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Sustainability Studies at the Intersection of Energy , Water , and Food Systems Elliott Campbell, Assistant Professo

Sustainability Studies at the Intersection of Energy , Water , and Food Systems Elliott Campbell, Assistant Professor, UC Merced. Overview. Background SEED Application Service Learning Capstone. Background. Research.

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Sustainability Studies at the Intersection of Energy , Water , and Food Systems Elliott Campbell, Assistant Professo

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  1. Sustainability Studies at the Intersection of Energy, Water, and Food Systems Elliott Campbell, Assistant Professor, UC Merced

  2. Overview • Background • SEED Application • Service Learning • Capstone

  3. Background

  4. 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*

  5. Student Organizations

  6. SEED Application

  7. Solar Tracker Lab NSF CCLI #0942439 A Web-Enabled, Interactive Remote Laboratory for Renewable Energy,Joel Kubby, Ali Shakouri, Brook Haag Pyrheliometer  Pyranometer 

  8. Course Overview • Text: Renewable and Efficient Electric Power Systems • 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)

  9. Student Performance • Total insolation is similar between measurements and predictions • However the model does a poor job of predicting the partitioning to direct and diffuse insolation

  10. 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

  11. Service Learning

  12. Rural Electrification • Partner: ESW • Results: Survey development for social sustainability metrics; Data collection in Madhya Pradesh, India • Support: SunEdison (McKuin & Campbell, In Prep)

  13. Environmental Microfinance • Partner: Kiva.org • Results: Marketing data; pilot website; Kiva buy-in • Support: PGE

  14. Food and Climate • Partners: P&D Willey Farms, UC Merced Dining Services • Results: Climate impact assessment for local farmer and for campus. • Support: PGE

  15. Capstone

  16. Water Recycling • Partner: City of Merced • Results: Hydraulics; Carbon credits. • Support: Dean

  17. Harvesting Energy from Irrigation Canals • Results: Prototype testing; Energy efficiency, Patent application. • Support: Dean

  18. Conclusions

  19. Summary • Engineering technical elective that integrates SEED curriculum • Service-learning with local and international partners • Capstone for students from social science and engineering

  20. Next Steps • 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

  21. Extra Slides

  22. Seasonal Storage

  23. Bioenergy Without Land? Efficiency of CO2 input and harvesting critical to sustainability (Wiley, Campbell, McKuin, WER, 2011) (Trent, 2010)

  24. Bioenergy Without Land II? (Mendu et al., PNAS, In Press)

  25. Science Communication (McDade and Campbell, 2009)

  26. 4. Use Google maps and a sun-path diagram to estimate the timing of obstructions in the afternoon. Azimuth of obstruction (φ): Altitude angle of obstruction (β): φ = -tan-1(Y/X) = -58° φ X Y Z Height (H) roughly 9 meters β = tan-1(H/Z) = 72°

  27. 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?

  28. 2. Comment on the reason for the difference and on what parameter adjustments might be required to obtain a better match. • Larger optical depth (k) to get less direct. • Larger sky diffuse factor (C) to get more diffuse

  29. 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.

  30. Lab Overview • Background:Two lectures on insolation theory. • Objective:Students will be able to develop solar forecasting models and evaluate with measurements. • Format: • Clicker Pre/Post Quiz • Introduction • Parallel Investigations • Group Reporting • Survey

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