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

3. Overview Background SEED Application Service Learning Capstone

4. Background

5. 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*

6. Student Organizations

7. SEED Application

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

9. 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)

10. 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

11. 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

12. Service Learning

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

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

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

16. Capstone

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

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

19. Conclusions

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

21. 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

22. Extra Slides

23. Seasonal Storage

24. Bioenergy Without Land? Efficiency of CO 2 input and harvesting critical to sustainability (Wiley, Campbell, McKuin, WER, 2011) (Trent, 2010)

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

26. Science Communication (McDade and Campbell, 2009)

27. 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°

28. 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?

29. 2. Comment on the reason for the difference and on what parameter adjustments might be required to obtain a better match. 1.Larger optical depth (k) to get less direct. 2.Larger sky diffuse factor (C) to get more diffuse

32. 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.

33. 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