CN107069716A - A kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error - Google Patents
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Abstract
The invention discloses a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error, method is first with real-time measurement data, the probability Estimation model of injection transfer distribution factor is established based on Bayes's linear regression theory, and solves the error burst for obtaining injection transfer distribution factor.And then, the present invention is constructed while considering the Real-Time Scheduling model of the disturbance of node injecting power and injection transfer distribution factor evaluated error, and based on Soyster robust Optimal methods, give the derivation algorithm of model using operation of power networks economy as target.The present invention is by simple 6 node system, the measuring and calculation of the node systems of IEEE 118 and the node systems of IEEE 300 and analysis, demonstrating the validity of method.
Description
Technical field
The present invention relates to a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error.
Background technology
For the global energy and environment crisis of reply, the novel power supply such as wind-powered electricity generation, photovoltaic is obtained extensively in power system
Using, its intrinsic randomness, intermittent feature enhance the uncertainty in Operation of Electric Systems, so that so that it is uncertain
Electric power system dispatching decision problem under service condition, the focus as currently associated domain expert, focus of attention.Robust optimizes
It is the effective ways that a class solves decision under uncertainty problem, it is interval according to the disturbance of uncertain variables, finds uncertain variables
Best decision scheme in the case of most bad realization.Because the decision process of robust Optimal methods need not have according to uncertain variables
The probability-distribution function of body, also, with the advantage in computational efficiency, in recent years, in Unit Commitment, economic load dispatching
It is used widely in problem.
The development of electric power system dispatching decision-making robust Optimal methods is rapid, and achievement is significant.Existing method institute pin
To, mainly load power demand, new energy output power of power supply and hair transmission facility failure it is this kind of have it is uncertain from
The object of right attribute.It will, however, be appreciated that when being modeled to decision of power system dispatching problem, for the mould of power network characteristic
Plan can usually introduce modeling error with approximate, so that new uncertain factor is introduced, among these, representative uncertain ginseng
Amount is the injection transfer distribution factor (ISF) of power network.
Injection transfer distribution factor be power network a kind of important linearisation factor, power transmission distribution factor (PTDF),
Branch breaking distribution factor (LODF) and cut-off transmission distribution factor (OTDF), can by injection transfer distribution factor derive obtain
.In electric power system dispatching decision model, injection transfer distribution factor is widely used in building the ability to transmit electricity of transmission of electricity branch road
Constraint, to ensure the security of transmission of electricity.
However, conventional at present shifts distribution factor based on the injection that DC power flow is derived, in reflection power system injection
During metastatic rule, there is larger error, reason is：
1) method depends on branch parameters, and in actual applications, branch parameters drift about with run time, and due to meter
Calculate, safeguard improper and there is error, the inaccurate of injection transfer distribution factor estimation will be caused；
2) need to set balance nodes when method is calculated, but the setting actual power not always with power network of balance nodes
Equilibrium strategy is consistent, and this will also influence the accuracy of injection transfer distribution factor estimation；
3) distribution factor is shifted based on the constant injection that DC power flow is derived, it is impossible to embody power network under different running statuses
The change of the injection transfer regularity of distribution.The error of injection transfer distribution factor estimation, can exist scheduling result and cause transmission of electricity branch
The out-of-limit possibility of road transimission power, so as to influence the security of Operation of Electric Systems.And prior art does not provide injection transfer
The acquisition methods of distribution factor indeterminacy section, meanwhile, in practice, the uncertainty degree of each injection transfer distribution factor is often
Nor consistent.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of to count and the robust of injection transfer distribution factor evaluated error is real
When dispatching method, the present invention can effectively count in decision-making and node inject shift distribution factor estimated bias, it is ensured that system
The security of operation, with higher computational efficiency.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error, based on Bayes's linear regression
The Theory Construction using the injection transfer distribution factor of metric data online probability Estimation model, using operation of power networks economy as
Target, using AGC unit operations basic point and the participation factor as decision variable, constructs the two benches robust optimization mould of Real-Time Scheduling
Type, while the bounded-but-unknown uncertainty of meter and injection transfer distribution factor and node injecting power, according to power network own physical characteristic,
Two classes in decision model are not known into parameter separating treatment, and are deterministic line by model conversation with Soyster methods
Property optimization problem is solved.
