CN110212584A - A kind of dispatching method of wind-powered electricity generation and extensive electric car coordination optimization - Google Patents
A kind of dispatching method of wind-powered electricity generation and extensive electric car coordination optimization Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 45
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
- B60L53/52—Wind-driven generators
-
- 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
- B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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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- H02J3/386—
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- 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
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The present invention relates to the dispatching methods of a kind of wind-powered electricity generation and extensive electric car coordination optimization, comprising the following steps: 1) constructs wind-powered electricity generation and extensive electric car coordinates and optimizes scheduling model；2) in the Optimized model of upper layer, foundation makes the smallest Optimized model of conventional electric generators operating cost；3) it will consider that the probabilistic robust Unit Combination primal problem of wind-powered electricity generation is decomposed into robust Unit Combination primal problem and wind-powered electricity generation uncertainty verification subproblem；4) in lower layer's Optimized model, the charge and discharge plan of total electric car that administer the operation plan of the electric car polymerization quotient at each moment should with it is equal and provides electric car charge and discharge constraint；5) electric car constraint subproblem receives the optimization solution for considering that the probabilistic robust Unit Combination primal problem transmitting of wind-powered electricity generation comes；6) it carries out solving the scheduling scheme for obtaining wind-powered electricity generation, electric car, thermoelectricity.Compared with prior art, the present invention has many advantages, such as that fast and reliable, strong applicability, effect of optimization are good.
Description
Technical field
The present invention relates to wind power plant and electric car dispatching technique field, more particularly, to a kind of wind-powered electricity generation with it is extensive electronic
The dispatching method of automobile coordination optimization.
Background technique
Wind-power electricity generation is with fastest developing speed, most promising renewable energy, is sent out in terms of alleviating energy crisis, energy-saving and emission-reduction
Important function is waved.However, due to its randomness and intermittence, large-scale wind power it is grid-connected brought to Operation of Electric Systems it is all
More challenges, seriously affect the safe and stable operation of electric system.In recent years, popularizing with electric car, it is extensive to cope with
The influence of wind-electricity integration bring provides new thinking, needs the coordinated scheduling further investigation to wind-powered electricity generation and electric car.
Currently, in windmill coordinated scheduling for how to handle the research of wind power output in terms of, generally use probabilistic model
The uncertainty of wind-powered electricity generation is described, but it is in practical applications there is also some problems, for example computationally intensive and reliability can not
The problems such as guarantee.And robust optimization describes uncertain parameters using uncertain collection, does not require the specific probability for obtaining variable
Distribution, preferably solves the problems, such as this, is applied to wind-powered electricity generation optimization in recent years.But its less application in windmill coordinated scheduling,
It needs further to study.
Participated in windmill coordinated scheduling in electric car, it is contemplated that a large amount of electric car participates in scheduling, centralized control by
Then the unidirectionally controlled charge requirement for being unable to satisfy user, it is difficult to feasible charging scheme is provided, and decentralised control is due to being certainly
The advantages of controlling and be easy to lack coordination, therefore layered framework is more feasible, combining centralized control and decentralised control.However, should
The key of method is how to coordinate different levels.Therefore, set forth herein two layers of coordinated schedulings based on approximate Benders algorithm
PROBLEM DECOMPOSITION is two levels by model: upper layer, which introduces electric car and polymerize quotient, handles the restricted problem with thermoelectricity and wind-powered electricity generation,
Lower layer is extensive electric car optimization problem, and coordinates the two levels with penalty function.
Aspect is optimized for extensive electric car, document is proposed to be made to bear using Nash Equilibrium principle control electric car
The fluctuation of lotus is minimum.It is excellent to solve electric car charge and discharge for tracking given load curve using convextiry analysis method in document
Change, but this method be used for extensive electric car when, will affect its applicability.It is found that these methods only account for electric car
Between coordination, have ignored the coordination of electric car Yu fired power generating unit and wind-powered electricity generation；In addition it is not suitable for when electric car is large number of.
