CN107404127A - Consider the wind-powered electricity generation Robust Interval trace scheduling method that Multiple Time Scales are coordinated - Google Patents
Consider the wind-powered electricity generation Robust Interval trace scheduling method that Multiple Time Scales are coordinated Download PDFInfo
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Abstract
The present invention relates to a kind of wind-powered electricity generation Robust Interval trace scheduling method for considering Multiple Time Scales and coordinating.This method binding model PREDICTIVE CONTROL optimizes with robust, robust optimization is rolled under the Scheduling Framework of Multiple Time Scales, generation wind power plant can dissolve power interval track boundary and conventional power unit generation schedule, system security constraint is satisfied by when wind power output can be being dissolved in the boundary of power interval track, alleviate the system potential safety hazard that wind power point prediction in traditional scheduler is inaccurately left, wind power's supervision system Real-time Feedback wind power plant is actual simultaneously contributes, calculate prediction error and predicted value is corrected, make forecasted future value closer to actual value, the plan deviation of decision content caused by predicting error due to wind-powered electricity generation is cut down step by step, make optimal planning index more accurate.
Description
Technical field
The present invention relates to operation and control of electric power system field, more particularly to a kind of wind-powered electricity generation for considering Multiple Time Scales and coordinating
Robust Interval trace scheduling method.
Background technology
As the ratio of wind power integration in power system gradually rises, the uncertainty and randomness of wind-powered electricity generation will give power train
Regiment commander carrys out many influences.Core component in being controlled as Operation of Electric Systems, active power dispatch are directly connected to electricity
Active power balance and frequency stabilization in Force system, the safe and reliable and economical operation to power system have and can not replaced
The effect in generation.
Active power dispatch operation after wind power integration will depend on wind power prediction technology.But wind power point prediction is still deposited
In larger error, and with the growth of predicted time, the error of predicted value and actual value also gradually increases.In wind-powered electricity generation at high proportion
On the premise of grid-connected, optimize obtained generation schedule using traditional active power dispatch method of wind power point prediction result, it can
It will be reduced by property, the strong randomness of wind-powered electricity generation will cause wind power plant to may deviate from planned value, and the extreme method of operation occur, and this will
Threaten the security of system operation；And increase wind power plant is cut machine behavior by the big ups and downs of wind-powered electricity generation, cause to abandon air quantity rise,
And the behavior for limiting wind power output can only be carried out after the unreliable method of operation is undergone in power system, can not predict in advance.
At present, the research both at home and abroad for the management and running control containing wind power system is more and more deep, how to utilize probability
Property information carry out power system active power dispatch research also gradually increase.Wherein, stochastic programming is adjusted with fuzzy programming in system
Certain application has been obtained in degree.Probability distribution information of the stochastic programming according to Uncertainty, by by the peace in Optimized model
Staff cultivation is configured to chance constraint, to solve the economic load dispatching model comprising Uncertainty probabilistic information.But the probability of wind-powered electricity generation
Difficulty and the complexity of calculating that information obtains in practice limit the application of stochastic programming.Fuzzy programming is by setting degree of membership
Function represents that policymaker to Uncertainty and its attitude of caused consequence, optimizes to obtain by maximizing membership function
Satisfied decision value, but fuzzy programming is subjective, and parameter optimization method is open loop optimization, lacks feedback control and makees
For compensation.
The content of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the invention provides a kind of wind-powered electricity generation Shandong for considering Multiple Time Scales and coordinating
Rod section trace scheduling method.This method binding model PREDICTIVE CONTROL optimizes with robust, under the Scheduling Framework of Multiple Time Scales
Robust optimization is rolled, generation wind power plant can dissolve power interval track boundary and conventional power unit generation schedule, when wind power output exists
System security constraint is satisfied by when can dissolve in the boundary of power interval track, alleviates in traditional scheduler wind power point prediction not
The system potential safety hazard accurately left, while the actual output of wind power's supervision system Real-time Feedback wind power plant, make forecasted future value
Closer to actual value, the plan deviation of decision content caused by predicting error due to wind-powered electricity generation is cut down step by step, makes optimal planning index more
Accurately.
