CN107104454A - Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain - Google Patents
Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—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
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- 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
- H02J2310/56—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 characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention relates to a kind of meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain, belong to intelligent grid field.Consider that electric automobile moves the effect of energy storage, set up electric automobile load spatial and temporal distributions simulated sampling method, set up the urgent degree of charging and electric discharge adequacy indexes, quantify electric automobile controllable ability and be ranked up, determine each node Time-space serial power adjustable control domain bound, and then meter and the node electricity price optimal load flow model of spatial character are set up, solution obtains optimal space nodes electricity price.Guide automobile user to change discharge and recharge by Spot Price to be accustomed to, reduce operating cost.
Description
Technical field
The invention belongs to intelligent grid field, it is related to a kind of meter and the optimal load flow node in electric automobile power adjustable control domain
Prices Calculation.
Background technology
At present, many achievements in research for being directed to electric automobile discharge and recharge are had both at home and abroad, and single is established such as LuoX
The urgent level index of charging electric vehicle, further analysis electric automobile charge-discharge electric power under the conditions of virtual public electricity price from
Dynamic response strategy, realizes frequency modulation and stabilizes the power swing that power grid wind access is produced；FlathCM etc. points out, temporal reality
When electricity price and node electricity price spatially, region electricity price good guiding function can be played to electric automobile, realize electric voltage equalization,
Peak load shifting etc.；Pan Zhanghui etc. is from electricity market angle, and user side electric automobile agent is using simulation Generation Side hair
The mode that power plant surfs the Net at a competitive price, is established and is discharged the Optimal Operation Model bidded based on Demand-side, realize power network peak load shifting and
Reduce the target of user side charging cost；Numb show model etc. makes further research on Load aggregation business's Research foundation, space
On set up the dual-layer optimization scheduling model of Nodal congestion price, upper strata is from supply side, it is considered to which cost of electricity-generating, which is minimized, to be set up
Direct current optimal power flow model obtains Nodal congestion price；Lower floor is from Demand-side, according to the Nodal congestion price of upper layer transfers,
Consider that charging expense, battery loss and period of reservation of number set up Model for Multi-Objective Optimization, then charge and discharge electric load after optimization is anti-
Feed upper strata, to realize the maximization of economic benefit of user and power network；Chang Fangyu etc. is from user side, by consumer psychology
Principle is applied to response condition of the description demanding side of the electrical power net user to tou power price price differential, and how research effectively formulates tou power price
Price and corresponding charging peak Pinggu period, user power utilization is guided, realize reduction peak load rate and operator, the warp of user
The target of Ji benefit win-win.
The above method mainly guides electric automobile discharge and recharge by electricity price, it is intended to meet power network peak load shifting and user's economy
Property, rarely have the urgent and equivalent energy storage for considering electric automobile discharge and recharge, and do not provide clear and definite index and distinguish discharge and recharge elder generation
Afterwards sequentially；Further analysis, part document considers the urgent degree of charging, but without the relaxation that further analysis discharge and recharge is urgently spent
Property, i.e. discharge and recharge existence time is interval, on the premise of ensureing that electric automobile discharge and recharge is completed, electronic vapour in the unit interval is interval
There is bound in car charge-discharge electric power.
The content of the invention
In view of this, it is an object of the invention to provide a kind of meter and the optimal load flow section in electric automobile power adjustable control domain
Point Prices Calculation, in the case where ensureing that electric automobile trip needs with the urgent degree of discharge and recharge, with operation of power networks cost
Target is minimised as, meter and the node electricity price optimal load flow model of spatial character is set up, solves and obtains optimal space nodes electricity
Valency, guides automobile user to change discharge and recharge and is accustomed to, reduces operating cost by Spot Price.
To reach above-mentioned purpose, the present invention provides following technical scheme：
The optimal load flow node electricity price computational methods this method in meter and electric automobile power adjustable control domain comprises the following steps：
S1：Electric automobile (Electric Vehicle, EV) trip parameter is estimated using Maximum Likelihood Estimation Method,
Including EV days operating range, starting discharge and recharge time；
According to EV days operating ranges, starting discharge and recharge time and battery charge state characteristic, list is obtained by convolution algorithm
Individual EV charging load desired value, reapplies central-limit theorem and obtains EV clusters and always charge load PEVProbability density function；
S2：Each time cross-section EV travelings destination is defined as a state, according to Markov theory, further
By dispatching cycle according to setting time spaced discrete, i.e., characterize EV destinations space transfer using M × m × m three-dimensional matrices general
Rate matrix, obtains correspondence arbitrary time span t two-dimensional space transition probability matrix Pt；
Wherein, the state characterized for the element in state transition probability matrix in continuous time interval is shifted, when ignoring
Between be spaced in using itself as destination or the shorter state transfer case of berthing time, obtain the pact of state transition probability matrix
Beam condition；
S3：The P respectively obtained by step S1 and S2EV、PtAnd the constraints of state transition probability matrix, sample and obtain N
EV trip situation, so as to obtain EV scales in initial demand of the time series to grid power；
S4：Γ is urgently spent in the charging for defining vehicle nch,n, electric discharge abundant intensity be Γdis,n, and confirm that scheduling is suitable according to definition
Sequence, further determines that time series power adjustable control domain bound；When EV chargings are at the interval top of time regulatable, it is determined that when current
The section power adjustable control domain upper limit；When EV chargings at the interval top of time regulatable but are not discharged at the interval top of time regulatable, it is determined that
Present period controllable domain lower limit；
S5：Object function is turned to the fuel cost of whole conventional power generation usage units, economical operation cost minimization in the cycle, built
Found the object function of multi-period optimal load flow；Consider that space each node voltage constraint, the constraint of each node power flow equation, EV power can
Regulate and control region constraint, LA demand responses and interrupt constraint, EV networkings power constraint, EV discharge and recharge time-constrains, the section set up after improving
Point electricity price optimal load flow model, solution obtains optimal space nodes electricity price.
