CN110065410A - A kind of electric car charge and discharge rate control method based on fuzzy control - Google Patents
A kind of electric car charge and discharge rate control method based on fuzzy control Download PDFInfo
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- CN110065410A CN110065410A CN201910373998.2A CN201910373998A CN110065410A CN 110065410 A CN110065410 A CN 110065410A CN 201910373998 A CN201910373998 A CN 201910373998A CN 110065410 A CN110065410 A CN 110065410A
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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
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
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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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
-
- 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
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
Abstract
The electric car charge and discharge rate control method based on fuzzy control that the invention discloses a kind of, include the following steps: S1, utilize the real time execution voltage of the real-time acquisition node of the self-operated measuring unit for being installed on power distribution network node, and compared with the predetermined reference voltage at node, obtain voltage difference；The charged Δ SOC of S2, the batteries of electric automobile according to needed for current time (abbreviation that SOC is state of charge) and maximum relevant to remaining down time can supplement charged SOCmaxEstablish charging emergency Degree Model；S3, according to voltage difference, charging emergency degree and power grid electricity price signal, design fuzzy control inference rule table；S4, controller carry out the control of electric car charge and discharge rate according to Controlling model.The present invention not only allows for the energy requirement of automobile user, charging emergency, the charging requirement such as fairness and lower charging cost, can also be limited in grid nodes voltage in service requirement restriction range.
Description
Technical field
The invention belongs to the technical fields of electric car, are related to a kind of electric car charge and discharge rate control based on fuzzy control
Method processed.
Background technique
Electric car is more more and more universal in recent years, wherein the quantity of private electric car increases year by year.Many studies have shown that
Many negative effects will be caused to distribution system using the electric car of no control strategy, such as voltage deviation, load peak, network
Increase, route/transformer overload etc. is lost.Have more research and has carried out centralization and distributed control method, these sides
Method considers technology and economic goal, including electromobile charging management is a few days ago, in a few days and Real time optimal dispatch.In the prior art
Have document propose solve the problems, such as power distribution network overload Control of Electric Vehicles, it is proposed that overload controlling method charging curve meter
It calculates, the distributing as price driven most preferably responds update.In addition it proposes and electric car is counted in intelligent distribution network
The on-line coordination method taken, the method proposed are the satisfactions in order to maximize the electric car owner and are not violating electric power
System operation cost is minimized in the case where system restriction, still, on condition that following electric car electric power demand forecasting needs
Ensure accurate and effective, is just able to achieve coordination optimization.The above method needs good communication mechanism that can execute control strategy.It is existing
Also the scheduling a few days ago of electric car in the activity distribution system using network-based register system realizes social good fortune in technology
Benefit maximizes, and still, pricing policy of the operation plan based on static electricity price, causes this kind of control method that can not accurately track a few days ago
Real-time markets electricity price signal does not adapt to the electric car charge and discharge control under Spot Price.
The fuzzy logic control methodology that can be independently controlled is a kind of widely used Engineering Control method, due to it
It is a kind of advanced control strategy that decision is carried out using language rule representation method, by fuzzy reasoning, without establishing system
Mathematical model, and they are also proved to can be applied to Control of Electric Vehicles.Someone devises V2G in prior art person
(Vehicle to Grid) controller and charging station control, carry out voltage stabilization using fuzzy logic and load peak control
Management.There is also the fuzzy logic power flow controller for parking lot infrastructure, charge rate depends on the pre- of subsequent period
Survey the power demand and energy cost of photovoltaic power generation output power, electric car；This means that needing accurate electricity needs
Prediction model is estimated.On the other hand, although control strategy can reach battery charge state when electric car leaves all
Higher than the charging requirement in case study, but the main sudden change requirement of electric bicycle (such as may in down time
Ask and more start early) in the case where, the performance of controller can not determine.Fuzzy control method is used for by someone in the prior art
Electric car charger, wherein charging end time preference as charge rate using the battery charge state of electric car and user
Two input quantities of control.Somebody, which proposes, considers the fuzzy of two factors of batteries of electric automobile state-of-charge and system voltage
Controller.But the simulation result of the two shows that proposed fuzzy controller really can be out-of-limit to avoid system voltage, and really
Protect the charging fairness problem of the different electric cars connected in different location.It will be appreciated, however, that two kinds of fuzzy controllers
The current electric car state-of-charge considered can not reflect the urgency of user's charging, citing: state-of-charge is very low
With long residence time, in the case where current voltage is horizontal lower than normal voltage, electric car is not necessarily in current time
Charging takes delay charging will more rationally in this case.For already existing technology in the prior art, electricity price scheme
Economic feasibility does not account for, and economy is exactly to be paid close attention to by automobile user.