To achieve the above objectives, the present invention adopts the technical scheme that：
A kind of wind-powered electricity generation Robust Interval trace scheduling method for considering Multiple Time Scales and coordinating, comprises the following steps：
S1, predicted according to history wind power actual value (i.e. history wind power plant actual contribute), history short-term wind-electricity power
Value and history super short-period wind power predicted value, error range, the super short-period wind power of statistics short-term wind-electricity power prediction are pre-
The error range of survey, it is short-term with reference to newest short-term wind-electricity power predicted value, newest super short-period wind power predicted value, generation
Wind power prediction section, super short-period wind power forecast interval, the input information as Optimized model；
S2, in a few days in rolling planning module, based on short-term wind-electricity power forecast interval and short-term load forecasting, with routine
Unit generation cost is minimum, the minimum object function of short-term wind-electricity power forecast interval upper limit deviation, with system safely for constraint
Robust Interval rolling optimization is carried out, power interval track boundary and conventional power unit generation schedule can be dissolved by calculating wind power plant；
S3, in real-time plan for adjustment module, based on super short-period wind power forecast interval and ultra-short term, with
It is base value that the wind power plant that in a few days rolling optimization module obtains, which can dissolve power interval track boundary with conventional power unit generation schedule, after
Continuous to roll robust optimization, adjustment wind power plant can dissolve power interval track boundary and conventional power unit generation schedule, obtain amendment wind
Electric field can dissolve power interval track boundary and amendment conventional power unit generation schedule；
S4, in AGC real-time control modules, non-AGC units tracking amendment conventional power unit generation schedule, wind power plant is being corrected
Wind power plant can dissolve and maximum power point tracking pattern is used in the boundary of power interval track, and AGC units tackle the small of non-regularity
Amplitude wave moves and the out-of-limit situation of wind-powered electricity generation, adjusts AGC unit basic point performance numbers in real time, is suitably lifted when standby sufficient under AGC units
Wind electricity digestion amount, finally issue generation schedule instruction to wind power plant and conventional power unit；
S5, contribute according to wind power's supervision system Real-time Feedback wind power plant is actual, correct the input information of Optimized model, from
And roll the accuracy of lifting operation plan.
In the above-mentioned methods, in step S1, short-term wind-electricity power forecast interval is to predict newest short-term wind-electricity power
Value obtains plus the error range of short-term wind-electricity power prediction；
Super short-period wind power forecast interval is that newest super short-period wind power predicted value is added into ultra-short term wind-powered electricity generation work(
The error range of rate prediction obtains.
In the above-mentioned methods, in step S2, object function is：
Wherein,Represent that wind power plant can dissolve the lower and upper limit of power interval track boundary；Represent t
Wind power plant i plans base value；Pj,tRepresent the generation schedule of j-th of conventional power unit of t；aj,bj,cjRespectively j-th conventional
The secondary term system of unit generation cost
Number, Monomial coefficient and constant term coefficient；Represent t wind power plant i short-term wind-electricity power forecast interval
The upper limit；λiRepresent the power prediction upper bound deviation penalty coefficient to wind power plant i；T represents optimization time domain；NGRepresent conventional power unit number
Amount, NWRepresent wind-powered electricity generation number.
In the above-mentioned methods, in step S2, constraints includes：
A1) power-balance constraint：
Wherein,Represent the short-term load forecasting of t；
B1) conventional power unit output restriction：
Wherein, Pj,t、The output lower and upper limit of j-th of conventional power unit of t are represented respectively；
C1) conventional power unit climbing rate constrains：
Wherein,Respectively the maximum of j-th of conventional power unit of t downwards with maximum climb by climbing power
Slope power；
D1) spinning reserve constrains：
Wherein,Lower spinning reserve and upper spinning reserve of j-th of conventional power unit in t are represented respectively；The lower rotation stand-by requirement of power system and upper spinning reserve demand are represented respectively；
E1 section security constraint) is transmitted：
Wherein, φj-lThe generating transfer factor for being j-th of conventional power unit to section l, φi-lIt is wind power plant i to section l's
Generating transfer factor；Fl min、Fl maxSection l trend lower limit and the trend upper limit is represented respectively；
F1) wind power output constrains：
Wherein,Represent t wind power plant i short-term wind-electricity power forecast interval lower limit.
In the above-mentioned methods, in step S2, in a few days in rolling planning module, rolled once per 1h, using 15min as the time point
Resolution, optimize following 4h decision value, totally 16 points, only carry out preceding 4 points every time.
In the above-mentioned methods, in step S3, the object function for rolling robust optimization is：
Wherein,Represent that in a few days rolling planning module optimizes the generation schedule of obtained j-th of conventional power unit in t；Represent that the wind power plant i that in a few days rolling planning module optimizes to obtain dissolves the power interval track boundary upper limit in t；Represent the super short-period wind power forecast interval upper limit；Represent that amendment wind power plant i can dissolve power interval respectively
Track boundary lower and upper limit；Represent amendment wind power plant plan base value；ΔPj,tRepresent amendment conventional power unit generation schedule
Value；T represents optimization time domain.
In the above-mentioned methods, in step S3, rolling the constraints of robust optimization includes：
A2) power-balance constraint：
Wherein,The t wind power plant i that in a few days rolling planning module obtains plan base values are represented,Represent the super of t
Short-term load forecasting；
B2) conventional power unit output restriction：
C2) conventional power unit climbing rate constrains：
D2) spinning reserve constrains：
E2 section security constraint) is transmitted：
F2) wind power output constrains：
G2) AGC basic points adjustment constraint：
Wherein, γmin、γmaxThe lower limit coefficient and upper limit coefficient of AGC units adjustment nargin are represented respectively；Pk,tFor kth platform
The plan that AGC set optimizations obtain is contributed；For the capacity of kth platform AGC units.
