CN113162089A - Scheduling method for electric automobile to participate in wind power consumption - Google Patents

Scheduling method for electric automobile to participate in wind power consumption Download PDF

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CN113162089A
CN113162089A CN202110474431.1A CN202110474431A CN113162089A CN 113162089 A CN113162089 A CN 113162089A CN 202110474431 A CN202110474431 A CN 202110474431A CN 113162089 A CN113162089 A CN 113162089A
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王玮
刘柳
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North China Electric Power University
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    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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    • H—ELECTRICITY
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    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
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    • H—ELECTRICITY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H02J2300/28—The renewable source being wind energy
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    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
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    • H02J2310/58—The condition being electrical
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Abstract

The invention discloses a scheduling method for electric vehicles to participate in wind power consumption, and belongs to the technical field of new energy power generation and energy storage scheduling. The method comprises the following steps of 1: determining mathematical models of electric vehicle energy storage and super capacitor energy storage; step 2: decomposing the wind power output power by utilizing a wavelet packet decomposition method; and step 3: performing spectrum analysis on other branches of the wind power output power of different branches decomposed by the wavelet packet in the step 2, and primarily determining energy storage configuration through a Pasteval law; and 4, step 4: when the electric automobile cluster can completely absorb wind power, acquiring an electric automobile optimal dispatching cluster by utilizing dynamic planning based on knapsack problem; the super capacitor is used as a standby energy storage device, so that timely supply can be realized when the electric automobile cannot meet the dispatching requirement; and 5: and verifying whether the grid-connected power subjected to energy storage scheduling meets the grid-connected fluctuation requirement. The result shows that the method meets the requirement of stabilizing the fluctuation of the wind power and solves the problem of overlarge fluctuation of the wind power generation.

Description

Scheduling method for electric automobile to participate in wind power consumption
Technical Field
The invention relates to the technical field of new energy power generation and energy storage scheduling, in particular to a scheduling method for electric vehicles to participate in wind power consumption.
Background
In recent years, in order to solve problems such as deterioration of ecological environment and rapid consumption of fossil energy, the scale of global renewable energy generation is rapidly expanded, and new energy represented by wind power is rapidly developed. However, wind power naturally has volatility and uncertainty, and large-scale wind power integration can bring challenges to safe and stable operation of a power grid. The energy storage system is one of effective modes for stabilizing wind power fluctuation, the electric automobile is gradually considered as an energy storage element to participate in energy storage scheduling, and the energy storage of the electric automobile is vigorously developed, so that the utilization rate of new energy is increased, and the low-carbon economy, energy conservation and emission reduction are facilitated to be developed.
The capacity of the power system for receiving the fluctuating energy is limited, so that the wind power output meets the limit value requirement of active power change proposed by technical regulation of accessing the wind power plant to the power system, a wavelet packet decomposition method can be adopted to determine target grid-connected power, and different layers of decomposition results are analyzed to configure the energy storage capacity.
With the vigorous development of electric automobiles in China, the energy storage capacity of the electric automobiles presents an ascending trend, the energy storage potential is huge, a great deal of research shows that the traditional energy storage mode can effectively stabilize the power generation fluctuation of new energy, compared with the traditional energy storage element, the electric automobiles have the characteristics of mobility and randomness, the characteristic enables the electric automobiles to be difficult to control and schedule, the conventional scheduling strategy of the electric automobiles at the present stage depends on the traditional queuing strategy, and the optimal state of the electric automobiles cannot be guaranteed on the premise of reaching the expected value.
