Use of evolutionary computation and guide curves to optimize the operating policies of a reservoir system established to supply drinking water

Abstract

AbstractThe objective of the present study was to develop a genetic algorithm capable of establishing optimal operating policies for monthly extractions from the three main reservoirs of the Cutzamala System, which supply drinking water to the Mexico City metropolitan area. In previous studies, annual water extraction defined with an annual Z curve in terms of the total water storage in the reservoirs on November 1 was optimized using genetic algorithms. In this study, a percentage of total annual extraction for each reservoir was also optimized, but monthly water extractions were adjusted too, when the water level fell outside the upper or lower limits of guide curves stablished for each reservoir. The capabilities of the genetic algorithms combined with a detailed simulation of reservoirs operation were used to optimize the levels of the guide curves and also to optimize the adjusted monthly programed extractions as linear functions of the difference between the actual storage level at the beginning of each month and the corresponding level of the guide curves. Therefore, 90 parameters were established: four to define the Z curve, two to establish the percentage assigned to each reservoir, 72 to establish the monthly levels of the guide curves and 12 to define the parameters of the linear functions used to adjust the monthly programed extractions when the actual water level exceeds the limits of the guide curves. For each alternative of the 90 parameters, a detailed simulation is done using the last 20 years of hydrological data on the inflow of water to the three main reservoirs, including the net contributions of five diversion dams, and the objective function sought to maximize water delivery to the treatment plant, while penalizing possible spills and deficits in the system is evaluated. The optimal policies found in this research resulted in smaller spills than those that occurred during the historical operation of the reservoir system. Therefore, the optimal monthly operating decisions required for each reservoir are provided by the genetic algorithm.