CN106934496B - Couple power station two dimension scheduling graph drafting and the application method of Runoff Forecast information - Google Patents

Couple power station two dimension scheduling graph drafting and the application method of Runoff Forecast information Download PDF

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CN106934496B
CN106934496B CN201710135621.4A CN201710135621A CN106934496B CN 106934496 B CN106934496 B CN 106934496B CN 201710135621 A CN201710135621 A CN 201710135621A CN 106934496 B CN106934496 B CN 106934496B
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雷晓辉
王旭
谭乔风
王超
王浩
蒋云钟
秦韬
纪毅
陆梦恬
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention discloses a kind of construction method of the power station two dimension scheduling graph based on coupling Runoff Forecast information, it is related to reservoir operation technical field.The described method includes：Power station two dimension scheduling graph citation form is generally changed；Establish power station two dimension scheduling graph Optimized model；Using multi-objective genetic algorithm, establishment is optimized to the generalization form of power station two dimension scheduling graph on the basis of the two dimension scheduling graph Optimized model of power station, the two-dimentional scheduling graph after being optimized；Based on forecast two Phase flow grade probability, power station decision-making is asked to contribute using posterior probability weighted mean method.The short-term benefit and long-term benefit of energy effective coordination reservoir of the invention, be conducive to the raising of power station panorama generated energy, meanwhile, the decision-making that the present invention obtains power station based on posterior probability weighted mean method is contributed, be conducive to take into full account the randomness of runoff, reduce the possibility of incorrect decision；Adapt to the formulation of reservoir Medium and long term generation scheduling and real-time reservoir operation.

Description

Couple power station two dimension scheduling graph drafting and the application method of Runoff Forecast information
Technical field
The present invention relates to reservoir operation technical field, more particularly to a kind of power station two dimension for coupling Runoff Forecast information to adjust Degree figure is drawn and application method.
Background technology
Reservoir is the important engineering measure that the mankind redistribute water resource spatial and temporal distributions, and reservoir operation technology is to realize water One of indispensable means that storehouse is normally and efficiently run.With the spatial and temporal distributions of the Regulation capacity Optimization of Water Resource Allocation of reservoir, can carry High water reservoir management operation level, achievees the purpose that Xing Li, removes the evil, and can improve water resource and hydraulic power potentials utilization rate.Power station tune Figure is spent due to simple to operate, is the important tool of existing reservoir operation technology middle finger water guide power station day-to-day operation.
Existing power station scheduling graph citation form is as shown in Figure 1.In the scheduling graph of power station generally according to reservoir level, when Between divide reservoir basin into flood control zone, ensure output area, reduce output area and increase output area Deng Ge operation areas, its management and running Mode is：First, when reservoir level is positioned at reduction output area, power station is by reduction output power generation, and control time end reservoir level is not Contribute less than level of dead water and not higher than reducing；2nd, when reservoir level is positioned at guarantee output area, power station, which is pressed, ensures power generation of contributing, And control time end reservoir level is not less than reducing out the line of force and not higher than tamper-proof line；3rd, when reservoir level is contributed positioned at increasing Qu Shi, power station generate electricity by output is increased, and control time end reservoir level is not less than tamper-proof line and not higher than flood control restraining line； 4th, when reservoir level is positioned at flood control restricted area, then control time end reservoir level is only needed not less than flood control restraining line, by whole dresses Machine anticipation, which is contributed, to generate electricity.
It can be seen that from the form and application mode of above-mentioned existing scheduling graph：Use existing power station scheduling graph scheduling decision When, only using the water level at the beginning of current time and scheduling slot as Rule of judgment, do not account for Runoff Forecast information.And in fact, The scheduling decision of reservoir present period, not only has relation with facing the water level at moment, and also carrying out water with facing the possibility of period has Close.Both：Existing power generation dispatching figure lacks for Runoff Forecast information and its probabilistic consideration.
The content of the invention
It is an object of the invention to provide a kind of power station two dimension scheduling graph for coupling Runoff Forecast information to draw and use Method, so as to solve foregoing problems existing in the prior art.
