CN108717581A - A kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation - Google Patents
A kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation Download PDFInfo
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Abstract
The invention discloses a kind of random multiple attributive decision making methods of the reservoir operation based on Monte Carlo simulation, include the statistical distribution characteristic of analysis and prediction relative error；Certainty reservoir operation Model for Multi-Objective Optimization is established, solution obtains a limited number of gate control program；Based on current forecast peb process and forecast relative error distribution, peb process collection is generated at random using statistical sampling；Flood routing is carried out to the flood generated at random according to gate control program, and quantifies the uncertainty of each index value；The uncertainty of quantizating index weight；Random Multiple Attribute Decision Model is established, the optimal scheduling scheme for enable policymaker to be satisfied with is inquired into using Monte Carlo simulation.The present invention has considered the uncertain factor during reservoir operation multiple attribute decision making (MADM), can realize the probability sorting of random environment dispatching scheme with preferably, abundant risk of policy making information is provided, the reliability for improving reservoir operation decision, can be widely applied to basin reservoir operation multiple attribute decision making (MADM).
Description
Technical field
The present invention relates to reservoir regulation for flood control decision-making technique more particularly to a kind of reservoir operations based on Monte Carlo simulation
Random multiple attributive decision making method.
Background technology
Reservoir regulation for flood control is one of important non-engineering measure of river basin flood management, with nature, society, economy, environment
Etc. factors it is closely related.It inherently sees, reservoir regulation for flood control is a multiple target, more attributes, multi-level, multistage complexity
Decision process, optimal solution are often difficult to determine.Way commonplace at present is that one group of non-bad Flood Control Dispatch is generated in advance
Scheme collection is therefrom selected the satisfactory solution of each Objective benefits of choosing comprehensively to be put to decision by policymaker, therefore, reservoir tune
Degree is a typical Multiple Attribute Decision Problems.
All there is many uncertainties for the links of reservoir regulation for flood control, such as due to Watershed Runoff process complexities
With the forecast flood into reservoir caused by hydrologic forecasting method limitation it is uncertain, change due to scouring and silting in reservoir caused by storage capacity it is bent
Line uncertainty, scheduling time sluggishness uncertainty etc..Uncertain factor in reservoir regulation for flood control directly results in flood control
The uncertainty of the regulation goal factor (such as the indexs such as peak level, maximum storage outflow, end of term water level).In uncertain environment
Under, each evaluation index in multiple attribute decision making (MADM) is no longer constant, and shows as having certain discrete feature stochastic variable.This
Outside, reservoir regulation for flood control usually requires the flood control safety from reservoir itself, upstream return water floods loss reduction, downstream flood control object
Safely, it abandons minimum, the big vast end of water loss and retains multiple targets such as big vast tail and Flood process of reservoir is adjusted, ensuring flood control safety
Under the premise of, the comprehensive utilization benefit of reservoir should be given full play to.In order to coordinate above-mentioned regulation goal, it must be determined that participate in evaluation and electing index
Weight relationship.According to the difference for assigning power form, index weights are divided into subjective weight and objective weight.Subjective weight embodies decision
The wish preference of person allows the Heuristics of policymaker incorporating Multiple Attribute Decision Model.However, due between each regulation goal
Competitiveness remained under high-strung flood-proofing terrain to make different interests main body reach completely the same subjective weight
In larger difficulty.Objective weight can reflect the attribute information of scheme collection data itself, but various different Objective Weightings
The weight acquired all having a certain difference property.Therefore, the evaluation index value in reservoir regulation for flood control multiple attribute decision making (MADM) and refer to
Marking weight, all there is a degree of uncertainties.
Existing reservoir operation multiple attributive decision making method (such as fuzzy optimum selection method, fussy matter-element, Method of extenics, D-S
Means of proof etc.) it is all confined among certainty environment, fail the influence for fully considering uncertain factor, especially has ignored finger
Mark the uncertainty of weight.Therefore, how comprehensively to consider the uncertain factor during multiple attribute decision making (MADM), establish reservoir tune
Spend the technical barrier that random multiple attributive decision making method is current urgent need to resolve.
Invention content
Goal of the invention：In view of the deficiencies of the prior art, during the present invention has considered reservoir operation multiple attribute decision making (MADM)
Uncertain factor, it is proposed that a kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation.