Disclosure of Invention
The invention aims to provide a scheduling method for an electric automobile to participate in wind power consumption, which is characterized by comprising the following steps:
step 1: determining mathematical models of electric vehicle energy storage and super capacitor energy storage;
step 2: decomposing the wind power output power by utilizing a wavelet packet decomposition method; when the number of wavelet packet decomposition layers is n, 2 is obtainednWind power output power branch (P)wind(n,0)～Pwind(n,2n-1)), under normal operating conditions, the first branch Pwind(n,0) if the limit value constraints of wind power output active power change at 1 minute and 10 minutes are completely met, stopping decomposition, and adding Pwind(n,0) as target grid-connected power, wherein PwindOutputting power for the wind power plant;
and step 3: 2 for wavelet packet decomposition in step 2nOther branch (P) of a wind power output branchwind(n,1)～Pwind(n, 2n-1)) performing a spectral analysis, calculating P in combination with Pasteval's lawwind(n,1)～Pwind(n,2n-1) power energy of each branch, thereby preliminarily determining an energy storage configuration;
and 4, step 4: designing a specific optimized scheduling model; when the electric automobile cluster can completely absorb wind power, the scheduling problem of the energy storage equipment is converted into a knapsack problem, and the optimal scheduling cluster of the electric automobile is obtained by dynamic planning of the knapsack problem; the super capacitor is used as a standby energy storage device, and timely supply is performed when the electric automobile cannot meet the dispatching requirement, so that the wind power is maximally consumed;
and 5: and verifying whether the grid-connected power subjected to energy storage scheduling meets the grid-connected fluctuation requirement.
The mathematical model of the electric vehicle energy storage in the step 1 is as follows:
the electric vehicle SOC is calculated as follows:
Figure BDA0003046506440000021
in the formula SiIs SOC of the ith electric vehicle, t is a sampling point, h is a sampling period, PEV,iIs the output power of the ith electric vehicle, etaEV,iFor the operating efficiency of the ith electric vehicle, Erated,iThe rated energy storage capacity of the ith electric automobile;
the energy storage optimization scheduling constraint of the electric automobile participating in stabilizing the new energy power generation fluctuation is as follows:
Smin,i≤Si(t)≤Smax,i
tarr,i≤t≤tdep,i
Si(t)+Pcηc(tdep,i-t)/Erated,i-Sdep,i≥0
Figure BDA0003046506440000022
Figure BDA0003046506440000023
in the formula Smin,iAnd Smax,iMinimum and maximum values of SOC constraints, t, for the ith vehicle to participate in dispatching the electric vehiclearr,iAnd tdep,iThe network access time and the network leaving time of the ith electric vehicle, PcCharging power, η, for electric vehiclescFor the efficiency of charging of electric vehicles, Sdep,iTarget off-grid SOC, T set for the ith electric vehicle to meet user travel demandc2dTime interval for converting charging action into discharging action, Td2cFor a time interval between the discharging operation and the charging operation, couc2d,iAnd coud2c,iT of ith electric vehiclec2dAnd Td2cAn aggregate value of the respective action transitions;
according to the energy storage optimization scheduling constraint condition, the maximum scheduling potential of the single electric vehicle at the t moment is obtained as follows:
Ec,i(t)＝Erated,i(Semax,i(t)-Si(t))
Figure BDA0003046506440000031
Ed,i(t)＝Erated,i(Si(t)-Semin,i(t))
Figure BDA0003046506440000032
in the formula, i and E for electric vehiclesc,iAnd Ed,iRespectively estimating the maximum charging potential and the maximum discharging potential; semax,iMaximum estimation value of SOC (state of charge) for charging electric vehicle till off-grid time, Semin,iFor minimum estimation of SOC for discharging action up to forced charging, Sesmax,iTo do not consider Smax,iUntil the SOC maximum estimated value, t, when off-griddis,maxForced charging time is required for the electric automobile; pdIs the discharge power of the electric vehicle etadThe discharge efficiency of the electric automobile;
the mathematical model of super capacitor energy storage is as follows:
the super-capacitor SOC is calculated as follows:
Figure BDA0003046506440000033
in the formula SscIs the SOC of the super capacitor; pSC,cAnd PSC,dRespectively charging and discharging power of the super capacitor; etaSC,cAnd ηSC,dCharge and discharge efficiencies, respectively; eSC,ratedRated energy storage capacity; n is a radical ofSC,cAnd NSC,dA charge state and a discharge state flag, respectively, whose values are 0 or 1;
the operation constraint of the super capacitor is as follows:
SSC,min≤SSC(t)≤SSC,max
0≤PSC,c(t)≤PSC,c,rated
PSC,d,rated≤-PSC,d(t)≤0
0≤NSC,c(t)+NSC,d(t)≤1
in the formula SSC,minAnd SSC,maxRespectively the most agreed for super capacitorSmall and maximum SOC allowed values; pSC,c,ratedAnd PSC,d,ratedRespectively, rated charge and discharge power.