To achieve these goals, the structure of the power station two dimension scheduling graph of the present invention based on coupling Runoff Forecast information Construction method, the described method includes：
S1, power station two dimension scheduling graph citation form are generally changed；
S11, scheduling is optimized using deterministic optimization dispatching method to long serial two Phase flow；
S12, draws the diagram of block of water level-reservoir inflow-output three；
S13, the plane projected to where water level and reservoir inflow that power station is contributed, observation obtain power station two dimension scheduling The generalization form of figure；
S2, establishes power station two dimension scheduling graph Optimized model, during power station two dimension scheduling graph Optimized model is established The target for needing to optimize is formula (1) and (2)；
Wherein, E is generated energy in schedule periods；KQtHtFor reservoir t period average outputs；Segment number when t is represented, when △ t are Segment length；T is period sum；P is output fraction, sum (Nt≥Nmin) contributing to be more than for the period in schedule periods ensures the total of output When hop count；NtRepresent that period t contributes, NminRepresent to ensure to contribute；
S3, using multi-objective genetic algorithm, to power station two dimension on the basis of the two dimension scheduling graph Optimized model of power station The generalization form of scheduling graph optimizes establishment, the two-dimentional scheduling graph after being optimized；
S4, based on forecast two Phase flow grade probability, asks power station decision-making to contribute using posterior probability weighted mean method.
Preferably, the generalization form of the power station two dimension scheduling graph obtained in step S1 is specially：Using Z as abscissa, institute It is water level or reservoir water requirement to state Z, to forecast two Phase flow as ordinate；According to the grade of the two Phase flow divided in advance and in advance The grade of the Z first divided, multiple output sections are separated into by the power station two dimension scheduling graph, and setting each output section has One independent power generating value, the relation between each power generating value represented by there are formula (3) and formula (4)：
Ni-1,j≤Ni,j≤Ni+1,j (3)
Ni,j-1≤Ni,j≤Ni,j+1 (4)
Wherein, i represents the Z grades residing for output section；J represents the forecast two Phase flow grade residing for output section；
Ni-1,jRepresent the power generating value in the output section that Z grades are i-1 in scheduling graph, and forecast two Phase flow grade is j；
Ni,jRepresent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j；
Ni+1,jRepresent the power generating value in the output section that Z grades are i+1 in scheduling graph, and forecast two Phase flow grade is j；
Ni,j-1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j-1；
Ni,j+1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j+1.
Preferably, in S2, the power station two dimension scheduling graph Optimized model of foundation meets five kinds of constraintss, is specially：
Reservoir water Constraints of Equilibrium, is specially formula (5)：
V (t+1)=V (t)+WI (t)-WO (t)-WL (t) (5)
In formula (5), V (t), V (t+1) represent storage capacity of the reservoir with period Mo at the beginning of the t periods respectively；WI(t),WO(t),WL (t) Incoming water quantity, outbound water and the water loss of reservoir t periods is represented respectively；
Pondage constrains, and is specially formula (6)：
Vmin(t)≤V(t)≤Vmax(t) (6)
V (t), V in formula (6)max(t),Vmin(t) reservoir is represented respectively in the storage capacity of t periods, the storage capacity upper limit of permission and is permitted Perhaps storage capacity lower limit；
Reservoir letdown flow constrains, and is specially formula (7)：
Qmin(t)≤Q(t)≤Qmax(t) (7)
In formula (7), Q (t), Qmax(t),Qmin(t) represent that reservoir is let out in the storage outflow of t periods, the maximum of permission respectively Flow and the minimum discharging flow allowed；
Power station units limits, are specially formula (8)：
Nmin(t)≤N(t)≤Nmax(t) (8)
N (t), N in formula (8)max(t),Nmin(t) power station is represented respectively in the output of t periods, permission maximum output and is permitted Perhaps minimum load；
Scheduling graph output Operations of Interva Constraint, is specially formula (9)：
Ni-1,j≤Ni,j≤Ni+1,j；Ni,j-1≤Ni,j≤Ni,j+1 (9)
In formula (9), i represents the grade in output section；J represents the forecast two Phase flow grade residing for output section, Ni-1,j Represent the power generating value in the output section that Z grades are i-1 in scheduling graph, and forecast two Phase flow grade is j；
Ni,jRepresent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j；
Ni+1,jRepresent the power generating value in the output section that Z grades are i+1 in scheduling graph, and forecast two Phase flow grade is j；
Ni,j-1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j-1；
Ni,j+1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j+1.
