell charging and discharging；SOCSBtIt is that accumulator exists
The state-of-charge of t periods；SOCSBmin、SOCSBmaxIt is the minimum value and maximum value of storage battery charge state respectively.
Further, since micro-capacitance sensor shows cycle dynamics to the Optimized Operation of accumulator, the SOC of accumulator is assumed herein
Whole story dispatching cycle at one day is consistent, that is, meets constraints：
SOCend=SOC0 (10)
Wherein, SOC0And SOCend24 accumulator of initial time 0 and end time is charged in a respectively dispatching cycle
State.
(6) power constraint of electric vehicle and charged constraint
For electric vehicle, the charge-discharge electric power and state-of-charge that should also meet are constrained to：
PEVmin≤PEVt≤PEVmax (11)
SOCEVmin≤SOCEVt≤SOCEVmax (12)
In formula, PEVmin、PEVmaxThe respectively minimum power and maximum power of electric vehicle charge and discharge；SOCEVtIt is electronic vapour
State-of-charge of the vehicle battery in the t periods；SOCmin、SOCmaxIt is the minimum value and maximum of batteries of electric automobile state-of-charge respectively
Value.
3, electric vehicle model
(1) power characteristic when the unordered charging of electric vehicle
It is related that electric vehicle puts the uncertain factors such as charge power and electric automobile during traveling mileage, charging time, therefore
It needs to obey the chance event of statistical law to emulating by platform electric vehicle by generation.By all electric vehicle power song
Total charge power curve just can be obtained in line superposition, and flow is as shown in Figure 1.
To every electric vehicle, about 14% probability will not go on a journey, if trip, daily travel d approximations clothes
From logarithm normal distribution, probability density function is：
In formula：Distributed constant μd=3.2, σd=0.88, it is distributed as electric vehicle daily travel mean value and standard variance.
Corresponding electric vehicle charge capacity demand is EEV：
EEV=ηEV·d (14)
Under the unordered access modules of VOG, it will charge at once to electric vehicle after being gone home due to most of car owners, so false
If it is exactly to start to charge up the moment to be finally returned in one day constantly, then starts to charge up and meet normal distribution constantly：
In formula：μs=17.6；σs=3.4.
(2) power characteristic when the orderly charge and discharge of electric vehicle
The orderly charge and discharge of electric vehicle, refer in the case where electric vehicle largely accesses, through electricity price guiding etc. policies,
Regulate and control electric vehicle in the way of tou power price under the premise of meeting automobile user use habit first to electronic vapour
Vehicle charge and discharge are orderly dispatched.It discharges in electricity price peak period when grid-connected, the charging of electricity price low-valley interval；When isolated island, in load
Peak period discharges, and charges in the load valley period.
Electric vehicle daily consumption energy can be obtained by daily travel S, to obtain lotus when batteries of electric automobile networks
Electricity condition：
In formula, C is the total capacity of batteries of electric automobile.
Discharge period is：
In formula：Tall-discIt is put when fully charged for electric vehicle to the total duration that discharges needed for state-of-charge lower limit；Tdisc
For the actual discharge duration；PdiscFor electric vehicle discharge power；SOC,maxAnd SOC,minRespectively storage battery charge state
Bound.
Networking discharging time Tstart_discBy last time return moment t0It is obtained judged with the energy requirement of micro-capacitance sensor.
Discharge finish time Tend_discDischarging time and discharge period codetermine by, and the upper limit is that the day terminates
Moment 24:00.
The electric discharge period is Tstart_disc~Tend_disc, during this period, electric vehicle discharges according to discharge power, by N
The electric discharge load of electric vehicle is cumulative to get to total discharge power in the electric discharge period.
Due to the limitation of discharge time, part electric vehicle is not discharged completely, so initial lotus when starting to charge up
Electricity condition, different due to the difference of discharged condition, which is uniquely determined by above-mentioned discharge scenario.Single electronic vapour
Charging load is by consuming gross energy needed for vehicle among one day, i.e.,
Duration of charge can be obtained in conjunction with the charge power of electric vehicle, to obtain charging power load distributing.
