The efficient operation of water distribution systems is increasingly important due to rising energy demand and the growing integration of renewable energy sources. This study presents a Mixed-Integer Linear Programming (MILP) model for the optimal scheduling of pumps and energy sources in a water pumping system over a 24-hour horizon. The model integrates pump operation, storage tank dynamics and multiple energy sources, including grid electricity, photovoltaic (PV) generation and a diesel generator, with the objective of minimizing total operating cost while satisfying system constraints. The formulation incorporates key operational features such as pump capacity limits, binary on/off decisions, storage balance and energy supply-demand matching. A linear approximation of pump power is adopted to ensure computational tractability within the MILP framework. The model is implemented in Python using the PuLP library and solved using the CBC solver. Results show that the optimal schedule prioritizes renewable energy usage, with PV generation supplying most of the system demand during daylight hours. Grid electricity is used only when necessary while diesel generation is avoided due to its higher cost and operational constraints. The storage tank plays a critical role in enabling load shifting, allowing pumping during low-cost periods and reducing reliance on expensive energy sources. Sensitivity analysis further confirms that the model responds consistently to changes in diesel cost parameters demonstrating economic robustness. Overall, the proposed approach provides a practical and cost-effective framework for integrating water and energy system optimization, improving operational efficiency and supporting sustainable water management.

Keywords

Pump scheduling, MILP, Energy optimization, Water systems and Renewable energy.