CN101599143A - A kind of dispatching zone of reservoir determinacy Optimization Dispatching is determined method and equipment thereof - Google Patents
A kind of dispatching zone of reservoir determinacy Optimization Dispatching is determined method and equipment thereof Download PDFInfo
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Abstract
The present invention relates to a kind of dispatching zone method of estimation and equipment thereof, especially relate to a kind of dispatching zone method of estimation and equipment thereof of reservoir determinacy Optimization Dispatching.The present invention is the method for estimation between a kind of optimal scheduling track region of reservoir determinacy Optimization Dispatching.Invention has proposed to carry out according to the optimal scheduling territory method of reservoir operation first, and its key is the estimation problem in optimal scheduling territory.The scheduling track of looking reservoir determinacy Optimization Dispatching problem is the parameter with Probability Characteristics, and looking the regulation goal functional value is likelihood function, adopts bayes method to estimate that the interval of optimal scheduling track distributes.The present invention can clear and definite reservoir the scope in optimal scheduling territory, analyze the dispatching efficiency of optimal scheduling track adjacent domain, estimate crucial schedule periods, can be scheduling decision more support information be provided.
Description
Technical field
The present invention relates to a kind of dispatching zone method of estimation and equipment thereof, the dispatching zone that especially relates to a kind of reservoir determinacy Optimization Dispatching is determined method and equipment thereof.
Background technology
The reservoir operation technology is one of indispensable means that realize the normal operation of reservoir, the comprehensive utilization task that it is born according to each reservoir, the spatial and temporal distributions of the ability of the regulating and storing optimized distribution water resource of utilization reservoir, improve the water reservoir management operation level, the purpose of reach Xing Li, removing the evil can improve water resource and hydraulic power potentials utilization factor.
The adoptable method of tradition reservoir determinacy Optimization Dispatching is linear programming, nonlinear programming, dynamic programming and other heuritic approaches (as simulated annealing method, genetic algorithm, ant group algorithm and particle cluster algorithm etc.).Wherein using is dynamic programming method more widely, and key step is:
(1) sets up the optimizing scheduling of reservoir model
Model mainly comprises objective function and constraint condition two parts.For deterministic reservoir (group) Optimization Dispatching problem, can suppose reservoir inflow I
i(i=1,2 ..., t) known, ask optimum reservoir operation strategy, make and satisfying under the prerequisite of each constraint condition, the total benefit maximization (perhaps minimization of loss is example here with the maximizing the benefits) of obtaining, promptly optimization aim is:
Max
E wherein
iRepresent the benefit of i period, F
tRepresent the total benefit of whole period; When describe be multi-objective problem the time, they are vectorial.
Constraint condition is:
1. water balance constraint: V
I+1=V
i+ (I
i-O
i) Δ t; O wherein
iIt is the outbound flow of i period; Segment length when Δ t is.
2. go up the water balance equation between (k), following (k+1) reservoir:
I wherein
i K+1, Q
i k, Q
iRepresent that respectively i period upper storage reservoir (i) is to lower storage reservoir (i+1) warehouse-in, outbound and interval flow.
3. reservoir capacity constraint: V
I+1≤ V
I+1≤ V
I+1V wherein
I+1, V
I+1Be respectively the storage capacity bound of i+1 period.
4. outbound traffic constraints: O
I+1≤ O
I+1≤ O
I+1O wherein
I+1, O
I+1Be respectively the outbound flow bound of i+1 period.
5. the whole story state boundaries condition: Z
1=Z
b, Z
T+1=Z
eZ in the formula
bThe expression schedule periods is the reservoir filling position just; Z
eExpression scheduling end of term reservoir filling position.
(2) adopt dynamic programming method to find the solution
The determinacy optimizing scheduling of reservoir is a typical multistage decision problem, and optimization aim has separability, and decision-making has without aftereffect, therefore the optimizing scheduling of reservoir problem can be generalized as dynamic programming problems, and concrete model is:
1. stage variable: the stage variable is reservoir operation calculation interval sequence number i.