Preferably, S4, is specifically realized as steps described below：
S41：The uncertainty of description two Phase flow graded forecast is forecast using Bayesian probability
Assuming that discharge process obeys single order markoff process, H is madet-1Represent the measured discharge at t-1 moment, Ht、StRespectively Represent the actual flow and forecasting runoff of t moment；ht-1、ht、stH is represented respectivelyt-1、Ht、StImplementation value, it is public according to Bayes Formula, actual flow HtPosterior density function be formula (10)：
In formula, g (ht|ht-1) it is flow priori probability density；Work as St=stWhen, function f (st|ht-1,ht) it is HtLikelihood Function；Φ(ht|ht-1,st) it is HtPosterior density function；
S42, with the renewal of forecast information, passes through likelihood function f (st|ht-1,ht) to priori probability density g (ht|ht-1) It is modified, the posterior probability more to be tallied with the actual situation；
It is [0, Q by reservoir inflow grade classification in conjunction with the two-dimentional scheduling graph after the optimization obtained in step S31]、[Q1, Q2]、[Q2,Q3] three grades, t moment forecast two Phase flow can be obtained by Bayesian formula and is in [0, Q1]、[Q1,Q2]、 [Q2,Q3] three grades probability P1、P2And P3, wherein, Q1、Q2And Q3Reservoir inflow three grades division threshold value is represented respectively, and Q1＜ Q2＜ Q3；
S43：Final decision-making output N is obtained using the average weighted mode of posterior probability, the weighted average formula of use For formula (11)：
N=P1×Ni,1+P2×Ni,2+P3×Ni,3 (11)
Wherein, i is rank residing for reservoir current level；
P1Grade [0, Q is in for two Phase flow1] probability；
P2Grade [Q is in for two Phase flow1,Q2] probability；
P3Grade [Q is in for two Phase flow2,Q3] probability；
Ni,1Expression current level is i ranks, runoff is in [0, Q1] when corresponding output section；
Ni,2Expression current level is i ranks, runoff is in [Q1,Q2] when corresponding output section；
Ni,3Expression current level is i ranks, runoff is in [Q2,Q3] when corresponding output section.
The present invention generally changes the citation form of power station two dimension scheduling graph according to the result that deterministic optimization is dispatched, basic herein On, optimized using parameter of the multi-objective genetic algorithm for two-dimentional scheduling graph, obtain taking into account that generated energy is optimal and power generation is protected The optimization two dimension scheduling graph of card rate maximum；Lack the shortcomings that considering uncertain to forecast for routine dispactching figure, using based on The runoff grade probability forecast of bayesian theory, it is uncertain to quantify Runoff Forecast；Based on Bayesian probability forecast model products, use Posterior probability weighted mean method obtains power station Real-time Decision power generating value.
The beneficial effects of the invention are as follows：
Consideration Runoff Forecast information guiding reservoir operation decision-making, the short-term benefit and long-term benefit of energy effective coordination reservoir, Be conducive to the raising of power station panorama generated energy.In particular with the development of numerical weather forecast so that believed using Runoff Forecast Breath instruct reservoir operation to be possibly realized, it is disclosed by the invention coupling Runoff Forecast information power station two dimension scheduling graph drafting and Application method, has the following advantages：
(1) prior art is generally all without considering Runoff Forecast information, and the present invention can generate coupling Runoff Forecast information Power station two dimension scheduling graph；
(2) the two Phase flow grade probability forecast based on bayesian theory can take into full account Runoff Forecast uncertainty；
(3) decision-making that power station is obtained based on posterior probability weighted mean method is contributed, be conducive to take into full account runoff with Machine, reduces the possibility of incorrect decision；
(4) formulation of reservoir Medium and long term generation scheduling and real-time reservoir operation are adapted to.
Brief description of the drawings
Fig. 1 is existing conventional power generation dispatching schematic diagram；M is level of dead water, and J1 is offline to ensure to contribute, and J2 contributes for guarantee Reach the standard grade, J3 is to increase out the line of force；I is flood control safety control zone, and ii is increases output area, and for iii to ensure output area, iv is to reduce Output area；
Fig. 2 is the flow diagram of the method for the present invention；
Fig. 3 is the generalization form of power station two dimension scheduling graph.
Fig. 4 is NSGA-II multi-objective genetic algorithm flow charts.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with attached drawing, to the present invention into Row is further described.It should be appreciated that the specific embodiments described herein are not used to only to explain the present invention Limit the present invention.