In the step 2, the limit value of the active power change of the wind power output in 1 minute and 10 minutes is restricted as follows:
Figure BDA0003046506440000034
in the formula,. DELTA.Pgrid,1minAnd Δ Pgrid,10minDeviation of maximum and minimum values of power fluctuation within 1 minute and 10 minutes, respectively, Pwind,insAnd the installed capacity of the wind power plant.
The branch bandwidth of the wavelet packet decomposition in the step 3 is as follows:
Figure BDA0003046506440000041
in the formula (f)sTaking the sampling frequency as the reference, and taking n as the decomposition layer number of the wavelet packet;
calculation of one of the branches [ f ] using Parceval's law1,f2]The power energy within the band is expressed as:
Figure BDA0003046506440000042
in the formula f1<f2X (f) is a discrete fourier transform of the time domain signal to be analyzed;
find [ f1,f2]The expression for the actual electrical power energy within the frequency band is:
Figure BDA0003046506440000043
the step 4 specifically includes the following substeps:
step 41: selecting a charge-discharge strategy according to the target smooth deviation delta P;
when Δ P (t)>0, scheduling actionDetermining a quantity requirement N of electric vehicles for chargingr(t) obtaining a dispatchable cluster N according to the real-time status of the electric vehicleEV(t)；
When Δ P (t)<0, scheduling action as discharging, obtaining N similarlyr(t) and NEV(t); in addition, the electric vehicle which is forcibly charged does not discharge under the dispatching action;
step 42: determining an electric automobile cluster participating in scheduling according to supply and demand balance;
when N is presentEV(t)≤Nr(t), the supply quantity of the electric vehicles can not meet or just meet the demand, and at the moment, all the electric vehicles which can participate in scheduling;
when N is presentEV(t)>Nr(t) the supply of electric vehicles meets the demand and has a margin, at which time the best dispatch cluster N is sought by dynamic planning based on knapsack problemopt(t)；
Step 43: calculating the real-time power output of the electric automobile cluster;
when the electric automobile cluster cannot stabilize the target deviation power, extra supply is carried out by using the super capacitor;
when the electric automobile cluster and the super capacitor reach a saturated state and cannot absorb wind power, wind abandon is considered;
step 44: and performing control action at the next moment until the control time length is finished.
The dynamic planning of the knapsack problem comprises the following steps:
step S1: defining the value of the electric automobile; the value of the individual electric vehicle i at time t is defined as:
ci(t)＝-αcoui(t)+βEcd,i(t)+γTrem,i
Figure BDA0003046506440000051
Figure BDA0003046506440000052
in the formula, ciAs a value of the ith electric vehicle, couiChanging the accumulated value of the number of charging and discharging times of the ith electric vehicle after the ith electric vehicle is connected to the networkcd,iFor schedulable potential of the ith electric vehicle, Trem,iThe remaining on-line time of the ith electric automobile is defined, and alpha, beta and gamma are weight coefficients;
step S2: determining an optimal value objective function; the optimal value objective function is expressed as follows:
Figure BDA0003046506440000053
Figure BDA0003046506440000054
wherein y is the total value of the electric vehicle cluster, Pc,dIs charging power or discharging power;
step S3: a segmentation problem; loading the ith electric automobile into a backpack for the ith time;
step S4: determining state variables
Figure BDA0003046506440000055
Figure BDA0003046506440000056
The residual capacity of the backpack after the ith dispatching is obtained;
step S5: determining a decision variable Di(ii) a Whether the ith electric automobile is dispatched at the ith time or not is judged, if so, DiTrue, otherwise DiIs false;
step S6: establishing a state transition equation
Figure BDA0003046506440000057
In the formula (I), the compound is shown in the specification,
Figure BDA0003046506440000058
for putting the load into the front i electric automobiles
Figure BDA0003046506440000059
The maximum value of the backpack of (a);
Figure BDA00030465064400000510
the weight of the electric vehicle;
step S7: and determining an optimal strategy according to the optimal value objective function of the step S2, thereby obtaining an optimal dispatching cluster of the electric vehicle.