For high-voltage transmission network, there is approximate linear relationship in Branch Power Flow between being injected with each node power, according to
Bayes's linear regression theory, linear regression mould is estimated using dependent variable in linear regression model (LRM) and independent variable sample observations
The probability distribution of unknown regression coefficient in type, by the sample observations to Branch Power Flow and node injecting power, sets up to note
Enter to shift Bayes's linear regression model (LRM) that distribution factor is regression coefficient, realize the probability Estimation of injection transfer distribution factor.
In the case where meeting DC power flow assumed condition, the active injection of the effective power flow of high-voltage transmission network branch road and each node
It is expressed as linear relationship：
In formula, branch road k active-power PsBranch,kAnd in correspondence system N number of node active injection power N-dimensional column vector
PNodeObservation can be obtained by metric data；MkFor corresponding to branch road k N-dimensional injection transfer distribution factor column vector.
In view of linear regression residual error, the linear regression model (LRM) comprising injection transfer distribution factor is：
In formula, εkFor regression residuals, it is that 0, variance is σ to usually assume that it obeys averagek 2Normal distribution；Injection transfer point
Cloth factor vector MkWith variance scalar σk 2For stochastic variable.
Bayesian formula can set up the joint posterior probability density function of stochastic variable in formula：
In formula, P (Mk,σk 2/PBranch,k,PNode) it is given PBranch,kAnd PNodeOn M during measuring valuekAnd σk 2Joint after
Test probability density function；P(Mk,σk 2) it is MkAnd σk 2Joint priori probability density function；P(PBranch,k|Mk,σk 2,PNode) be
Likelihood function；P(PBranch,k) it is PBranch,kThe marginal probability density function obtained by statistics；
To σk 2Integration obtains vector MkJoint Posterior probability distribution be：
In formula, P (Mk|PBranch,k,PNode) it is MkJoint posterior probability density function.
The Posterior probability distribution of injection transfer distribution factor is approximately asked for using gibbs sampler numerical algorithm.
According to Real-Time Scheduling and AGC control to have it is close associate, Real-Time Scheduling is by setting the operation base of generating set
Point, according to the affine Regulation mechanism of AGC unit power outputs, controls the operation shape of power network in AGC control process with participating in the factor
State, decision-making is carried out to the operation basic point and the participation factor of AGC units.
Model turns to decision objective with AGC unit operation cost minimizations, specifically includes cost of electricity-generating and stand-by cost.
When building decision objective, build power-balance constraint, participate in the constraint of factor sum, unit reserve capacity-constrained, machine
The maximum adjustment capability constraint downward upwards of group, unit minimax units limits, unit ramping rate constraints and tributary capacity are about
Beam.
Further, model to regulation and control variable by optimizing, it is ensured that scheduling result is for given interval interior arbitrary
Load fluctuation and injection transfer distribution factor estimated bias so that the constraint of structure is set up.
Compared with prior art, beneficial effects of the present invention are：
(1) present invention can effectively be counted in decision-making and the estimated bias of distribution factor is shifted in node injection, it is ensured that system
The security of operation；
(2) present invention meter and the evaluated error of node injection transfer distribution factor, in the same of strengthening system decision-making robustness
When, the increase of system operation cost is likely to result in, but test result shows, in reasonable estimation injection transfer distribution factor error model
On the premise of enclosing, cost increase rate of the invention is not notable；
(3) present invention demonstrates tune in real time of the invention by the measuring and calculation to the nodes of IEEE 118 and 300 node systems
Spending algorithm has higher computational efficiency.
Brief description of the drawings
The Figure of description for constituting the part of the application is used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its illustrate be used for explain the application, do not constitute the improper restriction to the application.
Fig. 1 is the 6 node system wiring diagrams of the present invention；
Fig. 2 is the effective power flow calculated value of circuit 2 and measuring value comparison diagram of the present invention；
Fig. 3 is the operating cost contrast schematic diagram of the present invention；
Fig. 4 is that the node systems of IEEE 118 of the present invention calculate time diagram；
Fig. 5 is that the node systems of IEEE 300 of the present invention calculate the time.
Embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provide further instruction to the application.Unless another
Indicate, all technologies and scientific terminology that the present invention is used have logical with the application person of an ordinary skill in the technical field
The identical meanings understood.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
As background technology is introduced, prior art does not provide injection transfer distribution factor and not known in the prior art
Interval acquisition methods, meanwhile, in practice, the uncertainty degree of each injection transfer distribution factor is often nor unanimously not
Foot, in order to solve technical problem as above, present applicant proposes a kind of robust counted and inject transfer distribution factor evaluated error
Real-time scheduling method.
