CN105932741A - Charging control method and system of electric automobile group - Google Patents

Charging control method and system of electric automobile group Download PDF

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Publication number
CN105932741A
CN105932741A CN201610394740.7A CN201610394740A CN105932741A CN 105932741 A CN105932741 A CN 105932741A CN 201610394740 A CN201610394740 A CN 201610394740A CN 105932741 A CN105932741 A CN 105932741A
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China
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electric automobile
charging
automobile group
cost
period
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CN105932741B (en
Inventor
宋艺航
张翔
蒙文川
冷媛
王玲
席云华
傅蔷
陈政
曾鸣
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North China Electric Power University
Research Institute of Southern Power Grid Co Ltd
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North China Electric Power University
Power Grid Technology Research Center of China Southern Power Grid Co Ltd
Research Institute of Southern Power Grid Co Ltd
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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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
    • 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
    • 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
    • Y02T90/14—Plug-in electric vehicles

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention relates to a charging control method and system of an electric automobile group. The charging control method of the electric automobile group comprises the steps of building a double-layer random dynamic planning model of the electric automobile group for participating in a market bidding strategy; solving the double-layer random dynamic planning model based on a constraint condition to obtain an optimal solution, and determining a charging strategy of the electric automobile group based on the optimal solution; and controlling the charging of the electric automobile group based on the charging strategy. As the upper-layer optimization problem of the double-layer random dynamic planning model corresponds to cost minimization in a charging period of the electric automobile group, and the lower-layer optimization problem corresponds to scheduling of a demand-side resource to realize minimum power generation cost, the true optimal bidding is realized under a precondition of considering coordination of a electricity purchasing cost of the electric automobile group and a minimum power generation cost; and as the charging strategy is determined based on the optimal bidding to schedule a electric automobile group user to adjust a charging behavior and control the charging of the electric automobile group at a suitable bidding cost, the electricity purchasing cost of the electric automobile group is reduced.

Description

The charge control method of electric automobile group and system
Technical field
The present invention relates to technical field of electric power, particularly relate to charge control method and the system of a kind of electric automobile group.
Background technology
Following China will have a substantial amounts and scattered electric automobile, if its charging not being carried out effective management and control to leave that it is the most grid-connected, peak load increase, voltage pulsation, harmonic pollution etc. will be caused, reduce electric energy delivery quality and Distribution Network Equipment generation is had a strong impact on.Therefore, it is necessary to the orderly management problem of the further investigation grid-connected charging of electric automobile.
The orderly Charge Management of electric automobile to be carried out, its prerequisite is made by scientific and effective optimization method, i.e. determine when, where with which kind of electricity price level be charged to electric automobile, thus on the basis of reaching in the management cycle supply of the electric energy required for electric automobile, it is achieved the target of electricity consumption the lowest cost.For electric automobile discharge and recharge strategy, the most widely used optimization method is: first, based on electric automobile discharge and recharge cost, performance analysis, builds electric automobile charge-discharge power demand function；Then, under meeting charging electric vehicle demand condition, minimize as target using total discharge and recharge period cost, optimize the charging strategy of electric automobile.
Said method does not consider that electric automobile group's is actively engaged in market competitive bidding, and the bid price obtained is not best price.In actual application, the discharge and recharge of electric motor car is a lasting process.Along with the increase of electric motor car quantity, charge requirement is also increasing.But, if being charged when not being best price, then will increase the purchases strategies of electric automobile group.
Summary of the invention
Based on this, it is necessary to provide the charge control method of a kind of electric automobile group of the charging cost that can reduce electric automobile.
A kind of charge control method of electric automobile group, including:
Build electric automobile group and participate in the double-deck stochastic dynamic programming of market Bidding Strategies, described double-deck stochastic dynamic programming includes upper strata optimization problem and lower floor's optimization problem, described upper strata optimization problem correspondence electric automobile group period cost of charging minimizes, and it is minimum that described lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating；
Build the constraints of described double-deck stochastic dynamic programming；
According to described constraints, use Stochastic Dynamic Programming Method to solve described double-deck stochastic dynamic programming, obtain the optimal solution of described double-deck stochastic dynamic programming；
The charging strategy of electric automobile group is determined according to described optimal solution；
Described electric automobile group charging is controlled according to described charging strategy.