Further, EV days operating ranges meet logarithm normal distribution described in step S1, and its probability density function is
In formula, μdFor the desired value of day operating range, value 2.98 is fitted；σdFor the standard deviation of day operating range, fitting takes
Value 1.14；D represents EV days operating ranges, and span 0≤d≤200, unit is km；
The starting discharge and recharge time meets segmentation normal distribution, and its probability density function is
In formula, μsFor the mathematical expectation of initiation of charge time, value 17.47 is fitted；σsFor the mathematics of initiation of charge time
Standard deviation, is fitted value 3.41；T is the starting discharge and recharge time；
Total charging load PEVProbability density function be
In formula, μEV, σEVTotal charging load P is represented respectivelyEVExpected value and standard deviation.
Further, the two-dimensional space transition probability matrix P in the step S2tFor
In formula,Represent in (t-1)-t periods, departure place is DiIt is D to destinationjTrip probability；
The constraints of the state transition probability matrix is
Further, the sampling in the step S3 obtains N EV trips situation and specifically includes following steps：
S301：Electric automobile quantity in region is determined according to car ownership and electric automobile infiltration situation；
S302：Simulated using the distance travelled with statistical law and running time probability density function, single electronic vapour
Car initial parameter random sampling；
S303：Initial value is set：Electric automobile numbering n=1；
S304：Travel time, the initial i=1 of mileage are set, and destination uses the space migrating probability with statistical law close
Spend functional simulation sampling；
S305：Electric automobile simulation trip, updates electric automobile SOC states, and be superimposed discharge and recharge load curve；
S306：Determine whether last trip, it is no, go to step S304；It is to go to step S307；
S307：Last electric automobile is determine whether, it is no, go to step S303 and update electric automobile numbering n；It is to turn
Step S308；
S308：Export workload demand of the N random discharge and recharge of electric automobile space-time to power network.
Further, the urgent degree Γ of EV chargings in the step S4ch,nFor
In formula：tin,nAnd tout,nVehicle n turn-on time and specified off-network time is represented respectively；ηchRepresent EV charging effects
Rate；tch,nRepresent vehicle n charging interval；SOCin,nAnd SOCex,nThe vehicle n initial state-of-charge of battery and off-network is represented respectively
When state-of-charge desired value；Pch,nRepresent vehicle n specified charge power；Bc,nRepresent vehicle n battery capacity；
When specified charge power is definite value, the bigger reflection of the ratio more priority scheduling of charging interval and berthing time；When
When charging urgently spends identical, dispatching sequence is determined with urgent spend of next scheduling slot charging, the more big more preferential tune of the urgent degree of charging
Degree
The electric discharge abundant intensity of the vehicle n is Γdis,nFor
In formula：ηdisRepresent EV discharging efficiencies；tdis,nRepresent vehicle n discharge time；Pdis,nRepresent vehicle n specified putting
Electrical power；
When nominal discharge power is definite value, the ratio of discharge time and berthing time is more big more priority scheduling；Work as electric discharge
When abundant intensity is identical, dispatching sequence is determined with next scheduling slot electric discharge abundant intensity, electric discharge abundant intensity is more big more priority scheduling；
Further, the object function of multi-period optimal load flow is in the step S5
In formula：CTRepresent power network total operating cost；T represents scheduling slot number；CGEN,tRepresent the unit of all unit periods t
Cost of electricity-generating；Pgen,iRepresent generator i active power output；H represents generator number；ai、bi、ciThe consumption of generator is represented respectively
Flow characteristic parameter；CS,tRepresent the start-up and shut-down costs of all unit periods t；Sgen,iRepresent Unit Commitment expense；ui(t) period t is represented
Unit Commitment state, 1 represents normal operation, and 0 represents to shut down；
Each node voltage is constrained to
Vmin≤Vi(t)≤Vmax,
In formula：Vmin、VmaxThe bound of node voltage amplitude is represented respectively；
Each node power flow equation is constrained to
PL,i(t)=PEV,i(t)+PLA,i(t)
In formula：PG,i(t)、QG,i(t) represent that the active and idle of period t node i generating set is exerted oneself respectively；PL,i(t)、
QL,i(t) represent that the active and idle of period t node i load is exerted oneself respectively, by EV total loads PEV,i(t) it can always be interrupted with LA negative
