CN106230020B - The electric vehicle interactive response control method of distributed generation resource consumption is considered under a kind of micro-capacitance sensor - Google Patents
The electric vehicle interactive response control method of distributed generation resource consumption is considered under a kind of micro-capacitance sensor Download PDFInfo
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- H02J3/383—
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/14—Plug-in electric vehicles
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Abstract
The electric vehicle interactive response control method that distributed generation resource consumption is considered under a kind of micro-capacitance sensor, includes the following steps：S1：One day continuous time for 24 hours was subjected to sliding-model control；S2：The battery information and client's charge requirement information of new networking electric vehicle are recorded by charge-discharge facility, and it is the access period to enable initial time period；S3：Read in current time information on load；S4：Photovoltaic power generation output forecasting；S5：It is contributed with load supply/demand, in conjunction with Spot Price and inclination blocking rate Developing Virtual Price Mechanisms based on photovoltaic；S6：Conversion maximizes photoelectricity and dissolves target under the guiding of virtual electricity price, solves object function, formulates electric vehicle charge and discharge plan in duration T；S7：Each electric vehicle carries out electricity consumption, idle or discharge operation according to control strategy under present period, updates prediction model information and uploads control information；S8：S3~S7 is repeated until vehicle leaves electrically-charging equipment.Photoelectricity consumption level of the present invention is higher, control effect is preferable.
Description
Technical field
The invention belongs to the orderly control fields of electric vehicle, and in particular to distributed generation resource consumption is considered under a kind of micro-capacitance sensor
Electric vehicle interactive response control method.
Background technology
Micro-capacitance sensor accesses the effective slow of power distribution network as regenerative resource (renewable energy sources, RES)
Its grid-connected or islet operation may be implemented by key technologies such as itself operation controls and energy management in punching, can be fully sharp
The adverse effect brought to power distribution network with distribution type renewable energy, the intermittent distributed generation resource of reduction, but can improve power supply can
By property and improve power quality, therefore has received widespread attention.With the continuous access of RES (such as wind-powered electricity generation, photoelectricity), contribute not
Influence of the certainty to micro-capacitance sensor optimization operation is increasingly apparent, it is therefore necessary to further how to be adapted under research RES Thief zones
The uncertain promotion new energy consumption rate that RES contributes, and then realize the optimization operation of micro-capacitance sensor.
Under Power Market, electric vehicle (electric vehicle, EV) conduct with energy saving and low emission
When a kind of Demand-side resource access micro-capacitance sensor, V2G (vehicle-to-grid) interactive response technology can be based on and participate in system energy
The grid-connected power generation system of supply side and the resource of Demand-side are carried out unified plan by amount regulation and control, weaken the intermittent band of RES power generations
The adverse effect come ensures the power supply reliability and power quality of micro-grid system, improves system economy etc..Therefore, as how
Efficient scheduling and control strategy are the regulation of energy effect that means give full play to EV, become and improve generation of electricity by new energy to greatest extent
Utilization rate, embodiment EV couple the key that synergy utilizes with extensive RES.
Some progress being had been achieved with both at home and abroad for the research in terms of the integrated utilization of RES and EV at present.Wherein, pass through
It is the effective means that micro-capacitance sensor realizes demand side management, promotes distributed consumption that the mode of electricity price, which guides the orderly charge and discharge of EV,.
Invention content
In order to overcome, the photoelectricity consumption level of the existing grid type micro-capacitance sensor containing EV and high permeability photoelectricity is relatively low, controls effect
The poor deficiency of fruit, a kind of photoelectricity of present invention offer dissolves considers distribution under horizontal higher, the preferable micro-capacitance sensor of control effect
The electric vehicle interactive response control method of power supply consumption.