Probabilistic estimation of the present invention based on injection transfer distribution factor and the real-time scheduling method with robust property,
Propose a kind of while meter and power network injection transfer distribution factor and the probabilistic robust Real-Time Scheduling side of node injecting power
Method.First, the present invention based on Bayes's linear regression theory construct using metric data injection transfer distribution factor
Line probability Estimation model.And then, using operation of power networks economy as target, using AGC unit operations basic point and the participation factor as decision-making
Variable, constructs the two benches Robust Optimization Model of Real-Time Scheduling, and model is counted and injection transfer distribution factor and node note simultaneously
Enter the bounded-but-unknown uncertainty of power.Two classes in decision model are not known parameter point by method according to power network own physical characteristic
It is that deterministic linear optimization problem is solved by model conversation from processing, and with Soyster methods.The present invention is by right
The measuring and calculation of simple 6 node system, demonstrates the feasibility and validity of method, and by the nodes of IEEE 118 and 300
The measuring and calculation of node system, demonstrates the higher computational efficiency of method.
According to Bayes's linear regression theory, it is possible to use dependent variable and independent variable sample observations in linear regression model (LRM)
To estimate the probability distribution of unknown regression coefficient in linear regression model (LRM).For high-voltage transmission network, Branch Power Flow and each node
There is approximate linear relationship between power injection.Thus, it can be observed by the sample to Branch Power Flow and node injecting power
Value, sets up to inject Bayes linear regression model (LRM) of the transfer distribution factor as regression coefficient, so as to realize injection transfer distribution
The probability Estimation of the factor.
In the case where meeting DC power flow assumed condition, the active injection of the effective power flow of high-voltage transmission network branch road and each node
It is represented by the linear relationship shown in formula (1)：
In formula, branch road k active-power PsBranch,kAnd in correspondence system N number of node active injection power N-dimensional column vector
PNodeObservation can be obtained by metric data；MkFor corresponding to branch road k N-dimensional injection transfer distribution factor column vector.
In view of linear regression residual error, the linear regression model (LRM) comprising injection transfer distribution factor can be denoted as：
In formula, εkFor regression residuals, it is that 0, variance is σ to usually assume that it obeys averagek 2Normal distribution；Injection transfer point
Cloth factor vector MkWith variance scalar σ k2For stochastic variable.The joint posteriority that can set up stochastic variable in formula by Bayesian formula is general
Rate density function：
In formula, P (Mk,σk 2/PBranch,k,PNode) it is given PBranch,kAnd PNodeOn M during measuring valuekAnd σk 2Joint after
Test probability density function；P(Mk,σk 2) it is MkAnd σk 2Joint priori probability density function；P(PBranch,k|Mk,σk 2,PNode) be
Likelihood function；P(PBranch,k) it is PBranch,kThe marginal probability density function obtained by statistics.
According to formula (3), to σk 2Integration just can obtain vector MkJoint Posterior probability distribution be：
In formula, P (Mk|PBranch,k,PNode) it is MkJoint posterior probability density function.
However, in current large scale electric network, injection transfer distribution factor vector dimension is higher, and the parsing to formula (4) is asked
Solution feasibility is difficult to ensure.It is therefore proposed that the posteriority that gibbs sampler numerical algorithm approximately asks for injection transfer distribution factor is general
Rate is distributed：
Gibbs sampler algorithm in multivariable joint probability distribution by sampling, and statistic sampling value carrys out approximate obtaining portion
The edge distribution of the Joint Distribution of variation per minute or certain unitary variant., can be with random in the extraction-type (3) of iteration according to algorithm thinking
Variable MkWith σk 2Sample value, and then count form their own probability distribution.Sampling process can be summarized as following four step：
1) it is σk 2Assign initial value and start iteration count variable, start sampling process.
σ is obtained by least-squares estimationk 2Initial value, initializes gibbs sampler process.Start counting variable i simultaneously to unite
Count iterations.
2) according to MkJoint posterior distribution it is sampled.
Known σk 2Currency (iteration is initial value first), formula (3) turns into only about vector MkJoint Posterior probability distribution.
