Importance of multiple time step optimization in river basin planning and management: a case study of Damodar River basin in India

ABSTRACT This paper outlines the importance of multiple time step optimization (MTO) in river basin allocation. The principal novelty of the work presented here is to provide a methodology for how to use MTO solutions in river basin planning and real-time operation. Two approaches for using the MTO results were tested on Damodar River basin in India and are presented in the paper. Using the proposed approach, the model managed flood flows without exceeding the downstream full bank channel capacity in 35 years of available historical data, while at the same time increasing generated hydropower on average by 63% annually, and supplying an additional 350 million m3 to irrigation and industry compared to the historical levels. The results presented in this study were obtained using the new Web-based Basin Management (WEB.BM) water management model, the only water allocation model with full Linear Programming (LP) optimization capabilities available online free of charge.

[1]  S. Yakowitz,et al.  Constrained differential dynamic programming and its application to multireservoir control , 1979 .

[2]  David W. Watkins,et al.  LINEAR PROGRAMMING FOR FLOOD CONTROL IN THE IOWA AND DES MOINES RIVERS , 2000 .

[3]  Charles ReVelle,et al.  Linear Decision Rule in Reservoir Management and Design: 2. Performance Optimization , 1970 .

[4]  Deepti Rani,et al.  Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation , 2010 .

[5]  Nesa Ilich Shortcomings of linear programming in optimizing river basin allocation , 2008 .

[6]  Richard M. Shane,et al.  RIVERWARE: A GENERALIZED TOOL FOR COMPLEX RESERVOIR SYSTEM MODELING 1 , 2001 .

[7]  Nesa Ilich,et al.  An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series , 2014 .

[8]  Daniel P. Sheer,et al.  Water Supply Planning Simulation Model Using Mixed-Integer Linear Programming “Engine” , 1997 .

[9]  Kumaraswamy Ponnambalam,et al.  Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia , 2019, Journal of Hydroinformatics.

[10]  Ralph A. Wurbs Reservoir‐System Simulation and Optimization Models , 1993 .

[11]  David Haro,et al.  A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities , 2012, Water Resources Management.

[12]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[13]  Mark H. Houck,et al.  Daily operation of a multipurpose reservoir system , 1983 .

[14]  Francesca Pianosi,et al.  An argument-driven classification and comparison of reservoir operation optimization methods , 2019, Advances in Water Resources.

[15]  Nesa Ilich Limitations of network flow algorithms in river basin modeling. , 2009 .

[16]  Paresh Deka,et al.  Neural Network Based Decision Support Model for Optimal Reservoir Operation , 2005 .

[17]  R. Willis,et al.  Monte Carlo Optimization for Reservoir Operation , 1984 .