2. state variable: with pondage V
iBe state variable.
3. decision variable: with reservoir outbound flow O
iBe decision variable.
4. state transition equation: state transition equation is the water balance equation,
V
i+1＝V
i+(i
i-O
i)Δt
5. recurrence equation: the objective function of system benefit maximization is expressed as following recurrence equation by dynamic planning principle and by the period recursive algorithm:
In the formula, F
t *(V
t) the expression reservoir sets out by the given period original state and be transferred to V along the optimized operation track
tThe accumulation benefit.
For avoiding the dimension calamity, can adopt some to improve one's methods to the multidimensional dynamic programming, as discrete differential dynamic programming (DDDP), dynamic programming nibbling method (DPSA) and optimization method (POA) etc. one by one one by one.
Traditional reservoir operation method generally can only obtain an optimum solution, and the existence of multiple solution in the optimization problem (being a plurality of optimum solutions) makes under the identical optimal value to make up various feasible alternatives and become possibility, for decision-making provides choice widely.Though the not unique phenomenon ubiquity of optimum solution is paid close attention to less in the optimizing scheduling of reservoir problem.Document [7] is a background with power station Optimal Load assignment problem, by the method that writes down each all optimum solutions of stage dynamic programming is improved, confirmed to exist really in the dynamic programming problems multiple solution, and compare with the globally optimal solution that the 0-1 estimator of setting up obtains, be globally optimal solution by these multiple solutions of case verification.Example shows: multiple solution can be discrete point, also may be for straight line, be possible some faces, illustrate that thus the optimal scheduling territory may exist in dynamic programming problems.
For reservoir determinacy Optimization Dispatching problem, Luo Qiang etc.
[8]Based on the self-optimizing analogue technique, the optimum territory to reservoir operation proves theoretically.But how to seek optimum territory, and carry out interval estimation, Chinese scholars does not relate to as yet.
According to Systems Theory, optimizing scheduling of reservoir is an optimum identification problem, the scheduling track need carry out parameter recognition, classic method has only adopted the thinking of optimizing, if for identical scheduling model and identical model input, have a plurality of optimized parameter groups, as dispatch track, make the scheduling result that is obtained have this phenomenon outwardness of identical target function value, adopt so and discern the scheduling track based on the method for uncertainty analysis and just have possibility, and can further estimate the optimal scheduling interval.
The method of uncertainty analysis is a lot, IAHS (IAHS) is by the mode of Workshop, inquire into the Uncertainty Analysis Method in the environmental science in worldwide, wherein commonly used has: 1. pseudo-bayes method---the uncertain estimation technique (GLUE) of general likelihood.2. Bayesian statistics deduction method---Markov chain Monte Carlo method (MCMC), representative MCMC method commonly used has the Markov chain Monte Carlo method (AM-MCMC) based on adaptively sampled algorithm.
GLUE method and MCMC method are each has something to recommend him, often they are merged in the reality, as in the GLUE method, adopting the MCMC sampling method to replace traditional stochastic simulation sampling, and with the standard of forecast interval coverage rate as the feasible parameter group feasible zone of judgement GLUE method, can improve the error that causes by the selected feasible zone threshold value of subjectivity greatly, have posteriority distribution obvious statistical significance, the character optimum and corresponding posteriority amount thereof thereby derive.
Summary of the invention
The present invention solves existing in prior technology to adopt single optimal scheduling track, the technical matters that waits always; Provide the dispatching zone of the reservoir determinacy Optimization Dispatching that a kind of interval that draws the optimal scheduling track distributes to determine method.
It is to solve the technical matters that existing in prior technology can not draw optimal scheduling track interval range under any level of confidence etc. that the present invention also has a purpose; The dispatching zone that provides a kind of method can draw the reservoir determinacy Optimization Dispatching of the optimal scheduling track interval range under any level of confidence is determined method.
