Abstract
<p>Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak demands generate immediate costs due to the energy requirements of pumping large volumes of water. If peak demands are not mitigated, large investments in infrastructure expansion are needed to support urban growth and economic development. Through data collection and communication approaches available through advanced metering infrastructure (AMI), demand-side management approaches could reduce peak demands. AMI data can be disaggregated to identify end uses that contribute to peak demands, and feedback about hourly use can be used to encourage demand shifting behaviors. Demand-side management implements technical approaches, such as retrofitting households with smart and water-efficient devices, and social approaches, such as dynamic water pricing, mandatory restrictions, and persuasive games that encourage voluntary participation. A community of households that shift demands can distribute the volume of water provision evenly over the hours of a day and reduce peak demands. While demand-side management strategies can reduce energy requirements associated with water supply and the need for new infrastructure development, demand management relies on the behaviors and decision-making of individuals, creating uncertainty in the emergent cost savings and infrastructure impacts. This research develops an agent-based modeling methodology to simulate the performance of demand-management approaches to reduce peak water demands. A persuasive game is simulated that implements a leaderboard to encourage cooperation and competition within and among neighborhoods of water users. Household agents receive points for shifting end-uses, based on the difficulty and water savings associated with end-user behaviors. Opinion dynamics simulate agents’ information exchange using a leaderboard, which provides motivation for agents to increase individual and team scores. The methodology is applied for AMI data to test the effects of persuasive games on reducing peak demands.</p>
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