1. LoCal Retreat Summer 2010 Eric Brewer, David Culler, Randy Katz, Seth Sanders University of California, Berkeley

2. Presentation Outline Retreat Purpose and Agenda What is LoCal? Project Progress and Status 2

3. Retreat Goals & Technology Transfer UC Berkeley Project Team Industrial Collaborators Government Sponsors Friends People Project Status Work in Progress Prototype Technology Early Access to Technology Promising Directions Reality Check Feedback 3

4. Retreat Purpose Second LoCal remote retreat – Alternate between Lake Tahoe winter/Santa Cruz summer One year in, but industrial input is still critical! – Working bottom-up from machine room to buildings to Smart Grid Review first year’s progress Posters, breakouts over dinner, panel 4

5. Who is Here? Industrial – eBay – Fujitsu Labs USA – Intel – Nokia – Samsung Electronics – Siemens Academic – University of California, Berkeley Government/Labs – LBNL – Innovation Center Denmark Utilities – PG&E 5

6. Retreat Schedule Monday, May 24 0800 – 1000 Travel from Berkeley to Chaminade 1030 – 1200Introduction and Overview, Randy/David/Fred 1200 – 1330Lunch 1330 – 1500Energy Systems Control 1500 – 1530Break 1530 – 1700Building-Scale Energy Systems 1700 – 1730Discussion of Breakout Topics 1730 – 1800Break and Poster/Demonstration Session Set-up 1800 – 1930Poster Session 1930 – 2100Industry Poster Session 2100 – Lubricated Discussion 6

7. Retreat Schedule Tuesday, May 25 0800 – 1200 Excursion to Ano Nuevo State Park 1230 – 1330Lunch with Discussion on Breakout Topics 1330 – 1400Report from Breakouts 1400 – 1530Cluster-scale Power-proportional Systems 1530 – 1600Break 1600 – 1730Feedback 1730 – 1900Dinner 1900 – 2100Return to Berkeley

8. Draft Breakout Topics (1 of 2) What are good techniques for localizing people inside buildings? What should be found in a residential testbed? What should the building-to-grid connection and experiments look like? How should LoCal exploit regional and national-scale testbeds? Is DC distribution a realistic possibility? What is the value of high fidelity power sensor data? How do you obtain sufficient coverage with sensors to accurately understand energy consumption patterns? How to integrate load-side technology developments with grid-scale economic/market modeling? 8

9. Draft Breakout Topics (2 of 2) What is the best way to influence smart grid technology and policy? What are the data products that can be collected and how would they be used? What would be the design and implementation of a personal energy monitoring/preference capturing environment, different from institutional/building-scale systems? What should energy usage visualization look like at personal and aggregated scales? What disruptive technologies/world trends should we understand better than we do? E.g., electric scooters in China yielding cheap batteries Minigrids in the developing world? Other topics as suggested by Industry? 9

10. 10 What if the Energy Infrastructure were Designed like the Internet? Energy: the limited resource of the 21st Century Information Age approach: bits follow current flow – Break synchronization between sources and loads: energy storage/buffering is key – Lower cost, more incremental deployment, suitable for developing economies – Enhanced reliability and resilience to wide-area outages, such as after natural disasters Exploit information to match sources to loads, manage buffers, integrate renewables, signal demand response, and take advantage of locality

11. Evolution of the Grid 11

12. What is LoCal? Boosting the IQ of the Smart Grid: Information-centric Energy Infrastructure – “Energy permits things to exist; information, to behave purposefully.” W. Ware, 1997 – Concept of Energy Networks: bits follow where current flows – Pervasive information: monitor, model, manage – Multiscale aggregates: nodes, racks, buildings, grids Working inductively bottom-up towards the architecture of the Grid Operating System 12

13. Six Month Progress Technology – Residential-scale generation and storage – Power-proportional cluster (Manage) Infrastructure – Multi-building energy monitoring Architecture and Control (Monitor + Model) – Web-centric high fidelity monitoring collection – Building description and monitoring database – Scheduling and Slack/Supply-following Loads Fridge model system Wind-assisted datacenter/cluster Grid-Building-Machine Room Energy Analogies 13

14. Information Overlay to the Energy Grid 14 Conventional Electric Grid Generation Transmission Distribution Load Intelligent Energy Network Load IPS Source IPS energy subnet Intelligent Power Switch Conventional Internet

15. Aware Co-operative Grid 15 Monitor, Model, Manage Deep instrumentation Waste elimination Efficient Operation Shifting, Scheduling, Adaptation Forecasting Tracking Market Availability Pricing Planning

16. 16 Intelligent Power Switch (IPS) Energy Network PowerComm Interface Energy Storage Power Generation Host Load energy flows information flows Intelligent Power Switch PowerComm Interface: Network + Power connector Scale Down, Scale Out Machine-room- to-Building Storage- to-Building Generation- to-Building Facility- to-Building Building- to-Grid Generation- to-Grid Microgrid- to-Grid

17. Multi-Scale Energy Internet 17 comm power now Load profile w $ now Price profile w now Actual load w Datacenter Bldg Energy Network IPS Internet Grid IPS Power proportional kernel Power proportional service manager Quality- Adaptive Service M/R Energy Net IPS AHU Chill CT IPS

18. 18 Instrumentation Models Controls Building OS Plug Loads Lighting Facilities Building Instrumentation Models Routing/Control Grid OS Demand Response Load Following Supply Following Grid Facility-to- Building Gen-to- Building Instrumentation Models Control Compressor Scheduling Temperature Maintenance Supply Following Loads Storage- to-Building Instrumentation Models Power-Aware Cluster Manager Load Balancer/ Scheduler Web Server Web App Logic DB/Storage Machine Room MR-to- Building Multi-scale Energy Network Gen- to-Grid uGrid- to-Grid Building- to-Grid Wind Modeling

19. 19 Building-scale Testbeds Cory Hall Soda Hall

20. Building OS and Evolving Information Model 20

21. “Supply-Following” Loads: Scheduling the Datacenter LoCal Power-Proportional Cluster Renewables-Augmented DC Historical Data Consumption DataSupply Data B B Workload Model/ Predictor Energy Aware Workload Scheduler Cluster Manager Cluster Manager Building/Facility Manager Tasks SLAs Energy Supply Information Energy Consumption Application Resource Footprint Energy Aware Load Scheduling 21

22. 22 Summary and Conclusions Evolution of the Grid to the “Smart Grid” – Intelligence extends from supply to loads – From centralized to distributed/peer-to-peer LoCal: a scalable energy network – Information overlay on cluster, machine room, building-scale “grids” – Prototypes plus real deployments to interface sensors, facilities, clusters, and buildings to information buses at a variety of scales – Actuation: management of supply-following loads at facility (refrigerator) and cluster scales