To obtain the Posterior distrbutionp, vector M need to be setkPrior probability distribution and likelihood function, its prior distribution is typically set to many
Tie up normal distribution：
In formula, ZkFor mean vector；ΣkFor variance diagonal matrix.Z during the present invention is calculatedkValue is null vector, ΣkPair
Diagonal element is taken as 1000 σk 2。
Due to residual epsilonkNormal Distribution N (0, σk 2), thus, it will be assumed that the likelihood function in formula (3) is：
Formula (5), (6) are substituted into formula (3), abbreviation can obtain vector MkJoint Posterior probability distribution still obey multidimensional normal state point
Cloth, its mean vector Zk *With covariance matrix Σk *It can be expressed as respectively：
Substituting into each known quantity just can try to achieve mean vector Zk *With covariance matrix Σk *, so far, vector MkJoint posteriority it is general
Rate distribution is determined.By multiple normal distribution sampling routine, vector M can be extractedkMiddle each element sampled value, stores sampled value
And M is updated with itkCurrency.
3) according to σk 2Posterior distrbutionp it is sampled.
Known MkDuring currency, formula (3) turns on variance scalar σk 2Posteriority edge distribution.Equally, σ is set firstk 2
Prior distribution, it is assumed that for inverse gamma distribution：
In formula, TkFor the form parameter of prior distribution；θkFor the scale parameter of prior distribution；Γ(Tk) exist for gamma function
TkThe value at place.T in the present inventionkWith θkValue be taken as 0.5.
According to Bayesian formula, formula (6), formula (9) are substituted into formula (3), σ visible through abbreviationk 2Posterior marginal probability distribution
Still obey inverse gamma distribution, its form parameter Tk+ 0.5, scale parameter θk+0.5(PBranch,k–PNode·Mk)T(PBranch,k–
PNode·Mk) it can also substitute into known quantity determination.Then, σ is extracted by single stochastic variable sampling routinek 2Sampled value, storage should
It is worth and σ is updated with itk 2Currency.
4) check whether iterations reaches setting value.
If counting variable i is not up to setting value, counting variable i Jia 1 certainly, return to step 2).Otherwise, the M of output storagek
With σk 2Sampled value, terminate iteration sampling process.
Injection transfer distribution factor vector M is obtained by above-mentioned stepskSampled value after, statistical vector M respectivelykIn each yuan
The sample of element, just can obtain the probability distribution of each single injection transfer distribution factor, and then each available injection turns
Move confidential interval of the distribution factor under given confidence level.It should be noted that said process is to each injection transfer distribution
The sampling of the factor is separate progress.Needs are calculated in real time to meet, and can be used multinuclear or multiprocessing parallel calculation technology, be carried
The estimation efficiency of height injection transfer distribution factor error burst.
Real-Time Scheduling and AGC control have it is close associate, Real-Time Scheduling by set the operation basic point of generating set with
The factor is participated in, affine Regulation mechanism (the Δ p according to AGC unit power outputsi=αiΔ D, wherein, Δ piRepresent AGC units i's
Adjustment amount, αiFor the unit i participation factor, Δ D is area power control deviation), control the operation of power network in AGC control process
State.The inventive method carries out decision-making according to the affine Regulation mechanisms of this AGC to the operation basic point and the participation factor of AGC units.For
Description is convenient, on the premise of method versatility is not influenceed, it is assumed that the unit for participating in Real-Time Scheduling all participates in AGC regulations.
Model is minimised as decision objective, target letter with AGC unit operations cost (including cost of electricity-generating and stand-by cost)
Number can be expressed as：
In formula, NGFor AGC units sum in system；ciFor unit i cost of electricity-generating coefficient；piFor unit output basic point；Machine
The up-regulation of group offer, downward reserve level are respectivelyIts corresponding stand-by cost coefficient is respectively
, it is necessary to meet following constraints while object function shown in the formula of minimum (10).
(1) power-balance constraint
In formula, NdFor load bus sum；djFor predicted load；D is exerting oneself that non-AGC units are provided, in Real-Time Scheduling
In be determination value.
(2) constraint of factor sum is participated in
Because the load unbalanced power occurred in Real-Time Scheduling needs according to the respective participation factor to be entered by each AGC units
Row distribution undertakes, it is then desired to meet the constraint for participating in that factor sum is 1.
