CN108808737A - Promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption - Google Patents
Promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption Download PDFInfo
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Abstract
The invention discloses a kind of active distribution network Optimization Schedulings promoting renewable distributed generation resource consumption.Include the following steps：1）Estimate load and renewable distributed generation resource output probability density parameter according to historical information；2）On the basis of known each pdf model, the distributed generation resource upper layer Optimized model based on chance constraint is established；3）In the Optimization Solution iterative process of upper layer, judge whether each iteration result meets confidence interval, meet, carry out next iteration, be unsatisfactory for, optimizes into lower layer；4）Lower layer's optimization advanced optimizes the result that confidence interval is unsatisfactory in upper layer, and correcting upper layer using lower layer's optimum results optimizes, and upper layer and lower layer optimizes alternately, is finally met the optimal solution of confidence interval.Entire Optimized Operation process can make full use of the controllable resources in active distribution network, realize Optimized Operation target using bilevel mode, upper layer and lower layer coordination optimization is divided into.
Description
Technical field
The invention belongs to intelligent grid field, more particularly to a kind of active distribution promoting renewable distributed generation resource consumption
Net Optimization Scheduling.
Background technology
For Optimization of Energy Structure, the policy of energy-saving and emission-reduction, distribution of the China based on regenerative resource and clean energy resource are responded
Formula generation technology has obtained quick development.A certain amount of distributed generation resource access conventional electrical distribution net a degree of can reduce
System losses, the upgrading for promoting voltage stability, delaying equipment component, but when the access amount of distributed generation resource is excessive
When, power distribution network part of nodes voltage out-of-limit can be caused, reduce operational reliability, to limit power distribution network to distributed generation resource
Digestion capability.Distributed generation resource based on regenerative resource in distributed generation resource is contributed effected by environmental factors, is had
Stronger randomness and uncertainty, to the operational management ability of power distribution network, more stringent requirements are proposed, if operational management is not
When it is possible that largely abandoning wind, abandoning optical phenomenon, causing the loss of economic asset.
Based on above-mentioned present situation, for operational management ability of the power distribution network to distributed generation resource is improved, foreign countries propose " actively
The concept of power distribution network ".Under the frame of active distribution network, research improves receiving of the power distribution network to regenerative resource distributed power generation
Ability and reach low-carbon environment-friendly requirement, while reducing the active power loss in power distribution network power supply process, and ensures good voltage
Level is the target pursued in the industry, but there is no associated description in the prior art.
Invention content
There is provided a kind of active distribution promoting renewable distributed generation resource consumption for technical problem solved by the invention
Net Optimization Scheduling.
Realize that the technical solution of the object of the invention is：A kind of active distribution promoting renewable distributed generation resource consumption
Net Optimization Scheduling, includes the following steps：
Step 1: according to historical data informations such as active distribution network location wind speed, intensity of illumination, temperature, loads, estimate
The active probability density estimation parameter for counting wind distribution formula power supply, photovoltaic distributed generation resource and load, it is close to obtain probability
Spend model；Specifically with the Weibull fitting of distribution wind speed probability density of two parameters, wind distribution formula power supply output is further obtained
Probability density further obtains wind distribution formula power supply output with the Beta fitting of distribution intensity of illumination probability density of two parameters
Probability density is fitted Load Probability density with normal distribution.
Step 2: meter and renewable distributed generation resource are contributed and the randomness and uncertainty of load, in known each probability
On the basis of density model, the active distribution network distributed generation resource upper layer Optimized model based on chance constraint is established；Upper layer optimizes
Model is in terms of and the power distribution network operating cost minimum of environmental factor, renewable distributed generation resource permeability highest and system are active
Loss minimization is target；Control variable includes the stochastic variable based on renewable distributed generation resource generated energy and workload demand amount
With the decision variable based on controlled distribution formula power supply generated energy；Constraints includes that object function needs the probability constraints met
Condition, the active and reactive equation constraints of power distribution network, node voltage, branch transmission capacity ranging opportunity formula constraints,
Distributed generation resource output climbing inequality constraints condition.The step is converted upper layer multi-objective optimization question by the way that weight coefficient is arranged
For single-object problem, wherein weight coefficient is determined using a kind of deviation ranking method.
