CN104182807A - Reservoir dispatching risk evaluation method by considering runoff forecast uncertainty - Google Patents

Abstract

The embodiment of the invention discloses a reservoir dispatching risk evaluation method by considering runoff forecast uncertainty. The reservoir dispatching risk evaluation method comprises a data processing module, a runoff prediction module, a runoff probability distribution module, a runoff random sampling module and a risk evaluation calculation module, wherein the data processing module standardizes data required by the runoff prediction module and inputs the data into the runoff prediction module to carry out runoff forecast; the forecasted runoff data is input the runoff probability distribution module by the runoff prediction module to obtain a corresponding probability distribution matrix; the probability distribution matrix is input into the runoff random sampling module by the runoff probability distribution module to carry out random sampling to obtain a storage runoff set; and the storage runoff set is input the risk evaluation calculation module by the random sampling module to calculate module calculation risks. The invention fully considers runoff forecast uncertainty and randomness, establishes a reservoir dispatching risk evaluation model by considering the runoff forecast uncertainty, carries out comprehensive safety assessment on the dispatching operation of a reservoir under runoff forecast information and exhibits wide application prospect and social value.

Description

A kind of probabilistic reservoir operation methods of risk assessment of Runoff Forecast of taking into account

Technical field

The invention belongs to water conservancy system reservoir operation risk assessment field, be specifically related to a kind of probabilistic reservoir operation methods of risk assessment of Runoff Forecast of taking into account.

Technical background

Reservoir operation operation is according to the spatial and temporal distributions of the requirements of comprehensive utilization Optimization of Water Resource Allocation of reservoir, effectively improves the utilization factor of water resource, realizes the object of bringing good to and remove all evil.Economic benefit, ecological benefits and social benefit are huge, are the importances in water resources management.

Runoff is as one of most important input message of reservoir operation, be vulnerable to the many factors impacts such as meteorology, rainfall, forecasting model, inevitably there is uncertainty in Runoff Forecast, thereby causes the uncertainty of scheduling decision, makes the operation of reservoir scheduler routine be faced with certain risk.

For the impact of research Runoff Forecast uncertainty on reservoir operation operation, the present invention builds and takes into account the probabilistic reservoir operation risk evaluation model of Runoff Forecast, take into full account uncertainty and the randomness of Runoff Forecast, the management and running risk of comprehensive assessment reservoir, formulate the retaining plan of reasonably generating electricity more economically, the benefit that improves reservoir utilization flood resource, has economic benefit and social value widely.

Summary of the invention

The object of the present invention is to provide a kind of probabilistic reservoir operation methods of risk assessment of Runoff Forecast of taking into account, for building, take into account the probabilistic reservoir operation risk evaluation model of Runoff Forecast, take into full account uncertainty and the randomness of Runoff Forecast, the management and running risk of comprehensive assessment reservoir, formulate the retaining plan of reasonably generating electricity more economically, the benefit that improves reservoir utilization flood resource, has economic benefit and social value widely.

For achieving the above object, technical scheme of the present invention is:

Take into account the probabilistic reservoir operation methods of risk assessment of Runoff Forecast, comprise data processing module, Runoff Forecast module, runoff probability distribution module, runoff stochastic sampling module and risk assessment computing module.Data processing module normalization Runoff Forecast Model desired data is also input to Runoff Forecast module and carries out Runoff Forecast; Runoff Forecast module is input to runoff probability distribution module by forecast footpath flow data and obtains corresponding probability distribution matrix; Runoff probability distribution module is inputted runoff stochastic sampling module by probability distribution matrix and is carried out the set of random sampling acquisition warehouse-in runoff; Stochastic sampling module will be put runoff set input risk assessment computing module calculation risk in storage.

Compared with prior art, beneficial effect of the present invention is as follows:

(1), based on a large amount of historical measuring runoffs and forecast footpath flow data, take into full account the uncertainty of Runoff Forecast;

(2) water level, the risk distribution of exerting oneself, abandoning the multiple schedule informations such as discharge and feature expectation value under quick obtaining current scheduling level of decision-making;

(3) realize the integrated of Runoff Forecast and schedule risk assessment.

Accompanying drawing explanation

Accompanying drawing is the system architecture schematic diagram of the embodiment of the present invention.

Embodiment

For making object of the present invention, advantage and technical scheme more clear, below in conjunction with drawings and Examples, the specific embodiment of the present invention is elaborated.