The invention has the beneficial effects that:
the final grid-connected power obtained by dynamic planning and scheduling based on the knapsack problem is similar to the target grid-connected power obtained by wavelet packet decomposition to the great extent, so that the requirement of stabilizing wind power fluctuation is met, the problem of overlarge wind power generation fluctuation is solved, and the ordered scheduling of the electric automobile is realized.
Drawings
FIG. 1 is a flow chart of a scheduling method for an electric vehicle to participate in wind power consumption;
FIG. 2 shows an original output power and a target grid-connected power of a wind power plant;
FIG. 3 is a comparison graph of actual grid-connected power and target grid-connected power;
fig. 4 shows the remaining capacity variation of a part of electric vehicles.
Detailed Description
The invention provides a scheduling method for electric vehicles to participate in wind power consumption, which is further explained by combining the attached drawings and specific embodiments.
FIG. 1 is a flowchart of a scheduling method for an electric vehicle to participate in wind power consumption. The scheduling method comprises two parts of determining initial grid-connected power by wavelet packet decomposition and optimizing and scheduling by a dynamic programming algorithm for solving the knapsack problem, and the specific implementation steps are as follows:
1) and determining discretization mathematical models of the electric automobile and the super capacitor.
The SOC (State of charge) of the electric vehicle is calculated as follows:
Figure BDA0003046506440000061
the energy storage optimization scheduling constraint of the electric automobile participating in stabilizing the new energy power generation fluctuation is as follows:
Smin,i≤Si(t)≤Smax,i
tarr,i≤t≤tdep,i
Si(t)+Pcηc(tdep,i-t)/Erated,i-Sdep,i≥0
Figure BDA0003046506440000062
Figure BDA0003046506440000063
wherein i and S of the electric vehicle are SOC and P of the electric vehicleEVOutput power of electric vehicle, etaEVFor the operating efficiency of electric vehicles, EratedFor the rated energy storage capacity of an electric vehicle, SminAnd SmaxMinimum and maximum values of SOC constraints, t, which can participate in scheduling electric vehiclesarrAnd tdepThe network access time and the network off time of the electric automobile, PcCharging power, η, for electric vehiclescFor the efficiency of charging of electric vehicles, SdepTarget off-grid SOC, T set for meeting user travel demandc2dTime interval for converting charging action into discharging action, Td2cFor a time interval between the discharging operation and the charging operation, couc2dAnd coud2cRespectively, the former is the accumulated value of the corresponding action transition.
The super-capacitor SOC is calculated as follows:
Figure BDA0003046506440000071
according to the scheduling constraint conditions, the maximum scheduling potential of the single electric vehicle at the time t can be obtained as follows:
Ec,i(t)＝Erated,i(Semax,i(t)-Si(t))
Figure BDA0003046506440000072
Ed,i(t)＝Erated,i(Si(t)-Semin,i(t))
Figure BDA0003046506440000073
in the formula, i and E for electric vehiclescAnd EdRespectively estimating the maximum charging potential and the maximum discharging potential; semaxMaximum estimation value of SOC (state of charge) for charging electric vehicle till off-grid time, SeminFor minimum estimation of SOC for discharging action up to forced charging, tdis,maxForced charging time is required for the electric automobile; pdIs the discharge power of the electric vehicle etadThe discharge efficiency of the electric automobile.
The operation constraint of the super capacitor is as follows:
SSC,min≤SSC(t)≤SSC,max
0≤PSC,c(t)≤PSC,c,rated
PSC,d,rated≤-PSC,d(t)≤0
0≤NSC,c(t)+NSC,d(t)≤1
in the formula SscIs the SOC of the super capacitor; pSC,cAnd PSC,dRespectively charging and discharging power of the super capacitor; etaSC,cAnd ηSC,dCharge and discharge efficiencies, respectively; eSC,ratedRated energy storage capacity; sSC,minAnd SSC,maxMinimum and maximum SOC allowed values agreed for the super capacitor; pSC,c,ratedAnd PSC,d,ratedRated charge-discharge power; n is a radical ofSC,cAnd NSC,dThe charge state and discharge state flags, respectively, have values of 0 or 1.