Step 3: in the Optimization Solution iterative process of upper layer, is contributed based on renewable distributed generation resource and Load Probability is close
Model is spent, sample sampling is carried out using Monte Carlo Method of Stochastic, and bring Load flow calculation into, obtains meeting chance constraint item
The probability of part judges whether to meet confidence interval, meets, into following single-step iteration, be unsatisfactory for, and optimizes into lower layer；Upper layer is excellent
Change and is solved based on chaotic mutation small survival environment particle sub-group algorithm using one kind, the difference of algorithm and conventional ion group's algorithm
It is：The initialization of population process that chaos sequence is introduced to particle cluster algorithm, enables initial population fully to spread all over solution space；Base
Initial population is divided into multiple microhabitats by the Euclidean distance between primary；The speed update of particle not only considers this
History optimal solution and global history optimal solution are experienced personally, also considers history optimal solution in microhabitat；The selection of algorithm inertia weight is adopted
With adaptive re-configuration police, and deepen each particle to every generation of population；During algorithm iteration, based on each
Euclidean distance between microhabitat center, the microhabitat less than given threshold value of adjusting the distance are eliminated.
Step 4: output and power factor and adjustment on-load voltage regulation transformation that lower layer's optimization passes through adjusting distributed generation resource
Device no-load voltage ratio, the result that confidence interval is unsatisfactory in optimizing to upper layer advanced optimize, and are corrected using lower layer's optimum results
Layer optimization, upper layer and lower layer Optimized Operation alternately, are finally met the optimal solution of confidence interval.
Lower layer's optimization is up to mesh with the active reduction minimum of renewable distributed generation resource and power distribution network node voltage quality
Mark；Variable includes that the active adjusting of distributed generation resource and the tap of power factor regulation, on-load regulator transformer are adjusted；Constrain item
Part includes that object function needs the probabilistic constraints met, the active reduction of distributed generation resource, power factor, on-load voltage regulation transformation
Device no-load voltage ratio inequality constraints condition allows the chance constraint item of capacity by lower layer's Optimized Operation measure posterior nodal point voltage and branch
Part.
Lower layer's optimization only starts in upper layer optimum results in the presence of under the premise of being unsatisfactory for confidence interval situation, optimizes to upper layer
In be unsatisfactory for the result of confidence interval and advanced optimized, lower layer's optimum results are returned into upper layer optimization, rejudge optimization
As a result whether meet confidence interval, meet, modification upper layer optimized variable value and calculating target function value are unsatisfactory for, to upper layer
Optimization object function is punished, then, carries out next step iteration；Upper layer and lower layer Optimized Operation alternately, obtains finally full
The optimal solution of sufficient confidence interval.
Compared with prior art, the present invention its remarkable advantage is：(1) method of the invention is to active distribution network distributed electrical
The optimization in source considers the randomness and uncertainty that renewable distributed generation resource is contributed with load, as a result more to actual motion
Has directive significance；(2) entire Optimized Operation process is contributed using bilevel mode, upper layer Optimum distribution formula power supply is divided into,
The power distribution networks active control resources such as lower layer's Optimum distribution formula electrical source power factor, on-load regulator transformer no-load voltage ratio, upper layer and lower layer association
Tuning can make full use of the controllable resources in active distribution network, realize Optimized Operation target；(3) deviation ranking method is used
It determines the weight between multiple objective function, Model for Multi-Objective Optimization is converted into single object optimization model, is become using based on chaos
Different small survival environment particle sub-group optimization algorithm solves the Optimized model established, and can obtain satisfied model optimal solution.
Description of the drawings
Fig. 1 is the active distribution network hierarchy optimization dispatching method implementation flow chart for promoting renewable distributed generation resource consumption.
Fig. 2 is that deviation ranking method determines multiple target weight flow chart.
Fig. 3 is chaos sequence initialization flowchart.
Fig. 4 is that the small survival environment particle sub-group optimization algorithm based on chaotic mutation solves flow chart.
Representative meaning is numbered in figure is：1 estimates wind-force, photovoltaic distributed generation resource using historical data information and bears
The probability density function parameter of lotus, 2 establish upper layer Optimal Operation Model, and 3 judge whether upper layer optimum results meet confidence interval,
4 progress lower layer's Optimized Operations simultaneously return to scheduling result.