In one embodiment, the object function of described upper strata optimization problem is:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ] Σ l β t l + p e E [ P t | e ] Σ l β t l ;
Wherein, C is that electric automobile group charges period cost；PtFor the market clearing price of t electric energy, PRtFor the market clearing price that t is standby；The electric automobile group that object function represents period cost of charging is tetrameric summation, and wherein, Part I is that electric automobile is not up to desired charged state by schedule and just stops the marginal punishment cost of charging；Part II is the cost expected value of fixing competitive bidding；Part III be flexible competitive bidding by standby go out clear time cost expected value；Part IV is that flexible competitive bidding is by cost expected value during generation clearing；μ is limit punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t；αtlFor in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；R is standby, prFor by standby go out clear probability, βtlThe charge volume of the electric automobile that the departure time is l is distributed to for scalar in t period, flexible competitive bidding；E is electric energy, peFor by the probability of generation clearing.
In one embodiment, the object function of described lower floor optimization problem is:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s ;
Wherein, CtgenFor the marginal cost of generating set g, Q in the t periodtgenFor the generated energy of generating set g, p in the t periodtsFor the price of the t period load flexible competitive bidding of integrator s, qtsMiddle scalar for the t period load flexible competitive bidding of integrator s.
In one embodiment, corresponding with described upper strata optimization problem constraints includes at least one of power distribution network capacity-constrained, electric automobile power constraint, battery short of electricity capacity-constrained, charging electric vehicle constraint of demand and the constraint of charging electric vehicle duration.
In one embodiment, corresponding with described lower floor optimization problem constraints includes at least one of the constraint of the electric energy equilibrium of supply and demand, the constraint of peak-frequency regulation spare capacity, generated output constraint and load integrator flexible competitive bidding restriction.
A kind of charge control system of electric automobile group, including:
MBM, the double-deck stochastic dynamic programming of market Bidding Strategies is participated in for building electric automobile group, described double-deck stochastic dynamic programming includes upper strata optimization problem and lower floor's optimization problem, described upper strata optimization problem correspondence electric automobile group period cost of charging minimizes, and it is minimum that described lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating；
Constraint builds module, for building the constraints of described double-deck stochastic dynamic programming；
Computing module, for according to described constraints, uses Stochastic Dynamic Programming Method to solve described double-deck stochastic dynamic programming, obtains the optimal solution of described double-deck stochastic dynamic programming；
Determine charging strategy module, for determining the charging strategy of electric automobile group according to described optimal solution；
Charge control module, for controlling described electric automobile group charging according to described charging strategy.
In one embodiment, the object function of described upper strata optimization problem is:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ] Σ l β t l + p e E [ P t | e ] Σ l β t l ;
Wherein, C is that electric automobile group charges period cost；PtFor the market clearing price of t electric energy, PRtFor the market clearing price that t is standby；The electric automobile group that object function represents period cost of charging is tetrameric summation, and wherein, Part I is that electric automobile is not up to desired charged state by schedule and just stops the marginal punishment cost of charging；Part II is the cost expected value of fixing competitive bidding；Part III be flexible competitive bidding by standby go out clear time cost expected value；Part IV is that flexible competitive bidding is by cost expected value during generation clearing；μ is limit punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t；αtlFor in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；R is standby, prFor by standby go out clear probability, βtlThe charge volume of the electric automobile that the departure time is l is distributed to for scalar in t period, flexible competitive bidding；E is electric energy, peFor by the probability of generation clearing.
In one embodiment, the object function of described lower floor optimization problem is:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s ;
Wherein, CtgenFor the marginal cost of generating set g, Q in the t periodtgenFor the generated energy of generating set g, p in the t periodtsFor the price of the t period load flexible competitive bidding of integrator s, qtsMiddle scalar for the t period load flexible competitive bidding of integrator s.
In one embodiment, corresponding with described upper strata optimization problem constraints includes at least one of power distribution network capacity-constrained, electric automobile power constraint, battery short of electricity capacity-constrained, charging electric vehicle constraint of demand and the constraint of charging electric vehicle duration.
In one embodiment, corresponding with described lower floor optimization problem constraints includes at least one of the constraint of the electric energy equilibrium of supply and demand, the constraint of peak-frequency regulation spare capacity, generated output constraint and load integrator flexible competitive bidding restriction.
The charge control method of above-mentioned electric automobile group and system, the double-deck stochastic dynamic programming of market Bidding Strategies is participated in by building electric automobile group, by solving double-deck stochastic dynamic programming according to constraints, obtain optimal solution and determine the charging strategy of electric automobile group according to described optimal solution, thus controlling described electric automobile group charging according to described charging strategy.Owing to the upper strata optimization problem correspondence electric automobile group of double-deck stochastic dynamic programming period cost of charging minimizes, it is minimum that described lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating, thus on the premise of the purchases strategies considering electric automobile group and the coordination that cost of electricity-generating is minimum, realize truly optimum to bid, bid according to optimum and determine that charging strategy scheduling electric automobile group user adjusts charging behavior, such as, charge in the load valley period, in the period charging that price is low, it is charged when suitably bidding cost with control electric automobile group, thus reduce the purchases strategies of electric automobile group.
