A simple algorithm for reducing the operation frequency of residential water pumps during peak hours of power consumption

摘要

In developing countries where water supply pressure is low, or frequent water outages and electricity shortages happen, domestic end‐users are forced to install water pump‐storage systems, consisting of water pumps and ground‐level and rooftop tanks to satisfy their daily water demands. This contributes to increasing electrical energy consumption, particularly during peak electricity demand hours. This study presents a simple, practical, computational, and cost‐effective shifting water level control algorithm to manage water‐energy nexus, by reducing pump‐storage system electric energy consumption during peak hours. The proposed algorithm requires a simple modification to the existing water level control scheme, by installing an additional float switch in the rooftop water tank below the currently available float switch that is usually adjusted to trigger the pump when the level in the tank drops by 5‐10% from the maximum level. Based on the simulation results, the algorithm preserves the domestic end‐users’ comfortable daily water demand and reduces water pump energy consumption during peak hours by 90%. During off‐peak hours, the control algorithm triggers the pump to refill the rooftop tank based on the upper float switch when water level drops by 5%, while during peak hours, the pump is triggered only when the water drops by 30%. The performance of the algorithm is found to be comparable to the performance of the model predictive control (MPC) approach developed for the same purpose, but MPC needs a high computation capacity and a complex analog feedback level sensor. The algorithm succeeds in reducing and shifting pump energy consumption under various possible operation scenarios and water demand disturbances. A mathematical model is developed for the domestic water pump‐storage system using Matlab/Simscape to cope with the complexity of solving nonlinear fluid flow equations and measure the data required to develop the control algorithm. The performance of the algorithm is tested based on a real case study.