scholarly journals From Linear Programming Model to Mixed Integer Linear Programming Model for the Simultaneous Optimisation of Water Allocation and Reservoir Location in River Systems

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 594 ◽  
Author(s):  
Jaime Veintimilla-Reyes ◽  
Annelies De Meyer ◽  
Dirk Cattrysse ◽  
Jos Van Orshoven
Keyword(s):  
Linear Programming ◽  
Network Flow ◽  
Water Allocation ◽  
Programming Model ◽  
Water Levels ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Network Configuration ◽  
Spatially Distributed ◽  
Lp Model

The allocation of water flowing through a river-with-reservoirs system to optimally meet spatially distributed and temporally variable demands can be conceived as a Network Flow Optimisation (NFO) problem and addressed by Linear Programming (LP). In this paper we present an extension of the strategic NFO-LP model to simultaneously optimise the allocation of water and the location of one or more reservoirs. The applicability of the MILP model has been illustrated by applying it to a hypothetical river network configuration consisting of seven candidate reservoir nodes and seven demand nodes, and by comparing the outcome (water levels in selected reservoir, penalties) with the values obtained by the original LP-model for the same network with six reservoirs present.

scholarly journals MILP for Optimizing Water Allocation and Reservoir Location: A Case Study for the Machángara River Basin, Ecuador

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1011 ◽  
Author(s):  
Jaime Veintimilla-Reyes ◽  
Annelies De Meyer ◽  
Dirk Cattrysse ◽  
Eduardo Tacuri ◽  
Pablo Vanegas ◽  
...  
Keyword(s):  
Linear Programming ◽  
River Basin ◽  
Network Flow ◽  
Water Allocation ◽  
Mixed Integer ◽  
Spatially Distributed ◽  
Lp Model ◽  
Milp Model ◽  
Network Flow Optimization

The allocation of water flowing through a river-with-reservoirs system to optimally meet spatially distributed and temporally variable demands can be conceived as a network flow optimization (NFO) problem and addressed by linear programming (LP). In this paper, we present an extension of the strategic NFO-LP model of our previous model to a mixed integer linear programming (MILP) model to simultaneously optimize the allocation of water and the location of one or more new reservoirs; the objective function to minimize only includes two components (floods and water demand), whereas the extended LP-model described in this paper, establishes boundaries for each node (reservoir and river segments) and can be considered closer to the reality. In the MILP model, each node is called a “candidate reservoir” and corresponds to a binary variable (zero or one) within the model with a predefined capacity. The applicability of the MILP model is illustrated for the Machángara river basin in the Ecuadorian Andes. The MILP shows that for this basin the water-energy-food nexus can be mitigated by adding one or more reservoirs.

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