Embodiment
With reference to Fig. 2, the construction method of the power station two dimension scheduling graph based on coupling Runoff Forecast information described in the present embodiment, The described method includes：
S1, power station two dimension scheduling graph citation form are generally changed
S11, scheduling is optimized using deterministic optimization dispatching method to long serial two Phase flow；
S12, draws the diagram of block of water level-reservoir inflow-output three；
S13, the plane projected to where water level and reservoir inflow that power station is contributed, observation obtain power station two dimension scheduling The generalization form of figure；
S2, establishes power station two dimension scheduling graph Optimized model, during power station two dimension scheduling graph Optimized model is established The target for needing to optimize is formula (1) and (2)；
Wherein, E is generated energy in schedule periods；KQtHtFor balancing reservoir t period average outputs；Segment number when t is represented, △ t are When segment length；T is period sum；P is output fraction, sum (Nt≥Nmin) contributing to be more than for the period in schedule periods ensures what is contributed Hop count when total；NtRepresent period t power generating value, NminRepresent to ensure power generating value；
S3, using multi-objective genetic algorithm, to power station two dimension on the basis of the two dimension scheduling graph Optimized model of power station The generalization form of scheduling graph optimizes establishment, the two-dimentional scheduling graph after being optimized；
S4, based on forecast two Phase flow grade probability, asks power station decision-making to contribute using posterior probability weighted mean method.
Explanation is explained in more detail：
(1) the generalization form of the power station two dimension scheduling graph obtained in step S1 is specially：Using Z as abscissa, the Z For water level or reservoir water requirement, to forecast two Phase flow as ordinate；According to the grade of the two Phase flow divided in advance and in advance The grade of the Z of division, multiple output sections are separated into by the power station two dimension scheduling graph, and setting each output section has a one Independent power generating value, the relation between each power generating value represented by there are formula (3) and formula (4)：
Ni-1,j≤Ni,j≤Ni+1,j (3)
Ni,j-1≤Ni,j≤Ni,j+1 (4)
Wherein, i represents the Z grades residing for output section；J represents the forecast two Phase flow grade residing for output section； Ni-1,jRepresent the power generating value in the output section that Z grades are i-1 in scheduling graph, and forecast two Phase flow grade is j；Ni,jRepresent scheduling Z grades are i in figure, the power generating value in the output section that forecast two Phase flow grade is j；Ni+1,jRepresent that Z grades are i+ in scheduling graph 1, the power generating value in the output section that forecast two Phase flow grade is j；Ni,j-1Represent that Z grades are i in scheduling graph, forecast two Phase flow Grade is the power generating value in the output section of j-1；Ni,j+1Represent that Z grades are i in scheduling graph, forecast two Phase flow grade is j+1's The power generating value in output section.
(2) in S2, the power station two dimension scheduling graph Optimized model of foundation meets five kinds of constraintss, is specially：
Reservoir water Constraints of Equilibrium, is specially formula (5)：
V (t+1)=V (t)+WI (t)-WO (t)-WL (t) (5)
In formula (5), V (t), V (t+1) represent storage capacity of the reservoir with period Mo at the beginning of the t periods respectively；WI(t),WO(t),WL (t) Incoming water quantity, outbound water and the water loss of reservoir t periods is represented respectively；
Pondage constrains, and is specially formula (6)：
Vmin(t)≤V(t)≤Vmax(t) (6)
V (t), V in formula (6)max(t),Vmin(t) reservoir is represented respectively in the storage capacity of t periods, the storage capacity upper limit of permission and is permitted Perhaps storage capacity lower limit；
Reservoir letdown flow constrains, and is specially formula (7)：
Qmin(t)≤Q(t)≤Qmax(t) (7)
In formula (7), Q (t), Qmax(t),Qmin(t) represent that reservoir is let out in the storage outflow of t periods, the maximum of permission respectively Flow and the minimum discharging flow allowed；
Power station units limits, are specially formula (8)：
Nmin(t)≤N(t)≤Nmax(t) (8)
N (t), N in formula (8)max(t),Nmin(t) power station is represented respectively in the output of t periods, permission maximum output and is permitted Perhaps minimum load；
Scheduling graph output Operations of Interva Constraint, is specially formula (9)：
Ni-1,j≤Ni,j≤Ni+1,j；Ni,j-1≤Ni,j≤Ni,j+1 (9)
In formula (9), i represents the grade in output section；J represents the forecast two Phase flow grade residing for output section.