In the above-mentioned methods, in step S3, real-time plan for adjustment module rolls once per 5min, using 5min as time resolution
Rate, optimize following 1h decision value, totally 12 points, only carry out the 1st point every time.
In the above-mentioned methods, step S5 formulation is as follows：, both can be according to prediction mould after t implements control
Type calculates each output valve of the object in future time instance, wherein also including predicted value y of i-th of output valve at the t+1 momenti,1(t+
1|t)；Carved to t+1 and measure each reality output yi(t+1) after, you can relatively and form error amount e (t+1) with corresponding predicted value,
Following error is predicted with method of weighting using this control information, and the prediction based on model is compensated with this, can be obtained through school
Positive predicted value：
ycor(t+2 | t+1)=ypre(t+2|t+1)+He(t+2|t+1) (16)
Wherein, ypre(t+2 | t+1) represent the t+1 moment t+2 moment is corrected before predicted value；ycor(t+2 | t+1) represent t
+ 1 moment is to t+2 moment revised predicted value；E (t+2 | t+1) represent that the t+1 moment is based on the error that e (t+1) predictions obtain
Value；H is amendment weighted value, H ∈ [0,1], is determined by the degree of accuracy of error prediction.
The wind-powered electricity generation Robust Interval trace scheduling method of the present invention for considering Multiple Time Scales and coordinating, have following excellent
Point：
This method has considered following factor：
1st, history wind power actual value and predicted value；
2nd, short-term wind-electricity power, short-term load forecasting data, super short-period wind power, ultra-short term data；
3rd, the power system topology structure information containing wind-powered electricity generation：Transmit in section security constraint formula (6), be related to section letter
Breath, section popular can also be interpreted as circuit, and transfer factor therein just needs to be related to power system topology structure information, i.e. circuit
Annexation, power plant, wind power plant position relationship, the trend bound etc. of each section；
4th, the information such as conventional power unit output limit value, climbing rate, spare capacity.
This method binding model PREDICTIVE CONTROL optimizes with robust, and it is excellent that robust is rolled under the Scheduling Framework of Multiple Time Scales
Change, generation wind power plant can dissolve power interval track boundary and conventional power unit plan, when wind power output can dissolve work(in wind power plant
Power system security constraint is satisfied by when in the boundary of rate section track, it is inaccurate to alleviate wind power point prediction in traditional scheduler
The power system security hidden danger left, while the actual output of wind power's supervision system Real-time Feedback wind power plant, calculate prediction error
And predicted value is corrected, forecasted future value is cut down determined caused by predicting error due to wind-powered electricity generation step by step closer to actual value
The plan deviation of plan value, making optimal planning index, (i.e. AGC real-time control modules refer to the plan that issues of wind power plant and conventional power unit
Make) it is more accurate.
Brief description of the drawings
Fig. 1 is the schematic flow sheet for considering the wind-powered electricity generation Robust Interval trace scheduling method that Multiple Time Scales are coordinated.
Fig. 2 is that the wind-powered electricity generation for considering the wind-powered electricity generation Robust Interval trace scheduling method that Multiple Time Scales are coordinated can dissolve power interval
Track boundary and control time yardstick schematic diagram.
Embodiment
The present invention is described in further detail below in conjunction with attached Fig. 1 and 2.