Summary of the invention
The shortcomings that it is a primary object of the present invention to overcome the prior art and deficiency, provide a kind of electricity based on fuzzy control
Electrical automobile charge and discharge rate control method, the present invention not only allow for the energy requirement of automobile user, charging emergency, charging
Grid nodes voltage, can also be limited in service requirement restriction range by the requirement such as fairness and lower charging cost.
In order to achieve the above object, the invention adopts the following technical scheme:
A kind of electric car charge and discharge rate control method based on fuzzy control, includes the following steps:
S1, using the real time execution voltage of the real-time acquisition node of the self-operated measuring unit for being installed on power distribution network node, and and
Predetermined reference voltage at node compares, and obtains voltage difference；
The charged Δ SOC of S2, the batteries of electric automobile according to needed for current time and maximum relevant to remaining down time
The carrying capacity SOC that can be supplementedmaxEstablish charging emergency Degree Model；
S3, according to voltage difference, charging emergency degree and power grid electricity price signal, design fuzzy logic controller, and benefit
Fuzzy control inference rule is obtained with fuzzy logic controller；
S4, controller carry out the control of electric car charge and discharge rate according to Controlling model.
Further include following step as a preferred technical solution, in step S1:
Electric car is connected to electric car charging/discharging apparatus EVSE, when electric car reaches stroke destination by matching
Grid nodes provide, and the actual motion voltage value of tie point is obtained by the advanced measurement basis facility in smart grid, node
The reference voltage at place is preset by operator；Charge rate output is controlled by fuzzy logic controller, electric car charging/discharging apparatus
EVSE will provide corresponding charge power P in t moment to electric car mev(m, t), as shown in equation (1)；
Pev(m, t)=ρ (m, t) η Pch (1)
Wherein, m is electric car index, and t is time index；And assume that maximum charge-discharge power is Pch, it is with kW
Unit, efficiency for charge-discharge are η；ρ (m, t) is real-time charge and discharge rate of the electric car m in t moment, and ρ is positive value if charging,
On the contrary, discharge condition ρ is negative value.
As a preferred technical solution, in step S2, in step S2, the specific steps of charging emergency Degree Model are established
Are as follows:
E) remaining residence time:
Δ T (m, t)=Td(m)-t (2)
Δ T (m, t) is remaining residence time of the electric car m in moment t, TdFor the time departure of electric car m, t table
Show current time, it is however generally that, remaining residence time is more, and charging urgency level is lower；
F) the charged Soc of maximum that current time electric car can fillmax(m, t):
In formula, E (m) is the battery capacity of electric car m, and formula (3) shows electric car within the remaining time with maximum
Charge power charging can fill to obtain battery charge amount；
G) the carrying capacity Δ Soc (m, t) supplemented needed for current time electric car:
Formula (4) gives the charged magnitude relation supplemented needed for surrounding time gap, if Δ Soc (m, t)≤0, charge rate
Calculating should be ρ (m, t- Δ t)=x (m, t) E (m)/(PchΔ t), it is meant that electric car is in upper time clearance t-Δ
T achievable charging；
H) emergency degree CU (m, t) is charged:
Formula (5) defines the electric car charging urgency level at current time, i.e., also needs to mend in the remaining retention period
The SOC that fills and the maximum SOC ratio that can be filled, in general, ' the value of CU' is bigger, electric car charge rate should be higher.
It further include the variation for calculating grid nodes, calculation method as a preferred technical solution, in step S2 are as follows:
Δ V (m, t)=V (i, t)-Vref(i) (6)
The requirement of network system node voltage operates between 0.95-1.05 times of per unit value.