(4) with reference to Fig. 4, step S3 is non-to approach by non-bad border based on non-domination solution quicksort genetic algorithm Inferior solution collection, final establishment obtains two, power station scheduling graph, specifically according to following realizations：
Step 1：Generate the initial population that capacity is p；
Step 2：Each individual object function is calculated, the non-bad sequence of fast hierarchical is carried out based on each target；
Step 3：According to the grade of individual, each individual adaptation degree function is assigned；
Step 4：The strategy generating next generation populations such as selection, intersection, variation are retained by elite；
Step 5：Parent and progeny population are merged；
Step 6：Population is subjected to the non-bad sequence of fast hierarchical；
Step 7：Best solution is selected to enter new population (capacity p)；
Step 8：If the individual in same rank is very much, then these individual closeness functions are calculated so that enter The individual of new population is widely distributed on the layer；
Step 9：Repeat step (4)-(8) (are usually set to iterations) untill stop condition meets.
(5) step S4, is specifically realized as steps described below：
S41：The uncertainty of description two Phase flow graded forecast is forecast using Bayesian probability
Assuming that discharge process obeys single order markoff process, H is madet-1Represent the measured discharge at t-1 moment, Ht、StRespectively Represent the actual flow and forecasting runoff of t moment；ht-1、ht、stH is represented respectivelyt-1、Ht、StImplementation value, it is public according to Bayes Formula, actual flow HtPosterior density function be formula (10)：
In formula, g (ht|ht-1) it is flow priori probability density；Work as St=stWhen, function f (st|ht-1,ht) it is HtLikelihood Function；Φ(ht|ht-1,st) it is HtPosterior density function；
S42, with the renewal of forecast information, passes through likelihood function f (st|ht-1,ht) to priori probability density g (ht|ht-1) It is modified, the posterior probability more to be tallied with the actual situation；
It is [0, Q by reservoir inflow grade classification in conjunction with the two-dimentional scheduling graph after the optimization obtained in step S31]、[Q1, Q2]、[Q2,Q3] three grades, t moment forecast two Phase flow can be obtained by Bayesian formula and is in [0, Q1]、[Q1,Q2]、 [Q2,Q3] three grades probability P1、P2And P3, wherein, Q1、Q2And Q3Reservoir inflow three grades division threshold value is represented respectively, and Q1＜ Q2＜ Q3；
S42：Obtained with reference to Fig. 3, final decision-making output N using the average weighted mode of posterior probability, the weighting of use Average formula is formula (11)：
N=P1×Ni,1+P2×Ni,2+P3×Ni,3 (11)
Wherein, i is rank residing for reservoir current level；
P1Grade [0, Q is in for two Phase flow1] probability；
P2Grade [Q is in for two Phase flow1,Q2] probability；
P3Grade [Q is in for two Phase flow2,Q3] probability；
Ni,1Expression current level is i ranks, runoff is in [0, Q1] when corresponding output section；
Ni,2Expression current level is i ranks, runoff is in [Q1,Q2] when corresponding output section；
Ni,3Expression current level is i ranks, runoff is in [Q2,Q3] when corresponding output section.
The present invention is to be lacked based on existing power generation dispatching figure for Runoff Forecast information and its probabilistic consideration, and is carried Go out a kind of basic generalization form for the power station two dimension scheduling graph for considering Runoff Forecast information, and be based on two Phase flow grade probability Forecast, the decision-making that power station is obtained using posterior probability weighted mean method are contributed.The invention be conducive to take into full account runoff with Machine, reduces the possibility of incorrect decision, adapts to the formulation of reservoir Medium and long term generation scheduling and real-time reservoir operation.