Accompanying drawing explanation
The flow chart of the charge control method of the electric automobile group of mono-embodiment of Fig. 1；
Fig. 2 be an embodiment high/low blocking level under load integrator flexible competitive bidding electricity price；
Fig. 3 be an embodiment low blocking level under market Bidding Strategies flexibly with fixing competitive bidding electricity and loss of energy expectation；
Fig. 4 is the high-level schematic functional block diagram of the charge control system of the electric automobile group of an embodiment.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, does not limit the present invention.
After existing charging electric vehicle optimization method is analyzed, find that its limitation is as follows:
1) research is generally directed to be analyzed, in the case of electric automobile passively accepts electricity price, the economy charged in order；
2) have ignored extensive electric automobile group as important its discharge and recharge of the Demand-side resource effect to safeguarding electric network reliability and stability.
Therefore, the present invention provides the charge control method of a kind of electric automobile group, under open market environment, with the electric automobile group minimum target of total charging cost, comprehensive all kinds of market clearing constraint and charging electric vehicle constraint, load integrator manages extensive electric automobile and is fixed competitive bidding and flexible competitive bidding, by arranging three kinds of flexible competitive bidding possible market clearing events, the Demand-side resource value of electric automobile can be fully demonstrated, business manager is concentrated to provide one to be capable of being substantially reduced electric automobile grid-connected charging period cost for load, realize the feasible electric automobile market Bidding Strategies prioritization scheme of peak load shifting peace slipstream test simultaneously, and the charging behavior of electric automobile is controlled according to this prioritization scheme, to realize the control of the charging to electric automobile group.
In one embodiment, as shown in Figure 1, it is provided that the charge control method of a kind of electric automobile group, comprise the following steps:
S102: build electric automobile group and participate in the double-deck stochastic dynamic programming of market Bidding Strategies, double-deck stochastic dynamic programming includes upper strata optimization problem and lower floor's optimization problem, upper strata optimization problem correspondence electric automobile group period cost of charging minimizes, and it is minimum that lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating.
S104: build the constraints of double-deck stochastic dynamic programming.
S106: according to constraints, uses Stochastic Dynamic Programming Method to solve double-deck stochastic dynamic programming, obtains the optimal solution of double-deck stochastic dynamic programming.
Concrete, the optimal solution of double-deck stochastic dynamic programming is the electricity of different periods conclusion of the business and cost of bidding.
S108: determine the charging strategy of electric automobile group according to optimal solution.
Charging strategy is corresponding with the optimal solution of double-deck stochastic dynamic programming.
S110: control electric automobile group charging according to charging strategy.
The charge control method of the electric automobile group of the present embodiment, the double-deck stochastic dynamic programming of market Bidding Strategies is participated in by building electric automobile group, by solving double-deck stochastic dynamic programming according to constraints, obtain optimal solution and determine the charging strategy of electric automobile group according to optimal solution, thus controlling electric automobile group charging according to charging strategy.Owing to the upper strata optimization problem correspondence electric automobile group of double-deck stochastic dynamic programming period cost of charging minimizes, it is minimum that lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating, thus on the premise of the purchases strategies considering electric automobile group and the coordination that cost of electricity-generating is minimum, realize truly optimum to bid, bid according to optimum and determine that charging strategy scheduling electric automobile group user adjusts charging behavior, such as, charge in the load valley period, in the period charging that price is low, it is charged when suitably bidding cost with control electric automobile group, thus reduce the purchases strategies of electric automobile group.
Concrete, as follows with the charge object function of upper strata optimization problem that period cost C minimizes structure of electric automobile group:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ] Σ l β t l + p e E [ P t | e ] Σ l β t l - - - ( 1 )
Wherein, PtFor the market clearing price of t electric energy, PRtFor the market clearing price that t is standby.The electric automobile group that object function represents period cost of charging is tetrameric summation: Part I is that electric automobile is not up to desired charged state by schedule and just stops the marginal punishment cost of charging, and μ be marginal punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t.Part II is the cost expected value of fixing competitive bidding, αtlFor in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l.Part III be flexible competitive bidding by standby go out clear time cost expected value, r is standby, prFor by standby go out clear probability, βtlThe charge volume of the electric automobile that the departure time is l is distributed to for scalar in t period, flexible competitive bidding；Last part be flexible competitive bidding by cost expected value during generation clearing, e is electric energy, peFor by the probability of generation clearing.