2) Decomposing the wind power output power by utilizing a wavelet packet decomposition method, wherein when the number of decomposition layers is n, the first branch P iswind(n,0) stopping decomposition when the wind power output active power change is in the limit value conditions of 1min and 10min under the normal operation condition, and dividing Pwind(n,0) as target grid-connected power, wherein PwindThe wind power plant raw output power.
Figure BDA0003046506440000081
In the formula,. DELTA.Pgrid,1minAnd Δ Pgrid,10minDeviation of maximum and minimum values of power fluctuation within 1 minute and 10 minutes, respectively, Pwind,insAnd the installed capacity of the wind power plant.
3) Other branches (P) based on the wavelet packet decomposition results in 2)wind(n,1)～Pwind(n,2n-1)) carrying out spectrum analysis, calculating the power energy of each branch according to the Pasteval law, and preliminarily determining the energy storage configuration by combining the frequency information of different branches.
The branch bandwidth of the wavelet packet decomposition is
Figure BDA0003046506440000082
In the formula (f)sSince n is the number of wavelet packet decomposition layers for the sampling frequency, P is knownwindThe bandwidth of (n,0) is 0-f0,PwindThe bandwidth of (n,1) is f0～2f0And so on.
Method for solving any bandwidth [ f ] by using Pasteval law1,f2]Inner signal energy component E:
Figure BDA0003046506440000083
in the formula f1<f2And x (f) is the discrete fourier transform of the signal.
According to the practical application of engineering, the frequency band f is obtained1,f2]Actual electric power energy E inPComprises the following steps:
Figure BDA0003046506440000084
4) a specific optimized dispatching model is designed, the super capacitor is used as a standby energy storage device, and timely supply is performed when the electric automobile cannot meet dispatching requirements, so that wind power is maximally consumed. And at the moment that the electric automobile cluster can completely absorb the wind power, the scheduling problem of the energy storage equipment is converted into a knapsack problem, and the optimal scheduling cluster of the electric automobile is obtained by using a dynamic programming algorithm.
Step 1: and selecting a charge-discharge strategy according to the target smooth deviation delta P.
(1) When Δ P (t)>0, scheduling action is charging, and determining the quantity requirement N of the electric vehiclesr(t) of (d). Obtaining schedulable cluster N according to real-time state of electric automobileEV(t)。
(2) When Δ P (t)<0, scheduling action is discharging, and N can be obtained by the same methodr(t) and NEV(t) of (d). In addition, the electric vehicle that is forcibly charged does not discharge in this scheduling operation.
Step 2: and determining the electric automobile clusters participating in scheduling according to the supply and demand balance.
(1) When N is presentEV(t)≤NrAnd (t), the supply quantity of the electric vehicles can not meet or just meet the demand, and all the electric vehicles capable of participating in scheduling participate in scheduling.
(2) When N is presentEV(t)>Nr(t), the supply of the electric automobile meets the demand and has certain margin, and at the moment, the best scheduling cluster N is sought by utilizing a dynamic programming algorithm based on knapsack problemsopt(t)。
And step 3: and calculating the real-time power output of the electric automobile cluster. If the electric automobile cluster cannot stabilize the target deviation power, the super capacitor is used for additional supply, and when both reach a saturation state and cannot absorb the wind power, appropriate wind abandoning is considered.
And 4, step 4: and performing control action at the next moment until the control time length is finished.
5) And verifying whether the grid-connected power subjected to energy storage scheduling meets the grid-connected fluctuation requirement.
Taking a certain 50MW wind farm as an example, the original output power of the wind farm is shown in a dotted line in FIG. 2, the sampling period is 10s, and the sampling point is 8640. Decomposing the wavelet packet into 5 layers, selecting Pwind(5,0) as the target grid-connected power is shown in solid line in fig. 2.
Calculating the power energy of each branch by using the Pasteval law, and selecting Pwind(5.1)～Pwind(5.15) estimating the number of the electric vehicles by combining the space-time characteristics of the electric vehicles, and finally determining the number of the electric vehicles to be 1300. And selecting 24 branches, namely power energy of (5.8) - (5.31), as reference to select the capacity of the super capacitor according to different branch energy and frequency, wherein the super capacitor is 500kWh, and the rated charge-discharge power is 3 MW.
