CN108365608A - A kind of Regional Energy internet uncertain optimization dispatching method and system - Google Patents
A kind of Regional Energy internet uncertain optimization dispatching method and system Download PDFInfo
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- CN108365608A CN108365608A CN201810009344.7A CN201810009344A CN108365608A CN 108365608 A CN108365608 A CN 108365608A CN 201810009344 A CN201810009344 A CN 201810009344A CN 108365608 A CN108365608 A CN 108365608A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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Abstract
A kind of Regional Energy internet uncertain optimization dispatching method and system, including：Obtain stochastic variable data；Probability of error distribution based on the stochastic variable generates multiple scenes；The multiple scene is cut down to obtain typical scene；Processing is carried out to the typical scene and obtains discrete combination optimization scene；Optimize scene according to the discrete combination to optimize by long-time uncertain optimization scheduling model, and optimum results are modified according to short time rolling amendment model.Technical scheme of the present invention helps to improve distributed energy permeability, copes with the uncertain fluctuation of intermittent energy, mitigates the scheduling pressure of next stage time scale.
Description
Technical field
The present invention relates to operation and control of electric power system fields, and in particular to a kind of Regional Energy internet uncertain optimization
Dispatching method and system.
Background technology
Active distribution network has energy active accommodation trend distribution, manages a variety of distributed energies, improves distributed energy profit
With the advantage of efficiency.Wherein, feeder line interconnection is carried out by flexible direct current device, the Power Exchange between internet can be completed, it is real
The now flexible modulation of controllable resources in larger scope, further increases the digestion capability to intermittent energy.With high permeability
Distributed energy access active distribution network, randomness and fluctuation to power distribution network optimizing scheduling bring it is many it is uncertain because
Element：First, uncertainty of contributing necessarily leads to certain prediction error, the scheduling strategy directly generated according to predicted value, warp
Ji property and safety cannot all be guaranteed；Secondly, intermittent energy output situation is affected by weather, when environmental catastrophe is led
Output big ups and downs are caused, larger pressure is caused to the dispatching of power netwoks of next stage time scale.In this background, traditional determination
Property optimizing scheduling strategy be no longer applicable in.Have part research at present and multi-scenario technique is applied to description power grid intermittent distribution
Formula power supply or uncertain load fluctuation, wherein most only discusses the uncertainty on sometime section, or considers
Multiple sections, and the relevance of power swing on adjacent time node is had ignored, it is complicated to there is calculating, it is difficult to which description is not known
Sex chromosome mosaicism.
In conclusion for the active distribution network of high permeability distributed energy, effectively to describe it not true there is an urgent need for a kind of
Qualitative scheduling strategy further increases the consumption of intermittent energy to cope with the uncertain fluctuation in network.
Invention content
In order to solve the above-mentioned deficiency in the presence of the prior art, it is uncertain that the present invention provides a kind of Regional Energy internet
Optimization Scheduling and system.
Technical solution provided by the invention is：
A kind of Regional Energy internet uncertain optimization dispatching method, including：
Obtain stochastic variable data；
Probability of error distribution based on the stochastic variable generates multiple scenes；
The multiple scene is cut down to obtain typical scene；
Processing is carried out to the typical scene and obtains discrete combination optimization scene；
Optimize scene according to the discrete combination to optimize by long-time uncertain optimization scheduling model, and to optimization
As a result it is modified according to short time rolling amendment model.
Preferably, the probability of error distribution based on the stochastic variable generates multiple scenes, including：
Error variance is turned into n section, each section institute envelope surface product is respectively S1,t,S2,t…Sn,t, constitute current time
Error state vector：Wherein, S1,t,S2,t…Sn,tRepresent each area
Between the probability that occurs；
The error state vector of the stochastic variable is corrected based on Markov theory；
To the error state vector random sampling, the corresponding error burst of the error state vector chosen is converted
For multiple scenes.
Preferably, the state vector that the stochastic variable is corrected based on Markov theory, is calculated as follows：
In formula, E indicates one step state transition matrix,Indicate t=t0The error state when moment to
Amount.
Preferably, described to the error state vector random sampling, the error state vector chosen is corresponding
Error burst is converted to multiple scenes, including：
According to each error state probability of t moment, random sampling is carried out to the error state vector；
After carrying out n times sampling, state matrix is constitutedSituation is chosen to constitute in wherein each section
One length is the binary vector (X of Nt)N×1, the binary vector (Xt)N×1In enable xi(i=1 ... n) indicates error region
Between selected state：If selected, xi=1, otherwise xi=0；
To the state matrixThe state matrix is constituted into t by Monte Carlo sampling0When-T
The scene of section.