Be to be understood that, specific embodiment described herein only, in order to explain the present invention, is not intended to limit the present invention.The present invention contain any by claim, defined in marrow of the present invention and scope, make substitute, modification, equivalent method and scheme.For the public is had a better understanding to the present invention, in below details of the present invention being described by the specific details of detailed descriptions part.Do not have for a person skilled in the art the description of these detail sections can understand the present invention completely yet.

Take into account the probabilistic reservoir operation methods of risk assessment of Runoff Forecast, comprise data processing module, Runoff Forecast module, runoff probability distribution module, runoff stochastic sampling module and risk assessment computing module.Data processing module normalization Runoff Forecast Model desired data is also input to Runoff Forecast module and carries out Runoff Forecast; Runoff Forecast module is input to runoff probability distribution module by forecast footpath flow data and obtains corresponding probability distribution matrix; Runoff probability distribution module is inputted runoff stochastic sampling module by probability distribution matrix and is carried out the set of random sampling acquisition warehouse-in runoff; Stochastic sampling module will be put runoff set input risk assessment computing module calculation risk in storage.Its course of work is as follows:

1. data processing module normalization original input data.

Obtaining by the Runoff Forecast Model being obtained by historical data calibration of original input data specified, and is provided with n item factor of influence, and maximum, the minimum value of i item factor of influence are respectively according to the following formula by raw data X _inormalization is to [a, b] interval, and normalization formula is:

$X_i^{\′}=a\×\frac{{\stackrel{\‾}{X}}_i-X_i}{{\stackrel{\‾}{X}}_i-{\underset{\‾}{X}}_i}+b$

X' wherein _ifor standardizing number certificate, X' _i∈ [a, b]; A, b, for normalization parameter, can be set according to demand by user, and this method is taken as a=0.8, and b=0.1 standardizes raw data to [0.1,0.9].

The original input data of n item factor of influence forms normalization input vector X'=[X' through normalization operation ₁, X' ₂..., X' _n], X' is input to Runoff Forecast module and carries out Runoff Forecast.

2. Runoff Forecast module be it is predicted footpath flow valuve according to the standardizing number of data processing module output.

X' is inputted to Runoff Forecast Model and obtain normalization Runoff Forecast sequence Y', the abstract formula of forecasting model is as follows:

Y'＝f(X',θ)

F () wherein, θ is respectively forecasting model structure and parameters.

Now Y', for normalization prediction footpath flow data, need carry out anti-normalization operation to it can obtain corresponding Runoff Forecast sequence Y, and normalization formula is as follows:

$Y=\stackrel{\‾}{Y}-\frac{(Y^{\′}-d)\×(\stackrel{\‾}{Y}-\underset{\‾}{Y})}{c}$

Wherein for the maximal value of Inflow Sequence, minimum value; C, d is the anti-parameter of standardizing of Runoff Forecast, native system is taken as c=0.8, d=0.1.

3. runoff probability distribution module reads the Runoff Forecast sequence Y of Runoff Forecast module output, according to level definition, judges other while of Y level of living in, obtains corresponding probability distribution matrix.

If runoff is divided m rank altogether, each rank is defined as follows:

$G=\left\{\begin{array}{cc}1& \mathrm{if}({\underset{\‾}{g}}_1\≤Y\≤{\stackrel{\‾}{g}}_1)\\ .& .\\ .& .\\ .& .\\ i& \mathrm{if}({\underset{\‾}{g}}_i\≤Y\≤{\stackrel{\‾}{g}}_i)\\ .& .\\ .& .\\ .& .\\ m& \mathrm{if}({\underset{\‾}{g}}_m\≤Y\≤{\stackrel{\‾}{g}}_m)\end{array}\right.$

Wherein be respectively the upper limit, the lower limit of rank i;

By a large amount of measuring runoffs and forecast runoff historical summary statistics, obtain m other probability risk distribution matrix of level P, as follows:

P wherein _i,jrepresent that measuring runoff is other probability of j level when forecast runoff is i rank. have while representing that forecast runoff is i rank, measuring runoff is bound to occur some in m rank or certain is several.Simultaneously all there is P _i,j∈ [0,1], P _i,jwhen less expression forecast runoff is i rank, measuring runoff is that other probability of j level is less.Especially, work as P _i,j, represent to forecast that when runoff is i rank, measuring runoff can not be j rank at=0 o'clock; Work as P _i,j, represent to forecast that when runoff is i rank, measuring runoff is entirely j rank at=1 o'clock.If all there is P _i,i=1, represent to forecast that when runoff is i rank, measuring runoff is also i rank, now probability risk distribution matrix is unit matrix, this is also ideal situation.Probability risk distribution matrix P is the precision statistics to current Runoff Forecast, has reflected current Runoff Forecast level, and P is the convergence actual distribution be day by day on the increase along with forecast data.