As a preferred technical solution, in step S3, the power grid electricity price signal is one of the influence factor of controller,
From the tissue such as grid company or intermediate operator, country variant and area use different electrovalence policies, including fixed electricity price
Policy, tou power price TOU, Spot Price RTP formulate Critical Peak Pricing policy CPP sometimes.
As a preferred technical solution, in step S3, the fuzzy logic controller is made of three inputs and one defeated
Out, three inputs include voltage deviation, and charge urgency and electricity price signal, export the charging for current time electric car/put
Electric rate；Specific steps are as follows:
S3.1, blurring:
Input variable voltage deviation is made of five membership functions: and NB: it is negative big, NS: bear small, ZE: zero, PS: just small, PB:
It is honest }, wherein NS, ZE and PS represent system and are separately operable in poor, normal and good state；Similarly, NB and PB is electricity
Press relatively low and overvoltage two kinds of extreme cases；Input variable ' CU' is also been described as tool, and there are five the corresponding fuzzy of linguistic variable
Signal: ultralow EL, low L, middle M, high H, superelevation EH；' CU' represents the charging requirement of electric car, EL indicates electric car charging
Urgency or the energy that it is needed are few for demand, and EH reflects high energy demand, the former has the ability to participate in demand response, the latter
It will be because the emergency of charge requirement be without concern for electricity price；Charging batteries of electric automobile/discharge rate output variable is divided into five moulds
Paste collection: high rate discharge HDR, low discharge rate LDR, zero ZE, low charge rate LCR and high rate discharge HCR；
S3.2, fuzzy controller output quantity change between -1.0 to 1.0, i.e., the electric discharge of different electric cars/fill
Electric rate；And the hypothesis electric car owner is only in very low-voltage in the not urgent, system that charges or is lower than the case where limiting voltage,
And electricity price signal be spike pumping signal when will highly participate in power grid discharge.
As a preferred technical solution, in step S3.1, low voltage refers to lower than 0.95p.u, and overvoltage, which refers to, to be higher than
1.05p.u。
The minimum value of HDR is set as 0.4 as a preferred technical solution, and when being greater than 0.8, discharge rate will be -1, this meaning
Taste the EV owner by 100% participate in discharge；Compared with HDR, HCR indicate voltage deviation be in positive value and it is higher, electricity price it is low with
And the case where without urgent charge requirement, the minimum value of HCR are also set to 0.4, but overriding concern is to the basic supplement of electric car
Energy requirement is charged when being set in 0.6 or more with maximum power.
As a preferred technical solution, in the step S3, the fuzzy control inference rule are as follows:
When being used for low electricity price and high electricity price, blurring mechanism are as follows: in low rate period, in addition to voltage negative value and charging is urgent
It spends outside not high, electric car is charged with low or high charge rate；
In electricity price peak period, electric car, which is taken, not to charge, or only urgency is very high and system voltage shape for charging
Charging decision is just done when condition is good,
The rule of electric discharge is participated in electric car, which only applies during electricity price signal is Critical Peak Pricing CPP, and
Do not implement charging decision when signal is CPP.
As a preferred technical solution, in the step S3, if what is implemented is Spot Price RTP policy, under needing online
The average price of electricity price is first calculated by formula (7), controller executes low electricity price when price signal is lower than the non-peak of average price
Period rule, on the contrary, controller will execute peak electricity tariff period rule when electricity price signal is higher than average electricity price value；
In formula, D is number of days, can use a natural season, needs to set with specific reference to local area control；T is to work as one day
In time slot number, pd,tFor Spot Price.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1, the present invention provides a kind of fuzzy controllers suitable for electric car charge and discharge rate management, are met with realizing
The driving requirements of car owner simultaneously reduce its charging cost.
2, two factors of fuzzy controller combination residence time of the present invention and battery charge situation, it is contemplated that electronic
The charging urgency level of user vehicle, and charge and discharge size can be arranged automatically according to charging urgency level.
3, the electric car charge and discharge rate control method proposed by the present invention based on fuzzy control is a kind of point of autonomous type
Cloth control strategy does not need the communication as centralized control complexity, thus economy is relatively preferable, is conducive to privacy of user yet
The protection of information.
4, the controller that the present invention designs has the ability for meeting the limitation of grid nodes voltage, and it is negative to reduce power grid peak
Lotus.Meanwhile the controller of design has stable performance under different Price Mechanisms, this is that not yet occurred in existing research
's.
