*4.3. Modeling Scenarios*

A set of scenarios are developed to explore the performance of the smart water grid for variations in model characteristics. This analysis explores changes in the number of households that join the market as prosumers to assess how many prosumers are needed to meet demands exerted by consumers. The participation of households as prosumers varies from 0% to 100% of the total number of households by increments of 5%. Households are randomly assigned as prosumer and consumer agents to meet the scenario definition. This analysis also explores changes in precipitation, as precipitation is expected to affect the amount of rainwater traded. Higher depths of rainfall and more frequent precipitation events lead to greater volumes of water stored, but reduce the amount of water needed for irrigation, as consumers do not exert demands immediately following precipitation events. Precipitation scenarios are explored where the depth of rainfall at each hourly time step is multiplied by a factor of 0.5, 1.0, 2.0, 3.0, 4.0, and 5.0. The total number of time steps with precipitation greater than zero is held constant to maintain consistency across simulations; for example, if 5.0 mm is recorded during the first hour of Seattle's April 2020 precipitation data, then a total of 15.0 mm falls during that time step in a scenario with a precipitation factor of 3*P*. Evapotranspiration (*ET*) values are held constant throughout all scenarios. According to the Blaney–Criddle equation, *ET* is correlated with temperature, wind, and daylight hours, which we assume as constant across all precipitation scenarios [94]. A total of 126 scenarios (six precipitation settings and 20 prosumer settings) are generated, and scenarios are labeled as the percentage of households that participate as prosumers in the market and the factor used to adjust rainfall depths. Each scenario was simulated over the one-month horizon using MASON, which required approximately 40 s to run using a 3.1 GHz Dual-Core Intel Core i5. Output from the agent-based model was used to create the input file depicting the demands of that scenario for EPANET, which required approximately 6.7 min when run using a 2.9 GHz 4-Core Intel Xeon W-2102.

#### **5. Results**

The results presented below demonstrate the dynamics of water storage, water consumption, energy consumption, and hydraulic performance for an example scenario. Subsequently, the performance of the ABM and the network across all scenarios are reported and explored.
