Use of Computational Intelligence for Scheduling of Pumps in Water Distribution Systems: a comparison between optimization algorithms

This work aims to study the operational scheduling of hydraulic pumps in a Treated Water Lift Station (TWLS) using computational intelligence techniques. This scheduling is very important to reduce electricity consumption of TWLS. For the experiments, a typical TWLS composed of two pumps and a reservoir is simulated. The choice of operation periods is obtained to minimize expenses with electrical energy, by means of an optimization task. From the hydraulic power spent, the TWLS electrical consumption is calculated. A factor $\lambda$ is used to correlate number of pumps starts and corresponding maintenance costs. An electrical consumption function, adjusted with this maintenance factor, is used as the objective function to be optimized. In this context, two meta-heuristics are compared: Simulated Annealing (SA) and a hybrid instance of Genetic Algorithms (HGA). Both meta-heuristic approaches were chosen because the reduction of energy and maintenance expenses can be seen as a nonlinear optimization problem, in addition to both techniques being used successfully to solve several real World problems. A statistical inference based objective comparison is done between results of both algorithms, and SA showed to achieve better results. After optimizing the activities related to this scheduling, it is possible to verify a reduction of up to 28.0% in electrical energy expenses, when compared to actual non-optimized operation.