It is to solve existing in prior technology to the uncertainty that exists in the prior art technical matters without any solution etc. that the present invention has a purpose again; Provide the dispatching zone of the reservoir determinacy Optimization Dispatching in a kind of interval distribution estimating that Uncertainty Analysis Method is incorporated into the optimal scheduling track to determine method.
It is to solve the technical matters of existing in prior technology to critical period that can not draw scheduling according to the width analysis of scheduling interval of existing in the prior art etc. that the present invention has a purpose again; Provide the dispatching zone of the reservoir determinacy Optimization Dispatching of a kind of critical period that can draw scheduling to determine method according to the width analysis of scheduling interval.
Above-mentioned technical matters of the present invention is mainly solved by following technical proposals:
The dispatching zone of reservoir determinacy Optimization Dispatching is determined method, may further comprise the steps:
Step 1, set up reservoir operation simulation model by the operation simulation model system, concrete grammar is: according to the water level-storage capacity relation curve of reservoir, water level-discharge capacity relation curve, deterministic reservoir reservoir inflow process, and the constraint condition of reservoir operation and objective function, with day part storage capacity value as input variable, objective function is set up the operation simulation model as output variable, and concrete orientation is:
Step 2, dispatched the interval of track estimates by analytic system between the scheduling track region, concrete grammar is: the operation simulation model that step 1 has been finished embeds in the uncertainty analysis calculating, look the scheduling track of reservoir determinacy Optimization Dispatching problem, be that day part storage capacity value is the parameter with Probability Characteristics, looking the regulation goal functional value is likelihood function, determines that the interval of scheduling track distributes;
Step 3, dispatch evaluation between track region by evaluation system between the scheduling track region, concrete grammar is: adopt reservoir determinacy Optimization Dispatching solution technique, be dynamic programming or its improvement algorithm, draw the regulation goal value, with the sample that obtains of analytic system between the scheduling track region whether be theoretical optimal objective value serve as to estimate foundation 95% or more, correctness and the validity between track region is dispatched in evaluation;
Step 4, carry out application between the optimal scheduling track region by application system between the scheduling track region, concrete grammar is: the interval width of statistics day part scheduling track, determine the critical period of scheduling according to the width of scheduling interval, selecting feasible scheduling track between the scheduling track region in step 3, is exactly required scheduling track.
Dispatching zone in above-mentioned reservoir determinacy Optimization Dispatching is determined method, and in the described step 2, analytic system is incorporated into Uncertainty Analysis Method in the dispatching zone calculating between the scheduling track region, can determine between the scheduling track region of each level of confidence.
Dispatching zone in above-mentioned reservoir determinacy Optimization Dispatching is determined method, in the described step 2, determines that the concrete steps that the interval of optimal scheduling track distributes are as follows:
Step 3.1 is even distribution by the prior distribution of grab sample module regulation reservoir capacity, and promptly up-and-down boundary is respectively the maximal value and the minimum value of reservoir capacity, and the stochastic simulation sampling generates 1000-10000 and organizes feasible reservoir capacity group then;
Step 3.2, utilize reservoir operation simulation model, calculate the likelihood function value of respectively organizing the reservoir capacity correspondence, it is the regulation goal functional value, the α of selected optimal scheduling desired value doubly is a threshold value then, and wherein, α is the random number between 0-1, the likelihood function value is lower than the reservoir capacity group of this threshold value, and making its corresponding likelihood function value is 0; To being higher than the parameter group of this threshold value, sort from high to low according to the likelihood function value, the likelihood function value of establishing i group reservoir capacity group correspondence is F
i, then its weight is
Step 3.3, the reservoir capacity group that has weight in the above-mentioned steps 3.2 are exactly that the posteriority of reservoir capacity group distributes: if the weight of a preceding u storage capacity and be α just, u storage capacity value is exactly the coboundary under the level of confidence α so; If the weight of last l storage capacity and lucky be α, l storage capacity value is exactly the lower boundary under the α level of confidence α so; The level of confidence in the interval between u, a l storage capacity is 1-2 α.