(3) Monte Carlo Analogue Method solves
Monte Carlo Method (Monte Carlo Method) is that one kind is estimated based on random sampling and stochastic simulation
Count the statistical method of mathematic(al) function.Its basic solution throughway is：For problem to be solved, advised according to the statistics of physical phenomenon itself
Rule or the suitable probabilistic model dependent on stochastic variable of arteface one, make the statistic of certain stochastic variables be asked for band
The solution of solution problem.Monte carlo method is theoretical according to following two points：
1. the law of large numbers：In function f (x) domains [a, b], N number of several xi, letter are randomly extracted with non-uniform probability distribution
The arithmetic average of the sum of numerical value converges on the desired value of function.After extracting enough random samples, the Monte Carlo of integral
Estimated value will converge on the correct result of the integral, i.e. stochastic variable statistic is：
2. central-limit theorem：Stochastic variable obeys single normal distribution made of a large amount of faint factors are cumulative.Meng Teka
The error ε of Lip river method depends on standard deviation sigma and number of samples N, and directly proportional to standard deviation sigma, with number of samples N side square roots at
Inverse ratio, i.e.,：
Monte Carlo sampling approximation on the average is to solve for a kind of effective ways of random optimization, also known as Method of Stochastic,
Also referred to as statistical test method, it provides effective approach to verify the constraints of Probability Forms, it is mainly used in solution
The problem of mathematics, engineer application and production management etc., Monte Carlo sampling approximation on the average method basic thought be：First
A probabilistic model is established, the solution that its some parameter is equal to problem is made to be selected to stochastic variable then according to the distribution of hypothesis
Specific value (this process is called sampling) calculates result to construct a deterministic model；Again by repeatedly taking out
Sample experiment as a result, obtain the statistical property of parameter, the final approximation for calculating solution.Electric vehicle based on Monte Carlo simulation
The carry calculation flow that charges is as shown in Figure 2.
N is that electric vehicle simulates quantity in figure, and n is the electric vehicle that present day analog calculates.System input information includes electricity
Probability distribution, possible charge period and the probability of initiation of charge time point that electrical automobile total scale, various charging behaviors occur
Cloth, charge the corresponding starting SOC probability distribution of behavior from the constraint of more durations, different type.To separate unit electric vehicle charging load
It calculates, first has to the charging behavior for determining the vehicle, if the vehicle, there are many behavior of charging, it is equal that one systematically discussed meets U (0,1)
The random number of even distribution determines the charging behavior of vehicle according to the probability distribution that different charging behaviors occur.
(4) electric vehicle charge and discharge case
Carried model is verified by taking the micro-capacitance sensor that a residential block is formed as an example.There are 400 family residents in the residential block, existing
It is given below hypothesis：
1. average every 2 family family possesses an electric vehicle, i.e. the cell possesses 200 electric vehicles；
2. selecting BYD E6 vehicles herein, parameter is as follows：Capacity Q=60kWh；Charge power Pdh=10kW；Power consumption
Measure S1kWh=4.762km/ (kWh)；Discharge efficiency eta=85%；
3. the averagely daily stroke 34.76km of each electric vehicle, whole electric vehicle day charge volumes are 1460kWh.
4. in order to encourage car owner to participate in the orderly charge and discharge plan of electric vehicle under electricity price guiding, micro-capacitance sensor is drafted to electronic
The price of automobile power purchase is in real time to the price of conventional load sale of electricity, and such electric vehicle 1kWh car owners that often discharge can benefit 0.6
Member or so (wherein EVs charge when paddy period electricity price be 0.37 yuan/kWh, EVs discharge when peak period electricity price be 1.03 yuan/
kWh).With market maturation and technology it is perfect, micro-capacitance sensor manager can suitably turn down the price to electric vehicle power purchase,
To withdraw the cost of repacking charge and discharge device.
The present invention simulates the orderly charge and discharge part throttle characteristics of different scales electric vehicle access power grid, because of electric vehicle
Most important function is still used as the vehicles, it is contemplated that and user's is accustomed to vehicle, in orderly charge and discharge, 07:00-17:When 00
Section, be not involved in dispatch orderly charge and discharge charge and discharge carry calculation it is different from unordered charge and discharge Fig. 3, emulate daily load curve
It is as shown in Figure 4 respectively.
4, micro-capacitance sensor running optimizatin scheduling strategy：
The micro-capacitance sensor scheduler object that the present invention considers includes photovoltaic power generation equipment (Photovoltaic, PV), wind-power electricity generation
Unit (Wind Turbine, WT), miniature gas turbine (Micro Turbine, MT), diesel engine (Diesel
Generator, DG), fuel cell (Fuel Cell, FC), accumulator (Storage Battery, SB), electric vehicle
(electric vehicles, EVs) and the major network for exchanging electric energy.Micro-capacitance sensor Optimized Operation is formulated using tou power price pattern
Whole day is divided into the paddy period (00 by strategy for 24 hours according to external electrical network load condition:00-07:00 and 23:00-24:00), usually
Section (07:00-10:00、15:00-18:00 and 21:00-23:And the peak period (10 00):00-15:00 and 18:00-21:00).It is micro-
Dispatching of power netwoks, for a thread period, predicted the power load and cooling and heating load at current scheduling moment first with 1 hour, with
And the output situation of photovoltaic power generation equipment and wind power generating set, and the state-of-charge of accumulator is monitored, in each scheduling
In period, micro-capacitance sensor operating cost is minimum, environmental benefit is maximum and the minimum optimization aim of overall cost, according to different scheduling
Strategy, obtains the optimum results under different scheduling strategies, and determine the active power output state of controllable type generator unit in micro-capacitance sensor,
The charge-discharge electric power curve of accumulator and the active power situation exchanged with major network.
