CN104734200B - A kind of active distribution network Optimization Scheduling based on virtual generating - Google Patents
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Abstract
The invention discloses a kind of active distribution network Optimization Scheduling based on virtual generating, comprise the steps：(1) set up Optimized Operation object function；(2) obtain Optimized Operation bound for objective function；Constraints includes：System loading Constraints of Equilibrium, system spinning reserve constrains, and conventional power unit goes out power restriction with the technology of Demand-side resource, conventional power unit Climing constant, conventional power unit minimum run time and minimum idle time constraint, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction constrains；(3) according to Optimized Operation object function and constraints and by the discrete particle cluster algorithm based on heuristic rule come the start and stop state of all units of the determination of period one by one；(4) after completing the calculating of each period start and stop state of all units, on the basis of known start and stop state, economic allocation is carried out on the basis of according to constraints to each unit；Thus realizing active distribution network Optimized Operation.
Description
Technical field
The invention belongs to active distribution network Optimum Scheduling Technology field, more particularly, to a kind of based on virtual generating
Active distribution network Optimization Scheduling.
Background technology
The extensive access of distributed power source, flexible load and novel energy-storing system, and modern power electronic device exists
Extensive application in electrical network, makes power distribution network all there occurs a series of changes in system structure and the method for operation.Active distribution network
As the distribution network system of Comprehensive Control distributed energy (DG, flexible load and energy storage), in whole power distribution network aspect to distribution
The formula energy is managed, and is not concerned only with the Autonomous Control of local, and the optimization simultaneously also paying close attention to the whole power distribution network overall situation is coordinated, for solution
Certainly the extensive concentration of distributed energy accesses and provides new approaches.The cooperation of active distribution network source net lotus three, how
The effectively ground integrated schedulable resource coordinating Generation Side, grid side and load side, thus obtain safety, economy and environmental benefit
Optimum, and realize that power distribution network is safe and reliable, high-quality and efficient operation, be the problem of current urgent need to resolve.
Mostly the research of active distribution network Optimized Operation at this stage, be for distributed power source, electric automobile and energy-storage system
Scheduling research, or the research individually for Demand Side Response strategy.Have studied individually and disappeared based on the wind-powered electricity generation of Demand Side Response
Receive model and establish mixed-integer programming model containing wind power system generation schedule a few days ago, and using ILOG/CPLEX business
Software is solved, lack to conventional power unit, distributed power source (mainly comprising wind-powered electricity generation, photovoltaic generation), Demand Side Response and
The research of the synthesis optimizing and scheduling method of user power utilization satisfaction.The present invention proposes the virtual generating of integrated use and active distribution network
Technology, the demand response resource of load side is equivalent to virtual generation assets, and participates in power balance regulation as virtual robot arm.
Prediction curve of exerting oneself according to wind-powered electricity generation and photovoltaic generation simultaneously, in conjunction with conventional power unit, establishes comprehensive distributed power source, Demand-side
Resource and the Model for Multi-Objective Optimization of conventional power unit, scheduling problem is converted into Optimization of Unit Commitment By Improved, and using double after improving
Heavy particle group's algorithm is solved.Example shows on the basis of meeting higher user power utilization mode satisfaction, this scheduling
Method can effectively reduce power distribution network generator operation cost and environmental pollution, and can effectively reduce load peak-valley difference.
Content of the invention
Disadvantages described above for prior art or Improvement requirement, the invention provides a kind of joined based on the active of virtual generating
Optimal dispatch method, its object is to, with virtual generating thought, the demand response resource of load side is equivalent to virtual
Electric resources, participate in power balance as virtual robot arm and adjust, and solved using improving dual particle swarm optimization, realize active distribution network
The distributing rationally of resource, thus solve to consider in active distribution network simultaneously conventional power unit, distributed power source, Demand Side Response and
During user power utilization satisfaction, optimize the technical problem that calculating speed leads to be difficult to quickly be scheduling controlling more slowly.
The invention provides a kind of active distribution network Optimization Scheduling based on virtual generating, comprise the steps：
(1) set up Optimized Operation object function；First object function is set up with the minimum target of system generator operation cost
F1, the second object function F is set up with the minimum target of pollutant emission total yield number2；
(2) obtain described Optimized Operation bound for objective function；Described constraints includes：System loading balances about
Bundle, system spinning reserve constrains, and conventional power unit goes out power restriction, conventional power unit Climing constant, routine with the technology of Demand-side resource
Unit minimum run time and minimum idle time constraint, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction
Constraint；
(3) Demand Side Response is equivalent to a virtual synchronous generator, according to described Optimized Operation object function and described
Constraints and by the discrete particle cluster algorithm based on heuristic rule come the period one by one acquisition all units start and stop shape
State；
(4) the start and stop state according to all units, and by the continuous particle cluster algorithm based on heuristic rule to each
Individual unit carries out economic allocation on the basis of according to described constraints；And the result according to economic allocation and all units
The final start and stop state of each period and size of exerting oneself allocate exerting oneself of each unit, realize active distribution network Optimized Operation.
