CN109861202A - A method and system for dynamic optimal scheduling of flexible interconnected distribution network - Google Patents
A method and system for dynamic optimal scheduling of flexible interconnected distribution network Download PDFInfo
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Abstract
The invention discloses a kind of flexible interconnection distribution networks dynamic optimization dispatching method and systems, pass through the back-to-back soft direct-connected flexible interconnection power distribution network connect of multiterminal as object using each feeder line, for renewable energy power output and the uncertainty of workload demand, the flexible interconnection distribution networks dynamic optimization dispatching method based on Model Predictive Control is proposed.First, based on renewable energy and load ultra-short term power prediction information, by calculating each Node Voltage Sensitivity, establish voltage-prediction model, then flexible power distribution network optimal operation model is established, with predicted voltage and voltage rating deviation and the comprehensive minimum target of power supply cost, and the method and feedback compensation of combining adaptive changeable weight, Optimization Solution goes out the dispatch command value of MBVH and issues, this method not only effectively solves the problems such as voltage out-of-limit that distributed generation resource is contributed and workload demand random fluctuation generates, and the economy and safety of active balance flexible interconnection power distribution network operation.
Description
Technical field
The invention belongs to active distribution networks to optimize running technology field, and in particular to a kind of based on the soft of Model Predictive Control
Property interconnection distribution networks dynamic optimization dispatching method and system.
Background technique
" closed loop design, open loop operation " mode that China's power distribution network is carried out for a long time, not only affect power supply reliability into
One step is promoted, and is difficult to meet the friendly access of Thief zone distributed generation resource (distributed generation, DG).Multiterminal
Soft straight (multi-terminal back-to-back VSC-HVDC, MBVH) is the power grid flexible control of latest development back-to-back
Technology, not only may be implemented any feeder line long-term safety Electromagnetic coupling, and can trend distribution between accuracy controlling feeder line.Distribution
The operation of net flexible interconnection is the important channel for promoting power supply reliability, guaranteeing the consumption of Thief zone DG quota.
Renewable energy power output and workload demand predict error with the increasing of predicted time scale in flexible interconnection power distribution network
It is long and increase, therefore, Optimized Operation result it will directly apply to flexible interconnection power distribution network a few days ago and in a few days run, and fail to fully consider
Voltage limit risk brought by the uncertainty of scene and load.Model Predictive Control (model predictive
Control, MPC) thought based on rolling optimization and feedback compensation, it can solve the system optimization control containing a variety of uncertain factors
Problem processed.Coordination between active distribution network performance driving economy and safety belongs to multi-objective optimization question, to determine distribution fortune
Row totally optimal plan, it is necessary to which the coordination problem between each target is taken in.Current solution multi-objective optimization question master
To use multi-objective optimization algorithm and weighted sum method.It is solved using multi-objective optimization algorithm, is containing multiple local optimums
Xie Shi not can guarantee and converge to globally optimal solution；It is solved using weighted sum method, it is difficult to adapt to network operation state change to power
Gravity treatment takes bring difficult.In addition, being different from the regulation pair such as on-load regulator transformer, compensation capacitor group and interruptible load
As the MBVH in flexible interconnection power distribution network has the power regulation ability of fast and flexible, therefore, if Model Predictive Control can combine
The characteristics of regulation and control object MBVH, will generate better application effect in flexible interconnection power distribution network.
Summary of the invention
Goal of the invention: in order to overcome the shortcomings in the prior art, the present invention provides a kind of energy balancing flexible interconnection power distribution network
The economy of operation and the flexible interconnection distribution networks dynamic optimization dispatching method and system based on Model Predictive Control of safety.
A kind of technical solution: flexible interconnection distribution networks dynamic optimization scheduling based on Model Predictive Control of the present invention
Method includes the following steps:
(1) with multiterminal, each current transformer active power of soft straight MBVH, reactive power are control variable back-to-back, using building in advance
Vertical voltage-prediction model predicts each node voltage of k+1 period system；
(2) soft back-to-back to the multiterminal pre-established using each node voltage of k+1 period system predicted as input variable
The direct-connected flexible interconnection power distribution network mathematical model connect optimizes, and solving k+1 period multiterminal, soft straight each current transformer has back-to-back
Function power, reactive power, as the back-to-back soft straight dispatch command value of k+1 period multiterminal；
(3) by k+1 period multiterminal, soft straight dispatch command value is issued to control centre back-to-back；
(4) soft straight dispatch command value issues each node virtual voltage in rear system to measurement k+1 period multiterminal back-to-back, makees
For the initial value of next rolling optimization period voltage-prediction model, k=k+1 is enabled to go to step (1), optimized into a new round；Directly
To entire dispatching cycle is traversed, terminate optimization.