Lotus PLA,i(t) it is added to power flow equation；Vi(t)、Vj(t) period t node i, j voltage magnitude are represented respectively；Gij、BijDifference table
Show the conductance and susceptance of branch road i-j in system admittance matrix；θij(t) period t node voltage phase angle difference is represented；
The EV power adjustables control region constraint is
PEV,min(t)≤PEV,i(t)≤PEV,max(t)
In formula：PEV,max(t)、PEV,min(t) time series t power adjustable controls domain bound in step S4 is represented respectively；
The LA demand responses are interrupted and are constrained to
PLA,min≤PLA,i(t)≤PLA,max
In formula：PLA,min、PLA,maxThe bound of the total interruptible load power of LA is represented respectively；PLA,k(t) period t the is represented
K LA's can interrupt power；
The EV networkings power constraint is
|PEV,i(t)+PLA,i(t) | Δ t=| QE,i,t|≤Pgrid,i(t)Δt
SOCmin≤SOCn(t)≤SOCmax
statech,n(t)statedis,n(t)=0
In formula：PEV,iAnd P (t)LA,i(t) power that the EV and LA of period t node i response change is represented respectively；Pgrid,i
(t) limitation of power network period t node is rated power is represented；Pch,nAnd Pdis,nVehicle n specified charge power is represented respectively and specified
Discharge power；Bc,nRepresent vehicle n battery capacity；QE,i,tRepresent the electric energy changed after the optimization of period t node i；I represents node
Number；Ni(t) the EV quantity of period t node i is represented；N represents EV total quantitys；Δ t represents unit interval, takes 1h；
statech,n(t)、statedis,n(t) the discharge and recharge 0-1 state variables of period t vehicle n are represented respectively, and 1 represents discharge and recharge, 0 table
Show and neither charge nor discharge；SOCminAnd SOCmaxEV battery charge states minimum value and maximum are represented respectively；ηchAnd ηdisPoint
Biao Shi not EV efficiency for charge-discharges；ηselfRepresent EV self discharge coefficients；
The EV discharge and recharges time-constrain is
tin,n+tch,n≤tout,n
In formula：And tdis,nDischarge time and amendment discharge time are represented respectively；tin,nWhen representing vehicle n charging completes
Between；tout,nRepresent that vehicle n confirms to participate in after electric discharge, the discharge time during electric energy of part can only be discharged.
The beneficial effects of the present invention are：The node optimal load flow Spot Price Model of meter and spatial character can realize EV discharge and recharges
Reasonable layout, electric discharge concentrate on endpoint node close to load, charging concentrate on top node close to power supply, effectively reduce system
Economical operation cost, improves node voltage and node electricity price distribution situation, reduces system losses.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carried out
Explanation：
Fig. 1 is the random discharge and recharge flow chart of N electric automobile space-time；
Fig. 2 is Optimized Operation strategic process figure；
Fig. 3 is the node power distribution net schematic diagrames of IEEE 33；
Fig. 4 is power network period worst voltage distribution graph；
Fig. 5 is grid nodes distribution of electricity prices figure after optimization；
Fig. 6 is node space-time power dispatching figure；
Fig. 7 is day charge-discharge power demand curve map；
Fig. 8 is line voltage distribution map；
Fig. 9 is grid nodes distribution of electricity prices figure.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
1EV load spatial and temporal distributions probability and simulated sampling method
1.1EV Annual distribution probability
EV (electric automobile) and fuel-engined vehicle trip custom similitude are considered, with reference to DOT in 2009 to the whole America
Investigation statisticses running data (the National HouseholdTravel of 47641 dilly service conditions of family expenses
Survey, NHTS), wherein 96% private savings passenger car is daily in suspended state, with more controllable times, flexibility
Height, therefore analyzed herein using private savings passenger car as research object.EV trip parameters are carried out using Maximum Likelihood Estimation Method
Estimation, operating range meets logarithm normal distribution within EV days；Originate the satisfaction segmentation normal distribution of discharge and recharge time.
Day, operating range probability density function was as shown in formula.
In formula：μdFor the desired value of day operating range, value 2.98 is fitted；σdFor the standard deviation of day operating range, fitting takes
Value 1.14；D represents EV days operating ranges, and span 0≤d≤200, unit is km.
EV is originated shown in the probability density function such as formula (2) of discharge and recharge time at random.