Further, in step (1), described first object function is：
Described second object function is：
Wherein, F1Expression system generator operation cost；T represents time dispatching cycle；NGRepresent the total number of units of conventional power unit；fi
(PGi(t))=Ai×PGi(t)2+Bi×PGi(t)+Ci, fiRepresent i-th conventional power unit operating cost；PGiRepresent i-th routine
The output of unit；SiRepresent i-th conventional power unit start-up and shut-down costs；ui(t) and ui(t-1) represent that i-th unit is current respectively
Moment and the start and stop state of previous moment；fDRRepresent that Demand Side Response compensates total amount；fDR(PDR(t))=dDR×PDR(t), PDR
Represent that Demand-side adjusts power, i.e. virtual generated output；uDRT () represents the start and stop state of virtual generating current time；Ai、BiAnd
CiRepresent the operational factor of i-th conventional power unit；ShiRepresent i-th conventional power unit heat
Start-up cost；SciRepresent i-th conventional power unit cold start-up cost；Ti.off.minRepresent that the minimum that i-th conventional power unit allows is held
Continuous idle time；Ti.offRepresent the time that i-th conventional power unit was persistently shut down before certain period, if before this unit being
Open state, then for 0；Ti,csRepresent the cold start-up time of conventional power unit i；dDRReduce every kilowatt hour electricity by Demand-side to be compensated
The amount of money；F2Represent pollutant emission total yield number；gi(PGi(t))=ai×PGi(t)2+bi×PGi(t)+ci, giRepresent i-th
The pollutant emission equivalent of conventional power unit；ai、biAnd ciRepresent the disposal of pollutants coefficient of i-th conventional power unit.
Further, in step (2), described system loading Constraints of Equilibrium is
Wherein, NDGRepresent distributed power source species number；PDGjT () represents that jth kind distributed power source t is exerted oneself；PLT () represents t
Load power；Described system spinning reserve is constrained toIts
In, PGi,maxRepresent that i-th conventional power unit maximum technology is exerted oneself；PDR,maxRepresent that maximum available Demand-side adjusts load；PRLTable
Show system spinning reserve demand；Expression system is the exerting oneself uncertainty of reply distributed power source and newly-increased standby
With capacity it is considered to the uncertainty of distributed electrical source power 100% probability interval, P hereinDGjIt is jth kind distributed power source to go out
Power；The technology of described conventional power unit and Demand-side resource is exerted oneself and is limited toPGi,min、PGi,maxRespectively
Represent that i-th conventional power unit minimum, maximum technology are exerted oneself；Described conventional power unit Climing constant is
Wherein Pi(t) and Pi(t-1) represent the output of current time and i-th conventional power unit of previous moment respectively；ruiAnd rdiPoint
Biao Shi not i-th conventional power unit power rise speed and fall off rate；Described conventional power unit minimum run time and minimum stoppage in transit
Time-constrain isWherein, Ti,onRepresent i-th conventional power unit continuous operating time；Ti,offOften represent i-th
Rule unit continuous idle time；Ti,on,minRepresent the minimum continuous working period that i-th conventional power unit allows；Described Demand-side is
Continuous greatly controllable period of time is constrained to TDR≤TDR, max, wherein, TDRRepresent Demand-side controllable period of time；TDR, maxRepresent that Demand-side allows
Maximum continuous controllable period of time；Described power mode satisfaction is constrained toWherein msRepresent power mode satisfaction
Degree；ms,minRepresent the minimum power mode satisfaction allowing,Represent in a dispatching cycle T, each before and after optimization
The sum of the knots modification absolute value of period electricity；Represent in a dispatching cycle T, total power consumption before optimization.
Further it is characterised in that the discrete particle cluster algorithm based on heuristic rule described in step (3) is concrete
For：
(3.1) each conventional power unit, Demand-side characterisitic parameter, system prediction payload and distributed power source prediction are initialized
Exert oneself size；
(3.2) random initializtion each Unit Commitment state, updates formula and position more according to existing discrete particle group velocity
New formula carries out particle rapidity and the location updating of given number of times (desirable 100 times), completes discrete particle cluster algorithm first period
The calculating of start and stop state；
In discrete particle cluster algorithm, required minimum continuous working period constraint to be processed, minimum continue idle time about
Bundle, maximum continuous controllable period of time constraint using heuristic modification method are：After certain particle position updates, if certain unit operation
Or idle time is unsatisfactory for given constraint, then the running status of this unit of forcibly changing makes its meet the constraint.System spinning reserve
Constraint heuristic modification method be：First according to priority method, each unit is arranged to bad order from good according to performance driving economy
Sequence, after certain particle position updates, if discontented pedal system spinning reserve constraint, by performance driving economy from good to bad order
Open off-duty unit successively, until the constraint of system spinning reserve is satisfied；
Meanwhile, in order to avoid all units all operate near minimum load, using heuristic correction as follows：In certain grain
After sub- location updating, if the conventional power unit of all operations is more than system with the minimum load limit value sum of equivalent virtual synchronous generator
Load and distributed power source exert oneself 0.9 times of difference of summation, then close successively to good order sequence from bad by performance driving economy
The unit of operation, until above-mentioned inequality is satisfied, thus be further ensured that the economic allocation of unit output；
(3.3) determine in dispatching cycle total when hop count T (typically taking 24), repeat step (3.2) content, until complete from
The calculating of shot swarm optimization start and stop of whole period state.Store each conventional power unit and equivalent virtual synchronous generator each period of group
Final start and stop state.
