A Cooperative Use of Stochastic Dynamic Programming and Non-Linear Programming for Optimization of Reservoir Operation

Stochastic Dynamic Programming (SDP) is a conventional procedure to extract optimal reservoir operating policies. SDP applies exhaustive search based on the total combinations of discrete values of state variables. The accuracy of SDP can be improved by increasing the number of intervals of inflow o...

Full description

Saved in:
Bibliographic Details
Published in:KSCE Journal of Civil Engineering Vol. 22; no. 5; pp. 2035 - 2042
Main Authors: Saadat, Mohsen, Asghari, Keyvan
Format: Journal Article
Language:English
Published: Seoul Korean Society of Civil Engineers 01.05.2018
Springer Nature B.V
대한토목학회
Subjects:
Civil Engineering
Dynamic programming
Engineering
Geotechnical Engineering & Applied Earth Sciences
Industrial Pollution Prevention
Inflow
Interpolation
Intervals
Linear programming
Modules
Nonlinear programming
Objective function
Optimization
Policies
Reservoir management
Reservoir operation
State variable
Water inflow
Water Resources and Hydrologic Engineering
토목공학
reservoir operation
reservoir optimization
CSDP
SDP
ISSN:1226-7988, 1976-3808
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Stochastic Dynamic Programming (SDP) is a conventional procedure to extract optimal reservoir operating policies. SDP applies exhaustive search based on the total combinations of discrete values of state variables. The accuracy of SDP can be improved by increasing the number of intervals of inflow or reservoir volume domain; however, increasing the inflow intervals is limited and increasing the reservoir volume intervals does not guarantee the regular improvement of results. This study attempts to determine the near optimum discrete reservoir volume values by cooperating SDP and non-linear programming (CSDP) optimization modules, so the acceptable performance is achieved by limited intervals of the state variables. Towards this end, the objective function is utilized with a hedging policy to control the amount of the monthly reservoir release. The non-linear programming optimization module embodied in SDP, determines the proper values of reservoir volume taking into account the hedging policy and available constrains. Considering various numbers of inflow and reservoir volume intervals, the conventional SDP results are implemented and compared to CSDP approach. Our comparisons indicate an improved reliability performance obtained in the CSDP technique. A Distance Based Interpolation Formula (DBIF) is also illustrated and applied to employ optimal operating polices for reservoir operation management. Findings show better performance of DBIF as compared to conventional methods like Multiple Linear Regression (MLR) during long-time simulation of reservoir operation.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-017-1689-0