Specific implementation mode
In conjunction with attached drawing, a kind of active distribution network Optimized Operation side of the renewable distributed generation resource consumption of promotion of the invention
Method includes the following steps：
Step 1, according to active distribution network location wind speed, intensity of illumination, temperature, load these historical data informations,
The probability density estimation parameter of estimation wind distribution formula power supply, photovoltaic distributed generation resource active power output and load, determines
Each pdf model；
Step 2, on the basis of known each pdf model, it is distributed to establish active distribution network based on chance constraint
Power supply upper layer Optimized model；
Control variable includes stochastic variable based on renewable distributed generation resource generated energy and workload demand amount and with can
Control the decision variable based on distributed generation resource generated energy；Constraints includes that object function needs the probabilistic constraints met,
The active and reactive equation constraints of power distribution network, node voltage, branch transmission capacity inequality constraints condition of opportunity, distribution
Formula power supply output climbing inequality constraints condition.
It converts upper layer multi-objective optimization question to single-object problem, wherein weight coefficient value by the way that weight coefficient is arranged
It is determined using deviation ranking method；The operating cost expression formula of power distribution network meter and environment factor is
Wherein, T is dispatching cycle, NDGFor the distributed generation resource number accessed in active distribution network, r is money rate, ni、
Cins,i、Pr,i、τg,iDepreciable life, installation cost, rated generation power and the annual utilization hours of respectively i-th kind distributed generation resource
Number, PDG,i,tFor the active power output of t period distributed generation resources i, KOM,iIt is safeguarded for the unit generated energy of i-th kind of distributed generation resource
Cost, CgasIt is respectively cooler fuel price and calorific value, η with Li、Qgas,iThe generating efficiency and unit of respectively i-th kind distributed generation resource
The fuel quantity of generated energy consumption, MDGFor distributed generation resource pollutant emission type, Ven,j、Vp,jFor the environment valence of jth kind pollutant
Value and penalty standard, QDG,ijFor pollutant discharge amount in the jth of i-th kind of distributed generation resource；Pgrid,tFor the interaction of t periods
Electricity, CgridFor power distribution network electricity price, M are interacted with higher level's power gridGFor traditional thermal power generation pollutant emission type, Qgrid,jFor tradition
The discharge capacity of thermal power generation unit quantity of electricity pollutant j；
Renewable distributed generation resource computing permeability expression formula is
Wherein, NPVAnd NWTPhoto-voltaic power supply and wind-force number of power sources, P respectively in power distribution networkPV,i,tAnd PWT,j,tRespectively
The generated energy of t periods i-th of photo-voltaic power supply and j-th of wind-force power supply, PL,tFor t period distribution network load amounts.
Step 3, in the Optimization Solution iterative process of upper layer, based on renewable distributed generation resource contribute and Load Probability density
Function carries out random sampling using Monte Carlo Method of Stochastic, and brings Load flow calculation into, obtains meeting constraints condition of opportunity
Probability, judge whether to meet confidence interval, if satisfied, carry out next step iteration, be unsatisfactory for, into lower layer optimize；
Using being solved based on chaotic mutation small survival environment particle sub-group algorithm, chaos sequence is introduced grain by algorithm for upper layer optimization
The initialization of population process of swarm optimization, enables initial population fully to spread all over solution space；
The more new formula of algorithm particle state is：
In formula,GkIndividual particles history optimal components of the respectively particle i in kth time iterative process,
The optimal component of local history and the optimal component of group's global history where particle in microhabitat；For inertia weight；c1、c2、c3
For particle Studying factors；ξ1、ξ2、ξ3For [0,1] uniform random number；
The selection of algorithm inertia weight uses adaptive re-configuration police, and deepens each for the every generation for arriving population
Particle, calculation formula are：
In formula, ωminFor the minimum inertia weight value of setting；λ is adjustment factor, between 0~1；WithKth
Population minimum, the maximum adaptation value of secondary iteration；kmaxFor greatest iteration number.
Step 4, lower layer's optimization are become by the output and power factor and on-load regulator transformer of adjusting distributed generation resource
Than the result for being unsatisfactory for confidence interval in optimizing to upper layer advanced optimizes, and it is excellent to correct upper layer using lower layer's optimum results
Change, the upper layer and lower layer Optimized Operation alternately, until finally being met the optimal solution of confidence interval.