Further, described in step (4), the continuous particle cluster algorithm based on heuristic rule is specially：
(4.1) according to above-mentioned discrete particle cluster algorithm result, initialize each conventional power unit and equivalent virtual synchronous generator group is each
Individual period start and stop status information.
(4.2) output distribution of first period each conventional power unit of random initializtion and equivalent virtual synchronous generator group.Using
Existing continuous particle group velocity updates formula and location updating formula carries out particle rapidity and the position of given number of times (desirable 100 times)
Put renewal, complete each unit output economic allocation of continuous particle cluster algorithm first period and calculate.
In continuous particle cluster algorithm required technology to be processed exert oneself restriction and Climing constant exert oneself heuristic
Modification method is：After certain particle position updates, if certain unit output size is more than this unit EIAJ limit value and previous
This unit output value of period and this unit maximum unit time climb the smaller value exerting oneself in sum, then force this unit output to be
Smaller value in above-mentioned two values；If certain unit output size is less than this unit minimum load limit value and this unit output of previous period
Value declines higher value in the difference exerted oneself with this unit maximum unit time, then force this unit output to be larger in above-mentioned two values
Value.User power utilization mode satisfaction constraint heuristic modification method be：After certain particle position updates, if user power utilization side
The constraint of formula satisfaction is unsatisfactory for, then mandatory down low effect the exerting oneself until meeting user power utilization mode satisfaction about of virtual synchronous generator
Bundle.
System loading Constraints of Equilibrium adopts Means of Penalty Function Methods to process.Processing method is：Constraints is converted into and penalizes letter
Penalty function and object function are grouped together into the calculating that new fitness function participates in algorithm by penalty factor by number.
If constraints is unsatisfactory for, the functional value of penalty function is a positive number；If constraints meets, the functional value of penalty function is 0.
(4.3) repeat step (4.2) content, until completing each conventional power unit of continuous particle cluster algorithm whole period and equivalent
Virtual synchronous generator group is exerted oneself the calculating of economic allocation.Discrete particle cluster algorithm tries to achieve each conventional power unit and equivalent virtual synchronous generator group
The final start and stop state of each period and continuous particle cluster algorithm try to achieve each conventional power unit and equivalent virtual synchronous generator group each when
The size of exerting oneself of section, the as final result of Unit Combination, namely Optimized Operation result of the present invention.
In general, by the contemplated above technical scheme of the present invention compared with prior art, there is following beneficial effect
Really：
(1) adopt virtual generating thought, Demand-side resource is equivalent to virtual generation assets, with conventional power unit, distributed
Power supply participates in system power balance adjustment jointly, and traditional Optimal Scheduling is converted into Optimization of Unit Commitment problem, can
Convenient effectively unified allocation of resources is carried out to Demand-side resource.Example show this dispatching method can effectively reduce system cost and
Environmental pollution, can also meet higher user power utilization satisfaction simultaneously.
(2) using improving dual particle cluster algorithm, compensate for single continuous particle cluster algorithm and be difficult to determine the 2 of Unit Commitment
The shortcoming of state of value, by discrete particle cluster and continuous population decoupling, employing, by the method for period calculating, makes to ask this algorithm simultaneously
Solution speed greatly speeds up.The various heuristic correction adding in algorithm can make the solution of gained fully meet all of constraint bar
Part, the introducing of critical operator ensure that particle do not lose multifarious update towards more excellent direction simultaneously, thus ensureing algorithm
While meeting high accuracy, solving speed is effectively improved.
Brief description
Fig. 1 is the active distribution network Optimized Operation principle schematic of the embodiment of the present invention；
Fig. 2 be the embodiment of the present invention using improve dual particle swarm optimization be optimized scheduling schematic diagram；
Fig. 3 is the discrete particle cluster algorithm flow chart based on heuristic rule of the embodiment of the present invention；
Fig. 4 is the continuous particle cluster algorithm flow chart based on heuristic rule of the embodiment of the present invention；
Fig. 5 is wind-power electricity generation prediction curve provided in an embodiment of the present invention；
Fig. 6 is that ideal photovoltaic provided in an embodiment of the present invention is exerted oneself prediction curve；
Fig. 7 is system total load curve provided in an embodiment of the present invention；
Fig. 8 is system loading curve comparison figure before and after provided in an embodiment of the present invention optimization.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only in order to explain the present invention, and
It is not used in the restriction present invention.As long as additionally, involved technical characteristic in each embodiment of invention described below
The conflict of not constituting each other just can be mutually combined.