The state of charging electric vehicle dynamical system t period depends on electric automobile quantity n planning to complete charging at different periods ltl, and the charge volume L that electric automobile is not fully completetl。Ltl=(1-SOC) λ represents that the anticipated l moment completes the charge volume L that the electric automobile of charging is not fully complete in t finish timetlNow electric automobile state-of-charge SOC (State of Charge, SOC) and the product of battery capacity λ, and L is deducted equal to 1tlAnd ntlPossesses dynamic.L(t+1)l=Ltl+ΔLtl-αtl-θptsβtl, k ＞ t, θ represent the impact on dynamic battery capacity of the market clearing event, when flexible competitive bidding by standby go out clear duration be 1/2, when flexible competitive bidding is 1 by generation clearing duration, when in flexible competitive bidding, duration is 0.Additionally, n(t+1)l=ntl+Δntl, l ＞ t, it is stipulated that, mean allocation in the electric automobile that middle scalar l when leaving is identical,
Concrete, the constraints corresponding with upper strata optimization problem includes at least one of power distribution network capacity-constrained, electric automobile power constraint, battery short of electricity capacity-constrained, charging electric vehicle constraint of demand and the constraint of charging electric vehicle duration.
(1) power distribution network capacity-constrained:
In the t period, in fixing competitive bidding, in the flexible competitive bidding of scalar sum, scalar is distributed to the charge volume sum of the electric automobile that the departure time is l and should be not more than power distribution network capacity.
Σ l ( α t l + β t l ) ≤ c t - - - ( 2 )
Wherein, αtlRepresenting in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；βtlRepresent that scalar distributes to the charge volume of the electric automobile that the departure time is l, c in t period, flexible competitive biddingtFor power distribution network capacity.
(2) electric automobile power constraint:
In the t period, the departure time is the maximum that the charge volume sum of the electric automobile of l should be not more than charging electric vehicle power.
α t l + β t l ≤ max pn t l , ∀ l - - - ( 3 )
Wherein, p represents charging electric vehicle power；αtlRepresenting in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；βtlRepresent that scalar distributes to the charge volume of the electric automobile that the departure time is l in t period, flexible competitive bidding；For any period l；ntlComplete the electric automobile quantity of charging in the l period for planning of adding up in the t period.
(3) battery short of electricity capacity-constrained:
In the t period, the departure time is that the charge volume sum of the electric automobile of l should be not more than the electric automobile short of electricity amount in the t period.
α t l + β t l ≤ L t l , ∀ l - - - ( 4 )
αtlRepresenting in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；βtlRepresent that scalar distributes to the charge volume of the electric automobile that the departure time is l in t period, flexible competitive bidding；For any period l；LtlFor planning to complete, in the l period, the charge volume that the electric automobile of charging is not fully complete in t.
(4) charging electric vehicle constraint of demand:
p W δ Σ t x ( t ) ≤ L t l , x ( t ) ∈ { 0 , 1 } - - - ( 5 )
Wherein, pWRepresenting the specified charge power of electric automobile, δ represents that charge efficiency, x represent charge rate；LtlFor planning to complete, in the l period, the charge volume that the electric automobile of charging is not fully complete in t.
(5) charging electric vehicle time-constrain:
t i = x i ( t ) p i , W δ i , x i ( t ) ∈ { 0 , 1 } - - - ( 6 )
Wherein, tiRepresent the charging duration of electric automobile i, xiT () represents the charge rate of electric automobile i, pi,WRepresent the specified charge power of electric automobile i, δiRepresent the charge efficiency of electric automobile i.
In another embodiment, in open market environment, load integrator control user side electric automobile charges in order and participates in market competitive bidding with electricity power enterprise on an equal basis, market transaction center is under conditions of meeting power balance and peak regulation, frequency modulation Reserve Ancillary Service security constraint, optimize electricity market and go out clearly, it is achieved economic load dispatching reduces cost of electricity-generating.Therefore, the object function of lower floor's Optimized model corresponding to cost of electricity-generating minimum is realized by dispatching requirement side resource as follows:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s - - - ( 7 )
Wherein, CtgenFor the marginal cost of generating set g in the t period, unit is unit/kWh；QtgenFor the generated energy of generating set g in the t period, unit is kWh；ptsFor the price of the t period load flexible competitive bidding of integrator s, unit is unit/kWh；qtsFor the middle scalar of the t period load flexible competitive bidding of integrator s, unit is kWh.