The solid line of fig. 3 is the actual grid-connected power after being scheduled by using the dynamic programming algorithm for solving the knapsack problem, and it is verified that the actual grid-connected power meets the limit value constraints of the wind power output active power change in 1 minute and 10 minutes under the normal operation condition, and the correlation coefficient of the actual grid-connected power and the target grid-connected power is 0.998, and in addition, the electric quantity change condition of part of electric vehicle monomers is shown in fig. 4.
In conclusion, the final grid-connected power obtained by scheduling with the dynamic programming algorithm based on the knapsack problem can be greatly similar to the target grid-connected power obtained by decomposing the wavelet packet, and the requirement of stabilizing the wind power fluctuation is met.
The embodiment is only a preferred embodiment of the invention, but the scope of the invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the invention will be covered by the scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A scheduling method for electric vehicles to participate in wind power consumption is characterized by comprising the following steps:
step 1: determining mathematical models of electric vehicle energy storage and super capacitor energy storage;
step 2: decomposing the wind power output power by utilizing a wavelet packet decomposition method; when the number of wavelet packet decomposition layers is n, 2 is obtainednWind power output power branch (P)wind(n,0)～Pwind(n,2n-1)), under normal operating conditions, the first branch Pwind(n,0) if the limit value constraints of wind power output active power change at 1 minute and 10 minutes are completely met, stopping decomposition, and adding Pwind(n,0) as target grid-connected power, wherein PwindOutputting power for the wind power plant;
and step 3: 2 for wavelet packet decomposition in step 2nOther branch (P) of a wind power output branchwind(n,1)～Pwind(n,2n-1)) performing a spectral analysis, calculating P in combination with Pasteval's lawwind(n,1)～Pwind(n,2n-1) power energy of each branch, thereby preliminarily determining an energy storage configuration;
and 4, step 4: designing a specific optimized scheduling model; when the electric automobile cluster can completely absorb wind power, the scheduling problem of the energy storage equipment is converted into a knapsack problem, and the optimal scheduling cluster of the electric automobile is obtained by dynamic planning of the knapsack problem; the super capacitor is used as a standby energy storage device, and timely supply is performed when the electric automobile cannot meet the dispatching requirement, so that the wind power is maximally consumed;
and 5: and verifying whether the grid-connected power subjected to energy storage scheduling meets the grid-connected fluctuation requirement.
2. The method for scheduling participation of electric vehicles in wind power consumption according to claim 1, wherein the mathematical model of the energy storage of the electric vehicle in the step 1 is as follows:
the electric vehicle SOC is calculated as follows:
Figure FDA0003046506430000011
in the formula SiIs SOC of the ith electric vehicle, t is a sampling point, h is a sampling period, PEV,iIs the output power of the ith electric vehicle, etaEV,iFor the operating efficiency of the ith electric vehicle, Erated,iThe rated energy storage capacity of the ith electric automobile;
the energy storage optimization scheduling constraint of the electric automobile participating in stabilizing the new energy power generation fluctuation is as follows:
Smin,i≤Si(t)≤Smax,i
tarr,i≤t≤tdep,i
Si(t)+Pcηc(tdep,i-t)/Erated,i-Sdep,i≥0
Figure FDA0003046506430000021
Figure FDA0003046506430000022
in the formula Smin,iAnd Smax,iMinimum and maximum values of SOC constraints, t, for the ith vehicle to participate in dispatching the electric vehiclearr,iAnd tdep,iThe network access time and the network leaving time of the ith electric vehicle, PcCharging power, η, for electric vehiclescFor the efficiency of charging of electric vehicles, Sdep,iTarget off-grid SOC, T set for the ith electric vehicle to meet user travel demandc2dTime interval for converting charging action into discharging action, Td2cFor a time interval between the discharging operation and the charging operation, couc2d,iAnd coud2c,iT of ith electric vehiclec2dAnd Td2cAn aggregate value of the respective action transitions;
