Exploring Adaptive Demand-side and Supply-side Management of Urban Water Resources using a Multi-objective Optimization Approach

摘要

Urban water management specifies supply-side infrastructure and demand-side policies to balance water supply and demands for social and environmental systems. As the sustainability of water resources depends on the dynamic interactions among the environmental, technological, and social characteristics of the water system and local population, an adaptive water management approach can be used to update utility decisions based on the feedback among these systems and may enable a more efficient use of resources. Adaptive demand-side management strategies, such as regulating water for outdoor use, can be designed with increasing restrictions corresponding to the depletion of reservoirs. Adaptive supply-side strategies supplement supply by increasing the volume of water that is transferred among basins when reservoirs levels drop. Frequent water use restrictions, however, can have adverse effects on property values, due to prolonged periods without lawn watering, while pumping water from external basins carries a high cost due to energy requirements. A trade-off exists between the management costs to the water utility and the number of day when outdoor water use is restricted. In this study, a Complex Adaptive Systems (CAS) framework is used to simulate the adaptive behaviors of consumers, the adaptive decisions of the water utility, and an engineering model of the water supply infrastructure. The CAS framework is coupled with a multi-objective optimization methodology to evaluate a combination of supply-side and demand-side adaptive water management strategies in achieving the conflicting goals of minimizing management costs and minimizing the number of days with outdoor water use restrictions. An evolutionary computation-based methodology, Hypervolume Maximizing Multi-objective Evolutionary Algorithm (HM2EA), is applied to an illustrative case study of an urban water supply system to explore Pareto-optimal solution sets of adaptive water management strategies.