In formula：μsFor the mathematical expectation of initiation of charge time, value 17.47 is fitted；σsFor the mathematics of initiation of charge time
Standard deviation, is fitted value 3.41.
By formula (1)~(2) and battery charge state characteristic, the charging load that can obtain single EV by convolution algorithm is expected
Value, derives that EV clusters always charge load P using central-limit theoremEVProbability density function as shown in formula：
In formula：μEV、σEVTotal charging load P is represented respectivelyEVExpected value and standard deviation.
From economy and power network angle, the EV unordered stochastic regime of electric discharge behavior accesses electricity in high peak height electricity price period
Net simultaneously starts electric discharge.
1.2EV space migrating probability
Markoff process mainly characterizes the development trend of system to-be using transition probability, with transition probability size
Reflect the inherent law of each stochastic regime.Each time cross-section EV travelings destination is such as defined as a state, according to horse
Er Kefu is theoretical, and the next states of EV are simply determined by current state.Further dispatching cycle is isolated according between setting time
Dispersion, you can characterize EV destinations space migrating probability matrix using M × m × m three-dimensional matrices, wherein M is that time interval is discrete
Number afterwards, m is trip purpose ground number of classifying.Correspondence arbitrary time span t two-dimensional space transition probability matrix PtSuch as formula (4)
It is shown.
In formula：Represent in (t-1)-t periods, departure place is DiIt is D to destinationjTrip probability.
The state transfer in continuous time interval is characterized for the element in state transition probability matrix, the time is ignored herein
Using itself as destination or the shorter state transfer case of berthing time in interval, because both of which can regard short stay as
Stroke, consider can not possibly discharge and recharge be rational.Therefore the constraints of state transition probability matrix is as follows.
Extensive EV spatial and temporal distributions simulated sampling methods
Always charged load P by the 1.1 and 1.2 EV scales that respectively obtain of sectionEVThe probability of probability scenarios and trip purpose ground
Situation, further sampling obtains N EV trip situation, obtains EV scales in initial demand of the time series to grid power,
Simulated sampling flow is as shown in Figure 1.
2 meters and the optimal load flow model in power adjustable control domain
As shown in Figure 2, it is considered to each node electric automobile in space time series to the pressing degree of power demand not
Together, the EV discharge and recharges first against each node in day part power network space are sorted, and obtain in the period EV discharge and recharges to power network demand
Power adjustable control domain (i.e. power maximum and minimum value), and then considers that guiding of the node Spot Price to user power utilization is made
With, solved under EV power controllable domain constraintss based on operation of power networks financial cost minimize node electricity price optimal load flow mould
Type.
The power adjustable control domain of 2.1 meters and the urgent degree of charging and electric discharge abundant intensity
Define the urgent degree Γ of 1 chargingchFor EV charging intervals and the ratio of berthing time, the urgent degree of reaction EV chargings.When
When specified charge power is definite value, i.e., charging interval length can reflect the ratio of charge capacity size, charging interval and berthing time
It is worth equivalent to, to the desirability of electricity, ratio is bigger, and reflection is more urgent, therefore more priority scheduling in the unit interval；When charging is compeled
When degree of cutting is identical, dispatching sequence is determined with the urgent degree of next scheduling slot charging, more big more priority scheduling is urgently spent in charging.Vehicle
Γ is urgently spent in n chargingch,nDefinition as shown in formula.
In formula：tin,nAnd tout,nVehicle n turn-on time and specified off-network time is represented respectively；ηchRepresent EV charging effects
Rate；tch,nRepresent vehicle n charging interval；SOCin,nAnd SOCex,nThe vehicle n initial state-of-charge of battery and off-network is represented respectively
When state-of-charge desired value；Pch,nRepresent vehicle n specified charge power；Bc,nRepresent vehicle n battery capacity.
Define 2 electric discharge abundant intensity ΓdisFor EV discharge times and the ratio of berthing time, the urgent degree of reaction EV electric discharges.When
When nominal discharge power is definite value, i.e., discharge time length can reflect the ratio of discharge electricity amount size, discharge time and berthing time
It is worth equivalent to, to the abundant degree of electricity, the ratio bigger i.e. follow-up controllable time is relatively smaller, priority scheduling in the unit interval；
When the abundant intensity that discharges is identical, dispatching sequence is determined with next scheduling slot electric discharge abundant intensity, electric discharge abundant intensity is more big more preferential
Scheduling.The definition of electric discharge abundant intensity is as shown in formula.
In formula：ηdisRepresent EV discharging efficiencies；tdis,nRepresent vehicle n discharge time；Pdis,nRepresent vehicle n specified putting
Electrical power.