The constraints corresponding with the object function of lower floor optimization problem includes at least one of the constraint of the electric energy equilibrium of supply and demand, the constraint of peak-frequency regulation spare capacity, generated output constraint and load integrator flexible competitive bidding restriction.Concrete constraints is as follows:
(1) electric energy balance constraint:
The generated energy of electricity power enterprise is equal to all competitive biddings acceptance of the bid electricity sum of load integrator.
Σ g Q t g e n = Σ s q t s + Σ s Q t s g - - - ( 8 )
Wherein, QtsgRepresent that scalar in competitive bidding is fixed by load integrator；qtsMiddle scalar for the t period load flexible competitive bidding of integrator s；QtgenFor the generated energy of generating set g in the t period.
(2) peak-frequency regulation spare capacity constraint:
The standby sum of peak-frequency regulation provided by electricity power enterprise and load integrator should be not less than the minima of spare capacity needed for system.
Σ g R t g e n + Σ g R t s ≥ min R - - - ( 9 )
Wherein, RtgenFor the peak regulation provided by Power Generation in the t period, frequency modulation spare capacity；RtsFor the peak regulation provided by load integrator respectively in the t period, frequency modulation spare capacity, R represents required spare capacity.
(3) minimax generated output restrictive condition:
Generating set provides electric energy and the standby generated output minimax constrained that need to ensure to meet unit.
Rtgen+Qtgen≤maxQtgen (10)
Qtgen-Rtgen≥minQtgen (11)
Wherein, RtgenFor the peak regulation provided by Power Generation in the t period, frequency modulation spare capacity；QtgenFor the generated energy of generating set g in the t period.
(4) load integrator minimax flexible competitive bidding restrictive condition:
In load integrator, target electric energy should be not more than total competitive bidding amount with standby, and in electric energy, scalar need to be not less than standby middle scalar.
Rts+qts≤Qts (12)
qts-Rts≥0 (13)
Wherein, RtsFor the peak regulation provided by load integrator respectively in the t period, frequency modulation spare capacity；qtsMiddle scalar for the t period load flexible competitive bidding of integrator s；QtsFor total competitive bidding amount.
In addition, before when load integrator participates in, the form of power market bidding comprises fixing competitive bidding and flexible competitive bidding, there are three kinds of Possible events in the market transaction result of competitive bidding flexibly: one is that competitive bidding goes out clearly by load with electrical energy form, and two is that competitive bidding goes out clearly by energy storage device with backup form, and three is in competitive bidding not.Concrete which kind of competitive bidding of generation goes out clear event and depends on the relation between flexible bidding price and market clearing price.
If | Pt-pts|≤PRt (14)
Then have
T electric energy and standby market clearing price are respectively PtAnd PRt(unit/kWh).Now, the electric energy expenditure of market transaction mechanism clearing load integrator is 1/2PtQts, and load integrator provide active service income be 1/2PRtQts, the flexible competitive bidding half of load integrator with generation clearing, half with standby go out clear.
If pts＞ Pt+PRt (16)
Then there is qts=Qts,Rts=0 (17)
Now, the electric energy expenditure of market transaction mechanism clearing load integrator is PtQts, the flexible competitive bidding of load integrator all goes out clearly with electrical energy form.
If pts＜ Pt-PRt (18)
Then there is qts=Rts=0 (19)
Now, the flexible competitive bidding of market transaction mechanism refusal load integrator.
Load integrator management electric automobile charges in order and participates in the optimum Bidding Strategies optimization method of electricity market, consider load integrator overall power distribution network capacity amount restrictive condition, electric automobile is initial and expects the information such as state-of-charge and charge completion time, by model optimization Bidding Strategies, it is possible to realize meeting the charging electric vehicle cost minimization under users ' individualized requirement premise.
In another embodiment, extensive Rechargeable vehicle charging belongs to stochastic dynamic programming problem, solve dimension height constraint challenge, there is randomness, dynamic and distributivity feature, and Stochastic Dynamic Programming Method has well adapting to property to solving this problem, therefore, step S106 uses stochastic dynamic programming model is solved, specifically comprise the following steps that
A, problem divide, and according to time or the space characteristics of problem, problem are divided into some stages；
B, determine state and state variable, problem is developed into the different state representation of various objective circumstances present during each stage out；
C, determining decision-making and write out state transition equation, the state in this stage is i.e. derived in state transfer according to state on last stage and decision-making；
D, write out end condition or boundary condition；
E, output optimal solution.