Preferably, described that multiple scenes are cut down to obtain typical scene, including：
The multiple scene is subjected to scene reduction using Fuzzy c-Means Clustering Algorithm, and it is general to calculate the typical scene
Rate.
Preferably, the multiple scene is subjected to scene reduction using Fuzzy c-Means Clustering Algorithm as the following formula：
In formula, U：Subordinated-degree matrix, vector matrix centered on V, C：Typical scene number, M0：Original scene number, Vc：The
The center vector of c cluster scene collection, Xi：I-th of original scene vector, μci：I-th of scene vector clusters scene to c-th
The membership function of collection, m：Convergence factor, J：The similarity degree of scene and center vector inside each classification.
Preferably, the center vector V of c-th of cluster scene collectioncProbability is calculated as follows：
In formula, Nc：Vector V centered on can clusteringcScene quantity.
Preferably, the typical scene probability carries out processing and obtains discrete combination optimization scene, including：
The probability of the discrete combination optimization scene, is calculated as follows：
In formula, I：The optimal typical scene number of photovoltaic, K：The optimal typical scene number of wind turbine, J：The optimal typical scene of load
Number, M are combine scenes number；The probability of i-th of scene occurs for photo-voltaic power supply,Wind turbine k-th of scene of generation
Probability,The probability of j-th of scene occurs for load.
Preferably, the object function of the long-time uncertain optimization scheduling model includes：
The ideal distribution of global the lowest cost and network node voltage；
The totle drilling cost is calculated as follows：
In formula, λ1：Totle drilling cost；εs：The probability that combine scenes S occurs；c1：Adjustable distributed generation resource cost；pDG,t：T moment
Distributed generation resource generated output；Δt：Unit interval；c2：Flexible direct current device dispatches cost；pVSC,t：T moment flexible direct current fills
Set power；cIL：Interruptible load cost；pIL,t：T moment interruptible load power；cgrid：From upper level power grid purchase electricity at
This；pgrid,t：T moment upper level electrical grid transmission power；ce：Power supply income；pL,t：T moment supply load power；closs：Operation damage
Consume cost；pline_loss,s：Power loss when combine scenes S；pVSC_loss,s：The loss of flexible direct current device when combine scenes S
Power；M：Combine scenes number；T：Long time scale optimizes duration；
The network node voltage is calculated as follows：
In formula, λ2：Average voltage bias；Nnode：Number of nodes；ui,t,s：The voltage of i-th of node t moment under S scenes
Deviation；T：Long time scale optimizes duration；us,t：T moment variation, ui N：Node rated voltage.
Preferably, the short time rolling amendment object function such as following formula：
In formula, g：Adjustable resource is with respect to reference value deviation；T:Short-term time scale optimizes duration；U is adjustable resource quantity,
pi,t res：I-th of adjustable resource output reference value；pi,t：The optimum results of i-th of adjustable resource short-term time scale；PGi：I-th
Adjustable resource rated power.
Preferably, the acquisition stochastic variable data, including：Obtain wind power output, photovoltaic output and wavy load.
Another object of the present invention is to propose that a kind of Regional Energy internet uncertain optimization dispatches system, including：It obtains
Modulus block, generation module cut down module, processing composite module and determining module；
The acquisition module, for obtaining stochastic variable data；
The generation module generates multiple scenes for the probability of error distribution based on the stochastic variable；
The reduction module, for being cut down to obtain typical scene to the multiple scene；
The processing composite module obtains discrete combination optimization scene for carrying out processing to the typical scene probability；
The determining module passes through long-time uncertain optimization scheduling model for optimizing scene according to the discrete combination
It optimizes, and optimum results is modified according to short time rolling amendment model.
Preferably, the generation module, including：It divides submodule, correct submodule, sampling submodule；
Submodule is divided, for error variance to be turned to n section, each section institute envelope surface product is respectively S1,t,S2,t…
Sn,t, constitute the error state vector at current time：Wherein, S1,t,
S2,t…Sn,tRepresent the probability that each section occurs；
Correct submodule, the error state vector for correcting the stochastic variable based on Markov theory；
Sampling submodule, is used for the error state vector random sampling, by the error state vector pair chosen
The error burst answered is converted to multiple scenes.
