The Energy-Water Nexus and the “New” Environmental Engineer...2016/03/31 · Ashlynn S....
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The Energy-Water Nexus and the “New” Environmental Engineer Ashlynn S. Stillwell NSF-AEESP Grand Challenges Workshop | Rice University | 4/1/16
Transcript of The Energy-Water Nexus and the “New” Environmental Engineer...2016/03/31 · Ashlynn S....
The Energy-Water Nexus and the “New” Environmental Engineer
Ashlynn S. Stillwell
NSF-AEESP Grand Challenges Workshop | Rice University | 4/1/16
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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ENERGY WATER
• Collection and conveyance • Drinking water treatment and distribution
• Water heating • Wastewater treatment • Water reuse
• Mining energy resources and cultivating biomass
• Refining liquid fuels • Cooling thermoelectric power plants
• Generation of hydropower
[Stillwell, JWRPM, 2015]
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Green stormwater infrastructure overlaps many disciplines.
ENERGY WATER
GREEN ROOF
Civil & Environmental Engineering Environmental Science Architecture Agriculture / Plant Biology Urban Planning Public Policy Psychology …
How can we quantify the performance and energy benefits of green infrastructure?
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Calibrated 2D distributed surface-groundwater models can quantify green roof performance.
Run
off (
m3 /s
)
Time
Runoff hydrograph
Pro
babi
lity
of F
ailu
re
Rainfall 0
1 Slight damage
Extensive damage
Fragility curves
[William & Stillwell, in preparation]
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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0%
10%
20%
30%
40%
50%
60%
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80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 P
roba
bilit
y of
failu
re
Storm return period [yr]
>0% peak reduction standard
>20% peak reduction standard
>60% peak reduction standard
>90% peak reduction standard
Green roof hydrologic performance is dependent on storm conditions.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Pro
babi
lity
of fa
ilure
Storm return period [yr]
>0% peak reduction standard
>20% peak reduction standard
>60% peak reduction standard
>90% peak reduction standard
2-hour storm 24-hour storm
[William & Stillwell, in preparation]
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Electric power generation overlaps many disciplines.
ENERGY WATER
POWER PLANT
Civil & Environmental Engineering Electrical Engineering Mechanical Engineering Chemical Engineering Economics Business Administration Public Policy …
How can we maintain electricity grid reliability under drought conditions?
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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A network model of streamflow and water temperature can support power grid simulation.
q1
q2
q4
q3
q6
q5
q7
Power Plant
Downstreamdirection
[Lubega & Stillwell, in preparation]
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Electric power grid reliability depends on sufficient water (quantity and quality) for cooling.
±0 25 50 75 10012.5
Miles
Original analysis performed by
DeNooyer et al. utilizing 2012 data
from the U.S. Energy Information
Administration (EIA)
September 2014
Baseline case results are
based on 2012 data
Baseline Power PlantWater Withdrawal
(billion gallons/year)
0.002 - 20
20 - 150
150 - 250
250 - 450
450 - 680
[DeNooyer et al., Appl. Energy, 2016; Lubega & Stillwell, in preparation]
Riverq, T
1
q1, T1
eff
2
q2, T2
eff
x12
T2
-T1
+
P1 P2
125 MW 90 MW
100 MW
P2
P1
P3
Bus 1
Bus 1 Bus 7 Bus 8 Bus 9 Bus 3
Bus 6Bus 5
Bus 4
Generator
Transformer
Power Demand
Transmission Line
Figure 3: Small-Scale Example Eastern Case
Pi =
9X
j=1
Bi j ✓i j
Pi j Pmaxi j 8i, j
(14)
The N-1 reliability requirement is not included for this small-scale example. Constraints on the generators include mini-mum/maximum power outputs, and maximum water withdrawalrates limited by intake pumps:
Pmini Pi Pmax
i
qi qmaxi
(15)
The solution to the presented optimization problem is depen-dent on the following:
(i) Characteristics of the individual generators:HRi, �i, Pmin/max
i , qmaxi
(ii) Grid Characteristics:Bi j, Pmaxi j , and the power demands
(iii) Inter-plant Distance: x12
(iv) The ambient flow and temperature: q,T
For fixed generator characteristics, grid characteristics, andinter-plant distances, the solution to the optimization problemwould provide optimal rules conditioned on the streamflow qand the stream temperature T . These can be determined for dif-ferent percentiles of flow and temperature, and used as operatingguides.
As an illustration, the simple case depicted in Figure 3 wassolved using reasonable assumed values for the di↵erent gener-ator and grid characteristics. The example was set up such thatpower plant 1 has a lower heat rate than power plant 2. Powerplant 3 was set up to have no thermal pollution impact. A trans-mission constraint of 70MW was imposed on all transmissionlines. Power plants 1 and 2 have maximum capacities of 150MWwhile power plant 3 has a capacity of 100MW.
