Adel M. Abdallah David E. Rosenberg Identifying Collaborative Residential Water and Energy Conservation Programs EWRI CONGRESS June 3, 2014 Adel M. Abdallah David E. Rosenberg

Utah’s Conservation Target Reduce water use by 25% from 1995 to 2025 Reduce emissions by 80% from 2005 to 2050 I want to point out that there is need to conservation both water and energy in the State of Utah. So we need to exploit the linkages to collaboratively conserve both of them

Heterogeneous water and energy and uses (largest 12% of users use 21% and 24% of water and energy) The green circle in the middle represents the average value which is widely used to model water and energy linkages. But in our study we want to exploit this heterogeneity by targeting the high users first

Simulation/ Optimization Results Follow up work Conclusions How can we exploit urban water-energy uses to collaboratively conserve both resources? Direct Energy Embedded Energy Objectives Simulation/ Optimization Results Follow up work Conclusions

Objectives Identify feasible city-wide collaborative water and energy conservation targets Select and size water and energy conservation programs Identify synergies and tradeoffs between water and energy Consider payback periods of actions

Targeted approach Retrofit toilet to stand. 342 Action Cost ($US) Retrofit toilet to stand. 342 Retrofit Shower to stand. 30 Retrofit faucet to stand. 50 Retrofit clotheswasher to stand. 819 Reduce outdoor by 10% 200 Lower heater set point temp to 120 F Conservation actions target the highest users first and then the next highest This cost table is from a Retrofit study (average cost) but the heater and outdoor values are assumptions

Energy embedded to treat, pump, distribute water plus treat wastewater SW: surface water source GW: ground water source

Modeling Methods Simulation (Monte Carlo Simulations) Sample 1,000 households in Salt Lake City 50% of households have old appliances Water heater type Demographic, technologic, behavior factors Estimate HH water and energy Use Savings by adopting conservation actions Optimization (Mixed integer linear program) Find feasible city-wide water and energy savings Identify actions that minimize cost to meet targets

Optimization model formulation Decision variables Conservation actions implemented Binary by appliance and household (e.g., retrofit all toilets in a house or not) Objective function ($) Minimize city-wide implementation cost of conservation actions Subject to: Meet city water reduction target Meet city direct energy reduction target Lower and upper bounds on city conservation actions Upper bounds of payback period for actions (5 years)

Cost to achieve reduction targets Solving for Max water and energy savings s.t. to the upper bound of a city action (1000) I found that Max water % is about 10% and energy about 8%. The red circle points out to an example target that I want to show its results. The target is 4% water and 5% energy I want to pint out to the tradeoff between saving water and energy for the same cost line

Heterogeneity of household savings and payback periods The Payback legend is sorted to match the Action legend. So on average, the shower action has the shorted payback period of 1 year while the Toilet action has the longest payback period of 4 years

Range of payback periods for actions Shower, toilet, and faucet actions target 50% of the population because we assume that 50% already have standard appliances Interestingly the Optimization Model didn’t select the clothes washer action because its expensive and has high payback period

Contribution of Embedded Energy Water target (%) Embedded Energy (%) Total Energy (%)

Applying the results Profile customers Target customers with high potential to save Educate customers on potential for short payback period Motivate customers to act, e.g. 712 water and energy actions for 172 households Save ~7 MG/year ($1,000/MG) and ~ 2,500 KWh/year ($26/KWh) embedded energy

Further work Work with Salt Lake City Public Utilities: Represent ~40,000 single-family households Adjust embedded energy by topography Include more conservation actions and their interactions Leverage High Performance Computing (HPC) to compute in parallel

Conclusions Heterogeneous water and energy savings and payback periods Profile, target, educate, and motivate savings SLC can save 10% water and 8% energy Strong potential to coordinate water and energy conservation efforts

Thank you! Questions? Adel Abdallah – amabdallah@aggiemail.usu.edu David E. Rosenberg – david.Rosenberg@usu.edu http://rosenberg.usu.edu