By using above-mentioned technical proposal disclosed by the invention, following beneficial effect has been obtained：
Consideration Runoff Forecast information guiding reservoir operation decision-making, the short-term benefit and long-term benefit of energy effective coordination reservoir, Be conducive to the raising of power station panorama generated energy.In particular with the development of numerical weather forecast so that believed using Runoff Forecast Breath instruct reservoir operation to be possibly realized, it is disclosed by the invention coupling Runoff Forecast information power station two dimension scheduling graph drafting and Application method, has the following advantages：
(1) prior art is generally all without considering Runoff Forecast information, and the present invention can generate coupling Runoff Forecast information Power station two dimension scheduling graph；
(2) the two Phase flow grade probability forecast based on bayesian theory can take into full account Runoff Forecast uncertainty；
(3) decision-making that power station is obtained based on posterior probability weighted mean method is contributed, be conducive to take into full account runoff with Machine, reduces the possibility of incorrect decision；
(4) formulation of reservoir Medium and long term generation scheduling and real-time reservoir operation are adapted to.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should Depending on protection scope of the present invention.

Claims (1)

  1. A kind of 1. construction method of the power station two dimension scheduling graph based on coupling Runoff Forecast information, it is characterised in that the side Method includes：
    S1, power station two dimension scheduling graph citation form are generally changed；
    S11, scheduling is optimized using deterministic optimization dispatching method to long serial two Phase flow；
    S12, draws the diagram of block of water level-reservoir inflow-output three；
    S13, the plane projected to where water level and reservoir inflow that power station is contributed, observation obtain power station two dimension scheduling graph Generalization form；
    S2, establishes power station two dimension scheduling graph Optimized model, is needed during power station two dimension scheduling graph Optimized model is established The target of optimization is formula (1) and (2)；
    <mrow> <mi>E</mi> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msub> <mi>KQ</mi> <mi>t</mi> </msub> <msub> <mi>H</mi> <mi>t</mi> </msub> <mo>&amp;times;</mo> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mi>P</mi> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mfrac> <mrow> <mi>s</mi> <mi>u</mi> <mi>m</mi> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>t</mi> </msub> <mo>&amp;GreaterEqual;</mo> <msub> <mi>N</mi> <mi>min</mi> </msub> <mo>)</mo> </mrow> </mrow> <mi>T</mi> </mfrac> <mo>&amp;times;</mo> <mn>100</mn> <mi>%</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>....</mn> <mo>,</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, E is generated energy in schedule periods；KQtHtFor reservoir t period average outputs；Segment number when t is represented, segment length when △ t are； T is period sum；P is output fraction, sum (Nt≥Nmin) contribute for the period in schedule periods the total period for being more than and ensureing to contribute Number；NtRepresent that period t contributes, NminRepresent to ensure to contribute；
    S3, using multi-objective genetic algorithm, dispatches power station two dimension on the basis of the two dimension scheduling graph Optimized model of power station The generalization form of figure optimizes establishment, the two-dimentional scheduling graph after being optimized；
    S4, based on forecast two Phase flow grade probability, asks power station decision-making to contribute using posterior probability weighted mean method；
    The generalization form of the power station two dimension scheduling graph obtained in step S1 is specially：Using Z as abscissa, the Z for water level or Reservoir water requirement, to forecast two Phase flow as ordinate；According to the grade of the two Phase flow divided in advance and the Z divided in advance Grade, multiple output sections are separated into by the power station two dimension scheduling graph, and setting each output section has one independent to go out Force value, the relation between each power generating value represented by there are formula (3) and formula (4)：
    Ni-1,j≤Ni,j≤Ni+1,j (3)
    Ni,j-1≤Ni,j≤Ni,j+1 (4)
    Wherein, i represents the Z grades residing for output section；J represents the forecast two Phase flow grade residing for output section；
    Ni-1,jRepresent the power generating value in the output section that Z grades are i-1 in scheduling graph, and forecast two Phase flow grade is j；
    Ni,jRepresent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j；
    Ni+1,jRepresent the power generating value in the output section that Z grades are i+1 in scheduling graph, and forecast two Phase flow grade is j；
    Ni,j-1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j-1；
    Ni,j+1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j+1；