The technical solution adopted by the present invention to solve the technical problems is：
The electric vehicle interactive response control method of distributed generation resource consumption, the control method are considered under a kind of micro-capacitance sensor
Include the following steps：
Step 1：One day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, when for arbitrary kth
Section, there is a k ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period；
Step 2：As electric vehicle access l (l=1,2 ..., n, hereinafter referred to as electric vehicle l) charge-discharge facilities
When, electrically-charging equipment reads electric vehicle turn-on time, original state (State of Charge, SOC) S of battery0,l, and 0≤
S0,l≤1；
Step 3：Car owner inputs the expection time departure T of vehicle lout,lAnd desired state-of-charge S when leavingE,l, and have
0≤SE,l≤1；
Step 4：If the duration that electric vehicle l persistently networks is more than charges to Expected energy water by the battery of electric vehicle l
It is flat it is required most grow in short-term, then follow the steps 5, otherwise user allowed independently to choose whether information of modifying, if user agrees to hold
Row modification then skips to step 3, and the user is abandoned if user refuses to execute modification；
Step 5：It is the period that vehicle accesses electrically-charging equipment to enable initial time period k；
Step 6：Current time information on load is read in, and selects dynamic optimization section T, according to existing photovoltaic output power
Research conclusion, contributed as initial value with the photovoltaic of present period, predict the photovoltaic output in the T periods in future；
Step 7：Based in present period micro-capacitance sensor distributed photovoltaic contribute and load between supply/demand, in conjunction with real-time
Electricity price has developed virtual Price Mechanisms with blocking rate IBR is tilted, and process is as follows,
Step 7-1：Spot Price mechanism RTP and system net load relationship are as follows：
In formula：RTPkFor the Spot Price of k periods；When being accessed for electric vehicle l, the micro-grid system of k periods is born only
Lotus；ak、bkFor Spot Price coefficient, section takes different values in different times, depends on the demand dynamic of user；For system
Base load, i.e. all electric loads in the micro-capacitance sensor in addition to electric vehicle cluster load；It is energy-storage system in k
The charge-discharge electric power of section；Photovoltaic for the k periods goes out activity of force；When indicating vehicle l access micro-capacitance sensors, charge and discharge plan system
Fixed completed vehicle cluster load；Ml-1When indicating vehicle l access micro-capacitance sensors, the completed vehicle of charge and discharge plan combines；
Step 7-2：Three kinds of electricity price grades are arranged in the present invention in IBR：
In formula,WithFor the boundary between different electricity price grades；xk、ykWith zkIt is specific to count for the electricity price under three grades
Calculation method is as follows：
In formula, λ1With λ2For the price multiplying power under different brackets, and λ2＞ λ1＞ 1；
Step 7-3：In conclusion virtual electricity priceCalculation be：
In formula,When, it is meant that regenerative resource output is superfluous, at this point, extra photovoltaic power generation quantity superior power grid
It send, RTPrePrice is sent for unit electricity；
Step 8：Conversion maximizes photoelectricity and dissolves target under the guiding of virtual electricity price, formulates electric vehicle in duration T and fills
Electric discharge plan, the formulation process of object function is as follows,
Based on described in step 7 virtual Spot Price Model, with the minimum target of the virtual totle drilling cost of charge and discharge to it into Mobile state
Planning：
In formula, VlFor the virtual totle drilling cost of electric vehicle l；Pl kPower is exchanged for k period electric vehicle l and micro-capacitance sensor,
Pl k＞ 0 indicates the charge power of vehicle l；Pl k＜ 0 indicates discharge power；Pl k=0 indicates to be in idle state, EV power batteries
Model and constraints are：
-PEV,d≤Pl k≤PEV,c (10)
In formula,Respectively battery SOCs of the vehicle l in k periods and k-1 periods；QEV,lFor power cell of vehicle
Capacity；SEV,max、SEV,minThe respectively upper and lower limit of power battery SOC；ηEVPower of battery exchange efficiency is indicated, with Power Exchange
Direction is related, as shown in formula (12)；ηc、ηdCharge and discharge efficiency is indicated respectively；
It also needs to consider micro-grid system power-balance constraint and send its formula of power constraint to be：
In formula,Indicate the interaction power of k periods micro-capacitance sensor and bulk power grid,Power is sent for micro-capacitance sensor；For the maximum value for sending power to allow.
Step 9：In present period, each electric vehicle carries out specific electricity consumption, idle or discharge operation according to control strategy,
Meanwhile it updating prediction model information and control information is uploaded to electric energy public service platform；
Step 10：Step 6~9 are repeated when entering the new period until vehicle leaves electrically-charging equipment.