Technical solution：The present invention provides a kind of random multiple attribute decision making (MADM) sides of the reservoir operation based on Monte Carlo simulation
Method includes the following steps：
(1) data, the statistical distribution characteristic of analysis and prediction relative error are forecast according to historical flood；
(2) certainty reservoir operation Model for Multi-Objective Optimization is established, solution obtains a limited number of gate control program；
(3) it is based on current forecast peb process and forecast relative error distribution, flood is generated using statistical sampling at random
Process collection；
(4) flood routing is carried out to the flood generated at random according to gate control program, and quantifies the not true of each index value
It is qualitative；
(5) uncertainty of quantizating index weight；
(6) random Multiple Attribute Decision Model is established, the optimal tune for enable policymaker to be satisfied with is inquired into using Monte Carlo simulation
Degree scheme.
Further, forecast relative error calculation formula is as follows in the step (1)：
In formula, QftFor the forecast reservoir inflow of t periods；QotFor the actual measurement reservoir inflow of t periods；εtFor the t periods
Flood forecasting relative error.
Further, certainty reservoir operation Model for Multi-Objective Optimization is minimum with reservoir peak level in the step (2)
With the minimum optimization aim of maximum storage outflow, object function expression formula is as follows：
In formula, hop count when T is schedule periods；ZtFor the reservoir level of t periods；qtFor the reservoir storage outflow of t periods；
The expression formula of certainty reservoir operation Model for Multi-Objective Optimization constraints is as follows：
(a) water balance constrains：
In formula, Q (t-1), the reservoir inflow of Q (t) the reservoir t period whole story；Q (t-1), q (t) are to begin the reservoir t periods
The storage outflow at end；V (t-1), V (t) are the reservoir storage of the reservoir t period whole story；Segment length when △ t are；
(b) reservoir peak level constrains：
Z(t)≤Zmax(5)；
In formula, Z (t) is the water level of reservoir t moment；ZmaxThe highest allowed for reservoir controls water level；
(c) end of term restriction of water level is dispatched：
Zend≥Ze(6)；
In formula, ZendFor reservoir operation end of term water level；ZeWater level is controlled for the reservoir operation end of term；When meeting other constraints,
It can take "="；
(d) reservoir discharge capacity constrains：
q(t)≤q(Z(t)) (7)；
In formula, q (t) is the storage outflow of reservoir t moment；Q (Z (t)) is reservoir t moment letting out in water level Z (t)
Stream ability；
(e) outbound allows luffing to constrain：
In formula, | q (t)-q (t-1) | it is the luffing of reservoir adjacent time interval storage outflow；The outbound stream allowed for reservoir
Quantitative change width.
Further, according to the forecast relative error statistical distribution obtained in step (1) in the step (3), under
Formula carries out the stochastic simulation of peb process：
Qt'=Qft·(1+εt) (9)；
In formula, Qt' it is the flood discharge simulated the t periods, according to formula (9), N number of forecast is generated using statistical sampling
Relative error random number, and then generate peb process collection at random.
Further, each reservoir regulation for flood control index of Monte-Carlo Simulation Method quantitative analysis is used in the step (4)
Value (such as：Reservoir peak level, maximum storage outflow, end of term water level etc.) uncertainty, which further comprises following
Sub-step：
(41) for the peb process collection generated in step (3), using identified gate control program in step (2)
(i.e. reservoir storage outflow process or reservoir discharge water strategy) carries out flood routing one by one, and it is corresponding anti-to obtain each peb process
Big vast regulation index value；
(42) it sorts according to sequence from big to small to each Flood Control Dispatch index value, draws out each Flood Control Dispatch index value
The cummulative frequency curve of distribution counts the statistical moments such as mean value and the variance of each index, and inquires into and consideration index value uncertainty
Stochastic Decision-making matrix；
In formula, ξijFor the stochastic variable of j-th of index value in i-th of scheme；
(43) reservoir peak level and maximum storage outflow are quantitatively calculated more than the relative risk of given threshold, calculation formula is such as
Under：
In formula, PZAnd PQRespectively peak level and maximum storage outflow are more than the relative risk of given threshold；nZAnd nQRespectively
It is more than the number of given threshold for peak level and maximum storage outflow；N is the total degree of Monte Carlo simulation.
Further, the probabilistic quantization of index weights includes four kinds of forms in the step (5)：Weight information is complete
It is unknown, weight vectors are determining, weight obey specify section be uniformly distributed and weight obey specify section Arbitrary distribution；
Wherein, when weight is obeyed, and section is specified to be uniformly distributed, weight space is formulated as：
In formula, wjFor the weight of j-th of index；wj minAnd wj maxThe weight minimum value and maximum value of j-th of index respectively.