The multiterminal pre-established the constraint side that the soft direct-connected flexible interconnection power distribution network mathematical model connect is run back-to-back
Journey are as follows:
Active power balance constraint:
Converter Capacity constraint:
Wherein, NVSCFor the current transformer sum of MBVH；Pk(t)、Qk(t) be respectively k-th of current transformer of t moment active and nothing
Function power, inflow feeder line are positive direction；AkFor the loss factor of k-th of current transformer；SkFor the rated capacity of k-th of current transformer.
Described with multiterminal, each current transformer active power of soft straight MBVH, reactive power are control variable back-to-back, using preparatory
The voltage-prediction model of foundation predicts each node voltage of k+1 period system, comprising the following steps:
Local derviation is asked to each node voltage amplitude of power flow equation and phase respectively, obtains Jacobian matrix J, and invert to it,
Obtain sensitivity matrix of each node voltage relative to each node injection active and reactive power:
[Δδ1,ΔU1,…,Δδn,ΔUn]T=
J-1[ΔP1,ΔQ1,…,ΔPn,ΔQn]T
Wherein: Δ δ1,ΔU1Respectively 1 voltage-phase of distribution node and amplitude variable quantity；Δδn,ΔUnRespectively distribution
Node n voltage-phase and amplitude variable quantity；ΔP1,ΔQ1Respectively distribution node 1 injects active power and reactive power variation
Amount；ΔPn,ΔQnRespectively distribution node n injects active power and reactive power variable quantity；
Each node voltage is predicted according to the sensitivity matrix:
U (k+ Δ t)=U (k)+Δ UG(k)+ΔUD(k)
Wherein: U (k) indicates the vector that each node voltage of k moment distribution is constituted, Δ UG(k) indicate that the k moment is respectively become by MBVH
Flow the vector that each node voltage variable quantity caused by the variation of device output power is constituted, Δ UD(k) indicate the k moment by renewable energy
And the vector that each node voltage variable quantity caused by load power fluctuation is constituted；(k+ Δ t) indicates that the distribution of k+ time Δt is each to U
The vector that node voltage is constituted；
By iterating to voltage prediction equation, until forward prediction P step, each node voltage is obtained in prediction time domain P
The vector U that prediction output valve in Δ t is constitutedf, the vector U of the rated value composition of each node voltageRIt indicates:
UR=[U1 r(k+Δt)…Un r(k+Δt),…,
U1 r(k+PΔt)…Un r(k+PΔt)]T
Wherein, UfFor the vector of estimating output valve composition of each node voltage in prediction duration P Δ t；The respectively predicted voltage of node 1 and node n in k+ time Δt；The respectively predicted voltage of node 1 and node n in k+P time Δt；The respectively voltage rating of node 1 and node n in k+ time Δt；The respectively voltage rating of node 1 and node n in k+P time Δt；T representing matrix transposition
Symbol.
The multiterminal pre-established back-to-back the soft direct-connected flexible interconnection power distribution network mathematical model connect with comprehensive power supply at
The minimum optimization aim of deviation in sheet and system between each node voltage predicted value and rated value.
The objective function of the back-to-back soft direct-connected flexible interconnection power distribution network mathematical model connect of the multiterminal pre-established are as follows:
Wherein, t0For current time；F is catalogue scalar functions；F1For the comprehensive minimum objective function of power supply cost；F2For node
Voltage deviation minimum target function；f1(t) and f2It (t) is respectively t moment purchases strategies and Web-based exercise；Ci(t)、PSTi(t)、
PDGi(t)、PDi(t) be respectively bus nodes electricity price at t moment node i, substation exit power, distributed generation resource it is active go out
Power and load active power；CwIt (t) is t moment purchase electricity price；Δ t was indicated in a dispatching cycle between the time of single period
Every；N indicates the number of nodes in power distribution network；
The constraint condition of the back-to-back soft direct-connected flexible interconnection power distribution network mathematical model connect of the multiterminal pre-established are as follows:
Wherein, Ui(t)、UjIt (t) is the voltage magnitude of t moment node i and node j；Gij、BijRespectively node i and node j
Between transconductance and mutual susceptance；δij(t) phase difference between t moment node i and node j；PVSCi(t)、QVSCi(t) respectively
Active and reactive power is exported for MBVH current transformer at t moment node i；QDiIt (t) is the reactive power of load at t moment node j；
Sij(t) line power between t moment node i and node j；Variable subscript "-" and subscript " _ " indicate variable the upper limit and
Lower limit.
The soft direct-connected flexible interconnection power distribution network mathematical model connect optimizes the described pair of multiterminal pre-established back-to-back, wraps
It includes:
F is adaptively adjusted according to distribution operating condition1And F2Weight distribution between two objective functions establishes the catalogue offer of tender
Number F:
Wherein, α is adaptive weighting, with optimization object function F2Linear correlation, ε1With ε2For corresponding linear coefficient of relationship,
And ε1、ε2>=0, F2maxAnd F1maxThe respectively maximum value of voltage deviation and comprehensive power supply cost；
The comprehensive power supply cost target F1With voltage deviation target F2End value according to above-mentioned adaptive weighting determine.