Preferably, the reduction module, including cluster submodule and computational submodule；
The cluster submodule, for the multiple scene to be carried out scene using Fuzzy c-Means Clustering Algorithm as the following formula
It cuts down：
In formula, U：Subordinated-degree matrix, V：Center vector matrix, C：Typical scene number, M0：Original scene number, Vc：C
The center vector of a cluster scene collection, Xi：I-th of original scene vector, μci：I-th of scene vector clusters scene collection to c-th
Membership function, m：Convergence factor, J：The similarity degree of scene and center vector inside each classification；
The computational submodule, the center vector V for c-th of cluster scene collection to be calculated as followscProbability：
In formula, Nc：Vector V centered on can clusteringcScene quantity.
Preferably, the processing composite module, including,
Submodule is combined, optimizes the probability of scene for the discrete combination to be calculated as follows：
In formula, I：The optimal typical scene number of photovoltaic, K：The optimal typical scene number of wind turbine, J：The optimal typical scene of load
Number, M：Combine scenes number；The probability of i-th of scene occurs for photo-voltaic power supply,The general of k-th scene occurs for wind turbine
Rate,The probability of j-th of scene occurs for load.
Preferably, the determining module, including：Long-time uncertain optimization scheduling model and short time rolling amendment mould
Type；
The long-time uncertain optimization scheduling model, the reason for calculating global the lowest cost and network node voltage
Think the object function of distribution；
The short time rolling amendment model, for calculating object function of the adjustable resource with respect to reference value deviation.
Compared with prior art, beneficial effects of the present invention are：
Technical scheme of the present invention is generated by being distributed the probability of error of stochastic variable based on acquisition stochastic variable data
Multiple scenes；Multiple scenes are cut down to obtain typical scene；And processing is carried out to the typical scene and obtains discrete combination
Optimize scene；Optimize scene according to discrete combination to optimize by long-time uncertain optimization scheduling model, and optimization is tied
Fruit is modified according to short time rolling amendment model, when realizing using multi-scenario technique processing stochastic uncertainty problem,
Complexity, inenarrable uncertain problem can be converted to multiple certainty scenes that may occur, to which simplification is asked
Difficulty is solved, distributed energy permeability is helped to improve, copes with the uncertain fluctuation of intermittent energy, mitigates next stage time ruler
The scheduling pressure of degree.
When technical scheme of the present invention can be counted and is multiple between discontinuity surface uncertain variables error correlation, will not know
Problem is effectively described as multiple certainty scenes, simplifies original problem.
Description of the drawings
Fig. 1 is the Multiple Time Scales dispatching method implementing procedure of Markov Chain-multi-scenario technique of the present invention；
Fig. 2 is that the scene of the present invention cuts down flow chart；
Fig. 3 is the scene generation step flow chart that the present invention is the present invention.
Fig. 4 is a kind of Regional Energy internet uncertain optimization dispatching method flow chart of the present invention；
Specific implementation mode
For a better understanding of the present invention, present disclosure is done further with example with reference to the accompanying drawings of the specification
Explanation.
Unit is not known for the uncertain scheduling problem of current active distribution network, in meter of the present invention and active distribution network to go out
A kind of temporal associativity of power error, it is proposed that uncertain scheduling of the Multiple Time Scales based on Markov Chain-multi-scenario technique
Model.Include the following steps：
It initially sets up the markovian scene of the combination and generates model, contribute to wind power output, photovoltaic and uncertain
Load carries out uncertain sampling, generates a large amount of scenes；Then it establishes the scene based on Fuzzy c-Means Clustering Algorithm and cuts down model,
Above-mentioned uncertain scene is cut down to obtain typical scene；Multiple Time Scales scheduling optimization model is finally established, that is, is based on allusion quotation
The long time scale active distribution network uncertain optimization scheduling model and deterministic short time rolling amendment model of type scene.
Technical solution as shown in Figure 4 is as follows：
One, stochastic variable data are obtained；
The Uncertain Stochastic variable being related to includes that wind turbine is contributed, photovoltaic is contributed and wavy load.
Two, the probability of error distribution based on the stochastic variable generates multiple scenes；
Scene generates the probability of error distribution based on stochastic variable and generates a large amount of scenes, and description is uncertain.