Further, the step (6) includes：
(61) weighted sum formula is used to calculate the value of utility of each Flood Control Dispatch scheme；
Assuming that the Multiple Attribute Decision Problems in the present embodiment include m option A={ A1,A2,…,AmAnd n index C=
{C1,C2,…,Cn, X=(xij)m×nFor decision matrix, w={ w1,w2,…,wnIt is weight vectors, wherein i, j are respectively scheme
With index serial number；The value of utility of each Flood Control Dispatch scheme, the i.e. aggreggate utility etc. of some scheme are calculated using weighted sum formula
In the weighted sum of partial utility：
ui=u (xi,w) (14)；
In formula, uiFor option AiUtility function value, be equivalent to the effect of decision model, uiShow that scheme is more excellent more greatly；xi
For option AiIndex value vector；W is weight vectors；
(62) weight is inclined in the sequence for calculating each scheme；
With probability density function fXThe Probability Characteristics of (ξ) description indexes value, it is close with the probability in feasible weight space
Spend function fW(w) uncertainty of description weight is stated, and feasible weight space W is defined as：
In order to which each scheme of quantitative expression obtains the calculation formula accordingly to sort, following ranking function is defined：
In formula, ρ [true]=1, ρ [false]=0；ξiFor option AiIndex value random variable vector；Rank's ()
Value range is [1, m]；
Sequence tendency weight Wi r(ξ) refers to so that option AiThe weight space of specified sequence r (r=1,2 ..., m) is obtained,
It is defined as：
Wi r(ξ)={ w ∈ W:rank(ξi, w) and=r } (17)；
It is input with uncertain index value and weight by means of the concept of sequence tendency weight, sequence can receive property and refer to
Mark bi r, global receivable property index ai h, center weight vector wi cWith confidence factor pi cStatistical indicator is used for final decision；
(63) sequence for calculating each scheme can receive property index；
Sequence can receive property index bi rDescribing makes option AiThe weight for obtaining specified sequence r is integrated into feasible weight space
Shared scale, can be calculated by following multiple integral formula in W：
(64) overall situation for calculating each scheme can receive property index；
The global receivable property index a of definitioni h, ai hTo option AiThe b of all sequencesi rIt is integrated, is described on the whole
The acceptability of each scheme is horizontal, and calculation formula is as follows：
In formula, αrFor two level weight, option A is reflectediEach sequence acceptability index bi rTo the receivable property of the overall situation
The percentage contribution of index, αrCalculation formula it is as follows：
(65) the center weight vector of each scheme is calculated；
Center weight vector wi cIt is so that option AiThe center for the weight space being optimal, i.e., when the program is optimal
Exemplary weights vector；wi cWeight combination when each scheme is optimal substantially is described, calculation formula is as follows：
(66) confidence factor of each scheme is calculated；
Confidence factor pi cIt is defined as using option AiCenter weight vector wi cWhen, the probability that the program is optimal, i.e.,
The index space that the program is optimal accounts for the ratio in overall performane space：
Advantageous effect：Compared with prior art, the present invention has the following advantages and beneficial effect：
1, the prior art is generally all confined among certainty environment, without fully considering the shadow of uncertain factor
It rings, especially has ignored the uncertainty of index weights.And the method for the present invention has considered reservoir operation multiple attribute decision making (MADM) process
In uncertain factor (including flood forecasting uncertainty and index weights uncertainty etc.), by traditional certainty more belong to
Property decision is extended among random environment, and abundanter decision information can be provided for policymaker, can be widely applied to basin
Reservoir regulation for flood control multiple attribute decision making (MADM)；
2, the essence of the prior art can be summarized as a multi-level, multidimentional system evaluation index to be converted into single level, one-dimensional
The process of system comprehensive evaluation index, wherein index value matrix, index weights and decision model are essential.However, in index
Value be that stochastic variable, weight be totally unknown or part known under decision scene, the prior art all can not be solved effectively.
The method of the present invention breaches the theoretical frame of the prior art, it uses the decision process of anti-weight space analysis, can realize
The probability sorting of random environment dispatching scheme provides the risk information of multiple attribute decision making (MADM), to improve reservoir operation with preferably
The reliability of decision.
Description of the drawings
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is various forms of index weights spaces schematic diagram；
Fig. 3 is that the random Multiple Attribute Decision Model based on Monte Carlo simulation solves flow chart.
Specific implementation mode
By way of example and in conjunction with the accompanying drawings, technical scheme of the present invention is described in detail.