According to the dispatching sequence of confirmation, time series power adjustable control domain bound is further determined that.When allowing EV as far as possible
Charging is at the interval top of time regulatable, as the present period power adjustable control domain upper limit；Charged when allowing EV as far as possible not adjustable
Time interval top and discharge as far as possible at the interval top of time regulatable, as present period controllable domain lower limit.It will determine
The P of propertyEV,i(t) variable variable is converted to.
2.2 meters and the optimal load flow node electricity price model in power adjustable control domain
Spot Price can be updated with shorter period, can accurately reflect that each period power network is powered limit
Cost and user side changes in demand, can preferably overcome the shortcomings of traditional tou power price.Therefore, each node in space is considered herein
EV power adjustable control region constraints and LA demand responses interrupt the node electricity price optimal load flow model that constraint is set up after improving.
Fuel cost (or totle drilling cost), Unit Commitment cost with whole conventional power generation usage unit in the cycle be economical operation into
Originally object function is minimised as, the object function of multi-period optimal load flow is as shown in formula.
In formula：CTRepresent power network total operating cost；T represents scheduling slot number；CGEN,tRepresent the unit of all unit periods t
Cost of electricity-generating；Pgen,iRepresent generator i active power output；H represents generator number；ai、bi、ciThe consumption of generator is represented respectively
Flow characteristic parameter；CS,tRepresent the start-up and shut-down costs of all unit periods t；Sgen,iRepresent Unit Commitment expense；ui(t) period t is represented
Unit Commitment state, 1 represents normal operation, and 0 represents to shut down.
Constraints：
Node voltage is constrained
Vmin≤Vi(t)≤Vmax (14)
In formula：Vmin、VmaxThe bound of node voltage amplitude is represented respectively.
Node power flow equation is constrained
PL,i(t)=PEV,i(t)+PLA,i(t) (16)
In formula：PG,i(t)、QG,i(t) represent that the active and idle of period t node i generating set is exerted oneself respectively；PL,i(t)、
QL,i(t) represent that the active and idle of period t node i load is exerted oneself respectively, by EV total loads PEV,i(t) it can always be interrupted with LA negative
Lotus PLA,i(t) it is added to power flow equation；Vi(t)、Vj(t) period t node i, j voltage magnitude are represented respectively；Gij、BijDifference table
Show the conductance and susceptance of branch road i-j in system admittance matrix；θij(t) period t node voltage phase angle difference is represented.
EV power adjustable control region constraints
PEV,min(t)≤PEV,i(t)≤PEV,max(t) (17)
In formula：PEV,max(t)、PEV,min(t) represent respectively after 2.1 section Optimal schedulings above and below period t power adjustable control domain
Limit.
LA can interrupt power constraint
PLA,min≤PLA,i(t)≤PLA,max (18)
In formula：PLA,min、PLA,maxThe bound of the total interruptible load power of LA is represented respectively；PLA,k(t) period t the is represented
K LA's can interrupt power.
EV networking power constraints
|PEV,i(t)+PLA,i(t) | Δ t=| QE,i,t|≤Pgrid,i(t)Δt (20)
SOCmin≤SOCn(t)≤SOCmax (22)
statech,n(t)statedis,n(t)=0 (23)
In formula：PEV,iAnd P (t)LA,i(t) power that the EV and LA of period t node i response change is represented respectively；Pgrid,i
(t) limitation of power network period t node is rated power is represented；Pch,nAnd Pdis,nVehicle n specified charge power is represented respectively and specified
Discharge power；Bc,nRepresent vehicle n battery capacity；QE,i,tRepresent the electric energy changed after the optimization of period t node i；I represents node
Number；Ni(t) the EV quantity of period t node i is represented；N represents EV total quantitys；Δ t represents unit interval, takes 1h；
statech,n(t)、statedis,n(t) the discharge and recharge 0-1 state variables of period t vehicle n are represented respectively, and 1 represents discharge and recharge, 0 table
Show and neither charge nor discharge；SOCminAnd SOCmaxEV battery charge states minimum value and maximum are represented respectively；ηchAnd ηdisPoint
Biao Shi not EV efficiency for charge-discharges；ηselfRepresent EV self discharge coefficients.
EV discharge and recharge time-constrains
tin,n+tch,n≤tout,n (25)
In formula：And tDis, nDischarge time and amendment discharge time are represented respectively；Represent amendment discharge time.Formula table
Show vehicle n charge completion times before time departure, that is, ensure the following trip requirements of user；Formula represents that vehicle n confirms participation
After electric discharge, discharge time is modified when can only discharge part electric energy.
Model optimization obtains the electric energy Q that each node time sequence of space changesE, the Lagrange multiplier of power flow equation is
For node electricity price, further set up and responded based on each node meter of space and the discharge and recharge electricity price Optimal Operation Model of time scale
QE, the situation of user side demand response is included, guiding LA changes power mode and determines EV user's specific discharge and recharge time, reaches
To peak load shifting and reduction user power utilization expense purpose.