Have studies have shown that at sometime section t, regenerative resource is contributed can with the prediction error of fluctuating load
It is approximately considered Normal Distribution.Error variance is turned into n section, each section institute envelope surface product is respectively S1,t,S2,t…
Sn,t, the probability that each section occurs is represented, the error state vector at current time is constituted：
In time scale, there is certain relevance, to influence original prediction between the uncertain deviation at each moment
The probability of error is distributed.It, can will be pre- at random since Markov Chain shows superperformance in wind-powered electricity generation, photovoltaic output series model
It surveys the error process of changing with time and regards markoff process as, i.e., in moment tkIn the case of error state is known, process exists
Moment t (t ＞ tk) at state only and tkThe state at moment is related, and and tkState before is unrelated.Therefore uncertain output error
State model can be expressed as：
Wherein, XtFor the error state of t moment, EijIndicate that error is transitioned into the step state transfer of state j generally by state i
Rate can be acquired by statistical data, as shown in formula (3)：
Wherein, xijTo switch to the period by the state i of period t by statistical analysis historical data and numerical value data of weather forecast
The number that the state j of t+1 occurs.
After considering temporal associativity, moment t error state vector mtIt can be modified to：
Wherein, E is one step state transition matrix, there is E=[Eij]n×n, and
For t=t0The error state vector when moment.
Scenario simulation is carried out based on the modified state vector of Markov theory, is conducive to improve having for subsequent scenario reduction
Effect property and computational efficiency.
According to each error state probability of t moment, random sampling is carried out to it.Enable xi(i=1 ... n) indicates error burst
Selected state：If selected, xi=1, otherwise xi=0, then scene of sampling can use one group of binary number representation.After n times sampling,
Situation is chosen to constitute the binary vector (X that a length is N in each sectiont)N×1。
Error state section sample X is obtained by the above processt, constitute state matrixIt carries out later
It samples Monte Carlo.It samples for ith, successively from XsIn w row random samplings obtain sample value Xiw, constitute t0- T the periods
Scene.Entire scene generating process is concluded as shown in Figure 3.
Three, the multiple scene is cut down to obtain typical scene；
Large scale scene is cut down using Fuzzy c-means Clustering method, to ensure computational efficiency, is according to formula (5)
Target carries out scene clustering.
Wherein, U is subordinated-degree matrix, and vector matrix centered on V, C indicates typical scene number, M0Indicate original scene number
Mesh, VcIndicate the center vector of c-th of cluster scene collection, XiIndicate i-th of original scene vector, μciIndicate i-th of scene to
The membership function to c-th of cluster scene collection is measured, m is convergence factor.By M0A original scene is divided into C set, respectively
The center vector of cluster substitutes all scenes therein as typical scene, and typical scene probability is all scenes in clustering
Probability and.J characterizes the similarity degree of scene and center vector inside each classification, is completed to the excellent of J by below step
Change, determines center vector Vc。
Scene clustering flow chart as can be seen from Figure 2 is described in detail below：
Step1：Iterations h=0 determines initialization membership function μ according to formula (5)ci (0), U(0)=[μci (0)]；
Step2：H=h+1 is enabled, center vector is calculated
Step3:Update the membership function of each scene：
Step4：Judge whether to meet U(h)-U(h-1)＜ ε, if so, output center vector Vc, otherwise it is transferred to Step2.
Center vector V can be obtained after clustercProbability is：
In formula, NcFor vector V centered on can clusteringcScene quantity.δc:The center vector V of c-th of cluster scene collectioncGenerally
Rate.
The present invention constructs fuzzy clustering Validity Index PS, shown in expression formula such as formula (7).First item can characterize class in formula
Interior compactness, Section 2 characterize alienation degree between class.Its value is bigger, indicates that c-th of intra-cluster is compact and is clustered with other
Between have larger difference.In cluster process, optimal typical scene number C is determined by PS values*, as shown in formula (8).
Wherein, For the flat of all typical scenes
Mean value.VkIndicate the center vector of k-th of cluster scene collection.
Four, processing is carried out to the typical scene and obtains discrete combination optimization scene；
By the corresponding typical scene permutation and combination of each stochastic variable, series of discrete Combinatorial Optimization scene is obtained, as
Subsequent research object.Combine scenes probability of happening is the product of probability of corresponding typical scene, i.e.,：
Wherein, wherein I is the optimal typical scene number of photovoltaic, and K is the optimal typical scene number of wind turbine, and J is that load is optimal
Typical scene number, M are combine scenes number, i.e. M=IKJ.Indicate that the probability of i-th of scene occurs for photo-voltaic power supply,Similarly.