Assume that fixed and flexible is bidded as πk, the linear programming problem solving generation obtains quantity x of competitive biddingkWith expected cost c (πk).In the part of multistage dynamic programming, bid flexibly and do not affect the feasibility of quantity of bidding, only affect linear programming cost vector ck.Therefore, by disturbance of bidding, update cost vectorAnd assess xk, it is possible to calculate rapidly shown in gradient such as formula (18).
▿ c ( π k ) = ∂ c ( π k ) ∂ π k = c p e r ′ k x k - - - ( 20 )
Note to keep fixing at gradient method convergence time delay period cost, because changing extension cost can affect cost vector, so that gradient estimation before is invalid.
When gradient is estimated, use steepest descent method to update and bid, as shown in formula (19),
π k + 1 = π k - α k I ▿ c ( π k ) - - - ( 21 )
Wherein I is unit matrix, αkFor step-length.
The embodiment of the present invention consider extensive charging electric vehicle in power system for safeguard the electrical network equilibrium of supply and demand and provide Reserve Ancillary Service effect.The impact by Probability estimate electric automobile competitive bidding result is contained in object function, thus conscientiously ensure the realization of charging electric vehicle total period cost this basic goal minimum, to realize peak load shifting peace slipstream test curve, for promoting that electrical network has important practical significance and good promotion prospect with extensive electric automobile sustainable development.
One application example of the present invention given below.
The low-voltage network that this application example uses comprises 50 families totally 100 plug-in hybrid electric automobiles, assume that each household rated voltage is 10kW, when each electric automobile is come back, battery electric quantity all exhausts, all electric automobiles insert charging when model starts, and between 3:00-12:00, completing charging, SOC during charging complete is 100%, as shown in table 1, the capacity of batteries of electric automobile is 8kWh, and punishment cost is 0.8 yuan/kWh.
The quantity of the electric automobile of table 1 day part charging
The result of three kinds of charging electric vehicle strategies is contrasted.The first, be that market participates in strategy, the charging strategy that i.e. the double-deck stochastic dynamic programming of the present invention is corresponding.The second, oneself's scheduling strategy, load integrator only submits stationary electric competitive bidding to, and based on Research on electricity price prediction situation to charging electric vehicle.When market participates in strategy and oneself's scheduling strategy, feeder line capacity limit is all monitored by load integrator.The third, be chance charging strategy, i.e. start to charge up from insertion, until preferable charged state reaches, regardless of the availability of feeder line capacity.
By pair time before power distribution network active volume estimated value under the estimated value of electricity market electric energy and standby cleaing price and high/low blocking level, use CPLEX MIP to optimize engine and combine C++ Program model.Load integrator time front market Bidding Strategies optimum results as shown in Figure 2,3.The optimization method of the present embodiment is more reasonable on cost.Concrete result of calculation is shown in Table 2.
The result of 2 three kinds of charging electric vehicle strategies of table
Market participates in strategy can provide optimum selection to provide demand model for filling out paddy it is assumed that chance charging increases, and during peak, load extends several hours, and feeder line capacity limit changes.Limited feeder loss charge volume may be deducted by monitoring capacity limit by load integrator.Oneself's scheduling transfers to the charging of low blocked periods, but charging may result in second peak in load curve, sometimes referred to as " bounce-back peak ".Strategy is participated in for market, uncertainty due to market events, and therefore electric automobile must have excitation to charging earlier and charging with more low consumption rate such that it is able to control the charging behavior of electric automobile group to charge less than maximum consumption rate to provide standby.
In one embodiment, also provide for the charge control system of a kind of electric automobile group, as shown in Figure 4, including:
MBM 102, the double-deck stochastic dynamic programming of market Bidding Strategies is participated in for building electric automobile group, double-deck stochastic dynamic programming includes upper strata optimization problem and lower floor's optimization problem, upper strata optimization problem correspondence electric automobile group period cost of charging minimizes, and it is minimum that lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating.
Constraint builds module 104, participates in the constraints of market Bidding Strategies support model for building electric automobile group.
Computing module 106, for according to constraints, uses Stochastic Dynamic Programming Method to solve double-deck stochastic dynamic programming, obtains the optimal solution of double-deck stochastic dynamic programming.
Determine charging strategy module 108, for determining the charging strategy of electric automobile group according to optimal solution.
Charge control module 110, for controlling electric automobile group charging according to charging strategy.