As another kind of scheme, determine method at the dispatching zone of above-mentioned reservoir determinacy Optimization Dispatching, in the described step 2, determine that the concrete steps that the interval of optimal scheduling track distributes are as follows:
Step 4.1 generates i group (i=100) storage capacity group at random by the grab sample module
, and calculate and determine every group of pairing regulation goal value of storage capacity;
Step 4.2 is calculated the covariance square of storage capacity group sample by storage capacity variance matrix module, and computation process is based on formula:
Wherein
I is a t dimension unit matrix, and ε is the real number of 0.01-0.1, and t is a scheduling slot length;
Step 4.3 generates new storage capacity group sample by storage capacity sample generation module
Step 4.4 is calculated the analogy model in the storage capacity group sample evidence step 1 in the step 5.3 by the objective function determination module, obtains target function value F
i
Step 4.5 is by the definite probability of accepting of probability determination module
Step 4.6 produces at random and obeys [0,1] equally distributed real number α, if α＜β, then
Otherwise
Step 4.7, repeating step 5.2 to 5.6 is up to generating the individual storage capacity group of n (n=2000-10000) number;
The reservoir capacity group is exactly that the posteriority of parameter distributes in the step 4.8, step 4.7, sorts from big to small according to the storage capacity value: the n * α storage capacity value is exactly the coboundary under the α level of confidence; N * (1-α) individual storage capacity value is exactly the lower boundary under the α level of confidence; The level of confidence in the interval between the n * α, n * (1-α) individual storage capacity is 1-2 α.
A kind ofly realize that above-mentioned dispatching zone determines the equipment of method, comprise the operation simulation model system, the scheduling track region between analytic system, the scheduling track region between evaluation system and the scheduling track region between application system, between described scheduling track region analytic system respectively with operation simulation model system and scheduling track region between evaluation system link to each other, between described scheduling track region evaluation system respectively with the scheduling track region between analytic system and dispatch that application system links to each other between track region.
Therefore, the present invention has following advantage: 1. and reasonable in design, simple in structure, and long service life, be easy to promote; 2. high efficiency; 3. easy and simple to handle, convenient disassembly, maintenance is convenient; 4. the stability of work is high, and security is good, can not be subjected to vibration influence, and wearing and tearing are few, and operating noise is little.5. the structural strength height is not fragile, and easy for installation; It is 6. reasonable in design, ingenious,
Description of drawings
Fig. 1 is a kind of workflow diagram of the present invention;
Embodiment
Below by embodiment, and in conjunction with the accompanying drawings, technical scheme of the present invention is described in further detail.Application system between evaluation system, scheduling track region between analytic system, scheduling track region between operation simulation model system among the figure, scheduling track region.
Embodiment:
The dispatching zone of reservoir determinacy Optimization Dispatching is determined method, may further comprise the steps:
Step 1, set up reservoir operation simulation model by the operation simulation model system, concrete grammar is: according to the water level-storage capacity relation curve of reservoir, water level-discharge capacity relation curve, deterministic reservoir reservoir inflow process, and the constraint condition of reservoir operation and objective function, with day part storage capacity value as input variable, objective function is set up the operation simulation model as output variable, and concrete grammar is as follows:
The reservoir operation simulation comprises the mass balance calculating that reservoir inflow, outbound flow and storage capacity change, and its core is the water balance equation:
In the formula: I
tReservoir inflow for period t; O
tOutbound flow for period t; V
tPondage for period t; Δ t is that calculation interval is long, and the selection of its length is can reflect the principle that is shaped as of peb process more exactly.I in the equation
tAll as given value, according to boundary condition, initial storage capacity V
0, finish storage capacity V
nAlso be known, unknown number has O
tAnd V
t, be variable to be found the solution.