according to the energy storage optimization scheduling constraint condition, the maximum scheduling potential of the single electric vehicle at the t moment is obtained as follows:
Ec,i(t)＝Erated,i(Semax,i(t)-Si(t))
Figure FDA0003046506430000023
Ed,i(t)＝Erated,i(Si(t)-Semin,i(t))
Figure FDA0003046506430000024
in the formula, i and E for electric vehiclesc,iAnd Ed,iRespectively estimating the maximum charging potential and the maximum discharging potential; semax,iMaximum estimation value of SOC (state of charge) for charging electric vehicle till off-grid time, Semin,iFor minimum estimation of SOC for discharging action up to forced charging, Sesmax,iTo do not consider Smax,iUntil the SOC maximum estimated value, t, when off-griddis,maxForced charging time is required for the electric automobile; pdIs the discharge power of the electric vehicle etadThe discharge efficiency of the electric automobile;
the mathematical model of super capacitor energy storage is as follows:
the super-capacitor SOC is calculated as follows:
Figure FDA0003046506430000025
in the formula SscIs the SOC of the super capacitor; pSC,cAnd PSC,dRespectively charging and discharging power of the super capacitor; etaSC,cAnd ηSC,dCharge and discharge efficiencies, respectively; eSC,ratedRated energy storage capacity; n is a radical ofSC,cAnd NSC,dRespectively, a charge state and a discharge state, and the value of the charge state and the discharge state is 0 or 1;
the operation constraint of the super capacitor is as follows:
SSC,min≤SSC(t)≤SSC,max
0≤PSC,c(t)≤PSC,c,rated
PSC,d,rated≤-PSC,d(t)≤0
0≤NSC,c(t)+NSC,d(t)≤1
in the formula SSC,minAnd SSC,maxRespectively appointing minimum and maximum SOC allowable values for the super capacitor; pSC,c,ratedAnd PSC,d,ratedRespectively, rated charge and discharge power.
3. The method for scheduling participation of electric vehicles in wind power consumption according to claim 1, wherein the limit constraints of the wind power output active power change in the step 2 at 1 minute and 10 minutes are as follows:
Figure FDA0003046506430000031
in the formula,. DELTA.Pgrid,1minAnd Δ Pgrid,10minDeviation of maximum and minimum values of power fluctuation within 1 minute and 10 minutes, respectively, Pwind,insAnd the installed capacity of the wind power plant.
4. The method for scheduling participation of electric vehicles in wind power consumption according to claim 1, wherein the branch bandwidth of wavelet packet decomposition in the step 3 is as follows:
Figure FDA0003046506430000032
in the formula (f)sTaking the sampling frequency as the reference, and taking n as the decomposition layer number of the wavelet packet;
calculation of one of the branches [ f ] using Parceval's law1,f2]The power energy within the band is expressed as:
Figure FDA0003046506430000033
in the formula f1<f2X (f) is a discrete fourier transform of the time domain signal to be analyzed;
find [ f1,f2]The expression for the actual electrical power energy within the frequency band is:
Figure FDA0003046506430000034
5. the method for scheduling participation of electric vehicles in wind power consumption according to claim 1, wherein the step 4 specifically comprises the following substeps:
step 41: selecting a charge-discharge strategy according to the target smooth deviation delta P;
when Δ P (t)>0, scheduling action is charging, and determining the quantity requirement N of the electric vehiclesr(t) obtaining a dispatchable cluster N according to a real-time status of the electric vehicleEV(t)；
When Δ P (t)<0, scheduling action as discharging, obtaining N similarlyr(t) and NEV(t); in addition, the electric vehicle which is forcibly charged does not discharge under the dispatching action;
step 42: determining an electric automobile cluster participating in scheduling according to supply and demand balance;
when N is presentEV(t)≤Nr(t), the supply quantity of the electric vehicles can not meet or just meet the demand, and at the moment, all the electric vehicles which can participate in scheduling;
when N is presentEV(t)>Nr(t) the supply of electric vehicles meets the demand and has a margin, at which time the best dispatch cluster N is sought by dynamic planning based on knapsack problemopt(t)；
Step 43: calculating the real-time power output of the electric automobile cluster;
when the electric automobile cluster cannot stabilize the target deviation power, extra supply is carried out by using the super capacitor;
when the electric automobile cluster and the super capacitor reach a saturated state and cannot absorb wind power, wind abandon is considered;
step 44: and performing control action at the next moment until the control time length is finished.