The present invention technical concept be：A kind of virtual Price Mechanisms are developed, the mutual sounds of something astir of EV are constructed based on virtual electricity price
The mixed integer programming framework answered introduces model predictive control method and realizes the control of dynamic EV interactive responses on this basis.
Beneficial effects of the present invention are mainly manifested in：
1, the EV interactive response control strategies based on virtual Price Mechanisms, can be on the basis of meeting user power utilization demand
Improve part throttle characteristics, substantially consumption photovoltaic output, reduces the grid-connected impact to higher level's power grid of distributed photovoltaic.
2, the dynamic EV response control strategy performances based on MPC methods are more preferable, and with the raising of uncertainty grade, always
Increasing speed for cost is slower.Therefore the EV energy flow control policies based on MPC methods have stronger robustness.
3, institute's extracting method can effectively improve the economy of supply and demand both sides, reduce bilateral cost.Specific implementation mode.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
Obviously, or practice through the invention is recognized.
Description of the drawings
Fig. 1 is the implementation flow chart of the present invention；
Fig. 2 is the load power curve under 5 kinds of control models；
Fig. 3 is the control strategy of EV clusters and energy-storage system under 5 patterns of Case；
Fig. 4 is the net load peak-valley difference analysis of Case 3 under uncertainty, Case 5；
Fig. 5 is the micro-capacitance sensor operating cost analysis of Case 3 under uncertainty, Case 5；
Specific implementation mode
The present invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1~Fig. 5 considers the electric vehicle interactive response controlling party of distributed generation resource consumption under a kind of micro-capacitance sensor
Method, the control method include the following steps：
Step 1：One day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, when for arbitrary kth
Section, there is a k ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period；
Step 2：As electric vehicle access l (l=1,2 ..., n, hereinafter referred to as electric vehicle l) charge-discharge facilities
When, electrically-charging equipment reads electric vehicle turn-on time, original state (State of Charge, SOC) S of battery0,l, and 0≤
S0,l≤1；
Step 3：Car owner inputs the expection time departure T of vehicle lout,lAnd desired state-of-charge S when leavingE,l, and have
0≤SE,l≤1；
Step 4：If the duration that electric vehicle l persistently networks is more than charges to Expected energy water by the battery of electric vehicle l
It is flat it is required most grow in short-term, then follow the steps 5, otherwise user allowed independently to choose whether information of modifying, if user agrees to hold
Row modification then skips to step 3, and the user is abandoned if user refuses to execute modification；
Step 5：It is the period that vehicle accesses electrically-charging equipment to enable initial time period k；
Step 6：Current time information on load is read in, and selects dynamic optimization section T, according to existing photovoltaic output power
Research conclusion, contributed as initial value with the photovoltaic of present period, predict the photovoltaic output in the T periods in future；
Step 7：Based in present period micro-capacitance sensor distributed photovoltaic contribute and load between supply/demand, in conjunction with real-time
Electricity price has developed virtual Price Mechanisms with blocking rate (IBR) is tilted, and process is as follows,
Step 7-1：Spot Price mechanism (RTP) and system net load relationship are as follows：
In formula：RTPkFor the Spot Price of k periods；When being accessed for electric vehicle l, the micro-grid system of k periods is born only
Lotus；ak、bkFor Spot Price coefficient, section different values can be taken in different times, depend on the demand dynamic of user；For
System base load, i.e. all electric loads in the micro-capacitance sensor in addition to electric vehicle cluster load；Exist for energy-storage system
The charge-discharge electric power of k periods；Photovoltaic for the k periods goes out activity of force；When indicating vehicle l access micro-capacitance sensors, charge and discharge meter
Draw surely completed vehicle cluster load；Ml-1When indicating vehicle l access micro-capacitance sensors, the completed vehicle knot of charge and discharge plan
It closes；
Step 7-2：Three kinds of electricity price grades are arranged in the present invention in IBR：
