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Insights into water-energy cobenefits and trade-offs in water resource management

Gao, Jinjing, Li, Chenlong, Zhao, Peng, Zhang, Hongwei, Mao, Guozhu, Wang, Yuan
Journal of cleaner production 2019 v.213 pp. 1188-1203
algorithms, energy, energy conservation, freshwater, groundwater, issues and policy, model validation, models, regression analysis, resource allocation, surface water, urban planning, wastewater treatment, water allocation, water resources, China
Cobenefits between water and energy are always pursued for urban planning since their consumption has a significant influence on each resource. To achieve the optimal conservation of both resources, i.e., reducing energy consumption while resolving water resource problems, this study established a new water resource allocation optimization tool with traditional water resources (such as surface water and ground water) and untraditional water resources (such as reclaimed water and desalinated water) using a multiobjective model with an evolutionary algorithm, which minimized the water consumption of freshwater and lowered the energy consumption of the system. To validate the model, the water resource system in Tianjin, one of the metropolitans in China, was simulated to provide the optimal water and energy conservation plan on a Pareto frontier. Our results suggest that at least 17% of current freshwater consumption could be decreased with less energy contribution by using imported water and a centralized supply in Tianjin. Furthermore, a minimum of 3.15 kWh of energy input would be required to save 1 m3 of fresh water with the assumption of linear regression fitting of the Pareto frontier in the water allocation plan. In addition, a sensitivity analysis was performed to determine the effects of variations in the demand/supply volumes as well as the energy supply involved in untraditional water resources. Thus, our optimization model can help policy makers identify water and energy cobenefits under different water allocation plans and provide valuable references for the optimization of the water and energy nexus.