Detailed description of the invention
Fig. 1 is the electric car charge and discharge rate control framework figure based on fuzzy control；
Fig. 2 is the comprising modules figure of controller；
Fig. 3 (a), Fig. 3 (b) are respectively two input quantity subordinating degree functions of fuzzy controller；
Fig. 4 is fuzzy controller output quantity subordinating degree function figure；
Fig. 5 is residential quarter IEEE-33 node topology distribution system diagram；
Fig. 6 is diversity factor figure；
Fig. 7 is electricity price signal figure；
Total load and charging load chart when Fig. 8 is TOU Price Mechanisms；
The voltage curve of upstream and downstream node when Fig. 9 is TOU Price Mechanisms；
Figure 10 is charge under TOU-CPP Price Mechanisms load and total load curve graph；
Figure 11 is upstream and downstream node curve graph under TOU-CPP Price Mechanisms；
Figure 12 is charge under RTP Price Mechanisms load and total load curve graph；
Figure 13 is upstream and downstream node voltage curve graph under RTP Price Mechanisms.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited
In this.
Embodiment
A kind of electric car charge and discharge rate control method based on fuzzy control of the present embodiment, includes the following steps:
S1, using the real time execution voltage of the real-time acquisition node of the self-operated measuring unit for being installed on power distribution network node, and and
Predetermined reference voltage at node compares, and obtains voltage difference；
S2, the batteries of electric automobile state-of-charge SOC according to needed for current time and it is relevant to remaining down time most
SOC available greatly establishes charging emergency Degree Model；
S3, according to voltage difference, charging emergency degree and power grid electricity price signal, design fuzzy logic controller, and benefit
Fuzzy control inference rule is obtained with fuzzy logic controller；
S4, controller carry out the control of electric car charge and discharge rate according to Controlling model.
Concrete scheme of the invention is further elaborated below:
1. control framework
The general objectives of charging batteries of electric automobile control is that the node voltage of distribution system is maintained acceptable limit
In degree, while meeting the requirement of electric car car owner, while reducing the charging cost of electric car car owner.Therefore, this hair
Three key factors of bright consideration, including the voltage deviation (Δ V) at tie point, the charging based on required battery charge state is urgent
Spend (CU) and remaining residence time (Δ T) and electricity price signal.
Fig. 1 gives the frame of electric car charging scheme, and electric car is connected to electric car charging/discharging apparatus
(EVSE), it is provided when EV reaches stroke destination by power distribution network node.The actual motion voltage value (V (i, t)) of tie point
It is obtained by the advanced measurement basis facility (AMI) in smart grid, the reference voltage at node presets (V by operatorref
(i)).In figure fuzzy logic controller will the detailed design in subsequent content, charge rate output controlled by fuzzy logic controller,
Electric car charging/discharging apparatus EVSE will provide corresponding charge power P in t moment to electric car mev(m, t), such as equation (1)
It is shown.
Pev(m, t)=ρ (m, t) η Pch (1)
Wherein, i is power distribution network node, and m is electric car index, and t is time index；And assume maximum charge-discharge power
It is Pch(as unit of kW), efficiency for charge-discharge are η；ρ (m, t) is real-time charge and discharge rate of the electric car m in t moment, if
Then ρ is positive value for charging, on the contrary, discharge condition ρ is negative value.
1.1 charging emergency degree
A) remaining residence time:
Δ T (m, t)=Td(m)-t (2)
Δ T (m, t) is remaining residence time of the electric car m in moment t, TdFor the time departure of electric car m, t table
Show current time.Remaining residence time is more, and charging urgency level is lower.
B) the charged S of maximum that current time electric car can fillmax(m, t):
In formula, E (m) is the battery capacity of electric car m, and formula (3) shows electric car within the remaining time with maximum
Charge power charging can fill to obtain battery charge amount.
C) the carrying capacity Δ Soc (m, t) supplemented needed for current time electric car:
Formula (4) gives the charged magnitude relation supplemented needed for surrounding time gap.If Δ Soc (m, t)≤0, then charge rate
Calculating should be ρ (m, t- Δ t)=x (m, t) E (m)/(PchΔ t), it is meant that electric car is in upper time clearance t-Δ
T achievable charging.