According to feature data such as the water level-storage capacity of reservoir, water level-discharge capacities, and the constraint condition of reservoir operation and objective function, as input variable, objective function is set up the operation simulation model as output variable with day part storage capacity value.Given reservoir inflow process is as long as input reservoir capacity process according to the analogy model based on the water balance equation, just can obtain scheduling result, to estimate the performance of scheduling this time.
Outbound flow O according to day part
tPerhaps storage capacity V
t, can carry out the evaluation of dispatching efficiency.The evaluation of scheduling comprises: the evaluation of flood control aspect, as reservoir peak level or maximum outbound flow; The evaluation of generating aspect is as generated energy and generating fraction; The evaluation of the evaluation of water supply aspect, shipping aspect; The evaluation of ecological aspect etc.
Step 2, dispatched the interval of track estimates by analytic system between the scheduling track region, concrete grammar is: the operation simulation model that step 1 has been finished embeds in the uncertainty analysis calculating, look the scheduling track of reservoir determinacy Optimization Dispatching problem, be that day part storage capacity value is the parameter with Probability Characteristics, looking the regulation goal functional value is likelihood function, the interval of determining the scheduling track distributes, analytic system is incorporated into Uncertainty Analysis Method in the dispatching zone calculating between the scheduling track region, can determine between the scheduling track region of each level of confidence.
In the present embodiment, can adopt following method to determine that the interval of optimal scheduling track distributes, concrete steps are as follows:
Step 3.1 is even distribution by the prior distribution of grab sample module regulation reservoir capacity, and promptly up-and-down boundary is respectively the maximal value and the minimum value of reservoir capacity, and the stochastic simulation sampling generates 1000-10000 and organizes feasible reservoir capacity group then;
Step 3.2, utilize reservoir operation simulation model, calculate the likelihood function value of respectively organizing the reservoir capacity correspondence, it is the regulation goal functional value, the α of selected optimal scheduling desired value doubly is a threshold value then, and wherein, α is the random number between 0-1, the likelihood function value is lower than the reservoir capacity group of this threshold value, and making its corresponding likelihood function value is 0; To being higher than the parameter group of this threshold value, according to high to low ordering in the likelihood function value, the likelihood function value of establishing i group reservoir capacity group correspondence is F
i, then its weight is
Step 3.3, the reservoir capacity group that has weight in the above-mentioned steps 3.2 are exactly that the posteriority of reservoir capacity group distributes: if the weight of a preceding u storage capacity and be α just, u storage capacity value is exactly the coboundary under the level of confidence α so; If the weight of last 1 storage capacity and be α just, the 1st storage capacity value is exactly the lower boundary under the α level of confidence α so; The level of confidence in the interval between u, 1 storage capacity is 1-2 α.
In the present embodiment, can also adopt another kind of method to determine that the interval of optimal scheduling track distributes, concrete steps are as follows:
Step 4.1 generates i group (i=100) storage capacity group at random by the grab sample module
, and calculate and determine every group of pairing regulation goal value of storage capacity;
Step 4.2 is calculated the covariance square of storage capacity group sample by storage capacity variance matrix module, and computation process is based on formula:
Wherein
I is a t dimension unit matrix, and ε is the real number of 0.01-0.1, and t is a scheduling slot length;
Step 4.3 generates new storage capacity group sample by storage capacity sample generation module
Step 4.4 is calculated the analogy model in the storage capacity group sample evidence step 1 in the step 5.3 by the objective function determination module, obtains target function value F
i
Step 4.5 is by the definite probability of accepting of probability determination module
Step 4.6 produces at random and obeys [0,1] equally distributed real number α, if α＜β, then
Otherwise
Step 4.7, repeating step 5.2 to 5.6 is up to generating the individual storage capacity group of n (n=2000-10000) number;
The reservoir capacity group is exactly that the posteriority of parameter distributes in the step 4.8, step 4.7, sorts from big to small according to the storage capacity value: the n * α storage capacity value is exactly the coboundary under the α level of confidence; N * (1-α) individual storage capacity value is exactly the lower boundary under the α level of confidence; The level of confidence in the interval between the n * α, n * (1-α) individual storage capacity is 1-2 α.