When lower layer optimizes, variable includes active and power factor regulation amount, the on-load regulator transformer of distributed generation resource
Tap regulated quantity；Constraints include the active reduction of distributed generation resource, power factor, on-load regulator transformer no-load voltage ratio inequality about
Beam condition；Lower layer optimization with when, variable includes active adjusting and power factor regulation, the on-load regulator transformer of distributed generation resource
Tap adjust, the renewable active reduction expression formula of distributed generation resource is：
In formula, Δ PPV,i,tWith Δ PWT,i,tRespectively i-th of photo-voltaic power supply and each wind-force power supply having in the t periods of jth
Work(reduction；
Offset of the node voltage quality with voltage with respect to rated voltage indicates that expression formula is：
In formula, npFor power distribution network number of nodes, V0、ViFor power distribution network head end balance nodes voltage and node i voltage, wiFor table
Show the weight factor of node voltage material circumstance, meets
Lower layer's optimization only starts when upper layer optimum results are unsatisfactory for confidence interval, and lower layer is unsatisfactory for setting in optimizing upper layer
The result in letter section is advanced optimized, and result is returned to upper layer optimization after lower layer's optimization, whether rejudges optimum results
Meet confidence interval, meet, modification upper layer optimized variable value and calculating target function value are unsatisfactory for, in calculating target function
Then additional penalty item when value carries out next step iteration.
The method of the present invention considers renewable distributed generation resource to the optimization of active distribution network distributed generation resource and contributes
With the randomness and uncertainty of load, directive significance as a result is had more to actual motion.
It is described in more detail below.
The present invention proposes a kind of active distribution network hierarchy optimization dispatching method promoting renewable distributed generation resource consumption, packet
Include following steps：
Step 1: according to historical data informations such as active distribution network location wind speed, intensity of illumination, temperature, loads, estimate
The active probability density estimation parameter for counting wind distribution formula power supply, photovoltaic distributed generation resource and load, it is close to obtain probability
Spend model；
With the Weibull fitting of distribution wind speed probability density of two parameters, further obtains wind distribution formula power supply and contribute generally
Rate density is further obtained wind distribution formula power supply and is contributed generally with the Beta fitting of distribution intensity of illumination probability density of two parameters
Rate density is fitted Load Probability density with normal distribution.
Step 2: meter and renewable distributed generation resource are contributed and the randomness and uncertainty of load, in known each probability
On the basis of density model, the active distribution network distributed generation resource upper layer Optimized model based on chance constraint is established；
Upper layer Optimized model is in terms of and the power distribution network operating cost of environmental factor is minimum, renewable distributed generation resource permeability
Highest and the minimum target of system active power loss, variable include being with renewable distributed generation resource generated energy and workload demand amount
Main stochastic variable and the decision variable based on controlled distribution formula power supply generated energy.
The operating cost expression formula of power distribution network meter and environment factor is
Formula (1) is divided into five parts, and first part is the amortization charge of distributed generation resource power generation, wherein T is scheduling week
Phase, NDGFor the distributed generation resource number accessed in active distribution network, r is money rate, ni、Cins,i、Pr,i、τg,iRespectively the i-th kind point
Depreciable life, installation cost, rated generation power and the annual utilization hours of cloth power supply, PDG,i,tFor t period distributed electricals
The active power output of source i；Second part is the operation expense of distributed generation resource, wherein KOM,iFor i-th kind of distributed generation resource
Unit generated energy maintenance cost；Part III is the fuel consumption cost of distributed generation resource, wherein CgasIt is respectively fuel list with L
Valence and calorific value, ηi、Qgas,iThe fuel quantity of the generating efficiency and the consumption of unit generated energy of respectively i-th kind distributed generation resource；4th
Part is the pollutant emission cost of distributed generation resource, MDGFor distributed generation resource pollutant emission type, Ven,j、Vp,jFor jth kind
The environmental value and penalty standard of pollutant, QDG,ijFor pollutant discharge amount in the jth of i-th kind of distributed generation resource；Part V
The pollutant emission cost for interacting electricity with upper level power supply for power distribution network is calculated, P with traditional thermal power generationgrid,tFor the t times
The interaction electricity of section, CgridFor power distribution network electricity price, M are interacted with higher level's power gridGFor traditional thermal power generation pollutant emission type,
Qgrid,jFor the discharge capacity of traditional thermal power generation unit quantity of electricity pollutant j.
Renewable distributed generation resource computing permeability expression formula is
Formula (2) can ensure that each period renewable distributed power generation permeability is maximum in dispatching cycle, wherein NPVWith
NWTPhoto-voltaic power supply and wind-force number of power sources, P respectively in power distribution networkPV,i,tAnd PWT,j,tRespectively i-th of photovoltaic electric of t periods
The generated energy in source and j-th of wind-force power supply, PL,tFor t period distribution network load amounts.
