Abstract
Water consumption dynamics lead to pressure fluctuations at network nodes, potentially associated with pipe leakages or unreliable supply within a water distribution system. Efficient management of secondary water supply system (SWSS) could enhance inflow modes of its essential component (i.e., storage tank) of potential implication on pressure control and water quality maintenance. In this study, a novel computational framework was developed to determine the optimal inflow profiles of storage tanks, where a water supply system simulation model was integrated with the particle swarm algorithm-based optimization for demand peak staggering. Experimental investigations on an example water supply system revealed that, as compared to the control of float ball valves, the optimizing regulation of SWSS tanks remarkably reduced water pressure oscillations by approximately 70%, correspondingly with the minimum pressure elevating and the maximum pressure declining among network nodes. Furthermore, the enhancing regulation schemes allowed water levels to fluctuate within an effective range, thus decreasing water retention time and facilitating associated water quality safety. Sensitivity analysis from our simulations indicates that increasingly appropriate tank number and size magnified the regulation capability, thereby reinforcing the promotion effect of optimizing control schemes on the system performance. The proposed approach is expected to provide theoretical support for optimizing the dynamic operations and management of SWSSs.
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Some data are available from the corresponding author upon requests.
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All authors contributed to the study conception and design. BD, WW, and LL provided the idea of the model construction. JW, SJ and ZD collected study data and analyzed model performance results. The first draft of the manuscript was written by JW, and revised by LL and GC. All authors read and approved the final manuscript.
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Wang, J., Deng, B., Jiang, S. et al. Optimizing Control of Secondary Water Supply Tanks for Demand Peak Staggering. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03855-3
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DOI: https://doi.org/10.1007/s11269-024-03855-3