S1, predicted according to history wind power actual value (i.e. history wind power plant actual contribute), history short-term wind-electricity power
Value and history super short-period wind power predicted value, error range, the super short-period wind power of statistics short-term wind-electricity power prediction are pre-
The error range of survey, it is short-term with reference to newest short-term wind-electricity power predicted value, newest super short-period wind power predicted value, generation
Wind power prediction section, super short-period wind power forecast interval, that is, wind power prediction section is built as Optimized model
Input information.It is specific as follows：
Refering to shown in Fig. 1.It is real that optimizing scheduling flow is divided into a few days rolling planning module, real-time plan for adjustment module and AGC
When control module, in a few days rolling planning module, real-time plan for adjustment module be required to wind power prediction data as input, be
Generation wind power plant can dissolve power interval track boundary, and wind power point prediction value is converted into wind power interval prediction value
(i.e. wind power prediction section), counted by the probability of error for combining history wind power prediction value and actual value, a certain
The error range section [- δ ,+σ] of each wind power plant is obtained under confidence level, with reference to following short-term wind-electricity power predicted value and ultra-short term
Wind power prediction value Pwf, generate short-term wind-electricity power forecast interval and super short-period wind power forecast interval [Pwf-δ,Pwf+
σ]。
S2, in a few days in rolling planning module, based on short-term wind-electricity power forecast interval and short-term load forecasting, with routine
Unit generation cost is minimum, the minimum object function of short-term wind-electricity power forecast interval upper limit deviation, with system safely for constraint
Robust Interval rolling optimization is carried out, power interval track boundary and conventional power unit generation schedule can be dissolved by calculating wind power plant, specifically
It is as follows：
In a few days in rolling planning module, rolled once per 1h, using 15min as temporal resolution, optimize following 4h decision-making
Value, totally 16 points, only carry out preceding 4 points every time.It is uncertain in this Robust Optimization Model unlike the optimization of traditional robust
Scope is that i.e. decision value is wind power plant plan base value, and wind power plant can dissolve power interval rail as decision value rather than set-point
Mark boundary and conventional power unit generation schedule, object function are as follows：
Wherein,Represent that wind power plant can dissolve the lower and upper limit of power interval track boundary；Represent t
Wind power plant i plans base value；Pj,tRepresent the generation schedule of j-th of conventional power unit of t；aj,bj,cjRespectively j-th conventional
Secondary term coefficient, Monomial coefficient and the constant term coefficient of unit generation cost；Represent t wind power plant i short-term wind-electricity
The upper limit in power prediction section；λiRepresent the power prediction upper bound deviation penalty coefficient to wind power plant i；T represents optimization time domain, this
T=16 in module；NGRepresent conventional power unit quantity, NWRepresent wind-powered electricity generation number；
The object function is corresponding this patent method, is not prior art, this object function has considered conventional power unit
Cost of electricity-generating and wind electricity digestion, and can optimize to obtain wind power plant dissolves power interval compass, within the range
The fluctuation of wind-powered electricity generation can be received by power network, can provide reference frame for operation plan.
Constraints includes：Power-balance constraint, conventional power unit output restriction, the constraint of conventional power unit climbing rate, wind
Electric units limits, spinning reserve constraint and transmission section security constraint.Variable in economic load dispatching Optimized model is according to specific
Problem change.Here the variable in constraints is the variable considered in the method for corresponding this patent, the constraints energy
Enough ensure Optimized model acquired results in the range of safe operation of power system.Shown in specific as follows：
1) power-balance constraint
Wherein,Represent the short-term load forecasting of t.
2) conventional power unit output restriction
Wherein,P j,t、The output lower and upper limit of j-th of conventional power unit of t are represented respectively.
3) conventional power unit climbing rate constrains
Wherein,Respectively the maximum of j-th of conventional power unit of t downwards with maximum climb by climbing power
Slope power.
4) spinning reserve constrains
Wherein,Lower spinning reserve and upper spinning reserve of j-th of conventional power unit in t are represented respectively；The lower rotation stand-by requirement of power system and upper spinning reserve demand are represented respectively.Here power system refers to
The whole power system being made up of all conventional power units, wind power plant, transmission line etc., because power system is to have to spinning reserve
Demand capacity, therefore spinning reserve capacity mentioned here is in units of whole power system.
5) section security constraint is transmitted
Wherein, φj-lThe generating transfer factor for being j-th of conventional power unit to section l, φi-lIt is wind power plant i to section l's
Generating transfer factor；Fl min、Fl maxSection l trend lower limit and the trend upper limit is represented respectively.
6) wind power output constrains
Wherein,T wind power plant i short-term wind-electricity power forecast interval lower limit is represented, the formula represents that wind power plant can disappear
Power interval track boundary lower limit of receiving should be not less than short-term wind-electricity field power prediction interval limit, and wind power plant can dissolve power area
Between the track boundary upper limit be not higher than the short-term wind-electricity power forecast interval upper limit.
S3, in real-time plan for adjustment module, based on super short-period wind power forecast interval and ultra-short term, with
It is base value that the wind power plant that in a few days rolling optimization module obtains, which can dissolve power interval track boundary with conventional power unit generation schedule, after
Continuous to roll robust optimization, adjustment wind power plant can dissolve power interval track boundary and conventional power unit generation schedule, obtain amendment wind
Electric field can dissolve power interval track boundary and amendment conventional power unit generation schedule, specific as follows：
The decision value of object function can dissolve power interval track boundary, the conventional machine of amendment for amendment wind power plant in this module
Group generation schedule and amendment wind power plant plan base value, the adjustment constraint of AGC units basic point is added in constraints, is that AGC units are pre-
Enough adjustment nargin is stopped, real-time plan for adjustment module rolls once per 5min, using 5min as temporal resolution, optimizes following 1h
Decision value, totally 12 points, only carry out the 1st point every time.