Therefore, it is badly in need of the dispatching method of a kind of wind-powered electricity generation and extensive electric car coordination optimization, can either fully considers wind
The steric crowding and time smoothing effect of electric field, but it can be considered that electric car accesses the charge and discharge electro ultrafiltration of power grid.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of wind-powered electricity generation and on a large scale
The dispatching method of electric car coordination optimization.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of dispatching method of wind-powered electricity generation and extensive electric car coordination optimization, comprising the following steps:
1) building wind-powered electricity generation and extensive electric car coordinate and optimize scheduling model, which includes that upper layer Optimized model-is examined
Consider the probabilistic robust Unit Combination primal problem of wind-powered electricity generation and lower layer's Optimized model-electric car constrains subproblem；
2) in the Optimized model of upper layer, foundation makes the smallest Optimized model of conventional electric generators operating cost；
3) it will consider that the probabilistic robust Unit Combination primal problem of wind-powered electricity generation is decomposed into robust machine using robust optimization algorithm
Group combination primal problem and wind-powered electricity generation uncertainty verify subproblem；
4) in lower layer's Optimized model, the operation plan for making the electric car at each moment polymerize quotient should administer total with it
Electric car charge and discharge plan it is equal and provide electric car charge and discharge constraint；
5) electric car constraint subproblem, which receives, considers what the probabilistic robust Unit Combination primal problem transmitting of wind-powered electricity generation came
Optimization solution, and reduce solution scale；
6) it is solved using approximate Benders algorithm, obtains the scheduling scheme of wind-powered electricity generation, electric car, thermoelectricity.
In the step 2), make the objective function of the smallest Optimized model of conventional electric generators operating cost are as follows:
Wherein, F is the sum of cost of electricity-generating and abandonment punishment cost, suiFor the starting expense of system unit, sdiFor System Computer
The idleness expense of group, zi,tAnd ui,tIt is 0/1 variable of first stage, to indicate that the start and stop state of unit i changes, if unit
I becomes starting in period t from shutting down, then zi,t=1, otherwise zi,t=0, if unit i becomes shutting down in period t from running, ui,t
=1, otherwise zi,t=0, fi,t(pi,t) be unit operating cost quadratic function, T be scheduling slot sum, N be unit sum；
Constraint condition includes:
A) minimum available machine time constraint:
Wherein,For the minimum available machine time of unit i, yi,t-1For unit i under benchmark state the t-1 period operation shape
State variable, yi,tOperating status variable for unit i in the t period, yi,h/ operation in idle time is being run for unit i
State variable, h are to have run/idle time；
B) minimum downtime constraint:
Wherein,For the minimum downtime of unit i；
C) the logical constraint of set state and state conversion:
-yi,t-1+yi,t-zi,t≤0
yi,t-1-yi,t-ui,t≤0
zi,t,ui,t,yi,t∈{0,1}；
D) system reserve constrains:
Wherein, pb,tFor the load at t period node b, r is spinning reserve rate, pi,maxFor the maximum output value of unit i, ND
For node total number, xk,tIt is divided into xk,t> 0 and xk,tIt is whole to respectively indicate the electric car that polymerization quotient k is administered for 0 two states of <
Charge characteristic and flash-over characteristic are showed, NK is polymerization quotient's sum；
E) unit output constrains:
yi,tpi,min≤pi,t≤yi,tpi,max；
Wherein, pi,minThe respectively minimum load value of unit i；
F) power-balance constraint:
Wherein, pi,tPower output for unit i in the t period,It is j-th of wind power plant under benchmark state in the optimal of t moment
Scheduling power output, NW are wind power plant sum, and subscript w indicates wind-powered electricity generation；
G) unit ramp loss:
pi,t-pi,t-1≤yi,tRUi+(1-yi,t-1)pi,max+pi,min(yi,t-yi,t-1)
pi,t-1-pi,t≤yi,tRDi+(1-yi,t-1)pi,max+pi,min(yi,t-1-yi,t)
Wherein, RUiAnd RDiThe upper and lower climbing rate of respectively unit i；
H) transmission line of electricity capacity-constrained:
Wherein,Respectively unit i, wind power plant j, node b, polymerization quotient k to route l
Power transfer factor,For the maximum transmission capacity of route；
I) the units limits of wind power plant:
Wherein,For j-th of wind power plant t moment wind power output power,It is j-th of wind power plant in t moment
Optimal scheduling power output, W are the uncertain collection of wind-powered electricity generation room and time constraint；
J) wind power output robust Model constrains:
Description wind power output is optimized using robust, comprehensively considers the steric crowding and time smoothing effect structure of wind power plant
The uncertain collection of wind-powered electricity generation robust is built, specific as follows:
Wherein, wj,tExpectation for j-th of wind power plant in t moment is contributed, Δ wj,tFor wind power output power and desired value
Maximum deviation amount,For 0/1 variable,For the space constraint parameter of wind power output power,For wind-powered electricity generation output work
The time-constrain parameter of rate；
K) schedule constraints of the electric car polymerization quotient in day part:
xk,t,min≤xk,t≤xk,t,max
Wherein, xk,t,max, xK, t, minRespectively polymerization quotient k period t scheduling maximum value and minimum value, For maximum charge/discharge power of the v electric car in period t for polymerizeing quotient k, KV is electric car quantity.
In the step 3), the expression formula of the robust Unit Combination primal problem are as follows:
In the step 3), wind-powered electricity generation uncertainty verification subproblem is expressed as max-min problem, expression formula are as follows:
Constraint condition are as follows:
υ1,lt≥0
υ2t≥0
υ3t≥0
Wherein, υ1,lt,υ2,t,υ3,tThe slack variable respectively introduced,For the going out in the t period of unit i under benchmark state
Power,Operating status variable for unit i under benchmark state in the t period,It is obtained to solve robust Unit Combination primal problem
Operating status variable of the unit i in the t period,For consider wind-powered electricity generation uncertainty optimization wind power output,To consider
The unit output of wind-powered electricity generation uncertainty optimization, subscript b indicate normal condition, and subscript u indicates to consider the uncertain state of wind-powered electricity generation.
In the step 4), make each moment in lower layer's Optimized model electric car polymerize quotient operation plan and its
The charge and discharge plan of total electric car of administration is equal, then has:
Wherein, pk,v,tFor belong to polymerization quotient k the v electric car period t actual schedule as a result,It is poly-
Charge power of the v electric car in period t of quotient k is closed,For the v electric car putting in period t for polymerizeing quotient k
Electrical power.
In the step 4), electric car charge and discharge constraint includes:
L) schedule constraints of the electric car polymerization quotient in day part:
Wherein, Sk,v(t+1) and Sk,v(t) electric energy for the v electric car of polymerization quotient k in t+1 period and t period,
ηCAnd ηDThe respectively charging and discharging efficiency of electric car, Sconsk,v(t) it polymerize the v electric automobile during traveling of quotient k for the t period
Energy consumption, Δ t are window duration；
M) schedule constraints of the electric car polymerization quotient in day part:
Smin≤Sk,v(t+1)≤Smax
Wherein, SmaxAnd SminThe respectively bound of accumulator of electric car electric energy；
N) schedule constraints of the electric car polymerization quotient in day part:
Wherein, Xk,v,tFor the charged state of the v electric car t period of polymerization quotient k, value is that 1 representative accesses power grid
And it is in charged state, value is that 0 representative is in non-charged state, Yk,v,tFor the v electric car t period for polymerizeing quotient k
Discharge condition, value is 1 representative access power grid and is in discharge condition, and value represents for 0 in non-discharged state；
O) electric car charge and discharge do not constrain simultaneously:
Xk,v,t+Yk,v,t≤1。
In the step 5), electric car constrains the expression formula of subproblem are as follows:
In the step 5), since electric car quantity size is big, electric car constrains subproblem direct solution difficulty
Height solves electric car constraint subproblem with Auxiliary Problem Principle, comprising the following steps:
51) Lagrangian constructed fuction is followed:
Wherein, ηkFor the parameter of introducing, and), λk,tFor dual variable,K-th when for nth iteration
Agential the v electric car charge-discharge electric power,To solve the operation plan that obtained electric car polymerize quotient；
52) single motor automobile charge-discharge electric power is solved:
521) in nth iteration, the charge-discharge electric power of each electric car isEach electric car is initialized to fill
Discharge power
522) a new charge control plan is calculated to each electric carIt makes it through following formula and meets electricity
Electrical automobile charge and discharge constraint:
523) iteration gap is calculated, if it is less than given threshold value, stops iteration convergence, otherwise enables n=n+1, and
Return step 522), the calculating formula of iteration gap e are as follows:
The step 6) specifically includes the following steps:
61) robust Unit Combination primal problem is solved using Benders algorithm, it is poly- obtains wind-powered electricity generation, thermoelectricity and electric car
The scheduling scheme of quotient is closed, if it is examined by the uncertain of wind-powered electricity generation, passes to electric car constraint subproblem, it is otherwise, raw
It is cut at corresponding Benders and is added to robust Unit Combination primal problem；
62) electric car polymerization quotient receives the operation plan formulated, and transmits charge and discharge control meter to electric car car owner
It draws；
63) electric car is solved with Auxiliary Problem Principle constrain subproblem；When iteration gap is less than given threshold value,
Show iteration convergence, obtains the feasible charge and discharge plan of electric car；
64) the feasible charge and discharge scheduled transfer of electric car is tested to polymerization quotient, if result restrains, is obtained most
It is excellent as a result, otherwise, the corresponding approximation Benders of generation being cut and is added to robust Unit Combination primal problem.
Compared with prior art, the invention has the following advantages that
One, fast and reliable: method for solving disclosed by the invention can obtain the Optimized Operation of decision variable fast and reliablely
As a result, effectively improving computational efficiency.
Two, increase the applicability of model: considering on the basis of considering the Optimized Operation a few days ago of wind power plant space time correlation constraint
Electric car charge and discharge can guarantee to reduce the operating cost of conventional power unit and reduce abandonment amount, increase the applicability of model.
Three, consider the steric crowding and time smoothing effect of wind-powered electricity generation: consider wind-powered electricity generation time and space constraint not really
Fixed collection can more meticulously describe the uncertainty of wind-powered electricity generation, and then obtain that effect of optimization is more preferable, more meets actual motion feelings
The scheduling scheme of condition.
Detailed description of the invention
Fig. 1 is the IEEE39 node system figure that wind power plant is added.
Fig. 2 is the abandonment amount comparison diagram under different operational modes.
Fig. 3 is change curve schematic diagram of the error with the number of iterations.
Fig. 4 is schematic diagram of the totle drilling cost with the variation tendency of electric car varied number.
Fig. 5 is flow chart of the method for the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in figure 5, the invention proposes the dispatching methods of a kind of wind-powered electricity generation and extensive electric car coordination optimization, not only
It can consider the charge and discharge constraint of each electric car, and the operation of electric car Yu fired power generating unit and wind-powered electricity generation can be coordinated, drop
Low solution scale, specifically includes the following steps:
The present invention has initially set up electric car and wind-powered electricity generation/thermoelectricity cooperative scheduling mathematical model, and the model is with tradition hair
The minimum target of the operating cost of motor, while meeting a variety of fired power generating units, Wind turbines and the agential constraint of electric car.
Secondly, in underlying model, so that the operation plan of the electric car polymerization quotient at each moment should be administered with it
Total electric car charge and discharge plan it is equal.