F will be sought2Max problem is converted to the problem of minimizing：
In formula (3), ξ is a smaller positive number, ensures f '2> 0.
Active power loss expression formula is
In formula (4), Ploss,tFor t period power distribution network active power losses.
It converts multi-objective optimization question to single-object problem by setting weight coefficient：
MinF=λ1f1+λ2f′2+λ3f3 (5)
In formula (5), λ1+λ2+λ3=1, λ1、λ2、λ3Value is determined by a kind of deviation ranking method.It is poor that deviation is also named
Amount, for describing the gap between estimator and actual value.The deviation of a certain object function describes when taking different variate-values, with
Gap between the object function optimal value, deviation define expression formula and are：
In formula (6), m is the number of object function, fi(xj) it is object function fiTake xjWhen corresponding target function value.xi
For object function fiOptimal solution.Step flow chart such as Fig. 2 institutes of the weight coefficient of each object function are determined using deviation ranking method
Show.
It contributes and the stochastic variables such as load due to containing regenerative resource in object function, so setting object function needs
Meet probabilistic constraints：
In formula (7), Pr { } indicates the probability of happening of time in bracket, and X is decision variable, ζiFor ith stochastic variable
Model's Monte Carlo Simulation of Ions Inside sample, αFFor confidence level,For F (X, ζi) in confidence level it is at least αFWhen maximum target functional value.
Other constraintss include the constraint of node injecting power equation：
In formula (8), PG,iAnd QG,iThe active and reactive output of power supply, P respectively at node iL,i、QL,iRespectively save
Active and reactive load at point i.
Node voltage, branch transmission capacity chance constraint：
In formula (9), αVAnd αSFor given confidence level, Np、NbFor power distribution network node, set of fingers, Vmax、VminPoint
Not Wei node voltage bound, SmaxFor the power distribution network branch transmission capacity upper limit.
Distributed generation resource output Maximum Constraint and Climing constant：
In formula (10),WithRespectively distributed generation resource i outputs bound,WithRespectively it is distributed
The upward climbing power of maximum and maximum power of climbing downwards of formula power supply i.
Step 3: in the Optimization Solution iterative process of upper layer, is contributed based on renewable distributed generation resource and Load Probability is close
Model is spent, sample sampling is carried out using Monte Carlo Method of Stochastic, and bring Load flow calculation into, obtains meeting chance constraint item
The probability of part judges whether to meet confidence interval, meets, into following single-step iteration, be unsatisfactory for, and optimizes into lower layer；
Upper layer optimization is solved using one kind based on chaotic mutation small survival environment particle sub-group algorithm, algorithm and conventional ion group
Algorithm the difference is that：The initialization of population process that chaos sequence is introduced to particle cluster algorithm, enables initial population to fill
Divide and spread all over solution space, specific implementation flow chart is as shown in Figure 3；Based on the Euclidean distance between primary, by initial kind
Group is divided into multiple microhabitats；The speed of particle updates not only consideration history optimal solution itself and global history optimal solution, also examines
Consider history optimal solution in microhabitat；During algorithm iteration, based on the Euclidean distance between each microhabitat center,
The microhabitat less than given threshold value of adjusting the distance is eliminated.
The more new formula of algorithm particle state is：
In formula (11),GkIndividual particles history most optimal sortings of the respectively particle i in kth time iterative process
The optimal component of local history and the optimal component of group's global history where amount, particle in microhabitat；For inertia weight；c1、
c2、c3For particle Studying factors；ξ1、ξ2、ξ3For [0,1] uniform random number.
The selection of algorithm inertia weight uses adaptive re-configuration police, and deepens each for the every generation for arriving population
Particle, calculation formula are：
In formula (12), ωminFor the minimum inertia weight value of setting；λ is adjustment factor, between 0~1；WithPopulation minimum, the maximum adaptation value of kth time iteration；kmaxFor greatest iteration number.
Algorithm basic step flow chart is as shown in Figure 4.
Step 4: output and power factor and adjustment on-load voltage regulation transformation that lower layer's optimization passes through adjusting distributed generation resource
Device no-load voltage ratio, the result that confidence interval is unsatisfactory in optimizing to upper layer advanced optimize, and lower layer's optimum results are returned to upper layer
Optimization, upper layer and lower layer Optimized Operation alternately, are finally met the optimal solution of confidence interval.