The initial value of the voltage-prediction model are as follows:
U (k+1)=Ureal(k+1)
Wherein, Ureal(k+1) after issuing for k+1 period MBVH dispatch command value, k+1 is measured by practical measurement system
Period actual node voltage value；U (k+1) is the initial value of voltage-prediction model.
The dispatching cycle is one day.
A kind of flexible interconnection distribution networks dynamic optimization scheduling system, including prediction module, rolling optimization module and feedback school
Positive module；
The prediction module, for each current transformer active power of soft straight MBVH, reactive power to be control back-to-back with multiterminal
Variable predicts each node voltage of k+1 period system using the voltage-prediction model pre-established, and prediction result is passed to rolling
Dynamic optimization module；
The rolling optimization module, for using each node voltage of k+1 period system predicted as input variable, to preparatory
The soft direct-connected flexible interconnection power distribution network mathematical model connect optimizes the multiterminal of foundation back-to-back, solves k+1 period multiterminal back
Backrest soft straight each current transformer active power, reactive power, as the back-to-back soft straight dispatch command value of k+1 period multiterminal, and under
It is sent to control centre, repeating scrolling optimization；
The feedback compensation module, for measuring k+1 period multiterminal, soft straight dispatch command value issues rear system back-to-back
In each node virtual voltage, the initial value as next rolling optimization period voltage-prediction model passes to the prediction module.
Compared with prior art, the invention has the benefit that
The problems such as effectively solving the voltage out-of-limit that distributed generation resource is contributed and workload demand random fluctuation generates, Er Qieyou
Imitate the economy and safety of balancing flexible interconnection power distribution network operation.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is the Optimized Operation framework based on Model Predictive Control；
Fig. 3 is flexible interconnection power distribution network；
Fig. 4 is 33 Node power distribution systems；
Fig. 5 is before A feeder line DG day, in a few days, practical comparison diagram of contributing；
Fig. 6 is before B feeder line DG day, in a few days, practical comparison diagram of contributing；
Fig. 7 is before C feeder line DG day, in a few days, practical comparison diagram of contributing；
Fig. 8 is before D feeder line DG day, in a few days, practical comparison diagram of contributing；
Fig. 9 is MBVH scheduling result figure in example A；
Figure 10 is MBVH scheduling result figure in example B；
Figure 11 is system node voltage-contrast figure；
Figure 12 is comprehensive power supply cost comparison diagram；
Figure 13 is system voltage mean deviation figureofmerit figure；
Figure 14 is adaptive weighting figure of changing.
Specific embodiment
The present invention will be further explained with reference to the accompanying drawing.
Flexible interconnection power distribution network Optimization Scheduling proposed by the present invention based on Model Predictive Control mainly includes prediction
Three model, rolling optimization and feedback compensation links.Prediction model is mainly based upon the ultra-short term of wind-powered electricity generation, photovoltaic and load
Prediction data predicts each node voltage of distribution in prediction time domain in conjunction with the method for voltage sensibility, and by prediction result
Pass to rolling optimization link.Rolling optimization link with each node voltage of distribution control time domain interior prediction value and rated value it is inclined
Difference and comprehensive power supply cost minimum are as target, and obtaining multiterminal, the scheduling of soft straight (MBVH) in control time domain refers to back-to-back
It enables, the dispatch command of first period is issued, time window simultaneously successively moves back a period, repeating scrolling optimization.It needs
It is bright, the characteristics of present invention combination control object MBVH fast and flexible power regulation, controlled single time interval as one
Time domain processed can reduce calculating duration under the premise of not influencing effect of optimization.Feedback compensation link is mainly to each node of distribution
Actual voltage value measures, and passes to rolling optimization link for actual measured results as feedback information.Three above ring
Section is closely connected, and constitutes the flexible interconnection distribution networks dynamic optimization scheduling architecture based on Model Predictive Control, improves flexible mutual
Join the safety and economy of power distribution network operation, the Optimized Operation framework based on Model Predictive Control is as shown in Figure 2.
As shown in Figure 1, a kind of flexible interconnection distribution networks dynamic optimization dispatching method, includes the following steps:
1, with multiterminal, each current transformer active power of soft straight MBVH, reactive power are control variable back-to-back, using building in advance
Vertical voltage-prediction model predicts each node voltage of k+1 period system.