Refering to shown in Fig. 1.Real-time plan for adjustment module is mainly in shorter time scale, according to ultra-short term prediction number
According to the wind power plant for optimizing to obtain to previous step can dissolve power interval track boundary and conventional power unit generation schedule be adjusted or
Amendment, therefore the decision value form of Robust Interval economic load dispatching is changed into Δ P, object function is as follows：
Wherein,Represent that in a few days rolling planning module optimizes the generation schedule of obtained j-th of conventional power unit in t；Represent that the wind power plant i that in a few days rolling planning module optimizes to obtain dissolves the power interval track boundary upper limit in t；Represent the super short-period wind power forecast interval upper limit；Represent that amendment wind power plant i can dissolve power interval respectively
Track boundary lower and upper limit；Represent amendment wind power plant plan base value；ΔPj,tRepresent amendment conventional power unit generation schedule
Value；T represents optimization time domain, T=12 in this module.Object function herein is also the method for corresponding this patent, is not existing skill
Art.The decision value of the object function is the adjustment amount after upper module optimal value, is the further adjustment to plan, ensures meter
The accuracy and optimality drawn, while have also contemplated that conventional power unit cost of electricity-generating and wind electricity digestion.
The constraints of plan for adjustment module is in real time：
1) power-balance constraint
Wherein,The t wind power plant i that in a few days rolling planning module obtains plan base values are represented,Represent the super of t
Short-term load forecasting.
2) conventional power unit output restriction
3) conventional power unit climbing rate constrains
4) spinning reserve constrains
5) section security constraint is transmitted
6) wind power output constrains
In order to which to enough adjustment nargin is reserved in AGC real-time control modules, the adjustment of AGC basic points is added in this module about
Beam, such as formula (15)：
Wherein, γmin、γmaxThe lower limit coefficient and upper limit coefficient of AGC units adjustment nargin are represented respectively；Pk,tFor kth platform
The plan that AGC set optimizations obtain is contributed；For the capacity of kth platform AGC units.The formula of the step adds as constraints
Enter to real-time plan for adjustment module, it is ensured that for AGC, subsequently adjustment leaves enough nargin.
S4, in AGC real-time control modules, non-AGC units follow the trail of the conventional power unit hair of real-time plan for adjustment module optimization
Electricity plan (corrects conventional power unit generation schedule), and the positive wind power plant of wind power plant repairing can be dissolved in the boundary of power interval track and used
Maximum power point tracking pattern, the fuctuation within a narrow range and the out-of-limit situation of wind-powered electricity generation of AGC units reply non-regularity, adjusts AGC machines in real time
Group basic point performance number, suitably lifts wind electricity digestion amount when standby sufficient under AGC units, finally under wind power plant and conventional power unit
Send out generation schedule instruction.It is specific as follows：
Comprising AGC units and non-AGC units in conventional power unit, AGC units can refer generally to fast routine of regulating the speed of contributing
Unit, the present invention in non-AGC units regulate the speed slowly, so mainly tracking generation schedule.
In AGC real-time control modules, non-AGC units tracking correction conventional power unit generation schedule, AGC units reply load
Adjustment power generating value maintains power system frequency stable in real time for fluctuation, when the positive wind power plant of wind power output repairing can dissolve power interval rail
When in mark boundary, wind power plant uses maximum power point tracking pattern；When more than the upper limit, wind power output is limited in amendment wind-powered electricity generation
Field can dissolve the power interval track boundary upper limit.When standby sufficient under AGC units, wind-powered electricity generation forced partial outage can be suitably reduced, most
The consumption wind-powered electricity generation of big degree.
Prevent from being primarily due to wind power output uncertainty more than the upper limit it is larger, in order to ensure power demands, power system
Middle conventional power unit installed capacity considers the situation of the hair of wind-powered electricity generation zero, and total installation of generating capacity disclosure satisfy that workload demand, when wind-powered electricity generation has
When power, part conventional power unit installed capacity can be used as standby.Therefore, wind power output lower limit does not have to limitation, but the upper limit one
As think maximum wind contribute can not be more than wind power prediction value, the amendment wind power plant that the inventive method optimizes to obtain can dissolve
Power interval track boundary upper and lower bound is in pre- power scale bound (i.e. short-term wind-electricity power forecast interval, ultra-short term wind
Electrical power forecast interval) within, wind power output scope can be reduced, being made a plan for scheduling, offer is more accurate to be referred to.
The wind power plant that final optimization pass obtains can dissolve the association of power interval track boundary schematic diagram and each module time yardstick
Adjust refering to shown in Fig. 2.