Then, consider that wind-powered electricity generation-electric car cooperative scheduling optimization model is asked for extensive, multiple constraint, nonlinear optimization
Topic.For this purpose, wind-powered electricity generation-electric car cooperative scheduling optimization model decomposition is considered wind-powered electricity generation first with approximate Benders algorithm
Probabilistic robust Unit Combination primal problem (upper layer Optimized model) and electric car constraint subproblem (lower layer's Optimized model),
Recycle Benders algorithm that will consider that the probabilistic robust Unit Combination primal problem of wind-powered electricity generation is decomposed into robust Unit Combination examination in chief
Topic and wind-powered electricity generation uncertainty verify subproblem, and electric car constraint subproblem receives the optimization solution passed over, and utilizes auxiliary
Problem principle reduces its solution scale.
Embodiment 1:
Lower mask body combines the IEEE39 node system comprising 1 wind power plant to carry out detailed analysis, as shown in Figure 1.
In order to analyze the result under different charge modes, it is divided into Three models and is analyzed.
Mode 1 is unordered charge operation mode, in that case, without any control program, once electric car connects
Enter, electric car will charge；
Mode 2 is pure charge control operational mode, that is, controls the charging time of electric car, do not consider the anti-of electric car
To electric discharge；
Mode 3 is V2G operational mode, i.e., electric car can also discharge from grid charging to power grid.
In order to study the windmill coordinated scheduling system under 3 kinds of charge modes operating cost and abandonment amount variation, it is excellent
Change shown in result table 1 and Fig. 2.As can be seen from Table 1, compared to mode 1, mode 2 can reduce the fuel cost of system about
1.9%, and totle drilling cost about 2.1% is reduced, mode 3 can then advanced optimize system operation using V2G, this is because carrying out
There is greater flexibility than conventional power unit after V2G technology.In addition, as shown in Figure 2, the abandonment amount of mode 1 is significantly greater than mode 2
With mode 3, in load valley period (1~7h, 16~18h, 22~for 24 hours) mode 2 since pure charge control is than 3 abandonment amount of mode
It is few, and wind-powered electricity generation can be all dissolved in both load peak periods (8~15h, 19~21h).Although 3 abandonment amount of mode compares mode
2 is bigger, but V2G facilitates conventional power unit efficient operation, reduces its operating cost.
Optimum results comparison under the different operational modes of table 1
| Production target | Mode 1 | Mode 2 | Mode 3 |
| Fuel cost ($) | 256467 | 251505 | 248332 |
| Start-up and shut-down costs ($) | 2900 | 2680 | 2400 |
| Totle drilling cost ($) | 259376 | 254185 | 250732 |
For the validity for proving proposed wind-powered electricity generation robust Model herein, it is subjected to calculating knot with traditional randomized optimization process
Fruit comparative analysis, comparing result are as shown in table 2.
The result of 22 kinds of models of table compares
As known from Table 2, with the reduction of the conservative degree factor-alpha of robust, Robust Optimization Model operating cost is reduced, abandonment amount
It is reducing, this is because the conservative degree factor-alpha of robust is smaller, the uncertainty of wind power output is smaller.Compared with random optimization, robust
Robust Optimization Model operating cost and abandonment amount are bigger than normal when conservative degree factor-alpha=0.9,0.7, show that Robust Optimization Model is more random
Optimization is than more conservative, and Robust Optimization Model operating cost and abandonment amount are relatively low when robust conservative degree factor-alpha=0.5,0.3,
Show that Robust Optimization Model conservative has been weaker than random optimization.
In order to analyze the validity of approximate Benders algorithm, it is compared with traditional Benders decomposition method.By
Table 3 is it is found that the cost of electricity-generating obtained using approximation Benders algorithm is omited than the cost of electricity-generating that traditional Benders algorithm obtains
Greatly, but the number of iterations of approximation Benders algorithm is significantly less than traditional Benders algorithm.In addition, further two kinds of calculations of comparison
The convergence curve of method, as shown in figure 3, the convergence process of traditional Benders decomposition algorithm relatively slowly and will appear some small
Oscillation, and the iterative convergent process of approximation Benders algorithm is more reliable rapidly.Therefore, approximate Benders algorithm not only may be used
To realize layered framework, the convergence rate of model may also speed up.