Each feeder line passes through multiterminal soft straight interconnection back-to-back in flexible interconnection power distribution network, includes wind-powered electricity generation, light on feeder line
It lies prostrate distributed power supply and customer charge, structure is as shown in Figure 3.The back-to-back soft straight middle multiple groups AC/DC Bidirectional variable-flow of multiterminal
In on same DC bus, exchange side is connected with each feeder line device DC side parallel respectively.It can flexibly be exchanged between each feeder line
Power simultaneously forms mutually support, to realize unified flexible interconnection.Soft straight control variable is every group of current transformer to multiterminal back-to-back
Active power and reactive power.Each current transformer of MVBH can generate certain power loss in transfer effective power flow on a large scale,
The present invention pays attention to certain loss factor in modeling process.The isolation of DC link is so that current transformer output reactive power
It is independent of each other, to only need to meet the capacity-constrained of respective current transformer.The constraint equation of MBVH operation is as follows:
1) active power balance constraint
2) Converter Capacity constrains
In formula: NVSCFor the current transformer sum of MBVH；Pk(t)、Qk(t) be respectively k-th of current transformer of t moment active and nothing
Function power, inflow feeder line are positive direction；AkFor the loss factor of k-th of current transformer；SkFor the rated capacity of k-th of current transformer.
Prediction data based on renewable energy and load, in conjunction with the method for voltage sensibility, to flexible interconnection power distribution network
In each node voltage predicted.
Firstly, seeking local derviation to each node voltage amplitude of power flow equation and phase respectively, Jacobian matrix J is obtained, further
It inverts to it, obtains sensitivity matrix of each node voltage relative to each node injection active and reactive power, such as formula (3):
Secondly, based on DG power output in power distribution network and the ultra-short term power prediction information of workload demand, and convolution (3) institute
The sensitivity matrix shown, can Approximate prediction obtain each node voltage, as shown in formula (4).
U (k+ Δ t)=U (k)+Δ UG(k)+ΔUD(k) (4)
In formula: U (k) indicates the vector that each node voltage of k moment distribution is constituted；ΔUG(k) indicate that the k moment is respectively become by MBVH
Flow the vector that each node voltage variable quantity caused by the variation of device output power is constituted；ΔUD(k) indicate the k moment by renewable energy
And the vector that each node voltage variable quantity caused by load power fluctuation is constituted.
Then, according to voltage prediction equation, in conjunction with the ultra-short term power prediction data of DG and load, by pre- to voltage
It surveys equation to iterate, until forward prediction P step, prediction output valve of each node voltage in prediction time domain P Δ t can be obtained
The vector U of compositionf, the vector U of the rated value composition of each node voltageRIt indicates.
2, soft back-to-back to the multiterminal pre-established using each node voltage of k+1 period system predicted as input variable
The direct-connected flexible interconnection power distribution network mathematical model connect optimizes, and solving k+1 period multiterminal, soft straight each current transformer has back-to-back
Function power, reactive power, as the back-to-back soft straight dispatch command value of k+1 period multiterminal.
To integrate the minimum optimization mesh of deviation in power supply cost and system between each node voltage predicted value and rated value
Mark, formula (10) and (11) are respectively the objective function and constraint condition in rolling optimization stage.
Objective function:
In formula: t0For current time；F is catalogue scalar functions；F1For the comprehensive minimum objective function of power supply cost；F2For node
Voltage deviation minimum target function；f1(t) and f2It (t) is respectively t moment purchases strategies and Web-based exercise；Ci(t)、PSTi(t)、
PDGi(t)、PDi(t) be respectively bus nodes electricity price at t moment node i, substation exit power, distributed generation resource it is active go out
Power and load active power；CwIt (t) is t moment purchase electricity price.
Constraint condition:
In formula: Ui(t)、UjIt (t) is the voltage magnitude of t moment node i and node j；Gij、BijRespectively node i and node j
Between transconductance and mutual susceptance；δij(t) phase difference between t moment node i and node j；PVSCi(t)、QVSCi(t) respectively
Active and reactive power is exported for MBVH current transformer at t moment node i；QDiIt (t) is the reactive power of load at t moment node j.
Sij(t) line power between t moment node i and node j；Variable subscript "-" and subscript " _ " indicate variable the upper limit and
Lower limit.
To solve the multi-objective optimization question in above-mentioned power distribution network, the present invention is by the specific item scalar functions F in formula (10)1With F2
It is polymerized to single function by the method for weighted sum, and uses adaptive changeable weight optimization method, feelings are run according to distribution
Condition adaptively adjusts the weight distribution between two targets.Each objective function is carried out first to mark change processing, keeps its dimension identical,
Then catalogue scalar functions F is established:
Wherein, α is adaptive weighting, with optimization object function F2Linear correlation, ε1With ε2For corresponding linear coefficient of relationship,
And ε1、ε2>=0, F2maxAnd F1maxThe respectively maximum value of voltage deviation and comprehensive power supply cost.Relevant constraint such as formula (11)
It is shown.