S5, contribute according to wind power's supervision system Real-time Feedback wind power plant is actual, the following input for correcting Optimized model is pre-
Measured value (mainly corrects short-term wind-electricity power predicted value and super short-period wind power predicted value, that is, corrects P herewf, the value amendment
Afterwards, formula (1) and the short-term wind-electricity power forecast interval upper limit in formula (8) and the super short-period wind power forecast interval upper limit from
So also it is corrected), it is specific as follows so as to roll the accuracy that can lift operation plan：
Refering to shown in Fig. 1.The actual output Real-time Feedback of the wind power plant monitored is given and in a few days rolled by wind power's supervision system
Schedule module and real-time plan for adjustment module, correct prediction result, make predicted value closer to actual value, so that what optimization obtained
Decision value is more accurate, and formulation is as follows：
After t implements control, each output valve of the object in future time instance can have both been calculated according to forecast model, wherein
Also include predicted value y of i-th of output quantity at the t+1 momenti,1(t+1|t).Carved to t+1 and measure each reality output yi(t+1)
Afterwards, you can relatively and form error amount e (t+1) with corresponding predicted value, predicted not with method of weighting using this control information
The error come, and the prediction based on model is compensated with this, it can obtain calibrated predicted value：
ycor(t+2 | t+1)=ypre(t+2|t+1)+He(t+2|t+1) (16)；
Wherein, ypre(t+2 | t+1) represent the t+1 moment t+2 moment is corrected before predicted value；ycor(t+2 | t+1) represent t
+ 1 moment is to t+2 moment revised predicted value；E (t+2 | t+1) represent that the t+1 moment is based on the error that e (t+1) predictions obtain
Value；H is amendment weighted value, H ∈ [0,1], is determined by the degree of accuracy of error prediction.(if predicted value over-correction can reduce on the contrary
Follow-up optimization precision, H selection are determined by the degree of accuracy of error prediction, if the error degree of accuracy is high, amendment weighted value H is desirable big
Some, on the contrary take smaller, it can so ensure that predicted value does not have " over-correction ".)
Model Predictive Control includes three forecast model, rolling optimization and feedback compensation links.Step S5 is exactly to feed back school
Positive link, it is one of innovative point of the present invention that Model Predictive Control, which is applied in scheduling, so being not prior art.Feed back school
The output of forecast model can be just being corrected in real time, and the output of forecast model is the reference of rolling optimization, so feedback compensation
Optimization is formed into a closed loop, it is possible to increase the precision of optimization, formulate more accurate planned value.
Present invention incorporates Model Predictive Control and robust to optimize, and embeds it in the electric power system dispatching operation containing wind-powered electricity generation
In, there is obvious difference with existing dispatching technique.Hierarchical coordinative is carried out to scheduling in a variety of time scales, and can with wind power plant
Consumption power interval track boundary optimizes for decision value, and the boundary is prior art without successively refinement can improve
Plan precision；The lower spinning reserve capacity of AGC units is considered simultaneously, so as to consider that wind-powered electricity generation, model are issued additional under standby sufficiency
Feedback compensation link in PREDICTIVE CONTROL can lift the precision of wind power prediction, also can further lift the standard of plan
True property.
The content not being described in detail in this specification belongs to prior art known to professional and technical personnel in the field.
Claims (9)
1. a kind of wind-powered electricity generation Robust Interval trace scheduling method for considering Multiple Time Scales and coordinating, comprises the following steps：
S1, according to history wind power actual value, history short-term wind-electricity power predicted value and history super short-period wind power predict
Value, error range, the error range of super short-period wind power prediction of statistics short-term wind-electricity power prediction, with reference to newest short-term
Wind power prediction value, newest super short-period wind power predicted value, generation short-term wind-electricity power forecast interval, ultra-short term wind-powered electricity generation
Power prediction section, the input information as Optimized model；
S2, in a few days in rolling planning module, based on short-term wind-electricity power forecast interval and short-term load forecasting, with conventional power unit
Cost of electricity-generating is minimum, the minimum object function of short-term wind-electricity power forecast interval upper limit deviation, is carried out with system for constraint safely
Robust Interval rolling optimization, power interval track boundary and conventional power unit generation schedule can be dissolved by calculating wind power plant；
S3, in real-time plan for adjustment module, based on super short-period wind power forecast interval and ultra-short term, with a few days
It is base value that the wind power plant that rolling optimization module obtains, which can dissolve power interval track boundary with conventional power unit generation schedule, continues to roll
Dynamic robust optimization, adjustment wind power plant can dissolve power interval track boundary and conventional power unit generation schedule, obtain amendment wind power plant
Power interval track boundary and amendment conventional power unit generation schedule can be dissolved；
S4, in AGC real-time control modules, non-AGC units tracking amendment conventional power unit generation schedule, the positive wind-powered electricity generation of wind power plant repairing
Field can dissolve and maximum power point tracking pattern is used in the boundary of power interval track, and AGC units tackle the small amplitude wave of non-regularity
The dynamic and out-of-limit situation of wind-powered electricity generation, AGC unit basic point performance numbers are adjusted in real time, wind-powered electricity generation is suitably lifted when standby sufficient under AGC units
Consumption amount, finally issue generation schedule instruction to wind power plant and conventional power unit；
S5, contribute according to wind power's supervision system Real-time Feedback wind power plant is actual, the input information of Optimized model is corrected, so as to roll
The accuracy of dynamic lifting operation plan.
