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A Stochastic Optimal Scheduling Method for Hydropower Station Reservoirs

A technology of stochastic optimization and scheduling method, applied in data processing applications, forecasting, instruments, etc., and can solve problems such as infeasibility and difficulty

Active Publication Date: 2019-08-20
HOHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

At this time, when the measured runoff samples are not enough, the method of enriching the runoff samples by the stochastic simulation model will become infeasible, because the artificial runoff series and its forecast runoff series are produced at the same time, without changing the original runoff characteristics and its relationship with the runoff forecast series. complex correlations, it is very difficult

Method used

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  • A Stochastic Optimal Scheduling Method for Hydropower Station Reservoirs
  • A Stochastic Optimal Scheduling Method for Hydropower Station Reservoirs
  • A Stochastic Optimal Scheduling Method for Hydropower Station Reservoirs

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Experimental program
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Effect test

Embodiment 1

[0144] 1) Example 1 - no forecast SDP model

[0145] The No Forecasting SDP (No Forecasting SDP, NF-SDP) model does not consider the runoff forecast information of the current period, but only considers the random transfer law of the runoff itself. The runoff in period t changes from the runoff state q in the previous period t-1 Determined, the reservoir scheduling decision is determined by the measured runoff q t-1 and initial storage capacity s t-1 decided together. The recurrence equation of the NF-SDP model is:

[0146]

[0147] Among them, j is the measured runoff grade index; P(q t ∈j|q t-1 ) is the runoff prior state transition probability in period t; B t (s t-1 ,q t ∈j,s t ) is the storage capacity at the beginning and end of period t, respectively s t-1 , s t , runoff q t Immediate benefit when ∈j; f t (s t-1 ,q t-1 ) for a given initial state s t-1 ,q t-1 In the case of , the maximum expected benefit from period t to T. The recursive process of ...

Embodiment 2

[0149] 2) Embodiment 2-BSDP model

[0150] In addition to considering the random transfer law of runoff itself, BSDP also uses Bayesian theorem to take runoff forecast uncertainty into the recurrence equation in the form of likelihood probability. The runoff in period t changes from the runoff state q in the previous period t-1 and the runoff forecast q of this period f t jointly determined, the reservoir scheduling decision is determined by the measured runoff q t-1 , forecast runoff q f t and initial storage capacity s t-1 Decide. At this time, the recurrence equation of the BSDP model is:

[0151]

[0152] Among them, j and k are the level indicators of measured runoff and forecasted runoff respectively; q f t and q f t+1 are the runoff forecast values ​​of period t and period t+1, respectively; P(q t ∈j|q t-1 ,q f t ) is the posterior state transition probability of runoff in period t; P(q f t+1 ∈k|q t ∈j) is the predictability probability of runoff in...

Embodiment 3

[0154] 3) Embodiment 3-perfect forecast SDP model

[0155] The Perfect Forecasting SDP (Perfect Forecasting SDP, PF-SDP) model assumes that there is accurate runoff forecast information in the current period. The predicted value of runoff in period t is equal to the measured value, that is, q f t =q t . The reservoir scheduling decision in period t is determined by the measured runoff q t and initial storage capacity s t-1 decided together. The recurrence equation of the PF-SDP model is:

[0156]

[0157] Among them, j is the measured runoff grade index; P(q t+1 ∈j|q t ) is the prior state transition probability of runoff in period t+1; B t (s t-1 ,q t ,s t ) is the storage capacity at the beginning and end of period t, respectively s t-1 and s t , the runoff is q t The immediate benefit of the time; f t (s t-1 ,q t ) for a given initial state s t-1 ,q tIn the case of , the maximum expected benefit from period t to T. The recursive process of PF-SDP mod...

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Abstract

The invention discloses a stochastic optimal dispatching method for hydropower station reservoirs, which comprises the following steps of: establishing a quantitative model of the uncertainty of runoff and its forecast; proposing a theoretical estimation method for uncertainty of runoff and its forecast. The stochastic dynamic programming (SDP) models under different runoff description modes are constructed to extract the dispatching rules to guide the actual operation of hydropower stations and reservoirs. The method of the invention breaks through the defect that the traditional empirical estimation method is completely restricted by the measured runoff sample when quantifying the uncertainty of runoff, avoids the generation of artificial runoff sample, simplifies the calculation, greatly reduces the data storage space, and improves the benefit of stochastic optimal scheduling.

Description

technical field [0001] The invention relates to the technical field of reservoir dispatching, in particular to a stochastic optimal dispatching method for hydropower station reservoirs. Background technique [0002] The research on optimal operation of reservoirs emerged in the 1960s. So far, a series of fruitful research results have been obtained in optimal operation theory, construction and solution of operation models. However, these achievements are seldom applied to the actual dispatching of hydropower stations. The reason is mainly due to the problem of runoff uncertainty faced by reservoir dispatching. The optimal dispatching model often considers runoff uncertainty in two ways: (1) Implicit stochastic method, which assumes that the future runoff trajectory is a repeat of historical runoff, and reflects the uncertainty characteristics of future runoff through historical runoff scenarios. ②Apparent random method, that is, to describe random inflow based on non-deter...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06
Inventor 谭乔凤闻昕方国华雷晓辉王旭王超黄显峰高玉琴
Owner HOHAI UNIV
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