The virtual generating that the embodiment of the present invention proposes refers to regulatable Demand-side resource as system reserve power supply,
When network load value is higher, suitably reduce the electricity consumption of such resource, and corresponding valence compensation is carried out to user.Negative due to reducing
Lotus is equivalent to increase generated energy, and compensates electricity price and be equivalent to cost of electricity-generating, therefore such Demand-side resource can be equivalent to void
Send out electric resources, be actively engaged in power grid regulation.
The active distribution network Optimization Scheduling of the embodiment of the present invention comprises the steps：
(1) set up Optimized Operation object function F1And F2.
S1：Set up system generator operation cost objective function：
In formula, F1Expression system generator operation cost；T represents time dispatching cycle；NGRepresent the total number of units of conventional power unit；fi
Represent i-th conventional power unit operating cost；PGiRepresent the output of i-th conventional power unit；SiRepresent that i-th conventional power unit opens
Stop cost；ui(t) and ui(t-1) represent the start and stop state of i-th unit current time and previous moment respectively；fDRExpression demand
Side response compensates total amount；PDRRepresent that Demand-side adjusts power, i.e. virtual generated output；uDRWhen () represents that virtual generating is current t
The start and stop state carved；Ai、BiAnd CiRepresent the operational factor of i-th conventional power unit；ShiRepresent that i-th conventional power unit thermal starting becomes
This；SciRepresent i-th conventional power unit cold start-up cost；Ti.off.minRepresent that the minimum that i-th conventional power unit allows persistently is stopped transport
Time；Ti.offRepresent the time that i-th conventional power unit was persistently shut down before certain period, if before this unit being start shape
State, then for 0；Ti,csRepresent the cold start-up time of conventional power unit i；dDRReduce the gold that every kilowatt hour electricity is compensated by Demand-side
Volume.
S2：Set up pollutant emission object function：
In formula, F2Represent pollutant emission total yield number；giRepresent the pollutant emission equivalent of i-th conventional power unit；ai、
biAnd ciRepresent the disposal of pollutants coefficient of i-th conventional power unit.
(2) set up Optimized Operation constraints, including：System loading Constraints of Equilibrium, system spinning reserve constrains, conventional machine
Group goes out power restriction, conventional power unit Climing constant with the technology of Demand-side resource, and conventional power unit minimum run time is stopped transport with minimum
Time-constrain, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction constrains.
S3：Set up system loading Constraints of Equilibrium：
In formula, NDGRepresent distributed power source species number；PDGjT () represents that jth kind distributed power source t is exerted oneself；PL(t)
Represent the load power of t.
S4：Set up the constraint of system spinning reserve：
In formula, PGi,maxRepresent that i-th conventional power unit maximum technology is exerted oneself；PDR,maxRepresent that maximum available Demand-side is adjusted
Load；PRLExpression system spinning reserve demand；Expression system be the exerting oneself uncertainty of reply distributed power source and
Newly-increased spare capacity is it is considered to the uncertainty of distributed electrical source power 100% probability interval, P hereinDGjIt is the distribution of jth kind
Formula power supply is exerted oneself.
S5：The technology setting up conventional power unit with Demand-side resource goes out power restriction：
PGi,min≤PGi(t)≤PGi,max(5)
0≤PDR(t)≤PDR,max
In formula, PGi,min、PGi,maxRepresent that i-th conventional power unit minimum, maximum technology are exerted oneself respectively.
S6：Set up conventional power unit Climing constant：
Pi(t)-Pi(t-1)≤ruiT (6)
Pi(t-1)-Pi(t)≤rdiT
In formula, Pi(t) and Pi(t-1) represent the output of current time and i-th conventional power unit of previous moment respectively；
ruiAnd rdiRepresent i-th conventional power unit power rise speed and fall off rate respectively.
S7：Set up the minimum continuous working period of conventional power unit permission and minimum lasting idle time constraint：
Ti,on≥Ti,on,min(7)
Ti,off≥Ti,off,min
In formula, Ti,onRepresent i-th conventional power unit continuous operating time；Ti,offRepresent that i-th conventional power unit is continuously stopped transport
Time；Ti,on,minRepresent the minimum continuous working period that i-th conventional power unit allows.
S8：Set up the maximum continuous controllable period of time constraint (continuous working period that i.e. virtual power supply allows of Demand-side permission
Constraint)：
TDR≤TDR, max(8)
In formula, TDRRepresent Demand-side controllable period of time；TDR, maxRepresent the maximum continuous controllable period of time that Demand-side allows.
S9：Set up the constraint of user power utilization mode satisfaction：
In formula, msRepresent power mode satisfaction；ms,minRepresent the minimum power mode satisfaction allowing,Represent
In a dispatching cycle T, the sum of the knots modification absolute value of each period electricity before and after optimization；Represent and dispatch at one
In cycle T, total power consumption before optimization.
(3) Demand Side Response is equivalent to a virtual synchronous generator, is calculated by the discrete particle cluster based on heuristic rule
Method carrys out the start and stop state of all units of determination of period one by one.