Five, optimize scene according to the discrete combination to optimize by long-time uncertain optimization scheduling model, and right
Optimum results are modified according to short time rolling amendment model.
The present invention relates to the scheduling optimization models of two time scales：Long time scale overall scheduling is based on Markov
Typical scene obtained by chain-multi-scenario technique and corresponding probability show that interconnection and the plan of VSC devices are contributed, realize global total
The minimum ideal distribution with network node voltage of cost；Short-term time scale keeps long time scale result constant, and correcting other can
It raises wages source, makes its adjustment amount with respect to reference value minimum.
Long time scale regulation goal is the ideal distribution for realizing global the lowest cost and network node voltage, expression formula
Such as formula (10)-(11).
λ1：Totle drilling cost；εs：The probability that combine scenes S occurs；c1：Adjustable distributed generation resource cost；pDG,t：T moment is distributed
Formula power supply generated output；Δt：Unit interval；c2：Flexible direct current device dispatches cost；pVSC,t：T moment flexible direct current device work(
Rate；cIL：Interruptible load cost；pIL,t：T moment interruptible load power；cgrid：Energy cost is bought from upper level power grid；
pgrid,t：T moment upper level electrical grid transmission power；ce：Power supply income；pL,t：T moment supply load power；closs：Running wastage
Cost；pline_loss,s：Power loss when combine scenes S；pVSC_loss,s：The loss work(of flexible direct current device when combine scenes S
Rate；M：Combine scenes number；T：Long time scale optimizes duration；
λ2：Average voltage bias；Nnode：Number of nodes；ui,t,s：The voltage deviation of i-th of node t moment under S scenes；
T：Long time scale optimizes duration.
Formula (10) is the full probability representation of expected gross cost, and first five items are respectively adjustable DG costs, flexible direct current dress
It sets scheduling cost, interruptible load cost, buy energy cost and power supply income from upper level power grid, last is by net
The operating cost brought, ε is lost in damage, VSCsFor the probability of happening of scene s.Formula (11) is node voltage mean deviation amount.Wherein,
us,tFor t moment variation under S scenes, ui NFor i-th of node rated voltage.
Long time scale constraints includes power-balance constraint, the constraint of unbalanced power amount, each adjustable elements output
Constraint, node voltage constraint, the constraint of flexible direct current device etc..
Short-term time scale keep in touch linear heat generation rate and flexible direct current device contribute it is constant, with adjustable resource under long time scale
Power generating value is used as with reference to being worth, and optimizes adjustable resource, and object function is set as adjustable resource with respect to reference value deviation minimum, such as formula
(12), g is adjustable resource with respect to reference value deviation, T:Short-term time scale optimizes duration.
U is adjustable resource quantity, pi,t resFor i-th of adjustable resource output reference value, i.e. long time scale acquired results,
pi,tFor the optimum results of i-th of adjustable resource short-term time scale, PGiFor i-th of adjustable resource rated power.
Short-term time scale constraint is similar with long time scale constraint, in addition, increasing each unit power adjustment most
Big value constraint.
With reference to Fig. 1, the embodiment of the method for the present invention is described in detail.
Step 1 describes in Fig. 1, and Uncertain Stochastic variable of the present invention includes that wind turbine is contributed, photovoltaic is contributed
And wavy load.Discontinuity surface when for t, the original probability distribution based on stochastic variable, is repaiied in conjunction with Markov Chain principle
Positive probability distribution, random sampling generate state sample.Turn to step 2；
Step 2 describes in Fig. 1, judges the control week that long time scale whether is reached at the time of current state sampling
Otherwise phase turns to step 9 if so, turning to step 3；
Step 3 describes in Fig. 1, according to sampled obtained t0The state sample X of-T periodst(t=t0... T), structure
At state matrixCarry out Monte Carlo sampling：It samples for ith, successively from XsIn w row random samplings
Obtain sample value Xiw, constitute t0The original scene collection of-T periods turns to step 4；
Fig. 1 steps 4 describe, and after obtaining original scene, carry out scene according to Fuzzy c-means Clustering principle and cut down acquisition
Typical scene determines optimal typical scene number, according to step1-step4 with formula (5) for target by formula (7) and formula (8)
The step of carry out scene clustering, obtain typical scene and each scene correspond to probability, then combine scenes and corresponding general are determined by formula (9)