(1) micro-capacitance sensor Optimized Operation basic scheduling strategy
1. the scheduling strategy of photovoltaic and wind-powered electricity generation
Since solar energy and wind-force belong to clean energy resource, do not pollute the environment, therefore preferentially set using photovoltaic generation
The electric energy that standby and wind power generating set is sent out, and accumulator is arranged to stablize their output-power fluctuation, make their reality
Output more meets prediction power curve.
2. accumulator cell charging and discharging scheduling strategy
The present invention mainly from terms of three come consider the accumulator in micro-capacitance sensor scheduling system act on：
First, stablize the fluctuation that wind turbine and photovoltaic are contributed, it is ensured that they can contribute by prediction power curve；
Second, when micro-grid connection is run, accumulator is arranged to charge, in the disconnection of usually section and at peak in the paddy period
Duan Fang electricity, as shown in Figure 5.In view of the state-of-charge of accumulator in every day is all periodic cycle, i.e. charge and discharge in one day
After to return to this day beginning state-of-charge.Simultaneously, it is contemplated that the charge and discharge of accumulator are used for the influence in service life, this
In take its charged lower limit be 20%, the upper limit 100%；
Third, in micro-capacitance sensor islet operation, when the output of photovoltaic and wind-powered electricity generation meets power load need, and has surplus,
Accumulator can store extra electric energy；When photovoltaic and wind power output cannot meet power load, accumulator can discharge to mend
Fill electricity shortage.Here the same periodicity of meter and accumulator cell charging and discharging, and to take its charged lower limit be 20%, the upper limit 90%.
(2) micro-capacitance sensor Optimized Operation strategy
Since electric vehicle has the characteristic of storage electric energy, after largely accessing micro-capacitance sensors, it is considered as mobile energy storage device,
Conventional energy storage device or standby generator sets can be replaced to a certain extent by reasonable arrangement, to improve the profit of electric vehicle
The investment of micro-capacitance sensor is built with rate and reduction.The research emphasis of the present invention is to obtain the micro-capacitance sensor scheduling strategy with feasibility,
And reasonable arrangement electric vehicle access micro-capacitance sensor realizes the performance driving economy of bigger.Therefore, it is electronic that six considerations have been formulated herein
The micro-capacitance sensor Optimized Operation strategy of automobile access.
Strategy 1：Micro-grid connection is run, and distributed generation unit PV, WT, MT, DG, FC and major network participate in optimization and adjust jointly
Degree, the photovoltaic power generation equipment PV and wind power generating set WT that gives priority in arranging for contribute, and fuel cell FC, are transported under " with the fixed heat of electricity " pattern
Row, miniature gas turbine MT are run under the pattern of " electricity determining by heat ", and accumulator SB is in the electric discharge of peak period, the charging of paddy period, bavin
Fry dried food ingredients motor DG fills up remaining insufficient, meets electricity consumption and cooling and heating load jointly.It can be with two-way exchange electricity between micro-capacitance sensor and major network
Energy.
Strategy 2：Micro-capacitance sensor islet operation, the photovoltaic power generation equipment PV and wind power generating set WT that gives priority in arranging for contribute, fuel
Battery FC is run under " with the fixed heat of electricity " pattern, and miniature gas turbine MT is run under the pattern of " electricity determining by heat ".When sending out electricity
When can exceed that power load, accumulator SB stores extra electric energy；When sending out electric energy and cannot meet power load, diesel-driven generator
DG and accumulator SB output powers supplementary power are insufficient；
Strategy 3：Micro-grid connection is run, and electric vehicle uses the V0G patterns of unidirectional unordered charging.Only consider electric vehicle
The charging situation of EV regards electric vehicle as pure power load.Power load (conventional load+electric vehicle) is sent out by distribution
Electric unit PV, WT, MT, DG, FC and accumulator SB and major network collaboration contribute and provide electric energy.
Strategy 4：Micro-grid connection is run, and electric vehicle uses the two-way V2G patterns orderly to charge.Consider electric vehicle EV
Flash-over characteristic, concentrate charging storage low price electric energy in major network paddy period electric vehicle EVs and accumulator SB, load is (often at this time
Advise load+electric vehicle charging load) provide electric energy by distributed generation unit PV, WT, MT, DG, FC and major network；In usually section
Electric vehicle EV is as the vehicles without charge and discharge, and conventional load is by distributed generation unit PV, WT, MT, DG, FC and storage
Battery SB and major network provide electric energy；And starts electric discharge according to certain probability in peak period electric vehicle and provide electricity for conventional load
Can, it is contributed and electric energy is provided by distributed generation unit PV, WT, MT, DG, FC and accumulator SB collaborations, it is anti-if having extra electric energy
It is fed in major network.