The resulting power output levels and required thermal vari-ances are shown in Table 2 for assumed higher percentile tem-peratures and lower percentile flows (at lower temperatures andhigher flows there would be no need for thermal variances). As
Table 2: Optimal rules for simple example based on lower percentile flows(q30, q20, q10) in m3 s�1 and upper percentile temperatures (T 80,T 90) in �C. Allpowers in MW and all thermal variances (TV) in �C.
T 80 = 32 T 90 = 33
q30 = 65P1 = 84, TV1 = 1.4
P2 = 131, TV2 = 2.4P3 = 100
P1 = 84, TV1 = 2.4P2 = 131, TV2 = 3.4
P3 = 100
q20 = 55P1 = 84, TV1 = 1.6
P2 = 131, TV2 = 2.7P3 = 100
P1 = 84, TV1 = 2.6P2 = 131, TV2 = 3.7
P3 = 100
q10 = 45P1 = 84, TV1 = 1.8
P2 = 131, TV2 = 3.2P3 = 100
P1 = 84, TV1 = 2.8P2 = 131, TV2 = 4.2
P3 = 100
expected, the optimization seeks to maximize the contributionfrom power plant 3 up to its 100MW capacity because this unitdoes not cause any thermal pollution. Unexpectedly, althoughpower plant 1 has a lower heat rate than power plant 2, the opti-mization results in a higher contribution from power plant 2 thanfrom power plant 1. This is because power plant 1 is upstreamof power plant 2, and thus the heat discharged by power plant1 increases the inlet temperature of power plant 2 as shown byEquation 11. However, the power contributed by power plant 2is limited to 131MW in this example, by the 70MW transmis-sion constraint on the line from bus 5 to bus 7. As the flowreduces, or as the temperature increases, larger thermal vari-ances are required to maintain the power plant outputs at thedesired levels. In this example, the optimal generation outputs(84MW, 131MW, 100MW) do not change with changes in theflow q and temperature T as long as the load remains constant.However, in a real world case, additional constraints could be-come binding with changes in q and T . For example, some nu-clear plants reduce output when water intake temperatures areextremely high for safety reasons, and for some power plants,extremely low flows may result in the water level being belowthe level of their intake structures. These additional constraintscan readily be captured in an optimization framework.
The power outputs in Table 2 are not generator dispatch deci-sions but rather should be thought of as average power levels that
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Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Interdisciplinary education in CEE is growing at the University of Illinois.
Started 2012 • 2012: 13 students • 2013: 23 students • 2014: 20 students • 2015: 24 students
Started 2013 • 2013: 6 students • 2014: 12 students • 2015: 16 students
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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The “new” CEE students are different from the norm, in a good way.
Reshmina William • B.S. Civil Engineering,
University of Illinois, 2014 • M.S. Civil Engineering,
University of Illinois, 2015 • Ph.D. Civil Engineering,
University of Illinois, ~2018
William Lubega • B.S. Electrical Engineering,
Makerere University, 2010 • M.S. Engineering Systems and
Management, Masdar Institute of Science and Technology, 2014
• Ph.D. Civil Engineering, University of Illinois, ~2017
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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The “new” CEE students are different from the norm, in a good way.
Reshmina William • Research: hydrologic
performance and energy co-benefits of green infrastructure
• Coursework: CEE, NRES, LAW
William Lubega • Research: electricity grid reliability
response to droughts and heat waves
• Coursework: CEE, ECE, ECON
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
# of
day
s
°F
egareva lanosaes evoba
Year
Will County Variances (days/season)
Joliet 29 Variances (days/season)
Joliet 9 Variances (days/season)
Braidwood Variances (days/season)
Dresden Variances (days/season)
Climate Index
The future of the energy-water nexus is interdisciplinary and innovative.
De Facto Reuse
Power Plant
Wastewater Treatment Plant
Reclaimed water for power plant cooling
42%
17%
7%
5%
29%
Space Heating
Water Heating
Air Conditioning
Refrigerators
Other Appliances
8% 6%
7%
9%
11%
58%
1%
Leak/Other
Faucet
Shower
Clothes Washer
Toilet
Outdoor
Unknown
U.S. Residential Energy Consumption [EIA Residential Energy Consumption Survey, 2009]
U.S. Residential Water Consumption [AWWARF Residential End Uses of Water, 1999]
Energy-water at the household scale
Aquatic ecosystem risk from power plants
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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QUESTIONS?
Ashlynn S. Stillwell, Ph.D. NSF-AEESP Workshop | 4/1/16
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Contact information
Ashlynn S. Stillwell
Assistant Professor Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign
stillwell.cee.illinois.edu
@AStillwellPhD
Stillwell Research Group
water energy policy