The calculated performance of 32 kinds of algorithms of table compares
| Calculation method | Totle drilling cost/$ | The number of iterations |
| Benders algorithm | 250629 | 31 |
| Approximate Benders algorithm | 250732 | 13 |
In order to analyze different scales electric car quantity to the influence of wind-vehicle coordinative dispatching model scheduling decision, calculate
As a result as shown in Figure 4.
As shown in Figure 4, as the grid-connected quantity of electric car is more and more, the corresponding totle drilling cost that runs also constantly subtracts therewith
It is small.It is found that electric car, which participates in scheduling, can contribute to conventional power unit efficient operation really, its operating cost is reduced.However, working as
After electric car quantity reaches 60000, the corresponding totle drilling cost that runs increases instead, because extensive electric car charging needs
That asks is continuously increased, and system needs to sacrifice certain fuel cost thus.
In order to verify the validity of the electric car parallel computation based on Auxiliary Problem Principle, it is asked with without using auxiliary
The optimum results that topic principle obtains compare, and calculated result comparison is as shown in table 4.
Whether table 4 uses the comparison of Auxiliary Problem Principle
As shown in Table 4, it is significantly less than using the Auxiliary Problem Principle calculating time without using Auxiliary Problem Principle, can be at least contracted
Short an order of magnitude, and as the increase of electric car quantity, Auxiliary Problem Principle optimization time change are not obvious, this is
Because Auxiliary Problem Principle is used to decouple extensive electric car Solve problems for single electric car Solve problems, by simultaneously
Row, which calculates, reduces computation burden, substantially increases computational efficiency.Therefore, Auxiliary Problem Principle is for solving extensive electronic vapour
Vehicle has apparent advantage.
Claims (9)
1. the dispatching method of a kind of wind-powered electricity generation and extensive electric car coordination optimization, which comprises the following steps:
1) building wind-powered electricity generation and extensive electric car coordinate and optimize scheduling model, which includes upper layer Optimized model-consideration wind
The probabilistic robust Unit Combination primal problem of electricity and lower layer's Optimized model-electric car constrain subproblem；
2) in the Optimized model of upper layer, foundation makes the smallest Optimized model of conventional electric generators operating cost；
3) it will consider that the probabilistic robust Unit Combination primal problem of wind-powered electricity generation is decomposed into robust unit group using robust optimization algorithm
It closes primal problem and wind-powered electricity generation uncertainty verifies subproblem；
4) in lower layer's Optimized model, the electric car at each moment is made to polymerize total electricity that the operation plan of quotient should be administered with it
The charge and discharge plan of electrical automobile is equal and provides electric car charge and discharge constraint；
5) electric car constraint subproblem receives the optimization for considering that the probabilistic robust Unit Combination primal problem transmitting of wind-powered electricity generation comes
Solution, and reduce solution scale；
6) it is solved using approximate Benders algorithm, obtains the scheduling scheme of wind-powered electricity generation, electric car, thermoelectricity.