Rate, rear steering step 5；
Fig. 1 steps 5 describe, and it is 4h that long time scale, which is arranged, in the present invention, with formula (10) and formula (11) for target, carry out
Long time scale global coordination trend distribution scheduling process, constraints include power-balance constraint, unbalanced power amount about
Beam, node voltage constraint, each adjustable elements and deferrable load constraint and the constraint of flexible direct current device, pass through long time scale
Global optimization, can obtain dominant eigenvalues and flexible direct current device is contributed, and turn to step 6；
Fig. 1 steps 6 describe, and it is 1h that short-term time scale, which is arranged, in the present invention, to ensure the power swing inside region not
Coupled external network is influenced, keeps dominant eigenvalues and flexible direct current device output under long time scale constant, with it
His adjustable elements are contributed as with reference to value, with formula (12) for target, are carried out short-term time scale region automatic control adjustable elements process, are obtained
The power of distributed generation resource, energy storage device and deferrable load in a period, and issue, turn to step 7；
Fig. 1 steps 7 describe, and judge whether short-term time scale optimization carries out to a controlling cycle, if so, turning to step
Rapid 8, the short-term time scale optimization that step 6 continues subsequent period is otherwise turned to, present invention setting carries out primary at interval of 1h
Rolling optimization is realized in optimization；
Fig. 1 steps 8 describe, and update historical data, and historical data is added in the actual power of wind-powered electricity generation, photovoltaic and load
In library, and the prediction of the wind-powered electricity generation, photovoltaic and load of next controlling cycle is carried out, turns to step 9；
Fig. 1 steps 9 describe, and new error transfer matrix are solved according to formula (3) by statistical data, by error transfer square
Battle array passes to scene generating process, turns to step 1.
Another object of the present invention is to propose that a kind of Regional Energy internet uncertain optimization dispatches system, including：It obtains
Modulus block, generation module cut down module, processing composite module and determining module；
Aforementioned four module is described further below：
Acquisition module, for obtaining stochastic variable data；
Generation module generates multiple scenes for the probability of error distribution based on the stochastic variable；
Module is cut down, for being cut down to obtain typical scene to the multiple scene；
Composite module is handled, discrete combination optimization scene is obtained for carrying out processing to the typical scene probability；
The determining module passes through long-time uncertain optimization scheduling model for optimizing scene according to the discrete combination
It optimizes, and optimum results is modified according to short time rolling amendment model.
Generation module, including：It divides submodule, correct submodule, sampling submodule；
Submodule is divided, for error variance to be turned to n section, each section institute envelope surface product is respectively S1,t,S2,t…
Sn,t, constitute the error state vector at current time：Wherein, S1,t,
S2,t…Sn,tRepresent the probability that each section occurs；
Correct submodule, the error state vector for correcting the stochastic variable based on Markov theory；
Sampling submodule, is used for the error state vector random sampling, by the error state vector pair chosen
The error burst answered is converted to multiple scenes.
Module is cut down, including：Cluster submodule and computational submodule；
Submodule is clustered, for the multiple scene to be carried out scene reduction using Fuzzy c-Means Clustering Algorithm as the following formula：
In formula, U：Subordinated-degree matrix, V：Center vector matrix, C：Typical scene number, M0：Original scene number, Vc：C
The center vector of a cluster scene collection, Xi：I-th of original scene vector, μci：I-th of scene vector clusters scene collection to c-th
Membership function, m：Convergence factor, J：The similarity degree of scene and center vector inside each classification；
Computational submodule, the center vector V for described c-th cluster scene collectioncProbability is calculated as follows：
In formula, Nc：Vector V centered on can clusteringcScene quantity.
Composite module is handled, including, combine submodule
Submodule is combined, for the probability of discrete combination optimization scene, is calculated as follows：
In formula, I：The optimal typical scene number of photovoltaic, K：The optimal typical scene number of wind turbine, J：The optimal typical scene of load
Number, M：Combine scenes number；The probability of i-th of scene occurs for photo-voltaic power supply,The general of k-th scene occurs for wind turbine
Rate,The probability of j-th of scene occurs for load.
