Impact of the Pumping Regime on Electricity Cost Savings in Urban Water Supply System
| Autor: | Lucija Plantak, Bojan Đurin, Ebrahim Alamatian, Sara Dadar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Water pumping
Optimal design electricity cost Geography Planning and Development 0207 environmental engineering 02 engineering and technology 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry Automotive engineering Genetic algorithm genetic algorithm Retrofitting 020701 environmental engineering TD201-500 0105 earth and related environmental sciences Water Science and Technology pumping station efficiency optimization Water supply for domestic and industrial purposes Electric potential energy Mode (statistics) Hydraulic engineering Work (electrical) Environmental science TC1-978 Energy (signal processing) |
| Zdroj: | Water Volume 13 Issue 9 Water, Vol 13, Iss 1141, p 1141 (2021) |
| Popis: | The main purpose of the presented research is to raise the efficiency of pumping stations’ operational work by developing a model for reducing energy costs in urban water supply systems. Pumping systems are responsible for a significant portion of the total electrical energy use. Significant opportunities exist to reduce the pumping energy through smart design, retrofitting, and operating practices. Today, considering the increase in pumping energy prices in water conveyance systems, the problem of optimal operation of pumping stations is very actual. The optimal operation of pumping stations was determined using a Genetic Algorithm Optimization (GAO) to achieve the minimum energy cost. The paper presents a novel management model for the optimal design and operation of water pumping systems on a real case study for the town of Gonabad in Iran. To achieve this goal, three days in a year were selected randomly. The results indicate that the proposed mode in conjunction with a GAO is a versatile management model for the design and operation of the real pumping station. Modeling results show that optimization with a GAO reduces power consumption by about 15–20%. |
| Databáze: | OpenAIRE |
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