Lower layer's optimization is up to mesh with the active reduction minimum of renewable distributed generation resource and power distribution network node voltage quality
Mark, variable include that the active adjusting of distributed generation resource and the tap of power factor regulation, on-load regulator transformer are adjusted.
The renewable active reduction expression formula of distributed generation resource is：
In formula (13), Δ PPV,i,tWith Δ PWT,i,tRespectively i-th of photo-voltaic power supply and each wind-force power supply of jth are in the t times
The active reduction of section.
Offset of the node voltage quality with voltage with respect to rated voltage indicates that expression formula is：
In formula (14), npFor power distribution network number of nodes, V0、ViFor power distribution network head end balance nodes voltage and node i voltage,
wiTo indicate the weight factor of node voltage material circumstance, meet
With step 3, lower layer's optimization converts multi-objective problem to single goal by scheduling setting weight coefficient and asks
Topic, weight coefficient are determined also according to deviation ranking method.
Lower layer's Optimized model object function is：
MinF '=λ4f4+λ5f5 (15)
Lower layer's Optimized Operation power-balance equality constraint：
In formula (16), Δ PDG,i、ΔQDG,iThe active and reactive reduction of distributed generation resource of respectively access node i.
Active reduction, on-load regulator transformer no-load voltage ratio and distributed electrical active power factor inequality constraints：
In formula (17),The respectively bound of the active reduction of distributed generation resource,
For the adjustable bound of on-load regulator transformer no-load voltage ratio,For distributed electrical active power factor bound.
Allow the chance constraint of capacity by lower layer's Optimized Operation measure posterior nodal point voltage and branch：
Lower layer's Optimized Operation is equally solved using the small survival environment particle sub-group optimization algorithm based on chaotic mutation.
In step 4, lower layer's optimization only starts in upper layer optimum results in the presence of under the premise of being unsatisfactory for confidence interval situation,
The result that confidence interval is unsatisfactory in optimizing to upper layer advanced optimizes, and lower layer's optimum results are returned to upper layer optimization, weight
Newly judge whether optimum results meet confidence interval, meet, modification upper layer optimized variable value and calculating target function value are discontented with
Foot, punishes upper layer optimization object function, then, carries out next step iteration；Upper layer and lower layer Optimized Operation alternately, obtains
To the optimal solution for finally meeting confidence interval.
Claims (6)
1. a kind of active distribution network Optimization Scheduling promoting the consumption of renewable distributed generation resource, which is characterized in that including with
Lower step：
Step 1, according to active distribution network location wind speed, intensity of illumination, temperature, load these historical data informations, estimation
The probability density estimation parameter of wind distribution formula power supply, photovoltaic distributed generation resource active power output and load determines each general
Rate density model；
Step 2, on the basis of known each pdf model, establish the active distribution network distributed generation resource based on chance constraint
Upper layer Optimized model；
Step 3, in the Optimization Solution iterative process of upper layer, based on renewable distributed generation resource contribute and Load Probability density letter
Number carries out random sampling using Monte Carlo Method of Stochastic, and brings Load flow calculation into, obtains meeting constraints condition of opportunity
Probability judges whether to meet confidence interval, if satisfied, carrying out next step iteration, be unsatisfactory for, and optimizes into lower layer；
Step 4, lower layer's optimization, by adjusting the output and power factor and on-load regulator transformer no-load voltage ratio of distributed generation resource,
The result that confidence interval is unsatisfactory in optimizing to upper layer advanced optimizes, and correcting upper layer using lower layer's optimum results optimizes,
The upper layer and lower layer Optimized Operation alternately, until finally being met the optimal solution of confidence interval.
2. promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption as described in claim 1, it is special
Sign is, in step 2, control variable includes the stochastic variable based on renewable distributed generation resource generated energy and workload demand amount
With the decision variable based on controlled distribution formula power supply generated energy；Constraints includes that object function needs the probability constraints met
Condition, the active and reactive equation constraints of power distribution network, node voltage, branch transmission capacity inequality constraints condition of opportunity,
Distributed generation resource output climbing inequality constraints condition.
3. promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption as described in claim 1, it is special
Sign is, upper layer multi-objective optimization question is converted to single-object problem by the way that weight coefficient is arranged in step 2, wherein power system
Number value is determined using deviation ranking method；The operating cost expression formula of power distribution network meter and environment factor is
Wherein, T is dispatching cycle, NDGFor the distributed generation resource number accessed in active distribution network, r is money rate, ni、Cins,i、
Pr,i、τg,iDepreciable life, installation cost, rated generation power and the annual utilization hours of respectively i-th kind distributed generation resource,
PDG,i,tFor the active power output of t period distributed generation resources i, KOM,iFor i-th kind of distributed generation resource unit generated energy safeguard at
This, CgasIt is respectively cooler fuel price and calorific value, η with Li、Qgas,iThe generating efficiency and unit hair of respectively i-th kind distributed generation resource
The fuel quantity of electric quantity consumption, MDGFor distributed generation resource pollutant emission type, Ven,j、Vp,jFor the environmental value of jth kind pollutant
And penalty standard, QDG,ijFor pollutant discharge amount in the jth of i-th kind of distributed generation resource；Pgrid,tFor the interaction electricity of t periods
Amount, CgridFor power distribution network electricity price, M are interacted with higher level's power gridGFor traditional thermal power generation pollutant emission type, Qgrid,jFor traditional fire
The discharge capacity of power power generation unit quantity of electricity pollutant j；
Renewable distributed generation resource computing permeability expression formula is
Wherein, NPVAnd NWTPhoto-voltaic power supply and wind-force number of power sources, P respectively in power distribution networkPV,i,tAnd PWT,j,tThe respectively t times
The generated energy of section i-th of photo-voltaic power supply and j-th of wind-force power supply, PL,tFor t period distribution network load amounts.
4. promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption as described in claim 1, it is special
Sign is, in step 3, upper layer optimization based on chaotic mutation small survival environment particle sub-group algorithm using being solved, and algorithm is by chaos sequence
Row introduce the initialization of population process of particle cluster algorithm, and initial population is enable fully to spread all over solution space；
The more new formula of algorithm particle state is：
In formula,GkIndividual particles history optimal components of the respectively particle i in kth time iterative process, particle institute
The optimal component of local history in microhabitat and the optimal component of group's global history；For inertia weight；c1、c2、c3For particle
Studying factors；ξ1、ξ2、ξ3For [0,1] uniform random number；
The selection of algorithm inertia weight uses adaptive re-configuration police, and deepens each grain to every generation of population
Son, calculation formula are：
In formula, ωminFor the minimum inertia weight value of setting；λ is adjustment factor, between 0~1；WithKth time is repeatedly
Population minimum, the maximum adaptation value in generation；kmaxFor greatest iteration number.
5. promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption as described in claim 1, it is special
Sign is, in step 4, when lower layer optimizes, variable includes the active and power factor regulation amount of distributed generation resource, on-load voltage regulation change
The tap regulated quantity of depressor；Constraints include the active reduction of distributed generation resource, power factor, on-load regulator transformer no-load voltage ratio not
Equality constraint；Lower layer optimization with when, variable includes active adjusting and power factor regulation, the on-load voltage regulation of distributed generation resource
The tap of transformer is adjusted, and the renewable active reduction expression formula of distributed generation resource is：
In formula, Δ PPV,i,tWith Δ PWT,i,tRespectively i-th of photo-voltaic power supply and each wind-force power supply of jth are cut in the active of t periods
Decrement；
Offset of the node voltage quality with voltage with respect to rated voltage indicates that expression formula is：
In formula, npFor power distribution network number of nodes, V0、ViFor power distribution network head end balance nodes voltage and node i voltage, wiTo indicate node
The weight factor of voltage material circumstance meets
6. promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption as claimed in claim 5, it is special
Sign is, in step 4, lower layer's optimization only starts when upper layer optimum results are unsatisfactory for confidence interval, during lower layer optimizes upper layer
The result for being unsatisfactory for confidence interval is advanced optimized, and result is returned to upper layer optimization after lower layer's optimization, rejudges optimization
As a result whether meet confidence interval, meet, modification upper layer optimized variable value and calculating target function value are unsatisfactory for, are calculating
Then additional penalty item when target function value carries out next step iteration.
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| CN113285445B (en) * | 2021-05-08 | 2022-10-11 | 广西大学 | Multi-region economic scheduling method for double-layer distributed multi-target rotary slime bacteria |
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