Step 3, dispatch evaluation between track region by evaluation system between the scheduling track region, concrete grammar is: adopt reservoir determinacy Optimization Dispatching solution technique, be dynamic programming or its improvement algorithm, draw the regulation goal value, with the sample that obtains of analytic system between the scheduling track region whether be theoretical optimal objective value serve as to estimate foundation 95% or more, correctness and the validity between track region is dispatched in evaluation; According to reservoir determinacy Optimization Dispatching solution technique, improve algorithm as dynamic programming or its, can draw optimum regulation goal.Whether approaching the optimal objective value with the sample of GLUE or MCMC is foundation, estimates the validity of uncertainty analysis.
The interval of statistics day part optimal scheduling track distributes and interval width, according to interval estimated result, determines scheduling fiducial interval optimum under the given confidence level (as 99.9%).According to the width of scheduling interval, estimate the critical period of scheduling: if scheduling of a certain period width is bigger, then Tiao Du range of choice is bigger, does not belong to crucial schedule periods; If the scheduling width is less, then be the scheduling critical period, need scheduling meticulously.
Step 4, carry out application between the optimal scheduling track region by application system between the scheduling track region, concrete grammar is: the interval width of statistics day part scheduling track, determine the critical period of scheduling according to the width of scheduling interval, selecting feasible scheduling track between the scheduling track region in step 3, is exactly required scheduling track.According to the interval of optimal scheduling track, dispatcher-controlled territory can be masked as the unoptimizable zone and optimize regional two major types.Selecting the feasible schedule track in interval, is exactly one of optimum scheduling track, can be used as the foundation that decision-making is implemented.
In Real-Time Scheduling, can select the optimal scheduling strategy in the stage that faces according to the optimal scheduling territory, implemented; Behind the new breath of next period acquisition, calculate the optimal scheduling territory once more, thereby realize the rolling implementation method of " enforcement "-" decision-making "-" implementing again ", carry out Real-Time Scheduling.
Specific embodiment described herein only is that the present invention's spirit is illustrated.The technician of the technical field of the invention can make various modifications or replenishes or adopt similar mode to substitute described specific embodiment, but can't depart from spirit of the present invention or surmount the defined scope of appended claims.
Claims (5)
1. the dispatching zone of a reservoir determinacy Optimization Dispatching is determined method, may further comprise the steps:
Step 1, set up reservoir operation simulation model by the operation simulation model system, concrete grammar is: according to the water level-storage capacity relation curve of reservoir, water level-discharge capacity relation curve, deterministic reservoir reservoir inflow process, and the constraint condition of reservoir operation and objective function, with day part storage capacity value as input variable, objective function is set up the operation simulation model as output variable;
Step 2, dispatched the interval of track estimates by analytic system between the scheduling track region, concrete grammar is: the operation simulation model that step 1 has been finished embeds in the uncertainty analysis calculating, look the scheduling track of reservoir determinacy Optimization Dispatching problem, be that day part storage capacity value is the parameter with Probability Characteristics, looking the regulation goal functional value is likelihood function, determines that the interval of scheduling track distributes;
Step 3, dispatch evaluation between track region by evaluation system between the scheduling track region, concrete grammar is: adopt reservoir determinacy Optimization Dispatching solution technique, be dynamic programming or its improvement algorithm, draw the regulation goal value, with the sample that obtains of analytic system between the scheduling track region whether be theoretical optimal objective value serve as to estimate foundation 95% or more, evaluation is dispatched between track region;
Step 4, carry out application between the optimal scheduling track region by application system between the scheduling track region, concrete grammar is: the interval width of statistics day part scheduling track, determine the critical period of scheduling according to the width of scheduling interval, selecting feasible scheduling track between the scheduling track region in step 3, is exactly required scheduling track.