Strategy 5：Micro-capacitance sensor islet operation, electric vehicle use the V0G patterns of unidirectional unordered charging.Only consider electric vehicle
The charging situation of EV regards electric vehicle as pure power load.Power load (conventional load+electric vehicle) is sent out by distribution
Electric unit PV, WT, MT, DG, FC and accumulator SB collaborations contribute and provide electric energy.As distributed generation unit PV, WT, MT, DG, FC
When cannot meet load electric energy demand with accumulator SB maximum output, make entire micro-capacitance sensor by temporarily cutting off interruptible load
Active power balance between supply and demand.
Strategy 6：Micro-capacitance sensor islet operation, electric vehicle use the two-way V2G patterns orderly to charge.Consider electric vehicle EV
Flash-over characteristic, electric vehicle EV concentrates the electric energy that charging storage regenerative resource is sent out when load valley, and load is (conventional at this time
Load+electric vehicle charging load) being contributed by distributed generation unit PV, WT, MT, DG, FC provides electric energy；When load is gentle
Electric vehicle EV is as the vehicles without charge and discharge, and conventional load is by distributed generation unit PV, WT, MT, DG, FC and storage
Battery SB collaborations, which are contributed, provides electric energy；And electric vehicle starts to discharge according to certain probability in load peak, at the same it is conventional negative
Lotus is contributed by distributed generation unit PV, WT, MT, DG, FC and accumulator SB collaborations and is provided electric energy, works as distributed generation unit
When PV, WT, MT, DG, FC and accumulator SB maximum output cannot meet load electric energy demand, by temporarily cutting off interruptible load
Make the active power balance between supply and demand of entire micro-capacitance sensor.
4, sample calculation analysis
(1) parameter setting
The search time of present case is one day of summer, and the same day 00 is formulated within 1 hour according to time interval:00-24:00 period
Operational plan.Fig. 6 give photovoltaic, wind energy 24 when discontinuity surface power generation prediction curve；Each distributed generation resource related data
As shown in table 1.Table 2 gives micro-capacitance sensor real-time electrical load requirement, and table 3 gives micro-capacitance sensor real-time cooling workload demand；Table 4 is given
The segmentation electricity price of power distribution network is gone out；Table 5 gives each generator unit pollutant discharge coefficient.
Each distributed electrical source dates in 1 micro-capacitance sensor of table
The real-time electrical load requirement of 2 micro-capacitance sensor of table (kW)
3 micro-capacitance sensor real-time cooling workload demand (kW) of table
4 micro-capacitance sensor of table and power distribution network electricity price scheme
Note：The peak period is：10:00~15:00,18:00~21:00；Usually section is：7:00~10:00,15:00~18:
00,21:00~23:00；The paddy period is：23:00~24:00,0:00~7:00.
5 each generator unit pollutant discharge coefficient of table
(2) interpretation of result and discussion
Optimized Operation total cost is as shown in table 6 under different scheduling strategies and different target function.
6 Optimized Operation result total cost of table compares
According to the data in table 6, analysis is made：
1) it is both grid-connected state in the comparison of scheduling strategy 1,3,4, using the optimization aim of V2G access modules
1,2,3 times operation total costs are compared to having dropped 8.2%, 7.9% and 8.0% respectively before EVs accesses, compared to using V0G patterns
12.8%, 12.0% and 12.4% is even more had dropped respectively.Illustrating under V0G patterns, electric vehicle increases merely power load,
Only increase the operating cost of micro-capacitance sensor；And under V2G patterns, electric vehicle absorbs distributed electrical as mobile energy storage device
The extra electric energy in source, and the electricity price for taking full advantage of major network peak interval of time is poor, when low electricity price, concentrate charging, when high electricity price electric discharge cut
Peak, the output burden for reducing distributed generation resource and the dependence to major network, as Figure 7-9.
2) it is both island operation state in the comparison of scheduling strategy 2,5,6, using the optimization aim 1,2,3 of V0G patterns
Lower operation total cost is to rise 7.6%, 6.2% and 7.5% respectively, and use the excellent of V2G access modules before being accessed compared to EVs
Change target 1,2,3 times operation total costs rise 9.8%, 12.4% and 12.9% more respectively before being accessed compared to EVs.Illustrate in orphan
Under the operation of island, the access of electric vehicle both increases the operating cost of micro-capacitance sensor, this is because distributed generation resource output cost is wanted
Higher than the cost from major network power purchase, simultaneously because a large amount of electric vehicles concentrate the load of charging excessive, conventional electric load is cut
The effect of peak load is not enough to make up the expense that distributed generation resource is additionally run.Therefore, it under the islet operation of short time, needs
Work out more rational electric vehicle charging plan.
3) in the comparison of scheduling strategy 3,5, electric vehicle is both under V0G access modules, and when islet operation optimizes mesh
Total cost under mark 1,2,3 is higher by 20.6%, 27.3% and 30.5% respectively when will be than being incorporated into the power networks, this is because isolated island is transported
The cost of electricity-generating of distributed generation resource is than from the of high cost of major network power purchase when row.