    In S2, the power station two dimension scheduling graph Optimized model of foundation meets five kinds of constraintss, is specially：
    Reservoir water Constraints of Equilibrium, is specially formula (5)：
    V (t+1)=V (t)+WI (t)-WO (t)-WL (t) (5)
    In formula (5), V (t), V (t+1) represent storage capacity of the reservoir with period Mo at the beginning of the t periods respectively；WI(t),WO(t),WL(t) Incoming water quantity, outbound water and the water loss of reservoir t periods is represented respectively；
    Pondage constrains, and is specially formula (6)：
    Vmin(t)≤V(t)≤Vmax(t) (6)
    V (t), V in formula (6)max(t),Vmin(t) represent reservoir in the storage capacity of t periods, the storage capacity upper limit of permission and permission respectively Storage capacity lower limit；
    Reservoir letdown flow constrains, and is specially formula (7)：
    Qmin(t)≤Q(t)≤Qmax(t) (7)
    In formula (7), Q (t), Qmax(t),Qmin(t) represent reservoir in the storage outflow of t periods, the maximum vent flow of permission respectively With the minimum discharging flow of permission；
    Power station units limits, are specially formula (8)：
    Nmin(t)≤N(t)≤Nmax(t) (8)
    N (t), N in formula (8)max(t),Nmin(t) power station is represented respectively in the output of t periods, permission maximum output and is allowed most Small output；
    Scheduling graph output Operations of Interva Constraint, is specially formula (9)：
    Ni-1,j≤Ni,j≤Ni+1,j；Ni,j-1≤Ni,j≤Ni,j+1 (9)
    In formula (9), i represents the grade in output section；J represents the forecast two Phase flow grade residing for output section, Ni-1,jRepresent Z grades are i-1 in scheduling graph, the power generating value in the output section that forecast two Phase flow grade is j；
    Ni,jRepresent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j；
    Ni+1,jRepresent the power generating value in the output section that Z grades are i+1 in scheduling graph, and forecast two Phase flow grade is j；
    Ni,j-1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j-1；
    Ni,j+1Represent the power generating value in the output section that Z grades are i in scheduling graph, and forecast two Phase flow grade is j+1；
    Step S4, is specifically realized as steps described below：
    S41：The uncertainty of description two Phase flow graded forecast is forecast using Bayesian probability
    Assuming that discharge process obeys single order markoff process, H is madet-1Represent the measured discharge at t-1 moment, Ht、StT is represented respectively The actual flow and forecasting runoff at moment；ht-1、ht、stH is represented respectivelyt-1、Ht、StImplementation value, it is real according to Bayesian formula Border flow HtPosterior density function be formula (10)：
    <mrow> <mi>&amp;Phi;</mi> <mrow> <mo>(</mo> <msub> <mi>h</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>s</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>h</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mi>g</mi> <mrow> <mo>(</mo> <msub> <mi>h</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <munderover> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </munderover> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>h</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mi>g</mi> <mrow> <mo>(</mo> <msub> <mi>h</mi> <mi>t</mi> </msub> <mo>|</mo> <msub> <mi>h</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mi>dh</mi> <mi>t</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    In formula, g (ht|ht-1) it is flow priori probability density；Work as St=stWhen, function f (st|ht-1,ht) it is HtLikelihood function； Φ(ht|ht-1,st) it is HtPosterior density function；
    S42, with the renewal of forecast information, passes through likelihood function f (st|ht-1,ht) to priori probability density g (ht|ht-1) carry out Correct, the posterior probability more to be tallied with the actual situation；
    It is [0, Q by reservoir inflow grade classification in conjunction with the two-dimentional scheduling graph after the optimization obtained in step S31]、[Q1,Q2]、 [Q2,Q3] three grades, t moment forecast two Phase flow can be obtained by Bayesian formula and is in [0, Q1]、[Q1,Q2]、[Q2, Q3] three grades probability P1、P2And P3, wherein, Q1、Q2And Q3Reservoir inflow three grades division threshold value, and Q are represented respectively1＜ Q2＜ Q3；
    S43：Final decision-making output N is obtained using the average weighted mode of posterior probability, and the weighted average formula used is public affairs Formula (11)：
    N=P1×Ni,1+P2×Ni,2+P3×Ni,3 (11)
    Wherein, i is rank residing for reservoir current level；
    P1Grade [0, Q is in for two Phase flow1] probability；
    P2Grade [Q is in for two Phase flow1,Q2] probability；
    P3Grade [Q is in for two Phase flow2,Q3] probability；
    Ni,1Expression current level is i ranks, runoff is in [0, Q1] when corresponding output section；
    Ni,2Expression current level is i ranks, runoff is in [Q1,Q2] when corresponding output section；
    Ni,3Expression current level is i ranks, runoff is in [Q2,Q3] when corresponding output section.
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