In formula,WithFor the boundary between different electricity price grades；xk、ykWith zkIt is specific to count for the electricity price under three grades
Calculation method is as follows：
In formula, λ1With λ2For the price multiplying power under different brackets, and λ2＞ λ1＞ 1；
Step 7-3：In conclusion virtual electricity priceCalculation be：
In formula,When, it is meant that regenerative resource output is superfluous, at this point, extra photovoltaic power generation quantity can be with superior
Power grid is sent, RTPrePrice is sent for unit electricity；
Step 8：Conversion maximizes photoelectricity and dissolves target under the guiding of virtual electricity price, formulates electric vehicle in duration T and fills
Electric discharge plan, the formulation process of object function is as follows,
Based on described in step 7 virtual Spot Price Model, with the minimum target of the virtual totle drilling cost of charge and discharge to it into Mobile state
Planning：
In formula, VlFor the virtual totle drilling cost of electric vehicle l；Pl kPower is exchanged for k period electric vehicle l and micro-capacitance sensor,
Pl k＞ 0 indicates the charge power of vehicle l；Pl k＜ 0 indicates discharge power；Pl k=0 indicates to be in idle state, EV power batteries
Model and constraints are：
-PEV,d≤Pl k≤PEV,c (10)
In formula,Respectively battery SOCs of the vehicle l in k periods and k-1 periods；QEV,lFor power cell of vehicle
Capacity；SEV,max、SEV,minThe respectively upper and lower limit of power battery SOC；ηEVPower of battery exchange efficiency is indicated, with Power Exchange
Direction is related, as shown in formula (12)；ηc、ηdCharge and discharge efficiency is indicated respectively；
It also needs to consider micro-grid system power-balance constraint and send its formula of power constraint to be：
In formula,Indicate the interaction power of k periods micro-capacitance sensor and bulk power grid,Power is sent for micro-capacitance sensor；For the maximum value for sending power to allow.
Step 9：In present period, each electric vehicle carries out specific electricity consumption, idle or discharge operation according to control strategy,
Meanwhile it updating prediction model information and control information is uploaded to electric energy public service platform；
Step 10：Step 6~9 are repeated when entering the new period until vehicle leaves electrically-charging equipment.To make art technology
Personnel more fully understand that the present invention, applicant's also application consider the electric vehicle interactive response control method of distributed generation resource consumption
Simulation analysis is carried out by taking certain Administrative Area micro-capacitance sensor as an example.
The photovoltaic installed capacity of the micro-capacitance sensor is 1 300kW, and the EV scales of service are 20, and EV battery capacities are 60kWh,
Specified charge and discharge power is 7kW, and charge and discharge efficiency is 0.92, and battery SOC boundary is 0.1 and 0.9 (SEV,min、
SEV,max).ESS capacity 1200kWh, SOC bound is set as 0.9 and 0.45；Vehicular charging rises, stops time Normal Distribution
(desired value is respectively 7:00、17:30, standard deviation is 1h), (desired value is Vehicular battery state-of-charge Normal Distribution
0.45, standard deviation 0.1), the parameters such as electric vehicle charge and discharge beginning and ending time and starting SOC are mutual indepedent.
The Administrative Area electricity price is based on time-of-use tariffs, the peak period (6：00-22：00) electricity price is 1.006 4 yuan/kWh, Gu Shi
Section (22：00- next day 6：00) electricity price is 0.249 5 yuan/kWh；EV user participate in demand response discharging compensation be 0.8 yuan/
kWh；Photovoltaic generation, which is utilized, can get 0.62 yuan/kWh of subsidy.Novel Spot Price coefficient ak、bkIt is set to 0.003 He
0.4；IBR electricity price threshold valuesIt is set to 200 and 300；Price multiplying power λ1With λ2Respectively 1.20 and 1.45；Photovoltaic is to electricity
It is 100kW that net, which send power limit, and it is 0.485 yuan/kWh to send price.
In order to more directly embody control effect of the put forward EV interactive responses control strategy in micro-capacitance sensor, this section emulates simultaneously
Following four control model is calculated to compare with institute extracting method：
(1)Case 1：Unordered charge mode, charge-discharge facility provide lasting invariable power charging service for the EV of access,
Until vehicle leaves, if filling with electricity before leaving, stop charging.
(2)Case 2：Fixed electricity price model, it is continuous as target, power to minimize user cost under constant off-time electricity price
Adjustable electric vehicle response control pattern.