2. the dispatching method of a kind of wind-powered electricity generation according to claim 1 and extensive electric car coordination optimization, feature exist
In making the objective function of the smallest Optimized model of conventional electric generators operating cost in the step 2) are as follows:
Wherein, F is the sum of cost of electricity-generating and abandonment punishment cost, suiFor the starting expense of system unit, sdiFor system unit
Idleness expense, zi,tAnd ui,tIt is 0/1 variable of first stage, to indicate that the start and stop state of unit i changes, if unit i exists
Period t becomes starting from shutting down, then zi,t=1, otherwise zi,t=0, if unit i becomes shutting down in period t from running, ui,t=
1, otherwise zi,t=0, fi,t(pi,t) be unit operating cost quadratic function, T be scheduling slot sum, N be unit sum；
Constraint condition includes:
A) minimum available machine time constraint:
Wherein,For the minimum available machine time of unit i, yi,t-1Become for unit i under benchmark state in the operating status of t-1 period
Amount, yi,tOperating status variable for unit i in the t period, yi,h/ operating status in idle time is being run for unit i
Variable, h are to have run/idle time；
B) minimum downtime constraint:
Wherein,For the minimum downtime of unit i；
C) the logical constraint of set state and state conversion:
-yi,t-1+yi,t-zi,t≤0
yi,t-1-yi,t-ui,t≤0
zi,t,ui,t,yi,t∈{0,1}；
D) system reserve constrains:
Wherein, pb,tFor the load at t period node b, r is spinning reserve rate, pi,maxFor the maximum output value of unit i, ND is section
Point sum, xk,tIt is divided into xk,t> 0 and xk,t0 two states of < respectively indicate the electric car that polymerization quotient k is administered and integrally present
Charge characteristic and flash-over characteristic out, NK are polymerization quotient's sum；
E) unit output constrains:
yi,tpi,min≤pi,t≤yi,tpi,max；
Wherein, pi,minThe respectively minimum load value of unit i；
F) power-balance constraint:
Wherein, pi,tPower output for unit i in the t period,For j-th of wind power plant under benchmark state t moment optimal scheduling
Power output, NW are wind power plant sum, and subscript w indicates wind-powered electricity generation；
G) unit ramp loss:
pi,t-pi,t-1≤yi,tRUi+(1-yi,t-1)pi,max+pi,min(yi,t-yi,t-1)
pi,t-1-pi,t≤yi,tRDi+(1-yi,t-1)pi,max+pi,min(yi,t-1-yi,t)
Wherein, RUiAnd RDiThe upper and lower climbing rate of respectively unit i；
H) transmission line of electricity capacity-constrained:
Wherein,Respectively unit i, wind power plant j, node b, the power for polymerizeing quotient k to route l
Transfer factor,For the maximum transmission capacity of route；
I) the units limits of wind power plant:
Wherein,For j-th of wind power plant t moment wind power output power,It is j-th of wind power plant in the optimal of t moment
Scheduling power output, W are the uncertain collection of wind-powered electricity generation room and time constraint；
J) wind power output robust Model constrains:
Description wind power output is optimized using robust, comprehensively considers the steric crowding and time smoothing effect building wind of wind power plant
The uncertain collection of electric robust, specific as follows:
Wherein, wj,tExpectation for j-th of wind power plant in t moment is contributed, Δ wj,tFor the maximum of wind power output power and desired value
Departure,For 0/1 variable,For the space constraint parameter of wind power output power,For wind power output power
Time-constrain parameter；
K) schedule constraints of the electric car polymerization quotient in day part:
xk,t,min≤xk,t≤xk,t,max
Wherein, xk,t,max, xK, t, minRespectively polymerization quotient k period t scheduling maximum value and minimum value, For
It polymerize maximum charge/discharge power of the v electric car in period t of quotient k, KV is electric car quantity.
3. the dispatching method of a kind of wind-powered electricity generation according to claim 2 and extensive electric car coordination optimization, feature exist
In, in the step 3), the expression formula of the robust Unit Combination primal problem are as follows:
4. the dispatching method of a kind of wind-powered electricity generation according to claim 2 and extensive electric car coordination optimization, feature exist
In in the step 3), wind-powered electricity generation uncertainty verification subproblem is expressed as max-min problem, expression formula are as follows:
Constraint condition are as follows:
υ1,lt≥0
υ2t≥0
υ3t≥0
Wherein, υ1,lt,υ2,t,υ3,tThe slack variable respectively introduced,For power output of the unit i in the t period under benchmark state,Operating status variable for unit i under benchmark state in the t period,Robust Unit Combination primal problem is solved to obtain
Operating status variable of the unit i in the t period,For consider wind-powered electricity generation uncertainty optimization wind power output,To consider wind
The unit output of electric uncertainty optimization, subscript b indicate normal condition, and subscript u indicates to consider the uncertain state of wind-powered electricity generation.