(3) unit reserve capacity-constrained
According to the affine Regulation mechanism of AGC units, the load unbalanced power occurred in system is needed by AGC units by each
From the participation factor provide spare capacity.Thus, the spare capacity needs of corresponding each AGC units are：
In formula,The respectively upper and lower disturbance border of load power.
(4) unit maximum is upward, the constraint of downward adjustment capability
In formula,The maximum up-regulation amount that respectively AGC units can be provided and maximum downward amount.The constraint
Represent, the spare capacity provided by unit is influenceed, and the regulating power that unit can be provided is limited.
(5) unit maximum, minimum load constraint
In formula,Respectively unit output upper and lower limit.The constraint shows, due to maximum, most by unit
The limitation that small technology is exerted oneself, the power output of unit needs within the specific limits.
(6) unit ramping rate constraints
In formula, Rd,i、Ru,i,Respectively unit i in scheduling time inter downwards with upward creep speed；For unit
I initial power generating value.
(7) tributary capacity is constrained
In formula, L is branch road quantity；For the branch road transmission capacity upper limit；AGC units i is represented respectively, born
Power injection transfer distribution factors of the lotus j to branch road k, it isIn element, be Uncertainty, it is upper save try to achieve it is not true
Fixed interval interior value；It is uncertain variables for node j load fluctuation amount, is located at the uncertain set of boxlikeIt is interior
Value；For AGC unit i corresponding with load fluctuation power output adjustment amount.
Formula (10)~formula (17) constitutes meter and node injecting power and injection transfer distribution factor is probabilistic in real time
Scheduling model.The decision variable of model includes：pi、αi、Model to regulation and control variable by optimizing, it is ensured that scheduling knot
Fruit is set up for given interval interior arbitrary load fluctuation and injection transfer distribution factor estimated bias, above-mentioned each item constraint.
In model, constraint formula (17) contains uncertain variables, and the key of model solution is treated as to it.
In the Branch Power Flow constraint shown in formula (17), there is the form that two Uncertainties are multiplied.But fortunately, exist
ProcessingDuring coefficient, because Real-Time Scheduling time interval is very short, the power injection direction of node be to determine (node is power supply section
Known to point or load bus), i.e., in above-mentioned model,WithBe on the occasion of.So as to which Branch Power Flow will be
Increasing function, therefore, it can to take according to monotonic function Branch Power Flow onLimiting case, i.e., on Branch Power Flow
Limit constraint,Capping valueFor the constraint of Branch Power Flow lower limit,Remove limit valueHere,Difference table
Show that injection transfer distribution factor gives the upper and lower boundary value in confidential interval.
And then,After value is determined, according to the affine Regulation mechanism of AGC units：
Formula (17) can be converted into：
It is clear to, only has uncertain variables in this up-to-date style (19)
According to the robust principle of optimality, for formula (19), as long as each Branch Power Flow constraint disclosure satisfy that in the case of most bad, then institute
Thanksing for your hospitality the constraint of the Branch Power Flow in the case of moving can meet.Herein according to Soyster methods describeds, formula (19) most bad situation is built
Certainty equivalence model, be shown below (by positive tributary capacity constraint exemplified by).
In formula,
Easily find, model is certainty linear programming problem after conversion, quickly can effectively be solved by existing software.
The present invention is by taking simple 6 node system of certain actual 220kV system equivalent of province as an example, to the validity of proposed model
Analyzed, data used in the system are PMU (Phasor Measurement Unit PMU) data of field measurement.In addition,
The present invention is also tested using IEEE 118 and the systems of IEEE 300 to model calculating speed.Optimized model is flat using GAMS
Platform CPLEX solvers are solved, and allocation of computer is Intel's Duo i5 dual core processors, dominant frequency 3.2GHz, internal memory 2GB.
6 bus test systems are all set to AGC units, unit parameter as shown in figure 1, system has 3 generators herein
As shown in table 1, it is assumed that unit reserve cost is the 10% of cost of electricity-generating.Branch parameters are as shown in table 2.System is in 2,4,6 nodes
Load is connected to, the specific data of load are shown in Table 3.Test system data represent that power reference value is 100MW with perunit value, combustion
Material cost base value is 400 yuan/MWh.