Final comprehensive power supply cost target F1With voltage deviation target F2It is determined by the mentioned adaptive weighting of formula (12).If
Voltage deviation F2Smaller, by formula (12) it is found that its weight α will be reduced accordingly, and integrating power supply cost weight 1- α will rise；Instead
It, if voltage deviation F2Larger, weight α will also increase therewith, corresponding comprehensive power supply cost weight 1- α decline.
3, by k+1 period multiterminal, soft straight dispatch command value is issued to control centre back-to-back.
The optimal control sequence that MBVH dispatch command value is constituted in control time domain can be obtained later by solving above-mentioned Optimized model
Column only issue the dispatch command value of first period in control time domain, when next control time domain being waited to arrive, repeat above-mentioned rolling
Dynamic optimization process.
4, soft straight dispatch command value issues each node virtual voltage in rear system to measurement k+1 period multiterminal back-to-back, makees
For the initial value of next rolling optimization period voltage-prediction model, k=k+1 is enabled to go to step (1), optimized into a new round；Directly
To entire dispatching cycle is traversed, terminate optimization.
After k+1 period MBVH dispatch command value is issued, the practical measuring value of each node voltage of system is rolled as next round
The initial value of voltage-prediction model in optimization process, so that the error of node voltage predicted value is reduced, so that entire scheduling process
More fitting is practical.
U (k+1)=Ureal(k+1) (13)
In formula: Ureal(k+1) after issuing for k+1 period MBVH dispatch command value, k+1 is measured by practical measurement system
Period actual node voltage value；U (k+1) is the initial value of voltage-prediction model.
When due to executing rolling optimization every time Model Predictive Control (model predictive control, MPC) with
System voltage actual value updates renewable energy ultra-short term prediction power value as the initial value in voltage-prediction model, protects
The stability and robustness of Rolling optimal strategy are demonstrate,proved.
In order to verify the feasibility and validity of the proposed Optimization Scheduling of the present invention, the present invention is with as shown in Figure 4 33
It is analyzed for node example system, which is connected to form by 4 feeder lines from 4 different substations by MBVH.
System nominal voltage is 10kV, the YJV22-3*400 type cable that route selects China's urban distribution network mainstream to use.It calculates
5 groups of photovoltaic systems and 4 Wind turbines are accessed in example, configuration parameter is referring to table 1.The electricity price when peak of power purchase (07:00~19:
00) with electricity price (19:00~07:00) when paddy referring to table 2.The current transformer rated capacity of MBVH is 3MVA, and loss factor is
0.02.Substation exit power interval is 0MW~8MW (not allowing power to send), and capacity of trunk 8MVA, each node voltage takes
Being worth range is [0.93,1.07] (per unit value).
MPC parameter setting takes prediction duration and control duration is all 5min, and rolling optimization control executes the period 5min/ times,
It is executed in total in one day 288 times, adaptive weighting coefficient ε1And ε2It is taken as 0.6,0.4 respectively, it is only to show that the present invention, which chooses the coefficient,
Model setting can be according to net state and needing adjust the coefficient when actual motion.Simulation Example is in MATLABR2014a
Program under environment.
Table 1DG configuration parameter
2 electric price parameter of table
The analysis of MPC optimum results:
Two examples are respectively set in validity for contrast verification based on MPC Optimization Scheduling, the present invention: to flexible mutual
MBVH in connection power distribution network is dispatched a few days ago, and scheduling result is applied to flexible interconnection power distribution network actual motion, is set as example
A；The MBVH in flexible interconnection power distribution network is scheduled using MPC Optimization Scheduling, scheduling result is applied to flexible mutual
Join power distribution network actual motion, is set as example B.
Before each feeder line DG days, in a few days and practical power output correlation curve as shown in figures 5-8.MBVH scheduling knot in example A, B
Fruit difference is as shown in Figures 9 and 10.Figure 11 is the highest and lowest voltage condition of each node of system in example A, B.Figure 12 is example A, B
The comprehensive power supply cost comparative situation of middle system.
From Fig. 5~8 as can be seen that each feeder line DG power output has stronger randomness and fluctuation, power output prediction a few days ago
Precision is lower, and the deviation between practical power output is larger, and the out-of-limit situation of node voltage, such as Figure 11 occurs in partial period in example A
Shown in middle example A, so that the dispatch command a few days ago of MBVH is not able to satisfy flexible interconnection power distribution network actual motion requirement.In this regard, this
Invention is using predictive information in following a period of time window of DG and load as input variable, with system node voltage deviation and comprehensive
Close the minimum target of power supply cost, the method for combining adaptive changeable weight, and using the actual value of day part system voltage as
Feedback information solves each current transformer dispatch command value of MBVH using MPC rolling optimization, as shown in Figure 10.As can be seen from the figure
The power regulation ability of control object MBVH fast and flexible in flexible interconnection power distribution network.