S10：Initialize each conventional power unit characterisitic parameter, including：Unit output limits, greenhouse gas, minimum start and stop
The machine time, the cold start-up time, climbing limits, start-up cost, emission performance coefficient, continuous service/idle time；Initialization needs
Seek side characterisitic parameter, including：The maximum technology of Demand-side resource is exerted oneself, the maximum continuous controllable period of time that Demand-side allows；Initially
Change system prediction payload and distributed power source predicts size of exerting oneself.
S11：Initialize certain period conventional power unit and the start and stop state of equivalent virtual synchronous generator group；It is provided with bpopIndividual (general
Integer between desirable [20,80]) represent the particle in certain period start and stop state for the unit, the dimension of each particle is normal with system
Rule unit is consistent with equivalent virtual synchronous generator group number sum, the start and stop shape of corresponding unit in the often one-dimensional value expression system of particle
State.The start and stop state of all units of random initializtion first is that 0 or 1, wherein 0 expression is closed, and 1 represents unlatching.Sentence first afterwards
Whether the particle after disconnected initialization meets the minimum continuous working period constraint of permission and minimum continues idle time constraint, goes forward side by side
The correction of row particle position, correction formula is as follows：
In formula,Represent the value of the 0th iteration i-th dimension of k-th particle, that is, i-th conventional power unit a certain period is initial
The state of unit after change.If in initial runtime, be stopped status before i-th conventional power unit, then Ti,on=0；If i-th often
Open state before rule unit, then Ti.off=0.
Each particle also needs to judge whether to meet the maximum continuous controllable period of time constraint of Demand-side, and carries out particle position
Revise, correction formula is as follows：
In formula,Represent k-th particle, the 0th iteration, the value of the n-th dimension, that is, virtual synchronous generator n is at the beginning of a certain period
The state of unit after beginningization.Tn,DRRepresent the time that n-th virtual synchronous generator was persistently started shooting before certain period, if this machine
It is stopped status before group, then for 0.TN, DR, maxRepresent the maximum continuous controllable period of time that Demand-side allows be equivalent n-th virtual
The maximum continuous working period that electromotor allows.
Each particle is made whether meet with minimum run time constraint, minimum idle time constraint and Demand-side are maximum
After the detection of continuous controllable period of time constraint, correction, judge whether each particle meets the constraint of system spinning reserve.Introduce preferential row
Sequence method sorts from the order got well to differing from according to performance driving economy successively to each conventional power unit.Performance driving economy passes through averagely at full capacity
Expense AFLC (Average Full-Load Cost) is used for being judged, expression formula is as follows：
fi(PGi,max)=Ai×PGi,max 2+Bi×PGi,max+Ci
In formula, fi(PGi.max) represent i-th conventional power unit full load situation under operating cost.Average expense at full capacity
Less, the performance driving economy of unit is better.
If a certain particle is discontented with the constraint of pedal system spinning reserve, open successively from the order got well to differing from according to performance driving economy
Open the unit being not turned on before and judge whether particle meets the constraint of system spinning reserve, until all particles all meet system
Spinning reserve constrains.
Afterwards heuristic correction is carried out to each particle start and stop state, judge whether each particle start and stop state meets following constraint
Condition：
In formula,Represent the summation of exerting oneself in the t period for the distributed power source.N represents conventional power unit and equivalent virtual
Electromotor number sum.
When the minimum technology of certain moment all operating units sum of exerting oneself is both greater than total load and distributed power source gross capability
Difference 90% when, in order to meet system loading Constraints of Equilibrium, all units almost have to operate at about minimum load, so
It is not necessarily economic optimum, therefore, add heuristic correction to accelerate convergence rate.Equally to each conventional power unit according to operation
Economy is sorted successively by the good order to difference, judges that the index of conventional power unit performance driving economy quality is shown in formula (12).If a certain
Particle is unsatisfactory for constraint equation (13), then be not turned off before from bad close successively to good order according to performance driving economy
Unit simultaneously judges whether meet the constraint conditional (13), until all particles all meet the constraint conditional (13).
S12：The single step completing discrete particle cluster updates.First carry out according to the discrete particle cluster algorithm based on heuristic rule
Speed updates, and speed more new formula is as follows：
vk(t+1)=wvk(t)+c1r1(Pk,best(t)-xk(t))+c2r2(Pg(t)-xk(t)) (14)
In formula, vkT () represents the speed after the t time iteration of k-th particle, xkT () represents the t time iteration of k-th particle
Position afterwards, Pk,bestT () represents the history optimal value of k-th particle, PgT () represents global optimum.W represents that inertia is weighed
Weight (typically between [0.4,0.9]), c1、c2Represent Studying factors (typically can use 2), r1、r2For the random number between [0,1].