(3)Case 3：Scheduling method a few days ago, under virtual electricity price, charge-discharge facility contributed based on the photovoltaic predicted a few days ago, base
This information on load carries out the continuously adjustable response of power with the electric vehicle that the virtual minimum target of charge and discharge totle drilling cost is access
Control.
(4)Case 4：Only charge Optimizing Mode, and similar with carried EV interactive responses control model, difference lies in the patterns
Do not consider V2G, orderly charging response control is only carried out, at this point, there is 0≤Pl k≤PEV,c。
Statistical information is listed in table 1 under different control models.
Table 1
Under 1 patterns of Case, a large amount of electric vehicles concentrate on the access of working peak period period, and charging modes lack flexibility,
Micro-capacitance sensor net load peak-valley difference and system economy are poor.Under 2 patterns of Case, vehicle is only simple to be tended in electricity price height
It charges when electric discharge, low ebb when peak, performance is excellent in terms of user's economy, but part throttle characteristics is poor, it is difficult to effectively facilitate photovoltaic hair
The consumption of electricity.Under Case 3,5 patterns of Case, web response body Web improves load water when photovoltaic contributes higher by largely charging
It is flat, and electric energy is sent back to by system by V2G as far as possible when photovoltaic contributes relatively low to reduce load level, thus the confession of system
It needs relationship more to balance, is effectively improved in terms of economy.Further, EV clusters and energy-storage system under 5 patterns of Case
Control strategy is as shown in Fig. 3.Under 4 patterns of Case, EV clusters are not involved in system V2G services, part throttle characteristics and photovoltaic utilization rate
It can not improve to the greatest extent.
In conjunction with 2~attached drawing of attached drawing 3 and table 1, it can be deduced that following phenomenon and conclusion：
The user side cost of 2 patterns of Case is minimum, but performance is poor in terms of part throttle characteristics and micro-capacitance sensor operating cost；
4 patterns of Case are superior to Case 3 and Case 5 in terms of energy loss amount and battery loss, in terms of micro-capacitance sensor operating cost
Part throttle characteristics and photovoltaic utilization rate compared with the reductions of Case 5 24.18%, but after the model-based optimization is still undesirable；5 patterns of Case
System equilibrium of supply and demand degree is high, but economy and non-optimal, thus micro-capacitance sensor operator need according to demand, user-responsiveness,
Cash flow abundant intensity etc. carries out interests feedback dynamics the management and control of varying strength.
In conclusion with the development of V2G technologies, there is the EV clusters of certain trip rule to play distributed energy storage effect
Part can be substituted and fix energy storage battery, solar photovoltaic utilization rate can be effectively improved, improve micro-capacitance sensor overall economics, fully sent out
The energy-saving and emission-reduction potentiality for waving EV, promote the large-scale development of new energy.As it can be seen that the integrated utilization of EV and new energy is a kind of effective
Synergistic means, can effectively reduce current energy framework some adverse effect.
Due to the accurate prediction for considering to contribute to photovoltaic, the advantage of MPC methods can not be embodied, to fully demonstrate
5 patterns of the case specific performance true in forecasting inaccuracy contributes not to distributed photovoltaic using random scene analysis method
The influence that certainty generates optimum results quantifies, and analyses in depth model predictive control method with uncertainty in traffic
To the robustness of EV cluster energy managements under environment.The reference prediction percentage error of photovoltaic and maximum uncertainty it is specific
Numerical value is as shown in table 2.
Table 2
Net load mean value is held essentially constant after showing 600 times using the analog result of random scene analysis method, is flat
Weighing apparatus calculates time and computational accuracy, determines that simulation times are 600 times.5 two kinds of Case 3, Case control models are not known at 6
The box traction substation of the micro-capacitance sensor net load peak-valley difference and operating cost spent under grade is compared as shown in attached drawing 4-5, the broken line table in figure
Show the mean value of random scene operation result.
By attached drawing 4-5 it is found that with uncertainty grade increase, the micro-capacitance sensor net load peak under Case 3, Case 5
Paddy difference and operating cost box height are progressively longer, and mean value broken line is presented dullness and increases trend, show the prediction that photovoltaic is contributed
Error can bring adverse effect to system operation economy, part throttle characteristics etc., also, this kind adverse effect is with the increasing of prediction error
Aggravate greatly.But in contrast, the net load peak-valley difference of Case 5 and micro-capacitance sensor operating cost are with uncertainty M raisings
And increased speed is significantly lower than Case 3.