As shown in Figure 1, a kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation, including following step
Suddenly：
(1) data, the statistical distribution characteristic of analysis and prediction relative error are forecast according to historical flood.
There is presently no theoretic proof and final conclusions for the distribution obeyed about forecast relative error.It is analyzed in hydrological statistics
In, using it is more include normal distribution, the distribution of III types of P-, Geng Beier distributions, the extreme value distribution etc., Chinese most of basins with
III type distribution applications of P- are the most extensive.The present invention forecasts data according to reservoir historical flood, is determined using the method for statistical analysis
The distribution of flood forecasting relative error and distributed constant.Flood forecasting relative error calculation formula is as follows：
In formula, QftFor the forecast reservoir inflow of t periods；QotFor the actual measurement reservoir inflow of t periods；εtFor the t periods
Flood forecasting relative error.
(2) certainty reservoir operation Model for Multi-Objective Optimization is established, solution obtains a limited number of gate control program (i.e.
Reservoir storage outflow process or reservoir discharge water strategy).
In the present embodiment, the certainty reservoir operation Model for Multi-Objective Optimization established is minimum and most with reservoir peak level
The minimum optimization aim of big storage outflow.Object function expression formula is as follows：
In formula, hop count when T is schedule periods；ZtFor the reservoir level of t periods；qtFor the reservoir storage outflow of t periods.
In the present embodiment, the constraints of model includes：Water balance constraint, the constraint of reservoir peak level, the scheduling end of term
Restriction of water level, the constraint of reservoir discharge capacity, outbound allow luffing to constrain.The expression formula of constraints is as follows：
(a) water balance constrains：
In formula, Q (t-1), the reservoir inflow of Q (t) the reservoir t period whole story；Q (t-1), q (t) are to begin the reservoir t periods
The storage outflow at end；V (t-1), V (t) are the reservoir storage of the reservoir t period whole story；Segment length when △ t are.
(b) reservoir peak level constrains：
Z(t)≤Zmax(5)；
In formula, Z (t) is the water level of reservoir t moment；ZmaxThe highest allowed for reservoir controls water level.
(c) end of term restriction of water level is dispatched：
Zend≥Ze(6)；
In formula, ZendFor reservoir operation end of term water level；ZeWater level is controlled for the reservoir operation end of term；When meeting other constraints,
It can take "=".
(d) reservoir discharge capacity constrains：
q(t)≤q(Z(t)) (7)；
In formula, q (t) is the storage outflow of reservoir t moment；Q (Z (t)) is reservoir t moment letting out in water level Z (t)
Stream ability.
(e) outbound allows luffing to constrain：
In formula, | q (t)-q (t-1) | it is the luffing of reservoir adjacent time interval storage outflow；The outbound stream allowed for reservoir
Quantitative change width.
There are many solutions that method can be used for reservoir operation Model for Multi-Objective Optimization at present, such as：Leash law, weight
Method, multi-objective Evolutionary Algorithm (such as multi-objective particle swarm algorithm, multiple target differential evolution algorithm).The present embodiment is using non-dominant
Sorting Genetic Algorithm (NSGA- II) solves above-mentioned Model for Multi-Objective Optimization, generates Noninferior Solution Set and (generates a limited number of gate
Control program).Since non-dominated sorted genetic algorithm (NSGA- II) belongs to prior art, do not repeat in the present embodiment.
(3) it is based on current forecast peb process and forecast relative error distribution, flood is generated using statistical sampling at random
Process collection.
In the present embodiment, it will forecast that peb process is considered as the mean value process of practical flood, superposition is random on this basis
The forecast relative error series of generation, you can stochastic simulation goes out reservoir Flood process of reservoir collection.According to the forecast obtained in step 1
Relative error statistical distribution carries out the stochastic simulation of peb process using following formula：
Qt'=Qft·(1+εt) (9)；
In formula, Qt' it is the flood discharge simulated the t periods.According to above-mentioned formula, generated using statistical sampling N number of pre-
Relative error random number is reported, and then generates N peb processes at random.
(4) flood routing is carried out to the flood generated at random according to gate control program, and quantifies the not true of each index value
It is qualitative.
In the present embodiment, with the uncertain of each reservoir regulation for flood control index value of Monte-Carlo Simulation Method quantitative analysis
Property, which further comprises following sub-step：
(41) for the fields the N peb process generated in step (3), using identified gate control program in step (2)
Carry out flood routing one by one, obtain the corresponding Flood Control Dispatch index value of each peb process (including：Reservoir peak level, most
Big storage outflow, end of term water level etc.)；
(42) it sorts according to sequence from big to small to each regulation index value, draws out the cumulative frequency of each index distribution
Curve counts the statistical moments such as mean value and the variance of each index, and inquires into and the probabilistic Stochastic Decision-making matrix of consideration index value；
In formula, ξijFor the stochastic variable of j-th of index value in i-th of scheme.