6. The method for dispatching electric vehicles to participate in wind power consumption according to claim 1 or 5, wherein the dynamic planning of the knapsack problem comprises the following steps:
step S1: defining the value of the electric automobile; the value of the individual electric vehicle i at time t is defined as:
ci(t)＝-αcoui(t)+βEcd,i(t)+γTrem,i
Figure FDA0003046506430000041
Figure FDA0003046506430000042
in the formula, ciAs a value of the ith electric vehicle, couiChanging the accumulated value of the number of charging and discharging times of the ith electric vehicle after the ith electric vehicle is connected to the networkcd,iFor schedulable potential of the ith electric vehicle, Trem,iThe remaining on-line time of the ith electric automobile is defined, and alpha, beta and gamma are weight coefficients;
step S2: determining an optimal value objective function; the optimal value objective function is expressed as follows:
Figure FDA0003046506430000043
Figure FDA0003046506430000044
in the formula, y is an electric automobile setTotal value of the population, Pc,dIs charging power or discharging power;
step S3: a segmentation problem; loading the ith electric automobile into a backpack for the ith time;
step S4: determining state variables
Figure FDA0003046506430000051
Figure FDA0003046506430000052
The residual capacity of the backpack after the ith dispatching is obtained;
step S5: determining a decision variable Di(ii) a Whether the ith electric automobile is dispatched at the ith time or not is judged, if so, DiTrue, otherwise DiIs false;
step S6: establishing a state transition equation
Figure FDA0003046506430000053
In the formula (I), the compound is shown in the specification,
Figure FDA0003046506430000054
for putting the load into the front i electric automobiles
Figure FDA0003046506430000055
The maximum value of the backpack of (a);
Figure FDA0003046506430000056
the weight of the electric vehicle;
step S7: and determining an optimal strategy according to the optimal value objective function of the step S2, thereby obtaining an optimal dispatching cluster of the electric automobile.
CN202110474431.1A 2021-04-29 2021-04-29 Scheduling method for electric automobile to participate in wind power consumption Pending CN113162089A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100179704A1 (en) * 2009-01-14 2010-07-15 Integral Analytics, Inc. Optimization of microgrid energy use and distribution
WO2012171147A1 (en) * 2011-06-17 2012-12-20 辽宁省电力有限公司 Coordination and control system for regulated charging and discharging of pure electric vehicle in combination with wind power generation
CN103632205A (en) * 2013-11-05 2014-03-12 常州大学 Optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty
CN105375507A (en) * 2015-07-10 2016-03-02 华北电力大学（保定） Power two-stage interactive optimization scheduling system of virtual power plant in haze environment
CN107482690A (en) * 2017-09-26 2017-12-15 广东工业大学 The electric power system dispatching optimization method and system of wind-powered electricity generation and electric automobile cooperative scheduling

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100179704A1 (en) * 2009-01-14 2010-07-15 Integral Analytics, Inc. Optimization of microgrid energy use and distribution
WO2012171147A1 (en) * 2011-06-17 2012-12-20 辽宁省电力有限公司 Coordination and control system for regulated charging and discharging of pure electric vehicle in combination with wind power generation
CN103632205A (en) * 2013-11-05 2014-03-12 常州大学 Optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty
CN105375507A (en) * 2015-07-10 2016-03-02 华北电力大学（保定） Power two-stage interactive optimization scheduling system of virtual power plant in haze environment
CN107482690A (en) * 2017-09-26 2017-12-15 广东工业大学 The electric power system dispatching optimization method and system of wind-powered electricity generation and electric automobile cooperative scheduling

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
WEI WANG: "Energy management and optimization of vehicle-to-grid systems for wind power integration", 《CSEE JOURNAL OF POWER AND ENERGY SYSTEMS》, vol. 7, no. 1, pages 172 - 179 *
陈姝: "基于频谱分析法的备用容量优化研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》, no. 2, pages 13 *

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