To sum up, the energy storage that EV clusters are played using rational response control as means acts on, and load is made to exist from photovoltaic generation
The period of vacancy is transferred to the noon photovoltaic generation power period more than needed, effectively improves part throttle characteristics, it is made to be more in line with light
Lie prostrate power generation situation.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiments or example.In addition, those skilled in the art can be by this specification
Described in different embodiments or examples be combined.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art within the scope of the invention can be to above-mentioned
Embodiment is changed, changes, replacing and modification.
Claims (1)
1. considering the electric vehicle interactive response control method of distributed generation resource consumption under a kind of micro-capacitance sensor, it is characterised in that：Institute
Control method is stated to include the following steps：
Step 1：One day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, for arbitrary kth time period, there is k
∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period；
Step 2：When electric vehicle accesses l charge-discharge facilities, l=1,2 ..., n, hereinafter referred to as electric vehicle l, charging
Facility reads electric vehicle turn-on time, the original state S of battery0,l, and 0≤S0,l≤1；
Step 3：Car owner inputs the expection time departure T of electric vehicle lout,lAnd desired state-of-charge S when leavingE,l, and have
0≤SE,l≤1；
Step 4：If the duration that electric vehicle l persistently networks is more than charges to the horizontal institute of Expected energy by the battery of electric vehicle l
What is needed most grows in short-term, thens follow the steps 5, otherwise user is allowed independently to choose whether information of modifying, and is repaiied if user agrees to execute
Change, skip to step 3, the user is abandoned if user refuses to execute modification；
Step 5：It is the period that vehicle accesses electrically-charging equipment to enable initial time period k；
Step 6：Current time information on load is read in, and selects dynamic optimization section T, according to grinding for existing photovoltaic output power
Study carefully conclusion, contributed as initial value with the photovoltaic of present period, predicts that the photovoltaic in the T periods in future is contributed；
Step 7：Based in present period micro-capacitance sensor distributed photovoltaic contribute and load between supply/demand, in conjunction with Spot Price
Developing virtual Price Mechanisms with blocking rate IBR is tilted, process is as follows,
Step 7-1：Spot Price mechanism RTP and system net load relationship are as follows：
In formula：RTPkFor the Spot Price of k periods；When being accessed for electric vehicle l, the micro-grid system net load of k periods；ak、
bkFor Spot Price coefficient, section takes different values in different times, depends on the demand dynamic of user；It is born substantially for system
Lotus, i.e. all electric loads in the micro-capacitance sensor in addition to electric vehicle cluster load；For energy-storage system filling in the k periods
Discharge power；Photovoltaic for the k periods goes out activity of force；When indicating electric vehicle l access micro-capacitance sensors, charge and discharge plan
Completed vehicle cluster load；Ml-1When indicating electric vehicle l access micro-capacitance sensors, the completed vehicle of charge and discharge plan combines；
Step 7-2：Three kinds of electricity price grades are arranged in the present invention in IBR：
In formula,WithFor the boundary between different electricity price grades；xk、ykWith zkFor the electricity price under three grades, specific calculating side
Method is as follows：
In formula, λ1With λ2For the price multiplying power under different brackets, and λ2＞ λ1＞ 1；
Step 7-3：In conclusion virtual electricity priceCalculation be：
In formula,When, it is meant that regenerative resource output is superfluous, at this point, extra photovoltaic power generation quantity superior power grid falls
It send, RTPrePrice is sent for unit electricity；
Step 8：Conversion maximizes photoelectricity and dissolves target under the guiding of virtual electricity price, formulates electric vehicle charge and discharge in duration T
Plan, the formulation process of object function is as follows,