The node system unit parameter of table 16
The node system line parameter circuit value of table 26
The node system load parameter of table 36
The validation verification of injection transfer distribution factor probability Estimation
Data sample for estimating injection transfer distribution factor, on the premise of ensureing that sample size is constant, with data
The mode continuous updating of first in first out, and the estimation of injection transfer distribution factor is gradually carried out, make estimated result closely reflect to be
System current operating conditions.
The present invention chooses continuous 601 groups of sequential PMU metric data, test injection transfer distribution factor Probabilistic estimation
Validity, for convenience of description, it is data set A to remember first 300 groups, and latter 301 groups are data set B.To ensure that the posterior probability obtained is estimated
Counting result can be as far as possible accurate, and gibbs sampler number of times is set to 10000.With data set A estimation gained injection transfer distribution because
The upper and lower border of sub 95% confidential interval is respectively as shown in table 4 and table 5.Correspondingly, calculate and obtain according to DC power flow method
Injection transfer distribution factor value it is as shown in table 6.
The injection transfer distribution factor 95% of table 4 confidential interval coboundary
The injection transfer confidential interval lower boundary of distribution factor 95% of table 5
Contrast table 4, table 5 and the result of table 6 can be seen that according to the inventive method, not occur turning the injection of all branch roads
The node that distribution factor is 0 is moved, this based on the result obtained by the calculating of DC power flow method with having significant difference (according to direct current tide
Evaluation method is flowed, it is 0 that distribution factor is shifted in injection of the reference mode to all branch roads, as shown in the respective column of 6 interior joint of table 4).
Injection transfer distribution factor obtained by the DC power flow method of table 6
The accuracy of injection transfer distribution factor estimated result can pass through the calculating to Branch Power Flow obtained by probability Estimation
Precision is weighed, by data set A estimations gained injection transfer distribution factor result and first group of injecting data phase in data set B
Respective branch trend estimated value can be obtained by multiplying, and then be calculated in proper order in the way of data first in first out, result of calculation with according to
The Branch Power Flow Comparative result for calculating gained injection transfer distribution factor estimation according to DC flow model is plotted in Fig. 2.
In Fig. 2, shadow region is the branch road effective power flow obtained according to injection transfer distribution factor probability Estimation result
95% confidential interval.As can be seen from Figure 2 measuring value can substantially be followed the trail of according to the counted trend value of DC power flow method, but
Differ larger therewith, maximum deviation is more than 10MW.And utilize the present invention injection transfer counted branch of distribution factor distributed area
Road trend interval can preferably cover trend measuring value.It can be seen that, injection transfer distribution factor estimation side proposed by the invention
Method, with higher Branch Power Flow estimated accuracy, it is possible to the uncertainty degree of quantificational description injection transfer distribution factor estimation.
In addition, the estimated efficiency that Real-Time Scheduling shifts distribution factor for injection has higher requirements.For the 6 node system
System, under aforementioned computer configuration, the testing time with MATLAB programmed environment serial implementation numerical samples is about 2.04s.For
Explanation can further improve estimated efficiency using parallel computation, and the present invention designs sampling routine with MATLAB parallel computations,
It is limited to dual core processor computing capability, opens 2 " worker ", the testing time that parallel sampling is 10000 times is about 1.16s, meter
Efficiency is calculated to significantly improve.As can be seen here, even for large scale electric network, according to multimachine or multi-core parallel concurrent computing technique, also may be used
Effectively shorten the sampling time, it is ensured that enough estimation efficiency.
Distribution factor estimation result is shifted according to above-mentioned injection, when load bus power swing scope is prediction performance number
During 1%-10%, the scheduling result that model solution of the present invention is obtained is as shown in table 7.In contrast, in a model using certainty
The scheduling result of injection transfer distribution factor is as shown in table 8.From two tables contrast in as can be seen that do not count and inject transfer be distributed because
The generator operation basic point of sub- evaluated error scheduling model institute decision-making load fluctuation by 1% increase to 10% during always
Do not change, only participate in the factor and change, moreover, in this course, Branch Power Flow constraint is never acted as
With.And after counting and injecting transfer distribution factor evaluated error, the unit operation basic point result of decision is always with load fluctuation model
The change enclosed and adjust, this is due to the restriction of branch road 4, unit power output is shifted from unit 3 to unit 1.