3 example data and basic model
EV space-times discharge and recharges is considered in power distribution network, therefore bibliography (He L, Yang J, Yan J, et al.A bi-
layer optimization based temporal and spatial scheduling for large-scale
electric vehicles[J].Applied Energy,2016,168:179-192.) the modification Node power distribution systems of IEEE 33
Exemplified by, checking puies forward the validity of model and algorithm herein, and node destination is classified and the topological structure of power network is shown in Fig. 3, is generated electricity
Side and distribution network users are connected by simplified transformer and transmission line of electricity, and the unit parameter and coefficient correlation that node 1 is connected are such as
Following table：
The node system unit parameters of 1 IEEE of Table A 33
Note：Generator G1~G3 is located at node 1.
The interruptible load with high compensation parameter of Table A 2
The typical day day part node load (unit of Table A 3:MW)
Reference capacity 100MVA, voltage class 12.66kV.Node 2,21,24 is chosen as Office Area load access point, section
Point 6,10,30 is as business Recreation area load access point, and node 16,18,33 is as residential block load access point, and each node is pacified
Equipped with EV charging/discharging apparatus and control of intelligent terminal.LA can outage capacity take the 10% of Real-time Load, interruptible load with high compensation
Parameter and response reliability level are shown in Table A2.EV relevant parameters and quantity ask for bibliography (Li Zhenkun, Tian Yuan, Dong Chengming,
Deng distributed power source planning [J] Automation of Electric Systems in power distribution networks containing electric automobile of the based on probabilistic loadflow, 2014,38
(16):60-66.
LI Zhenkun,TIAN Yuan,DONG Chengming,et al.Distributed Generators
Programming in Distribution Network Involving Vehicle to Grid Based on
Probabilistic Power Flow[J].Automation ofElectric Power Systems,2014,38(16):
60-66.), (Yang Jiajia, Zhao Junhua, Wen Fushuan wait void of the containing electric automobile and Wind turbines to battery cost of compensation bibliography
Intend Bidding strategy for power plant [J] Automation of Electric Systems, 2014,38 (13):92-102.
YANG Jiajia,ZHAO Junhua,WEN Fushuan,et al.Development of Bidding
Strategies for Virtual Power Plants Considering Uncertain Outputs from Plug-
in Electric Vehicles and Wind Generators[J].Automation ofElectric Power
Systems,2014,38(13):92-102.), permeability selection 50%, typical day EV space-times discharge and recharge typical sampling discharge and recharge
Load curve as shown in fig. 7, each node LA be superimposed EV days charging and discharging curves after, typical day node active power tables of data A3 institutes
Show.The upper limit δ of truncated distributionmax=Qdis,n, correspond to EV user and persistently respond electric discharge electricity price until scheduling is completed；Its truncated distribution
Lower limit δmin=0, correspondence EV user has neither part nor lot in response, and average and variance are set as u=0, σ=Qdis,n。
Basic example considers to be electrically accessed under grid condition in EV initial sample charge and discharges, and it is carried out with reference to node load data
Optimal load flow is calculated, and obtains space-time voltage's distribiuting and each node electricity price situation, as shown in Figure 8 and Figure 9.Find out from voltage's distribiuting,
Node 18,33 occurs voltage in the load peak period 14,19 less than 0.95, and threat is brought to power grid operation.From node electricity
Valency is analyzed, and different loads are to that should have different node electricity prices in time scale, and such as load peak period 14-19 correspondences are higher
The relatively low node electricity price of correspondence, guarantee is provided for the demand response of time scale before node electricity price, load valley period 9；
On Spatial Dimension, same period each node electricity price is different, and condition is provided for the demand response of Spatial Dimension.
The node electricity price optimal load flow model optimization of 3.2 meters and spatial character
Fig. 4 will become apparent from the voltage after space optimal load flow optimizes and be obviously improved, and load peak period voltage is 0.971
3% is improved compared to basic model 0.942, guarantee is provided for the safe and reliable operation of power network.Fig. 5 node electricity prices are distributed, right
It can be seen that node electricity price has risen in load valley period 1-9 and period 22-24 than Fig. 9, load peak period node 11-20
Electricity price is decreased obviously.
Typical day EV discharge and recharges space-time scheduling result is as shown in Figure 6.Know that load power increase is concentrated mainly on node by figure
2,6,16,33, load power is cut down and is concentrated mainly on 10,18,21,24,30.33 node distributions are typical radial pattern network,
Power flow is by 1 node (power supply) to endpoint node (load), therefore, during system space optimal load flow Optimized Operation, same area
Domain class scheduling EV chargings can reduce network loss, improve electricity close to mains side, scheduling LA demand responses and EV electric discharges in endpoint node
Network operation financial cost, such as following table consistent with network loss and economical operation cost result.
