My presentation at #AGU2014 (12/15/2014) about my ongoing research at the University of California, Davis and the Polytechnic University of Valencia (Spain).

1. COUPLING RESIDENTIAL END USE
AND UTILITY WATER-ENERGY MODELS
by Alvar Escriva-Bou, Jay R. Lund, Manuel Pulido-Velazquez, Edward Spang and Frank Loge
ALVAR ESCRIVA-BOU
alesbou@gmail.com
@alesbou
notjustwater.wordpress.com
AGU 2014 FALL MEETING SAN FRANCISCO, DECEMBER 15TH 2014

2. Water
Energy
Environment
Food
Climate
Industry

3. OUTLINE
• Residential Water-Energy-CO2 optimization
model.
 Household minimize their bills and conservation costs
facing water and energy price shocks.
• Utility-scale hourly Water-Energy simulation
model.
 Based on actual data we build a model that can simulate
demand changes.
3

4. RESIDENTIAL END-USE OPTIMIZATION MODEL
• Based on a previous water-energy-
GHG assessment
study.
• Using probability
distribution functions based
on a water end-use survey.
• 10,000 MC simulations for
10 different cities in CA.
4

5. Water
The economics behind the model: Demand
Energy
Uo
Indoor hot water
Indoor cold water
Outdoor water
Air conditioned
Appliances
Space heating
Water heating
Complementarity
0
1
2
qw0 qw2 qw1
qE1
qE0
qE2
5

6. DEMAND: CONSERVATION ACTIONS
6
• Each household has a set of available actions:
– Long-term: Retrofits.
– Short-term: Behavioral.
• Each action has:
– Cost.
• Annualized costs for retrofits.
• Hassle costs on a daily basis for behavioral changes.
– Effectiveness (Water or energy savings).

7. Conservation Actions: Savings - Technological
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0 1 2 3 4 5 6 7 8 9 10
Flow (GPM)
Retrofitted Appliance
Normal Appliance
7

8. Conservation Actions: Savings - Behavioral
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Household Reduction Factor
CDF of previous behavioral factor
Potential conservation
Consciousness
factor
0 2 4 6 8 10 12 14 16 18 20
Non-excedance probability
Reduction Factor
Behavioral Factor - Shower Length (min/shower)
8

9. Quantity
The economics behind the model: Water Supply
Price
D
Supply0
Q0
Supply ‘
Supply ‘’
Q’’ Q’
P’’
P’
P0
Pw=120%
(ρ=0.1)
Pw=110%
(ρ=0.2)
Pw=100%
(ρ=0.7)
9

10. The economics behind the model: Energy Supply
8
8.5
9
9.5
10
10.5
11
11.5
12
Jan-09
Mar-09
May-09
Jul-09
Sep-09
Nov-09
Jan-10
Mar-10
May-10
Jul-10
Sep-10
Nov-10
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
May-12
Jul-12
Sep-12
Nov-12
Jan-13
Mar-13
May-13
Jul-13
Sep-13
Nov-13
Jan-14
Mar-14
Residential Natural Gas Price
Mean
110%
90%
10
Pe=115%
(ρ=0.1)
Pe=100%
(ρ=0.8)
Pe=85%
(ρ=0.1)

11. OPTIMIZATION
푀푖푛푖푚푖푧푒 푇푂푇퐴퐿 퐶푂푆푇 =
푤푙푡
퐶푤푙푡 ∙ 푋푤푙푡 +
푒푙푡
퐶푒푙푡 ∙ 푋푒푙푡 +
퐵 ∙
푤푒
푝푤푒 ∙
푒푒
푝푒푒 ∙ 퐷 ∙
푤푠푡
퐶푤푠푡 ∙ 푋푤푠푡푤푒,푒푒 +
푒푠푡
퐶푒푠푡 ∙ 푋푒푠푡푤푒,푒푒 + 퐵푊푤푒 + 퐵퐸푒푒
• Subject to:
– Decision variables are binary
– Savings are less than initial use (upper bound)
– Mutually exclusive actions
– Interdependence among actions
11

12. Results: Adoption rate and water savings for long-term actions
12

13. Results: Adoption rate and energy savings for water-related actions
13

14. Results: When energy cost is included
(respect the only-water scenario)
14
• Adoption rate:
• Retrofit shower: +7.9%
• Retrofit clotheswasher: +1.7%
• Reduce shower length: +3.2%
• …
• Increased savings:
• Indoor water savings: +24%
• Energy savings: +30%
• GHG savings: +53%

15. Results: Demand function and elasticities
15

16. Results: Own- and cross-price
elasticities (averages)
16
• Water own-price elasticity Ɛww = -0.05
• Energy own-price elasticity Ɛee = -0.03
• Energy water-price elasticity Ɛew = -0.02
• Water energy-price elasticity Ɛwe = -0.004
• Own-price values are relatively low.
• Water price affects energy consumption more than energy price
affects water use.
• Literature about cross-price elasticities reviewed: Only 1 paper!!
Lars Garn Hansen (Land Economics, 1996) obtained a Ɛwe = -0.2,
but none obtained Ɛew.

17. UTILITY-SCALE HOURLY WATER-ENERGY SIMULATION MODEL
• Based on a real data from
EBMUD water utility.
• We select a only a part that
represents 27% of total
EBMUD water use.
• We want to simulate real
operation to obtain results
for different scenarios.
17

18. EBMUD: Selected scheme of study
WTP
WWTP
PP
PP
PP
Leland
Pop. ≈ 130,000
6,391 MG/year
Elevation:
150 feet – 45 m
Danville
Pop. ≈ 75,000
3661 MG/year
Elevation:
350 feet – 107 m
San Ramon
Pop. ≈ 150,000
7553 MG/year
Elevation:
550 feet – 168 m
Pardee and
Camanche Reservoirs
Total Supply:
17604 MG/year
(out of 64868 MG/year)
18

19. Assembling the model
Water users Water utility
19
Total Annual
Water Use
Energy Utility
Hourly water
demand
Hourly water
supply
Water-related
energy
GHG
Emissions
Shares of use by
customer category
Indoor vs. Outdoor
Hourly distribution
of end uses
Irrigation
Necessities (P-ET)
Pumping and
treatment
patterns
Water regulation
Water treatment
Pumping and
distribution
Wastewater
treatment
Water-related
energy
Regressions
and pumping
patterns
End-uses
energy
intensity

20. Some Results: Energy break down
20
• Annual water use: 17,604 MG/year
• Annual energy use: 558,000 MWh/year
• Energy Intensity: 31.7 MWh/MG
• Water utility energy cost: $3,355,451

21. Simulating scenarios
21
• Scenario 1: Residential optimal conservation
• Annual water use: 16,541 MG/year (-6%)
• Reduction Energy Use: 36,940 MWh/year (-6.6%)
• Water utility energy savings: $115,493 (3.5%)
• GHG savings: 7023 metric tons / year (93.5%
residential and 6.5% Utility)

22. Simulating scenarios
22
• Scenario 2: Peak shaving
• Outdoor consumption shift to off-peak hours
• The same water and energy use (but different
hours)
• Water utility energy savings: $51,023 (1.5%)
• Energy utility benefits: ??? But some!

23. TAKE HOME MESSAGES
• Increased water (and water-related energy
and GHG emissions) conservation when
energy is included.
• Most of water-related energy is from water
heating in households.
• There are gains for water and energy utilities
working together.
23

24. Thanks ;)
ALVAR ESCRIVA-BOU
alesbou@gmail.com
@alesbou
notjustwater.wordpress.com
AGU 2014 FALL MEETING SAN FRANCISCO, DECEMBER 15TH 2014