The scheduling result of transfer distribution factor evaluated error is counted and injected to table 7
The scheduling result of transfer distribution factor evaluated error is not counted and injected to table 8
Table 7 shows with the result of table 8, in operation, if unit operation basic point and the participation factor are set according to result shown in table 8
Fixed, when range of load fluctuation exceedes predicted value 2%, out-of-limit situation may occur in branch road 4, and when range of load fluctuation increases
When greatly to 10%, then it is likely to result in out-of-limit while branch road 4 and branch road 7.
The interval range of injection transfer distribution factor evaluated error is an important parameter in model of the present invention, for analysis
The interval size influences on the result of decision, takes 90% confidential interval of injection transfer distribution factor evaluated error to substitute above-mentioned test
95% confidential interval set, the result of decision is as shown in table 9.Contrast table 7, table 8 are understood with table 9, are less than or are waited in load fluctuation
When 5%, the scheduling result of table 9 is consistent with the scheduling result of table 8, illustrates in the case of load fluctuation is less, injection transfer distribution
The less predicated error of the factor does not produce influence to the result of decision, now, and Branch Power Flow constraint is not acted upon.Work as load fluctuation
When more than 6%, Branch Power Flow constraint is functioned to, and the result of table 9 is immediately different from the result of table 8.It can be seen that, in scheduling decision
Not considering the uncertainty of injection transfer distribution factor has certain scope of application, and as power network interior joint injects fluctuation range
Increase, it is considered to injection transfer distribution factor evaluated error be necessary.
The injection transfer distribution factor of table 9 takes the corresponding scheduling result of 90% confidential interval
Injection transfer distribution factor evaluated error, can be from the operating cost of unit for the influence of scheduling result economy
Find out in contrast.In the case of three kinds shown in table 7, table 8 and table 9, the contrast of unit operation cost is as shown in Figure 3.From figure
As can be seen that when load fluctuation is less than 2%, three kinds of situation operating costs are basically identical.With the increase of range of load fluctuation,
Operating cost in the case of three kinds increases, and this is due to load power demand and standby undertaken by relatively inexpensive machine
Group is shifted to more uneconomic unit.When range of load fluctuation rises to 10%, the result of table 7 is corresponding with the result of table 8
Operating cost difference 1.44%.Correspond to therewith, the result of table 9 operating cost corresponding with the result of table 8 is closer to, difference 0.2%.
In order to verify the computational efficiency of decision model of the present invention, the nodes of IEEE 118 and the node standard systems of IEEE 300 are chosen
System is tested.Because ieee standard system does not have corresponding metric data, thus, used in model and institute is calculated based on DC power flow
The injection transfer distribution factor obtained, and set its error burst to be ± 2%.AGC units in two systems are set to 15, fluctuation
Load number increases to 30 from 5, corresponding to calculate the time respectively as shown in Figure 4 and Figure 5.
It can be seen that increasing with uncertain load quantity from Fig. 4 and Fig. 5, the calculating time increased, this be because
Decision variable and constraint conditional number amount for model can accordingly increase with the increase of uncertain load quantity.For 118 sections
Dot system, when fluctuating load number is by rising to 30 for 5, the calculating time rises to 1.095s from 0.162s.And for 300
Node system, the calculating time then rises to 1.150s by 0.228s.As can be seen that the calculating time phase difference of two systems is less,
Illustrate that model of the present invention is insensitive to the scale of system, also, computational efficiency can meet the demand of Real-Time Scheduling completely.
The preferred embodiment of the application is the foregoing is only, the application is not limited to, for the skill of this area
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent substitution, improvement etc., should be included within the protection domain of the application.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, not to present invention protection model
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deform still within protection scope of the present invention that creative work can make.
Claims (10)
1. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error, it is characterized in that：Based on Bayes
Linear regression theory constructs the online probability Estimation model of the injection transfer distribution factor using metric data, with operation of power networks
Economy is target, using AGC unit operations basic point and the participation factor as decision variable, constructs the two benches robust of Real-Time Scheduling
Optimized model, while the bounded-but-unknown uncertainty of meter and injection transfer distribution factor and node injecting power, according to power network itself thing
Characteristic is managed, two classes in decision model are not known into parameter separating treatment, and is determination by model conversation with Soyster methods
The linear optimization problem of property is solved.
2. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：For high-voltage transmission network, there is approximate linear relationship in Branch Power Flow, between being injected with each node power according to shellfish
This linear regression theory of leaf, linear regression model (LRM) is estimated using dependent variable in linear regression model (LRM) and independent variable sample observations
In unknown regression coefficient probability distribution, by the sample observations to Branch Power Flow and node injecting power, set up to inject
Bayes's linear regression model (LRM) that distribution factor is regression coefficient is shifted, the probability Estimation of injection transfer distribution factor is realized.
3. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：In the case where meeting DC power flow assumed condition, the active injection of the effective power flow of high-voltage transmission network branch road and each node
It is expressed as linear relationship：
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Observation can be obtained by metric data；MkFor corresponding to branch road k N-dimensional injection transfer distribution factor column vector.
4. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：In view of linear regression residual error, the linear regression model (LRM) comprising injection transfer distribution factor is：
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In formula, εkFor regression residuals, it is that 0, variance is σ to usually assume that it obeys averagek 2Normal distribution；Injection transfer distribution because
Subvector MkWith variance scalar σk 2For stochastic variable.
5. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：Bayesian formula can set up the joint posterior probability density function of stochastic variable in formula：
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Rate density function；P(Mk,σk 2) it is MkAnd σk 2Joint priori probability density function；P(PBranch,k|Mk,σk 2,PNode) it is likelihood
Function；P(PBranch,k) it is PBranch,kThe marginal probability density function obtained by statistics；
To σk 2Integration obtains vector MkJoint Posterior probability distribution be：
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6. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：The Posterior probability distribution of injection transfer distribution factor is approximately asked for using gibbs sampler numerical algorithm.
7. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：According to Real-Time Scheduling and AGC control to have it is close associate, Real-Time Scheduling is by setting the operation basic point of generating set
With participating in the factor, according to the affine Regulation mechanism of AGC unit power outputs, the running status of power network in AGC control process is controlled,
Decision-making is carried out to the operation basic point and the participation factor of AGC units.
8. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：Model turns to decision objective with AGC unit operation cost minimizations, specifically includes cost of electricity-generating and stand-by cost.
9. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 1, its
It is characterized in：When building decision objective, build power-balance constraint, participate in the constraint of factor sum, unit reserve capacity-constrained, unit
Maximum adjustment capability constraint downwards upwards, unit minimax units limits, unit ramping rate constraints and tributary capacity constraint.
10. a kind of robust real-time scheduling method counted and inject transfer distribution factor evaluated error as claimed in claim 9, its
It is characterized in：Model to regulation and control variable by optimizing, it is ensured that scheduling result for arbitrary load fluctuation in given interval and
Injection transfer distribution factor estimated bias so that the constraint of structure is set up.
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| CN107516909A (en) * | 2017-08-31 | 2017-12-26 | 华北电力大学（保定） | The optimization method and device of wind power output are can access in a kind of rack restructuring procedure |
| CN107516909B (en) * | 2017-08-31 | 2019-09-13 | 华北电力大学（保定） | It can access the optimization method and device of wind power output in a kind of rack restructuring procedure |
| CN107591844A (en) * | 2017-09-22 | 2018-01-16 | 东南大学 | Consider the probabilistic active distribution network robust reconstructing method of node injecting power |
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| CN108388954A (en) * | 2018-01-05 | 2018-08-10 | 上海电力学院 | A kind of cascade hydropower robust Optimization Scheduling based on random security domain |
| CN108388954B (en) * | 2018-01-05 | 2021-07-20 | 上海电力学院 | Cascade hydropower robust optimization scheduling method based on random security domain |
| CN112421636A (en) * | 2020-10-31 | 2021-02-26 | 国网河南省电力公司漯河供电公司 | Method for calculating injection transfer distribution factor of power system |
| CN112670983A (en) * | 2020-12-16 | 2021-04-16 | 国网河南省电力公司漯河供电公司 | Method for calculating distribution factor of power system based on measured data exponential forgetting weight |
| CN113516172A (en) * | 2021-05-19 | 2021-10-19 | 电子科技大学 | Image classification method based on random computation Bayesian neural network error injection |
| CN113516172B (en) * | 2021-05-19 | 2023-05-12 | 电子科技大学 | Image classification method based on Bayesian neural network error injection by random calculation |
| EP4312329A1 (en) * | 2022-07-25 | 2024-01-31 | Siemens Aktiengesellschaft | Method and control unit for controlling a power network |
| WO2024022814A1 (en) * | 2022-07-25 | 2024-02-01 | Siemens Aktiengesellschaft | Method and control unit for controlling a power network |
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