First according to the scope of level definition, determine the residing rank k of Runoff Forecast value Y, then from probability risk distribution matrix, search the corresponding probability distribution matrix P of rank k _k=[P _{k, 1}, P _{k, 2}..., P _k,m], finally by level definition G and probability distribution matrix P _kinput runoff stochastic sampling module.

4. runoff stochastic sampling module is according to level definition G and probability distribution P _kcarry out the random sampling of runoff, obtain Runoff Forecast set.

If extract altogether N sample, according to probability distribution matrix P _kdetermine each rank sample drawn number, the number of samples computing formula of rank i is as follows:

N wherein _ifor the theoretical sampling number of rank i; represent to be greater than the smallest positive integral of x.

Each rank is carried out respectively the random sampling of corresponding number of times, and the footpath flow valuve of extraction is added in Runoff Forecast set to rank i n _iinferior sampling formula is as follows:

$Y_{n_i}=r_1\×({\stackrel{\‾}{g}}_i-{\underset{\‾}{g}}_i)+{\underset{\‾}{g}}_i$

R wherein ₁for equally distributed random number between [0,1]; for rank i n _ithe footpath flow valuve that inferior sampling obtains.

Corresponding runoff set when being obtained Runoff Forecast and be Y by random sampling, can be similar to by the known multiple sampling of law of great number the institute's sight likely that represents following reservoir inflow.

5. risk assessment computing module utilizes the runoff set that runoff stochastic sampling module obtains, under the current level of decision-making of evaluates calculation abandon water, the risk of the schedule information such as exert oneself.

Fixing current level of decision-making, determine and input successively all possible warehouse-in runoff in runoff set, allocating conventional algorithm calculates respectively, obtain the schedule information (as exerting oneself, abandon water etc.) of various water situations in runoff set, then to different schedule informations, adopt mathematical statistics method can obtain corresponding risk distribution and individual features information.Take below and abandon water and abandon discharge calculating mode as example summary relative risk and expectation, exert oneself, last water level etc. is similar with it.

Risk rate estimation formula is:

Wherein P is for abandoning water risk probability, P ∈ [0,1], if P=0 represents devoid of risk, P=1 represents to be bound to abandon water; Num is for abandoning the number of samples of water; N is total sample number order in runoff set.

Discharge calculating formula is abandoned in expectation:

Wherein for discharge is abandoned in the expectation under current scheduling information; Q _ithe discharge of abandoning that represents i sample in runoff set.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (1)

1. take into account the probabilistic reservoir operation methods of risk assessment of Runoff Forecast, it is characterized in that following steps,

(1) data processing module normalization original input data

Obtaining by the Runoff Forecast Model being obtained by historical data calibration of original input data specified, and is provided with n item factor of influence, and maximum, the minimum value of i item factor of influence are respectively according to the following formula by raw data X _inormalization is to [a, b] interval, and normalization formula is:

$X_i^{\′}=a\×\frac{{\stackrel{\‾}{X}}_i-X_i}{{\stackrel{\‾}{X}}_i-{\underset{\‾}{X}}_i}+b$

X' wherein _ifor standardizing number certificate, X' _i∈ [a, b]; A, b, for normalization parameter, can be set according to demand by user, and this method is taken as a=0.8, and b=0.1 standardizes raw data to [0.1,0.9];

The original input data of n item factor of influence forms normalization input vector X'=[X' through normalization operation ₁, X' ₂..., X' _n], X' is input to Runoff Forecast module and carries out Runoff Forecast;

(2) Runoff Forecast module be it is predicted footpath flow valuve according to the standardizing number of data processing module output;

X' is inputted to Runoff Forecast Model and obtain normalization Runoff Forecast sequence Y', the abstract formula of forecasting model is as follows:

Y'＝f(X',θ)

F () wherein, θ is respectively forecasting model structure and parameters;

Now Y', for normalization prediction footpath flow data, need carry out anti-normalization operation side to it to obtain corresponding Runoff Forecast sequence Y, and normalization formula is as follows:

$Y=\stackrel{\‾}{Y}-\frac{(Y^{\′}-d)\×(\stackrel{\‾}{Y}-\underset{\‾}{Y})}{c}$

Wherein for the maximal value of Inflow Sequence, minimum value; C, d is the anti-parameter of standardizing of Runoff Forecast, native system is taken as c=0.8, d=0.1;

