Sustainability Studies at the Intersection of Energy,
Water, and Food Systems
Elliott Campbell, Assistant Professor, UC Merced
Overview
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Background
SEED Application
Service Learning
Capstone
Background
Research
• Themes (Sustainable Energy, Systems Engineering,
Integrated Assessment)
• Discovery (Campbell et al., Science, 2008; Campbell
et al., Science, 2009; Tsao et al., Nature Climate
Change, 2012; Mendu et al., PNAS, 2012)
• Support (*with UCSC co-I's)
– NSF/CAREER
– CITRIS*
– DOE/BER
– USDA/AFRI*
Student Organizations
SEED Application
Solar Tracker Lab
NSF CCLI #0942439 A Web-Enabled, Interactive Remote Laboratory for
Renewable Energy, Joel Kubby, Ali Shakouri, Brook Haag
Pyrheliometer
Pyranometer
Course Overview
• Text: Renewable and Efficient Electric Power Systems
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Soon to become SoE requirement
Teaching Technologies: Clickers, Adobe Connect
Assessment: Consultant, Surveys, Pre/Post Meeting Quizzes
Developing new SEED labs (Electro-Coagulation, International
Climate Negotiations, Land-Use/GIS, Economics/HOMER)
Student Performance
1000
Appendix
Simulated Direct
800
Simulated Total
600
Class - Total
Class - Direct
400
200
0
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Total insolation is similar between measurements and predictions
However the model does a poor job of predicting the partitioning to direct and
diffuse insolation
Assessment
• High student engagement suggested by survey
and outside assessment
• Student understanding of theory and
measurements improved
• Limited understanding of optimization and
model calibration achieved
Service Learning
Rural Electrification
• Partner: ESW
• Results: Survey development for social sustainability
metrics; Data collection in Madhya Pradesh, India
• Support: SunEdison
(McKuin & Campbell, In Prep)
Environmental Microfinance
• Partner: Kiva.org
• Results: Marketing data; pilot website; Kiva buy-in
• Support: PGE
Food and Climate
• Partners: P&D Willey Farms, UC Merced Dining Services
• Results: Climate impact assessment for local farmer
and for campus.
• Support: PGE
Capstone
Water Recycling
• Partner: City of Merced
• Results: Hydraulics; Carbon credits.
• Support: Dean
Harvesting Energy from Irrigation Canals
• Results: Prototype testing; Energy efficiency, Patent
application.
• Support: Dean
Conclusions
Summary
• Engineering technical elective that integrates
SEED curriculum
• Service-learning with local and international
partners
• Capstone for students from social science and
engineering
Next Steps
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Social science contributions on technology use
GE Course on Energy, Water, Food Nexus
SEED Lab Curriculum
EILS Collaborations!
Elliott Campbell
Assistant Professor
UC Merced
Email: ecampbell3@ucmerced.edu
Phone: 209.631.9312
Skype: elliott.campbell
Extra Slides
Seasonal Storage
Bioenergy Without Land?
(Trent, 2010)
Efficiency of CO2 input and harvesting critical to
sustainability (Wiley, Campbell, McKuin, WER, 2011)
Bioenergy Without Land II?
(Mendu et al., PNAS, In Press)
Science Communication
(McDade and Campbell, 2009)
4. Use Google maps and a sun-path diagram to estimate the timing of
obstructions in the afternoon.
Azimuth of obstruction (φ):
φ
X
Y
Altitude angle of obstruction (β):
Z
Height (H) roughly 9 meters
β = tan-1(H/Z) = 72°
φ = -tan-1(Y/X) = -58°
4. Use Google maps and a sun-path diagram to estimate the timing of
obstructions in the afternoon.
5. Does your sun-path diagram analysis agree with the measured data?
2. Comment on the reason for the difference and on what parameter
adjustments might be required to obtain a better match.
1.
2.
Larger optical depth (k) to get less direct.
Larger sky diffuse factor (C) to get more diffuse
1200
Total
Direct
Insolation (W/m^2)
1000
Diffuse
800
600
400
200
0
Measured
Model (k = 0.171, C = 0.092)
Model (k = 0.45, C = 0.75)
Solar Resource Lab
Learning Goals
• Students will be able to understand sources of variation in insolation,
construct insolation forecasting models, validate these models with
solar radiation measurements, and gain an appreciation for solar
forecasting as an intriguing challenge for the design of renewable
energy systems.
Learning Outcome
• Forecast seasonal and daily variation in insolation on a collector surface
using clear-sky insolation theory.
• Estimate model error using pyrheliometer and pyranometer
measurements.
• Propose plausible sources of error in model and derive optimal
parameter estimates.
• Predict the quantity and timing of insolation losses due to obstructions
using site maps and sun-path diagrams.
Lab Overview
• Background: Two lectures on insolation theory.
• Objective: Students will be able to develop solar
forecasting models and evaluate with measurements.
• Format:
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Clicker Pre/Post Quiz
Introduction
Parallel Investigations
Group Reporting
Survey