D) emergency degree CU (m, t) is charged:
Formula (5) defines the electric car charging urgency level at current time, i.e., also needs to mend in the remaining retention period
The SOC that fills and the maximum SOC ratio that can be filled, in general, ' the value of CU' is higher, electric car charge rate should be higher.
1.2 grid nodes variations；
Δ V (m, t)=V (i, t)-Vref(i) (6)
Network system node voltage generally requires to operate between 0.95-1.05 times of per unit value.
Reference voltage should show the fairness of power distribution network difference node, and therefore, the reference voltage of power distribution network downstream node is answered
Higher than upstream node.In general, when load is lighter, voltage deviation is positive value in radiation profiles formula distribution system, on the contrary, when electricity
When pressure deviation is negative value, load is heavier.Therefore, for the input quantity, voltage deviation should be given for timing fuzzy controller and charge
Facility charging signals, on the contrary, fuzzy controller should provide discharge signal when voltage deviation is negative value.
1.3 electricity price signal；
Electricity price signal comes from grid company, and country variant and area use different electrovalence policies, including fixed electricity price political affairs
Plan, tou power price (time of use, TOU), Spot Price (real time price, RTP) formulate Critical Peak Pricing sometimes
Policy (critical price policy, CPP).Due to different incentive policies, the behavior of electric car will have different tables
Existing, stable charging-discharging controller should have high-adaptability, should adapt to different electrovalence policies.Therefore, the present invention believes electricity price
Number it is considered as one of influence factor of controller.If what is implemented is fixed electrovalence policy, controller is only to voltage deviation and charging
Urgency level ' CU' response.
2. the specific design of fuzzy controller
Fuzzy logic controller generally includes four modules, is respectively as follows: blurring, rule base, Fuzzy inferential engine reconciliation
It is fuzzy, as shown in Figure 2.In the present invention, fuzzy logic controller scheme is made of that (voltage deviation, charging are urgent three inputs
Degree and electricity price signal) and an output (the charge/discharge rate of current time electric car).
2.1 blurring
Input variable voltage deviation is made of five membership functions: and NB: it is negative big, NS: bear small, ZE: zero, PS: just small, PB:
It is honest }, wherein NS, ZE and PS represent system and are separately operable in poor, normal and good state.Similarly, NB and PB is electricity
Press two kinds of extreme cases of relatively low (lower than 0.95p.u) and overvoltage (being higher than 1.05p.u.).Input variable ' CU' is also described
It is low (L) to have the corresponding blurred signal there are five linguistic variable: ultralow (EL), in (M), high (H), superelevation (EH)；' CU' representative
The charging requirement of electric car, EL indicates charging demand for electric vehicles, and urgency or the energy that it is needed are few, and EH is reflected
High energy demand, the former has the ability to participate in demand response, and the latter will be because the emergency of charge requirement be without concern for electricity price.EV battery
The output variable of charge/discharge rate is divided into five fuzzy sets: high rate discharge (HDR), low discharge rate (LDR), zero (ZE), low charging
Rate (LCR) and high rate discharge (HCR).Membership function is as shown in Fig. 3 (a), Fig. 3 (b), Fig. 4.
The output quantity of fuzzy controller changes between -1.0 to 1.0, i.e., the charged/discharged rate of different EV.The present invention
It is assumed that the electric car owner is only in very low-voltage in the not urgent, system that charges or is lower than the case where limiting voltage, and electricity price
Signal will highly participate in discharging to power grid when being spike pumping signal.And the minimum value of HDR is set as 0.4, when being greater than 0.8,
Discharge rate will be 1, it means that the EV owner participates in discharging by 100%.Compared with HDR, HCR indicates that voltage deviation is in positive value
And it is higher, electricity price is low and the case where without urgent charge requirement, and the minimum value of HCR is also set to 0.4, but considers electric car
The requirement of basic complementary capabilities, present invention prize charged with maximum power when being set in 0.6 or more.
2) rule base: the fuzzy control rule that the present invention designs such as table 1 to table 3, according to IF Δ V is A and CU is
The fuzzy inference rule of B, THEN ρ is C make inferences.
