Energy saving and management of water pumping networks

Abdelazeem A Abdelsalam; Hossam A Gabbar

doi:10.1016/j.heliyon.2021.e07820

Energy saving and management of water pumping networks

Heliyon. 2021 Aug 16;7(8):e07820. doi: 10.1016/j.heliyon.2021.e07820. eCollection 2021 Aug.

Authors

Abdelazeem A Abdelsalam^{1

2}, Hossam A Gabbar^{1

3}

Affiliations

¹ Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, L1H7K4, ON, Canada.
² Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt.
³ Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, L1H7K4, ON, Canada.

Abstract

The main consumption of energy in water systems is the pumps. Due to the different tariff of energy consumption during the one day, the operation of these pumps should be controlled to minimize their consumption and consequently decrease the cost of operation. This paper utilizes an optimization algorithm to control the on/off operation of water pumps to minimize the cost of energy consumption and number of pump switching of water networks. This objective function is subjected to some optimization and hydraulic constraints such as the tanks upper and lower limits, and water network pressure limit. The proposed methodology is an iterative combination process between an optimization algorithm and EPANet hydraulic simulator where optimization algorithm generates the schedules and the hydraulic simulator is used to check the feasibility of these schedules. The suggested optimization method is the artificial electric field algorithm (AEFA). This methodology is applied to three water networks; EPANet practical example network, Richmond network and a part from Toronto network with a variable energy consumption tariff. The AEFA is tested and trained to select the best values of its controlling parameters for each network. The results show that the energy consumption cost is significantly decreased by the optimal schedules of water pumps. Also AEFA is compared with other optimization algorithms such as the genetic and particle swarm algorithms on the same networks and energy tariff and the results show the superiority of AEFA in the convergence and saving of the cost of energy consumption.

Keywords: Artificial electric field algorithm; EPANet; Energy management; Optimization; Water pump scheduling.