2. the method as described in claim 1, it is characterised in that：In step S1, short-term wind-electricity power forecast interval is will be newest
Short-term wind-electricity power predicted value plus short-term wind-electricity power prediction error range obtain；
Super short-period wind power forecast interval is that newest super short-period wind power predicted value is pre- plus super short-period wind power
The error range of survey obtains.
3. method as claimed in claim 1 or 2, it is characterised in that：In step S2, object function is：
Wherein,Represent that wind power plant can dissolve the lower and upper limit of power interval track boundary；Represent t wind-powered electricity generation
Field i plan base values；Pj,tRepresent the generation schedule of j-th of conventional power unit of t；aj,bj,cjRespectively j-th of conventional power unit
Secondary term coefficient, Monomial coefficient and the constant term coefficient of cost of electricity-generating；Represent t wind power plant i short-term wind-electricity power
The upper limit of forecast interval；λiRepresent the power prediction upper bound deviation penalty coefficient to wind power plant i；T represents optimization time domain；NGRepresent
Conventional power unit quantity, NWRepresent wind-powered electricity generation number.
4. method as claimed in claim 1 or 2, it is characterised in that：In step S2, constraints includes：
A1) power-balance constraint：
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<mi>G</mi>
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<mi>t</mi>
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<mi>w</mi>
</msubsup>
<mo>=</mo>
<msub>
<mover>
<mi>L</mi>
<mo>^</mo>
</mover>
<mi>t</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein,Represent the short-term load forecasting of t；
B1) conventional power unit output restriction：
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<mi>P</mi>
<mo>&OverBar;</mo>
</munder>
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<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
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<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
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1
Wherein,P j,t、The output lower and upper limit of j-th of conventional power unit of t are represented respectively；
C1) conventional power unit climbing rate constrains：
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<mo>-</mo>
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<mi>P</mi>
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<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>&le;</mo>
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<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
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<mi>U</mi>
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<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein,The downward climbing power of maximum of respectively j-th of conventional power unit of t and maximum work(of climbing upwards
Rate；
D1) spinning reserve constrains：
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<mi>j</mi>
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<mi>N</mi>
<mi>G</mi>
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</munder>
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<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mo>-</mo>
</msubsup>
<mo>&GreaterEqual;</mo>
<msubsup>
<mi>R</mi>
<mi>S</mi>
<mo>-</mo>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein,Lower spinning reserve and upper spinning reserve of j-th of conventional power unit in t are represented respectively；
The lower rotation stand-by requirement of power system and upper spinning reserve demand are represented respectively；
E1 section security constraint) is transmitted：
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<mi>l</mi>
<mi>min</mi>
</msubsup>
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</mrow>
</munder>
<msub>
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<mi>j</mi>
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<mi>l</mi>
</mrow>
</msub>
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<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msup>
<mi>N</mi>
<mi>W</mi>
</msup>
</mrow>
</munder>
<msub>
<mi>&phi;</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>l</mi>
</mrow>
</msub>
<msubsup>
<mi>P</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mi>w</mi>
</msubsup>
<mo>&le;</mo>
<msubsup>
<mi>F</mi>
<mi>l</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein, φj-lThe generating transfer factor for being j-th of conventional power unit to section l, φi-lFor generatings of the wind power plant i to section l
Transfer factor；Fl min、Fl maxSection l trend lower limit and the trend upper limit is represented respectively；
F1) wind power output constrains：
Wherein,Represent t wind power plant i short-term wind-electricity power forecast interval lower limit.
5. method as claimed in claim 1 or 2, it is characterised in that：In step S2, in a few days in rolling planning module, rolled per 1h
Move once, using 15min as temporal resolution, optimize following 4h decision value, totally 16 points, only carry out preceding 4 points every time.
6. method as claimed in claim 1 or 2, it is characterised in that：In step S3, the object function for rolling robust optimization is：
Wherein,Represent that in a few days rolling planning module optimizes the generation schedule of obtained j-th of conventional power unit in t；Table
Show that the wind power plant i that in a few days rolling planning module optimizes to obtain dissolves the power interval track boundary upper limit in t；Table
Show the super short-period wind power forecast interval upper limit；Represent that amendment wind power plant i can dissolve power interval track respectively
Boundary lower and upper limit；Represent amendment wind power plant plan base value；ΔPj,tRepresent amendment conventional power unit power generation plan value；T tables
Show optimization time domain.