Table 1-2 is the rule of tou power price TOU mechanism, when Tables 1 and 2 is respectively used to low electricity price and high electricity price.Fuzzy machine
Be made as: in low rate period, in addition to voltage negative value and charging urgency is not high outer, and electric car is with the charging of low or high charge rate；
In electricity price peak period, electric car, which is taken, not to charge, or only charging urgency is very high and system voltage in order when
Just do charging decision.Table 3 participates in the rule of electric discharge then for electric car, which is only the Critical Peak Pricing CPP phase in electricity price signal
Between apply, and when signal be CPP when do not implement charging decision.
The rule base of the low rate period of table 1
The rule base of the high rate period of table 2
The rule base of 3 Critical Peak Pricing period of table
If what is implemented is Spot Price RTP policy, the present invention claims the flat fares for first calculating electricity price under online by formula (7)
Lattice, the controller low rate period of real-time table 1 rule in the sub-average non-peak of price signal, otherwise, controller will be
Electricity price signal implements the peak electricity tariff period rule of table 2 when being higher than average electricity price value.
In formula, D is number of days, can use a natural season, needs to set with specific reference to local area control；T is to work as one day
In time slot number, pd,tFor Spot Price.
Example:
1. basic parameter is arranged
Electric car is usually connected to low voltage power distribution network in arrival residential quarter, therefore, for case study, using improvement
Modified IEEE-33 node power distribution network such as Fig. 5, the residential quarter distribution network node total number be 33, including one balance
Node (node number 0) and 32 nodes (from 1 to 32).
1) base load and electric car quantity
Assuming that there is a few family residents under each node, each household resident has an electric car, is mounted on containing fuzzy
The low pressure charge-discharge facility EVSE of controller, electric car are connected with it, the resident's quantity and node under each node it is active
See Table 4 for details for power termination distribution.
Each node base load of 4 residential quarter power distribution network of table and electric car quantity
The diversity factor LVC of each node is as shown in Figure 6.
2) electric car and charge-discharge facility parameter
In example, arrival time of electric car, time departure and battery SOC when reaching using truncation Gauss point
Cloth function modelling N (19,22)、N(7,22) and N (0.75,0.252), battery capacity is set as 24kWh, SOC mesh when eventually off
Mark is set as 0.9.The efficiency for charge-discharge of the maximum charge power 3.3kW, EV of charge-discharge facility EVSE are assumed to 92%.
3) electricity price signal used in test cases, as shown in Figure 7.
4) for including unordered charging scheme and the control of average charge rate with the control strategy of control program of the present invention comparison
Scheme.
2. simulation result
1) various control program Comparative results under tou power price TOU
Curve in Fig. 8 shows that unordered charging clearly results in the huge electrical energy demands to power distribution network, this increased charging
Load will lead to serious voltage decline, as shown in figure 9, and distribution transformer is caused to overload.Under the control of average charge rate
Peak load decreases, but voltage is limited still in minimum hereinafter, unfavorable to operation of power networks.And it is obscured when using of the invention
When load curve is more smooth when control strategy, charging load is transferred to off-peak period, and the voltage of node is also wanted in operation
It asks within range.
2) various control program Comparative results under the tou power price TOU-CPP mechanism containing Critical Peak Pricing
Figure 10 give under TOU-CPP Price Mechanisms it is unordered charging and average charge rate control strategy and it is proposed that
Fuzzy control strategy result.Compared with TOU Price Mechanisms, under the stimulation of CPP peak structure, the charging of average control strategy is negative
Lotus and voltage level do not change.When using fuzzy control strategy, electric car participates in electric discharge during CPP, this is effectively reduced
Load peak, and the control program can guarantee that electric car meets its energy requirement before morning sets out.?
Charge rate increases during low ebb electricity price, and charging load is compared with trough period (including CPP).Figure 11 correspondingly reflects
Electric discharge participation of the electric car during CPP effectively increases voltage value, to ensure the voltage of each node reasonable
It is run in level.
3) various control program Comparative results under Spot Price RTP mechanism
The performance of fuzzy control strategy of the present invention is as shown in figure 12 under Spot Price, it can be seen from the figure that fuzzy control
The performance of device is still stable, the effect that fuzzy controller plays peak load shifting, maintains node voltage stable.