The charge control system of the electric automobile group of the present embodiment, the double-deck stochastic dynamic programming of market Bidding Strategies is participated in by building electric automobile group, by solving double-deck stochastic dynamic programming according to constraints, obtain optimal solution and determine the charging strategy of electric automobile group according to optimal solution, thus controlling electric automobile group charging according to charging strategy.Owing to the upper strata optimization problem correspondence electric automobile group of double-deck stochastic dynamic programming period cost of charging minimizes, it is minimum that lower floor's optimization problem correspondence dispatching requirement side resource realizes cost of electricity-generating, thus on the premise of the purchases strategies considering electric automobile group and the coordination that cost of electricity-generating is minimum, realize truly optimum to bid, bid according to optimum and determine that charging strategy scheduling electric automobile group user adjusts charging behavior, such as, charge in the load valley period, in the period charging that price is low, it is charged when suitably bidding cost with control electric automobile group, thus reduce the purchases strategies of electric automobile group.
In one embodiment, the object function of upper strata optimization problem is:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ) Σ l β t l + p e E [ P t | e ] Σ l β t l ;
Wherein, C is that electric automobile group charges period cost；PtFor the market clearing price of t electric energy, PRtFor the market clearing price that t is standby；The electric automobile group that object function represents period cost of charging is tetrameric summation, and wherein, Part I is that electric automobile is not up to desired charged state by schedule and just stops the marginal punishment cost of charging；Part II is the cost expected value of fixing competitive bidding；Part III be flexible competitive bidding by standby go out clear time cost expected value；Part IV is that flexible competitive bidding is by cost expected value during generation clearing；μ is limit punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t；αtlFor in the t period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；R is standby, prFor by standby go out clear probability, βtlThe charge volume of the electric automobile that the departure time is l is distributed to for scalar in t period, flexible competitive bidding；E is electric energy, peFor by the probability of generation clearing.
In one embodiment, the object function of lower floor's optimization problem is:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s ;
Wherein, CtgenFor the marginal cost of generating set g, Q in the t periodtgenFor the generated energy of generating set g, p in the t periodtsFor the price of the t period load flexible competitive bidding of integrator s, qtsMiddle scalar for the t period load flexible competitive bidding of integrator s.
In one embodiment, corresponding with upper strata optimization problem constraints includes at least one of power distribution network capacity-constrained, electric automobile power constraint, battery short of electricity capacity-constrained, charging electric vehicle constraint of demand and the constraint of charging electric vehicle duration.
In one embodiment, corresponding with lower floor optimization problem constraints includes at least one of the constraint of the electric energy equilibrium of supply and demand, the constraint of peak-frequency regulation spare capacity, generated output constraint and load integrator flexible competitive bidding restriction.
Each technical characteristic of above example can combine arbitrarily, for making description succinct, all possible combination of each technical characteristic in above-described embodiment is not all described, but, as long as the combination of these technical characteristics does not exist contradiction, all it is considered to be the scope that this specification is recorded.
Embodiment described above only have expressed the several embodiments of the present invention, and it describes more concrete and detailed, but can not therefore be construed as limiting the scope of the patent.It should be pointed out that, for the person of ordinary skill of the art, without departing from the inventive concept of the premise, it is also possible to make some deformation and improvement, these broadly fall into protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. the charge control method of electric automobile group, including:
Build electric automobile group and participate in the double-deck stochastic dynamic programming of market Bidding Strategies, described bilayer with Machine dynamic programming model includes upper strata optimization problem and lower floor's optimization problem, described upper strata optimization problem correspondence electricity Electrical automobile group period cost of charging minimizes, and described lower floor's optimization problem correspondence dispatching requirement side resource realizes sending out Electricity cost minimization；
Build the constraints of described double-deck stochastic dynamic programming；
According to described constraints, Stochastic Dynamic Programming Method is used to solve described double-deck stochastic dynamic programming mould Type, obtains the optimal solution of described double-deck stochastic dynamic programming；
The charging strategy of electric automobile group is determined according to described optimal solution；
Described electric automobile group charging is controlled according to described charging strategy.
The charge control method of electric automobile group the most according to claim 1, it is characterised in that described The object function of upper strata optimization problem is:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ] Σ l β t l + p e E [ P t | e ] Σ l β t l ;
Wherein, C is that electric automobile group charges period cost；PtFor the market clearing price of t electric energy, PRt For the market clearing price that t is standby；The electric automobile group that object function represents period cost of charging is four Point summation, wherein, Part I is that electric automobile is not up to desired charged state by schedule and just stops The only marginal punishment cost of charging；Part II is the cost expected value of fixing competitive bidding；Part III is flexible Competitive bidding by standby go out clear time cost expected value；Part IV is that flexible competitive bidding is by one-tenth current period during generation clearing Prestige value；μ is limit punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t；αtlFor at t Period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；R is standby, pr For by standby go out clear probability, βtlThe electricity that the departure time is l is distributed to for scalar in t period, flexible competitive bidding The charge volume of electrical automobile；E is electric energy, peFor by the probability of generation clearing.