(43) reservoir peak level and maximum storage outflow are quantitatively calculated more than the relative risk of given threshold, calculation formula is such as
Under：
In formula, PZAnd PQRespectively peak level and maximum storage outflow are more than the relative risk of given threshold；nZAnd nQRespectively
It is more than the number of given threshold for peak level and maximum storage outflow；N is the total degree of Monte Carlo simulation.
(5) uncertainty of quantizating index weight.
The present invention proposes the weight of four kinds of forms：Weight information is totally unknown, weight vectors are determining, weight is obeyed and specified
Section is uniformly distributed, weight obeys and specifies section Arbitrary distribution.For containing the Multiple Attribute Decision Problems there are three index, four kinds
The weight space that various forms of index weights are constituted is as shown in Figure 2.Wherein, w1, w2, w3The weight of respectively three indexs；
w1 max, w2 max, w3 maxThe value upper limit of respectively three index weights；w1 min, w2 min, w3 minRespectively three index weights take
It is worth lower limit.
1) weight information is totally unknown
At the initial stage of reservoir regulation for flood control multiple attribute decision making (MADM) modeling, the method for the present invention allows policymaker not provide any weight
Information.For a three-dimensional decision problem, weight space at this time can be described as the triangle projective planum as shown in (a) in Fig. 2.
In the case where weight information is totally unknown, optimal case is not known simultaneously, and the method for the present invention can be empty by searching for entire weight
Between mode distinguish apparent excellent and apparent bad scheme.
2) weight vectors determine
If policymaker has information sufficient enough to uniquely determine weight vectors, weight vectors at this time are punctured into space
In a bit (w1, w2, w3), in Fig. 2 shown in (b).
3) weight, which is obeyed, specifies section to be uniformly distributed
Due to the competitiveness between reservoir regulation for flood control target, the subjectivity that the expert group of different interests main body provides is represented
Weight often has certain inconsistency.In addition, objective weight determined by various Objective Weightings also all exists centainly
Difference.Therefore, index weights usually have certain randomness.In the weight point estimate that expert group can not reach an agreement
When, the interval estimation of the method for the present invention weight states uncertainty.When weight is obeyed, and section is specified to be uniformly distributed, power
Weight space is the hexagon plane region as shown in (c) in Fig. 2, can be formulated as：
In formula, wjFor the weight of j-th of index；wj minAnd wj maxThe weight minimum value and maximum value of j-th of index respectively.
4) weight, which is obeyed, specifies section Arbitrary distribution
In addition to outer with the uncertainty being uniformly distributed to portray weight, the method for the present invention allows weight obedience Arbitrary distribution,
Such as (d) in Fig. 2 of weight space at this time is shown.
(6) random Multiple Attribute Decision Model is established, the optimal tune for enable policymaker to be satisfied with is inquired into using Monte Carlo simulation
Degree scheme.
The random Multiple Attribute Decision Model that the method for the present invention is established breaches the theoretical frame of the prior art, it simultaneously should not
Policymaker is asked to give index weights in advance, but it is uncertain in weight information, using determining for anti-weight space analysis
Plan flow, basic ideas are to search for the weight combination of random distribution in entire feasible weight space using decision model to disclose
The combination of which kind of weight can be such that certain scheme is optimal or a certain sequence, and calculate its ratio in entire feasible weight space
Example is measured the program and is optimal or the probability of a certain sequence accordingly, to realize the schemes ranking under random environment with preferably.
The step further comprises following sub-step：
(61) weighted sum formula is used to calculate the value of utility of each Flood Control Dispatch scheme；
Assuming that the Multiple Attribute Decision Problems in the present embodiment include m option A={ A1,A2,K,AmAnd n index C=
{C1,C2,K,Cn}.X=(xij)m×nFor decision matrix, w={ w1,w2,K,wnBe weight vectors, wherein i, j be respectively scheme and
Index serial number.The value of utility of each Flood Control Dispatch scheme is calculated using weighted sum formula, i.e. the aggreggate utility of some scheme is equal to
The weighted sum of partial utility：
ui=u (xi,w) (14)；
In formula, uiFor option AiUtility function value, be equivalent to the effect of decision model, uiShow that scheme is more excellent more greatly；xi
For option AiIndex value vector；W is weight vectors.
