1. Cyber-Physical Energy Systems: Focus on Smart Buildings Presentation by Francis Usher CS 525 Fall 2012 Monday September 19, 2012

2. Goals ● Examine challenges for energy systems ● Virtual case study – Implementation challenges in RL scenario – Estimated energy costs and prodction ● Thought experiment – How close can we get to self-sufficiency?

3. Motivation ● Buildings: 70% electricity, 40% GHG ● Push for Zero Net Energy Buildings (ZNEB) ● Coupled generation, sensing, & control – Just one or two of these inadequate

4. Energy Metrics & Concepts ● Detailed breakdown of energy usage – “Mixed-use” building – Lighting, mechanical, machine-room, plug – Baseline load ● Large % total load ● Unreasonably high

6. Experimental Design ● UCSD CSE building (mixed-use) ● Normal weekday occupancy ● Lots of permanent heavy-duty IT equipment ● Large roof space ● Climate control (overlooked)

8. Mitigation Approach ● IT Infrastructure (Somniloquy) ● Lighting (Motion sensors & LED) ● Generation (Solar PV) – Other generation forms not widely suitable

9. Somniloquy ● People don't put their computers to sleep! ● Must allow lightweight, always-on net apps ● Pilot test on 30 CSE desktop users – 60-80% savings ● Server estimate 50% – Workload consolidation – Virtual machine migration

12. Lighting ● Off-hours mitigation – Don't jeopardize productivity – 20% of baseline = safety critical lights – Other lights on motion sensor ● LED lights – Reduction to 1/3

13. Solar PhotoVoltaic (PV) ● Function of available roof space (2700m²) ● AC conversion efficiency vs. requirements – 15% (standard) – 25-48% (necessary with tracking) – 31-66% (thermodynamic limit) – 32% (laboratory multi-junction) ● Update – 30% (ZTJ space-application multi-junction) – 43% (laboratory multi-junction)

14. Main Results ● Breakdown of metered energy use – Baseline plug load (PC energy use) ~ 50% ● Savings capacity – Mostly IT, some light ● Comparison with PV generation capability – Contemporaneous PV technology inadequate

15. Future Work ● Real-world tests – Implement changes – Collect data – Fine-tune, tweak ● Analysis of cooling & ventilation optimization – Make climate control ~ actual needs – More efficient fans & pumps – Closed-loop zonal climate control – (assumed savings of 20%)