(3) runoff probability distribution module reads the Runoff Forecast sequence Y of Runoff Forecast module output, according to level definition, judges other while of Y level of living in, obtains corresponding probability distribution matrix; If runoff is divided m rank altogether, each rank is defined as follows:

$G=\left\{\begin{array}{cc}1& \mathrm{if}({\underset{\‾}{g}}_1\≤Y{\≤\stackrel{\‾}{g}}_1)\\ .& .\\ .& .\\ .& .\\ i& \mathrm{if}({\underset{\‾}{g}}_i\≤Y\≤{\stackrel{\‾}{g}}_i)\\ .& .\\ .& .\\ .& .\\ m& \mathrm{if}({\underset{\‾}{g}}_m\≤Y\≤{\stackrel{\‾}{g}}_m)\end{array}\right.$

Wherein be respectively the upper limit, the lower limit of rank i;

By a large amount of measuring runoffs and forecast runoff historical summary statistics, obtain m other probability risk distribution matrix of level P, as follows:

P wherein _i,jrepresent that measuring runoff is other probability of j level when forecast runoff is i rank; have while representing that forecast runoff is i rank, measuring runoff is bound to occur some in m rank or certain is several; Simultaneously all there is P _i,j∈ [0,1], P _i,jwhen less expression forecast runoff is i rank, measuring runoff is that other probability of j level is less; Especially, work as P _i,j, represent to forecast that when runoff is i rank, measuring runoff can not be j rank at=0 o'clock; Work as P _i,j, represent to forecast that when runoff is i rank, measuring runoff is entirely j rank at=1 o'clock; If all there is P _i,i=1, represent to forecast that when runoff is i rank, measuring runoff is also i rank, now probability risk distribution matrix is unit matrix, this is also ideal situation; Probability risk distribution matrix P is the precision statistics to current Runoff Forecast, has reflected current Runoff Forecast level, and P is the convergence actual distribution be day by day on the increase along with forecast data;

First according to the scope of level definition, determine the residing rank k of Runoff Forecast value Y, then from probability risk distribution matrix, search the corresponding probability distribution matrix P of rank k _k=[P _{k, 1}, P _{k, 2}..., P _k,m], finally by level definition G and probability distribution matrix P _kinput runoff stochastic sampling module;

(4) runoff stochastic sampling module is according to level definition G and probability distribution P _kcarry out the random sampling of runoff, obtain Runoff Forecast set; If extract altogether N sample, according to probability distribution matrix P _kdetermine each rank sample drawn number, the number of samples computing formula of rank i is as follows:

N wherein _ifor the theoretical sampling number of rank i; represent to be greater than the smallest positive integral of x;

Each rank is carried out respectively the random sampling of corresponding number of times, and the footpath flow valuve of extraction is added in Runoff Forecast set to rank i n _iinferior sampling formula is as follows:

$Y_{n_i}=r_1\×({\stackrel{\‾}{g}}_i-{\underset{\‾}{g}}_i)+{\underset{\‾}{g}}_i$

R wherein ₁for equally distributed random number between [0,1]; for rank i n _ithe footpath flow valuve that inferior sampling obtains;

Corresponding runoff set when being obtained Runoff Forecast and be Y by random sampling, can be similar to by the known multiple sampling of law of great number the institute's sight likely that represents following reservoir inflow;

(5) risk assessment computing module utilizes the runoff set that runoff stochastic sampling module obtains, the current level of decision-making of evaluates calculation

Under abandon water, the risk of the schedule information such as exert oneself;

Fixing current level of decision-making, determine and input successively all possible warehouse-in runoff in runoff set, allocating conventional algorithm calculates respectively, obtain the schedule information (as exerting oneself, abandon water etc.) of various water situations in runoff set, then to different schedule informations, adopt mathematical statistics method can obtain corresponding risk distribution and individual features information; Take below and abandon water and abandon discharge calculating mode as example summary relative risk and expectation, exert oneself, last water level etc. is similar with it;

Risk rate estimation formula is:

$P=\frac{\mathrm{num}}{N}\×100\%$

Wherein P is for abandoning water risk probability, P ∈ [0,1], if P=0 represents devoid of risk, P=1 represents to be bound to abandon water; Num is for abandoning the number of samples of water; N is total sample number order in runoff set;

Discharge calculating formula is abandoned in expectation:

$\stackrel{\‾}{Q}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{Q}_i$

Wherein for discharge is abandoned in the expectation under current scheduling information; Q _ithe discharge of abandoning that represents i sample in runoff set.