(62) weight is inclined in the sequence for calculating each scheme；
With probability density function fXThe Probability Characteristics of (ξ) description indexes value.It is close with the probability in feasible weight space
Spend function fW(w) uncertainty of description weight is stated.Feasible weight space W is defined as：
In order to which each scheme of quantitative expression obtains the calculation formula accordingly to sort, the method for the present invention defines following ranking letter
Number：
In formula, ρ [true]=1, ρ [false]=0；ξiFor option AiIndex value random variable vector；Rank's ()
Value range is [1, m].
Sequence tendency weight Wi r(ξ) refers to so that option AiThe weight space of specified sequence r (r=1,2 ..., m) is obtained,
It is defined as：
Wi r(ξ)={ w ∈ W:rank(ξi, w) and=r } (17)；
It is input with uncertain index value and weight by means of the concept of sequence tendency weight, the method for the present invention is main
It is proposed that four statistical indicators are used for final decision, respectively：Sequence can receive property index bi r, global receivable property index ai h、
Center weight vector wi cWith confidence factor pi c。
(63) sequence for calculating each scheme can receive property index；
Sequence can receive property index bi rDescribing makes option AiThe weight for obtaining specified sequence r is integrated into feasible weight space
Shared scale, can be calculated by following multiple integral formula in W：
(64) overall situation for calculating each scheme can receive property index；
The method of the present invention further defines global receivable property index ai h, ai hTo option AiThe b of all sequencesi rCarry out
Comprehensive, the acceptability for describing each scheme on the whole is horizontal, and calculation formula is as follows：
In formula, αrFor two level weight, option A is reflectediEach sequence acceptability index bi rTo the receivable property of the overall situation
The percentage contribution of index.According to normal policy-making thought, people always want to that those forward acceptable indexs that sort is allowed to obtain
Larger two level weight is obtained, and assigns those the acceptable indexs of sequence rearward smaller two level weight.Therefore, αrIt is one
M dimensional vectors that are non-negative, successively decreasing, calculation formula are as follows：
(65) the center weight vector of each scheme is calculated；
Center weight vector wi cIt is so that option AiThe center for the weight space being optimal, i.e., when the program is optimal
Exemplary weights vector.wi cWeight combination when each scheme is optimal substantially is described, calculation formula is as follows：
(66) confidence factor of each scheme is calculated；
Confidence factor pi cIt is defined as using option AiCenter weight vector wi cWhen, the probability that the program is optimal, i.e.,
The index space that the program is optimal accounts for the ratio in overall performane space：
Include Higher Dimensional Integration in above-mentioned established random Multiple Attribute Decision Model, the dimension of integral is multiple with decision problem
The increase of miscellaneous degree and linear increase are often difficult to Analytical Solution under higher dimensional space in practical applications, complex decision environment.For
This, the method for the present invention carries out numerical solution using Monte Carlo simulation to above-mentioned random Multiple Attribute Decision Model, can according to the overall situation
Acceptance index is ranked up each reservoir regulation for flood control scheme and preferably, inquires into the optimal tune for enable policymaker the most satisfied
Degree scheme, and according to the risk information of sequence receivable property index and confidence factor qualitative assessment multiple attribute decision making (MADM).It is special based on covering
The random Multiple Attribute Decision Model of Monte Carlo Simulation of Ions Inside solves flow chart and sees Fig. 3.
The probability sorting of random environment lower storage reservoir scheduling scheme, optimal reservoir tune can be obtained by implementing above-mentioned technical proposal
Degree scheme and corresponding risk information.Compared with prior art, above-mentioned technical proposal breaches certainty multiple attribute decision making (MADM)
Theoretical frame, substantially envisaging various uncertain factors in reservoir operation, (including flood forecasting is uncertain, index weights
Uncertainty etc.) influence.And then abundanter decision information is provided for policymaker, improve the reliable of reservoir operation decision
Property, it can be widely applied to basin reservoir operation multiple attribute decision making (MADM).
The preferred embodiment of the present invention has been described above in detail.But during present invention is not limited to the embodiments described above
Detail can carry out a variety of equivalents to technical scheme of the present invention within the scope of the technical concept of the present invention, this
A little equivalents all belong to the scope of protection of the present invention.