After completing speed renewal, carry out location updating, location updating step is as follows：
First determine whether whether the start and stop state of unit meets minimum run time constraint, minimum idle time constraint and maximum
Sustainable operation time-constrain, if meeting, according to the location updating formula of normal scatter population, is carried out using critical operator
Location updating；If being unsatisfactory for, it is updated with (10), (11) formula.Wherein, the introducing of critical operator is desirable to so that iteration mistake
Journey more preferable to dominance.Ordinary particle group's algorithm adopts a random number λ, after several s that speed is converted between 0～1, λ
Take is excessive or too small, and the probability that set state is transformed into 0 or 1 is excessive, then have a strong impact on iteration to dominance.Critical operator
0.1<λ1<0.4,0.6<λ2<0.9, by the range shorter of random number λ to [λ1,λ2], so not lose particle multifarious simultaneously
Ensure that particle updates towards more excellent direction.
The location updating formula of discrete particle cluster is as follows：
In formula, xkT () represents the position after the t time iteration of k-th particle；Rand is the random number between [0,1]；
Sigmoid is self-defining function.
Next, it is judged that whether particle meets spinning reserve constraint, and it is updated, renewal process is standby with rotating in S11
Renewal process with constraint.
Finally, heuristic correction is carried out to each particle start and stop state, makeover process is with makeover process heuristic in S11.
After completing particle position renewal, each particle is brought into solution adaptive value in fitness function, carries out optimum
Value updates.In this problem, fitness function is unit starting cost, and formula is as follows：
F=Si(t)×(1-ui(t-1))
In formula, F represents unit starting cost.
Optimal value update step be：If k-th particle brings the adaptive value P that fitness function calculates intokT () is less than the
History optimal value P of k particlek,bestT (), then make Pk,best(t)=Pk(t), otherwise Pk,bestT () keeps constant.If currently more
Global optimum P of population after the completion of newg,nowT () is less than global optimum P beforegT (), then make Pg,now(t)=Pg(t).
S13：Complete the calculating of discrete particle cluster algorithm list period start and stop state；Determine each period need iteration repeatedly
Generation number CB(typically can use 100 times), first carries out being S11 step, repeats S12 step afterwards until cycle-index and reaches CB
Secondary, obtain the start and stop state of certain period final, and store this data.
S14：Complete the calculating of discrete particle cluster algorithm start and stop of whole period state.Determining in what a dispatching cycle
(24 are typically taken) it is possible to carry out the meter of each period start and stop state successively according to the method for S13 step after hop count T when total
Calculate.The final start and stop state outcome result of all each periods of unit is stored.
(4) after completing the calculating of each period start and stop state of all units, on the basis of known start and stop state, lead to
Cross the continuous particle cluster algorithm based on heuristic rule and each unit is carried out on the basis of according to constraints with economical point
Join, specific implementation step is as follows：
S15：Initialize the output distribution of a certain each unit of period.It is provided with cpopIndividual (between typically desirable [20,80]
Random integers) represent the particle that all units are exerted oneself in certain period, the dimension of each particle and conventional power unit in system and equivalent
Virtual synchronous generator number sum is consistent, the size of exerting oneself of corresponding unit in the often one-dimensional value expression system of particle.Maximum in each unit
In the range of minimum load, the size of exerting oneself of all units of random initializtion.
S16：Complete standard particle group's single step to update.First carry out speed renewal according to standard particle group's algorithm, speed updates
Formula, with the speed more new formula of discrete particle cluster, is shown in formula (14), then carries out location updating.Continuous particle cluster algorithm location updating
Formula is：
xk(t+1)=xk(t)+vk(t) (17)
After location updating, each particle needs to judge whether to meet the Climing constant of conventional power unit, and carries out particle position
Correction, correction formula is as follows：
In formula,WithRepresent k-th particle value in t period and t-1 period i-th dimension respectively, that is, in the t period and
The size of exerting oneself of i-th conventional power unit of t-1 period.UPiAnd DNiThe maximum that the expression conventional power unit i unit interval can climb respectively
Size of exerting oneself and the EIAJ size declining.
Each particle also needs to meet the constraint of user power utilization mode satisfaction, and carries out the correction of particle position, correction formula
As follows：
In formula,Represent the value that k-th particle is tieed up, i.e. exerting oneself in n-th virtual synchronous generator of t period in the t period n-th
Size.PLT () represents the system total load of period t.
After completing location updating, each particle is brought into solution adaptive value in fitness function, carries out optimal value
Update.The fitness function of this problem is by the operating cost of unit, the equivalent price of system blowdown and system loading Constraints of Equilibrium
Penalty composition.Fitness function formula is：
In formula, c represents blowdown Price factor.λ represents penalty factor.Last expression system loading of multinomial balances about
The penalty of bundle.
The step that the step that optimal value updates is updated with optimal value in S12 is identical.
S17：The economic allocation completing single each unit output of period calculates；Determining each period needs the iteration of iteration
Number of times CC(typically can use 100 times), first carries out S15 step, repeats S16 step afterwards until cycle-index and reaches CCSecondary, obtain
To the situation of final each unit output economic allocation of certain period, and store this data.
S18：Complete the calculating of each unit output economic allocation of standard particle group's algorithm whole period；Determining the period
It is possible to carry out the calculating of each period Unit Economic distribution successively according to the method for S17 step after number T.By all units
The size storage of finally exerting oneself of each period.