The charge control method of electric automobile group the most according to claim 1, it is characterised in that described The object function of lower floor's optimization problem is:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s ;
Wherein, CtgenFor the marginal cost of generating set g, Q in the t periodtgenFor generating set g in the t period Generated energy, ptsFor the price of the t period load flexible competitive bidding of integrator s, qtsFor t period load integrator s The middle scalar of competitive bidding flexibly.
The charge control method of electric automobile group the most according to claim 2, it is characterised in that with institute State constraints corresponding to upper strata optimization problem and include power distribution network capacity-constrained, electric automobile power constraint, electricity Pond short of electricity capacity-constrained, charging electric vehicle constraint of demand and at least the one of the constraint of charging electric vehicle duration Kind.
The charge control method of electric automobile group the most according to claim 3, it is characterised in that with institute State constraints corresponding to lower floor's optimization problem and include the constraint of the electric energy equilibrium of supply and demand, peak-frequency regulation spare capacity about At least one of the constraint of bundle, generated output and load integrator flexible competitive bidding restriction.
6. the charge control system of electric automobile group, including:
MBM, participates in the double-deck stochastic dynamic programming mould of market Bidding Strategies for building electric automobile group Type, described double-deck stochastic dynamic programming includes upper strata optimization problem and lower floor's optimization problem, described upper strata Optimization problem correspondence electric automobile group period cost of charging minimizes, and the optimization problem correspondence scheduling of described lower floor needs Side resource is asked to realize cost of electricity-generating minimum；
Constraint builds module, for building the constraints of described double-deck stochastic dynamic programming；
Computing module, for according to described constraints, uses Stochastic Dynamic Programming Method to solve described bilayer Stochastic dynamic programming, obtains the optimal solution of described double-deck stochastic dynamic programming；
Determine charging strategy module, for determining the charging strategy of electric automobile group according to described optimal solution；
Charge control module, for controlling described electric automobile group charging according to described charging strategy.
The charge control system of electric automobile group the most according to claim 6, it is characterised in that described The object function of upper strata optimization problem is:
min C = μL t ( l = t ) + E [ P t ] Σ l α t l + 1 2 p r E [ P t - P R t | r ] Σ l β t l + p e E [ P t | e ] Σ l β t l ;
Wherein, C is that electric automobile group charges period cost；PtFor the market clearing price of t electric energy, PRt For the market clearing price that t is standby；The electric automobile group that object function represents period cost of charging is four Point summation, wherein, Part I is that electric automobile is not up to desired charged state by schedule and just stops The only marginal punishment cost of charging；Part II is the cost expected value of fixing competitive bidding；Part III is flexible Competitive bidding by standby go out clear time cost expected value；Part IV is that flexible competitive bidding is by one-tenth current period during generation clearing Prestige value；μ is limit punishment cost coefficient, LtThe charge volume being not fully complete for electric automobile t；αtlFor at t Period, in fixing competitive bidding, scalar distributes to the charge volume of the electric automobile that the departure time is l；R is standby, pr For by standby go out clear probability, βtlThe electricity that the departure time is l is distributed to for scalar in t period, flexible competitive bidding The charge volume of electrical automobile；E is electric energy, peFor by the probability of generation clearing.
The charge control system of electric automobile group the most according to claim 6, it is characterised in that described The object function of lower floor's optimization problem is:
m i n Σ g C t g e n Q t g e n - Σ s p t s q t s ;
Wherein, CtgenFor the marginal cost of generating set g, Q in the t periodtgenFor generating set g in the t period Generated energy, ptsFor the price of the t period load flexible competitive bidding of integrator s, qtsFor t period load integrator s The middle scalar of competitive bidding flexibly.
The charge control system of electric automobile group the most according to claim 7, it is characterised in that with institute State constraints corresponding to upper strata optimization problem and include power distribution network capacity-constrained, electric automobile power constraint, electricity Pond short of electricity capacity-constrained, charging electric vehicle constraint of demand and at least the one of the constraint of charging electric vehicle duration Kind.
The charge control system of electric automobile group the most according to claim 8, it is characterised in that with Constraints corresponding to described lower floor optimization problem includes the constraint of the electric energy equilibrium of supply and demand, peak-frequency regulation spare capacity At least one of constraint, generated output constraint and load integrator flexible competitive bidding restriction.
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CN110733370B (en) * 2019-11-20 2022-11-11 国网江苏省电力有限公司南通供电分公司 Electric vehicle charging station ordered charging method based on double-layer optimization algorithm
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