Optimizing Water Tank Levels Using Genetic Algorithms

Abstract

This thesis presents a practical optimization framework for energy-efficient pump scheduling in water distribution systems. It combines the practicality of rule-based control with the global-search power of genetic algorithms. A novel setpoint curve encoding scheme is introduced, in which daily tank level targets are parameterized by a small set of meaningful coefficients (baseline, peak/dip timing and amplitude, and curvature descriptors). These key parameters are then optimized using a custom genetic algorithm, coupled with EPANET-driven hydraulic simulations. Constraint handling is managed by penalty functions for demand security, hydraulic feasibility, reservoir volume balance and pump maintenance. The framework is first demonstrated on the simplified NET-1 hydraulic network, providing insight on how to algorithm operate. The optimization algorithm is subsequently applied to a calibrated high-pressure zone (HPZ-G) of the Gothenburg water network, using historical operational data for model validation. Results indicate that the optimized setpoint curves can reduce energy cost, whilst still hydraulic and operational constraints. However, certain data gaps are identified which would need to be addressed to improve the model’s validity.