4) index compares under various control programs
Table 5 gives power distribution network operation indicator under different Price Mechanisms and different control strategies and electric car is charged to
This.As can be seen from the table, index is relatively poor when not taking the unordered charging of control strategy, after the control of average charge rate,
Index is improved, and charging cost also accordingly reduces.But generally speaking, no matter which kind of electrovalence policy is used, using the present invention
When the fuzzy control strategy of proposition, operating index is effectively promoted, and charging cost is effectively reduced, or even is generated electric discharge and received
Benefit.In addition, fuzzy control strategy not only can satisfy the service requirement of power distribution network, it can also be ensured that the charging of automobile user
Demand, and charging emergency is considered, while reducing user cost.Moreover, because the fuzzy controller that the present invention designs is embedded
It is mounted in charge-discharge facility, is a kind of distributed control method for not needing complex communication and calculating, need to only pass through automatic measuring
The real time execution voltage that device AMI obtains power grid is surveyed, time departure set by user and expectation are detected by charge-discharge facility
SOC, fuzzy controller can provide the charge and discharge rate at current time according to the electricity price signal received.
Index result when 5 difference control program of table
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (10)
1. a kind of electric car charge and discharge rate control method based on fuzzy control, which is characterized in that include the following steps:
S1, using the real time execution voltage of the real-time acquisition node of the self-operated measuring unit for being installed on power distribution network node, and and node
The predetermined reference voltage at place compares, and obtains voltage difference；
The charged Δ SOC of S2, the batteries of electric automobile according to needed for current time and maximum relevant to remaining down time can mend
The carrying capacity SOC filledmaxEstablish charging emergency Degree Model；
S3, according to voltage difference, charging emergency degree and power grid electricity price signal, design fuzzy logic controller, and utilize mould
Fuzzy logic controller obtains fuzzy control inference rule；
S4, controller carry out the control of electric car charge and discharge rate according to Controlling model.
2. the electric car charge and discharge rate control method based on fuzzy control according to claim 1, which is characterized in that step
Further include following step in S1:
Electric car is connected to electric car charging/discharging apparatus EVSE, when electric car reaches stroke destination by power distribution network
Node provides, and the actual motion voltage value of tie point is obtained by the advanced measurement basis facility in smart grid, at node
Reference voltage is preset by operator；Charge rate output is controlled by fuzzy logic controller, and electric car charging/discharging apparatus EVSE is in t
Moment will provide corresponding charge power P to electric car mev(m, t), as shown in equation (1)；
Pev(m, t)=ρ (m, t) η Pch (1)
Wherein, m is electric car index, and t is time index；And assume that maximum charge-discharge power is Pch, as unit of kW,
Efficiency for charge-discharge is η；ρ (m, t) is real-time charge and discharge rate of the electric car m in t moment, and ρ is positive value if charging, on the contrary,
Discharge condition ρ is negative value.
3. the electric car charge and discharge rate control method based on fuzzy control according to claim 1, which is characterized in that step
In S2, in step S2, the specific steps of charging emergency Degree Model are established are as follows:
A) remaining residence time:
Δ T (m, t)=Td(m)-t (2)
Δ T (m, t) is remaining residence time of the electric car m in moment t, TdFor the time departure of electric car m, t indicates current
Moment, it is however generally that, remaining residence time is more, and charging urgency level is lower；
B) the charged Soc of maximum that current time electric car can fillmax(m, t):
In formula, E (m) is the battery capacity of electric car m, and formula (3) shows electric car within the remaining time with maximum charge
Power charging can fill to obtain battery charge amount；
C) the carrying capacity Δ Soc (m, t) supplemented needed for current time electric car:
Formula (4) gives the charged magnitude relation supplemented needed for surrounding time gap, if Δ Soc (m, t)≤0, the meter of charge rate
Calculation should be ρ (m, t- Δ t)=x (m, t) E (m)/(PchΔ t), it is meant that electric car upper time clearance t-Δ t
Achievable charging；
D) emergency degree CU (m, t) is charged:
Formula (5) defines the electric car charging urgency level at current time, i.e., also requires supplementation in the remaining retention period
SOC and the maximum SOC ratio that can be filled, in general, ' the value of CU' is bigger, electric car charge rate should be higher.