Network loss and economical operation Cost comparisons
| Example | Network loss (MW) | Economical operation cost ($) |
| Random sampling basic model | 4.4761 | 194212 |
| Space optimal load flow model | 2.4898 | 180887 |
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
Cross above preferred embodiment the present invention is described in detail, it is to be understood by those skilled in the art that can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (6)
1. meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain, it is characterised in that：This method bag
Include following steps：
S1：Electric automobile (Electric Vehicle, EV) trip parameter is estimated using Maximum Likelihood Estimation Method, including
EV days operating range, starting discharge and recharge time；
According to EV days operating ranges, starting discharge and recharge time and battery charge state characteristic, single EV is obtained by convolution algorithm
Charging load desired value, reapply central-limit theorem and obtain EV clusters and always charge load PEVProbability density function；
S2：Each time cross-section EV travelings destination is defined as a state, according to Markov theory, will further be adjusted
The cycle is spent according to setting time spaced discrete, i.e., characterize EV destinations space migrating probability square using M × m × m three-dimensional matrices
Battle array, obtains correspondence arbitrary time span t two-dimensional space transition probability matrix Pt；
Wherein, the state characterized for the element in state transition probability matrix in continuous time interval is shifted, and is ignored between the time
Every interior using itself as destination or the shorter state transfer case of berthing time, the constraint bar of state transition probability matrix is obtained
Part；
S3：The P respectively obtained by step S1 and S2EV、PtAnd the constraints of state transition probability matrix, sample and obtain N EV
Trip situation, so as to obtain EV scales in initial demand of the time series to grid power；
S4：Γ is urgently spent in the charging for defining vehicle nch,n, electric discharge abundant intensity be Γdis,n, and dispatching sequence is confirmed according to definition, enter
One step determines time series power adjustable control domain bound；When EV chargings are at the interval top of time regulatable, present period work(is determined
The rate controllable domain upper limit；When EV chargings at the interval top of time regulatable but are not discharged at the interval top of time regulatable, it is determined that currently
Period controllable domain lower limit；
S5：Object function is turned to the fuel cost of whole conventional power generation usage units, economical operation cost minimization in the cycle, set up many
The object function of period optimal load flow；Consider each node voltage constraint in space, the constraint of each node power flow equation, EV power adjustable controls
Region constraint, LA demand responses interrupt constraint, EV networkings power constraint, EV discharge and recharge time-constrains, the node electricity set up after improving
Valency optimal load flow model, solution obtains optimal space nodes electricity price.
2. the optimal load flow node electricity price computational methods of the meter and electric automobile power adjustable control domain described in claim 1, it is special
Levy and be：EV days operating ranges meet logarithm normal distribution described in step S1, and its probability density function is
In formula, μdFor the desired value of day operating range, value 2.98 is fitted；σdFor the standard deviation of day operating range, value is fitted
1.14；D represents EV days operating ranges, and span 0≤d≤200, unit is km；
The starting discharge and recharge time meets segmentation normal distribution, and its probability density function is
In formula, μsFor the mathematical expectation of initiation of charge time, value 17.47 is fitted；σsFor the mathematical standard of initiation of charge time
Difference, is fitted value 3.41；T is the starting discharge and recharge time；
Total charging load PEVProbability density function be
In formula, μEV, σEVTotal charging load P is represented respectivelyEVExpected value and standard deviation.
3. the optimal load flow node electricity price computational methods of the meter and electric automobile power adjustable control domain described in claim 1, it is special
Levy and be：Two-dimensional space transition probability matrix P in the step S2tFor
In formula,Represent in (t-1)-t periods, departure place is DiIt is D to destinationjTrip probability；
The constraints of the state transition probability matrix is
4. the optimal load flow node electricity price computational methods of the meter and electric automobile power adjustable control domain described in claim 1, it is special
Levy and be：Sampling in the step S3 obtains N EV trips situation and specifically includes following steps：
S301：Electric automobile quantity in region is determined according to car ownership and electric automobile infiltration situation；
S302：Simulated using the distance travelled with statistical law and running time probability density function, at the beginning of single electric automobile
Beginning stochastic parameter is sampled；
S303：Initial value is set：Electric automobile numbering n=1；
S304：Travel time, the initial i=1 of mileage are set, and destination uses the space migrating probability density letter with statistical law
Number simulated sampling；
S305：Electric automobile simulation trip, updates electric automobile SOC states, and be superimposed discharge and recharge load curve；
S306：Determine whether last trip, it is no, go to step S304；It is to go to step S307；
S307：Last electric automobile is determine whether, it is no, go to step S303 and update electric automobile numbering n；It is to go to step
S308；
S308：Export workload demand of the N random discharge and recharge of electric automobile space-time to power network.