5. the dispatching method of a kind of wind-powered electricity generation according to claim 2 and extensive electric car coordination optimization, feature exist
In in the step 4), the operation plan for making the electric car at each moment in lower layer's Optimized model polymerize quotient is administered with it
Total electric car charge and discharge plan it is equal, then have:
Wherein, pk,v,tFor belong to polymerization quotient k the v electric car period t actual schedule as a result,To polymerize quotient k
The v electric car period t charge power,For electric discharge function of the v electric car in period t for polymerizeing quotient k
Rate.
6. the dispatching method of a kind of wind-powered electricity generation according to claim 5 and extensive electric car coordination optimization, feature exist
In in the step 4), electric car charge and discharge constraint includes:
L) schedule constraints of the electric car polymerization quotient in day part:
Wherein, Sk,v(t+1) and Sk,v(t) electric energy for the v electric car of polymerization quotient k in t+1 period and t period, ηCAnd ηD
The respectively charging and discharging efficiency of electric car, Sconsk,v(t) it polymerize the v electric automobile during traveling energy consumption of quotient k for the t period,
Δ t is window duration；
M) schedule constraints of the electric car polymerization quotient in day part:
Smin≤Sk,v(t+1)≤Smax
Wherein, SmaxAnd SminThe respectively bound of accumulator of electric car electric energy；
N) schedule constraints of the electric car polymerization quotient in day part:
Wherein, Xk,v,tFor the charged state of the v electric car t period of polymerization quotient k, value is 1 representative access power grid and locates
In charged state, value is that 0 representative is in non-charged state, Yk,v,tFor the electric discharge of the v electric car t period of polymerization quotient k
State, value is 1 representative access power grid and is in discharge condition, and value represents for 0 in non-discharged state；
O) electric car charge and discharge do not constrain simultaneously:
Xk,v,t+Yk,v,t≤1。
7. the dispatching method of a kind of wind-powered electricity generation according to claim 5 and extensive electric car coordination optimization, feature exist
In in the step 5), electric car constrains the expression formula of subproblem are as follows:
8. the dispatching method of a kind of wind-powered electricity generation according to claim 7 and extensive electric car coordination optimization, feature exist
In in the step 5), since electric car quantity size is big, electric car constrains subproblem direct solution difficulty height, fortune
Electric car constraint subproblem is solved with Auxiliary Problem Principle, comprising the following steps:
51) Lagrangian constructed fuction is followed:
Wherein, ηkFor the parameter of introducing, andλk,tFor dual variable,K-th of agency when for nth iteration
The v electric car charge-discharge electric power of quotient,To solve the operation plan that obtained electric car polymerize quotient；
52) single motor automobile charge-discharge electric power is solved:
521) in nth iteration, the charge-discharge electric power of each electric car isInitialize each electric car charge and discharge
Power
522) a new charge control plan is calculated to each electric carIt makes it through following formula and meets electronic vapour
Vehicle charge and discharge constraint:
523) iteration gap is calculated, if it is less than given threshold value, stops iteration convergence, otherwise enables n=n+1, and return
Step 522), the calculating formula of iteration gap e are as follows:
9. the dispatching method of a kind of wind-powered electricity generation according to claim 1 and extensive electric car coordination optimization, feature exist
In, the step 6) specifically includes the following steps:
61) robust Unit Combination primal problem is solved using Benders algorithm, obtains wind-powered electricity generation, thermoelectricity and electric car polymerization quotient
Scheduling scheme pass to electric car constraint subproblem if it is examined by the uncertain of wind-powered electricity generation and otherwise generate phase
The Benders answered, which is cut, is added to robust Unit Combination primal problem；
62) electric car polymerization quotient receives the operation plan formulated, and transmits charge and discharge control plan to electric car car owner；
63) electric car is solved with Auxiliary Problem Principle constrain subproblem；When iteration gap is less than given threshold value, show
Iteration convergence obtains the feasible charge and discharge plan of electric car；
64) the feasible charge and discharge scheduled transfer of electric car is tested to polymerization quotient, if result restrains, obtains optimal knot
Otherwise the corresponding approximation Benders of generation is cut and is added to robust Unit Combination primal problem by fruit.
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