4. the electric car charge and discharge rate control method based on fuzzy control according to claim 2, which is characterized in that step
It further include the variation for calculating grid nodes, calculation method in S2 are as follows:
Δ V (m, t)=V (i, t)-Vref(i) (6)
The requirement of network system node voltage operates between 0.95-1.05 times of per unit value.
5. the electric car charge and discharge rate control method based on fuzzy control according to claim 1, which is characterized in that step
In S3, the power grid electricity price signal is one of the influence factor of controller, is organized from grid company or intermediate operator etc.,
Country variant and area use different electrovalence policies, including fixed electrovalence policy, and tou power price TOU, Spot Price RTP have
When can formulate Critical Peak Pricing policy CPP.
6. the electric car charge and discharge rate control method based on fuzzy control according to claim 1, which is characterized in that step
In S3, the fuzzy logic controller is made of three inputs and an output, and three inputs include voltage deviation, and charging is urgent
Degree and electricity price signal, export the charge/discharge rate for current time electric car；Specific steps are as follows:
S3.1, blurring:
Input variable voltage deviation is made of five membership functions: and NB: it is negative big, NS: bear small, ZE: zero, PS: just small, PB: just
Greatly }, wherein NS, ZE and PS represent system and are separately operable in poor, normal and good state；Similarly, NB and PB is voltage
Relatively low and overvoltage two kinds of extreme cases；Input variable ' CU' is also been described as corresponding fuzzy letter of the tool there are five linguistic variable
Number: ultralow EL, low L, middle M, high H, superelevation EH；' CU' represents the charging requirement of electric car, EL indicates that electric car charging needs
Ask not anxious or its needs energy few, and EH reflects high energy demand, the former has the ability to participate in demand response, and the latter will
Because the emergency of charge requirement is without concern for electricity price；Charging batteries of electric automobile/discharge rate output variable is divided into five and obscures
Collection: high rate discharge HDR, low discharge rate LDR, zero ZE, low charge rate LCR and high rate discharge HCR；
S3.2, fuzzy controller output quantity change between -1.0 to 1.0, i.e., the charged/discharged rate of different electric cars；
And the hypothesis electric car owner is only in very low-voltage in the not urgent, system that charges or is lower than the case where limiting voltage, and it is electric
Bivalent signal will highly participate in discharging to power grid when being spike pumping signal.
7. the electric car charge and discharge rate control method based on fuzzy control according to claim 6, which is characterized in that step
In S3.1, low voltage refers to that, lower than 0.95p.u, overvoltage refers to higher than 1.05p.u.
8. the electric car charge and discharge rate control method based on fuzzy control according to claim 6, which is characterized in that HDR
Minimum value be set as 0.4, when be greater than 0.8 when, discharge rate will be -1, it means that the EV owner by 100% participate in discharge；With
HDR is compared, and HCR indicates that voltage deviation is in positive value and higher, and electricity price is low and the case where without urgent charge requirement, and HCR is most
Small value is also set to 0.4, but overriding concern is to the basic supplement energy requirement of electric car, with maximum when being set in 0.6 or more
Power charges.
9. the electric car charge and discharge rate control method based on fuzzy control according to claim 1, which is characterized in that described
In step S3, the fuzzy control inference rule are as follows:
When being used for low electricity price and high electricity price, blurring mechanism are as follows: in low rate period, in addition to voltage negative value and charging urgency is not
High outer, electric car is charged with low or high charge rate；
In electricity price peak period, electric car, which is taken, not to charge, or only charging urgency is very high and system voltage situation is good
Charging decision is just done when good,
The rule of electric discharge is participated in electric car, which only applies during electricity price signal is Critical Peak Pricing CPP, and when letter
Number be CPP when do not implement charging decision.
10. the electric car charge and discharge rate control method based on fuzzy control according to claim 9, which is characterized in that institute
It states in step S3,
If what is implemented is Spot Price RTP policy, the average price of electricity price is first calculated under needing online by formula (7), controller exists
Price signal executes low rate period rule when being lower than the non-peak of average price, on the contrary, controller will be higher than in electricity price signal
Peak electricity tariff period rule is executed when average electricity price value；
In formula, D is number of days, can use a natural season, needs to set with specific reference to local area control；T is in one day
Time slot number, pd,tFor Spot Price.
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