5. the optimal load flow node electricity price computational methods of the meter and electric automobile power adjustable control domain described in claim 1, it is special
Levy and be：The urgent degree Γ of EV chargings in the step S4ch,nFor
In formula：tin,nAnd tout,nVehicle n turn-on time and specified off-network time is represented respectively；ηchRepresent EV charge efficiencies；
tch,nRepresent vehicle n charging interval；SOCin,nAnd SOCex,nWhen representing the vehicle n initial state-of-charge of battery and off-network respectively
State-of-charge desired value；Pch,nRepresent vehicle n specified charge power；Bc,nRepresent vehicle n battery capacity；
When specified charge power is definite value, the bigger reflection of the ratio more priority scheduling of charging interval and berthing time；Work as charging
When urgently spending identical, dispatching sequence is determined with the urgent degree of next scheduling slot charging, more big more priority scheduling is urgently spent in charging
The electric discharge abundant intensity of the vehicle n is Γdis,nFor
In formula：ηdisRepresent EV discharging efficiencies；tdis,nRepresent vehicle n discharge time；Pdis,nRepresent vehicle n nominal discharge work(
Rate；
When nominal discharge power is definite value, the ratio of discharge time and berthing time is more big more priority scheduling；It is abundant when discharging
When spending identical, dispatching sequence is determined with next scheduling slot electric discharge abundant intensity, electric discharge abundant intensity is more big more priority scheduling.
6. the optimal load flow node electricity price computational methods of the meter and electric automobile power adjustable control domain described in claim 1, it is special
Levy and be：The object function of multi-period optimal load flow is in the step S5
In formula：CTRepresent power network total operating cost；T represents scheduling slot number；CGEN,tRepresent the unit generation of all unit periods t
Cost；Pgen,iRepresent generator i active power output；H represents generator number；ai、bi、ciRepresent that the consumption of generator is special respectively
Property parameter；CS,tRepresent the start-up and shut-down costs of all unit periods t；Sgen,iRepresent Unit Commitment expense；ui(t) machine of period t is represented
Group start and stop state, 1 represents normal operation, and 0 represents to shut down；
Each node voltage is constrained to
Vmin≤Vi(t)≤Vmax,
In formula：Vmin、VmaxThe bound of node voltage amplitude is represented respectively；
Each node power flow equation is constrained to
PL,i(t)=PEV,i(t)+PLA,i(t)
In formula：PG,i(t)、QG,i(t) represent that the active and idle of period t node i generating set is exerted oneself respectively；PL,i(t)、QL,i
(t) represent that the active and idle of period t node i load is exerted oneself respectively, by EV total loads PEV,i(t) with the total interruptible loads of LA
PLA,i(t) it is added to power flow equation；Vi(t)、Vj(t) period t node i, j voltage magnitude are represented respectively；Gij、BijRepresent respectively
Branch road i-j conductance and susceptance in system admittance matrix；θij(t) period t node voltage phase angle difference is represented；
The EV power adjustables control region constraint is
PEV,min(t)≤PEV,i(t)≤PEV,max(t)
In formula：PEV,max(t)、PEV,min(t) time series t power adjustable controls domain bound in step S4 is represented respectively；
The LA demand responses are interrupted and are constrained to
PLA,min≤PLA,i(t)≤PLA,max
In formula：PLA,min、PLA,maxThe bound of the total interruptible load power of LA is represented respectively；PLA,k(t) k-th of LA of period t is represented
Can interrupt power；
The EV networkings power constraint is
|PEV,i(t)+PLA,i(t) | Δ t=| QE,i,t|≤Pgrid,i(t)Δt
-Pdis,nηdisstatedis,n(t)-ηselfSOCn(t))×Bc,nΔt)
SOCmin≤SOCn(t)≤SOCmax
statech,n(t)statedis,n(t)=0
In formula：PEV,iAnd P (t)LA,i(t) power that the EV and LA of period t node i response change is represented respectively；Pgrid,i(t) table
Show that power network period t node is rated power is limited；Pch,nAnd Pdis,nVehicle n specified charge power and nominal discharge is represented respectively
Power；Bc,nRepresent vehicle n battery capacity；QE,i,tRepresent the electric energy changed after the optimization of period t node i；I represents interstitial content；
Ni(t) the EV quantity of period t node i is represented；N represents EV total quantitys；Δ t represents unit interval, takes 1h；statech,n
(t)、statedis,n(t) the discharge and recharge 0-1 state variables of period t vehicle n are represented respectively, and 1 represents discharge and recharge, and 0 represents neither to fill
Electricity does not also discharge；SOCminAnd SOCmaxEV battery charge states minimum value and maximum are represented respectively；ηchAnd ηdisEV is represented respectively
Efficiency for charge-discharge；ηselfRepresent EV self discharge coefficients；
The EV discharge and recharges time-constrain is
tin,n+tch,n≤tout,n
In formula：And tdis,nDischarge time and amendment discharge time are represented respectively；tin,nRepresent vehicle n charge completion times；
tout,nRepresent that vehicle n confirms to participate in after electric discharge, the discharge time during electric energy of part can only be discharged.
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