Claims (7)
1. a kind of random multiple attributive decision making method of reservoir operation based on Monte Carlo simulation, which is characterized in that including following step
Suddenly：
(1) data, the statistical distribution characteristic of analysis and prediction relative error are forecast according to historical flood；
(2) certainty reservoir operation Model for Multi-Objective Optimization is established, solution obtains a limited number of gate control program；
(3) it is based on current forecast peb process and forecast relative error distribution, peb process is generated using statistical sampling at random
Collection；
(4) flood routing is carried out to the flood generated at random according to gate control program, and quantifies the uncertainty of each index value；
(5) uncertainty of quantizating index weight；
(6) random Multiple Attribute Decision Model is established, the optimal scheduling side for enable policymaker to be satisfied with is inquired into using Monte Carlo simulation
Case.
2. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
It is characterized in that, forecast relative error calculation formula is as follows in the step (1)：
In formula, QftFor the forecast reservoir inflow of t periods；QotFor the actual measurement reservoir inflow of t periods；εtFor the flood of t periods
Water forecasts relative error.
3. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
Be characterized in that, in the step (2) certainty reservoir operation Model for Multi-Objective Optimization with reservoir peak level it is minimum and it is maximum go out
The minimum optimization aim of library flow, object function expression formula are as follows：
In formula, hop count when T is schedule periods；ZtFor the reservoir level of t periods；qtFor the reservoir storage outflow of t periods；
The expression formula of certainty reservoir operation Model for Multi-Objective Optimization constraints is as follows：
(a) water balance constrains：
In formula, Q (t-1), the reservoir inflow of Q (t) the reservoir t period whole story；Q (t-1), q (t) are the reservoir t period whole story
Storage outflow；V (t-1), V (t) are the reservoir storage of the reservoir t period whole story；Segment length when △ t are；
(b) reservoir peak level constrains：
Z(t)≤Zmax(5)；
In formula, Z (t) is the water level of reservoir t moment；ZmaxThe highest allowed for reservoir controls water level；
(c) end of term restriction of water level is dispatched：
Zend≥Ze(6)；
In formula, ZendFor reservoir operation end of term water level；ZeWater level is controlled for the reservoir operation end of term；It, can be with when meeting other constraints
It takes "="；
(d) reservoir discharge capacity constrains：
q(t)≤q(Z(t)) (7)；
In formula, q (t) is the storage outflow of reservoir t moment；Q (Z (t)) is aerial drainage energy of the reservoir t moment in water level Z (t)
Power；
(e) outbound allows luffing to constrain：
In formula, | q (t)-q (t-1) | it is the luffing of reservoir adjacent time interval storage outflow；The storage outflow allowed for reservoir becomes
Width.
4. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
It is characterized in that, according to the forecast relative error statistical distribution obtained in step (1) in the step (3), using following formula into flood passage
The stochastic simulation of water process：
Qt'=Qft·(1+εt) (9)；
In formula, Qt' forecast relatively according to formula (9) using statistical sampling generation is N number of for the flood discharge that the t periods simulate
Error random number, and then generate peb process collection at random.
5. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
It is characterized in that, with the not true of each reservoir regulation for flood control index value of Monte-Carlo Simulation Method quantitative analysis in the step (4)
Qualitative, which further comprises following sub-step：
(41) for the peb process collection generated in step (3), using identified gate control program in step (2) carry out by
One flood routing obtains the corresponding Flood Control Dispatch index value of each peb process；
(42) it sorts according to sequence from big to small to each Flood Control Dispatch index value, draws out each Flood Control Dispatch index Distribution value
Cummulative frequency curve, count the statistical moments such as mean value and the variance of each index, and inquire into consideration index value it is probabilistic with
Machine decision matrix；
In formula, ξijFor the stochastic variable of j-th of index value in i-th of scheme；
(43) relative risk of reservoir peak level and maximum storage outflow more than given threshold is quantitatively calculated, calculation formula is as follows：
In formula, PZAnd PQRespectively peak level and maximum storage outflow are more than the relative risk of given threshold；nZAnd nQRespectively most
High water level and maximum storage outflow are more than the number of given threshold；N is the total degree of Monte Carlo simulation.
6. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
It is characterized in that, the probabilistic quantization of index weights includes four kinds of forms in the step (5)：Weight information is totally unknown, weighs
The determination of weight vector, weight obedience specify section to be uniformly distributed and specify section Arbitrary distribution with weight obedience；
Wherein, when weight is obeyed, and section is specified to be uniformly distributed, weight space is formulated as：
In formula, wjFor the weight of j-th of index；wj minAnd wj maxThe weight minimum value and maximum value of j-th of index respectively.