7. method as claimed in claim 1 or 2, it is characterised in that：In step S3, the constraints bag of robust optimization is rolled
Include：
A2) power-balance constraint：
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<mo>&Element;</mo>
<msup>
<mi>N</mi>
<mi>G</mi>
</msup>
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</munder>
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<mo>(</mo>
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<mo>^</mo>
</mover>
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<mo>,</mo>
<mi>t</mi>
</mrow>
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<mo>+</mo>
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<mi>&Delta;P</mi>
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<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msup>
<mi>N</mi>
<mi>W</mi>
</msup>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mi>w</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&Delta;P</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mi>w</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mover>
<mi>L</mi>
<mo>~</mo>
</mover>
<mi>t</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein,The t wind power plant i that in a few days rolling planning module obtains plan base values are represented,Represent the ultra-short term of t
Load prediction；
B2) conventional power unit output restriction：
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<msub>
<munder>
<mi>P</mi>
<mo>&OverBar;</mo>
</munder>
<mrow>
<mi>j</mi>
<mo>,</mo>
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<mo>&le;</mo>
<msub>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&Delta;P</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
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<mi>P</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>j</mi>
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<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
C2) conventional power unit climbing rate constrains：
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<mo>-</mo>
<msubsup>
<mi>&Delta;P</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
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<mi>D</mi>
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<mo>(</mo>
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<mi>t</mi>
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<mo>-</mo>
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<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
D2) spinning reserve constrains：
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8. method as claimed in claim 1 or 2, it is characterised in that：In step S3, real-time plan for adjustment module rolls per 5min
Once, using 5min as temporal resolution, optimize following 1h decision value, totally 12 points, only carry out the 1st point every time.
9. method as claimed in claim 1 or 2, it is characterised in that：Step S5 formulation is as follows：Implement to control in t
After system, each output valve of the object in future time instance can be both calculated according to forecast model, had been existed wherein also including i-th of output valve
The predicted value y at t+1 momenti,1(t+1|t)；Each reality output y is measured to the t+1 momenti(t+1) after, you can with corresponding predicted value
Relatively and error amount e (t+1) is formed, following error is predicted with method of weighting using this control information, and base is compensated with this
In the prediction of model, calibrated predicted value can obtain：
ycor(t+2 | t+1)=ypre(t+2|t+1)+He(t+2|t+1) (16)；
Wherein, ypre(t+2 | t+1) represent the t+1 moment t+2 moment is corrected before predicted value；ycorWhen (t+2 | t+1) represents t+1
Carve to t+2 moment revised predicted value；E (t+2 | t+1) represent the t+1 moment be based on error amount that e (t+1) predictions obtain to
Amount；H is amendment weighted value, H ∈ [0,1], is determined by the degree of accuracy of error prediction.
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| CN108306287A (en) * | 2018-02-09 | 2018-07-20 | 清华大学 | A kind of small interference unstability Risk Identification Method and system of the electric system containing wind-powered electricity generation |
| CN109742799A (en) * | 2019-01-24 | 2019-05-10 | 福州大学 | Based on the multiattribute wind energy turbine set installed capacity Interval Decision Making method of D-U spatial mixing |
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| CN108063453B (en) * | 2017-12-04 | 2021-07-13 | 国网辽宁省电力有限公司 | Power grid full-time multivariate optimization scheduling method for improving wind power receiving capacity |
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| CN108090632B (en) * | 2018-01-23 | 2020-06-02 | 南方电网科学研究院有限责任公司 | New energy grid-connected power system multi-time scale scheduling method based on robust optimization |
| CN108306287A (en) * | 2018-02-09 | 2018-07-20 | 清华大学 | A kind of small interference unstability Risk Identification Method and system of the electric system containing wind-powered electricity generation |
| CN109787279B (en) * | 2019-01-14 | 2022-05-17 | 三峡大学 | Interval quantity solving method for uncertain scheduling problem of wind power grid-connected system |
| CN109787279A (en) * | 2019-01-14 | 2019-05-21 | 三峡大学 | A kind of wind-electricity integration system does not know the section amount method for solving of scheduling problem |
| CN109742799A (en) * | 2019-01-24 | 2019-05-10 | 福州大学 | Based on the multiattribute wind energy turbine set installed capacity Interval Decision Making method of D-U spatial mixing |
| CN109742799B (en) * | 2019-01-24 | 2022-07-01 | 福州大学 | D-U space mixed multi-attribute wind power plant installed capacity interval decision method |
| CN110458344A (en) * | 2019-07-26 | 2019-11-15 | 湘潭大学 | A kind of super short-period wind power prediction technique of auto-adaptive time resolution ratio |
| CN110458344B (en) * | 2019-07-26 | 2022-05-06 | 湘潭大学 | Ultrashort-term wind power prediction method adaptive to time resolution |
| CN113969873A (en) * | 2021-10-18 | 2022-01-25 | 国网湖南省电力有限公司 | Ultra-short term output prediction method and system for wind turbine generator and storage medium |
| CN113969873B (en) * | 2021-10-18 | 2023-04-07 | 国网湖南省电力有限公司 | Ultra-short term output prediction method and system for wind turbine generator and storage medium |
| CN115207913A (en) * | 2022-07-27 | 2022-10-18 | 中国华能集团清洁能源技术研究院有限公司 | Method and system for correcting ultra-short-term power prediction data of wind storage combined operation |
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