S19：Try to achieve the final start and stop state of all each periods of unit and exert oneself size, the as termination of Unit Combination
Really.According to Unit Combination result, control centre in a planned way allocates exerting oneself of each unit, realizes active distribution network Optimized Operation.
In embodiments of the present invention, by checking proposition Optimization Scheduling effectiveness, the method for proposition is applied to
The calculating of active distribution network Optimized Operation.Choose the technical parameter of conventional fired power generating unit as shown in table 1, wind-powered electricity generation, photovoltaic generation go out
Respectively as shown in Figure 5 and Figure 6, system total load is as shown in Figure 7 for the prediction curve of power.In actual electric network, Demand-side resource type
There is multiformity, user side Demand Side Response need to compensate electricity price, and the Demand-side resource such as energy storage only needs to consider maintenance cost, need not
Carry out price benefication.Calculated for simplified model, set all Demand-side schedulable resource total sizes as 80MW, equivalent is flat
All every kilowatt hour amount of compensation is 0.016 $, and user power utilization mode satisfaction is not less than 90%.
The technical parameter of the conventional fired power generating unit of table 1
The technical parameter of the conventional fired power generating unit of continued 1
Simulation result is as follows：
A () is not optimized scheduling before, supply workload demand only with conventional power unit, in now 24 hours, system operation is total
Cost is 426300 $, and pollutant emission equivalents is 82985kg.
B () adds distributed power source, after being optimized scheduling, can get system operation totle drilling cost in system 24 hours is
390660 $, have dropped 35640 $ compared with situation (a)；Pollutant emission equivalents is 74433kg, have dropped compared with situation (a)
8552kg.
If c () considers distributed power source and Demand Side Response simultaneously, after being optimized scheduling, can get in system 24 hours
System operation totle drilling cost is 370698 $, have dropped 19962 $ compared with situation (b)；Pollutant emission equivalents is 72487kg, compared with feelings
Condition (b) have dropped 1946kg.Before and after Optimized Operation, load curve contrast is as shown in Figure 8 it can be seen that consider Demand Side Response
Afterwards, load peak is decreased obviously, and effectively reduces the peak-valley difference of load.
By example as can be seen that adopting Optimization Scheduling, distributed power source and the Demand-side that the embodiment of the present invention proposes
Resource simultaneously participates in electrical network and adjusts, and can effectively reduce system generator operation cost, reduces load peak-valley difference, reduces environment dirty simultaneously
Dye, the optimization operation to electrical network has positive effect.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not in order to
Limit the present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc., all should comprise
Within protection scope of the present invention.
Claims (2)
1. a kind of active distribution network Optimization Scheduling based on virtual generating is it is characterised in that comprise the steps：
(1) set up Optimized Operation object function；First object function F is set up with the minimum target of system generator operation cost1, with
The minimum target of pollutant emission total yield number sets up the second object function F2；
(2) obtain described Optimized Operation bound for objective function；Described constraints includes：System loading Constraints of Equilibrium,
System spinning reserve constrains, and conventional power unit goes out power restriction, conventional power unit Climing constant, conventional power unit with the technology of Demand-side resource
Minimum run time and minimum idle time constraint, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction constrains；
(3) Demand Side Response is equivalent to a virtual synchronous generator, according to described Optimized Operation object function and described constraint
Condition and by the discrete particle cluster algorithm based on heuristic rule come the period one by one acquisition all units start and stop state；
(4) the start and stop state according to all units, and by the continuous particle cluster algorithm based on heuristic rule to each machine
Group carries out economic allocation on the basis of according to described constraints；And the result according to economic allocation and all units each
The final start and stop state of period and size of exerting oneself allocate exerting oneself of each unit, realize active distribution network Optimized Operation；
In step (1), described first object function is：
Described second object function is：
Wherein, F1Expression system generator operation cost；T represents time dispatching cycle；NGRepresent the total number of units of conventional power unit；fi(PGi
(t))=Ai×PGi(t)2+Bi×PGi(t)+Ci, fiRepresent i-th conventional power unit operating cost；PGiRepresent i-th conventional power unit
Output；SiRepresent i-th conventional power unit start-up and shut-down costs；ui(t) and ui(t-1) represent i-th unit current time respectively
Start and stop state with previous moment；fDRRepresent that Demand Side Response compensates total amount；fDR(PDR(t))=dDR×PDR(t), PDRRepresent
Demand-side adjusts power, i.e. virtual generated output；uDRT () represents the start and stop state of virtual generating current time；Ai、BiAnd CiTable
Show the operational factor of i-th conventional power unit；ShiRepresent i-th conventional power unit thermal starting
Cost；SciRepresent i-th conventional power unit cold start-up cost；Ti.off.minRepresent that the minimum that i-th conventional power unit allows persistently is stopped
The fortune time；Ti.offRepresent the time that i-th conventional power unit was persistently shut down before certain period, if before this unit being start
State, then for 0；Ti,csRepresent the cold start-up time of conventional power unit i；dDRReduce the gold that every kilowatt hour electricity is compensated by Demand-side
Volume；F2Represent pollutant emission total yield number；gi(PGi(t))=ai×PGi(t)2+bi×PGi(t)+ci, giRepresent i-th routine
The pollutant emission equivalent of unit；ai、biAnd ciRepresent the disposal of pollutants coefficient of i-th conventional power unit.
