Improving energy efficiency in water supply systems with pump scheduling optimization

Abstract

Water supply systems have a significant environmental and energetic impact due to the large amount of energy consumed in water pumping and water losses. The safe and efficient operation of these systems is crucial, where digital tools, such as monitoring, hydro-informatics, and optimization algorithms, are key approaches that can play an important role on support decisions.This paper presents a hybrid optimization method to improve the energy efficiency of a water supply system towards a more sustainable water management concerning the water-energy nexus. A genetic algorithm was used to optimize the pumping schedule during the day. Knowing the water consumption a priori, it is possible to define the optimal pump status for a specific timeframe (e.g. every 1 h), minimizing the operation costs, and also the energy consumption and associated carbon dioxide emissions. Knowledge-based mechanisms, like introducing known feasible solutions in the population and selective mutation mechanisms, were introduced in order to boost the algorithm convergence. A model of the water network developed in the hydraulic simulator EPANET was used to evaluate the solutions. All the physical constraints of the water supply system (e.g. hydraulic compliances) and water demands must be met for each solution, including the level limits of the water storage tanks.From the obtained solutions, it is found that optimizing the pump scheduling can improve the energy efficiency up to 15% in average (maximum of 25%) comparatively to the real operation, although this value can severely decrease if a conservative approach is assumed of maintaining more water stored in the tanks (low-risk approach). Similar improvements were achieved for cost and carbon dioxide emissions. Besides knowledge-based mechanisms, the analysis of the water storage risk was also an innovative outcome of this paper.Finally, digital tools can be used to optimize the system with minimal investment in equipment or physical intervention, although optimal solutions depend on water availability, water demand, and water storage risk.