The MBVH dispatch command obtained using MPC Optimization Scheduling is applied to node voltage obtained in real system
Curve is as shown in example B in Figure 11, it can be seen from the figure that the partial period node voltage as caused by DG power output prediction error
Out-of-limit problem is effectively solved, and the safety of system operation is improved.In addition, it can be recognized from fig. 12 that compared to calculation
Synthesis power supply cost in example A, example B is significantly reduced on the whole.Although through analysis it is found that example A with comprehensive power supply at
Originally minimum to solve to obtain MBVH dispatch command and issue as optimization aim, but before DG days between power output prediction data and actual value
Deviation the economy of running is reduced.And example B uses MPC Optimization Scheduling, is existed using DG and load
Predictive information in future time period carries out rolling optimization solution as input variable, and prediction error is dropped compared to example A
Low, the economy that the MBVH dispatch command value obtained after Optimization Solution is applied in running is obtained compared to example A
It is promoted.
To sum up, after using MPC Optimization Scheduling, system voltage deviation is reduced, and comprehensive power supply cost obtains
It reduces, improves the economy and safety of system operation on the whole.
In addition, the power regulation ability of present invention combination control object MBVH fast and flexible, in MPC optimization process selection with
Single time interval is the rolling optimization period.For the superiority for verifying this method, the present invention is respectively set example B1 and B2 and carries out
Comparative analysis.The MPC optimization method that example B1 is used is identical as example B, is all using single time interval as the rolling optimization period；
Example B2 is using three time intervals as the rolling optimization period.By calculating the comprehensive confession it can be found that in example B1 and example B2
Electric cost is not much different, and respectively 47463.57 yuan and 47548.26 yuan, but single rolling calculation duration is compared in example B1
Reduce 1.819s in example B2, and with the increase of flexible interconnection power distribution network scale, example B1 is calculating the advantage in duration
It can be more obvious.
Adaptive changeable weight multiple-objection optimization interpretation of result:
The present invention is respectively adopted following three kinds of schemes and carrys out having for the adaptive changeable weight Multipurpose Optimal Method of contrast verification
Effect property: scheme 1 is that voltage deviation and comprehensive power supply cost take fixed weight 0.7 and 0.3 respectively；Scheme 2 is voltage deviation and comprehensive
It closes power supply cost and takes fixed weight 0.9 and 0.1 respectively；Scheme 3 is the adaptive changeable weight multiple-objection optimization that the present invention uses
Method.For the ease of analysis, the present invention defines day part i mark and changes treated system voltage mean deviation figureofmerit (AVOi)
Are as follows:
System whole service period internal standard changes treated node voltage evaluation index (VI) are as follows:
In formula: segment number when i is indicated；J indicates node serial number；Number of segment when m indicates total；N indicates system node sum；|Δ
Uij| i-th of period of expression, the absolute value of j node voltage amplitude offset, | Δ Uij.max| indicate i-th of period, j node voltage
The maximum value of magnitude shift amount absolute value.
Figure 13 compared system voltage mean deviation figureofmerit under three kinds of schemes by taking 08:00~18:00 period as an example
AVOi, it can be seen from the figure that scheme 2 increases voltage deviation corresponding weight on the basis of scheme 1, with voltage deviation
The increase of weight, system voltage mean deviation figureofmerit AVOiOptimization is significant, is substantially reduced within the entire period.But
Lower average voltage offset figureofmerit also brings the increase of comprehensive power supply cost, as shown in table 3.The program uses fixed weight
Method, balance is being difficult in distribution actual moving process and is rationally being accepted or rejected between node voltage deviation and comprehensive power supply cost
Weight setting.Meanwhile in flexible interconnection power distribution network renewable distributed generation resource permeability be stepped up, distribution runs shape
State also becomes more changeable.In this regard, the present invention use adaptive changeable weight method (scheme 3), simulation result such as Figure 13 and
Shown in Figure 14.
3 three kinds of scheme operation result comparisons of table
It can be seen from the figure that the weight α of voltage deviation has according to adaptive adjustment is made with Running State variation
Imitate balance nodes voltage deviation and comprehensive power supply cost.Wherein, by taking 08:00~18:00 period as an example, the dynamic of analytical weight becomes
Change process, as shown in figure 14.In conjunction with Figure 13 as can be seen that in 08:00~11:00 and 15:15~18:00 period, system section
Point voltage deviation is relatively low, and corresponding weight is smaller, and comprehensive power supply cost weight is larger, and the economy of system operation obtains
It is promoted.In 11:15~15:00 period, system node voltage deviation is larger, and corresponding weight is increased, to guarantee system
The safety of operation.It can be seen that the weight of voltage deviation and comprehensive power supply cost is done according to the practical operation situation of system
Adaptive adjustment out, the economy and safety of the operation of active balance system, realize flexible interconnection power distribution network overall operation from
Adapt to optimal coordinated control.