Determining module, including：Long-time uncertain optimization scheduling model and short time rolling amendment model；
Long-time uncertain optimization scheduling model, the ideal point for calculating global the lowest cost and network node voltage
The object function of cloth；
Short time rolling amendment model, for calculating object function of the adjustable resource with respect to reference value deviation.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, the application can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The application is with reference to method, the flow of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
It these are only the embodiment of the present invention, be not intended to restrict the invention, it is all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (16)
1. a kind of Regional Energy internet uncertain optimization dispatching method, which is characterized in that including：
Obtain stochastic variable data；
Probability of error distribution based on the stochastic variable generates multiple scenes；
The multiple scene is cut down to obtain typical scene；
Processing is carried out to the typical scene and obtains discrete combination optimization scene；
Optimize scene according to the discrete combination to optimize by long-time uncertain optimization scheduling model, and to optimum results
It is modified according to short time rolling amendment model.
2. Regional Energy internet uncertain optimization dispatching method as described in claim 1, which is characterized in that described to be based on institute
The probability of error distribution for stating stochastic variable generates multiple scenes, including：
Error variance is turned into n section, each section institute envelope surface product is respectively S1,t,S2,t…Sn,t, constitute the mistake at current time
Poor state vector：Wherein, S1,t,S2,t…Sn,tRepresent each section hair
Raw probability；
The error state vector of the stochastic variable is corrected based on Markov theory；
To the error state vector random sampling, the corresponding error burst of the error state vector chosen is converted to more
A scene.
3. Regional Energy internet uncertain optimization dispatching method as claimed in claim 2, which is characterized in that described to be based on horse
The state vector of stochastic variable, is calculated as follows described in Er Kefu theoretical corrections：
In formula, E indicates one step state transition matrix,Indicate t=t0The error state vector when moment.
4. Regional Energy internet uncertain optimization dispatching method as claimed in claim 2, which is characterized in that described to described
The corresponding error burst of the error state vector chosen is converted to multiple scenes by error state vector random sampling, packet
It includes：
According to each error state probability of t moment, random sampling is carried out to the error state vector；
After carrying out n times sampling, state matrix is constitutedSituation is chosen to constitute one in wherein each section
A length is the binary vector (X of Nt)N×1, the binary vector (Xt)N×1In enable xi(i=1 ... n) indicates error burst
Selected state：If selected, xi=1, otherwise xi=0；
To the state matrixThe state matrix is constituted into t by Monte Carlo sampling0- T the periods
Scene.
5. Regional Energy internet uncertain optimization dispatching method as described in claim 1, which is characterized in that described to multiple
Scene is cut down to obtain typical scene, including：
The multiple scene is subjected to scene reduction using Fuzzy c-Means Clustering Algorithm, and calculates the typical scene probability.
6. Regional Energy internet uncertain optimization dispatching method as claimed in claim 5, which is characterized in that as the following formula by institute
It states multiple scenes and scene reduction is carried out using Fuzzy c-Means Clustering Algorithm：
In formula, U：Subordinated-degree matrix, vector matrix centered on V, C：Typical scene number, M0：Original scene number, Vc：C-th
Cluster the center vector of scene collection, Xi：I-th of original scene vector, μci：I-th of scene vector clusters scene collection to c-th
Membership function, m：Convergence factor, J：The similarity degree of scene and center vector inside each classification.
7. Regional Energy internet uncertain optimization dispatching method as claimed in claim 6, which is characterized in that described c-th
Cluster the center vector V of scene collectioncProbability is calculated as follows：
In formula, Nc：Vector V centered on can clusteringcScene quantity.
8. Regional Energy internet uncertain optimization dispatching method as claimed in claim 7, which is characterized in that the typical field
Scape probability carries out processing and obtains discrete combination optimization scene, including：
The probability of the discrete combination optimization scene, is calculated as follows：
In formula, I：The optimal typical scene number of photovoltaic, K：The optimal typical scene number of wind turbine, J：The optimal typical scene number of load
Mesh, M are combine scenes number；The probability of i-th of scene occurs for photo-voltaic power supply,The general of k-th scene occurs for wind turbine
Rate,The probability of j-th of scene occurs for load.
9. Regional Energy internet uncertain optimization dispatching method as claimed in claim 8, which is characterized in that the long-time
The object function of uncertain optimization scheduling model includes：
The ideal distribution of global the lowest cost and network node voltage；
The totle drilling cost is calculated as follows：
{εs[c1(pDG, t)Δt+c2(pVSC, t)Δt
In formula, λ1：Totle drilling cost；εs：The probability that combine scenes S occurs；c1：Adjustable distributed generation resource cost；pDG,t：T moment is distributed
Formula power supply generated output；Δt：Unit interval；c2：Flexible direct current device dispatches cost；pVSC,t：T moment flexible direct current device work(
Rate；cIL：Interruptible load cost；pIL,t：T moment interruptible load power；cgrid：Energy cost is bought from upper level power grid；
pgrid,t：T moment upper level electrical grid transmission power；ce：Power supply income；pL,t：T moment supply load power；closs：Running wastage
Cost；pline_loss,s：Power loss when combine scenes S；pVSC_loss,s：The loss work(of flexible direct current device when combine scenes S
Rate；M：Combine scenes number；T：Long time scale optimizes duration；
The network node voltage is calculated as follows：
In formula, λ2：Average voltage bias；Nnode：Number of nodes；ui,t,s：The voltage deviation of i-th of node t moment under S scenes；
T：Long time scale optimizes duration；us,t：T moment variation, ui N：Node rated voltage.
