1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

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

8. 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

9. 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)

10. 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

11. 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

12. 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

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

14. 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

15. 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

16. 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

17. Thank you! Questions? Adel Abdallah – amabdallah@aggiemail.usu.edu
David E. Rosenberg –