Based on described in step 7 virtual Spot Price Model, Dynamic Programming carried out to it with the minimum target of the virtual totle drilling cost of charge and discharge：
In formula, VlFor the virtual totle drilling cost of electric vehicle l；Pl kFor the power that exchanges of k period electric vehicle l and micro-capacitance sensor, Pl k＞ 0
Indicate the charge power of electric vehicle l；Pl k＜ 0 indicates discharge power；Pl k=0 indicates to be in idle state, EV power battery moulds
Type and constraints are：
-PEV,d≤Pl k≤PEV,c (10)
In formula,Respectively battery SOCs of the electric vehicle l in k periods and k-1 periods；QEV,lHold for power cell of vehicle
Amount；SEV,max、SEV,minThe respectively upper and lower limit of power battery SOC；ηEVPower of battery exchange efficiency is indicated, with Power Exchange side
To related, as shown in formula (12)；ηc、ηdCharge and discharge efficiency is indicated respectively；
It also needs to consider micro-grid system power-balance constraint and send its formula of power constraint to be：
In formula,Indicate the interaction power of k periods micro-capacitance sensor and bulk power grid,Power is sent for micro-capacitance sensor；For
The maximum value for sending power to allow；
Step 9：In present period, each electric vehicle carries out specific electricity consumption, idle or discharge operation according to control strategy, together
When, it updates prediction model information and control information is uploaded to electric energy public service platform；
Step 10：Step 6~9 are repeated when entering the new period until vehicle leaves electrically-charging equipment.
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| CN107196348B (en) * | 2017-07-19 | 2020-08-14 | 国电南瑞科技股份有限公司 | Day-ahead power generation planning method considering multi-end flexibility and straightness |
| CN107482675A (en) * | 2017-07-31 | 2017-12-15 | 东南大学 | A kind of computational methods of the electric automobile consumption regenerative resource based on off-network type microgrid |
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| CN107618393B (en) * | 2017-09-29 | 2020-09-01 | 重庆邮电大学 | Electric automobile charging load regulation and control system and method based on lever electricity price |
| CN108764554B (en) * | 2018-05-21 | 2021-12-07 | 上海电力学院 | Robust optimization method for guiding orderly charging of electric automobile |
| CN108944517B (en) * | 2018-07-20 | 2021-06-04 | 湖北理工学院 | Online charging method for electric automobile |
| CN109217310A (en) * | 2018-10-25 | 2019-01-15 | 三峡大学 | A kind of orderly charge control method of electric car considering new energy consumption |
| CN109636056B (en) * | 2018-12-24 | 2021-08-03 | 浙江工业大学 | Multi-energy microgrid decentralized optimization scheduling method based on multi-agent technology |
| CN109866645A (en) * | 2019-04-02 | 2019-06-11 | 东北大学 | A kind of orderly charge control method of electric car based on virtual Spot Price |
| CN109986989A (en) * | 2019-04-17 | 2019-07-09 | 三峡大学 | A kind of orderly charging method of electric car promoting new energy consumption |
| CN110445159A (en) * | 2019-08-08 | 2019-11-12 | 国网天津市电力公司 | Improve the orderly charge control method of electric car of large-scale photovoltaic consumption |
| CN110429631A (en) * | 2019-09-10 | 2019-11-08 | 周封 | Distributed power grid energy adjustment consumption system based on electric vehicle |
| CN110774929A (en) * | 2019-10-25 | 2020-02-11 | 上海电气集团股份有限公司 | Real-time control strategy and optimization method for orderly charging of electric automobile |
| CN111062513B (en) * | 2019-11-14 | 2023-08-18 | 四川大学 | Distributed community energy trading system and method based on self-adaptive consensus mechanism |
| CN112129995B (en) * | 2020-09-25 | 2023-03-21 | 中国电力科学研究院有限公司 | Renewable energy power production and consumption metering method and system |
| CN117811051B (en) * | 2024-02-27 | 2024-05-07 | 华东交通大学 | Micro-grid elasticity control method based on demand side response |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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-
2016
- 2016-08-11 CN CN201610658639.8A patent/CN106230020B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793758A (en) * | 2014-01-23 | 2014-05-14 | 华北电力大学 | Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system |
Non-Patent Citations (1)
| Title |
|---|
| 基于动态分时电价的电动汽车充电站有序充电策略;徐智威;《中国电机工程学报》;20140805;第34卷(第22期);第3638-3646页 * |
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