4) in the comparison of scheduling strategy 4,6, electric vehicle is both under V2G access modules, and when islet operation optimizes mesh
The lower total cost of mark 1,2,3 also all than being incorporated into the power networks when be higher by 41.0%, 53.2% and 56.5% respectively, this is because isolated island fortune
The cost of electricity-generating of distributed generation resource is than from the of high cost of major network power purchase when row, but compared to being higher by under V0G patterns in analysis (3)
Ratio, it is more go out it is very much, it is a large amount of that diesel-driven generators is called to contribute this is because EVs concentrates the increase load of charging excessive,
The total cost of islet operation is improved, is especially being taken into account with the total cost under the optimization aim 2 of environmental benefit and 3, amplification point
53.2% and 56.5% are not reached.
Claims (6)
1. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle, which is characterized in that this approach includes the following steps：
1) pattern for determining electric vehicle access micro-capacitance sensor, is put by the separate unit electric vehicle under different access modules and fills load
Distribution character superposition obtains putting for electric vehicle and fills load distribution performance；
2) it is added to electric vehicle as micro-capacitance sensor scheduler object in micro-capacitance sensor Optimized Operation, and is filled according to putting for electric vehicle
Load distribution performance establishes the micro-capacitance sensor Optimal Operation Model for considering extensive electric vehicle access, and the consideration is electric on a large scale
The optimization object function of micro-capacitance sensor Optimal Operation Model of electrical automobile access is：
Micro-capacitance sensor management and running cost Obj1It is minimum：
The processing cost Obj to discharge pollutants2It is minimum：
It is minimum that micro-capacitance sensor dispatches overall cost：
min Obj3=m1Obj1+m2Obj2
Wherein, CGFor the fuel cost of distributed generation resource, COMFor operation expense, CDPFor the depreciable cost of generator unit,
CGridFor micro-capacitance sensor and bulk power grid electric energy switching cost, CEVFor micro-capacitance sensor and electric car electric energy switching cost, CLStop for load
Transport cost of compensation, Δ TtFor the unit period, T is dispatching cycle, and j is that distributed generation unit is numbered in micro-capacitance sensor, and t is operation
Period, k are the pollutant type discharged, CkTo handle every kilogram of expense to discharge pollutants, γjk(Pjt) be in micro-capacitance sensor
Generator unit j exports PjtThe weight of the pollutant k generated when electric energy, γgridk(Pgridt) it is that power distribution network exports PgridtIt is produced when electric energy
The weight of raw pollutant k, m1、m2For the weight of operating cost and discharge costs；
Constraints is：
Power-balance constraint：
Cold heat power-balance constraint：
WLoad=WMT+WFC
Distributed generation unit active-power PjtBound constrains：
Micro-capacitance sensor exchanges power P with major networkgridLimit value constrains：
The power P of energy-storage unitsSBtConstraint and charged SOCSBtConstraint：
PSBmin≤PSBt≤PSBmax
SOCSBmin≤SOCSBt≤SOCSBmax
SOCend=SOC0
The power P of electric vehicleEvtConstraint and charged SOCEVtConstraint：
PEVmin≤PEVt≤PEVmax
SOCEVmin≤SOCEVt≤SOCEVmax
Wherein, PjtFor the power that generator unit j in t period micro-capacitance sensors is sent out, PgridtIt is transmitted to micro-capacitance sensor for t periods power distribution network
Power, PbatterytFor the power that t period accumulators are sent out, PloadtFor the workload demand in t period micro-capacitance sensors, WLoadIt is entire micro-
The cold heat workload demand of network system, WMTFor the cold heat power that miniature gas turbine waste heat flue gas provides, WFCFor fuel cell
Power generation generates the cold heat power that heat provides,For the minimum output power of distributed generation unit j,It is sent out for distribution
The peak power output of electric unit j,The minimum power that common point circuit can transmit between micro-capacitance sensor and power distribution network,The maximum power that common point circuit can transmit between micro-capacitance sensor and power distribution network, PSBmin、PSBmaxRespectively accumulator fills
The minimum power and maximum power of electric discharge, SOCSBmin、SOCSBmaxIt is the minimum value and maximum value of storage battery charge state respectively,
SOC0And SOCendThe state-of-charge of 24 accumulator of initial time 0 and end time, P in a respectively dispatching cycleEVmin、
PEVmaxThe respectively minimum power and maximum power of electric vehicle charge and discharge, SOCmin、SOCmaxIt is batteries of electric automobile lotus respectively
The minimum value and maximum value of electricity condition；
3) micro-capacitance sensor of extensive electric vehicle access is considered using the particle swarm optimization algorithm based on automatic recombination mechanism
Optimal Operation Model, and the micro-capacitance sensor under a variety of different scheduling strategies of comparative analysis dispatches economy, to obtain optimal scheduling
Strategy.
2. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle according to claim 1, which is characterized in that
Pattern in the step 1) includes the V2G patterns of the V0G patterns and two-way orderly charge and discharge of unidirectional unordered charging.
3. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle according to claim 2, which is characterized in that
Under the V0G patterns of unidirectional unordered charging, the electrical demand that the putting of electric vehicle fills electric vehicle in load distribution performance is：
EEV=ηEV·d
Wherein, ηEVFor the electrical demand coefficient of electric vehicle mileage, the mileage travelled d obediences pair of every electric vehicle
Number normal distribution, probability density function are：
Charging time fs(x) meet normal distribution：
Wherein, μdAnd μsFor desired value, σdAnd σsFor standard deviation.
4. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle according to claim 2, which is characterized in that
Under the V2G patterns of two-way orderly charge and discharge, electric vehicle puts the discharge period T filled in load distribution performancedisc1For：
Duration of charge can be obtained in conjunction with the charge power of electric vehicle, to obtain continuing charging time Tdisc2For：
Charging load is by consuming gross energy, i.e. P needed for single electric vehicle among one dayEVFor：
Wherein, Tall-discIt is put when fully charged for electric vehicle to the total duration that discharges needed for state-of-charge lower limit, PdiscFor electricity
Electrical automobile discharge power, SOCmaxAnd SOCminThe respectively bound of storage battery charge state, D are in electric vehicle day traveling
Journey, W100For hundred kilometers of power consumption of electric vehicle, Tend_discFor finish time of discharging, Tstart_discFor networking discharging time, PcFor
Electric vehicle charge power.
5. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle according to claim 1, which is characterized in that
A variety of different scheduling strategies in the step 3) include micro-grid connection operation reserve and micro-capacitance sensor islet operation strategy.
6. a kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle according to claim 1, which is characterized in that
The step 3) specifically includes following steps：
31) according to the access module of electric vehicle, the model parameter of each distributed generation unit, each target letter in micro-capacitance sensor are set
Number parameter and each constraints parameter, and introduce uncontrollable predictable distributed generation resource and contribute and cold electric load parameter；
32) will controllably output unit miniature gas turbine, fuel cell, diesel-driven generator, accumulator and major network interchange power make
Particles are tieed up for five, and set particle cluster algorithm parameter, including the maximum speed of population, solution space dimension, maximum iteration, particle
Degree and recombination index r；
33) fitness of each particle is calculated, and records the current individual extreme value of each particle and corresponding target letter
Numerical value, and then all extreme values and corresponding target function value are obtained, and select individual optimal value and global optimum；
34) the current number of iteration adds one, and update population carries out position and speed；
35) whether judging result meets Premature Convergence standard and whether recombination number reaches preset value, if as a result meeting
Premature Convergence standard and recombination number does not reach preset value, then recombinate population and recombination index r=r+1, and return
Step 33) is returned, step 36) is otherwise carried out；
36) judge whether to meet the condition of convergence, if so, obtaining global optimum or reaching maximum iteration, terminate iteration
Process, if it is not, then return to step 33), continue iterative operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510400856.2A CN105160451B (en) | 2015-07-09 | 2015-07-09 | A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510400856.2A CN105160451B (en) | 2015-07-09 | 2015-07-09 | A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105160451A CN105160451A (en) | 2015-12-16 |
| CN105160451B true CN105160451B (en) | 2018-08-24 |
Family
ID=54801302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510400856.2A Active CN105160451B (en) | 2015-07-09 | 2015-07-09 | A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105160451B (en) |
Families Citing this family (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105552894B (en) * | 2015-12-29 | 2019-02-05 | 中国电力科学研究院 | A kind of optimal control method of micro-capacitance sensor low-carbon operation |
| CN105574620A (en) * | 2016-01-06 | 2016-05-11 | 重庆邮电大学 | Micro-grid dynamic optimization scheduling method combined with double master control cooperation and MPSO algorithm |
| CN105894123B (en) * | 2016-04-20 | 2017-10-17 | 广州供电局有限公司 | The determination method and device of electric vehicle charging operational mode |
| CN105811409B (en) * | 2016-04-28 | 2018-06-26 | 上海电力学院 | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile |
| CN106026152B (en) | 2016-05-19 | 2017-06-06 | 合肥工业大学 | A kind of electric automobile accesses the discharge and recharge dispatching method of micro-capacitance sensor |
| CN106532688B (en) * | 2016-11-22 | 2019-02-26 | 国电南瑞科技股份有限公司 | It is a kind of for assessing the method and system of micro-capacitance sensor operational reliability |
| CN106712037B (en) * | 2016-11-28 | 2019-11-08 | 武汉大学 | A kind of power system steady state voltage stability appraisal procedure considering electric car charge characteristic and the load fluctuation limit |
| CN107196586A (en) * | 2017-05-15 | 2017-09-22 | 国网安徽省电力公司电力科学研究院 | Micro-grid system optimizing operation method is stored up containing the light bavin that electric automobile is accessed |
| CN107220730B (en) * | 2017-05-23 | 2020-06-16 | 西安交通大学 | Dynamic route planning method for pure electric bus capable of prolonging service life of power battery |
| CN107104454A (en) * | 2017-06-06 | 2017-08-29 | 重庆大学 | Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain |
| CN107482680B (en) * | 2017-08-28 | 2020-06-05 | 南京工程学院 | Electric vehicle dispatching method based on islanding division |
| CN107704947A (en) * | 2017-08-31 | 2018-02-16 | 合肥工业大学 | A kind of micro-capacitance sensor Multiobjective Optimal Operation method for considering electric automobile Stochastic accessing |
| CN107657373B (en) * | 2017-09-23 | 2021-04-06 | 华北电力大学 | Method for verifying scheduling plan |
| CN107634532B (en) * | 2017-10-23 | 2020-09-01 | 上海电力学院 | Charging, discharging and storing integrated station control method based on improved V2G and priority scheduling |
| CN108734350A (en) * | 2018-05-17 | 2018-11-02 | 燕山大学 | A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor |
| CN108923536B (en) * | 2018-07-12 | 2020-09-08 | 中国南方电网有限责任公司 | Schedulable potential analysis method, system, computer device and storage medium |
| CN110350535A (en) * | 2018-09-07 | 2019-10-18 | 国网甘肃省电力公司电力科学研究院 | A kind of meter and large-scale wind power and the grid-connected distribution network voltage probability of stability appraisal procedure of electric car |
| CN109460853B (en) * | 2018-09-29 | 2021-10-29 | 中国电力科学研究院有限公司 | Method and system for determining charging load demand of electric automobile |
| CN109301852B (en) * | 2018-11-23 | 2021-09-03 | 武汉理工大学 | Micro-grid hierarchical multi-target combined economic dispatching method |
| CN109472422A (en) * | 2018-11-28 | 2019-03-15 | 中国电力科学研究院有限公司 | A kind of comprehensive energy network cost optimization method and device |
| CN109710882B (en) * | 2018-12-21 | 2023-06-09 | 重庆大学 | Off-grid micro-grid electric vehicle ordered charge-discharge load modeling and solving method based on optimized operation |
| CN109871981B (en) * | 2019-01-10 | 2021-07-13 | 国家电网有限公司 | Load characteristic prediction method considering distributed power supply and electric vehicle influence |
| CN109658012B (en) * | 2019-01-22 | 2022-12-02 | 武汉理工大学 | Micro-grid multi-target economic dispatching method and device considering demand side response |
| CN110048411B (en) * | 2019-04-23 | 2023-05-12 | 广东电网有限责任公司 | Micro-grid networking method, device and equipment based on multiport electric energy router |
| CN109978285B (en) * | 2019-05-06 | 2022-07-15 | 南京邮电大学 | Regional microgrid optimization scheduling method for large-scale access of electric vehicles |
| CN110084443B (en) * | 2019-05-23 | 2022-11-01 | 哈尔滨工业大学 | QPSO optimization algorithm-based power change station operation optimization model analysis method |
| CN110570007B (en) * | 2019-06-17 | 2023-05-09 | 上海交通大学 | Multi-time-scale optimal scheduling method for electric automobile |
| CN110443415B (en) * | 2019-07-24 | 2022-07-15 | 三峡大学 | Electric vehicle charging station multi-objective optimization scheduling method considering dynamic electricity price strategy |
| CN111900727B (en) * | 2020-07-15 | 2024-02-02 | 国网上海市电力公司 | PSO-based optical storage, charging and replacement integrated charging station collaborative optimization scheduling method and device |
| CN111953023B (en) * | 2020-07-20 | 2022-02-22 | 上海电力大学 | Charging pile-containing regional energy network management method based on MACPSO algorithm |
| CN112418605A (en) * | 2020-10-19 | 2021-02-26 | 国网上海市电力公司 | Optimal operation method for energy storage system of optical storage type charging station |
| CN112467767B (en) * | 2020-11-02 | 2023-06-09 | 国网内蒙古东部电力有限公司呼伦贝尔供电公司 | Electric automobile grid-connected cooperative control method under comprehensive energy system environment |
| CN112874368A (en) * | 2021-03-26 | 2021-06-01 | 国网黑龙江省电力有限公司电力科学研究院 | Electric vehicle charging strategy optimization method based on QPSO algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103001259A (en) * | 2012-12-29 | 2013-03-27 | 南方电网科学研究院有限责任公司 | Annealing algorithm based grid-connected type micro-grid optimal dispatching method |
| CN103077429A (en) * | 2013-01-10 | 2013-05-01 | 华北电力大学 | Capacity-optimizing method of isolated micro-electrical network containing wind-solar electricity-generating and electric-automobile electricity-transforming station |
| CN103593717A (en) * | 2013-11-21 | 2014-02-19 | 国网上海市电力公司 | Micro-grid energy real-time optimization control method |