7. the random multiple attributive decision making method of a kind of reservoir operation based on Monte Carlo simulation according to claim 1,
It is characterized in that, the step (6) includes：
(61) weighted sum formula is used to calculate the value of utility of each Flood Control Dispatch scheme；
Assuming that the Multiple Attribute Decision Problems in the present embodiment include m option A={ A1,A2,…,AmAnd n index C={ C1,
C2,…,Cn, X=(xij)m×nFor decision matrix, w={ w1,w2,…,wnIt is weight vectors, wherein i, j are respectively scheme and refer to
Mark serial number；The value of utility of each Flood Control Dispatch scheme is calculated using weighted sum formula, i.e. the aggreggate utility of some scheme is equal to portion
Divide the weighted sum of effectiveness：
ui=u (xi,w) (14)；
In formula, uiFor option AiUtility function value, be equivalent to the effect of decision model, uiShow that scheme is more excellent more greatly；xiFor side
Case AiIndex value vector；W is weight vectors；
(62) weight is inclined in the sequence for calculating each scheme；
With probability density function fXThe Probability Characteristics of (ξ) description indexes value, with the probability density function in feasible weight space
fW(w) uncertainty of description weight is stated, and feasible weight space W is defined as：
In order to which each scheme of quantitative expression obtains the calculation formula accordingly to sort, following ranking function is defined：
In formula, ρ [true]=1, ρ [false]=0；ξiFor option AiIndex value random variable vector；The value of rank ()
Ranging from [1, m]；
Sequence tendency weight Wi r(ξ) refers to so that option AiObtain the weight space of specified sequence r (r=1,2 ..., m), definition
For：
Wi r(ξ)={ w ∈ W:rank(ξi, w) and=r } (17)；
By means of the concept of sequence tendency weight, it is input with uncertain index value and weight, sorts and can receive property index bi r、
The receivable property index a of the overall situationi h, center weight vector wi cWith confidence factor pi cStatistical indicator is used for final decision；
(63) sequence for calculating each scheme can receive property index；
Sequence can receive property index bi rDescribing makes option AiThe weight for obtaining specified sequence r is integrated into feasible weight space W
Shared scale can be calculated by following multiple integral formula：
(64) overall situation for calculating each scheme can receive property index；
The global receivable property index a of definitioni h, ai hTo option AiThe b of all sequencesi rIt is integrated, is described on the whole each
The acceptability of scheme is horizontal, and calculation formula is as follows：
In formula, αrFor two level weight, option A is reflectediEach sequence acceptability index bi rTo global receivable property index
Percentage contribution, αrCalculation formula it is as follows：
(65) the center weight vector of each scheme is calculated；
Center weight vector wi cIt is so that option AiThe center for the weight space being optimal, i.e., the allusion quotation when program is optimal
Type weight vectors；wi cWeight combination when each scheme is optimal substantially is described, calculation formula is as follows：
(66) confidence factor of each scheme is calculated；
Confidence factor pi cIt is defined as using option AiCenter weight vector wi cWhen, the probability that the program is optimal, the i.e. party
The index space that case is optimal accounts for the ratio in overall performane space：
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| CN116070886A (en) * | 2023-04-04 | 2023-05-05 | 水利部交通运输部国家能源局南京水利科学研究院 | Multidimensional adaptive regulation and control method and system for water resource system |
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| CN110033164A (en) * | 2019-03-04 | 2019-07-19 | 华中科技大学 | A kind of Risk assessment and decision method of multi-reservoir joint Flood Control Dispatch |
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| CN111400655A (en) * | 2020-06-08 | 2020-07-10 | 中国水利水电科学研究院 | Correction optimization method and system for warehousing traffic |
| CN111400655B (en) * | 2020-06-08 | 2020-09-18 | 中国水利水电科学研究院 | Correction optimization method and system for warehousing traffic |
| CN112950033A (en) * | 2021-03-04 | 2021-06-11 | 吴统明 | Reservoir dispatching decision method and system based on reservoir dispatching rule synthesis |
| CN112950033B (en) * | 2021-03-04 | 2024-03-29 | 吴统明 | Reservoir dispatching decision method and system based on reservoir dispatching rule synthesis |
| CN116070886A (en) * | 2023-04-04 | 2023-05-05 | 水利部交通运输部国家能源局南京水利科学研究院 | Multidimensional adaptive regulation and control method and system for water resource system |
| CN116070886B (en) * | 2023-04-04 | 2023-06-20 | 水利部交通运输部国家能源局南京水利科学研究院 | Multidimensional adaptive regulation and control method and system for water resource system |
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