TY - GEN
T1 - A Decision Support Tool for Minimizing Energy Consumption in Water Distribution Networks
AU - Mohammed, Abdulla S.
AU - Abdallah, Mohammed
AU - Sleptchenko, Andrei
AU - Bouabid, Ali
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Economic development and population growth are continuously increasing the pressure on water utilities. Water transmission and distribution networks are considered an energy-intensive process. The primary energy consumption in water networks is due to pumping. Minimizing the energy consumption of the pumps becomes the primary target for many water utilities around the world. This research aims to develop a decision tool to optimize pumping operation costs in water distribution networks. A pilot case study from a utility water network company is selected to develop and test a new optimization tool. The case study consists of two water pumping stations, six variable speed pumps, six tanks, and nine demand nodes. The operation system of these pumping stations automatically controls all the variables (motor speed, pressure, flowrate, valve opening, and closing) that will satisfy the hydraulic requirement of the water network. Currently, the start and stop of each pump are decided based on human experience regardless of the electricity tariff. In this study, a new optimization decision tool is developed to help the operation decide the optimum pumping flowrate for the upcoming 24 hours, minimizing pumping cost and satisfying all requirements and constraints. The model required the user to fill the hourly demand forecast at each demand node, electricity tariff, initial tanks level, maximum and minimum pump flowrate. The optimization model uses a linear programming algorithm for finding the optimum flowrate for 24 hours. AIMMS optimization software was selected for the implementation of the optimization model and the development of a user-friendly interface. The optimum flowrates were tested on EPANET to verify the feasibility of the optimum schedule from hydraulics perspectives. Results show that the new optimization tool is robust and has successfully reduced the energy cost of a pumping station by up to 9%.
AB - Economic development and population growth are continuously increasing the pressure on water utilities. Water transmission and distribution networks are considered an energy-intensive process. The primary energy consumption in water networks is due to pumping. Minimizing the energy consumption of the pumps becomes the primary target for many water utilities around the world. This research aims to develop a decision tool to optimize pumping operation costs in water distribution networks. A pilot case study from a utility water network company is selected to develop and test a new optimization tool. The case study consists of two water pumping stations, six variable speed pumps, six tanks, and nine demand nodes. The operation system of these pumping stations automatically controls all the variables (motor speed, pressure, flowrate, valve opening, and closing) that will satisfy the hydraulic requirement of the water network. Currently, the start and stop of each pump are decided based on human experience regardless of the electricity tariff. In this study, a new optimization decision tool is developed to help the operation decide the optimum pumping flowrate for the upcoming 24 hours, minimizing pumping cost and satisfying all requirements and constraints. The model required the user to fill the hourly demand forecast at each demand node, electricity tariff, initial tanks level, maximum and minimum pump flowrate. The optimization model uses a linear programming algorithm for finding the optimum flowrate for 24 hours. AIMMS optimization software was selected for the implementation of the optimization model and the development of a user-friendly interface. The optimum flowrates were tested on EPANET to verify the feasibility of the optimum schedule from hydraulics perspectives. Results show that the new optimization tool is robust and has successfully reduced the energy cost of a pumping station by up to 9%.
KW - Optimization of Pumping Station
KW - Pumping Cost Optimization
KW - Water Distribution Optimization
KW - Water Network
UR - http://www.scopus.com/inward/record.url?scp=85130140290&partnerID=8YFLogxK
U2 - 10.1109/DASA54658.2022.9765225
DO - 10.1109/DASA54658.2022.9765225
M3 - Conference contribution
AN - SCOPUS:85130140290
T3 - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
SP - 1749
EP - 1753
BT - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
Y2 - 23 March 2022 through 25 March 2022
ER -