| CN103996075A (en) * | 2014-05-08 | 2014-08-20 | 南方电网科学研究院有限责任公司 | Diesel engine and storage battery coordinated micro grid scheduling method with multiple optimization objectives |
| CN104268652A (en) * | 2014-09-28 | 2015-01-07 | 南方电网科学研究院有限责任公司 | Micro-grid operation optimizing method in consideration of real-time electricity price and controllable load |
-
2015
- 2015-07-09 CN CN201510400856.2A patent/CN105160451B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103001259A (en) * | 2012-12-29 | 2013-03-27 | 南方电网科学研究院有限责任公司 | Annealing algorithm based grid-connected type micro-grid optimal dispatching method |
| CN103077429A (en) * | 2013-01-10 | 2013-05-01 | 华北电力大学 | Capacity-optimizing method of isolated micro-electrical network containing wind-solar electricity-generating and electric-automobile electricity-transforming station |
| CN103593717A (en) * | 2013-11-21 | 2014-02-19 | 国网上海市电力公司 | Micro-grid energy real-time optimization control method |
| CN103996075A (en) * | 2014-05-08 | 2014-08-20 | 南方电网科学研究院有限责任公司 | Diesel engine and storage battery coordinated micro grid scheduling method with multiple optimization objectives |
| CN104268652A (en) * | 2014-09-28 | 2015-01-07 | 南方电网科学研究院有限责任公司 | Micro-grid operation optimizing method in consideration of real-time electricity price and controllable load |
Non-Patent Citations (2)
| Title |
|---|
| 含电动汽车微网的经济调度研究;杨赞;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20140315(第3期);全文 * |
| 含移动储能单元的微网优化调度模型研究;吴婷;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20130715(第7期);C042-897 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105160451A (en) | 2015-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105160451B (en) | A kind of micro-capacitance sensor Multiobjective Optimal Operation method containing electric vehicle | |
| Hou et al. | Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load | |
| Wang et al. | Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method | |
| Meng et al. | Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system | |
| CN105591406B (en) | A kind of optimized algorithm of the microgrid energy management system based on non-cooperative game | |
| CN105811409B (en) | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile | |
| CN105071389B (en) | The alternating current-direct current mixing micro-capacitance sensor optimizing operation method and device of meter and source net load interaction | |
| CN104966127B (en) | A kind of electric vehicle economic load dispatching method based on demand response | |
| CN105515110B (en) | A kind of electric automobile charges real-time control system in order | |
| CN110533225A (en) | A kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming | |
| Yang et al. | A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem | |
| CN106026152A (en) | Charging and discharging scheduling method for electric vehicles connected to micro-grid | |
| CN105787605A (en) | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm | |
| CN106058855A (en) | Active power distribution network multi-target optimization scheduling method of coordinating stored energy and flexible load | |
| CN107104454A (en) | Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain | |
| CN109672202A (en) | A kind of automatic demand response method of energy-storage system based on block chain technology | |
| CN109657993A (en) | A kind of automatic demand response method of energy local area network energy-storage system based on non-cooperative game | |
| CN109710882B (en) | Off-grid micro-grid electric vehicle ordered charge-discharge load modeling and solving method based on optimized operation | |
| CN105896596B (en) | A kind of the wind power layering smoothing system and its method of consideration Demand Side Response | |
| CN107612041A (en) | One kind consideration is probabilistic to be based on the automatic demand response method of event driven micro-capacitance sensor | |
| CN107482690A (en) | The electric power system dispatching optimization method and system of wind-powered electricity generation and electric automobile cooperative scheduling | |
| Mei et al. | Multi-objective optimal scheduling of microgrid with electric vehicles | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| Li et al. | Multi-objective optimal dispatching of electric vehicle cluster considering user demand response | |
| CN106227986A (en) | A kind of distributed power source combines dispositions method and device with intelligent parking lot |
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 | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 200090 Pingliang Road, Shanghai, No. 2103, No. Patentee after: Shanghai Electric Power University Address before: 200090 Pingliang Road, Shanghai, No. 2103, No. Patentee before: SHANGHAI University OF ELECTRIC POWER |
|
| CP01 | Change in the name or title of a patent holder | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210427 Address after: 200124 2 floors, No. 84, Sanlin Road, Pudong New Area, Shanghai Patentee after: Shanghai Shunyi Energy Technology Co.,Ltd. Address before: 200090 Pingliang Road, Shanghai, No. 2103, No. Patentee before: Shanghai Electric Power University |
|
| TR01 | Transfer of patent right |