10. Regional Energy internet uncertain optimization dispatching method as claimed in claim 9, which is characterized in that it is described in short-term
Between rolling amendment object function such as following formula：
In formula, g：Adjustable resource is with respect to reference value deviation；T:Short-term time scale optimizes duration；U is adjustable resource quantity, pi,t res：
I-th of adjustable resource output reference value；pi,t：The optimum results of i-th of adjustable resource short-term time scale；PGi：It can raise wages for i-th
Source rated power.
11. Regional Energy internet uncertain optimization dispatching method as described in claim 1, which is characterized in that the acquisition
Stochastic variable data, including：Obtain wind power output, photovoltaic output and wavy load.
12. a kind of Regional Energy internet uncertain optimization dispatches system, which is characterized in that including：Acquisition module generates mould
Block cuts down module, processing composite module and determining module；
The acquisition module, for obtaining stochastic variable data；
The generation module generates multiple scenes for the probability of error distribution based on the stochastic variable；
The reduction module, for being cut down to obtain typical scene to the multiple scene；
The processing composite module obtains discrete combination optimization scene for carrying out processing to the typical scene probability；
The determining module is carried out for optimizing scene according to the discrete combination by long-time uncertain optimization scheduling model
Optimization, and optimum results are modified according to short time rolling amendment model.
13. Regional Energy internet as claimed in claim 12 uncertain optimization dispatches system, which is characterized in that the generation
Module, including：It divides submodule, correct submodule, sampling submodule；
Submodule is divided, for error variance to be turned to n section, each section institute envelope surface product is respectively S1,t,S2,t…Sn,t,
Constitute the error state vector at current time：Wherein, S1,t,S2,t…Sn,t
Represent the probability that each section occurs；
Correct submodule, the error state vector for correcting the stochastic variable based on Markov theory；
Sampling submodule is used for the error state vector random sampling, and the error state vector chosen is corresponding
Error burst is converted to multiple scenes.
14. Regional Energy internet as claimed in claim 12 uncertain optimization dispatches system, which is characterized in that the reduction
Module, including cluster submodule and computational submodule；
The cluster submodule, for the multiple scene to be carried out scene reduction using Fuzzy c-Means Clustering Algorithm as the following formula：
In formula, U：Subordinated-degree matrix, V：Center vector matrix, C：Typical scene number, M0：Original scene number, Vc：C-th poly-
The center vector of class scene collection, Xi：I-th of original scene vector, μci：Person in servitude of i-th of scene vector to c-th of cluster scene collection
Category degree function, m：Convergence factor, J：The similarity degree of scene and center vector inside each classification；
The computational submodule, the center vector V for c-th of cluster scene collection to be calculated as followscProbability：
In formula, Nc：Vector V centered on can clusteringcScene quantity.
15. Regional Energy internet as claimed in claim 14 uncertain optimization dispatches system, which is characterized in that the processing
Composite module, including,
Submodule is combined, optimizes the probability of scene for the discrete combination to be calculated as follows：
In formula, I：The optimal typical scene number of photovoltaic, K：The optimal typical scene number of wind turbine, J：The optimal typical scene number of load
Mesh, M：Combine scenes number；The probability of i-th of scene occurs for photo-voltaic power supply,The general of k-th scene occurs for wind turbine
Rate,The probability of j-th of scene occurs for load.
16. Regional Energy internet as claimed in claim 12 uncertain optimization dispatches system, which is characterized in that the determination
Module, including：Long-time uncertain optimization scheduling model and short time rolling amendment model；
The long-time uncertain optimization scheduling model, the ideal point for calculating global the lowest cost and network node voltage
The object function of cloth；
The short time rolling amendment model, for calculating object function of the adjustable resource with respect to reference value deviation.
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