2. the dispatching zone of reservoir determinacy Optimization Dispatching according to claim 1 is determined method, it is characterized in that, in the described step 2, analytic system is incorporated into Uncertainty Analysis Method in the dispatching zone calculating between the scheduling track region, can determine between the scheduling track region of each level of confidence.
3. the dispatching zone of reservoir determinacy Optimization Dispatching according to claim 1 is determined method, it is characterized in that, in the described step 2, determines that the concrete steps that the interval of optimal scheduling track distributes are as follows:
Step 3.1 is even distribution by the prior distribution of grab sample module regulation reservoir capacity, and promptly up-and-down boundary is respectively the maximal value and the minimum value of reservoir capacity, and the stochastic simulation sampling generates 1000-10000 and organizes feasible reservoir capacity group then;
Step 3.2, utilize reservoir operation simulation model, calculate the likelihood function value of respectively organizing the reservoir capacity correspondence, it is the regulation goal functional value, the α of selected optimal scheduling desired value doubly is a threshold value then, and wherein, α is the random number between 0-1, the likelihood function value is lower than the reservoir capacity group of this threshold value, and making its corresponding likelihood function value is 0; To being higher than the parameter group of this threshold value, sort from high to low according to the likelihood function value, the likelihood function value of establishing i group reservoir capacity group correspondence is F
i, then its weight is
Step 3.3, the reservoir capacity group that has weight in the above-mentioned steps 3.2 are exactly that the posteriority of reservoir capacity group distributes: if the weight of a preceding u storage capacity and be α just, u storage capacity value is exactly the coboundary under the level of confidence α so; If the weight of last l storage capacity and lucky be α, l storage capacity value is exactly the lower boundary under the α level of confidence α so; The level of confidence in the interval between u, a l storage capacity is 1-2 α.
4. the dispatching zone of reservoir determinacy Optimization Dispatching according to claim 1 is determined method, it is characterized in that, in the described step 2, determines that the concrete steps that the interval of optimal scheduling track distributes are as follows:
Step 4.1 generates i group (i=100) storage capacity group at random by the grab sample module
And calculate and determine every group of pairing regulation goal value of storage capacity;
Step 4.2 is calculated the covariance square of storage capacity group sample by storage capacity variance matrix module, and computation process is based on formula:
Wherein
I is a t dimension unit matrix, and ε is the real number of 0.01-0.1, and t is a scheduling slot length;
Step 4.3 generates new storage capacity group sample by storage capacity sample generation module
Step 4.4 is calculated the analogy model in the storage capacity group sample evidence step 1 in the step 5.3 by the objective function determination module, obtains target function value F
i
Step 4.5 is by the definite probability of accepting of probability determination module
Step 4.6 produces at random and obeys [0,1] equally distributed real number α, if α＜β, then
Otherwise
Step 4.7, repeating step 5.2 to 5.6 is up to generating the individual storage capacity group of n (n=2000-10000) number;
The reservoir capacity group is exactly that the posteriority of parameter distributes in the step 4.8, step 4.7, sorts from big to small according to the storage capacity value: the n * α storage capacity value is exactly the coboundary under the α level of confidence; N * (1-α) individual storage capacity value is exactly the lower boundary under the α level of confidence; The level of confidence in the interval between the n * α, n * (1-α) individual storage capacity is 1-2 α.
5. realize that the described dispatching zone of claim 1 determines the equipment of method for one kind, comprise the operation simulation model system, the scheduling track region between analytic system, the scheduling track region between evaluation system and the scheduling track region between application system, between described scheduling track region analytic system respectively with operation simulation model system and scheduling track region between evaluation system link to each other, between described scheduling track region evaluation system respectively with the scheduling track region between analytic system and dispatch that application system links to each other between track region.
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2009
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