2. active distribution network Optimization Scheduling as claimed in claim 1 is it is characterised in that in step (2), described system is born
Lotus Constraints of Equilibrium isWherein, NDGRepresent distributed power source species number；PDGj(t)
Represent that jth kind distributed power source t is exerted oneself；PLT () represents the load power of t；
Described system spinning reserve is constrained toWherein, PGi,max
Represent that i-th conventional power unit maximum technology is exerted oneself；PDR,maxRepresent that maximum available Demand-side adjusts load；PRLExpression system is revolved
Turn stand-by requirement；Expression system be reply distributed power source exert oneself uncertainty and newly-increased spare capacity, examine
Consider the uncertainty of distributed electrical source power 100% probability interval, P hereinDGjIt is jth kind distributed power source to exert oneself；
The technology of described conventional power unit and Demand-side resource is exerted oneself and is limited toPGi,min、PGi,maxPoint
Biao Shi i-th conventional power unit minimum, maximum technology not exert oneself；
Described conventional power unit Climing constant isWherein Pi(t) and Pi(t-1) represent current time respectively
Output with i-th conventional power unit of previous moment；ruiAnd rdiRepresent respectively i-th conventional power unit power rise speed and
Fall off rate；
Described conventional power unit minimum run time and minimum idle time are constrained toWherein, Ti,onRepresent i-th
Conventional power unit continuous operating time；Ti,offI-th conventional power unit of expression continuous idle time；Ti,on,minRepresent i-th conventional machine
The minimum continuous working period that group allows；
The maximum continuous controllable period of time of described Demand-side is constrained to TDR≤TDR, max, wherein, TDRRepresent Demand-side controllable period of time；TDR, max
Represent the maximum continuous controllable period of time that Demand-side allows；
Described power mode satisfaction is constrained toWherein msRepresent power mode satisfaction；ms,minRepresent
The minimum power mode satisfaction allowing,Represent that before and after optimization, each period electricity changes in a dispatching cycle T
The sum of variable absolute value；Represent in a dispatching cycle T, total power consumption before optimization.
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| CN105870976B (en) * | 2016-04-15 | 2018-05-29 | 国家电网公司 | A kind of low-carbon dispatching method and device based on energy environment efficiency |
| CN105956247B (en) * | 2016-04-26 | 2019-04-16 | 大连理工大学 | The three public scheduling model construction methods based on Gini coefficient |
| CN106779237B (en) * | 2016-12-29 | 2020-12-29 | 天津大学 | Load curvilinearization prediction method for active power distribution network from bottom to top |
| CN106655232B (en) * | 2017-01-13 | 2018-12-11 | 东北电力大学 | It is a kind of meter and three-phrase burden balance electric car distribution charge and discharge strategy |
| CN106816879B (en) * | 2017-01-16 | 2019-08-20 | 华南理工大学 | A kind of active distribution network voltage control method for coordinating based on urgent need response |
| CN107967567B (en) * | 2017-12-14 | 2022-02-11 | 西南石油大学 | Wind power plant scheduling method and system based on virtual power supply |
| CN108288854A (en) * | 2018-01-04 | 2018-07-17 | 国网江苏省电力有限公司电力科学研究院 | One introduces a collection net lotus control method for coordinating and system |
| CN108493992A (en) * | 2018-03-27 | 2018-09-04 | 国网江苏省电力有限公司电力科学研究院 | A kind of wind power plant Optimization Scheduling of the controller containing Distributed Power Flow |
| CN108964021B (en) * | 2018-06-25 | 2022-07-01 | 国网陕西省电力公司经济技术研究院 | Method for controlling capacity space-time distribution characteristic of frequency-modulated electric vehicle capable of accessing network |
| CN109617133B (en) * | 2018-12-18 | 2023-05-12 | 深圳供电局有限公司 | Power system scheduling method considering flexible load |
| CN109934450A (en) * | 2018-12-29 | 2019-06-25 | 国网天津市电力公司电力科学研究院 | More scene active distribution network planning appraisal methods based on Demand Side Response |
| CN109858774B (en) * | 2019-01-09 | 2020-10-27 | 燕山大学 | Source network load planning method for improving system safety and coordination |
| CN112165086B (en) * | 2020-08-12 | 2022-05-13 | 华南理工大学 | Online optimization system of active power distribution network |
| CN112396232B (en) * | 2020-11-19 | 2022-03-08 | 燕山大学 | Economic dispatching method and system for electric power system with valve point effect |
| CN112751369A (en) * | 2020-12-30 | 2021-05-04 | 国网河北省电力有限公司沧州供电分公司 | Optimized scheduling method for offshore power distribution network and terminal equipment |
| CN113240547B (en) * | 2021-05-21 | 2022-04-12 | 河北工业大学 | Scheduling method of hydrogen production unit array under wind power consumption |
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