It should be understood by those skilled in the art that, embodiments herein can provide 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, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process 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 every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or 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 counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, 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 (10)
1. a kind of flexible interconnection distribution networks dynamic optimization dispatching method, which comprises the following steps:
(1) with multiterminal, each current transformer active power of soft straight MBVH, reactive power are control variable back-to-back, utilize what is pre-established
Voltage-prediction model predicts each node voltage of k+1 period system；
(2) soft direct-connected back-to-back to the multiterminal pre-established using each node voltage of k+1 period system predicted as input variable
The flexible interconnection power distribution network mathematical model connect optimizes, and solves k+1 period multiterminal soft straight each current transformer wattful power back-to-back
Rate, reactive power, as the back-to-back soft straight dispatch command value of k+1 period multiterminal；
(3) by k+1 period multiterminal, soft straight dispatch command value is issued to control centre back-to-back；
(4) soft straight dispatch command value issues each node virtual voltage in rear system to measurement k+1 period multiterminal back-to-back, as under
The initial value of one rolling optimization period voltage-prediction model, enables k=k+1 go to step (1), optimizes into a new round；Until time
Entire dispatching cycle is gone through, optimization is terminated.
2. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1, which is characterized in that described pre-
The multiterminal first the established constraint equation that the soft direct-connected flexible interconnection power distribution network mathematical model connect is run back-to-back are as follows:
Active power balance constraint:
Converter Capacity constraint:
Wherein, NVSCFor the current transformer sum of MBVH；Pk(t)、Qk(t) be respectively k-th of current transformer of t moment active and idle function
Rate, inflow feeder line are positive direction；AkFor the loss factor of k-th of current transformer；SkFor the rated capacity of k-th of current transformer.
3. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1, which is characterized in that it is described with
Each current transformer active power of soft straight MBVH, reactive power are control variable to multiterminal back-to-back, utilize the voltage prediction pre-established
Each node voltage of model prediction k+1 period system, comprising the following steps:
Local derviation is asked to each node voltage amplitude of power flow equation and phase respectively, obtains Jacobian matrix J, and invert to it, obtains
Sensitivity matrix of each node voltage relative to each node injection active and reactive power:
[Δδ1,ΔU1,…,Δδn,ΔUn]T=
J-1[ΔP1,ΔQ1,…,ΔPn,ΔQn]T
Wherein: Δ δ1,ΔU1Respectively 1 voltage-phase of distribution node and amplitude variable quantity；Δδn,ΔUnRespectively distribution node n
Voltage-phase and amplitude variable quantity；ΔP1,ΔQ1Respectively distribution node 1 injects active power and reactive power variable quantity；Δ
Pn,ΔQnRespectively distribution node n injects active power and reactive power variable quantity；
Each node voltage is predicted according to the sensitivity matrix:
U (k+ Δ t)=U (k)+Δ UG(k)+ΔUD(k)
Wherein: U (k) indicates the vector that each node voltage of k moment distribution is constituted, Δ UG(k) indicate the k moment by each current transformer of MBVH
The vector that each node voltage variable quantity caused by output power variation is constituted, Δ UD(k) indicate the k moment by renewable energy and
The load power vector that each node voltage variable quantity is constituted caused by fluctuating；(k+ Δ t) indicates each node of k+ time Δt distribution to U
The vector that voltage is constituted；
By iterating to voltage prediction equation, until forward prediction P step, each node voltage is obtained in prediction time domain P Δ t
Prediction output valve constitute vector Uf, the vector U of the rated value composition of each node voltageRIt indicates:
Wherein, UfFor the vector of estimating output valve composition of each node voltage in prediction duration P Δ t；The respectively predicted voltage of node 1 and node n in k+ time Δt；The respectively predicted voltage of node 1 and node n in k+P time Δt；The respectively voltage rating of node 1 and node n in k+ time Δt；The respectively voltage rating of node 1 and node n in k+P time Δt；T representing matrix transposition
Symbol.
4. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1, which is characterized in that described pre-
The soft direct-connected flexible interconnection power distribution network mathematical model connect is respectively saved the multiterminal first established back-to-back with integrating in power supply cost and system
The minimum optimization aim of deviation between point voltage prediction value and rated value.
5. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1 or 4, which is characterized in that institute
State the objective function of the back-to-back soft direct-connected flexible interconnection power distribution network mathematical model connect of the multiterminal pre-established are as follows:
Wherein, t0For current time；F is catalogue scalar functions；F1For the comprehensive minimum objective function of power supply cost；F2For node voltage
Deviation minimum target function；f1(t) and f2It (t) is respectively t moment purchases strategies and Web-based exercise；Ci(t)、PSTi(t)、PDGi
(t)、PDi(t) be respectively bus nodes electricity price at t moment node i, substation exit power, distributed generation resource active power output and
Load active power；CwIt (t) is t moment purchase electricity price；Δ t indicates the time interval of single period in a dispatching cycle；N table
Show the number of nodes in power distribution network；
6. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 5, which is characterized in that described pre-
The constraint condition of the back-to-back soft direct-connected flexible interconnection power distribution network mathematical model connect of the multiterminal first established are as follows:
Wherein, Ui(t)、UjIt (t) is the voltage magnitude of t moment node i and node j；Gij、BijRespectively between node i and node j
Transconductance and mutual susceptance；δij(t) phase difference between t moment node i and node j；PVSCi(t)、QVSCiIt (t) is respectively t
MBVH current transformer exports active and reactive power at moment node i；QDiIt (t) is the reactive power of load at t moment node j；Sij
(t) line power between t moment node i and node j；The subscript "-" and subscript " _ " of variable indicate the upper limit of variable under
Limit.
7. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 5, which is characterized in that described right
The soft direct-connected flexible interconnection power distribution network mathematical model connect optimizes the multiterminal pre-established back-to-back, comprising:
F is adaptively adjusted according to distribution operating condition1And F2Weight distribution between two objective functions establishes catalogue scalar functions F:
Wherein, α is adaptive weighting, with optimization object function F2Linear correlation, ε1With ε2For corresponding linear coefficient of relationship, and ε1、
ε2>=0, F2maxAnd F1maxThe respectively maximum value of voltage deviation and comprehensive power supply cost；
The comprehensive power supply cost target F1With voltage deviation target F2End value according to above-mentioned adaptive weighting determine.
8. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1, which is characterized in that the electricity
The initial value of pressure prediction model are as follows:
U (k+1)=Ureal(k+1)
Wherein, Ureal(k+1) after issuing for k+1 period MBVH dispatch command value, the k+1 period is measured by practical measurement system
Actual node voltage value；U (k+1) is the initial value of voltage-prediction model.
9. a kind of flexible interconnection distribution networks dynamic optimization dispatching method according to claim 1, which is characterized in that the tune
Spending the period is one day.
10. a kind of flexible interconnection distribution networks dynamic optimization dispatches system, which is characterized in that the system comprises prediction modules, rolling
Dynamic optimization module and feedback compensation module；
The prediction module, for multiterminal, each current transformer active power of soft straight MBVH, reactive power to be control variable back-to-back,
Predict each node voltage of k+1 period system using the voltage-prediction model that pre-establishes, and prediction result passed to roll it is excellent
Change module；
The rolling optimization module, for using each node voltage of k+1 period system predicted as input variable, to pre-establishing
Multiterminal back-to-back the soft direct-connected flexible interconnection power distribution network mathematical model connect optimize, it is back-to-back to solve k+1 period multiterminal
Soft straight each current transformer active power, reactive power as the back-to-back soft straight dispatch command value of k+1 period multiterminal, and are issued to
Control centre, repeating scrolling optimization；
The feedback compensation module, for measuring k+1 period multiterminal, soft straight dispatch command value is issued in rear system respectively back-to-back
Node virtual voltage, the initial value as next rolling optimization period voltage-prediction model pass to the prediction module.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820673A (en) * | 2012-08-10 | 2012-12-12 | 沈阳工业大学 | Multiterminal flexible direct current power transmission system included power grid operation control system and method |
| CN104269861A (en) * | 2014-10-13 | 2015-01-07 | 国家电网公司 | Electromagnetic looped network reactive power ring current optimal control method based on flexible looped network controller |
| CN105740980A (en) * | 2016-01-29 | 2016-07-06 | 国网福建省电力有限公司 | Optimization planning method for alternating current and direct current hybrid grid with flexible direct current power transmission system |
| CN107749635A (en) * | 2017-11-15 | 2018-03-02 | 江苏方天电力技术有限公司 | A kind of closed loop network running optimizatin method |
-
2018
- 2018-11-15 CN CN201811356652.3A patent/CN109861202B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820673A (en) * | 2012-08-10 | 2012-12-12 | 沈阳工业大学 | Multiterminal flexible direct current power transmission system included power grid operation control system and method |
| CN104269861A (en) * | 2014-10-13 | 2015-01-07 | 国家电网公司 | Electromagnetic looped network reactive power ring current optimal control method based on flexible looped network controller |
| CN105740980A (en) * | 2016-01-29 | 2016-07-06 | 国网福建省电力有限公司 | Optimization planning method for alternating current and direct current hybrid grid with flexible direct current power transmission system |
| CN107749635A (en) * | 2017-11-15 | 2018-03-02 | 江苏方天电力技术有限公司 | A kind of closed loop network running optimizatin method |
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|---|---|---|---|---|
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